US20150167085A1

US20150167085A1 - Methods and Systems for Analysis of Organ Transplantation

Info

Publication number: US20150167085A1
Application number: US14/481,167
Authority: US
Inventors: Daniel R. Salomon; Sunil M. Kurian; Steven Head; Michael M. Abecassis; John J. Friedewald; Josh Levitsky
Original assignee: Scripps Research Institute; Northwestern University
Current assignee: Scripps Research Institute; Northwestern University
Priority date: 2013-09-09
Filing date: 2014-09-09
Publication date: 2015-06-18
Also published as: US10870888B2; US20180371546A1; EP3044333A1; AU2014317834A1; AU2024200059A1; GB201603464D0; AU2020277267A1; US20200407791A1; US20170191128A1; WO2015035367A1; CA2923700A1; GB2532672A; AU2020277267B2; EP3044333A4

Abstract

Disclosed herein are methods of detecting, predicting or monitoring a status or outcome of a transplant in a transplant recipient.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 61/875,276 filed on Sep. 9, 2013, U.S. Provisional Application No. 61/965,040 filed on Jan. 16, 2014, U.S. Provisional Application No. 62/001,889 filed on May 22, 2014, U.S. Provisional Application No. 62/029,038 filed on Jul. 25, 2014, U.S. Provisional Application No. 62/001,909 filed on May 22, 2014, and U.S. Provisional Application No. 62/001,902 filed on May 22, 2014, all of which are incorporated herein by reference in their entireties.

GOVERNMENT RIGHTS

The invention described herein was made with government support under Grant Numbers U19 A152349, U01 A1084146, and A1063603 awarded by the National Institutes of Health. The United States Government has certain rights in the invention.

BACKGROUND

The current method for detecting organ rejection in a patient is a biopsy of the transplanted organ. However, organ biopsy results can be inaccurate, particularly if the area biopsied is not representative of the health of the organ as a whole (e.g., as a result of sampling error). There can be significant differences between individual observors when they read the same biopsies independently and these discrepancies are particularly an issue for complex histologies that can be challenging for clinicians. Biopsies, especially surgical biopsies, can also be costly and pose significant risks to a patient. In addition, the early detection of rejection of a transplant organ may require serial monitoring by obtaining multiple biopsies, thereby multiplying the risks to the patients, as well as the associated costs.
Transplant rejection is a marker of ineffective immunosuppression and ultimately if it cannot be resolved, a failure of the chosen therapy. The fact that 50% of kidney transplant patients will lose their grafts by ten years post transplant reveals the difficulty of maintaining adequate and effective longterm immunosuppression. There is a need to develop a minimally invasive, objective metric for detecting, identifying and tracking transplant rejection. In particular, there is a need to develop a minimally invasive metric for detecting, identifying and tracking transplant rejection in the setting of a confounding diagnosis, such as acute dysfunction with no rejection. This is especially true for identifying the rejection of a transplanted kidney. For example, elevated creatinine levels in a kidney transplant recipient may indicate either that the patient is undergoing an acute rejection or acute dysfunction without rejection. A minimally-invasive test that is capable of distinguishing between these two conditions would therefore be extremely valuable and would diminish or eliminate the need for costly, invasive biopsies.

SUMMARY

The methods and systems disclosed herein may be used for detecting or predicting a condition of a transplant recipient (e.g., acute transplant rejection, acute dysfunction without rejection, subclinical acute rejection, hepatitis C virus recurrence, etc.). In some aspects, a method for detecting or predicting a condition of a transplant recipient comprises a) obtaining a sample, wherein the sample comprises one or more gene expression products from the transplant recipient; b) performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and c) detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is a classifier capable of distinguishing between at least two conditions that are not normal conditions, and wherein one of the at least two conditions is transplant rejection or transplant dysfunction. In another embodiment, a method for detecting or predicting a condition of a transplant recipient comprises a) obtaining a sample, wherein the sample comprises one or more gene expression products from the transplant recipient; b) performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and c) detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is a classifier capable of distinguishing between at least two conditions that are not normal conditions, and wherein one of the at least two conditions is transplant rejection. In another embodiment, a method for detecting or predicting a condition of a transplant recipient comprises a) obtaining a sample, wherein the sample comprises one or more gene expression products from the transplant recipient; b) performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and c) detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is a classifier capable of distinguishing between at least two conditions that are not normal conditions, and wherein one of the at least two conditions is transplant dysfunction. In some cases, the transplant recipient is a kidney transplant recipient. In some cases, the transplant recipient is a liver transplant recipient.
In some embodiments, a method of detecting or predicting a condition of a transplant recipient comprises: a) obtaining a sample, wherein the sample comprises one or more gene expression products from the transplant recipient; b) performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and c) detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is capable of distinguishing between acute rejection and transplant dysfunction with no rejection. In some cases, the transplant dysfunction with no rejection is acute transplant dysfunction with no rejection. In some cases, the transplant recipient is a kidney transplant recipient. In some cases, the transplant recipient is a liver transplant recipient.
In an embodiment, a method of detecting or predicting a condition of a transplant recipient comprises: a) obtaining a sample, wherein the sample comprises five or more gene expression products from the transplant recipient; b) an assay to determine an expression level of the five or more gene expression products from the transplant recipient, wherein the five or more gene expression products correspond to five or more genes listed in Table 1a, 1b, 1c, or 1d, or any combination thereof; and c) detecting or predicting the condition of the transplant recipient based on the expression level determined in step (b). In another embodiment, a method of detecting or predicting a condition of a transplant recipient comprises: a) obtaining a sample, wherein the sample comprises five or more gene expression products from the transplant recipient; b) an assay to determine an expression level of the five or more gene expression products from the transplant recipient, wherein the five or more gene expression products correspond to five or more genes listed in Table 1a; and c) detecting or predicting the condition of the transplant recipient based on the expression level determined in step (b). In another embodiment, a method of detecting or predicting a condition of a transplant recipient comprises: a) obtaining a sample, wherein the sample comprises five or more gene expression products from the transplant recipient; b) an assay to determine an expression level of the five or more gene expression products from the transplant recipient, wherein the five or more gene expression products correspond to five or more genes listed in Table 1a, 1b, 1c, or 1d, in any combination.; and c) detecting or predicting the condition of the transplant recipient based on the expression level determined in step (b). In another embodiment, a method of detecting or predicting a condition of a transplant recipient comprises: a) obtaining a sample, wherein the sample comprises five or more gene expression products from the transplant recipient; b) an assay to determine an expression level of the five or more gene expression products from the transplant recipient, wherein the five or more gene expression products correspond to five or more genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.; and c) detecting or predicting the condition of the transplant recipient based on the expression level determined in step (b). In some cases, the transplant recipient is a kidney transplant recipient.
In an embodiment, a method of detecting or predicting a condition of a transplant recipient comprises: a) obtaining a sample, wherein the sample comprises one or more gene expression products from the transplant recipient; b) performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and c) detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is a three-way classifier capable of distinguishing between at least three conditions, and wherein one of the at least three conditions is transplant rejection. In some embodiments, one of the at least three conditions is normal transplant function. In some embodiments, one of the at least three conditions is transplant dysfunction. In some embodiments, the transplant dysfunction is transplant dysfunction with no rejection. In some cases, the transplant dysfunction with no rejection is acute transplant dysfunction with no rejection. In another embodiment, the method disclosed herein further comprises providing or terminating a treatment for the transplant recipient based on the detected or predicted condition of the transplant recipient.
In another aspect, a method of diagnosing, predicting or monitoring a status or outcome of a transplant in a transplant recipient comprises: a) determining a level of expression of one or more genes in a sample from a transplant recipient, wherein the level of expression is determined by RNA sequencing; and b) diagnosing, predicting or monitoring a status or outcome of a transplant in the transplant recipient.
In another aspect, a method disclosed herein comprises the steps of: a) determining a level of expression of one or more genes in a sample from a transplant recipient; b) normalizing the expression level data from step (a) using a frozen robust multichip average (fRMA) algorithm to produce normalized expression level data; c) producing one or more classifiers based on the normalized expression level data from step (b); and d) diagnosing, predicting or monitoring a status or outcome of a transplant in the transplant recipient based on the one or more classifiers from step (c). In another aspect, a method disclosed herein comprises the steps of: a) determining a level of expression of a plurality of genes in a sample from a transplant recipient; and b) classifying the sample by applying an algorithm to the expression level data from step (a), wherein the algorithm is validated by a combined analysis of a sample with an unknown phenotype and a subset of a cohort with known phenotypes.
In another aspect, the methods disclosed herein have an error rate of less than about 40%. In some embodiments, the method has an error rate of less than about 40%, 35%, 30%, 25%, 20%, 15%, 10%, 5%, 3%, 2%, or 1%. For example, the method has an error rate of less than about 10%. In some embodiments, the methods disclosed herein have an accuracy of at least about 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%. For example, the method has an accuracy of at least about 70%. In some embodiments, the methods disclosed herein have a sensitivity of at least about 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%. For example, the method has a sensitivity of at least about 80%. In some embodiments, the methods disclosed herein have a positive predictive value of at least about 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%. In some embodiments, the methods disclosed herein have a negative predictive value of at least about 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%.
In some embodiments, the gene expression products described herein are RNA (e.g., mRNA). In some embodiments, the gene expression products are polypeptides. In some embodiments, the gene expression products are DNA complements of RNA expression products from the transplant recipient.
In an embodiment, the algorithm described herein is a trained algorithm. In another embodiment, the trained algorithm is trained with gene expression data from biological samples from at least three different cohorts. In another embodiment, the trained algorithm comprises a linear classifier. In another embodiment, the linear classifier comprises one or more linear discriminant analysis, Fisher's linear discriminant, Naïve Bayes classifier, Logistic regression, Perceptron, Support vector machine (SVM) or a combination thereof. In another embodiment, the algorithm comprises a Diagonal Linear Discriminant Analysis (DLDA) algorithm. In another embodiment, the algorithm comprises a Nearest Centroid algorithm. In another embodiment, the algorithm comprises a Random Forest algorithm or statistical bootstrapping. In another embodiment, the algorithm comprises a Prediction Analysis of Microarrays (PAM) algorithm. In another embodiment, the algorithm is not validated by a cohort-based analysis of an entire cohort. In another embodiment, the algorithm is validated by a combined analysis with an unknown phenotype and a subset of a cohort with known phenotypes.
In another aspect, the one or more gene expression products comprises five or more gene expression products with different sequences. In another embodiment, the five or more gene expression products correspond to 200 genes or less. In another embodiment, the five or more gene expression products correspond to less than (or at most) 200 genes listed in Table 1c. In another embodiment, the five or more gene expression products correspond to less than (or at most) 200 genes listed in Table 1a. In another embodiment, the five or more gene expression products correspond to less than (or at most) 200 genes listed in Table 1a, 1b, 1c, or 1d, in any combination. In another embodiment, the five or more gene expression products correspond to less than about 200 genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In another embodiment, the five or more gene expression products correspond to less than or equal to 500 genes, to less than or equal to 400 genes, to less than or equal to 300 genes, to less than or equal to 250 genes, to less than or equal to 200 genes, to less than or equal to 150 genes, to less than or equal to 100 genes, to less than or equal to genes, to less than or equal to 80 genes, to less than or equal to 50 genes, to less than or equal to 40 genes, to less than or equal to genes, to less than or equal to 25 genes, to less than or equal to 20 genes, at most 15 genes, or to less than or equal to 10 genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.
In one aspect, the biological samples are differentially classified based on one or more clinical features. For example, the one or more clinical features comprise status or outcome of a transplanted organ.
In another aspect, a three-way classifier is generated, in part, by comparing two or more gene expression profiles from two or more control samples. In another embodiment, the two or more control samples are differentially classified as acute rejection, acute dysfunction no rejection, or normal transplant function. In another embodiment, the two or more gene expression profiles from the two or more control samples are normalized. In another embodiment, the two or more gene expression profiles are not normalized by quantile normalization. In another embodiment, the two or more gene expression profiles from the two or more control samples are normalized by frozen multichip average (fRMA). In another embodiment, the three-way classifier is generated by creating multiple computational permutations and cross validations using a control sample set. In some cases, a four-way classifier is used instead or in addition to a three-way classifier.
In another aspect, the sample is a blood sample or is derived from a blood sample. In another embodiment, the blood sample is a peripheral blood sample. In another embodiment, the blood sample is a whole blood sample. In another embodiment, the sample does not comprise tissue from a biopsy of a transplanted organ of the transplant recipient. In another embodiment, the sample is not derived from tissue from a biopsy of a transplanted organ of the transplant recipient.
In another aspect, the assay is a microarray, SAGE, blotting, RT-PCR, sequencing and/or quantitative PCR assay. In another embodiment, the assay is a microarray assay. In another embodiment, the microarray assay comprises the use of an Affymetrix Human Genome U133 Plus 2.0 GeneChip. In another embodiment, the mircroarray uses the Hu133 Plus 2.0 cartridge arrays plates. In another embodiment, the microarray uses the HT HG-U133+PM array plates. In another embodiment, determining the assay is a sequencing assay. In another embodiment, the assay is a RNA sequencing assay. In another embodiment, the gene expression products correspond to five or more genes listed in Table 1c. In another embodiment, the gene expression products correspond to five or more genes listed in Table 1a. In another embodiment, the gene expression products correspond to five or more genes listed in Table 1a, 1b, 1c, or 1d, in any combination. In another embodiment, the gene expression products correspond to five or more genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.
In some embodiments, the transplant recipient has a serum creatinine level of at least 0.4 mg/dL, 0.6 mg/dL, 0.8 mg/dL, 1.0 mg/dL, 1.2 mg/dL, 1.4 mg/dL, 1.6 mg/dL, 1.8 mg/dL, 2.0 mg/dL, 2.2 mg/dL, 2.4 mg/dL, 2.6 mg/dL, 2.8 mg/dL, 3.0 mg/dL, 3.2 mg/dL, 3.4 mg/dL, 3.6 mg/dL, 3.8 mg/dL, or 4.0 mg/dL. For example, the transplant recipient has a serum creatinine level of at least 1.5 mg/dL. In another example, the transplant recipient has a serum creatinine level of at least 3 mg/dL.
In another aspect, the transplant recipient is a recipient of an organ or tissue. In some embodiments, the organ is an eye, lung, kidney, heart, liver, pancreas, intestines, or a combination thereof. In some embodiments, the transplant recipient is a recipient of tissue or cells comprising: stem cells, induced pluripotent stem cells, embryonic stem cells, amnion, skin, bone, blood, marrow, blood stem cells, platelets, umbilical cord blood, cornea, middle ear, heart valve, vein, cartilage, tendon, ligament, or a combination thereof. In preferred embodiments of any method described herein, the transplant recipient is a kidney transplant recipient. In other embodiments, the transplant recipient is a liver recipient.
In another aspect, this disclosure provides classifier probe sets for use in classifying a sample from a transplant recipient, wherein the classifier probe sets are specifically selected based on a classification system comprising two or more classes. In another embodiment, a classifier probe set for use in classifying a sample from a transplant recipient, wherein the classifier probe set is specifically selected based on a classification system comprising three or more classes. In another embodiment, at least two of the classes are selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. In another embodiment, three of the three or more classes are transplant rejection, transplant dysfunction with no rejection and normal transplant function. In some cases, the transplant dysfunction with no rejection is acute transplant dysfunction with no rejection.
In another aspect, a non-transitory computer-readable storage media disclosed herein comprises: a) a database, in a computer memory, of one or more clinical features of two or more control samples, wherein i) the two or more control samples are from two or more transplant recipients; and ii) the two or more control samples are differentially classified based on a classification system comprising three or more classes; b) a first software module configured to compare the one or more clinical features of the two or more control samples; and c) a second software module configured to produce a classifier set based on the comparison of the one or more clinical features. In another embodiment, at least two of the classes are selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. In another embodiment, all three classes are selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function.
In another aspect, the storage media further comprising one or more additional software modules configured to classify a sample from a transplant recipient. In another embodiment, classifying the sample from the transplant recipient comprises a classification system comprising three or more classes. In another embodiment, at least two of the classes are selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. In another embodiment, at least three of the classes are transplant rejection, transplant dysfunction with no rejection and normal transplant function.
In another aspect, a system comprising: a) a digital processing device comprising an operating system configured to perform executable instructions and a memory device; b) a computer program including instructions executable by the digital processing device to classify a sample from a transplant recipient comprising: i) a software module configured to receive a gene expression profile of one or more genes from the sample from the transplant recipient; ii) a software module configured to analyze the gene expression profile from the transplant recipient; and iii) a software module configured to classify the sample from the transplant recipient based on a classification system comprising three or more classes. In another embodiment, at least one of the classes is selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. In another embodiment, at least two of the classes are selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. In another embodiment, all three of the classes are selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function.
In another aspect, analyzing the gene expression profile from the transplant recipient comprises applying an algorithm. In another embodiment, analyzing the gene expression profile comprises normalizing the gene expression profile from the transplant recipient. In another embodiment, normalizing the gene expression profile does not comprise quantile normalization.

INCORPORATION BY REFERENCE

All publications and patent applications mentioned in this specification are herein incorporated by reference in their entireties to the same extent as if each individual publication or patent application was specifically and individually incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

FIG. 1 shows a schematic overview of certain methods in the disclosure.

FIG. 2 shows a schematic overview of certain methods of acquiring samples, analyzing results, transmitting reports over a computer network.

FIG. 3 shows a schematic of the workflows for cohort and bootstrapping strategies for biomarker discovery and validation.

FIG. 4 shows a graph of the Area Under the Curve (AUCs) for the normal transplant function (TX) versus acute rejection (AR), normal transplant function (TX) versus acute dysfunction no rejection (ADNR), and the acute rejection (AR) versus acute dysfunction no rejection (ADNR) comparisons for the locked nearest centroid (NC) classifier in the validation cohort.

FIG. 5 shows a graph of the Area Under the Curve (AUCs) for the normal transplant function (TX) versus acute rejection (AR), normal transplant function (TX) versus acute dysfunction no rejection (ADNR), and the acute rejection (AR) versus acute dysfunction no rejection (ADNR) using the locked nearest centroid (NC) classifier on 30 blinded validation acute rejection (AR), acute dysfunction no rejection (ADNR) and normal function (TX) samples using the one-by-one strategy.

FIG. 6 shows a system for implementing the methods of the disclosure.

FIG. 7 shows a graph of AUCs for the 200-classifier set obtained from the full study sample set of 148 samples. These results demonstrate that there is no over-fitting of the classifier.

DETAILED DESCRIPTION OF THE INVENTION

Overview

The present disclosure provides novel methods for characterizing and/or analyzing samples, and related kits, compositions and systems, particularly in a minimally invasive manner. Methods of classifying one or more samples from one or more subjects are provided, as well as methods of determining, predicting and/or monitoring an outcome or status of an organ transplant, and related kits, compositions and systems. The methods, kits, compositions, and systems provided herein are particularly useful for distinguishing between two or more conditions or disorders associated with a transplanted organ or tissue. For example, they may be used to distinguish between acute transplant rejection (AR), acute dysfunction with no rejection (ADNR), and normally functioning transplants (TX). Often, a three-way analysis or classifier is used in the methods provided herein.
This disclosure may be particularly useful for kidney transplant recipients with elevated serum creatinine levels, since elevated creatinine may be indicative of AR or ADNR. The methods provided herein may inform the treatment of such patiecants and assist with medical decisions such as whether to continue or change immunosuppressive therapies. In some cases, the methods provided herein may inform decisions as to whether to increase immunosuppression to treat immune-mediated rejection if detected or to decrease immunosuppression (e.g., to protect the patient from unintended toxicities of immunosuppressive drugs when the testing demonstrates more immunosuppression is not required). The methods disclosed herein (e.g., serial blood monitoring for rejection) may allow clinicians to make a change in an immunosuppression regimen (e.g., an increase, decrease or other modification in immunosuppression) and then follow the impact of the change on the blood profile for rejection as a function of time for each individual patient through serial monitoring of a bodily fluid, such as by additional blood drawings.
An overview of certain methods in the disclosure is provided in FIG. 1. In some instances, a method comprises obtaining a sample from a transplant recipient in a minimally invasive manner (110), such as via a blood draw, urine capture, saliva sample, throat culture, etc. The sample may comprise gene expression products (e.g., polypeptides, RNA, mRNA isolated from within cells or a cell-free source) associated with the status of the transplant (e.g., AR, ADNR, normal transplant function, etc.). In some instances, the method may involve reverse-transcribing RNA within the sample to obtain cDNA that can be analyzed using the methods described herein. The method may also comprise assaying the level of the gene expression products (or the corresponding DNA) using methods such as microarray or sequencing technology (120). The method may also comprise applying an algorithm to the assayed gene expression levels (130), wherein the algorithm is capable of distinguishing signatures for two or more transplantation conditions (e.g., AR, ADNR, TX, SCAR, CAN/IFTA, etc.) such as two or more non-normal transplant conditions (e.g., AR vs ADNR). Often, the algorithm is a trained algorithm obtained by the methods provided herein. In some instances, the algorithm is a three-way classifier and is capable of performing multi-class classification of the sample (140). The method may further comprise detecting, diagnosing, predicting, or monitoring the condition (e.g., AR, ADNR, TX, SCAR, CAN/IFTA etc.) of the transplant recipient. The methods may further comprise continuing, stopping or changing a therapeutic regimen based on the results of the assays described herein.
The methods, systems, kits and compositions provided herein may also be used to generate or validate an algorithm capable of distinguishing between at least two conditions of a transplant recipient (e.g., AR, ADNR, TX, SCAR, CAN/IFTA, etc.). The algorithm may be produced based on gene expression levels in various cohorts or sub-cohorts described herein.
The methods, systems, kits and compositions provided herein may also be capable of generating and transmitting results through a computer network. As shown in FIG. 2, a sample (220) is first collected from a subject (e.g. transplant recipient, 210). The sample is assayed (230) and gene expression products are generated. A computer system (240) is used in analyzing the data and making classification of the sample. The result is capable of being transmitted to different types of end users via a computer network (250). In some instances, the subject (e.g. patient) may be able to access the result by using a standalone software and/or a web-based application on a local computer capable of accessing the internet (260). In some instances, the result can be accessed via a mobile application (270) provided to a mobile digital processing device (e.g. mobile phone, tablet, etc.). In some instances, the result may be accessed by physicians and help them identify and track conditions of their patients (280). In some instances, the result may be used for other purposes (290) such as education and research.
Subjects
Often, the methods are used on a subject, preferably human, that is a transplant recipient. The methods may be used for detecting or predicting a condition of the transplant recipient such as acute rejection (AR), acute dysfunction with no rejection (ADNR), chronic allograft nephropathy (CAN), interstitial fibrosis and tubular atrophy (IF/TA), subclinical rejection acute rejection (SCAR), hepatitis C virus recurrence (HCV-R), etc. In some cases, the condition may be AR. In some cases, the condition may be ADNR. In some cases, the condition may be SCAR. In some cases, the condition may be transplant dysfunction. In some cases, the condition may be transplant dysfunction with no rejection. In some cases, the condition may be acute transplant dysfunction.
Typically, when the patient does not exhibit symptoms or test results of organ dysfunction or rejection, the transplant is considered a normal functioning transplant (TX: Transplant eXcellent). An unhealthy transplant recipient may exhibit signs of organ dysfunction and/or rejection (e.g., an increasing serum creatinine). However, a subject (e.g., kidney transplant recipient) with subclinical rejection may have normal and stable organ function (e.g. normal creatinine level and normal eGFR). In these subjects, at the present time, rejection may be diagnosed histologically through a biopsy. A failure to recognize, diagnose and treat subclinical AR before significant tissue injury has occurred and the transplant shows clinical signs of dysfunction could be a major cause of irreversible organ damage. Moreover, a failure to recognize a chronic, subclinical immune-mediated organ damage and a failure to make appropriate changes in immunosuppressive therapy to restore a state of effective immunosuppression in that patient could contribute to late organ transplant failure. The methods disclosed herein can reduce or eliminate these and other problems associated with transplant rejection or failure.
Acute rejection (AR) occurs when transplanted tissue is rejected by the recipient's immune system, which damages or destroys the transplanted tissue unless immunosuppression is achieved. T-cells, B-cells and other immune cells as well as possibly antibodies of the recipient may cause the graft cells to lyse or produce cytokines that recruit other inflammatory cells, eventually causing necrosis of allograft tissue. In some instances, AR may be diagnosed by a biopsy of the transplanted organ. In the case of kidney transplant recipients, AR may be associated with an increase in serum creatinine levels. The treatment of AR may include using immunosuppressive agents, corticosteroids, polyclonal and monoclonal antibodies, engineered and naturally occurring biological molecules, and antiproliferatives. AR more frequently occurs in the first three to 12 months after transplantation but there is a continued risk and incidence of AR for the first five years post transplant and whenever a patient's immunosuppression becomes inadequate for any reason for the life of the transplant.
Acute dysfunction with no rejection (ADNR) is an abrupt decrease or loss of organ function without histological evidence of rejection from a transplant biopsy. Kidney transplant recipients with ADNR will often exhibit elevated creatinine levels. Unfortunately, the levels of kidney dysfunction based on serum creatinines are usually not significantly different between AR and ADNR subjects.
Another condition that can be associated with a kidney transplant is chronic allograft nephropathy (CAN), which is characterized by a gradual decline in kidney function and, typically, accompanied by high blood pressure and hematuria. Histopathology of patients with CAN is characterized by interstitial fibrosis, tubular atrophy, fibrotic intimal thickening of arteries and glomerulosclerosis typically described as IFTA. CAN/IFTA usually happens months to years after the transplant though increased amounts of IFTA can be present early in the first year post transplant in patients that have received kidneys from older or diseased donors or when early severe ischemia perfusion injury or other transplant injury occurs. CAN is a clinical phenotype characterized by a progressive decrease in organ transplant function. In contrast, IFTA is a histological phenotype currently diagnosed by an organ biopsy. In kidney transplants, interstitial fibrosis (IF) is usually considered to be present when the supporting connective tissue in the renal parenchyma exceeds 5% of the cortical area. Tubular atrophy (TA) refers to the presence of tubules with thick redundant basement membranes, or a reduction of greater than 50% in tubular diameter compared to surrounding non-atrophic tubules. In certain instances, finding interstitial fibrosis and tubular atrophy (IFTA) on the biopsy may be early indicators that predict the later organ dysfunction associated with the clinical phenotype of CAN. Immunologically, CAN/IFTA usually represents a failure of effective longterm immunosuppression and mechanistically it is immune-mediated chronic rejection (CR) and can involve both cell and antibody-mediated mechanisms of tissue injury as well as activation of complement and other blood coagulation mechanisms and can also involve inflammatory cytokine-mediated tissue activation and injury.
Subclinical rejection (SCAR) is generally a condition that is histologically identified as acute rejection but without concurrent functional deterioration. For kidney transplant recipients, subclinical rejection (SCAR) is histologically defined acute rejection that is characterized by tubulointerstitial mononuclear infiltration identified from a biopsy specimen, but without concurrent functional deterioration (variably defined as a serum creatinine not exceeding about 10%, 20% or 25% of baseline values). A SCAR subject typically shows normal and/or stable serum creatinine levels. SCAR is usually diagnosed through biopsies that are taken at a fixed time after transplantation (e.g. protocol biopsies or serial monitoring biopsies) which are not driven by clinical indications but rather by standards of care. SCAR may be subclassified by some into acute SCAR (SCAR) or a milder form called borderline SCAR (suspicious for acute rejection) based on the biopsy histology.
A subject therefore may be a transplant recipient that has, or is at risk of having a condition such as AR, ADNR, TX, CAN, IFTA, or SCAR. In some instances, a normal serum creatinine level and/or a normal estimated glomerular filtration rate (eGFR) may indicate healthy transplant (TX) or subclinical rejection (SCAR). For example, typical reference ranges for serum creatinine are 0.5 to 1.0 mg/dL for women and 0.7 to 1.2 mg/dL for men, though typical kidney transplant patients have creatinines in the 0.8 to 1.5 mg/dL range for women and 1.0 to 1.9 mg/dL range for men. This may be due to the fact that most kidney transplant patients have a single kidney. In some instances, the trend of serum creatinine levels over time can be used to evaluate the recipient's organ function. This is why it may be important to consider both “normal” serum creatinine levels and “stable” serum creatinine levels in making clinical judgments, interpreting testing results, deciding to do a biopsy or making therapy change decisions including changing immunosuppressive drugs. For example, the transplant recipient may show signs of a transplant dysfunction or rejection as indicated by an elevated serum creatinine level and/or a decreased eGFR. In some instances, a transplant subject with a particular transplant condition (e.g., AR, ADNR, CAN, etc.) may have an increase of a serum creatinine level of at least 0.1 mg/dL, 0.2 mg/dL, 0.3 mg/dL, 0.4 mg/dL, 0.5 mg/dL, 0.6 mg/dL, 0.7 mg/dL 0.8 mg/dL, 0.9 mg/dL, 1.0 mg/dL, 1.1 mg/dL, 1.2 mg/dL, 1.3 mg/dL, 1.4 mg/dL, 1.5 mg/dL, 1.6 mg/dL, 1.7 mg/dL, 1.8 mg/dL, 1.9 mg/dL, 2.0 mg/dL, 2.1 mg/dL, 2.2 mg/dL, 2.3 mg/dL, 2.4 mg/dL, 2.5 mg/dL, 2.6 mg/dL, 2.7 mg/dL, 2.8 mg/dL, 2.9 mg/dL, 3.0 mg/dL, 3.1 mg/dL, 3.2 mg/dL, 3.3 mg/dL, 3.4 mg/dL, 3.5 mg/dL, 3.6 mg/dL, 3.7 mg/dL, 3.8 mg/dL, 3.9 mg/dL, or 4.0 mg/dL. In some instances, a transplant subject with a certain transplant condition (e.g., AR, ADNR, CAN, etc.) may have an increase of a serum creatinine level of at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% from baseline. In some instances, a transplant subject with a certain transplant condition (e.g., AR, ADNR, CAN, etc.) may have an increase of a serum creatinine level of at least 1-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, or 10-fold from baseline. In some cases, the increase in serum creatinine (e.g., any increase in the concentration of serum creatinine described herein) may occur over about 0.25 days, 0.5 days, 0.75 days, 1 day, 1.25 days, 1.5 days, 1.75 days, 2.0 days, 3.0 days, 4.0 days, 5.0 days, 6.0 days, 7.0 days, 8.0 days, 9.0 days, 10.0 days, 15 days, 30 days, 1 month, 2 months, 3 months, 4 months, 5 months, or 6 months, or more. In some instances, a transplant subject with a particular transplant condition (e.g., AR, ADNR, CAN, etc.) may have a decrease of a eGFR of at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% from baseline. In some cases, the decrease in eGFR may occur over 0.25 days, 0.5 days, 0.75 days, 1 day, 1.25 days, 1.5 days, 1.75 days, 2.0 days, 3.0 days, 4.0 days, 5.0 days, 6.0 days, 7.0 days, 8.0 days, 9.0 days, 10.0 days, 15 days, 30 days, 1 month, 2 months, 3 months, 4 months, 5 months, or 6 months, or more. In some instances, diagnosing, predicting, or monitoring the status or outcome of a transplant or condition comprises determining transplant recipient-specific baselines and/or thresholds.
In some cases, the methods provided herein are used on a subject who has not yet received a transplant, such as a subject who is awaiting a tissue or organ transplant. In other cases, the subject is a transplant donor. In some cases, the subject has not received a transplant and is not expected to receive such transplant. In some cases, the subject may be a subject who is suffering from diseases requiring monitoring of certain organs for potential failure or dysfunction. In some cases, the subject may be a healthy subject.
A transplant recipient may be a recipient of a solid organ or a fragment of a solid organ. The solid organ may be a lung, kidney, heart, liver, pancreas, large intestine, small intestine, gall bladder, reproductive organ or a combination thereof. Preferably, the transplant recipient is a kidney transplant or allograft recipient. In some instances, the transplant recipient may be a recipient of a tissue or cell. The tissue or cell may be amnion, skin, bone, blood, marrow, blood stem cells, platelets, umbilical cord blood, cornea, middle ear, heart valve, vein, cartilage, tendon, ligament, nerve tissue, embryonic stem (ES) cells, induced pluripotent stem cells (IPSCs), stem cells, adult stem cells, hematopoietic stem cells, or a combination thereof.
The donor organ, tissue, or cells may be derived from a subject who has certain similarities or compatibilities with the recipient subject. For example, the donor organ, tissue, or cells may be derived from a donor subject who is age-matched, ethnicity-matched, gender-matched, blood-type compatible, or HLA-type compatible with the recipient subject.
The transplant recipient may be a male or a female. The transplant recipient may be patients of any age. For example, the transplant recipient may be a patient of less than about 10 years old. For example, the transplant recipient may be a patient of at least about 0, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 years old. The transplant recipient may be in utero. Often, the subject is a patient or other individual undergoing a treatment regimen, or being evaluated for a treatment regimen (e.g., immunosuppressive therapy). However, in some instances, the subject is not undergoing a treatment regimen. A feature of the graft tolerant phenotype detected or identified by the subject methods is that it is a phenotype which occurs without immunosuppressive therapy, e.g., it is present in a host that is not undergoing immunosuppressive therapy such that immunosuppressive agents are not being administered to the host.
In various embodiments, the subjects suitable for methods of the invention are patients who have undergone an organ transplant within 6 hours, 12 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 10 days, 15 days, 20 days, 25 days, 1 month, 2 months, 3 months, 4 months, 5 months, 7 months, 9 months, 11 months, 1 year, 2 years, 4 years, 5 years, 10 years, 15 years, 20 years or longer of prior to receiving a classification disclosed herein (e.g., a classification obtained by the methods disclosed herein). Some of the methods further comprise changing the treatment regime of the patient responsive to the detecting, prognosing, diagnosing or monitoring step. In some of these methods, the subject can be one who has received a drug before performing the methods, and the change in treatment comprises administering an additional drug, administering a higher or lower dose of the same drug, stopping administration of the drug, or replacing the drug with a different drug or therapeutic intervention.
The subjects can include transplant recipients or donors or healthy subjects. The methods can be useful on human subjects who have undergone a kidney transplant although can also be used on subjects who have gone other types of transplant (e.g., heart, liver, lung, stem cell, etc.). The subjects may be mammals or non-mammals. The methods can be useful on non-humans who have undergone kidney or other transplant. Preferably, the subjects are a mammal, such as, a human, non-human primate (e.g., apes, monkeys, chimpanzees), cat, dog, rabbit, goat, horse, cow, pig, rodent, mouse, SCID mouse, rat, guinea pig, or sheep. Even more preferably, the subject is a human. The subject may be male or female; the subject may be a fetus, infant, child, adolescent, teenager or adult.
In some methods, species variants or homologs of these genes can be used in a non-human animal model. Species variants may be the genes in different species having greatest sequence identity and similarity in functional properties to one another. Many of such species variants human genes may be listed in the Swiss-Prot database.
Samples
Methods for detecting molecules (e.g., nucleic acids, proteins, etc.) in a subject who has received a transplant (e.g., organ transplant, tissue transplant, stem cell transplant) in order to detect, diagnose, monitor, predict, or evaluate the status or outcome of the transplant are described in this disclosure. In some cases, the molecules are circulating molecules. In some cases, the molecules are expressed in blood cells. In some cases, the molecules are cell-free circulating nucleic acids.
The methods, kits, and systems disclosed herein may be used to classify one or more samples from one or more subjects. A sample may be any material containing tissues, cells, nucleic acids, genes, gene fragments, expression products, polypeptides, exosomes, gene expression products, or gene expression product fragments of a subject to be tested. Methods for determining sample suitability and/or adequacy are provided. A sample may include but is not limited to, tissue, cells, or biological material from cells or derived from cells of an individual. The sample may be a heterogeneous or homogeneous population of cells or tissues. In some cases, the sample is from a single patient. In some cases, the method comprises analyzing multiple samples at once, e.g., via massively parallel sequencing.
The sample is preferably a bodily fluid. The bodily fluid may be sweat, saliva, tears, urine, blood, menses, semen, and/or spinal fluid. In preferred embodiments, the sample is a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The sample may be a whole blood sample. The blood sample may be a peripheral blood sample. In some cases, the sample comprises peripheral blood mononuclear cells (PBMCs); in some cases, the sample comprises peripheral blood lymphocytes (PBLs). The sample may be a serum sample. In some instances, the sample is a tissue sample or an organ sample, such as a biopsy.
The methods, kits, and systems disclosed herein may comprise specifically detecting, profiling, or quantitating molecules (e.g., nucleic acids, DNA, RNA, polypeptides, etc.) that are within the biological samples. In some instances, genomic expression products, including RNA, or polypeptides, may be isolated from the biological samples. In some cases, nucleic acids, DNA, RNA, polypeptides may be isolated from a cell-free source. In some cases, nucleic acids, DNA, RNA, polypeptides may be isolated from cells derived from the transplant recipient.
The sample may be obtained using any method known to the art that can provide a sample suitable for the analytical methods described herein. The sample may be obtained by a non-invasive method such as a throat swab, buccal swab, bronchial lavage, urine collection, scraping of the skin or cervix, swabbing of the cheek, saliva collection, feces collection, menses collection, or semen collection. The sample may be obtained by a minimally-invasive method such as a blood draw. The sample may be obtained by venipuncture. In other instances, the sample is obtained by an invasive procedure including but not limited to: biopsy, alveolar or pulmonary lavage, or needle aspiration. The method of biopsy may include surgical biopsy, incisional biopsy, excisional biopsy, punch biopsy, shave biopsy, or skin biopsy. The sample may be formalin fixed sections. The method of needle aspiration may further include fine needle aspiration, core needle biopsy, vacuum assisted biopsy, or large core biopsy. In some embodiments, multiple samples may be obtained by the methods herein to ensure a sufficient amount of biological material. In some instances, the sample is not obtained by biopsy. In some instances, the sample is not a kidney biopsy.
Sample Data
The methods, kits, and systems disclosed herein may comprise data pertaining to one or more samples or uses thereof. The data may be expression level data. The expression level data may be determined by microarray, SAGE, sequencing, blotting, or PCR amplification (e.g. RT-PCR, quantitative PCR, etc.). In some cases, the expression level is determined by sequencing (e.g., RNA or DNA sequencing). The expression level data may be determined by microarray. Exemplary microarrays include but are not limited to the Affymetrix Human Genome U133 Plus 2.0 GeneChip or the HT HG-U133+PM Array Plate.
In some cases, arrays (e.g., Illumina arrays) may use different probes attached to different particles or beads. In such arrays, the identity of which probe is attached to which particle or beads is usually determinable from an encoding system. The probes can be oligonucleotides. In some cases, the probes may comprise several match probes with perfect complementarity to a given target mRNA, optionally together with mismatch probes differing from the match probes. See, e.g., (Lockhart, et al., Nature Biotechnology 14:1675-1680 (1996); and Lipschutz, et al., Nature Genetics Supplement 21: 20-24, 1999). Such arrays may also include various control probes, such as a probe complementary to a housekeeping gene likely to be expressed in most samples. Regardless of the specifics of array design, an array generally contains one or more probes either perfectly complementary to a particular target mRNA or sufficiently complementary to the target mRNA to distinguish it from other mRNAs in the sample. The presence of such a target mRNA can be determined from the hybridization signal of such probes, optionally by comparison with mismatch or other control probes included in the array. Typically, the target bears a fluorescent label, in which case hybridization intensity can be determined by, for example, a scanning confocal microscope in photon counting mode. Appropriate scanning devices are described by e.g., U.S. Pat. No. 5,578,832, and U.S. Pat. No. 5,631,734. The intensity of labeling of probes hybridizing to a particular mRNA or its amplification product may provide a raw measure of expression level.
The data pertaining to the sample may be compared to data pertaining to one or more control samples, which may be samples from the same patient at different times. In some cases, the one or more control samples may comprise one or more samples from healthy subjects, unhealthy subjects, or a combination thereof. The one or more control samples may comprise one or more samples from healthy subjects, subjects suffering from transplant dysfunction with no rejection, subjects suffering from transplant rejection, or a combination thereof. The healthy subjects may be subjects with normal transplant function. The data pertaining to the sample may be sequentially compared to two or more classes of samples. The data pertaining to the sample may be sequentially compared to three or more classes of samples. The classes of samples may comprise control samples classified as being from subjects with normal transplant function, control samples classified as being from subjects suffering from transplant dysfunction with no rejection, control samples classified as being from subjects suffering from transplant rejection, or a combination thereof.
Biomarkers/Gene Expression Products
Biomarker refers to a measurable indicator of some biological state or condition. In some instances, a biomarker can be a substance found in a subject, a quantity of the substance, or some other indicator. For example, a biomarker may be the amount of RNA, mRNA, tRNA, miRNA, mitochondrial RNA, siRNA, polypeptides, proteins, DNA, cDNA and/or other gene expression products in a sample. In some instances, gene expression products may be proteins or RNA. In some instances, RNA may be an expression product of non-protein coding genes such as ribosomal RNA (rRNA), transfer RNA (tRNA), micro RNA (miRNA), or small nuclear RNA (snRNA) genes. In some instances, RNA may be messenger RNA (mRNA). In certain examples, a biomarker or gene expression product may be DNA complementary or corresponding to RNA expression products in a sample.
The methods, compositions and systems as described here also relate to the use of biomarker panels and/or gene expression products for purposes of identification, diagnosis, classification, treatment or to otherwise characterize various conditions of organ transplant comprising AR, ANDR, TX, IFTA, CAN, SCAR, hepatitis C virus recurrence (HCV-R). Sets of biomarkers and/or gene expression products useful for classifying biological samples are provided, as well as methods of obtaining such sets of biomarkers. Often, the pattern of levels of gene expression biomarkers in a panel (also known as a signature) is determined and then used to evaluate the signature of the same panel of biomarkers in a sample, such as by a measure of similarity between the sample signature and the reference signature. In some instances, biomarker panels or gene expression products may be chosen to distinguish acute rejection (AR) from transplant dysfunction with no acute rejection (ADNR) expression profiles. In some instances, biomarker panels or gene expression products may be chosen to distinguish acute rejection (AR) from normally functioning transplant (TX) expression profiles. In some instances, biomarker panels or gene expression products may be selected to distinguish acute dysfunction with no transplant rejection (ADNR) from normally functioning transplant (TX) expression profiles. In some instances, biomarker panels or gene expression products may be selected to distinguish transplant dysfunction from acute rejection (AR) expression profiles. In certain examples, this disclosure provides methods of reclassifying an indeterminate biological sample from subjects into a healthy, acute rejection or acute dysfunction no rejection categories, and related kits, compositions and systems.
The expression level may be normalized. In some instances, normalization may comprise quantile normalization. Normalization may comprise frozen robust multichip average (fRMA) normalization.
Determining the expression level may comprise normalization by frozen robust multichip average (fRMA). Determining the expression level may comprise reverse transcribing the RNA to produce cRNA.
The methods provided herein may comprise identifying a condition from one or more gene expression products from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In some cases, AR of a kidney transplant (or other organ transplant) can be detected from one or more gene expression products from Table 1a, 1b, 1c, 1d, 8, 10b, or 12b, in any combination. In some cases, ADNR of a kidney transplant (or other organ transplant) can be detected from one or more gene expression products from Table 1a, 1b, 1c, 1d, 10b, or 12b, in any combination. In some cases, TX (or normal functioning) of a kidney transplant (or other organ transplant) can be detected from one or more gene expression products from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, or 14b, in any combination. In some cases, SCAR of kidney transplant (or other organ transplant) can be detected from one or more gene expression products from Table 8 or 9, in any combination. In some instances, AR of a liver transplant (or other organ transplant) can be detected from one or more gene expression products from Table 16b, 17b, or 18b, in any combination. In some instances, ADNR of liver can be detected from one or more gene expression products from Table 16b. In some cases, TX of liver can be detected from one or more gene expression products from Table 16b. In some cases, HCV of liver can be detected from one or more gene expression products from Table 17b or 18b, in any combination. In some cases, HCV+AR of liver can be detected from one or more gene expression products from Table 17b or 18b, in any combination.
The methods provided herein may also comprise identifying a condition from one or mor gene expression products from a tissue biopsey sample. From example, AR of kidney biopsey can be detected from one or more gene expression products from Table 10b or 12b, in any combination. ADNR of kidney biopsey can be detected from one or more gene expression products from Table 10b or 12b, in any combination. CAN of kidney biopsey can be detected from one or more gene expression products from Table 12b or 14b, in any combination. TX of kidney biopsey can be detected from one or more gene expression products from Table 10b, 12b, or 14b, in any combination. AR of liver biopsey can be detected from one or more gene expression products from Table 18b. HCV of liver biopsey can be detected from one or more gene expression products from Table 18b. HCV+AR of liver biopsey can be detected from one or more gene expression products from Table 18b.
The gene expression product may be a peptide or RNA. At least one of the gene expression products may correspond to a gene found in Table 1a. The gene expression product may be a peptide or RNA. At least one of the gene expression products may correspond to a gene found in Table 1c. At least one of the gene expression products may correspond to a gene found in Table 1a, 1b, 1c or 1d, in any combination. At least one of the gene expression products may correspond to a gene found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes found in Table 1a. The gene expression products may correspond to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes found in Table 1c. The gene expression products may correspond to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more genes found in Table 1a. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more genes found in Table 1c. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or less genes found in Table 1a. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or less genes found in Table 1c. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or less genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or less genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more genes found in Table 1a. The gene expression products may correspond to 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more genes found in Table 1c. The gene expression products may correspond to 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 10 or more genes found in Table 1a. The gene expression products may correspond to 10 or more genes found in Table 1c. The gene expression products may correspond to 10 or more genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 10 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 25 or more genes found in Table 1a. The gene expression products may correspond to 25 or more genes found in Table 1c. The gene expression products may correspond to 25 or more genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 25 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 50 or more genes found in Table 1a. The gene expression products may correspond to 50 or more genes found in Table 1c. The gene expression products may correspond to 50 or more genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 50 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 100 or more genes found in Table 1a. The gene expression products may correspond to 100 or more genes found in Table 1c. The gene expression products may correspond to 100 or more genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 100 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 200 or more genes found in Table 1a. The gene expression products may correspond to 200 or more genes found in Table 1c. The gene expression products may correspond to 200 or more genes found in Table 1a, 1b, 1c, or 1d in any combination. The gene expression products may correspond to 200 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.
At least a subset the gene expression products may correspond to the genes found in Table 1a. At least a subset the gene expression products may correspond to the genes found in Table 1c. At least a subset the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, or 1d, in any combination. At least a subset the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20% or more of the gene expression products may correspond to the genes found in Table 1a. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20% or more of the gene expression products may correspond to the genes found in Table 1c. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20% or more of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, or 1d, in any combination. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20% or more of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 100% of the gene expression products may correspond to the genes found in Table 1a. At least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 100% of the gene expression products may correspond to the genes found in Table 1c. At least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 100% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, or 1d, in any combination. At least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 100% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 5% of the gene expression products may correspond to the genes found in Table 1a. At least about 5% of the gene expression products may correspond to the genes found in Table 1c. At least about 5% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, or 1d, in any combination. At least about 5% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 10% of the gene expression products may correspond to the genes found in Table 1a. At least about 10% of the gene expression products may correspond to the genes found in Table 1c. At least about 10% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, or 1d, in any combination. At least about 10% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 25% of the gene expression products may correspond to the genes found in Table 1a. At least about 25% of the gene expression products may correspond to the genes found in Table 1c. At least about 25% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, or 1d, in any combination. At least about 25% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 30% of the gene expression products may correspond to the genes found in Table 1a. At least about 30% of the gene expression products may correspond to the genes found in Table 1c. At least about 30% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, or 1d, in any combination. At least about 30% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.
In another aspect, the invention provides arrays, which contain a support or supports bearing a plurality of nucleic acid probes complementary to a plurality of mRNAs fewer than 5000 in number. Typically, the plurality of mRNAs includes mRNAs expressed by at least five genes selected from Table 1a. In another embodiment, the plurality of mRNAs includes mRNAs expressed by at least five genes selected from Table 1c. The plurality of mRNAs may also include mRNAs expressed by at least five genes selected from Table 1a, 1b, 1c, or 1d, in any combination. The plurality of mRNAs may also include mRNAs expressed by at least five genes selected from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In some embodiments, the plurality of mRNAs are fewer than 1000 or fewer than 100 in number. In some embodiments, the plurality of nucleic acid probes are attached to a planar support or to beads. In a related aspect, the invention provides arrays that contain a support or supports bearing a plurality of ligands that specifically bind to a plurality of proteins fewer than 5000 in number. The plurality of proteins typically includes at least five proteins encoded by genes selected from Table 1a. The plurality of proteins typically includes at least five proteins encoded by genes selected from Table 1c. The plurality of proteins typically includes at least five proteins encoded by genes selected from Table 1a, 1b, 1c, or 1d, in any combination. The plurality of proteins typically includes at least five proteins encoded by genes selected from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In some embodiments, the plurality of proteins are fewer than 1000 or fewer than 100 in number. In some embodiments, the plurality of ligands are attached to a planar support or to beads. In some embodiments, the at least five proteins are encoded by genes selected from Table 1a. In some embodiments, the at least five proteins are encoded by genes selected from Table 1c. In some embodiments, the at least five proteins are encoded by genes selected from Table 1a, 1b, 1c, or 1d, in any combination. In some embodiments, the at least five proteins are encoded by genes selected from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In some embodiments, the ligands are different antibodies that bind to different proteins of the plurality of proteins.
Methods, kits, and systems disclosed herein may have a plurality of genes associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a. Methods, kits, and systems disclosed herein may have a plurality of genes associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1c. Methods, kits, and systems disclosed herein may also have a plurality of genes associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, or 1d, in any combination. Methods, kits, and systems disclosed herein may also have a plurality of genes associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In some instances, there may be genes selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16 or more biomarker panels and can have from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more gene expression products from each biomarker panel, in any combination. In some instances, the biomarkers within each panel are interchangeable (modular). The plurality of biomarkers in all panels can be substituted, increased, reduced, or improved to accommodate the classification system described herein. In some embodiments, the set of genes combined give a specificity or sensitivity of greater than 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 99.5%, or a positive predictive value or negative predictive value of at least 95%, 95.5%, 96%, 96.5%, 97%, 97.5%, 98%, 98.5%, 99%, 99.5% or more.
Classifiers may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 biomarkers disclosed in Table 1a. Classifiers may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 biomarkers disclosed in Table 1c. Classifiers may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. Classifiers may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. Classifiers may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 biomarkers disclosed in Table 1a. Classifiers may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 biomarkers disclosed in Table 1c. Classifiers may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. Classifiers may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. Classifiers may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 biomarkers disclosed in Table 1a. Classifiers may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 biomarkers disclosed in Table 1c. Classifiers may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. Classifiers may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.
At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1c. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 22%, 25%, 27%, 30%, 32%, 35%, 37%, 40%, 42%, 45%, 47%, 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a. At least about 22%, 25%, 27%, 30%, 32%, 35%, 37%, 40%, 42%, 45%, 47%, 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1c. At least about 22%, 25%, 27%, 30%, 32%, 35%, 37%, 40%, 42%, 45%, 47%, 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. At least about 22%, 25%, 27%, 30%, 32%, 35%, 37%, 40%, 42%, 45%, 47%, 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 3% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a. At least about 3% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1c. At least about 3% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. At least about 3% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 5% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a. At least about 5% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1c. At least about 5% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. At least about 5% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 10% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a. At least about 10% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1c. At least about 10% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. At least about 10% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.
Classifier probe sets may comprise one or more oligonucleotides. The oligonucleotides may comprise at least a portion of a sequence that can hybridize to one or more biomarkers from the panel of biomarkers. Classifier probe sets may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more oligonucleotides, wherein at least a portion of the oligonucleotide can hybridize to at least a portion of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more biomarkers from the panel of biomarkers. Classifier probe sets may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more oligonucleotides, wherein at least a portion of the oligonucleotide can hybridize to at least a portion of at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more biomarkers from the panel of biomarkers. Classifier probe sets may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or fewer oligonucleotides, wherein at least a portion of the oligonucleotide can hybridize to fewer than 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more biomarkers from the panel of biomarkers. Classifier probe sets may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more oligonucleotides, wherein at least a portion of the oligonucleotide can hybridize to at least a portion of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more biomarkers from the panel of biomarkers.
Training of multi-dimensional classifiers (e.g., algorithms) may be performed on numerous samples. For example, training of the multi-dimensional classifier may be performed on at least about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more samples. Training of the multi-dimensional classifier may be performed on at least about 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350, 400, 450, 500 or more samples. Training of the multi-dimensional classifier may be performed on at least about 525, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 2000 or more samples.
The total sample population may comprise samples obtained by venipuncture. Alternatively, the total sample population may comprise samples obtained by venipuncture, needle aspiration, fine needle aspiration, or a combination thereof. The total sample population may comprise samples obtained by venipuncture, needle aspiration, fine needle aspiration, core needle biopsy, vacuum assisted biopsy, large core biopsy, incisional biopsy, excisional biopsy, punch biopsy, shave biopsy, skin biopsy, or a combination thereof. In some embodiments, the samples are not obtained by biopsy. The percent of the total sample population that is obtained by venipuncture may be greater than about 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. The percent of the total sample population that is obtained by venipuncture may be greater than about 1%. The percent of the total sample population that is obtained by venipuncture may be greater than about 5%. The percent of the total sample population that is obtained by venipuncture may be greater than about 10%
There may be a specific (or range of) difference in gene expression between subtypes or sets of samples being compared to one another. In some examples, the gene expression of some similar subtypes are merged to form a super-class that is then compared to another subtype, or another super-class, or the set of all other subtypes. In some embodiments, the difference in gene expression level is at least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, or 50% or more. In some embodiments, the difference in gene expression level is at least about 2, 3, 4, 5, 6, 7, 8, 9, 10 fold or more.
The present invention may initialize gene expression products corresponding to one or more biomarkers selected from gene expression products derived from genes listed in Table 1a. The present invention may initialize gene expression products corresponding to one or more biomarkers selected from gene expression products derived from genes listed in Table 1c. The present invention may initialize gene expression products corresponding to one or more biomarkers selected from gene expression products derived from genes listed in Table 1a, 1b, 1c, or 1d, in any combination. The present invention may initialize gene expression products corresponding to one or more biomarkers selected from gene expression products derived from genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The methods, compositions and systems provided herein may include expression products corresponding to any or all of the biomarkers selected from gene expression products derived from genes listed in Table 1a, as well as any subset thereof, in any combination. The methods, compositions and systems provided herein may include expression products corresponding to any or all of the biomarkers selected from gene expression products derived from genes listed in Table 1c, as well as any subset thereof, in any combination. The methods, compositions and systems provided herein may include expression products corresponding to any or all of the biomarkers selected from gene expression products derived from genes listed in Table 1a, 1b, 1c, or 1d, in any combination, as well as any subset thereof, in any combination. The methods, compositions and systems provided herein may include expression products corresponding to any or all of the biomarkers selected from gene expression products derived from genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination, as well as any subset thereof, in any combination. For example, the methods may use gene expression products corresponding to at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 of the markers provided Table 1a. In another embodiment, the methods use gene expression products corresponding to at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 of the markers provided Table 1c. In another example, the methods may use gene expression products corresponding to at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 of the markers provided Table 1a, 1b, 1c, or 1d, in any combination. In another example, the methods may use gene expression products corresponding to at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 of the markers provided Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The methods may use gene expression products corresponding to at least about 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300 or more of the markers provided in gene expression products derived from genes listed in Table 1a. The methods may use gene expression products corresponding to at least about 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300 or more of the markers provided in gene expression products derived from genes listed in Table 1c. The methods may use gene expression products corresponding to at least about 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300 or more of the markers provided in gene expression products derived from genes listed in Table 1a, 1b, 1c, or 1d, in any combination. The methods may use gene expression products corresponding to at least about 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300 or more of the markers provided in gene expression products derived from genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.
Further disclosed herein are classifier sets and methods of producing one or more classifier sets. The classifier set may comprise one or more genes. The classifier set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes. The classifier set may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more genes. The classifier set may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more genes. The classifier set may comprise 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000 or more genes. The classifier set may comprise 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 110000, 120000, 130000, 140000, 150000, 160000, 170000, 180000, 190000, 200000 or more genes. The classifier set may comprise 10 or more genes. The classifier set may comprise 30 or more genes. The classifier set may comprise 60 or more genes. The classifier set may comprise 100 or more genes. The classifier set may comprise 125 or more genes. The classifier set may comprise 150 or more genes. The classifier set may comprise 200 or more genes. The classifier set may comprise 250 or more genes. The classifier set may comprise 300 or more genes.
The classifier set may comprise one or more differentially expressed genes. The classifier set may comprise one or more differentially expressed genes. The classifier set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more differentially expressed genes. The classifier set may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more differentially expressed genes. The classifier set may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more differentially expressed genes. The classifier set may comprise 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000 or more differentially expressed genes. The classifier set may comprise 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 110000, 120000, 130000, 140000, 150000, 160000, 170000, 180000, 190000, 200000 or more differentially expressed genes. The classifier set may comprise 10 or more differentially expressed genes. The classifier set may comprise 30 or more differentially expressed genes. The classifier set may comprise 60 or more differentially expressed genes. The classifier set may comprise 100 or more differentially expressed genes. The classifier set may comprise 125 or more differentially expressed genes. The classifier set may comprise 150 or more differentially expressed genes. The classifier set may comprise 200 or more differentially expressed genes. The classifier set may comprise 250 or more differentially expressed genes. The classifier set may comprise 300 or more differentially expressed genes.
In some instances, the method provides a number, or a range of numbers, of biomarkers or gene expression products that are used to characterize a sample. Examples of classification panels may be derived from genes listed in Table 1a. Examples of classification panels may be derived from genes listed in Table 1c. Examples of classification panels may be derived from genes listed in Table 1a, 1b, 1c, or 1d, in any combination. Examples of classification panels may be derived from genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. However, the present disclosure is not meant to be limited solely to the biomarkers disclosed herein. Rather, it is understood that any biomarker, gene, group of genes or group of biomarkers identified through methods described herein is encompassed by the present invention. In some embodiments, the method involves measuring (or obtaining) the levels of two or more gene expression products that are within a biomarker panel and/or within a classification panel. For example, in some embodiments, a biomarker panel or a gene expression product may contain at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1a. In some embodiments, a biomarker panel or a gene expression product may contain at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1c. In some embodiments, a biomarker panel or a gene expression product may contain at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1a, 1b, 1c, or 1d, in any combination. In some embodiments, a biomarker panel or a gene expression product may contain at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In some embodiments, a biomarker panel or a gene expression product may contain no more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1a. In some embodiments, a biomarker panel or a gene expression product may contain no more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1c. In some embodiments, a biomarker panel or a gene expression product may contain no more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1a, 1b, 1c, or 1d, in any combination. In some embodiments, a biomarker panel or a gene expression product may contain no more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In other embodiments, a biomarker panel or a gene expression product may contain about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 total genes chosen from Table 1a. In other embodiments, a biomarker panel or a gene expression product may contain about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 total genes chosen from Table 1c. In other embodiments, a biomarker panel or a gene expression product may contain about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 total genes chosen from Table 1a, 1b, 1c, or 1d, in any combination. In other embodiments, a biomarker panel or a gene expression product may contain about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 total genes chosen from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.
Measuring Expression Levels
The methods, kits and systems disclosed herein may be used to obtain or to determine an expression level for one or more gene products in a subject. In some instances, the expression level is used to develop or train an algorithm or classifier provided herein. In some instances, where the subject is a patient, such as a transplant recipient; gene expression levels are measured in a sample from the transplant recipient and a classifier or algorithm (e.g., trained algorithm) is applied to the resulting data in order to detect, predict, monitor, or estimate the risk of a transplant condition (e.g., acute rejection).
The expression level of the gene products (e.g., RNA, cDNA, polypeptides) may be determined using any method known in the art. In some instances, the expression level of the gene products (e.g., nucleic acid gene products such as RNA) is measured by microarray, sequencing, electrophoresis, automatic electrophoresis, SAGE, blotting, polymerase chain reaction (PCR), digital PCR, RT-PCR, and/or quantitative PCR (qPCR). In certain preferred embodiments, the expression level is determined by microarray. For example, the microarray may be an Affymetrix Human Genome U133 Plus 2.0 GeneChip or a HT HG-U133+PM Array Plate.
In certain preferred embodiments, the expression level of the gene products (e.g., RNA) is determined by sequencing, such as by RNA sequencing or by DNA sequencing (e.g., of cDNA generated from reverse-transcribing RNA (e.g., mRNA) from a sample). Sequencing may be performed by any available method or technique. Sequencing methods may include: high-throughput sequencing, pyrosequencing, classic Sangar sequencing methods, sequencing-by-ligation, sequencing by synthesis, sequencing-by-hybridization, RNA-Seq (Illumina), Digital Gene Expression (Helicos), next generation sequencing, single molecule sequencing by synthesis (SMSS) (Helicos), Ion Torrent Sequencing Machine (Life Technologies/Thermo-Fisher), massively-parallel sequencing, clonal single molecule Array (Solexa), shotgun sequencing, Maxim-Gilbert sequencing, primer walking, and any other sequencing methods known in the art.
Measuring gene expression levels may comprise reverse transcribing RNA (e.g., mRNA) within a sample in order to produce cDNA. The cDNA may then be measured using any of the methods described herein (e.g., PCR, digital PCR, qPCR, microarray, SAGE, blotting, sequencing, etc.). In some instances, the method may comprise reverse transcribing RNA originating from the subject (e.g., transplant recipient) to produce cDNA, which is then measured such as by microarray, sequencing, PCR, and/or any other method available in the art.
In some instances, the gene products may be polypeptides. In such instances, the methods may comprise measuring polypeptide gene products. Methods of measuring or detecting polypeptides may be accomplished using any method or technique known in the art. Examples of such methods include proteomics, expression proteomics, mass spectrometry, 2D PAGE, 3D PAGE, electrophoresis, proteomic chips, proteomic microarrays, and/or Edman degradation reactions.
The expression level may be normalized (e.g., signal normalization). In some instances, signal normalization (e.g., quantile normalization) is performed on an entire cohort. In general, quantile normalization is a technique for making two or more distributions identical in statistical properties. However, in settings where samples must be processed individually or in small batches, data sets that are normalized separately are generally not comparable. In some instances provided herein, the expression level of the gene products is normalized using frozen RMA (fRMA). fRMA is particularly useful because it overcomes these obstacles by normalization of individual arrays to large publicly available microarray databases allowing for estimates of probe-specific effects and variances to be pre-computed and “frozen” (McCall et al. 2010, Biostatistics, 11(2): 242-253; McCall et al. 2011, BMC bioinformatics, 12:369). In some instances, a method provided herein does not comprise performing a normalization step. In some instances, a method provided herein does not comprise performing quantile normalization. In some cases, the normalization does not comprise quantile normalization. In certain preferred embodiments, the methods comprise frozen robust multichip average (fRMA) normalization.
In some cases, analysis of expression levels initially provides a measurement of the expression level of each of several individual genes. The expression level can be absolute in terms of a concentration of an expression product, or relative in terms of a relative concentration of an expression product of interest to another expression product in the sample. For example, relative expression levels of genes can be expressed with respect to the expression level of a house-keeping gene in the sample. Relative expression levels can also be determined by simultaneously analyzing differentially labeled samples hybridized to the same array. Expression levels can also be expressed in arbitrary units, for example, related to signal intensity.
Biomarker Discovery and Validation
Exemplary workflows for cohort and bootstrapping strategies for biomarker discovery and validation are depicted in FIG. 3. As shown in FIG. 3, the cohort-based method of biomarker discovery and validation is outlined by the solid box and the bootstrapping method of biomarker discovery and validation is outlined in the dotted box. For the cohort-based method, samples for acute rejection (n=63) (310), acute dysfunction no rejection (n=39) (315), and normal transplant function (n=46) (320) are randomly split into a discovery cohort (n=75) (325) and a validation cohort (n=73) (345). The samples from the discovery cohort are analyzed using a 3-class univariate F-test (1000 random permutations, FDR<10%; BRB ArrayTools) (330). The 3-class univariate F-test analysis of the discovery cohort yielded 2977 differentially expressed probe sets (Table 1) (335). Algorithms such as the Nearest Centroid, Diagonal Linear Discriminant Analysis, and Support Vector Machines, are used to create a 3-way classifier for AR, ADNR and TX in the discovery cohort (340). The 25-200 classifiers are “locked” (350). The “locked” classifiers are validated by samples from the validation cohort (345). For the bootstrapping method, 3-class univariate F-test is performed on the whole data set of samples (n=148) (1000 random permutations, FDR<10%; BRB ArrayTools) (355). The significantly expressed genes are selected to produce a probe set (n=200, based on the nearest centroid (NC), diagonal linear discriminant analysis (DLDA), or support vector machines (SVM)). Optimism-corrected AUCs are obtained for the 200-probe set classifier discovered with the 2 cohort-based strategy (360). AUCs are obtained for the full data set (365). Optimism-corrected AUCs are obtained for the 200-probe set classifier by Bootstrapping from 1000 samplings of the full data set with replacement (370). Optimism-corrected AUCs are obtained for nearest centroid (NC), diagonal linear discriminant analysis (DLDA), or support vector machines (SVM) using the original 200 SVM classifier (375).
In some instances, the cohort-based method comprises biomarker discovery and validation. Transplant recipients with known conditions (e.g. AR, ADNR, CAN, SCAR, TX) are randomly split into a discovery cohort and a validation cohort. One or more gene expression products may be measured for all the subjects in both cohorts. In some instances, at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1500, 2000, 2500 or more gene expression products are measured for all the subjects. In some instances, the gene expression products with different conditions (e.g. AR, ADNR, CAN, SCAR, TX) in the discovery cohort are compared and differentially expressed probe sets are discovered as biomarkers. For example, the discovery cohort in FIG. 3 yielded 2977 differentially expressed probe sets (Table 1). In some instances, the difference in gene expression level is at least 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45% or 50% or more. In some instances, the difference in gene expression level is at least 2, 3, 4, 5, 6, 7, 8, 9, 10 fold or more. In some instances, the accuracy is calculated using a trained algorithm. For example, the present invention may provide gene expression products corresponding to genes selected from Table 1a. The present invention may also provide gene expression products corresponding to genes selected from Table 1c. In some instances, the accuracy is calculated using a trained algorithm. For example, the present invention may provide gene expression products corresponding to genes selected from Table 1a, 1b, 1c, or 1d, in any combination. In some instances, the accuracy is calculated using a trained algorithm. For example, the present invention may provide gene expression products corresponding to genes selected from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In some instances, the identified probe sets may be used to train an algorithm for purposes of identification, diagnosis, classification, treatment or to otherwise characterize various conditions (e.g. AR, ADNR, CAN, SCAR, TX) of organ transplant.
The differentially expressed probe sets and/or algorithm may be subject to validation. In some instances, classification of the transplant condition may be made by applying the probe sets and/or algorithm generated from the discovery cohort to the gene expression products in the validation cohort. In some instances, the classification may be validated by the known condition of the subject. For example, in some instances, the subject is identified with a particular condition (e.g. AR, ADNR, CAN, SCAR, TX) with an accuracy of greater than 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99% or more. In some instances, the subject is identified with a particular condition (e.g. AR, ADNR, CAN, SCAR, TX) with a sensitivity of greater than 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99% or more. In some instances, the subject is identified with a particular condition (e.g. AR, ADNR, CAN, SCAR, TX) with a specificity of greater than 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99% or more. In some instances, biomarkers and/or algorithms may be used in identification, diagnosis, classification and/or prediction of the transplant condition of a subject. For example, biomarkers and/or algorithms may be used in classification of transplant conditions for an organ transplant patient, whose condition may be unknown.
Biomarkers that have been validated and/or algorithms may be used in identification, diagnosis, classification and/or prediction of transplant conditions of subjects. In some instances, gene expression products of the organ transplant subjects may be compared with one or more different sets of biomarkers. The gene expression products for each set of biomarkers may comprise one or more reference gene expression levels. The reference gene expression levels may correlate with a condition (e.g. AR, ADNR, CAN, SCAR, TX) of an organ transplant.
The expression level may be compared to gene expression data for two or more biomarkers in a sequential fashion. Alternatively, the expression level is compared to gene expression data for two or more biomarkers simultaneously. Comparison of expression levels to gene expression data for sets of biomarkers may comprise the application of a classifier. For example, analysis of the gene expression levels may involve sequential application of different classifiers described herein to the gene expression data. Such sequential analysis may involve applying a classifier obtained from gene expression analysis of cohorts of transplant recipients with a first status or outcome (e.g., transplant rejection), followed by applying a classifier obtained from analysis of a mixture of different samples, some of such samples obtained from healthy transplant recipients, transplant recipients experiencing transplant rejection, and/or transplant recipients experiencing organ dysfunction with no transplant rejection. Alternatively, sequential analysis involves applying at least two different classifiers obtained from gene expression analysis of transplant recipients, wherein at least one of the classifiers correlates to transplant dysfunction with no rejection.
Classifiers and Classifier Probe Sets
Disclosed herein is the use of a classification system comprises one or more classifiers. In some instances, the classifier is a 2-, 3-, 4-, 5-, 6-, 7-, 8-, 9-, or 10-way classifier. In some instances, the classifier is a 15-, 20-, 25-, 30-, 35-, 40-, 45-, 50-, 55-, 60-, 65-, 70-, 75-, 80-, 85-, 90-, 95-, or 100-way classifier. In some preferred embodiments, the classifier is a three-way classifier. In some embodiments, the classifier is a four-way classifier.
A two-way classifier may classify a sample from a subject into one of two classes. In some instances, a two-way classifier may classify a sample from an organ transplant recipient into one of two classes comprising acute rejection (AR) and normal transplant function (TX). In some instances, a two-way classifier may classify a sample from an organ transplant recipient into one of two classes comprising acute rejection (AR) and acute dysfunction with no rejection (ADNR). In some instances, a two-way classifier may classify a sample from an organ transplant recipient into one of two classes comprising normal transplant function (TX) and acute dysfunction with no rejection (ADNR). In some instances, a three-way classifier may classify a sample from a subject into one of three classes. A three-way classifier may classify a sample from an organ transplant recipient into one of three classes comprising acute rejection (AR), acute dysfunction with no rejection (ADNR) and normal transplant function (TX). In some instances, a three-way classifier may a sample from an organ transplant recipient into one of three classes wherein the classes can include a combination of any one of acute rejection (AR), acute dysfunction with no rejection (ADNR), normal transplant function (TX), chronic allograft nephropathy (CAN), interstitial fibrosis and/or tubular atrophy (IF/TA), or Subclinical Acute Rejection (SCAR). In some cases, the three-way classifier may classify a sample as AR/HCV-R/Tx. In some cases, the classifier is a four-way classifier. In some cases, the four-way classifier may classify a sample as AR, HCV-R, AR+HCV, or TX.
Classifiers and/or classifier probe sets may be used to either rule-in or rule-out a sample as healthy. For example, a classifier may be used to classify a sample as being from a healthy subject. Alternatively, a classifier may be used to classify a sample as being from an unhealthy subject. Alternatively, or additionally, classifiers may be used to either rule-in or rule-out a sample as transplant rejection. For example, a classifier may be used to classify a sample as being from a subject suffering from a transplant rejection. In another example, a classifier may be used to classify a sample as being from a subject that is not suffering from a transplant rejection. Classifiers may be used to either rule-in or rule-out a sample as transplant dysfunction with no rejection. For example, a classifier may be used to classify a sample as being from a subject suffering from transplant dysfunction with no rejection. In another example, a classifier may be used to classify a sample as not being from a subject suffering from transplant dysfunction with no rejection.
Classifiers used in sequential analysis may be used to either rule-in or rule-out a sample as healthy, transplant rejection, or transplant dysfunction with no rejection. For example, a classifier may be used to classify a sample as being from an unhealthy subject. Sequential analysis with a classifier may further be used to classify the sample as being from a subject suffering from a transplant rejection. Sequential analysis may end with the application of a “main” classifier to data from samples that have not been ruled out by the preceding classifiers. For example, classifiers may be used in sequential analysis of ten samples. The classifier may classify 6 out of the 10 samples as being from healthy subjects and 4 out of the 10 samples as being from unhealthy subjects. The 4 samples that were classified as being from unhealthy subjects may be further analyzed with the classifiers. Analysis of the 4 samples may determine that 3 of the 4 samples are from subjects suffering from a transplant rejection. Further analysis may be performed on the remaining sample that was not classified as being from a subject suffering from a transplant rejection. The classifier may be obtained from data analysis of gene expression levels in multiple types of samples. The classifier may be capable of designating a sample as healthy, transplant rejection or transplant dysfunction with no rejection.
Classifier probe sets, classification systems and/or classifiers disclosed herein may be used to either classify (e.g., rule-in or rule-out) a sample as healthy or unhealthy. Sample classification may comprise the use of one or more additional classifier probe sets, classification systems and/or classifiers to further analyze the unhealthy samples. Further analysis of the unhealthy samples may comprise use of the one or more additional classifier probe sets, classification systems and/or classifiers to either classify (e.g., rule-in or rule-out) the unhealthy sample as transplant rejection or transplant dysfunction with no rejection. Sample classification may end with the application of a classifier probe set, classification system and/or classifier to data from samples that have not been ruled out by the preceding classifier probe sets, classification systems and/or classifiers. The classifier probe set, classification system and/or classifier may be obtained from data analysis of gene expression levels in multiple types of samples. The classifier probe set, classification system and/or classifier may be capable of designating a sample as healthy, transplant rejection or transplant dysfunction which may include transplant dysfunction with no rejection. Alternatively, the classifier probe set, classification system and/or classifier is capable of designating an unhealthy sample as transplant rejection or transplant dysfunction with no rejection.
The differentially expressed genes may be genes that may be differentially expressed in a plurality of control samples. For example, the plurality of control samples may comprise two or more samples that may be differentially classified as acute rejection, acute dysfunction no rejection or normal transplant function. The plurality of control samples may comprise three or more samples that may be differentially classified. The samples may be differentially classified based on one or more clinical features. The one or more clinical features may comprise status or outcome of a transplanted organ. The one or more clinical features may comprise diagnosis of transplant rejection. The one or more clinical features may comprise diagnosis of transplant dysfunction. The one or more clinical features may comprise one or more symptoms of the subject from which the sample is obtained from. The one or more clinical features may comprise age and/or gender of the subject from which the sample is obtained from. The one or more clinical features may comprise response to one or more immunosuppressive regimens. The one or more clinical features may comprise a number of immunosuppressive regimens.
The classifier set may comprise one or more genes that may be differentially expressed in two or more control samples. The two or more control samples may be differentially classified. The two or more control samples may be differentially classified as acute rejection, acute dysfunction no rejection or normal transplant function. The classifier set may comprise one or more genes that may be differentially expressed in three or more control samples. The three or more control samples may be differentially classified.
The method of producing a classifier set may comprise comparing two or more gene expression profiles from two or more control samples. The two or more gene expression profiles from the two or more control samples may be normalized. The two or more gene expression profiles may be normalized by different tools including use of frozen robust multichip average (fRMA). In some instances, the two or more gene expression profiles are not normalized by quantile normalization.
The method of producing a classifier set may comprise applying an algorithm to two or more expression profiles from two or more control samples. The classifier set may comprise one or more genes selected by application of the algorithm to the two or more expression profiles. The method of producing the classifier set may further comprise generating a shrunken centroid parameter for the one or more genes in the classifier set.
The classifier set may be generated by statistical bootstrapping. Statistical bootstrapping may comprise creating multiple computational permutations and cross validations using a control sample set.
Disclosed herein is the use of a classifier probe set for determining an expression level of one or more genes in preparation of a kit for classifying a sample from a subject, wherein the classifier probe set is based on a classification system comprising three or more classes. At least two of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. All three classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function.
Further disclosed herein is a classifier probe set for use in classifying a sample from a subject, wherein the classifier probe set is based on a classification system comprising three or more classes. At least two of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. All three classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function.
Further disclosed herein is the use of a classification system comprising three or more classes in preparation of a probe set for classifying a sample from a subject. At least two of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. At least three of the three or more classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. Often, the classes are different classes.
Further disclosed herein are classification systems for classifying one or more samples from one or more subjects. The classification system may comprise three or more classes. At least two of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. All three classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function.
Classifiers may comprise panels of biomarkers. Expression profiling based on panels of biomarkers may be used to characterize a sample as healthy, transplant rejection and/or transplant dysfunction with no rejection. Panels may be derived from analysis of gene expression levels of cohorts containing healthy transplant recipients, transplant recipients experiencing transplant rejection and/or transplant recipients experiencing transplant dysfunction with no rejection. Panels may be derived from analysis of gene expression levels of cohorts containing transplant recipients experiencing transplant dysfunction with no rejection. Exemplary panels of biomarkers can be derived from genes listed in Table 1a. Exemplary panels of biomarkers can also be derived from genes listed in Table 1c. Exemplary panels of biomarkers can be derived from genes listed in Table 1a, 1b, 1c, or 1d, in any combination. Exemplary panels of biomarkers can be derived from genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.
Sample Cohorts
In some embodiments, the methods, kits and systems of the present invention seek to improve upon the accuracy of current methods of classifying samples obtained from transplant recipients. In some embodiments, the methods provide improved accuracy of identifying samples as normal function (e.g., healthy), transplant rejection or transplant dysfunction with no rejection. In some embodiments, the methods provide improved accuracy of identifying samples as normal function (e.g., healthy), AR or ADNR. Improved accuracy may be obtained by using algorithms trained with specific sample cohorts, high numbers of samples, samples from individuals located in diverse geographical regions, samples from individuals with diverse ethnic backgrounds, samples from individuals with different genders, and/or samples from individuals from different age groups.
The sample cohorts may be from female, male or a combination thereof. In some cases, the sample cohorts are from at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, or 80 or more different geographical locations. The geographical locations may comprise sites spread out across a nation, a continent, or the world. Geographical locations include, but are not limited to, test centers, medical facilities, medical offices, hospitals, post office addresses, zip codes, cities, counties, states, nations, and continents. In some embodiments, a classifier that is trained using sample cohorts from the United States may need to be retrained for use on sample cohorts from other geographical regions (e.g., Japan, China, Europe, etc.). In some cases, the sample cohorts are from at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20 or more different ethnic groups. In some embodiments, a classifier that is trained using sample cohorts from a specific ethnic group may need to be retrained for use on sample cohorts from other ethnic groups. In some cases, the sample cohorts are from at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more different age groups. The age groups may be grouped into 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 or more years, or a combination thereof. Age groups may include, but are not limited to, under 10 years old, 10-15 years old, 15-20 years old, 20-25 years old, 25-30 years old, 30-35 years old, 35-40 years old, 40-45 years old, 45-50 years old, 50-55 years old, 55-60 years old, 60-65 years old, 65-70 years old, 70-75 years old, 75-80 years old, and over 80 years old. In some embodiments, a classifier that is trained using sample cohorts from a specific age group (e.g., 30-40 years old) may need to be retrained for use on sample cohorts from other age groups (e.g., 20-30 years old, etc.).
Methods of Classifying Samples
The samples may be classified simultaneously. The samples may be classified sequentially. The two or more samples may be classified at two or more time points. The samples may be obtained at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more time points. The samples may be obtained at 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more time points. The samples may be obtained at 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more time points. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more minutes apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more hours apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more days apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more weeks apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more months apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more years apart. The two or more time points may be at least about 6 hours apart. The two or more time points may be at least about 12 hours apart. The two or more time points may be at least about 24 hours apart. The two or more time points may be at least about 2 days apart. The two or more time points may be at least about 1 week apart. The two or more time points may be at least about 1 month apart. The two or more time points may be at least about 3 months apart. The two or more time points may be at least about 6 months apart. The three or more time points may be at the same interval. For example, the first and second time points may be 1 month apart and the second and third time points may be 1 month apart. The three or more time points may be at different intervals. For example, the first and second time points may be 1 month apart and the second and third time points may be 3 months apart.
Methods of simultaneous classifier-based analysis of one or more samples may comprise applying one or more algorithm to data from one or more samples to simultaneously produce one or more lists, wherein the lists comprise one or more samples classified as being from healthy subjects (e.g. subjects with a normal functioning transplant (TX)), unhealthy subjects, subjects suffering from transplant rejection, subjects suffering from transplant dysfunction, subjects suffering from acute rejection (AR), subjects suffering from acute dysfunction with no rejection (ADNR), subjects suffering from chronic allograft nephropathy (CAN), subjects suffering from interstitial fibrosis and/or tubular atrophy (IF/TA), and/or subjects suffering from subclinical acute rejection (SCAR).
Methods of sequential classifier-based analysis of one or more samples may comprise (a) applying a first algorithm to data from one or more samples to produce a first list; and (b) applying a second algorithm to data from the one or more samples that were excluded from the first list to produce a second list. The first list or the second list may comprise one or more samples classified as being from healthy subjects (e.g. subjects with a normal functioning transplant (TX)). The first list or the second list may comprise one or more samples classified as being from unhealthy subjects. The first list or the second list may comprise one or more samples classified as being from subjects suffering from transplant rejection. The first list or the second list may comprise one or more samples classified as being from subjects suffering from transplant dysfunction. The first list or the second list may comprise one or more samples classified as being from subjects suffering from acute rejection (AR). The first list or the second list may comprise one or more samples classified as being from subjects suffering from acute dysfunction with no rejection (ADNR). The first list or the second list may comprise one or more samples classified as being from subjects suffering from chronic allograft nephropathy (CAN). The first list or the second list may comprise one or more samples classified as being from subjects suffering from interstitial fibrosis and/or tubular atrophy (IF/TA). The first list or the second list may comprise one or more samples classified as being from subjects suffering from subclinical acute rejection (SCAR). For example, a sequential classifier-based analysis may comprise (a) applying a first algorithm to data from one or more samples to produce a first list, wherein the first list comprises one or more samples classified as being from healthy subjects; and (b) applying a second algorithm to data from the one or more samples that were excluded from the first list to produce a second list, wherein the second list comprises one or more samples classified as being from subjects suffering from transplant rejection.
The methods may undergo further iteration. One or more additional lists may be produced by applying one or more additional algorithms. The first algorithm, second algorithm, and/or one or more additional algorithms may be the same. The first algorithm, second algorithm, and/or one or more additional algorithms may be different. In some instances, the one or more additional lists may be produced by applying one or more additional algorithms to data from one or more samples from one or more previous lists. The one or more additional lists may comprise one or more samples classified as being from healthy subjects (e.g. subjects with a normal functioning transplant (TX)). The one or more additional lists may comprise one or more samples classified as being from unhealthy subjects. The one or more additional lists may comprise one or more samples classified as being from subjects suffering from transplant rejection. The one or more additional lists may comprise one or more samples classified as being from subjects suffering from transplant dysfunction. The one or more additional lists may comprise one or more samples classified as being from subjects suffering from acute rejection (AR). The one or more additional lists may comprise one or more samples classified as being from subjects suffering from acute dysfunction with no rejection (ADNR). The one or more additional lists may comprise one or more samples classified as being from subjects suffering from chronic allograft nephropathy (CAN). The one or more additional lists may comprise one or more samples classified as being from subjects suffering from interstitial fibrosis and/or tubular atrophy (IF/TA). The one or more additional lists may comprise one or more samples classified as being from subjects suffering from subclinical acute rejection (SCAR).
This disclosure also provides one or more steps or analyses that may be used in addition to applying a classifier or algorithm to expression level data from a sample, such as a clinical sample. Such series of steps may include, but are not limited to, initial cytology or histopathology study of the sample, followed by analysis of gene (or other biomarker) expression levels in the sample. In some embodiments, the one or more steps or analyses (e.g., cytology or histopathology study) occur prior to the step of applying any of the classifier probe sets or classification systems described herein. The one or more steps or analyses (e.g., cytology or histopathology study) may occur concurrently with the step of applying any of the classifier probe sets or classification systems described herein. Alternatively, the one or more steps or analyses (e.g., cytology or histopathology study) may occur after the step of applying any of the classifier probe sets or classification systems described herein.
Sequential classifier-based analysis of the samples may occur in various orders. For example, sequential classifier-based analysis of one or more samples may comprise classifying samples as healthy or unhealthy, followed by classification of unhealthy samples as transplant rejection or non-transplant rejection, followed by classification of non-transplant rejection samples as transplant dysfunction or transplant dysfunction with no rejection. In another example, sequential classifier-based analysis of one or more samples may comprise classifying samples as transplant dysfunction or no transplant dysfunction, followed by classification of transplant dysfunction samples as transplant rejection or no transplant rejection. The no transplant dysfunction samples may further be classified as healthy. In another example, sequential classifier-based analysis comprises classifying samples as transplant rejection or no transplant rejection, followed by classification of the no transplant rejection samples as healthy or unhealthy. The unhealthy samples may be further classified as transplant dysfunction or no transplant dysfunction. Sequential classifier-based analysis may comprise classifying samples as transplant rejection or no transplant rejection, followed by classification of the no transplant rejection samples as transplant dysfunction or no transplant dysfunction. The no transplant dysfunction samples may further be classified as healthy or unhealthy. The unhealthy samples may further be classified as transplant rejection or no transplant rejection. The unhealthy samples may further be classified as chronic allograft nephropathy/interstitial fibrosis and tubular atrophy (CAN/IFTA) or no CAN/IFTA. The unhealthy samples may further be classified as transplant dysfunction or no transplant dysfunction. The transplant dysfunction samples may be further classified as transplant dysfunction with no rejection or transplant dysfunction with rejection. The transplant dysfunction samples may be further classified as transplant rejection or no transplant rejection. The transplant rejection samples may further be classified as chronic allograft nephropathy/interstitial fibrosis and tubular atrophy (CAN/IFTA) or no CAN/IFTA.
Algorithms
The methods, kits, and systems disclosed herein may comprise one or more algorithms or uses thereof. The one or more algorithms may be used to classify one or more samples from one or more subjects. The one or more algorithms may be applied to data from one or more samples. The data may comprise gene expression data. The data may comprise sequencing data. The data may comprise array hybridization data.
The methods disclosed herein may comprise assigning a classification to one or more samples from one or more subjects. Assigning the classification to the sample may comprise applying an algorithm to the expression level. In some cases, the gene expression levels are inputted to a trained algorithm for classifying the sample as one of the conditions comprising AR, ADNR, or TX.
The algorithm may provide a record of its output including a classification of a sample and/or a confidence level. In some instances, the output of the algorithm can be the possibility of the subject of having a condition, such as AR, ADNR, or TX. In some instances, the output of the algorithm can be the risk of the subject of having a condition, such as AR, ADNR, or TX. In some instances, the output of the algorithm can be the possibility of the subject of developing into a condition in the future, such as AR, ADNR, or TX.
The algorithm may be a trained algorithm. The algorithm may comprise a linear classifier. The linear classifier may comprise one or more linear discriminant analysis, Fisher's linear discriminant, Naïve Bayes classifier, Logistic regression, Perceptron, Support vector machine, or a combination thereof_The linear classifier may be a Support vector machine (SVM) algorithm.
The algorithm may comprise one or more linear discriminant analysis (LDA), Basic perceptron, Elastic Net, logistic regression, (Kernel) Support Vector Machines (SVM), Diagonal Linear Discriminant Analysis (DLDA), Golub Classifier, Parzen-based, (kernel) Fisher Discriminant Classifier, k-nearest neighbor, Iterative RELIEF, Classification Tree, Maximum Likelihood Classifier, Random Forest, Nearest Centroid, Prediction Analysis of Microarrays (PAM), k-medians clustering, Fuzzy C-Means Clustering, Gaussian mixture models, or a combination thereof. The algorithm may comprise a Diagonal Linear Discriminant Analysis (DLDA) algorithm. The algorithm may comprise a Nearest Centroid algorithm. The algorithm may comprise a Random Forest algorithm. The algorithm may comprise a Prediction Analysis of Microarrays (PAM) algorithm.
The methods disclosed herein may comprise use of one or more classifier equations. Classifying the sample may comprise a classifier equation. The classifier equation may be Equation 1:
$δ_{k} (x^{*}) = \sum_{i = 1}^{p} \frac{{(x_{i}^{*} - {\overline{x}}_{ik}^{'})}^{2}}{{(s_{i} + s_{0})}^{2}} - 2 \log π_{k},$
wherein:
k is a number of possible classes;
δ_kmay be the discriminant score for class k;
x*_irepresents the expression level of gene ?;
x* represents a vector of expression levels for all p genes to be used for classification drawn from the sample to be classified;
x′_kmay be a shrunken centroid calculated from a training data and a shrinkage factor;
x′_ikmay be a component of x′_kcorresponding to gene i;
s_iis a pooled within-class standard deviation for gene i in the training data;
s₀is a specified positive constant; and
π_krepresents a prior probability of a sample belonging to class k.
Assigning the classification may comprise calculating a class probability. Calculating the class probability {circumflex over (p)}_kmay be calculated by Equation 2:
${\hat{p}}_{k} (x^{*}) = \frac{e^{- \frac{1}{2} δ_{k} (x^{*})}}{\sum_{l = 1}^{K} e^{- \frac{1}{2} δ_{l} (x^{*})}} .$
Assigning the classification may comprise a classification rule. The classification rule C(x*) may be expressed by Equation 3:
$C (x^{*}) = \underset{k \in {1, K}}{\arg \max} {\hat{p}}_{k} (x^{*}) .$
Classification of Samples
The classifiers disclosed herein may be used to classify one or more samples. The classifiers disclosed herein may be used to classify 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more samples. The classifiers disclosed herein may be used to classify 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more samples. The classifiers disclosed herein may be used to classify 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more samples. The classifiers disclosed herein may be used to classify 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000 or more samples. The classifiers disclosed herein may be used to classify 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 110000, 120000, 130000, 140000, 150000, 160000, 170000, 180000, 190000, 200000 or more samples. The classifiers disclosed herein may be used to classify at least about 5 samples. The classifiers disclosed herein may be used to classify at least about 10 samples. The classifiers disclosed herein may be used to classify at least about 20 samples. The classifiers disclosed herein may be used to classify at least about 30 samples. The classifiers disclosed herein may be used to classify at least about 50 samples. The classifiers disclosed herein may be used to classify at least about 100 samples. The classifiers disclosed herein may be used to classify at least about 200 samples.
Two or more samples may be from the same subject. The samples may be from two or more different subjects. The samples may be from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more subjects. The samples may be from 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more subjects. The samples may be from 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more subjects. The samples may be from 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000 or more subjects. The samples may be from 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000 or more subjects. The samples may be from 2 or more subjects. The samples may be from 5 or more subjects. The samples may be from 10 or more subjects. The samples may be from 20 or more subjects. The samples may be from 50 or more subjects. The samples may be from 70 or more subjects. The samples may be from 80 or more subjects. The samples may be from 100 or more subjects. The samples may be from 200 or more subjects. The samples may be from 300 or more subjects. The samples may be from 500 or more subjects.
The two or more samples may be obtained at the same time point. The two or more samples may be obtained at two or more different time points. The samples may be obtained at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more time points. The samples may be obtained at 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more time points. The samples may be obtained at 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more time points. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more minutes apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more hours apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more days apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more weeks apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more months apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more years apart. The two or more time points may be at least about 6 hours apart. The two or more time points may be at least about 12 hours apart. The two or more time points may be at least about 24 hours apart. The two or more time points may be at least about 2 days apart. The two or more time points may be at least about 1 week apart. The two or more time points may be at least about 1 month apart. The two or more time points may be at least about 3 months apart. The two or more time points may be at least about 6 months apart. The three or more time points may be at the same interval. For example, the first and second time points may be 1 month apart and the second and third time points may be 1 month apart. The three or more time points may be at different intervals. For example, the first and second time points may be 1 month apart and the second and third time points may be 3 months apart.
Further disclosed herein are methods of classifying one or more samples from one or more subjects. The method of classifying one or more samples from one or more subjects may comprise (a) obtaining an expression level of one or more gene expression products of a sample from a subject; and (b) identifying the sample as normal transplant function if the gene expression level indicates a lack of transplant rejection and/or transplant dysfunction. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise identifying the sample as transplant dysfunction if the gene expression level indicates transplant rejection and/or transplant dysfunction. The method may further comprise identifying the sample as transplant dysfunction with no rejection if the gene expression level indicates transplant dysfunction and a lack transplant rejection. The method may further comprise identifying the sample as transplant rejection if the gene expression level indicates transplant rejection and/or transplant dysfunction. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The peg array may be a HT HG-U133+PM Array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Identifying the sample may comprise use of one or more classifier probe sets. Identifying the sample may comprise use of one or more algorithms. Identifying the sample may comprise use of one or more classification systems. The classification system may comprise a three-way classification. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, transplant rejection, or a combination thereof. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, and transplant rejection. The method may further comprise generating one or more reports based on the identification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject.
The method of classifying a sample may comprise (a) obtaining an expression level of one or more gene expression products of a sample from a subject; and (b) identifying the sample as transplant rejection if the gene expression level indicative of transplant rejection and/or transplant dysfunction. The one or more subjects may be transplant recipients. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise identifying the sample as transplant dysfunction if the gene expression level indicates transplant rejection and/or transplant dysfunction. The method may further comprise identifying the sample as transplant dysfunction with no rejection if the gene expression level indicates transplant dysfunction and a lack of transplant rejection. The method may further comprise identifying the sample as normal function if the gene expression level indicates a lacks of transplant rejection and transplant dysfunction. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The peg array may be a HT HG-U133+PM Array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Identifying the sample may comprise use of one or more classifier probe sets. Identifying the sample may comprise use of one or more algorithms. Identifying the sample may comprise use of one or more classification systems. The classification system may comprise a three-way classification. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, transplant rejection, or a combination thereof. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, and transplant rejection. The method may further comprise generating one or more reports based on the identification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject.
The method of classifying a sample may comprise (a) obtaining an expression level of one or more gene expression products of a sample from a subject; and (b) identifying the sample as transplant dysfunction with no rejection wherein the gene expression level indicative of transplant dysfunction and the gene expression level indicates a lack of transplant rejection. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise identifying the sample as normal transplant function if the gene expression level indicates a lack of transplant dysfunction. The method may further comprise identifying the sample as transplant rejection if the gene expression level indicates transplant rejection and/or transplant dysfunction. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The peg array may be a HT HG-U133+PM Array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Identifying the sample may comprise use of one or more classifier probe sets. Identifying the sample may comprise use of one or more algorithms. Identifying the sample may comprise use of one or more classification systems. The classification system may comprise a three-way classification. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, transplant rejection, or a combination thereof. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, and transplant rejection. The method may further comprise generating one or more reports based on the identification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject.
The method of classifying a sample may comprise (a) determining an expression level of one or more gene expression products in a sample from a subject; and (b) assigning a classification to the sample based on the level of expression of the one or more gene products, wherein the classification comprises transplant dysfunction with no rejection. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1c. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, or 1d, in any combination. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise classifying the sample as transplant dysfunction. The method may further comprise classifying the sample as transplant dysfunction with no rejection. The method may further comprise classifying the sample as normal function. The method may further comprise classifying the sample as transplant rejection. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The peg array may be a HT HG-U133+PM Array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Classifying the sample may comprise use of one or more classifier probe sets. Classifying the sample may comprise use of one or more algorithms. The classification system may further comprise normal transplant function. The classification system may further comprise transplant rejection. The classification system may further comprise CAN. The classification system may further comprise IF/TA. The classification system may comprise a three-way classification. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, transplant rejection, or a combination thereof. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, and transplant rejection. The method may further comprise generating one or more reports based on the identification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject.
The method of classifying a sample may comprise (a) determining an expression level of one or more gene expression products in a sample from a subject; and (b) assigning a classification to the sample based on the level of expression of the one or more gene products, wherein the classification comprises transplant rejection, transplant dysfunction with no rejection and normal transplant function. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1c. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, or 1d, in any combination. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise classifying the sample as transplant dysfunction. The method may further comprise classifying the sample as transplant dysfunction with no rejection. The method may further comprise classifying the sample as normal function. The method may further comprise classifying the sample as transplant rejection. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The peg array may be a HT HG-U133+PM Array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Classifying the sample may comprise use of one or more classifier probe sets. Classifying the sample may comprise use of one or more algorithms. The classification system may further comprise CAN. The classification system may further comprise IF/TA. The method may further comprise generating one or more reports based on the identification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject.
The method of classifying a sample may comprise (a) determining a level of expression of a plurality of genes in a sample from a subject; and (b) classifying the sample by applying an algorithm to the expression level data from step (a), wherein the algorithm is not validated by a cohort-based analysis of an entire cohort. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1c. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, or 1d, in any combination. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise classifying the sample as transplant dysfunction. The method may further comprise classifying the sample as transplant dysfunction with no rejection. The method may further comprise classifying the sample as normal function. The method may further comprise classifying the sample as transplant rejection. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Classifying the sample may comprise use of one or more classifier probe sets. Classifying the sample may comprise use of one or more algorithms. Classifying the sample may comprise use of a classification system. The classification system may further comprise normal transplant function. The classification system may further comprise transplant rejection. The classification system may further comprise CAN. The classification system may further comprise IF/TA. The classification system may comprise a three-way classification. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, transplant rejection, or a combination thereof. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, and transplant rejection. The method may further comprise generating one or more reports based on the identification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject. The algorithm may be validated by analysis of less than or equal to about 97%, 95%, 93%, 90%, 87%, 85%, 83%, 80%, 77%, 75%, 73%, 70%, 67%, 65%, 53%, 60%, 57%, 55%, 53%, 50%, 47%, 45%, 43%, 40%, 37%, 35%, 33%, 30%, 27%, 25%, 23%, 20%, 17%, 15%, 13%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, or 3% of the entire cohort. The algorithm may be validated by analysis of less than or equal to about 70% of the entire cohort. The algorithm may be validated by analysis of less than or equal to about 60% of the entire cohort. The algorithm may be validated by analysis of less than or equal to about 50% of the entire cohort. The algorithm may be validated by analysis of less than or equal to about 40% of the entire cohort.
The method of classifying a sample may comprise (a) determining a level of expression of a plurality of genes in a sample from a subject; and (b) classifying the sample by applying an algorithm to the expression level data from step (a), wherein the algorithm is validated by a combined analysis of expression level data from a plurality of samples, wherein the plurality of samples comprises at least one sample with an unknown phenotype and at least one sample with a known phenotype. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1c. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, or 1d, in any combination. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise classifying the sample as transplant dysfunction. The method may further comprise classifying the sample as transplant dysfunction with no rejection. The method may further comprise classifying the sample as normal function. The method may further comprise classifying the sample as transplant rejection. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Classifying the sample may comprise use of one or more classifier probe sets. Classifying the sample may comprise use of one or more algorithms. Classifying the sample may comprise use of a classification system. The classification system may further comprise normal transplant function. The classification system may further comprise transplant rejection. The classification system may further comprise CAN. The classification system may further comprise IF/TA. The classification system may comprise a three-way classification. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, transplant rejection, or a combination thereof. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, and transplant rejection. The method may further comprise generating one or more reports based on the identification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 12%, 15%, 17%, 20%, 23%, 25%, 27%, 30% or more of the samples from the plurality of samples may have an unknown phenotype. At least about 35%, 40%, 45%, 50%, 55%, 57%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97% or more of the samples from the plurality of samples may have an unknown phenotype. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 12%, 15%, 17%, 20%, 23%, 25%, 27%, 30% or more of the samples from the plurality of samples may have a known phenotype. At least about 35%, 40%, 45%, 50%, 55%, 57%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97% or more of the samples from the plurality of samples may have a known phenotype.
The method of classifying one or more samples from one or more subjects may comprise (a) determining an expression level of one or more gene expression products in a sample from a subject; and (b) assigning a classification to the sample based on the level of expression of the one or more gene products, wherein the classification comprises transplant rejection, transplant dysfunction with no rejection and normal transplant function. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise classifying the sample as transplant dysfunction. The method may further comprise classifying the sample as transplant dysfunction with no rejection. The method may further comprise classifying the sample as normal function. The method may further comprise classifying the sample as transplant rejection. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Identifying the sample may comprise use of one or more classifier probe sets. Classifying the sample may comprise use of one or more algorithms. The classification may further comprise CAN. The classification may further comprise IF/TA. The method may further comprise generating one or more reports based on the classification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject.
Classifying the sample may be based on the expression level of 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 or more gene products. Classifying the sample may be based on the expression level of 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 or more gene products. Classifying the sample may be based on the expression level of 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000 or more gene products. Classifying the sample may be based on the expression level of 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000 or more gene products. Classifying the sample may be based on the expression level of 25 or more gene products. Classifying the sample may be based on the expression level of 50 or more gene products. Classifying the sample may be based on the expression level of 100 or more gene products. Classifying the sample may be based on the expression level of 200 or more gene products. Classifying the sample may be based on the expression level of 300 or more gene products.
Classifying the sample may comprise statistical bootstrapping.
Clinical Applications
The methods, compositions, systems and kits provided herein can be used to detect, diagnose, predict or monitor a condition of a transplant recipient. In some instances, the methods, compositions, systems and kits described herein provide information to a medical practitioner that can be useful in making a therapeutic decision. Therapeutic decisions may include decisions to: continue with a particular therapy, modify a particular therapy, alter the dosage of a particular therapy, stop or terminate a particular therapy, altering the frequency of a therapy, introduce a new therapy, introduce a new therapy to be used in combination with a current therapy, or any combination of the above. In some cases, the methods provided herein can be applied in an experimental setting, e.g., clinical trial. In some instances, the methods provided herein can be used to monitor a transplant recipient who is being treated with an experimental agent such as an immunosuppressive drug or compound. In some instances, the methods provided herein can be useful to determine whether a subject can be administered an experimental agent (e.g., an agonist, antagonist, peptidomimetic, protein, peptide, nucleic acid, small molecule, or other drug candidate) to reduce the risk of rejection. Thus, the methods described herein can be useful in determining if a subject can be effectively treated with an experimental agent and for monitoring the subject for risk of rejection or continued rejection of the transplant.
Additionally or alternatively, the physician can change the treatment regime being administered to the patient. A change in treatment regime can include administering an additional or different drug, or administering a higher dosage or frequency of a drug already being administered to the patient. Many different drugs are available for treating rejection, such as immunosuppressive drugs used to treat transplant rejection calcineurin inhibitors (e.g., cyclosporine, tacrolimus), mTOR inhibitors (e.g., sirolimus and everolimus), anti-proliferatives (e.g., azathioprine, mycophenolic acid), corticosteroids (e.g., prednisolone and hydrocortisone) and antibodies (e.g., basiliximab, daclizumab, Orthoclone, anti-thymocyte globulin and anti-lymphocyte globulin). Conversely, if the value or other designation of aggregate expression levels of a patient indicates the patient does not have or is at reduced risk of transplant rejection, the physician need not order further diagnostic procedures, particularly not invasive ones such as biopsy. Further, the physician can continue an existing treatment regime, or even decrease the dose or frequency of an administered drug.
In some cases, a clinical trial can be performed on a drug in similar fashion to the monitoring of an individual patient described above, except that drug is administered in parallel to a population of transplant patients, usually in comparison with a control population administered a placebo.
Detecting/Diagnosing a Condition of a Transplant Recipient
The methods, compositions, systems and kits provided herein are particularly useful for detecting or diagnosing a condition of a transplant recipient such as a condition the transplant recipient has at the time of testing. Exemplary conditions that can be detected or diagnosed with the present methods include organ transplant rejection, acute rejection (AR), chronic rejection, Acute Dysfunction with No Rejection (ADNR), normal transplant function (TX) and/or Sub-Clinical Acute Rejection (SCAR). The methods provided herein are particularly useful for transplant recipients who have received a kidney transplant. Exemplary conditions that can be detected or diagnosed in such kidney transplant recipients include: AR, chronic allograft nephropathy (CAN), ADNR, SCAR, IF/TA, and TX.
The diagnosis or detection of condition of a transplant recipient may be particularly useful in limiting the number of invasive diagnostic interventions that are administered to the patient. For example, the methods provided herein may limit or eliminate the need for a transplant recipient (e.g., kidney transplant recipient) to receive a biopsy (e.g., kidney biopsies) or to receive multiple biopsies. In some instances, the methods provided herein may also help interpreting a biopsy result, especially when the biopsy result is inconclusive.
In a further embodiment, the methods provided herein can be used alone or in combination with other standard diagnosis methods currently used to detect or diagnose a condition of a transplant recipient, such as but not limited to results of biopsy analysis for kidney allograft rejection, results of histopathology of the biopsy sample, serum creatinine level, creatinine clearance, ultrasound, radiological imaging results for the kidney, urinalysis results, elevated levels of inflammatory molecules such as neopterin, and lymphokines, elevated plasma interleukin (IL)-1 in azathioprine-treated patients, elevated IL-2 in cyclosporine-treated patients, elevated IL-6 in serum and urine, intrarenal expression of cytotoxic molecules (granzyme B and perforin) and immunoregulatory cytokines (IL-2, -4, -10, interferon gamma and transforming growth factor-b1).
The methods provided herein are useful for distinguishing between two or more conditions or disorders (e.g., AR vs ADNR, SCAR vs ADNR, etc.). In some instances, the methods are used to determine whether a transplant recipient has AR, ADNR or TX. In some instances, the methods are used to determine whether a transplant recipient has AR, ADNR, SCAR and/or TX, or any subset or combination thereof. In some instances, the methods are used to determine whether a transplant recipient has AR, ADNR, SCAR, TX, HCV, or any subset or combination thereof. As previously described, elevated serum creatinine levels from baseline levels in kidney transplant recipients may be indicative of AR or ADNR. In preferred embodiments, the methods provided herein are used to distinguish AR from ADNR in a kidney transplant recipient. In some preferred embodiments, the methods provided herein are used to distinguish AR from ADNR in a liver transplant recipient. In some instances, the methods are used to determine whether a transplant recipient has AR, ADNR, SCAR, TX, acute transplant dysfunction, transplant dysfunction, transplant dysfunction with no rejection, or any subset or combination thereof. In some instances, the methods provided herein are used to distinguish AR from HCV from HCV+AR in a liver transplant recipient. In some instances, the methods provided herein are used to distinguish AR from HCV-R from HCV-R+AR in a liver transplant recipient. In some instances, the methods provided herein are used to distinguish HCV-R from HCV-R+AR in a liver transplant recipient. In some instances, the methods provided herein are used to distinguish AR from ADNR from CAN a kidney transplant recipient.
In some instances, the methods are used to distinguish between AR and ADNR in a kidney transplant recipient. In some instances, the methods are used to distinguish between AR and SCAR in a kidney transplant recipient. In some instances, the methods are used to distinguish between AR, TX, and SCAR in a kidney transplant recipient. In some instances, the methods are used to determine whether a kidney transplant recipient has AR, ADNR or TX. In some instances, the methods are used to determine whether a kidney transplant recipient has AR, ADNR, SCAR, CAN or TX, or any combination thereof. In some instances, the methods are used to distinguish between AR, ADNR, and CAN in a kidney transplant recipient.
In some instances, the methods provided herein are used to detect or diagnose AR in a transplant recipient (e.g., kidney transplant recipient) in the early stages of AR, in the middle stages of AR, or the end stages of AR. In some instances, the methods provided herein are used to detect or diagnose ADNR in a transplant recipient (e.g., kidney transplant recipient) in the early stages of ADNR, in the middle stages of ADNR, or the end stages of ADNR. In some instances, the methods are used to diagnose or detect AR, ADNR, IFTA, CAN, TX, SCAR, or other disorders in a transplant recipient with an accuracy, error rate, sensitivity, positive predictive value, or negative predictive value provided herein.
Predicting a Condition of a Transplant Recipient
In some embodiments, the methods provided herein can predict AR, CAN, ADNR, and/or SCAR prior to actual onset of the conditions. In some instances, the methods provided herein can predict AR, IFTA, CAN, ADNR, SCAR or other disorders in a transplant recipient at least 1 day, 5 days, 10 days, 30 days, 50 days or 100 days prior to onset. In other instances, the methods provided herein can predict AR, IFTA, CAN, ADNR, SCAR or other disorders in a transplant recipient at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or 31 days prior to onset. In other instances, the methods provided herein can predict AR, IFTA, CAN, ADNR, SCAR or other disorders in a transplant recipient at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 months prior to onset.
Monitoring a Condition of a Transplant Recipient
Provided herein are methods, systems, kits and compositions for monitoring a condition of a transplant recipient. Often, the monitoring is conducted by serial testing, such as serial non-invasive tests, serial minimally-invasive tests (e.g., blood draws), serial invasive tests (biopsies), or some combination thereof. Preferably, the monitoring is conducted by administering serial non-invasive tests or serial minimally-invasive tests (e.g., blood draws).
In some instances, the transplant recipient is monitored as needed using the methods described herein. Alternatively the transplant recipient may be monitored hourly, daily, weekly, monthly, yearly or at any pre-specified intervals. In some instances, the transplant recipient is monitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 or 24 hours. In some instances the transplant recipient is monitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or 31 days. In some instances, the transplant recipient is monitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 months. In some instances, the transplant recipient is monitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 years or longer, for the lifetime of the patient and the graft.
In some instances, gene expression levels in the patients can be measured, for example, within, one month, three months, six months, one year, two years, five years or ten years after a transplant. In some methods, gene expression levels are determined at regular intervals, e.g., every 3 months, 6 months or every year post-transplant, either indefinitely, or until evidence of a condition is observed, in which case the frequency of monitoring is sometimes increased. In some methods, baseline values of expression levels are determined in a subject before a transplant in combination with determining expression levels at one or more time points thereafter.
The results of diagnosing, predicting, or monitoring a condition of a transplant recipient may be useful for informing a therapeutic decision such as determining or monitoring a therapeutic regimen. In some instances, determining a therapeutic regimen may comprise administering a therapeutic drug. In some instances, determining a therapeutic regimen comprises modifying, continuing, initiating or stopping a therapeutic regimen. In some instances, determining a therapeutic regimen comprises treating the disease or condition. In some instances, the therapy is an immunosuppressive therapy. In some instances, the therapy is an antimicrobial therapy. In other instances, diagnosing, predicting, or monitoring a disease or condition comprises determining the efficacy of a therapeutic regimen or determining drug resistance to the therapeutic regimen.
Modifying the therapeutic regimen may comprise terminating a therapy. Modifying the therapeutic regimen may comprise altering a dosage of a therapy. Modifying the therapeutic regimen may comprise altering a frequency of a therapy. Modifying the therapeutic regimen may comprise administering a different therapy. In some instances, the results of diagnosing, predicting, or monitoring a condition of a transplant recipient may be useful for informing a therapeutic decision such as removal of the transplant. In some instances, the removal of the transplant can be an immediate removal. In other instances, the therapeutic decision can be a retransplant. Other examples of therapeutic regimen can include a blood transfusion in instances where the transplant recipient is refractory to immunosuppressive or antibody therapy.
Examples of therapeutic regimen can include administering compounds or agents that are e.g., compounds or agents having immunosuppressive properties (e.g., a calcineurin inhibitor, cyclosporine A or FK 506); a mTOR inhibitor (e.g., rapamycin, 40-O-(2-hydroxyethyl)-rapamycin, CC1779, ABT578, AP23573, biolimus-7 or biolimus-9); an ascomycin having immuno-suppressive properties (e.g., ABT-281, ASM981, etc.); corticosteroids; cyclophosphamide; azathioprene; methotrexate; leflunomide; mizoribine; mycophenolic acid or salt; mycophenolate mofetil; 15-deoxyspergualine or an immunosuppressive homologue, analogue or derivative thereof; a PKC inhibitor (e.g., as disclosed in WO 02/38561 or WO 03/82859); a JAK3 kinase inhibitor (e.g., N-benzyl-3,4-dihydroxy-benzylidene-cyanoacetamide a-cyano-(3,4-dihydroxy)-]N-benzylcinnamamide (Tyrphostin AG 490), prodigiosin 25-C(PNU156804), [4-(4′-hydroxyphenyl)-amino-6,7-dimethoxyquinazoline] (WHI-P131), [4-(3′-bromo-4′-hydroxylphenyl)-amino-6,7-dimethoxyquinazoline] (WHI-P154), [4-(3′,5′-dibromo-4′-hydroxylphenyl)-amino-6,7-dimethoxyquinazoline] WHI-P97, KRX-211, 3-{(3R,4R)-4-methyl-3-[methyl-(7H-pyrrolo[2,3-d]pyrimidin-4-yl)-amino]-piperidin-1-yl}-3-oxo-propionitrile, in free form or in a pharmaceutically acceptable salt form, e.g., mono-citrate (also called CP-690,550), or a compound as disclosed in WO 04/052359 or WO 05/066156); a SIP receptor agonist or modulator (e.g., FTY720 optionally phosphorylated or an analog thereof, e.g., 2-amino-2-[4-(3-benzyloxyphenylthio)-2-chlorophenyl]ethyl-1,3-propanediol optionally phosphorylated or 1-{4-[1-(4-cyclohexyl-3-trifluoromethyl-benzyloxyimino)-ethyl]-2-ethyl-benzyl}-azetidine-3-carboxylic acid or its pharmaceutically acceptable salts); immunosuppressive monoclonal antibodies (e.g., monoclonal antibodies to leukocyte receptors, e.g., MHC, CD2, CD3, CD4, CD7, CD8, CD25, CD28, CD40, CD45, CD52, CD58, CD80, CD86 or their ligands); other immunomodulatory compounds (e.g., a recombinant binding molecule having at least a portion of the extracellular domain of CTLA4 or a mutant thereof, e.g., an at least extracellular portion of CTLA4 or a mutant thereof joined to a non-CTLA4 protein sequence, e.g., CTLA4Ig (for ex. designated ATCC 68629) or a mutant thereof, e.g., LEA29Y); adhesion molecule inhibitors (e.g., LFA-1 antagonists, ICAM-1 or -3 antagonists, VCAM-4 antagonists or VLA-4 antagonists). These compounds or agents may also be used alone or in combination. Immunosuppressive protocols can differ in different clinical settings. In some instances, in AR, the first-line treatment is pulse methylprednisolone, 500 to 1000 mg, given intravenously daily for 3 to 5 days. In some instances, if this treatment fails, than OKT3 or polyclonal anti-T cell antibodies will be considered. In other instances, if the transplant recipient is still experiencing AR, antithymocyte globulin (ATG) may be used.
Kidney Transplants
The methods, compositions, systems and kits provided herein are particularly useful for detecting or diagnosing a condition of a kidney transplant. Kidney transplantation may be needed when a subject is suffering from kidney failure, wherein the kidney failure may be caused by hypertension, diabetes melitus, kidney stone, inherited kidney disease, inflammatory disease of the nephrons and glomeruli, side effects of drug therapy for other diseases, etc. Kidney transplantation may also be needed by a subject suffering from dysfunction or rejection of a transplanted kidney.
Kidney function may be assessed by one or more clinical and/or laboratory tests such as complete blood count (CBC), serum electrolytes tests (including sodium, potassium, chloride, bicarbonate, calcium, and phosphorus), blood urea test, blood nitrogen test, serum creatinine test, urine electrolytes tests, urine creatinine test, urine protein test, urine fractional excretion of sodium (FENA) test, glomerular filtration rate (GFR) test. Kidney function may also be assessed by a renal biopsy. Kidney function may also be assessed by one or more gene expression tests. The methods, compositions, systems and kits provided herein may be used in combination with one or more of the kidney tests mentioned herein. The methods, compositions, systems and kits provided herein may be used before or after a kidney transplant. In some instances, the method may be used in combination with complete blood count. In some instances, the method may be used in combination with serum electrolytes (including sodium, potassium, chloride, bicarbonate, calcium, and phosphorus). In some instances, the method may be used in combination with blood urea test. In some instances, the method may be used in combination with blood nitrogen test. In some instances, the method may be used in combination with a serum creatinine test. In some instances, the method may be used in combination with urine electrolytes tests. In some instances, the method may be used in combination with urine creatinine test. In some instances, the method may be used in combination with urine protein test. In some instances, the method may be used in combination with urine fractional excretion of sodium (FENA) test. In some instances, the method may be used in combination with glomerular filtration rate (GFR) test. In some instances, the method may be used in combination with a renal biopsy. In some instances, the method may be used in combination with one or more other gene expression tests. In some instances, the method may be used when the result of the serum creatinine test indicates kidney dysfunction and/or transplant rejection. In some instances, the method may be used when the result of the glomerular filtration rate (GFR) test indicates kidney dysfunction and/or transplant rejection. In some instances, the method may be used when the result of the renal biopsy indicates kidney dysfunction and/or transplant rejection. In some instances, the method may be used when the result of one or more other gene expression tests indicates kidney dysfunction and/or transplant rejection.
Sensitivity, Specificity, and Accuracy
The methods, kits, and systems disclosed herein for use in identifying, classifying or characterizing a sample may be characterized by having a specificity of at least about 50%. The specificity of the method may be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. The specificity of the method may be at least about 63%. The specificity of the method may be at least about 68%. The specificity of the method may be at least about 72%. The specificity of the method may be at least about 77%. The specificity of the method may be at least about 80%. The specificity of the method may be at least about 83%. The specificity of the method may be at least about 87%. The specificity of the method may be at least about 90%. The specificity of the method may be at least about 92%.
In some embodiments, the present invention provides a method of identifying, classifying or characterizing a sample that gives a sensitivity of at least about 50% using the methods disclosed herein. The sensitivity of the method may be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. The sensitivity of the method may be at least about 63%. The sensitivity of the method may be at least about 68%. The sensitivity of the method may be at least about 72%. The sensitivity of the method may be at least about 77%. The sensitivity of the method may be at least about 80%. The sensitivity of the method may be at least about 83%. The sensitivity of the method may be at least about 87%. The sensitivity of the method may be at least about 90%. The sensitivity of the method may be at least about 92%.
The methods, kits and systems disclosed herein may improve upon the accuracy of current methods of monitoring or predicting a status or outcome of an organ transplant. The methods, kits, and systems disclosed herein for use in identifying, classifying or characterizing a sample may be characterized by having an accuracy of at least about 50%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 63%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 68%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 72%. The accuracy of the method may be at least about 77%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 80%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 83%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 87%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 90%. The accuracy of the method may be at least about 92%.
The methods, kits, and/or systems disclosed herein for use in identifying, classifying or characterizing a sample may be characterized by having a specificity of at least about 50% and/or a sensitivity of at least about 50%. The specificity may be at least about 50% and/or the sensitivity may be at least about 70%. The specificity may be at least about 70% and/or the sensitivity may be at least about 70%. The specificity may be at least about 70% and/or the sensitivity may be at least about 50%. The specificity may be at least about 60% and/or the sensitivity may be at least about 70%. The specificity may be at least about 70% and/or the sensitivity may be at least about 60%. The specificity may be at least about 75% and/or the sensitivity may be at least about 75%.
The methods, kits, and systems for use in identifying, classifying or characterizing a sample may be characterized by having a negative predictive value (NPV) greater than or equal to 90%. The NPV may be at least about 90%, 91%, 92%, 93%, 94%, 95%, 95.2%, 95.5%, 95.7%, 96%, 96.2%, 96.5%, 96.7%, 97%, 97.2%, 97.5%, 97.7%, 98%, 98.2%, 98.5%, 98.7%, 99%, 99.2%, 99.5%, 99.7%, or 100%. The NPV may be greater than or equal to 95%. The NPV may be greater than or equal to 96%. The NPV may be greater than or equal to 97%. The NPV may be greater than or equal to 98%.
The methods, kits, and/or systems disclosed herein for use in identifying, classifying or characterizing a sample may be characterized by having a positive predictive value (PPV) of at least about 30%. The PPV may be at least about 32%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 95.2%, 95.5%, 95.7%, 96%, 96.2%, 96.5%, 96.7%, 97%, 97.2%, 97.5%, 97.7%, 98%, 98.2%, 98.5%, 98.7%, 99%, 99.2%, 99.5%, 99.7%, or 100%. The PPV may be greater than or equal to 95%. The PPV may be greater than or equal to 96%. The PPV may be greater than or equal to 97%. The PPV may be greater than or equal to 98%.
The methods, kits, and/or systems disclosed herein for use in identifying, classifying or characterizing a sample may be characterized by having a NPV may be at least about 90% and/or a PPV may be at least about 30%. The NPV may be at least about 90% and/or the PPV may be at least about 50%. The NPV may be at least about 90% and/or the PPV may be at least about 70%. The NPV may be at least about 95% and/or the PPV may be at least about 30%. The NPV may be at least about 95% and/or the PPV may be at least about 50%. The NPV may be at least about 95% and/or the PPV may be at least about 70%.
The methods, kits, and systems disclosed herein for use in identifying, classifying or characterizing a sample may be characterized by having an error rate of less than about 30%, 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9.5%, 9%, 8.5%, 8%, 7.5%, 7%, 6.5%, 6%, 5.5%, 5%, 4.5%, 4%, 3.5%, 3%, 2.5%, 2%, 1.5%, or 1%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 1%, 0.9%, 0.8%, 0.7%, 0.6%, 0.5%, 0.4%, 0.3%, 0.2%, 0.1% or 0.005%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 10%. The method may be characterized by having an error rate of less than about 5%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 3%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 1%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 0.5%.
The methods, kits, and systems disclosed herein for use in diagnosing, prognosing, and/or monitoring a status or outcome of a transplant in a subject in need thereof may be characterized by having an accuracy of at least about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%. The methods, kits, and systems disclosed herein may be characterized by having an accuracy of at least about 70%. The methods, kits, and systems disclosed herein may be characterized by having an accuracy of at least about 80%. The methods, kits, and systems disclosed herein may be characterized by having an accuracy of at least about 85%. The methods, kits, and systems disclosed herein may be characterized by having an accuracy of at least about 90%. The methods, kits, and systems disclosed herein may be characterized by having an accuracy of at least about 95%.
The methods, kits, and systems disclosed herein for use in diagnosing, prognosing, and/or monitoring a status or outcome of a transplant in a subject in need thereof may be characterized by having a specificity of at least about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%. The methods, kits, and systems disclosed herein may be characterized by having a specificity of at least about 70%. The methods, kits, and systems disclosed herein may be characterized by having a specificity of at least about 80%. The methods, kits, and systems disclosed herein may be characterized by having a specificity of at least about 85%. The methods, kits, and systems disclosed herein may be characterized by having a specificity of at least about 90%. The methods, kits, and systems disclosed herein may be characterized by having a specificity of at least about 95%.
The methods, kits, and systems disclosed herein for use in diagnosing, prognosing, and/or monitoring a status or outcome of a transplant in a subject in need thereof may be characterized by having a sensitivity of at least about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%. The methods, kits, and systems disclosed herein may be characterized by having a sensitivity of at least about 70%. The methods, kits, and systems disclosed herein may be characterized by having a sensitivity of at least about 80%. The methods, kits, and systems disclosed herein may be characterized by having a sensitivity of at least about 85%. The methods, kits, and systems disclosed herein may be characterized by having a sensitivity of at least about 90%. The methods, kits, and systems disclosed herein may be characterized by having a sensitivity of at least about 95%.
The methods, kits, and systems disclosed herein for use in diagnosing, prognosing, and/or monitoring a status or outcome of a transplant in a subject in need thereof may be characterized by having an error rate of less than about 30%, 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9.5%, 9%, 8.5%, 8%, 7.5%, 7%, 6.5%, 6%, 5.5%, 5%, 4.5%, 4%, 3.5%, 3%, 2.5%, 2%, 1.5%, or 1%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 1%, 0.9%, 0.8%, 0.7%, 0.6%, 0.5%, 0.4%, 0.3%, 0.2%, 0.1% or 0.005%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 10%. The method may be characterized by having an error rate of less than about 5%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 3%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 1%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 0.5%.
The classifier, classifier set, classifier probe set, classification system may be characterized by having a accuracy for distinguishing two or more conditions (AR, ANDR, TX, CAN) of at least about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%. The classifier, classifier set, classifier probe set, classification system may be characterized by having a sensitivity for distinguishing two or more conditions (AR, ANDR, TX, CAN) of at least about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%. The classifier, classifier set, classifier probe set, classification system may be characterized by having a selectivity for distinguishing two or more conditions (AR, ANDR, TX, CAN) of at least about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%.
Computer Program
The methods, kits, and systems disclosed herein may include at least one computer program, or use of the same. A computer program may include a sequence of instructions, executable in the digital processing device's CPU, written to perform a specified task. Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), data structures, and the like, that perform particular tasks or implement particular abstract data types. In light of the disclosure provided herein, those of skill in the art will recognize that a computer program may be written in various versions of various languages.
The functionality of the computer readable instructions may be combined or distributed as desired in various environments. The computer program will normally provide a sequence of instructions from one location or a plurality of locations. In various embodiments, a computer program includes, in part or in whole, one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plug-ins, extensions, add-ins, or add-ons, or combinations thereof.
Further disclosed herein are systems for classifying one or more samples and uses thereof. The system may comprise (a) a digital processing device comprising an operating system configured to perform executable instructions and a memory device; (b) a computer program including instructions executable by the digital processing device to classify a sample from a subject comprising: (i) a first software module configured to receive a gene expression profile of one or more genes from the sample from the subject; (ii) a second software module configured to analyze the gene expression profile from the subject; and (iii) a third software module configured to classify the sample from the subject based on a classification system comprising three or more classes. At least one of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. At least two of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. All three of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. Analyzing the gene expression profile from the subject may comprise applying an algorithm. Analyzing the gene expression profile may comprise normalizing the gene expression profile from the subject. In some instances, normalizing the gene expression profile does not comprise quantile normalization.
FIG. 4 shows a computer system (also “system” herein) 401 programmed or otherwise configured for implementing the methods of the disclosure, such as producing a selector set and/or for data analysis. The system 401 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 405, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The system 401 also includes memory 410 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 415 (e.g., hard disk), communications interface 420 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 425, such as cache, other memory, data storage and/or electronic display adapters. The memory 410, storage unit 415, interface 420 and peripheral devices 425 are in communication with the CPU 405 through a communications bus (solid lines), such as a motherboard. The storage unit 415 can be a data storage unit (or data repository) for storing data. The system 401 is operatively coupled to a computer network (“network”) 430 with the aid of the communications interface 420. The network 430 can be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet. The network 430 in some instances is a telecommunication and/or data network. The network 430 can include one or more computer servers, which can enable distributed computing, such as cloud computing. The network 430 in some instances, with the aid of the system 401, can implement a peer-to-peer network, which may enable devices coupled to the system 401 to behave as a client or a server.
The system 401 is in communication with a processing system 435. The processing system 435 can be configured to implement the methods disclosed herein. In some examples, the processing system 435 is a nucleic acid sequencing system, such as, for example, a next generation sequencing system (e.g., Illumina sequencer, Ion Torrent sequencer, Pacific Biosciences sequencer). The processing system 435 can be in communication with the system 401 through the network 430, or by direct (e.g., wired, wireless) connection. The processing system 435 can be configured for analysis, such as nucleic acid sequence analysis.
Methods as described herein can be implemented by way of machine (or computer processor) executable code (or software) stored on an electronic storage location of the system 401, such as, for example, on the memory 410 or electronic storage unit 415. During use, the code can be executed by the processor 405. In some examples, the code can be retrieved from the storage unit 415 and stored on the memory 410 for ready access by the processor 405. In some situations, the electronic storage unit 415 can be precluded, and machine-executable instructions are stored on memory 410.
Digital Processing Device
The methods, kits, and systems disclosed herein may include a digital processing device, or use of the same. In further embodiments, the digital processing device includes one or more hardware central processing units (CPU) that carry out the device's functions. In still further embodiments, the digital processing device further comprises an operating system configured to perform executable instructions. In some embodiments, the digital processing device is optionally connected a computer network. In further embodiments, the digital processing device is optionally connected to the Internet such that it accesses the World Wide Web. In still further embodiments, the digital processing device is optionally connected to a cloud computing infrastructure. In other embodiments, the digital processing device is optionally connected to an intranet. In other embodiments, the digital processing device is optionally connected to a data storage device.
In accordance with the description herein, suitable digital processing devices include, by way of non-limiting examples, server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, netpad computers, set-top computers, handheld computers, Internet appliances, mobile smartphones, tablet computers, personal digital assistants, video game consoles, and vehicles. Those of skill in the art will recognize that many smartphones are suitable for use in the system described herein. Those of skill in the art will also recognize that select televisions, video players, and digital music players with optional computer network connectivity are suitable for use in the system described herein. Suitable tablet computers include those with booklet, slate, and convertible configurations, known to those of skill in the art.
The digital processing device will normally include an operating system configured to perform executable instructions. The operating system is, for example, software, including programs and data, which manages the device's hardware and provides services for execution of applications. Those of skill in the art will recognize that suitable server operating systems include, by way of non-limiting examples, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. Those of skill in the art will recognize that suitable personal computer operating systems include, by way of non-limiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU/Linux®. In some embodiments, the operating system is provided by cloud computing. Those of skill in the art will also recognize that suitable mobile smart phone operating systems include, by way of non-limiting examples, Nokia® Symbian® OS, Apple® iOS®, Research In Motion® BlackBerry OS®, Google® Android®, Microsoft Windows Phone® OS, Microsoft Windows Mobile® OS, Linux®, and Palm® WebOS®.
The device generally includes a storage and/or memory device. The storage and/or memory device is one or more physical apparatuses used to store data or programs on a temporary or permanent basis. In some embodiments, the device is volatile memory and requires power to maintain stored information. In some embodiments, the device is non-volatile memory and retains stored information when the digital processing device is not powered. In further embodiments, the non-volatile memory comprises flash memory. In some embodiments, the non-volatile memory comprises dynamic random-access memory (DRAM). In some embodiments, the non-volatile memory comprises ferroelectric random access memory (FRAM). In some embodiments, the non-volatile memory comprises phase-change random access memory (PRAM). In other embodiments, the device is a storage device including, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, magnetic disk drives, magnetic tapes drives, optical disk drives, and cloud computing based storage. In further embodiments, the storage and/or memory device is a combination of devices such as those disclosed herein.
A display to send visual information to a user will normally be initialized. Examples of displays include a cathode ray tube (CRT, a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT-LCD, an organic light emitting diode (OLED) display. In various further embodiments, on OLED display is a passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display. In some embodiments, the display may be a plasma display, a video projector or a combination of devices such as those disclosed herein.
The digital processing device would normally include an input device to receive information from a user. The input device may be, for example, a keyboard, a pointing device including, by way of non-limiting examples, a mouse, trackball, track pad, joystick, game controller, or stylus; a touch screen, or a multi-touch screen, a microphone to capture voice or other sound input, a video camera to capture motion or visual input or a combination of devices such as those disclosed herein.
Non-Transitory Computer Readable Storage Medium
The methods, kits, and systems disclosed herein may include one or more non-transitory computer readable storage media encoded with a program including instructions executable by the operating system to perform and analyze the test described herein; preferably connected to a networked digital processing device. The computer readable storage medium is a tangible component of a digital that is optionally removable from the digital processing device. The computer readable storage medium includes, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, optical disk drives, cloud computing systems and services, and the like. In some instances, the program and instructions are permanently, substantially permanently, semi-permanently, or non-transitorily encoded on the media.
A non-transitory computer-readable storage media may be encoded with a computer program including instructions executable by a processor to create or use a classification system. The storage media may comprise (a) a database, in a computer memory, of one or more clinical features of two or more control samples, wherein (i) the two or more control samples may be from two or more subjects; and (ii) the two or more control samples may be differentially classified based on a classification system comprising three or more classes; (b) a first software module configured to compare the one or more clinical features of the two or more control samples; and (c) a second software module configured to produce a classifier set based on the comparison of the one or more clinical features.
At least two of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. All three classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. The storage media may further comprise one or more additional software modules configured to classify a sample from a subject. Classifying the sample from the subject may comprise a classification system comprising three or more classes. At least two of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. All three classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function.
Web Application
In some embodiments, a computer program includes a web application. In light of the disclosure provided herein, those of skill in the art will recognize that a web application, in various embodiments, utilizes one or more software frameworks and one or more database systems. In some embodiments, a web application is created upon a software framework such as Microsoft .NET or Ruby on Rails (RoR). In some embodiments, a web application utilizes one or more database systems including, by way of non-limiting examples, relational, non-relational, object oriented, associative, and XML database systems. In further embodiments, suitable relational database systems include, by way of non-limiting examples, Microsoft® SQL Server, mySQL™, and Oracle®. Those of skill in the art will also recognize that a web application, in various embodiments, is written in one or more versions of one or more languages. A web application may be written in one or more markup languages, presentation definition languages, client-side scripting languages, server-side coding languages, database query languages, or combinations thereof. In some embodiments, a web application is written to some extent in a markup language such as Hypertext Markup Language (HTML), Extensible Hypertext Markup Language (XHTML), or eXtensible Markup Language (XML). In some embodiments, a web application is written to some extent in a presentation definition language such as Cascading Style Sheets (CSS). In some embodiments, a web application is written to some extent in a client-side scripting language such as Asynchronous Javascript and XML (AJAX), Flash® Actionscript, Javascript, or Silverlight®. In some embodiments, a web application is written to some extent in a server-side coding language such as Active Server Pages (ASP), ColdFusion, Perl, Java™, JavaServer Pages (JSP), Hypertext Preprocessor (PHP), Python™, Ruby, Tcl, Smalltalk, WebDNA®, or Groovy. In some embodiments, a web application is written to some extent in a database query language such as Structured Query Language (SQL). In some embodiments, a web application integrates enterprise server products such as IBM® Lotus Domino®. In some embodiments, a web application includes a media player element. In various further embodiments, a media player element utilizes one or more of many suitable multimedia technologies including, by way of non-limiting examples, Adobe® Flash®, HTML 5, Apple® QuickTime®, Microsoft® Silverlight®, Java™, and Unity®.
Mobile Application
In some embodiments, a computer program includes a mobile application provided to a mobile digital processing device. In some embodiments, the mobile application is provided to a mobile digital processing device at the time it is manufactured. In other embodiments, the mobile application is provided to a mobile digital processing device via the computer network described herein.
In view of the disclosure provided herein, a mobile application is created by techniques known to those of skill in the art using hardware, languages, and development environments known to the art. Those of skill in the art will recognize that mobile applications are written in several languages. Suitable programming languages include, by way of non-limiting examples, C, C++, C#, Objective-C, Java™, Javascript, Pascal, Object Pascal, Python™, Ruby, VB.NET, WML, and XHTML/HTML with or without CSS, or combinations thereof.
Suitable mobile application development environments are available from several sources. Commercially available development environments include, by way of non-limiting examples, AirplaySDK, alcheMo, Appcelerator®, Celsius, Bedrock, Flash Lite, .NET Compact Framework, Rhomobile, and WorkLight Mobile Platform. Other development environments are available without cost including, by way of non-limiting examples, Lazarus, MobiFlex, MoSync, and Phonegap. Also, mobile device manufacturers distribute software developer kits including, by way of non-limiting examples, iPhone and iPad (iOS) SDK, Android™ SDK, BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, and Windows® Mobile SDK.
Those of skill in the art will recognize that several commercial forums are available for distribution of mobile applications including, by way of non-limiting examples, Apple® App Store, Android™ Market, BlackBerry® App World, App Store for Palm devices, App Catalog for webOS, Windows® Marketplace for Mobile, Ovi Store for Nokia® devices, Samsung® Apps, and Nintendo® DSi Shop.
Standalone Application
In some embodiments, a computer program includes a standalone application, which is a program that is run as an independent computer process, not an add-on to an existing process, e.g., not a plug-in. Those of skill in the art will recognize that standalone applications are often compiled. A compiler is a computer program(s) that transforms source code written in a programming language into binary object code such as assembly language or machine code. Suitable compiled programming languages include, by way of non-limiting examples, C, C++, Objective-C, COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB .NET, or combinations thereof. Compilation is often performed, at least in part, to create an executable program. In some embodiments, a computer program includes one or more executable complied applications.
Web Browser Plug-in
In some embodiments, the computer program includes a web browser plug-in. In computing, a plug-in is one or more software components that add specific functionality to a larger software application. Makers of software applications support plug-ins to enable third-party developers to create abilities which extend an application, to support easily adding new features, and to reduce the size of an application. When supported, plug-ins enable customizing the functionality of a software application. For example, plug-ins are commonly used in web browsers to play video, generate interactivity, scan for viruses, and display particular file types. Those of skill in the art will be familiar with several web browser plug-ins including, Adobe® Flash® Player, Microsoft® Silverlight®, and Apple® QuickTime®. In some embodiments, the toolbar comprises one or more web browser extensions, add-ins, or add-ons. In some embodiments, the toolbar comprises one or more explorer bars, tool bands, or desk bands.
In view of the disclosure provided herein, those of skill in the art will recognize that several plug-in frameworks are available that enable development of plug-ins in various programming languages, including, by way of non-limiting examples, C++, Delphi, Java™, PHP, Python™, and VB .NET, or combinations thereof
Web browsers (also called Internet browsers) are software applications, designed for use with network-connected digital processing devices, for retrieving, presenting, and traversing information resources on the World Wide Web. Suitable web browsers include, by way of non-limiting examples, Microsoft® Internet Explorer®, Mozilla® Firefox®, Google® Chrome, Apple® Safari®, Opera Software® Opera®, and KDE Konqueror. In some embodiments, the web browser is a mobile web browser. Mobile web browsers (also called mircrobrowsers, mini-browsers, and wireless browsers) are designed for use on mobile digital processing devices including, by way of non-limiting examples, handheld computers, tablet computers, netbook computers, subnotebook computers, smartphones, music players, personal digital assistants (PDAs), and handheld video game systems. Suitable mobile web browsers include, by way of non-limiting examples, Google® Android® browser, RIM BlackBerry® Browser, Apple® Safari®, Palm® Blazer, Palm® WebOS® Browser, Mozilla® Firefox® for mobile, Microsoft® Internet Explorer® Mobile, Amazon® Kindle® Basic Web, Nokia® Browser, Opera Software® Opera® Mobile, and Sony® PSP™ browser.
Software Modules
The methods, kits, and systems disclosed herein may include software, server, and/or database modules, or use of the same. In view of the disclosure provided herein, software modules are created by techniques known to those of skill in the art using machines, software, and languages known to the art. The software modules disclosed herein are implemented in a multitude of ways. In various embodiments, a software module comprises a file, a section of code, a programming object, a programming structure, or combinations thereof. In further various embodiments, a software module comprises a plurality of files, a plurality of sections of code, a plurality of programming objects, a plurality of programming structures, or combinations thereof. In various embodiments, the one or more software modules comprise, by way of non-limiting examples, a web application, a mobile application, and a standalone application. In some embodiments, software modules are in one computer program or application. In other embodiments, software modules are in more than one computer program or application. In some embodiments, software modules are hosted on one machine. In other embodiments, software modules are hosted on more than one machine. In further embodiments, software modules are hosted on cloud computing platforms. In some embodiments, software modules are hosted on one or more machines in one location. In other embodiments, software modules are hosted on one or more machines in more than one location.
Databases
The methods, kits, and systems disclosed herein may comprise one or more databases, or use of the same. In view of the disclosure provided herein, those of skill in the art will recognize that many databases are suitable for storage and retrieval of information pertaining to gene expression profiles, sequencing data, classifiers, classification systems, therapeutic regimens, or a combination thereof. In various embodiments, suitable databases include, by way of non-limiting examples, relational databases, non-relational databases, object oriented databases, object databases, entity-relationship model databases, associative databases, and XML databases. In some embodiments, a database is internet-based. In further embodiments, a database is web-based. In still further embodiments, a database is cloud computing-based. In other embodiments, a database is based on one or more local computer storage devices.
Data Transmission
The methods, kits, and systems disclosed herein may be used to transmit one or more reports. The one or more reports may comprise information pertaining to the classification and/or identification of one or more samples from one or more subjects. The one or more reports may comprise information pertaining to a status or outcome of a transplant in a subject. The one or more reports may comprise information pertaining to therapeutic regimens for use in treating transplant rejection in a subject in need thereof. The one or more reports may comprise information pertaining to therapeutic regimens for use in treating transplant dysfunction in a subject in need thereof. The one or more reports may comprise information pertaining to therapeutic regimens for use in suppressing an immune response in a subject in need thereof.
The one or more reports may be transmitted to a subject or a medical representative of the subject. The medical representative of the subject may be a physician, physician's assistant, nurse, or other medical personnel. The medical representative of the subject may be a family member of the subject. A family member of the subject may be a parent, guardian, child, sibling, aunt, uncle, cousin, or spouse. The medical representative of the subject may be a legal representative of the subject.
The term “about,” as used herein and throughout the disclosure, generally refers to a range that may be 15% greater than or 15% less than the stated numerical value within the context of the particular usage. For example, “about 10” would include a range from 8.5 to 11.5.
The term “or” as used herein and throughout the disclosure, generally means “and/or”.

EXAMPLES

The following illustrative examples are representative of embodiments of the software applications, systems, and methods described herein and are not meant to be limiting in any way.

Example 1

Introduction

Improvements in kidney transplantation have resulted in significant reductions in clinical acute rejection (AR) (8-14%) (Meier-Kriesche et al. 2004, Am J Transplant, 4(3): 378-383). However, histological AR without evidence of kidney dysfunction (i.e. subclinical AR) occurs in >15% of protocol biopsies done within the first year. Without a protocol biopsy, patients with subclinical AR would be treated as excellent functioning transplants (TX). Biopsy studies also document significant rates of progressive interstitial fibrosis and tubular atrophy in >50% of protocol biopsies starting as early as one year post transplant.
Two factors contribute to AR: the failure to optimize immunosuppression and individual patient non-adherence. Currently, there is no validated test to measure or monitor the adequacy of immunosuppression; the failure of which is often first manifested directly as an AR episode. Subsequently, inadequate immunosuppression results in chronic rejection and allograft failure. The current standards for monitoring kidney transplant function are serum creatinine and estimated glomerular filtration rates (eGFR). Unfortunately, serum creatinine and eGFR are relatively insensitive markers requiring significant global injury before changing and are influenced by multiple non-immunological factors.
Performing routine protocol biopsies is one strategy to diagnose and treat AR prior to extensive injury. A study of 28 patients one week post-transplant with stable creatinines showed that 21% had unsuspected “borderline” AR and 25% had inflammatory tubulitis (Shapiro et al. 2001, Am J Transplant, 1(1): 47-50). Other studies reveal a 29% prevalence of subclinical rejection (Hymes et al. 2009, Pediatric transplantation, 13(7): 823-826) and that subclinical rejection with chronic allograft nephropathy was a risk factor for late graft loss (Moreso et al. 2006, Am J Transplant, 6(4): 747-752). A study of 517 renal transplants followed after protocol biopsies showed that finding subclinical rejection significantly increased the risk of chronic rejection (Moreso et al. 2012, Transplantation 93(1): 41-46).
We originally reported a peripheral blood gene expression signature by DNA microarrays to diagnose AR (Flechner et al. 2004, Am J Transplant, 4(9): 1475-1489). Subsequently, others have reported qPCR signatures of AR in peripheral blood based on genes selected from the literature or using microarrays (Gibbs et al. 2005, Transpl Immunol, 14(2): 99-108; Li et al. 2012, Am J Transplant, 12(10): 2710-2718; Sabek et al. 2002, Transplantation, 74(5): 701-707; Sarwal et al. 2003, N Engl J Med, 349(2): 125-138; Simon et al. 2003, Am J Transplant, 3(9): 1121-1127; Vasconcellos et al. 1998, Transplantation, 66(5): 562-566). As the biomarker field has evolved, validation requires independently collected sample cohorts and avoidance of over-training during classifier discovery (Lee et al. 2006, Pharm Res, 23(2): 312-328; Chau et al. 2008, Clin Cancer Res, 14(19): 5967-5976). Another limitation is that the currently published biomarkers are designed for 2-way classifications, AR vs. TX, when many biopsies reveal additional ADNR.
We prospectively followed over 1000 kidney transplants from 5 different clinical centers (Transplant Genomics Collaborative Group) to identify 148 instances of unequivocal biopsy-proven AR (n=63), ADNR (n=39), and TX (n=45). Global gene expression profiling was done on peripheral blood using DNA microarrays and robust 3-way class prediction tools (Dabney et al. 2005, Bioinformatics, 21(22): 4148-4154; Shen et al. 2006, Bioinformatics, 22(21): 2635-2642; Zhu et al. 2009, BMC bioinformatics, 10 Suppl 1:S21). Classifiers were comprised of the 200 highest value probe sets ranked by the prediction accuracies with each tool were created with three different classifier tools to insure that our results were not subject to bias introduced by a single statistical method. Importantly, even using three different tools, the 200 highest value probe set classifiers identified were essentially the same. These 200 classifiers had sensitivity, specificity, positive predictive accuracy (PPV), negative predictive accuracy (NPV) and Area Under the Curve (AUC) for the Validation cohort depending on the three different prediction tools used ranging from 82-100%, 76-95%, 76-95%, 79-100%, 84-100% and 0.817-0.968, respectively. Next, the Harrell bootstrapping method (Miao et al. 2013, SAS Global Forum, San Francisco; 2013) based on sampling with replacement was used to demonstrate that these results, regardless of the tool used, were not the consequence of statistical over-fitting. Finally, to model the use of our test in real clinical practice, we developed a novel one-by-one prediction strategy in which we created a large reference set of 118 samples and then randomly took 10 samples each from the AR, ADNR and TX cohorts in the Validation set. These were then blinded to phenotype and each sample was tested by itself against the entire reference set to model practice in a real clinical situation where there is only a single new patient sample obtained at any given time.
Materials and Methods
Patient Populations:
We studied 46 kidney transplant patients with well-functioning grafts and biopsy-proven normal histology (TX; controls), 63 patients with biopsy-proven acute kidney rejection (AR) and 39 patients with acute kidney dysfunction without histological evidence of rejection (ADNR). Inclusion/exclusion criteria are in Table 2. Subjects were enrolled serially as biopsies were performed by 5 different clinical centers (Scripps Clinic, Cleveland Clinic, St. Vincent Medical Center, University of Colorado and Mayo Clinic Arizona). Human Subjects Research Protocols approved at each Center and by the Institutional Review Board of The Scripps Research Institute covered all studies.
Pathology:
All subjects had kidney biopsies (either protocol or “for cause”) graded for evidence of acute rejection by the Banff 2007 criteria (Solez et al. 2008, Am J Transplant, 8(4): 753-760). All biopsies were read by local pathologists and then reviewed and graded in a blinded fashion by a single pathologist at an independent center (LG). The local and single pathologist readings were then reviewed by DRS to standardize and finalize the phenotypes prior to cohort construction and any diagnostic classification analysis. C4d staining was done per the judgment of the local clinicians and pathologists on 69 of the 148 samples (47%; Table 3). Positive was defined as linear, diffuse staining of peritubular capillaries. Donor specific antibodies were not measured on these patients and thus, we cannot exclude the new concept of C4d negative antibody-mediated rejection (Sis et al. 2009, Am J Transplant, 9(10): 2312-2323; Wiebe et at 2012, Am J Transplant, 12(5): 1157-1167).
Gene Expression Profiling and Statistical Analysis:
RNA was extracted from Paxgene tubes using the Paxgene Blood RNA system (PreAnalytix) and GlobinClear (Ambion). Biotinylated cRNA was prepared with Ambion MessageAmp Biotin H kit (Ambion) and hybridized to Affymetrix Human Genome U133 Plus 2.0 GeneChips. Normalized Signals were generated using frozen RMA (fRMA) in R (McCall et al. 2010, Biostatistics, 11(2): 242-253; McCall et al. 2011, BMC bioinformatics, 12:369). The complete strategy used to discover, refine and validate the biomarker panels is shown in FIG. 1. Class predictions were performed with multiple tools: Nearest Centroids, Support Vector Machines (SVM) and Diagonal Linear Discriminant Analysis (DLDA). Predictive accuracy is calculated as true positives+true negatives/true positives+false positives+false negatives+true negatives. Other diagnostic metrics given are sensitivity, specificity, Postive Predictive Value (PPV), Negative Predictive Value (NPV) and Area Under the Curve (AUC). Receiver Operating Characteristic (ROC) curves were generated using pROC in R (Robin et al. 2011, BMC bioinformatics, 12:77). Clinical study parameters were tested by multivariate logistic regression with an adjusted (Wald test) p-value and a local false discovery rate calculation (q-value). Chi Square analysis was done using GraphPad. CEL files and normalized signal intensities are posted in NIH Gene Expression Omnibus (GEO) (accession number GSE15296).
Results
Patient Population
Subjects were consented and biopsied in a random and prospective fashion at five Centers (n=148; Table 3). Blood was collected at the time of biopsy. TX represented protocol biopsies of transplants with excellent, stable graft function and normal histology (n=45). AR patients were biopsied “for cause” based on elevated serum creatinine (n=63). We excluded subjects with recurrent kidney disease, BKV or other infections. ADNRs were biopsied “for cause” based on suspicion of AR but had no AR by histology (n=39). Differences in steroid use (less in TX) reflect more protocol biopsies done at a steroid-free center. As expected, creatinines were higher in AR and ADNR than TX. Creatinine was the only significant variable by multivariable logistic regression by either phenotype or cohort. C4d staining, when done, was negative in TX and ADNR. C4d staining was done in 56% of AR subjects by the judgment of the pathologists and was positive in 1⅔6 (33%) of this selected group.
Three-Way Predictions
We randomly split the data from 148 samples into two cohorts, Discovery and Validationas shown in FIG. 1. Discovery was 32 AR, 20 ADNR, 23 TX and Validation was 32 AR, 19 ADNR, 22 TX. Normalization used Frozen Robust Multichip Average (fRMA) (McCall et al. 2010, Biostatistics, 11(2): 242-253; McCall et al. 2011, BMC bioinformatics, 12:369). Probe sets with median Log₂signals less than 5.20 in >70% of samples were eliminated. A 3-class univariate F-test was done on the Discovery cohort (1000 random permutations, FDR<10%; BRB ArrayTools) yielding 2977 differentially expressed probe sets using the Hu133 Plus 2.0 cartridge arrays plates (Table 1b). In another experiment, 4132 differentially expressed probe sets were yield using the HT HG-U133+PM array plates (Table 1 d). The Nearest Centroid algorithm (Dabney et al. 2005, Bioinformatics, 21(22): 4148-4154) was used to create a 3-way classifier for AR, ADNR and TX in the Discovery cohort revealing 200 high-value probe sets (Table 1a: using the Hu133 Plus 2.0 cartridge arrays plates; Table 1c: using the HT HG-U133+PM array plates) defined by having the lowest class predictive error rates (Table 4; see also Supplemental Statistical Methods).
Thus testing our locked classifier in the validation cohort demonstrated predictive accuracies of 83%, 82% and 90% for the TX vs. AR, TX vs. ADNR and AR vs. ADNR respectively (Table 4). The AUCs for the TX vs. AR, TX vs. ADNR and the AR vs. ADNR comparisons were 0.837, 0.817 and 0.893, respectively as shown in FIG. 5. The sensitivity, specificity, PPV, NPV for the three comparisons were in similar ranges and are shown in Table 4. To determine a possible minimum classifier set, we ranked the 200 probe sets by p values and tested the top 25, 50, 100 and 200 (Table 4). The conclusion is that given the highest value classifiers discovered using unbiased whole genome profiling, the total number of classifiers necessary for testing may be 25. However, below that number the performance of our 3-way classifier falls off to about 50% AUC at 10 or lower (data not shown).
Alternative Prediction Tools
Robust molecular diagnostic strategies should work using multiple tools. Therefore, we repeated the entire 3-way locked discovery and validation process using DLDA and Support Vector Machines (Table 5). All the tools perform nearly equally well with 100-200 classifiers though small differences were observed.
It is also important to test whether a new classifier is subject to statistical over-fitting that would inflate the claimed predictive results. This testing can be done with the method of Harrell et al. using bootstrapping where the original data set is sampled 1000 times with replacement and the AUCs calculated for each (Miao et al, 2013, SAS Global Forum, San Francisco; 2013). The original AUCs minus the calculated AUCs for each tool create the corrections in the AUCs for “optimism” in the original predictions that adjust for potential over-fitting (Table 6). Therefore we combined the Discovery and Validation cohorts and performed a 3-class univariate F-test on the whole data set of 148 samples (1000 random permutations, FDR<10%; BRB ArrayTools). This yielded 2666 significantly expressed genes from which we selected the top 200 by p-values. Results using NC, SVM and DLDA with these 200 probe sets are shown in Table 6. Optimism-corrected AUCs from 0.823-0,843 were obtained for the 200-probe set classifier discovered with the 2 cohort-based strategy. Results for the 200-classifier set obtained from the full study sample set of 148 were 0.851-0.866. These results demonstrate that over-fitting is not a major problem as would be expected from a robust set of classifiers (FIG. 7). These results translate to sensitivity, specificity, PPV and NPV of 81%, 93%, 92% and 84% for AR vs. TX; 90%, 85%, 86% and 90% for ADNR vs. TX and 85%, 96%, 95% and 87% for AR vs. ADNR.
Validation in One-by-One Predictions
In clinical practice the diagnostic value of a biomarker is challenged each time a single patient sample is acquired and analyzed. Thus, prediction strategies based on large cohorts of known clinical classifications do not address the performance of biomarkers in their intended application. Two problems exist with cohort-based analysis. First, signal normalization is typically done on the entire cohort, which is not the case in a clinical setting for one patient. Quantile normalization is a robust method but has 2 drawbacks; it cannot be used in clinical settings where samples must be processed individually or in small batches and data sets normalized separately are not comparable. Frozen RMA (fRMA) overcomes these limitations by normalization of individual arrays to large publicly available microarray databases allowing for estimates of probe-specific effects and variances to be pre-computed and “frozen” (McCall et al. 2010, Biostatistics, 11(2): 242-253; McCall et al. 2011, BMC bioinformatics, 12:369). The second problem with cohort analysis is that all the clinical phenotypes are already known and classification is done on the entire cohort. To address these challenges, we removed 30 random samples from the Validation cohort (10 AR, 10 ADNR, 10 TX), blinded their classifications and left a Reference cohort of 118 samples with known phenotypes. Classification was done by adding one blinded sample at a time to the Reference cohort. Using the 200-gene, 3-way classifier derived in NC, we demonstrated an overall predictive accuracy of 80% and individual accuracies of 80% AR, 90% ADNR and 70% TX and AUCs of 0.885, 0.754 and 0.949 for the AR vs. TX, ADNR vs. TX and the AR vs. ADNR comparisons, respectively as shown in FIG. 6.

DISCUSSION

Ideally, molecular markers will serve as early warnings for immune-mediated injury, before renal function deteriorates, and also permit optimization of immunosuppression. We studied a total of 148 subjects with biopsy-proven phenotypes identified in 5 different clinical centers by following over 1000 transplant patients. Global RNA expression of peripheral blood was used to profile 63 patients with biopsy-proven AR, 39 patients with ADNR and 46 patients with excellent function and normal histology (TX).
We addressed several important and often overlooked aspects of biomarker discovery. To avoid over training, we used a discovery cohort to establish the predictive equation and its corresponding classifiers, then locked these down and allowed no further modification. We then tested the diagnostic on our validation cohort. To demonstrate the robustness of our approach, we used multiple, publically available prediction tools to establish that our results are not simply tool-dependent artifacts. We used the bootstrapping method of Harrell to calculate optimism-corrected AUCs and demonstrated that our predictive accuracies are not inflated by over-fitting. We also modeled the actual clinical application of this diagnostic, with a new strategy optimized to normalizing individual samples by fRMA. We then used 30 blinded samples from the validation cohort and tested them one-by-one. Finally, we calculated the statistical power of our analysis and determined that we have greater than 90% power at a significance level of p<0.001. We concluded that peripheral blood gene expression profiling can be used to diagnose AR and ADNR in patients with acute kidney transplant dysfunction. An interesting finding is that we got the same results using the classic two-cohort strategy (discovery vs. validation) as we did using the entire sample set and creating our classifiers with the same tools but using the Harrell bootstrapping method to control for over-fitting. Thus, the current thinking that all biomarker signatures require independent validation cohorts may need to be reconsidered.
In the setting of acute kidney transplant dysfunction, we are the first to address the common clinical challenge of distinguishing AR from ADNR by using 3-way instead of 2-way classification algorithms.
Additional methods may comprise a prospective, blinded study. The biomarkers may be further validated using a prospective, blinded study. Methods may comprise additional samples. The additional samples may be used to classify the different subtypes of T cell-mediated, histologically-defined AR. The methods may further comprise use of one or more biopsies. The one or more biopsies may be used to develop detailed histological phenotyping. The methods may comprise samples obtained from subjects of different ethnic backgrounds. The methods may comprise samples obtained from subjects treated with various therapies (e.g., calcineurin inhibitors, mycophenolic acid derivatives, and steroids. The methods may comprise samples obtained from one or more clinical centers. The use of samples obtained from two or more clinical centers may be used to identify any differences in the sensitivity and/or specificity of the methods to classify and/or characterize one or more samples. The use of samples obtained from two or more clinical centers may be used to determine the effect of race and/or therapy on the sensitivity and/or specificity of the methods disclosed herein. The use of multiple samples may be used to determine the impact of bacterial and/or viral infections on the sensitivity and/or specificity of the methods disclosed herein.
The samples may comprise pure ABMR (antibody mediated rejection). The samples may comprise mixed ABMR/TCMR (T-cell mediated rejection). In this example, we had 12 mixed ABMR/TCMR instances but only 1 of the 12 was misclassified for AR. About 30% of our AR subjects had biopsies with positive C4d staining. However, supervised clustering to detect outliers did not indicate that our signatures were influenced by C4d status. At the time this study was done it was not common practice to measure donor-specific antibodies. However, we note the lack of correlation with C4d status for our data.
The methods disclosed herein may be used to determine a mechanism of ADNR since these patients were biopsied based on clinical judgments of suspected AR after efforts to exclude common causes of acute transplant dysfunction. While our results from this example do not address this question, it is evident that renal transplant dysfunction is common to both AR and ADNR. The levels of kidney dysfunction based on serum creatinines were not significantly different between AR and ADNR subjects. Thus, these gene expression differences are not based simply on renal function or renal injury. Also, the biopsy histology for the ADNR patients revealed nonspecific and only focal tubular necrosis, interstitial edema, scattered foci of inflammatory cells that did not rise to even borderline AR and nonspecific arteriolar changes consistent but not diagnostic of CNI toxicity.
Biopsy-based diagnosis may be subject to the challenge of sampling errors and differences between the interpretations of individual pathologists (Mengel et al. 2007, Am J Transplant, 7(10): 2221-2226). To mitigate this limitation, we used the Banff schema classification and an independent central biopsy review of all samples to establish the phenotypes. Another question is how these signatures would reflect known causes of acute kidney transplant dysfunction (e.g. urinary tract infection, CMV and BK nephropathy). Our view is that there are already well-established, clinically validated and highly sensitive tests available to diagnose each of these. Thus, for implementation and interpretation of our molecular diagnostic for AR and ADNR clinicians would often do this kind of laboratory testing in parallel. In complicated instances a biopsy will still be required, though we note that a biopsy is also not definitive for sorting out AR vs. BK nephropathy.
The methods may be used for molecular diagnostics to predict outcomes like AR, especially diagnose subclinical AR, prior to enough tissue injury to result in kidney transplant dysfunction. The methods may be used to measure and ultimately optimize the adequacy of long term immunosuppression by serial monitoring of blood gene expression. The design of the present study involved blood samples collected at the time of biopsies. The methods may be used to predict AR or ADNR. The absence of an AR gene profile in a patient sample may be a first measure of adequate immunosuppression and may be integrated into a serial blood monitoring protocol. Demonstrating the diagnosis of subclinical AR and the predictive capability of our classifiers may create the first objective measures of adequate immunosuppression. One potential value of our approach using global gene expression signatures developed by DNA microarrays rather than highly reduced qPCR signatures is that these more complicated predictive and immunosuppression adequacy signatures can be derived later from prospective studies like CTOT08. In turn, an objective metric for the real-time efficacy of immunosuppression may allow the individualization of drug therapy and enable the long term serial monitoring necessary to optimize graft survival and minimize drug toxicity.
While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention.
Supplemental Statistical Methods
All model selection was done in Partek Genomics Suite v6.6 using the Partek user guide model selection, 2010: Nearest Centroid
The Nearest Centroid classification method was based on [Tibshirani, R., Hastie, T., Narasimham, B., and Chu, G (2003): Class Prediction by Nearest Shrunken Centroids, with Applications to DNA Microarrays. Statist. Sci. Vol. 18 (1):104-117] and [Tou, J. T., and Gonzalez, R. C. (1974): Pattern Recognition Principals, Addison- Wesley, Reading, Massachusetts]. The centroid classifications were done by assigning equal prior probabilities.
Support Vector Machines
Support Vector Machines (SVMs) attempt to find a set of hyperplanes (one for each pair of classes) that best classify the data. It does this by maximizing the distance of the hyperplanes to the closest data points on both sides. Partek uses the one-against-one method as described in “A comparison of methods for multi-class support vector machines” (C. W. Hsu and C. J. Lin. IEEE Transactions on Neural Networks, 13(2002), 415-425).
To run model selection with SVM cost with shrinking was used. Cost of 1 to 1000 with Step 100 was chosen to run several models. The radial basis kernel (gamma) was used. The kernel parameters were 1/number of columns.
Diagonal Linear Discriminant Analysis
The Discriminant Analysis method can do predictions based on the class variable. The linear with equal prior probability method was chosen.
Linear Discriminant Analysis is performed in Partek using these steps:

- Calculation of a common (pooled) covariance matrix and within-group means
- Calculation of the set of linear discriminant functions from the common covariance and the within-group means
- Classification using the linear discriminant functions

The common covariance matrix is a pooled estimate of the within-group covariance matrices:
ΣSWi
S=i
Σni−Ci
Thus, for linear discriminant analysis, the linear discriminant function for class i is defined as: d(x)=−1(x−m)t S−1(x−m)+In P(w).
Optimism-Corrected AUC's
The steps for estimating the optimism-corrected AUCs are based on the work of F. Harrell published in [Regression Modeling Strategies: With applications to linear models, logistic regression, and survival analysis. Springer, New York (2001)].
The basic approach is described in [Miao Y M, Cenzer I S, Kirby K A, Boscardin J W. Estimating Harrell's Optimism on Predictive Indices Using Bootstrap Samples. SAS Global Forum 2013; San Francisco]:

1. Select the predictors and fit a model using the full dataset and a particular variable selection method. From that model, calculate the apparent discrimination (capp).
2. Generate M=100 to 200 datasets of the same sample size (n) using bootstrap samples with replacement.
3. For each one of the new datasets m . . . M, select predictors and fit the model using the exact same algorithmic approach as in step 1 and calculate the discrimination (cboot (m)).
4. For each one of the new models, calculate its discrimination back on the original data set (corig(m)). For this step, the regression coefficients can either be fixed to their values from step 3 to determine the joint degree of over-fitting from both selection and estimation or can be re-estimated to determine the degree of over-fitting from selection only.
5. For each one of the bootstrap samples, the optimism in the fit is o(m)=corig(m)−cboot(m). The average of these values is the optimism of the original model.
6. The optimism-corrected performance of the original model is then cadj=capp−o. This value is a nearly unbiased estimate of the expected values of the optimism that would be obtained in external validation.

We adapted this model in Partek Genomics Suite using 1000 samplings with replacement of our dataset (n=148). An original AUC was calculated on the full dataset, and then the average of the M=1000 samplings was also estimated. The difference between the original and the estimated AUC's was designated as the optimism and this was subtracted from the original AUC to arrive at the “optimism-corrected AUC”. In the text, we specifically compared the AUC's that we reported by testing our locked 200-probe set classifiers on only our Validation cohort (see Table 4) to the optimism-corrected AUC's (see Table 5). The results demonstrate little difference consistent with the conclusion that our high predictive accuracies are not the result of over-fitting.

TABLE 1a

The top 200 gene probeset used in the 3-Way AR, ADNR TX ANOVA Analysis (using the Hu133 Plus 2.0 cartridge arrays plates)

	Geom	Geom	Geom
	mean of	mean of	mean of
Parametric	intensities	intensities	intensities						Pairwise
p-value	in class 1	in class 2	in class 3	ProbeSet	Symbol	Name	EntrezID	DefinedGenelist	significant

1	<1e−07	6.48	7.08	7.13	212167_s_at	SMARCB1	SWI/SNF	6598	Chromatin	(1, 2), (1, 3)
							related, matrix		Remodeling by
							associated, actin		hSWI/SNF ATP-
							dependent		dependent Complexes
							regulator of
							chromatin,
							subfamily b,
							member 1
2	<1e−07	9.92	9.42	10.14	201444_s_at	ATP6AP2	ATPase, H+	10159		(2, 1), (2, 3)
							transporting,
							lysosomal
							accessory
							protein 2
3	<1e−07	5.21	5.01	5.68	227658_s_at	PLEKHA3	pleckstrin	65977		(1, 3), (2, 3)
							homology
							domain
							containing,
							family A
							(phosphoinositide
							binding
							specific)
							member 3
4	1.00E−07	6.73	7.62	7.73	201746_at	TP53	tumor protein	7157	Apoptotic Signaling	(1, 2), (1, 3)
							p53		in Response to DNA
									Damage, ATM
									Signaling Pathway,
									BTG family proteins
									and cell cycle
									regulation, Cell Cycle:
									G1/S Check Point,
									Cell Cycle: G2/M
									Checkpoint,
									Chaperones modulate
									interferon Signaling
									Pathway, CTCF: First
									Multivalent Nuclear
									Factor, Double
									Stranded RNA
									Induced Gene
									Expression, Estrogen-
									responsive protein Efp
									controls cell cycle and
									breast tumors growth,
									Hypoxia and p53 in
									the Cardiovascular
									system, Overview of
									telomerase protein
									component gene hTert
									Transcriptional
									Regulation, p53
									Signaling Pathway,
									RB Tumor
									Suppressor/Checkpoint
									Signaling in
									response to DNA
									damage, Regulation of
									cell cycle progression
									by Plk3, Regulation of
									transcriptional activity
									by PML, Role of
									BRCA1, BRCA2 and
									ATR in Cancer
									Susceptibility,
									Telomeres,
									Telomerase, Cellular
									Aging, and
									Immortality, Tumor
									Suppressor Arf
									Inhibits Ribosomal
									Biogenesis,
									Amyotrophic lateral
									sclerosis (ALS),
									Apoptosis, Cell cycle,
									Colorectal cancer,
									Huntington\'s disease,
									MAPK signaling
									pathway, Wnt sign . . .
5	1.00E−07	6.64	6.07	6.87	218292_s_at	PRKAG2	protein kinase,	51422	ChREBP regulation	(2, 1), (2, 3)
							AMP-activated,		by carbohydrates and
							gamma 2 non-		cAMP, Reversal of
							catalytic subunit		Insulin Resistance by
									Leptin, Adipocytokine
									signaling pathway,
									Insulin signaling
									pathway
6	1.00E−07	11.08	11.9	12.02	1553551_s_at	ND2	MTND2	4536		(1, 2), (1, 3)
7	2.00E−07	8.6	9.41	9.21	210996_s_at	YWHAE	tyrosine 3-	7531	Cell cycle	(1, 2), (1, 3)
							monooxygenase/
							tryptophan 5-
							monooxygenase
							activation
							protein, epsilon
							polypeptide
8	2.00E−07	5.89	6.92	6.23	243037_at					(1, 2), (3, 2)
9	2.00E−07	7.61	7	7.81	200890_s_at	SSR1	signal sequence	6745		(2, 1), (2, 3)
							receptor, alpha
10	2.00E−07	5.18	6.71	6.12	1570571_at	CCDC91	coiled-coil	55297		(1, 2), (1, 3)
							domain
							containing 91
11	3.00E−07	7.01	6.55	7.21	233748_x_at	PRKAG2	protein kinase,	51422	ChREBP regulation	(2, 1), (2, 3)
							AMP-activated,		by carbohydrates and
							gamma 2 non-		cAMP, Reversal of
							catalytic subunit		Insulin Resistance by
									Leptin, Adipocytokine
									signaling pathway,
									Insulin signaling
									pathway
12	3.00E−07	7.96	7.29	7.86	224455_s_at	ADPGK	ADP-dependent	83440		(2, 1), (2, 3)
							glucokinase
13	3.00E−07	8.42	7.93	8.41	223931_s_at	CHFR	checkpoint with	55743	Tryptophan	(2, 1), (2, 3)
							forkhead and		metabolism
							ring finger
							domains, E3
							ubiquitin protein
							ligase
14	3.00E−07	4.84	5.44	5.11	236766_at					(1, 2), (1, 3),
										(3, 2)
15	3.00E−07	7.95	8.72	7.73	242068_at					(1, 2), (3, 2)
16	3.00E−07	6.96	6.58	7.31	215707_s_at	PRNP	prion protein	5621	Prion Pathway,	(2, 1), (2, 3)
									Neurodegenerative
									Disorders, Prion
									disease
17	3.00E−07	6.68	7.73	7.4	1558220_at					(1, 2), (1, 3)
18	3.00E−07	6.53	6.19	6.87	203100_s_at	CDYL	chromodomain	9425		(2, 1), (2, 3)
							protein, Y-like
19	3.00E−07	6.19	5.66	6.28	202278_s_at	SPTLC1	serine	10558	Sphingolipid	(2, 1), (2, 3)
							palmitoyltransferase,		metabolism
							long chain
							base subunit 1
20	4.00E−07	7.73	7.83	6.71	232726_at					(3, 1), (3, 2)
21	4.00E−07	9.78	9.22	9.81	218178_s_at	CHMP1B	charged	57132		(2, 1), (2, 3)
							multivesicular
							body protein 1B
22	4.00E−07	7.08	6.35	7.25	223585_x_at	KBTBD2	kelch repeat and	25948		(2, 1), (2, 3)
							BTB (POZ)
							domain
							containing 2
23	4.00E−07	4.85	4.53	5.49	224407_s_at	MST4	serine/threonine	51765		(1, 3), (2, 3)
							protein kinase
							MST4
24	4.00E−07	9.49	9.74	8.95	239597_at					(3, 1), (3, 2)
25	4.00E−07	4.3	4.76	4.42	239987_at					(1, 2), (3, 2)
26	5.00E−07	5.49	6.06	5.84	243667_at					(1, 2), (1, 3)
27	6.00E−07	8.08	7.32	7.95	209287_s_at	CDC42EP3	CDC42 effector	10602		(2, 1), (2, 3)
							protein (Rho
							GTPase binding) 3
28	6.00E−07	7.81	7.17	8	212008_at	UBXN4	UBX domain	23190		(2, 1), (2, 3)
							protein 4
29	6.00E−07	4.88	4.57	5.27	206288_at	PGGT1B	protein	5229		(1 ,3), (2, 3)
							geranylgeranyl-
							transferase type I,
							beta subunit
30	6.00E−07	9.75	9.98	9.26	238883_at					(3, 1), (3, 2)
31	7.00E−07	6.19	5.42	6.79	207794_at	CCR2	chemokine (C-C	729230		(2, 1), (2, 3)
							motif) receptor 2
32	7.00E−07	8.17	8.58	7.98	242143_at					(1, 2), (3, 2)
33	7.00E−07	4.52	5.01	5.07	205964_at	ZNF426	zinc finger	79088		(1, 2), (1, 3)
							protein 426
34	8.00E−07	6.68	5.68	6.75	1553685_s_at	SP1	Sp1	6667	Agrin in Postsynaptic	(2, 1), (2, 3)
							transcription		Differentiation,
							factor		Effects of calcineurin
									in Keratinocyte
									Differentiation,
									Human
									Cytomegalovirus and
									Map Kinase
									Pathways,
									Keratinocyte
									Differentiation,
									MAPKinase Signaling
									Pathway, Mechanism
									of Gene Regulation by
									Peroxisome
									Proliferators via
									PPARa(alpha),
									Overview of
									telomerase protein
									component gene hTert
									Transcriptional
									Regulation, Overview
									of telomerase RNA
									component gene
									hTerc Transcriptional
									Regulation, TGF-beta
									signaling pathway
35	8.00E−07	5.08	5.8	6.03	219730_at	MED18	mediator complex	54797		(1, 2), (1, 3)
							subunit 18
36	9.00E−07	5.74	6.07	5.74	233004_x_at					(1, 2), (3, 2)
37	9.00E−07	5.5	6.4	6.06	242797_x_at					(1, 2), (1, 3)
38	9.00E−07	8.4	8.03	8.65	200778_s_at	2-Sep	septin 2	4735		(2, 1), (2, 3)
39	1.00E−06	7.64	6.65	7.91	211559_s_at	CCNG2	cyclin G2	901		(2, 1), (2, 3)
40	1.00E−06	6.71	7.3	7.23	221090_s_at	OGFOD1	2-oxoglutarate	55239		(1, 2), (1, 3)
							and iron-
							dependent
							oxygenase
							domain
							containing 1
41	1.00E−06	4.36	5.14	4.87	240232_at					(1, 2), (1, 3)
42	1.10E−06	6.55	6.86	7.12	221650_s_at	MED18	mediator complex	54797		(1, 2), (1, 3),
							subunit 18			(2, 3)
43	1.10E−06	8	8.48	8.26	214670_at	ZKSCAN1	zinc finger with	7586		(1, 2), (1, 3),
							KRAB and			(3, 2)
							SCAN domains 1
44	1.20E−06	6.55	6.17	7.04	202089_s_at	SLC39A6	solute carrier	25800		(1, 3), (2, 3)
							family 39 (zinc
							transporter),
							member 6
45	1.20E−06	7.05	6.31	7.45	211825_s_at	FLI1	Friend leukemia	2313		(2, 1), (2, 3)
							virus integration 1
46	1.20E−06	6.05	6.85	6.71	243852_at	LUC7L2	LUC7-like 2 (S.	51631		(1, 2), (1, 3)
							cerevisiae)
47	1.20E−06	8.27	7.44	8.6	207549_x_at	CD46	CD46 molecule,	4179	Complement and	(2, 1), (2, 3)
							complement		coagulation cascades
							regulatory
							protein
48	1.30E−06	4.7	5.52	4.65	242737_at					(1, 2), (3, 2)
49	1.30E−06	4.93	4.67	4.57	239189_at	CASKIN1	CASK	57524		(2, 1), (3, 1)
							interacting
							protein 1
50	1.30E−06	7.66	8.08	7.47	232180_at	UGP2	UDP-glucose	7360	Galactose	(1,2), (3, 2)
							pyrophosphorylase 2		metabolism,
									Nucleotide sugars
									metabolism, Pentose
									and glucuronate
									interconversions,
									Starch and sucrose
									metabolism
51	1.40E−06	7.47	6.64	7.31	210971_s_at	ARNTL	aryl hydrocarbon	406	Circadian Rhythms	(2, 1), (2, 3)
							receptor nuclear
							translocator-like
52	1.40E−06	8.66	8.99	8.1	232307_at					(3, 1), (3, 2)
53	1.40E−06	7.06	6.48	7.56	222699_s_at	PLEKHF2	pleckstrin	79666		(2, 1), (2, 3)
							homology
							domain
							containing,
							family F (with
							FYVE domain)
							member 2
54	1.60E−06	6.59	6.62	6.13	234435_at					(3, 1), (3, 2)
55	1.60E−06	3.94	3.51	4.17	207117_at	ZNF117	zinc finger	51351		(2, 1), (2, 3)
							protein 117
56	1.60E−06	7.57	7.25	8.26	1553530_a_at	ITGB1	integrin, beta 1	3688	Adhesion and	(1, 3), (2, 3)
							(fibronectin		Diapedesis of
							receptor, beta		Lymphocytes,
							polypeptide,		Adhesion Molecules
							antigen CD29		on Lymphocyte,
							includes MDF2,		Agrin in Postsynaptic
							MSK12)		Differentiation,
									Aspirin Blocks
									Signaling Pathway
									Involved in Platelet
									Activation, B Cell
									Survival Pathway,
									Cells and Molecules
									involved in local acute
									inflammatory
									response, Eph Kinases
									and ephrins support
									platelet aggregation,
									Erk and PI-3 Kinase
									Are Necessary for
									Collagen Binding in
									Corneal Epithelia,
									Erk1/Erk2 Mapk
									Signaling pathway,
									Integrin Signaling
									Pathway, mCalpain
									and friends in Cell
									motility, Monocyte
									and its Surface
									Molecules, PTEN
									dependent cell cycle
									arrest and apoptosis,
									Ras-Independent
									pathway in NK cell-
									mediated cytotoxicity,
									Signaling of
									Hepatocyte Growth
									Factor Receptor,
									Trefoil Factors Initiate
									Mucosal Healing,
									uCalpain and friends
									in Cell spread, Axon
									guidance, Cell
									adhesion molecules
									(CAMs), ECM-
									receptor interaction,
									Focal adhesion,
									Leukocyte
									transendothelial
									migration, Regulation
									of actin cytoskeleton
57	1.60E−06	5.67	5.02	6.16	214786_at	MAP3K1	mitogen-	4214	Angiotensin II	(2, 1), (2, 3)
							activated protein		mediated activation of
							kinase kinase		JNK Pathway via
							kinase 1, E3		Pyk2 dependent
							ubiquitin protein		signaling, BCR
							ligase		Signaling Pathway,
									CD40L Signaling
									Pathway, Ceramide
									Signaling Pathway,
									EGF Signaling
									Pathway, FAS
									signaling pathway
									(CD95), Fc Epsilon
									Receptor I Signaling
									in Mast Cells, fMLP
									induced chemokine
									gene expression in
									HMC-1 cells, HIV-I
									Nef: negative effector
									of Fas and TNF,
									Human
									Cytomegalovirus and
									Map Kinase
									Pathways, Inhibition
									of Cellular
									Proliferation by
									Gleevec, Keratinocyte
									Differentiation, Links
									between Pyk2 and
									Map Kinases, Map
									Kinase Inactivation of
									SMRT Corepressor,
									MAPKinase Signaling
									Pathway,
									Neuropeptides VIP
									and PACAP inhibit
									the apoptosis of
									activated T cells, NF-
									kB Signaling
									Pathway, p38 MAPK
									Signaling Pathway,
									PDGF Signaling
									Pathway, Rac 1 cell
									motility signaling
									pathway, Role of
									MAL in Rho-
									Mediated Activation
									of SRF, Signal
									transduction through
									IL1R, T Cell Receptor
									Signaling Pathway,
									The 4-1BB-dependent
									immune response,
									TNF/Stress Related
									Signaling, TNFR1
									Signaling Pathway,
									TNFR2 Sig . . .
58	1.70E−06	7.49	7.27	7.73	222729_at	FBXW7	F-box and WD	55294	Cyclin E Destruction	(2, 1), (2, 3)
							repeat domain		Pathway,
							containing 7, E3		Neurodegenerative
							ubiquitin protein		Disorders, Ubiquitin
							ligase		mediated proteolysis
59	1.80E−06	7.73	7.41	8.08	208310_s_at					(2, 1), (2, 3)
60	1.80E−06	9.06	9.28	8.54	242471_at	SRGAP2B	SLIT-ROBO	647135		(3, 1), (3, 2)
							Rho GTPase
							activating
							protein 2B
61	1.80E−06	7.84	8.15	7.5	238812_at					(3, 1), (3, 2)
62	1.80E−06	6.64	7.28	7.08	206240_s_at	ZNF136	zinc finger	7695		(1, 2), (1, 3)
							protein 136
63	1.80E−06	10.29	9.86	10.35	1555797_a_at	ARPC5	actin related	10092	Regulation of actin	(2, 1), (2, 3)
							protein 2/3		cytoskeleton
							complex,
							subunit 5,
							16 kDa
64	1.90E−06	5.05	5.49	5.24	215068_s_at	FBXL18	F-box and	80028		(1, 2), (3, 2)
							leucine-rich
							repeat protein 18
65	2.00E−06	6.84	6.08	7.16	204426_at	TMED2	transmembrane	10959		(2, 1), (2, 3)
							emp24 domain
							trafficking
							protein 2
66	2.00E−06	5.6	5.19	5.13	234125_at					(2, 1), (3, 1)
67	2.10E−06	9.87	9.38	10.27	200641_s_at	YWHAZ	tyrosine 3-	7534	Cell cycle	(2, 1), (2, 3)
							monooxygenase/
							tryptophan 5-
							monooxygenase
							activation
							protein, zeta
							polypeptide
68	2.10E−06	7.6	6.83	7.89	214544_s_at	SNAP23	synaptosomal-	8773	SNARE interactions	(2, 1), (2, 3)
							associated		in vesicular transport
							protein, 23 kDa
69	2.10E−06	9.57	10.14	9.29	238558_at					(1, 2), (3, 2)
70	2.20E−06	6.79	7.12	6.72	221071_at					(1, 2), (3, 2)
71	2.40E−06	6.98	6.38	7.29	232591_s_at	TMEM30A	transmembrane	55754		(2, 1), (2, 3)
							protein 30A
72	2.40E−06	7.22	7.49	6.35	1569477_at					(3, 1), (3, 2)
73	2.60E−06	7.97	7.26	8.22	211574_s_at	CD46	CD46 molecule,	4179	Complement and	(2, 1), (2, 3)
							complement		coagulation cascades
							regulatory
							protein
74	2.60E−06	7.63	7.37	8.18	201627_s_at	INSIG1	insulin induced	3638		(1, 3), (2, 3)
							gene 1
75	2.60E−06	5.27	5.97	5.53	215866_at					(1, 2), (3, 2)
76	2.80E−06	9.6	9.77	9.42	201986_at	MED13	mediator complex	9969		(3, 2)
							subunit 13
77	2.80E−06	9.21	8.85	9.44	200753_x_at	SRSF2	serine/arginine-	6427	Spliceosomal	(2, 1), (2, 3)
							rich splicing		Assembly
							factor 2
78	3.00E−06	4.65	4.75	5.36	214959_s_at	API5	apoptosis	8539		(1, 3), (2, 3)
							inhibitor 5
79	3.10E−06	7.39	8.28	7.81	217704_x_at	SUZ12P1	suppressor of	440423		(1, 2), (3, 2)
							zeste 12
							homolog
							pseudogene 1
80	3.30E−06	7.38	7.93	7	244535_at					(1, 2), (3, 2)
81	3.40E−06	7.17	6.66	7.86	210786_s_at	FLI1	Friend leukemia	2313		(1, 3), (2, 3)
							virus integration 1
82	3.40E−06	7.33	7.87	7.47	235035_at	SLC35E1	solute carrier	79939		(1, 2), (3, 2)
							family 35,
							member E1
83	3.40E−06	10.42	10.84	10.08	241681_at					(1, 2), (3, 2)
84	3.40E−06	7.13	6.16	7.1	212720_at	PAPOLA	poly(A)	10914	Polyadenylation of	(2, 1), (2, 3)
							polymerase		mRNA
							alpha
85	3.50E−06	5.81	5.47	6.03	205408_at	MLLT10	myeloid/lymphoid	8028		(2, 1), (2, 3)
							or mixed-
							lineage leukemia
							(trithorax
							homolog,
							Drosophila);
							translocated to, 10
86	3.50E−06	5.51	6.12	5.83	238418_at	SLC35B4	solute carrier	84912		(1, 2), (1, 3)
							family 35,
							member B4
87	3.50E−06	6.03	7.01	6.37	1564424_at					(1, 2), (3, 2)
88	3.60E−06	8.65	9.02	8.39	243030_at					(1, 2), (3, 2)
89	3.60E−06	5.52	5.25	5.78	215207_x_at					(2, 1), (2, 3)
90	3.90E−06	6.77	7.36	7.21	235058_at					(1, 2), (1, 3)
91	4.20E−06	8.15	7.94	8.48	202092_s_at	ARL2BP	ADP-	23568		(1, 3), (2, 3)
							ribosylation
							factor-like 2
							binding protein
92	4.40E−06	8.55	8.24	8.6	202162_s_at	CNOT8	CCR4-NOT	9337		(2, 1), (2, 3)
							transcription
							complex,
							subunit 8
93	4.40E−06	8.21	8.1	8.68	201259_s_at	SYPL1	synaptophysin-	6856		(1, 3), (2, 3)
							like 1
94	4.40E−06	7.68	7.79	7.2	236168_at					(3, 1), (3, 2)
95	4.40E−06	6.72	7.58	6.89	1553252_a_at	BRWD3	bromodomain	254065		(1, 2), (3, 2)
							and WD repeat
							domain
							containing 3
96	4.50E−06	6.71	7.67	7.19	244872_at	RBBP4	retinoblastoma	5928	The PRC2 Complex	(1, 2), (3, 2)
							binding protein 4		Sets Long-term Gene
									Silencing Through
									Modification of
									Histone Tails
97	4.50E−06	5.58	6.53	6.36	215390_at					(1, 2), (1, 3)
98	4.60E−06	4.93	6.29	5.36	1566966_at					(1, 2), (3, 2)
99	4.90E−06	5.46	5.07	5.68	225700_at	GLCCI1	glucocorticoid	113263		(2, 1), (2, 3)
							induced
							transcript 1
100	5.00E−06	4.96	5.17	4.79	236324_at	MBP	myelin basic	4155		(1, 2), (3, 2)
							protein
101	5.10E−06	8.08	7.26	8.33	222846_at	RAB8B	RAB8B,	51762		(2, 1), (2, 3)
							member RAS
							oncogene family
102	5.10E−06	6.24	5.75	6.58	1564053_a_at	YTHDF3	YTH domain	253943		(2, 1), (2, 3)
							family, member 3
103	5.20E−06	7	6.36	7.35	216100_s_at	TOR1AIP1	torsin A	26092		(2, 1), (2, 3)
							interacting
							protein 1
104	5.20E−06	6.15	5.97	6.63	1565269_s_at	ATF1	activating	466	TNF/Stress Related	(1, 3), (2, 3)
							transcription		Signaling
							factor 1
105	5.30E−06	8.13	7.73	8.57	220477_s_at	TMEM230	transmembrane	29058		(2, 3)
							protein 230
106	5.30E−06	7.45	8.09	7.72	1559490_at	LRCH3	leucine-rich	84859		(1, 2), (3, 2)
							repeats and
							calponin
							homology (CH)
							domain
							containing 3
107	5.30E−06	7.44	8.05	7.44	225490_at	ARID2	AT rich	196528		(1, 2), (3, 2)
							interactive
							domain 2
							(ARID, RFX-
							like)
108	5.50E−06	7.49	8.18	7.83	244766_at					(1, 2), (3, 2)
109	5.50E−06	7.71	8.41	8	242673_at					(1, 2), (3, 2)
110	5.60E−06	8.97	8.59	9.24	202164_s_at	CNOT8	CCR4-NOT	9337		(2, 1), (2, 3)
							transcription
							complex,
							subunit 8
111	5.70E−06	7.75	8.26	7.52	222357_at	ZBTB20	zinc finger and	26137		(1, 2), (3, 2)
							BTB domain
							containing 20
112	5.90E−06	5.07	5.52	4.71	240594_at					(1, 2), (3, 2)
113	6.00E−06	7.78	7.45	7.96	1554577_a_at	PSMD10	proteasome	5716		(2, 1), (2, 3)
							(prosome,
							macropain) 26S
							subunit, non-
							ATPase, 10
114	6.00E−06	6.55	7.03	6.58	215137_at					(1, 2), (3, 2)
115	6.10E−06	9.46	9.66	9.05	243527_at					(3, 1), (3, 2)
116	6.30E−06	7.8	7.27	8.15	214449_s_at	RHOQ	ras homolog	23433	Insulin signaling	(2, 1), (2, 3)
							family member Q		pathway
117	6.30E−06	7.3	7.92	7.44	216197_at	ATF7IP	activating	55729		(1, 2), (3, 2)
							transcription
							factor 7
							interacting
							protein
118	6.40E−06	7.38	8.17	7.51	1558569_at	LOC100131541	uncharacterized	100131541		(1, 2), (3, 2)
							LOC100131541
119	6.50E−06	4.79	4.55	5.23	244030_at	STYX	serine/threonine/	6815		(1, 3), (2, 3)
							tyrosine
							interacting
							protein
120	6.70E−06	7.2	7.95	7.27	244010_at					(1, 2), (3, 2)
121	7.20E−06	6.36	6.78	6.05	232002_at					(1, 2), (3, 2)
122	7.20E−06	6.11	6.95	6.18	243051_at	CNIH4	cornichon	29097		(1, 2), (3, 2)
							homolog 4
							(Drosophila)
123	7.20E−06	5.89	6.55	6.32	212394_at	EMC1	ER membrane	23065		(1, 2), (1, 3)
							protein complex
							subunit 1
124	7.30E−06	5.6	6.37	5.86	1553407_at	MACF1	microtubule-	23499		(1, 2), (3, 2)
							actin
							crosslinking
							factor 1
125	7.50E−06	5.08	5.84	5.74	214123_s_at	NOP14-AS1	NOP14	317648		(1, 2), (1, 3)
							antisense RNA 1
126	7.50E−06	4.89	5.65	5.06	1564438_at					(1, 2), (3, 2)
127	7.60E−06	8.54	8.88	8.22	229858_at					(3, 2)
128	7.60E−06	9.26	8.77	9.49	215933_s_at	HHEX	hematopoietically	3087	Maturity onset	(2, 1), (2, 3)
							expressed		diabetes of the young
							homeobox
129	7.60E−06	7.97	8.14	7.59	239234_at					(3, 1), (3, 2)
130	7.70E−06	9.71	9.93	9.17	238619_at					(3, 1), (3, 2)
131	7.70E−06	5.46	6.05	5.61	1559039_at	DHX36	DEAH (Asp-	170506		(1, 2), (3, 2)
							Glu-Ala-His)
							box polypeptide 36
132	7.70E−06	9.19	8.57	9.36	222859_s_at	DAPP1	dual adaptor of	27071		(2, 1), (2, 3)
							phosphotyrosine
							and 3-
							phosphoinositides
133	7.80E−06	7.76	7.35	8.07	210285_x_at	WTAP	Wilms tumor 1	9589		(2, 1), (2, 3)
							associated
							protein
134	7.90E−06	5.64	5.2	5.67	238816_at	PSEN1	presenilin 1	5663	Generation of amyloid	(2, 1), (2, 3)
									b-peptide by PS1, g-
									Secretase mediated
									ErbB4 Signaling
									Pathway, HIV-I Nef:
									negative effector of
									Fas and TNF,
									Presenilin action in
									Notch and Wnt
									signaling, Proteolysis
									and Signaling
									Pathway of Notch,
									Alzheimer\'s disease,
									Neurodegenerative
									Disorders, Notch
									signaling pathway,
									Wnt signaling
									pathway
135	7.90E−06	5.6	5.42	5.26	239112_at					(2, 1), (3, 1),
										(3, 2)
136	8.40E−06	6.99	6.66	7.22	211536_x_at	MAP3K7	mitogen-	6885	ALK in cardiac	(2, 1), (2, 3)
							activated protein		myocytes, FAS
							kinase kinase		signaling pathway
							kinase 7		(CD95), MAPKinase
									Signaling Pathway,
									NFkB activation by
									Nontypeable
									Hemophilus
									influenzae, NF-kB
									Signaling Pathway,
									p38 MAPK Signaling
									Pathway, Signal
									transduction through
									IL1R, TGF beta
									signaling pathway,
									Thrombin signaling
									and protease-activated
									receptors, TNFR1
									Signaling Pathway,
									Toll-Like Receptor
									Pathway, WNT
									Signaling Pathway,
									Adherens junction,
									MAPK signaling
									pathway, Toll-like
									receptor signaling
									pathway, Wnt
									signaling pathway
137	8.40E−06	7.82	8.34	7.98	228070_at	PPP2R5E	protein	5529		(1, 2), (3, 2)
							phosphatase 2,
							regulatory
							subunit B′,
							epsilon isoform
138	8.60E−06	5.38	5.07	5.54	220285_at	FAM108B1	family with	51104		(2, 1), (2, 3)
							sequence
							similarity 108,
							member B1
139	8.60E−06	8.07	7.56	8.2	210284_s_at	TAB2	TGF-beta	23118	MAPK signaling	(2, 1), (2, 3)
							activated kinase		pathway, Toll-like
							1/MAP3K7		receptor signaling
							binding protein 2		pathway
140	8.60E−06	5.22	4.5	5.59	1558014_s_at	FAR1	fatty acyl CoA	84188		(2, 1), (2, 3)
							reductase 1
141	8.60E−06	6.25	6.53	6.04	240247_at					(1, 2), (3, 2)
142	8.80E−06	6.64	6.6	7.21	235177_at	METTL21A	methyltransferase	151194		(1, 3), (2, 3)
							like 21A
143	8.90E−06	6.46	7.47	6.7	1569540_at					(1, 2), (3, 2)
144	8.90E−06	6.8	6.34	7.16	224642_at	FYTTD1	forty-two-three	84248		(2, 1), (2, 3)
							domain
							containing 1
145	8.90E−06	7.94	7.19	8.19	204427_s_at	TMED2	transmembrane	10959		(2, 1), (2, 3)
							emp24 domain
							trafficking
							protein 2
146	8.90E−06	9.75	9.99	9.23	233867_at					(3, 1), (3, 2)
147	9.00E−06	10.08	10.61	10.12	212852_s_at	TROVE2	TROVE domain	6738		(1, 2), (3, 2)
							family, member 2
148	9.20E−06	7.39	7.76	7.12	215221_at					(1, 2), (3, 2)
149	9.30E−06	9.17	9.71	9.05	231866_at	LNPEP	leucyl/cystinyl	4012		(1, 2), (3, 2)
							aminopeptidase
150	9.50E−06	5.34	5.61	5.22	217293_at					(1, 2), (3, 2)
151	9.50E−06	7.2	6.59	7.35	224311_s_at	CAB39	calcium binding	51719	mTOR signaling	(2, 1), (2, 3)
							protein 39		pathway
152	9.60E−06	8.5	9	8.52	231716_at	RC3H2	ring finger and	54542		(1, 2), (3, 2)
							CCCH-type
							domains 2
153	9.70E−06	6.99	7.48	6.92	1565692_at					(1, 2), (3, 2)
154	9.70E−06	8.45	8.64	7.76	232174_at					(3, 1), (3, 2)
155	9.70E−06	6.72	7.22	6.23	243827_at					(3, 2)
156	9.90E−06	5.13	6.2	5.51	217536_x_at					(1, 2), (3, 2)
157	1.00E−05	9	8.88	9.33	206052_s_at	SLBP	stem-loop	7884		(1, 3), (2, 3)
							binding protein
158	1.00E−05	7.26	6.61	7.55	209131_s_at	SNAP23	synaptosomal-	8773	SNARE interactions	(2, 1), (2, 3)
							associated		in vesicular transport
							protein, 23 kDa
159	1.00E−05	4.46	5.13	4.73	1568801_at	VWA9	von Willebrand	81556		(1, 2), (3, 2)
							factor A domain
							containing 9
160	1.00E−05	8.01	7.85	8.32	211061_s_at	MGAT2	mannosyl	4247	Glycan structures -	(1, 3), (2, 3)
							(alpha-1,6-)-		biosynthesis 1, N-
							glycoprotein		Glycan biosynthesis
							beta-1,2-N-
							acetylglucosaminyl-
							transferase
161	1.01E−05	8.55	8.23	8.9	223010_s_at	OCIAD1	OCIA domain	54940		(2, 3)
							containing 1
162	1.01E−05	6.75	7.5	7.6	207460_at	GZMM	granzyme M	3004		(1, 2), (1, 3)
							(lymphocyte
							met-ase 1)
163	1.02E−05	4.77	4.59	5.46	1553176_at	SH2D1B	SH2 domain	117157	Natural killer cell	(1, 3), (2, 3)
							containing 1B		mediated cytotoxicity
164	1.02E−05	6.36	6.24	6.62	211033_s_at	PEX7	peroxisomal	5191		(1, 3), (2, 3)
							biogenesis factor 7
165	1.04E−05	7.01	7.75	7.77	203547_at	CD4	CD4 molecule	920	Activation of Csk by	(1, 2), (1, 3)
									cAMP-dependent
									Protein Kinase
									Inhibits Signaling
									through the T Cell
									Receptor, Antigen
									Dependent B Cell
									Activation, Bystander
									B Cell Activation,
									Cytokines and
									Inflammatory
									Response, HIV
									Induced T Cell
									Apoptosis, HIV-1
									defeats host-mediated
									resistance by CEM15,
									IL 17 Signaling
									Pathway, IL 5
									Signaling Pathway,
									Lck and Fyn tyrosine
									kinases in initiation of
									TCR Activation,
									NO2-dependent IL 12
									Pathway in NK cells,
									Regulation of
									hematopoiesis by
									cytokines, Selective
									expression of
									chemokine receptors
									during T-cell
									polarization, T Helper
									Cell Surface
									Molecules, Antigen
									processing and
									presentation, Cell
									adhesion molecules
									(CAMs),
									Hematopoietic cell
									lineage, T cell
									receptor signaling
									pathway
166	1.04E−05	8.82	8.4	9.05	200776_s_at	BZW1	basic leucine	9689		(2, 1), (2, 3)
							zipper and W2
							domains 1
167	1.07E−05	6.71	7.95	7.51	207735_at	RNF125	ring finger	54941		(1, 2), (1, 3)
							protein 125, E3
							ubiquitin protein
							ligase
168	1.08E−05	6.5	7.08	6.99	46947_at	GNL3L	guanine	54552		(1, 2), (1, 3)
							nucleotide
							binding protein-
							like 3
							(nucleolar)-like
169	1.08E−05	7.92	8.54	8.12	240166_x_at	TRMT10B	tRNA	158234		(1, 2), (3, 2)
							methyltransferase
							10 homolog B
							(S. cerevisiae)
170	1.13E−05	8.39	7.96	8.54	1555780_a_at	RHEB	Ras homolog	6009	mTOR Signaling	(2, 1), (2, 3)
							enriched in brain		Pathway, Insulin
									signaling pathway,
									mTOR signaling
									pathway
171	1.14E−05	8.37	8.82	8.56	214948_s_at	TMF1	TATA element	7110		(1, 2), (3, 2)
							modulatory
							factor 1
172	1.15E−05	6.77	7.43	7.07	221191_at	STAG3L1	stromal antigen	54441		(1, 2), (3, 2)
							3-like 1
173	1.16E−05	5.47	4.68	5.89	201295_s_at	WSB1	WD repeat and	26118		(2, 1), (2, 3)
							SOCS box
							containing 1
174	1.18E−05	7.31	6.73	7.62	211302_s_at	PDE4B	phosphodiesterase	5142	Purine metabolism	(2, 1), (2, 3)
							4B, cAMP-
							specific
175	1.19E−05	9.02	9.36	8.68	227576_at					(3, 2)
176	1.23E−05	7.34	7.82	7.28	1553349_at	ARID2	AT rich	196528		(1, 2), (3, 2)
							interactive
							domain 2
							(ARID, RFX-
							like)
177	1.23E−05	8.56	9.02	8.26	242405_at					(1, 2), (3, 2)
178	1.24E−05	5.32	6.33	5.71	238723_at	ATXN3	ataxin 3	4287		(1, 2), (3, 2)
179	1.25E−05	6.97	7.45	6.59	241508_at					(1, 2), (3, 2)
180	1.27E−05	7.73	7.49	7.74	225374_at					(2, 1), (2, 3)
181	1.29E−05	8.64	9.09	8.43	244414_at					(1, 2), (3, 2)
182	1.29E−05	7.52	6.99	7.57	202213_s_at	CUL4B	cullin 4B	8450		(2, 1), (2, 3)
183	1.29E−05	5.63	5.52	5.19	243002_at					(3, 1), (3, 2)
184	1.34E−05	4.51	5.32	4.8	210384_at	PRMT2	protein arginine	3275	Aminophosphonate	(1, 2), (3, 2)
							methyltransferase 2		metabolism,
									Androgen and
									estrogen metabolism,
									Histidine metabolism,
									Nitrobenzene
									degradation,
									Selenoamino acid
									metabolism,
									Tryptophan
									metabolism, Tyrosine
									metabolism
185	1.35E−05	4.65	4.25	4.81	1569952_x_at					(2, 1), (2, 3)
186	1.36E−05	12.44	12.13	12.55	202902_s_at	CTSS	cathepsin S	1520	Antigen processing	(2, 1), (2, 3)
									and presentation
187	1.37E−05	7.56	7.92	7.09	239561_at					(3, 2)
188	1.37E−05	6.8	7.38	7.18	218555_at	ANAPC2	anaphase	29882	Cell cycle, Ubiquitin	(1, 2), (1, 3)
							promoting		mediated proteolysis
							complex subunit 2
189	1.38E−05	8.03	7.89	8.57	200946_x_at	GLUD1	glutamate	2746	Arginine and proline	(1, 3), (2, 3)
							dehydrogenase 1		metabolism, D-
									Glutamine and D-
									glutamate
									metabolism,
									Glutamate
									metabolism, Nitrogen
									metabolism, Urea
									cycle and metabolism
									of amino groups
190	1.39E−05	5.15	4.77	5.96	221268_s_at	SGPP1	sphingosine-1-	81537	Sphingolipid	(1, 3), (2, 3)
							phosphate		metabolism
							phosphatase 1
191	1.40E−05	5.36	6.5	5.8	216166_at					(1, 2), (3, 2)
192	1.41E−05	7.07	7.64	7.09	1553909_x_at	FAM178A	family with	55719		(1, 2), (3, 2)
							sequence
							similarity 178,
							member A
193	1.42E−05	7.23	6.95	7.5	1554747_a_at	2-Sep	septin 2	4735		(2, 3)
194	1.45E−05	6.04	6.69	6.38	242751_at					(1, 2)
195	1.46E−05	7.74	8.22	7.79	239363_at					(1, 2), (3, 2)
196	1.47E−05	5.57	5.19	5.76	222645_s_at	KCTD5	potassium	54442		(2, 1), (2, 3)
							channel
							tetramerisation
							domain
							containing 5
197	1.53E−05	3.93	3.73	4.26	210875_s_at	ZEB1	zinc finger E−	6935	SUMOylation as a	(1, 3), (2, 3)
							box binding		mechanism to
							homeobox 1		modulate CtBP-
									dependent gene
									responses
198	1.55E−05	8.42	8	8.63	1567458_s_at	RAC1	ras-related C3	5879	Agrin in Postsynaptic	(2, 1), (2, 3)
							botulinum toxin		Differentiation,
							substrate 1 (rho		Angiotensin II
							family, small		mediated activation of
							GTP binding		JNK Pathway via
							protein Rac1)		Pyk2 dependent
									signaling, BCR
									Signaling Pathway,
									fMLP induced
									chemokine gene
									expression in HMC-1
									cells, How does
									salmonella hijack a
									cell, Influence of Ras
									and Rho proteins on
									G1 to S Transition,
									Links between Pyk2
									and Map Kinases,
									MAPKinase Signaling
									Pathway, p38 MAPK
									Signaling Pathway,
									Phosphoinositides and
									their downstream
									targets., Phospholipids
									as signalling
									intermediaries, Rac 1
									cell motility signaling
									pathway, Ras
									Signaling Pathway,
									Ras-Independent
									pathway in NK cell-
									mediated cytotoxicity,
									Role of MAL in Rho-
									Mediated Activation
									of SRF, Role of PI3K
									subunit p85 in
									regulation of Actin
									Organization and Cell
									Migration, T Cell
									Receptor Signaling
									Pathway,
									Transcription factor
									CREB and its
									extracellular signals,
									Tumor Suppressor Arf
									Inhibits Ribosomal
									Biogenesis, uCalpain
									and friends in Cell
									spread, Y branching
									of actin filaments,
									Adherens junction,
									Axon guidance, B cell
									receptor signaling pat
									. . .
199	1.56E−05	9	9.37	8.89	233893_s_at	UVSSA	UV-stimulated	57654		(1, 2), (3, 2)
							scaffold protein A
200	1.59E−05	5.95	6.42	6.55	226539_s_at					(1, 2), (1, 3)

TABLE 1b

The 2977 gene probeset used in the 3-Way AR, ADNR TX
ANOVA Analysis (using the Hu133 Plus 2.0 cartridge arrays plates)

SMARCB1	233900_at	RNPC3	FLJ44342	1563715_at	244088_at
ATP6AP2	KCNC4	COQ9	CNIH	ZBTB20	OXTR
PLEKHA3	GLRX3	HSPD1	HMGXB4	BLOC1S3	220687_at
TP53	BCL2L11	NFYA	GJC2	GMCL1	TLE4
PRKAG2	HNRPDL	TNRC18	236370_at	SRSF3	1556655_s_at
ND2	XRN2	238745_at	SH2B3	227730_at	TMEM161B
YWHAE	EXOC3	215595_x_at	232834_at	CLIP1	FTSJD1
243037_at	RNGTT	228799_at	OTUB2	236742_at	AAGAB
SSR1	ENY2	1555485_s_at	LINC00028	MAT2B	1570335_at
CCDC91	KLHL36	SEMA7A	INTS1	217185_s_at	CIZ1
PRKAG2	UBR4	ZNF160	DIS3	MTUS2	ZNF45
ADPGK	RALB	G6PD	ATP2A3	MOB3B	215650_at
CHFR	UBE2D3	1556205_at	LOC100128822	1558922_at	UBTF
236766_at	DR1	DSTN	PRKAR2A	233376_at	ZNF555
242068_at	CLEC7A	HSF1	214027_x_at	SLC16A7	MED28
PRNP	SDHD	NPHP4	TCF4	241106_at	SIGLEC10
1558220_at	1565701_at	242126_at	CCDC115	IRAK3	TM6SF2
CDYL	216813_at	SRSF11	244677_at	242362_at	PACSIN2
SPTLC1	C2orf72	231576_at	SRP72	ADAMTS16	DLX3
232726_at	236109_at	RBM4	221770_at	KIAA1715	PKM
CHMP1B	240262_at	FAM175B	1556003_a_at	202648_at	SUMO3
KBTBD2	1563364_at	ZNF592	1559691_at	PTPRO	GATM
MST4	235263_at	RAPGEF2	KANK2	ADAM12	NECAP1
239597_at	PRR12	MALAT1	PEX7	XBP1	LPPR3
239987_at	CBFB	216621_at	PTGS2	222180_at	CAMK2D
243667_at	JAK1	KIAA1683	RDH11	MGAT2	CRH
CDC42EP3	240527_at	1559391_s_at	PHF7	CUBN	243310_at
UBXN4	6-Sep	DR1	236446_at	ZNF626	DYNC1H1
PGGT1B	ITGB1	ARF4	242027_at	CD84	C17orf80
238883_at	1562948_at	RAB39B	1565877_at	LRRC8C	233931_at
CCR2	YRDC	GPR27	ACBD3	AMBP	C20orf78
242143_at	1566001_at	HPS3	SNAI3-AS1	C3orf38	MED13L
ZNF426	SETD5	215900_at	215630_at	ATG4C	226587_at
SP1	RNGTT	220458_at	POLR1B	217701_x_at	226543_at
MED18	240789_at	ARFGAP3	PON2	LOC100996246	LPP
233004_x_at	239227_at	1566428_at	MED27	244473_at	LOC100131043
242797_x_at	236802_at	EID2B	LOC100289058	FOLR1	AGA
2-Sep	TM2D1	1559347_at	1565976_at	231682_at	STX11
CCNG2	TBX4	ARPP19	YEATS4	NRP2	215586_at
OGFOD1	232528_at	IFRD1	VIPR2	PTPRD	244177_at
240232_at	TSEN15	1564227_at	233761_at	RBBP6	240892_at
MED18	MAP3K8	1561058_at	SLC25A16	SMARCD2	GPBP1L1
ZKSCAN1	TMEM206	SFT2D3	OSTC	CLEC4A	242872_at
SLC39A6	239600_at	LOC646482	ATP11B	RBM3	240550_at
FLI1	TMOD3	TMEM43	C1orf43	POLE3	KIAA0754
LUC7L2	GLS	CSNK1A1	CASK	MAPK9	213704_at
CD46	RPS10	233816_at	PAXBP1	ACTR10	GABPA
242737_at	TSN	C3orf17	ADSSL1	1557551_at	240347_at
CASKIN1	C7orf53	239901_at	C11orf58	ZNF439	G3BP1
UGP2	MTMR1	ABCC6	1569312_at	RTFDC1	PIK3AP1
ARNTL	TNPO1	7-Mar	STRN	ND6	KRIT1
232307_at	232882_at	NRIP2	243634_at	LIN7C	CGNL1
PLEKHF2	SSR1	1569930_at	240392_at	CNOT7	C11orf30
234435_at	233223_at	DLAT	SDK2	237846_at	1562059_at
ZNF117	VNN3	USP14	WNK1	GAPVD1	SPG20
ITGB1	ARFGEF1	UACA	AKAP10	CDAN1	1561749_at
MAP3K1	244100_at	PGK1	UNK	TECR	C16orf87
FBXW7	TMEM245	CHRNB2	GUCA1B	LINC00476	232234_at
208310_s_at	CDC42EP3	SUMF2	PTPRH	229448_at	MEGF9
SRGAP2B	244433_at	NEDD1	216756_at	ZBTB43	RAD18
238812_at	MYCBP2	MFN1	1557993_at	230659_at	ZCCHC3
ZNF136	ATF5	PIGY	MAEA	1560199_x_at	TOR2A
ARPC5	RBBP4	1564248_at	215628_x_at	BET1L	SMIM14
FBXL18	232929_at	APH1A	SPSB1	PAFAH1B1	SLMO2
TMED2	PCGF1	DCTN1	APC	243350_at	NAA15
234125_at	239567_at	KLHL42	TIMM50	TAF1B	ZNF80
YWHAZ	233799_at	OGFRL1	240080_at	MRPL42	GKAP1
SNAP23	FBXO9	FBXL14	SLC38A9	232867_at	FBXO8
238558_at	SS18	240013_at	MON1A	ASIC4	233027_at
221071_at	ARMC10	SREK1IP1	239809_at	WNT7B	237051_at
TMEM30A	232700_at	CDH16	243088_at	ZNF652	YJEFN3
1569477_at	UNKL	234278_at	233940_at	GMEB1	213048_s_at
CD46	1561389_at	LIX1L	KIAA1468	BCAS4	PPP1R12A
INSIG1	SLC35B3	NBR2	1563320_at	GFM1	TCEB3
215866_at	235912_at	SEC31A	APOL2	IGHMBP2	201380_at
MED13	234759_at	MLX	FLJ12334	BEX4	LOC283788
SRSF2	SLC15A4	ATP6V1H	FANCF	SNX24	RNA45S5
API5	TMEM70	USP36	242403_at	AKIRIN1	RBBP4
SUZ12P1	RANBP9	C2orf68	ERLIN2	ZNF264	SNTB2
244535_at	NMD3	LOC149373	241965_at	LYSMD2	234046_at
FLI1	LARP1	TMEM38B	SLC30A5	RUFY2	231346_s_at
SLC35E1	240765_at	TNPO1	238024_at	239925_at	AGFG2
241681_at	SCFD1	RSU1	1562283_at	SPSB4	NONO
PAPOLA	RAB28	FXR1	216902_s_at	ZNF879	THEMIS
MLLT10	242144_at	TET3	SMAP1	XIAP	NDUFS1
SLC35B4	PAFAH2	MGC57346	C6orf89	SNX19	RAB18
1564424_at	ZNF43	231107_at	FBXO33	RER1	NAP1L1
243030_at	CYP4V2	PHF20	ANKRD10	1566472_s_at	235701_at
215207_x_at	MREG	1560332_at	INSIG1	QSOX2	1557353_at
235058_at	BECN1	PAIP2	239106_at	DDR2	HOPX
ARL2BP	LOXL2	235805_at	KIAA1161	1566491_at	CCT6A
CNOT8	TPD52	KRBA2	1563590_at	PRNP	VGLL4
SYPL1	API5	BCKDHB	LOC100129175	HNMT	EXOSC6
236168_at	222282_at	VAV3	DLGAP4	GGA1	TM2D3
BRWD3	HHEX	MC1R	ARID5A	AQR	BAZ2A
RBBP4	237048_at	HMGCR	SMAD6	KIF3B	HIF1AN
215390_at	1562853_x_at	MBD4	ZNF45	SMO	MBD4
1566966_at	CCNL1	PPP2R2A	SLC38A10	241303_x_at	DET1
GLCCI1	TIGD1	242480_at	GFM1	MTPAP	227383_at
MBP	241391_at	INTS9	221579_s_at	230998_at	239005_at
RAB8B	DDX3X	ABHD6	CCIN	241159_x_at	PIGF
YTHDF3	FTX	CEP350	ASAP1-IT1	TGDS	SREBF2
TOR1AIP1	HIBADH	RHEB	FAM126B	CRLS1	TLE4
ATF1	PXK	1559598_at	232906_at	REXO1	PLCB1
TMEM230	231644_at	ING4	RALGAPA2	1557699_x_at	DNAAF2
LRCH3	FPR2	RHNO1	SPCS1	FAM208B	PALLD
ARID2	CPSF2	1552867_at	MAGT1	ZNF721	USP28
244766_at	G2E3	212117_at	TMEM19	PRDM11	217572_at
242673_at	237600_at	TLR8	ZNF24	243869_at	C1orf86
CNOT8	244357_at	231324_at	NAIP	G2E3	DAAM2
ZBTB20	215577_at	DDA1	232134_at	233270_x_at	GNB3
240594_at	CLEC7A	243207_at	B2M	DMPK	RNF145
PSMD10	GAREML	9-Sep	NPR3	NLK	RANBP9
215137_at	1566965_at	SRP72	ATP6AP2	TRIM28	1557543_at
243527_at	TLK1	233832_at	1563104_at	238785_at	ZNF250
RHOQ	231281_at	GABBR1	ERCC3	243691_at	PPP1R15B
ATF7IP	EXOC5	TRIM50	STEAP4	LOC283482	FLI1
LOC100131541	GHITM	KIAA1704	232937_at	LOC285300	SMURF1
STYX	ZCCHC9	LRRFIP2	238892_at	242310_at	EAF1
244010_at	ZNF330	LIG4	NDUFS4	239449_at	SUMO2
232002_at	TMEM230	HECA	ERLEC1	SOCS5	MED21
CNIH4	ZNF207	243561_at	UBL3	233121_at	PIGR
EMC1	LOC440993	215961_at	BBS12	NUMBL	240154_at
MACF1	PAPOLA	BRAP	242637_at	PCNP	UPF1
NOP14-AS1	CXorf36	SURF4	231125_at	PRG3	217703_x_at
1564438_at	AKAP13	PANK2	SMCHD1	SRSF2	SKAP2
229858_at	ASXL2	236338_at	PDE3B	MOAP1	ARL6IP5
HHEX	RAB14	FNBP1	TRPM7	SPG11	STIM2
239234_at	DIP2A	G2E3	RPL18	ZFP41	WBP11
238619_at	243391_x_at	FLT3LG	PTOV1	CARD11	ADAM10
DHX36	PBXIP1	233800_at	HAVCR2	MFSD8	PHF20L1
DAPP1	MFN1	SMIM7	242890_at	EEF1D	CR1
WTAP	DENND4C	ADRA2B	BFAR	NAPEPLD	STEAP4
PSEN1	SLC25A40	PTPN23	ARHGAP21	GM2A	MAML2
239112_at	242490_at	SNX2	SRPK2	EBAG9	236450_at
MAP3K7	LMBR1	P2RY10	PRKCA	242749_at	RTN4IP1
PPP2R5E	CDK6	ABHD15	TRAPPC2L	PRKCA	221454_at
FAM108B1	TMEM19	1565804_at	LRRFIP1	SUV39H1	RMDN2
TAB2	237310_at	236966_at	CCDC6	LOC100507281	UFM1
FAR1	PIK3C3	SRPK2	232344_at	PLXDC1	228911_at
240247_at	ABI1	242865_at	TRAK1	ZP1	BEST1
METTL21A	CHD8	AGMAT	SRGN	HR	239086_at
1569540_at	SLC30A5	GPHN	PAPOLA	RYBP	216380_x_at
FYTTD1	216056_at	ZNF75D	ATXN7L1	POPDC2	KCTD5
TMED2	MAPKAPK5	SON	TBATA	SNX2	SFTPC
233867_at	CSNK1A1	AP1AR	TTC17	BMP7	KLF7
TROVE2	243149_at	TXNDC12	ELP6	226532_at	ERBB2IP
215221_at	1560349_at	1569527_at	CACHD1	MAN2A2	232622_at
LNPEP	SERBP1	237377_at	ELOVL5	LOC100129726	234882_at
217293_at	MDM4	NPTN	1563173_at	PDE1B	225494_at
CAB39	217702_at	239431_at	CHRNA6	SCRN3	OSGIN1
RC3H2	RPAP3	242132_x_at	SLC9A5	FAM3C	SLC26A6
1565692_at	NR3C1	AP1S2	UBE4B	GPHA2	MALT1
232174_at	SMAD4	TMEM38B	233674_at	USP38	216593_s_at
243827_at	FBXO11	CDC42SE1	CHRNE	IDH3A	237176_at
217536_x_at	SNAPC3	TLR4	TIMM23	FGFR1OP2	1570087_at
SLBP	SVIL	C14orf169	FCGR2C	DST	ENTPD2
SNAP23	TSPAN14	BTF3L4	232583_at	NSMF	232744_x_at
VWA9	SERBP1	AUH	KIAA2026	241786_at	NUDT6
MGAT2	238544_at	RDX	242551_at	WHSC1L1	AGPAT1
OCIAD1	LRRFIP2	224175_s_at	MALAT1	234033_at	242926_at
GZMM	SNAP29	240813_at	229434_at	244086_at	CLASP2
SH2D1B	215648_at	FGL1	YWHAE	C14orf142	239241_at
PEX7	PPTC7	235028_at	COPS8	ME2	MIR143HG
CD4	241932_at	SENP7	215599_at	NFYB	232472_at
BZW1	CNBP	215386_at	KDM2A	AIF1L	FCAR
RNF125	239463_at	MGC34796	TFAP2D	MALT1	XPNPEP3
GNL3L	POGZ	ME2	PKHD1	226250_at	ACAD8
TRMT10B	215083_at	PPP6R1	OGFOD1	233099_at	PARP15
RHEB	TMEM64	211180_x_at	GPATCH2L	237655_at	TMEM128
TMF1	ERP44	GEM1	DSTN	ZBED3	PTPN7
STAG3L1	LOC100272216	CEP120	ZSCAN9	BAZ1B	215474_at
WSB1	1562062_at	MAN1A2	MFSD11	CDC42SE2	215908_at
PDE4B	1559154_at	STX3	SERPINI1	ISG20	AASDHPPT
227576_at	CDC40	243469_at	XRCC3	KLHDC8A	NCBP1
ARID2	PIGM	NDUFS1	ADNP	DLAT	233272_at
242405_at	238000_at	CAAP1	LOC100506651	DIABLO	240870_at
ATXN3	RAP2B	CHD4	242532_at	PDXDC1	LPIN1
241508_at	236685_at	ZNF644	CD200R1	PTGDR	AFG3L1P
225374_at	GLIPR1	FLJ13197	ZMYND11	231992_x_at	CNEP1R1
244414_at	OGT	HSPA14	PACRGL	221381_s_at	PMS2P5
CUL4B	CREB1	CNNM2	RNFT1	ZNF277	C14orf1
243002_at	YAF2	SENP2	CD58	RBM47	THOP1
PRMT2	215385_at	KLF4	USP42	SYN1	CLASP2
1569952_x_at	PPM1K	1558410_s_at	216745_x_at	SCP2	241477_at
CTSS	1562324_a_at	241837_at	235493_at	234159_at	233733_at
239561_at	SH2D1B	LOC100130654	CLASP1	FLJ10038	SNX19
ANAPC2	GNAI3	SNTB2	HACL1	FAM84B	ZNF554
GLUD1	AKAP11	233417_at	ANAPC7	BRAP	G2E3
SGPP1	1569578_at	236149_at	LOC286437	FOXJ1	SLC30A1
216166_at	ATF7	237404_at	RPS12	244845_at	ATF7IP
FAM178A	234260_at	DHRS4-AS1	PALLD	244550_at	228746_s_at
2-Sep	KLHDC10	KHSRP	MTO1	244422_at	232779_at
242751_at	RFWD3	SLC25A43	241114_s_at	RAB30	227052_at
239363_at	STX16	MESP1	UBXN7	TTLL5	240405_at
KCTD5	CTBP2	PSMD12	215376_at	NUCKS1	ZNF408
ZEB1	FOXN3	HMP19	241843_at	PRMT8	PIP5K1A
RAC1	239861_at	240020_at	TSR1	239659_at	CCPG1
UVSSA	LOC100505876	LRRFIP1	CD164	CENPL	ASCC1
226539_s_at	MLLT10	TNPO1	MRPS10	THADA	LINC00527
MS4A6A	243874_at	GALNT7	MACF1	MRPS11	UBQLN4
CNOT4	MDM4	VPS37A	213833_x_at	DBH	MAST4
1559491_at	TAOK1	PPP2CA	244665_at	LRRFIP1	RAP1GAP
NOP16	PIK3R5	TFEC	HPN	239445_at	SRSF4
HIRIP3	LINC00094	ENTPD6	PAK2	234753_x_at	LOC149401
PTPN11	242369_x_at	M6PR	MOB1A	TSHZ2	11-Sep
GFM2	242357_x_at	TAF9B	MTMR9LP	GTSE1	NIT1
SMCR8	243035_at	1564077_at	IGLJ3	243674_at	PTGS2
ZNF688	TMEM50B	PMAIP1	ADNP2	237201_at	ACTG1
KIAA0485	XAGE3	234596_at	PLA2G4F	HSP90AB1	ARHGEF1
ABHD10	FAS	1558748_at	NRBF2	216285_at	TRIM8
LOC729013	FBXO9	TMEM41A	SYTL3	ACTR3	TTC27
233440_at	239655_at	MIER3	C1orf174	KRCC1	GABPA
224173_s_at	VAMP3	UBAP2L	C16orf72	RHOA	243736_at
TAOK1	230918_at	NXT2	ZNF836	TRMT2B	TLR2
SLC39A6	ZNF615	PRRT3	KCNK3	ADK	VPS35
NAMPT	DLST	BRD3	UBXN8	PNRC1	MED28
CNBP	231042_s_at	243414_at	TIA1	216607_s_at	242542_at
MBP	TNPO3	MTMR2	NCOA2	GRM2	241893_at
PRKAR2A	CS	243178_at	CEP135	EIF3L	ZNF92
GNL3L	CLTC-IT1	TCF3	BCL7B	236704_at	GPC2
YWHAZ	MEF2C	SFTPB	HELB	MYO9B	TSPAN16
PPP6R2	PACSIN3	UBE2N	LOC642236	NGFRAP1	LOC100507602
RSPRY1	TROVE2	238836_at	PAK2	PSPC1	1561155_at
MBNL1	PPIF	RPRD1A	FPR2	239560_at	218458_at
DENR	RBM15B	RDH11	ITGA4	ERCC8	ZNF548
DNAJB9	242793_at	WWP2	MSI2	VPS13D	ADAM17
216766_at	ARHGAP32	CLYBL	SMPD1	MTM1	1561067_at
CLINT1	SHOX2	230240_at	TMEM92	ANKRD13D	207186_s_at
FBXO9	208811_s_at	VNN3	ARHGAP26	AKAP17A	MED29
ATXN1	217055_x_at	PECR	ZBTB1	LOC100506748	DTD2
1570021_at	LMNA	HLA-DPA1	GALNT9	RCN2	242695_at
ARFGEF1	PSEN1	TPCN2	CTNNB1	NTN5	238040_at
232333_at	CLCN7	LARS	227608_at	OAT	EIF1B-AS1
GOSR1	C15orf37	ZDHHC8	TMEM106C	ZNF562	1561181_at
FAM73A	TUG1	BCL6	PSD4	1560049_at	SERTAD2
244358_at	WDYHV1	IMPACT	DYNLRB1	215861_at	1561733_at
SPPL3	SELT	1558236_at	237868_x_at	A2M	FGFR1
ARFIP1	BBIP1	235295_at	PADI2	CADM3	PSMD10
SF3B3	TMCO3	APOBEC2	RALGPS1	CAMK2D	CCND3
TMEM185A	FBXO22	PIK3CG	ZNF790-AS1	TRIM4	DPYSL4
LARP1	PICALM	ZNF706	214194_at	CRYZL1	ANGPTL4
SETX	KLHL7	239396_at	FTCD	IL1R2	CAPS
POLR3A	233431_x_at	GATAD2B	1570299_at	WNT10A	238159_at
RSBN1	TNPO3	ZDHHC21	WDR20	SEH1L	225239_at
SNX13	SMAD4	MED4	208638_at	216789_at	STOX2
ZNF542	FAS	PRKACB	9-Sep	JAK3	207759_s_at
228623_at	RPL27A	HERPUD1	KLHL20	TFDP2	LOC100996349
242688_at	222295_x_at	PPFIA3	SOS2	C4orf29	ZNF805
237881_at	GHITM	222358_x_at	PTPN22	TACC2	LOC100131825
239414_at	IER5	TUBGCP4	CYB5R4	FBXO33	ZFP36L2
RASSF5	HSPD1	210338_s_at	232601_at	GOPC	KIF1B
222319_at	UEVLD	RABL5	IMPAD1	C1QBP	ZNF117
PPP1R3B	LPXN	242859_at	LOC100128751	NXPH3	SLC35D2
TAOK1	DENND2A	1554948_at	CXCR4	PLAUR	235441_at
MAP2K4	SPRTN	ZNF551	PODNL1	ZNF175	214996_at
NMT2	SF3A2	GPSM3	ZNF567	POLR2L	LOC100506127
STX16	DICER1	1556339_a_at	242839_at	ZAN	BTBD7
PDE7A	ANKRD17	236545_at	MAPRE2	239296_at	PNRC2
RALGAPA2	GOLGA7	MKRN2	PGM2	TBX2	232991_at
MED1	UBE2J1	AGAP2	PHF20L1	TUBB	PRKCSH
PICALM	215212_at	CCNK	TTF1	244847_at	PRM3
BTG2	236944_at	233103_at	ETV4	MEA1	GNAI3
KIAA2018	CREB1	239408_at	FAM159A	MLL	243839_s_at
230590_at	232835_at	NXPE3	EIF4E	SECISBP2L	PHTF2
AP5M1	LOC100631377	222306_at	232535_at	MCRS1	TSPYL5
217615_at	ZNF880	ANGPTL1	UBA1	219422_at	DCAF17
238519_at	TMEM63A	RYK	ZNF493	USP7	IDS
TBL1XR1	AKAP7	SCARB2	224105_x_at	TRDMT1	242413_at
MAT2A	LMBRD1	1557224_at	GPR137	EFS	213601_at
CDC42SE2	235138_at	DCUN1D4	ZKSCAN5	RAB30	INSIG2
228105_at	POLK	STX7	PRKAR2A	1562468_at	AP1G2
CHAMP1	TBCC	CCT2	SATB1	236592_at	SLC5A9
ASAH1	AK2	233107_at	ZNF346	MNX1	215462_at
TAF8	216094_at	ZNF765	ZFP90	236908_at	240046_at
PAPOLG	TRAPPC11	239933_x_at	SPG11	E2F5	PTPLAD1
240500_at	CSNK1A1	LOC283867	ALPK1	TRO	1559117_at
200041_s_at	PPP1R17	TNKS2	GOLGA2	MAN2A1	WARS2
233727_at	244383_at	ZNF70	PRRC2A	TPM2	SMAD4
DPP8	CNOT2	239646_at	216704_at	SH3BGRL	ZNF500
MAP3K7	1569041_at	NUFIP1	SMARCA2	FAM81A	KCTD12
SBNO1	MBIP	FOXK2	233626_at	MAST4	SRSF10
VPS13D	1555392_at	PRPF18	236404_at	FARSB	SAT1
1556657_at	RBM25	DARS	LOC100507918	HEATR3	1569538_at
TMEM170A	PRDX3	TLK2	NAP1L1	200653_s_at	REV1
ZFAND5	C1orf170	244219_at	FOPNL	SLC23A3	1558877_at
UBE2G1	FOXP1-IT1	LOC145474	LINC00526	LIN52	212929_s_at
233664_at	ERO1L	235422_at	ZMYND8	PDIA6	GRAMD4
1565743_at	TMCO1	244341_at	240118_at	242616_at	CNST
RAB5A	SORL1	HERC4	242380_at	ZNF419	NPY5R
243524_at	BRI3BP	MAST4	1565975_at	SETDB2	STK38
ATG5	PAFAH1B2	TMED10	MAP1A	XYLT2	ZFAND1
DSERG1	TRIM32	TRIM2	TMEM50B	PDXDC1	240216_at
ZNF440	1567101_at	236114_at	RABGAP1	1561128_at	P2RX6
IL18BP	PDK4	BAZ2A	GFM1	244035_at	239876_at
F2RL1	GOLGB1	FAM206A	GLOD4	GRIK2	UBE2D2
CRLS1	PRRC2A	ZC2HC1A	NAPB	HNMT	UBE2W
238277_at	ATM	239923_at	HYMAI	SP6	PLEKHB1
COL7A1	209084_s_at	SWAP70	PRKAB1	220719_at	232400_at
MAP3K2	240252_at	222197_s_at	1559362_at	244156_at	GJB1
TMX1	1557520_a_at	LRBA	EXOC5	227777_at	UBA6
MCFD2	216729_at	203742_s_at	C12orf43	PEX3	FCF1
SLMAP	ZCWPW1	235071_at	HAX1	HDAC2	240241_at
CNOT1	PRDX3	240520_at	CRISPLD2	229679_at	242343_x_at
242407_at	CWC25	NDNL2	TNPO1	RCOR2	ULK4
CDC5L	KIF5B	RPS23	ARF6	EIF3M	GATC
DCTN4	ETV5	235053_at	239661_at	SNRPA1	BET1
TM6SF1	232264_at	ZNF565	1562600_at	RBM8A	HIPK2
ZFP3	ARL8B	CCDC126	MAF	EMX1	GNB4
SRSF1	1562033_at	VEZF1	ETNK1	USP46	ERO1L
GDI2	NIN	COPS8	PSMD11	KCNE3	220728_at
TERF2	1558093_s_at	NSUN4	CNOT11	EDC3	MYOD1
ATM	236060_at	LOC283887	MEGF9	ALG13	1561318_at
1558425_x_at	243895_x_at	FBXO28	NNT	NUDT7	FAM213A
234148_at	244548_at	MRPL30	LUZP1	RAD1	1557688_at
ITGA4	VTA1	215278_at	MFGE8	EIF3B	208810_at
1556658_a_at	BRD2	243003_at	ZNF12	RPL35A	RNF207
POLR3E	UBXN2B	FNTA	KRAS	UBE2B	BHLHE40
BRD2	SLC33A1	ZKSCAN4	1555522_s_at	MBD4	S1PR1
RECQL4	SUZ12	HSPH1	PEX2	BIRC3	233690_at
PTPRC	QRSL1	OR4D1	FOXP1	240665_at	ZNF93
AURKAIP1	ARNTL	219112_at	238769_at	235123_at	BCR
TLR1	LTBP4	FAM170A	NIP7	242194_at	1559663_at
243203_at	CENPT	DDX51	STAM2	ARHGAP42	234942_s_at
PRPF4B	1556352_at	CANX	PSMB4	PQBP1	CHN2
239603_x_at	TTYH2	208750_s_at	NUDT13	221079_s_at	237013_at
ZBTB18	RAB40B	230350_at	232338_at	GTF2H2	SERPINB9
SLC35A3	TBL1XR1	234345_at	SSR3	229469_at	244597_at
239166_at	SPOP	SNX6	RABEP1	244123_at	230386_at
TM9SF2	NKTR	DCAF8	FCGR2C	MED28	SH2B1
237185_at	ATP2A2	ALG2	242691_at	ALDH3A2	234369_at
233824_at	RNF130	OSGIN2	230761_at	PLEKHG5	CALU
COG8	234113_at	CCDC85C	225642_at	1569519_at	DNM2
215528_at	NEK4	CNOT6L	ND4	RASA1	SRSF4
CDV3	ARPC5	ERBB4	DHDH	AKAP13	TMEM134
GINS4	ZBTB44	DNAJC16	FABP6	NT5DC1	TTF2
ATF1	YTHDF2	SPOPL	ZNF316	ATP5F1	E2F3
IBTK	1562412_at	FGD5-AS1	G3BP1	PMAIP1	239585_at
PRKAR1A	242995_at	ZNF44	KRCC1	212241_at	VANGL1
PSME3	TPD52	1568795_at	FOXK2	HTR7	CYP21A2
PRKCB	EML4	1555014_x_at	MORF4L1	TRIM65	214223_at
FAM27E3	PLA2G12A	HADH	FAM105B	244791_at	MTRF1L
244579_at	CANX	DPT	TLN1	CXorf56	STARD4
XYLT2	240666_at	GPSM3	220582_at	TAGLN	207488_at
5-Mar	PPP2R1B	TP73-AS1	241692_at	MRM1	ZNF451
DAZAP2	232919_at	234488_s_at	SLC35F5	ZNF503-AS2	LOC728537
CILP2	SYNPO2	240529_at	AKAP10	1569234_at	MPZL2
TMEM168	ORAI2	ZNF566	USP34	MAOB	ABCA11P
PDGFC	SETD5-AS1	GTF3C5	R3HDM1	MTM1	CSNK1G3
MYCN	BARD1	GSPT2	SMG6	CASC4	CDRT15L2
ZNF747	ND4	SAMD4B	PPP6R2	TMEM214	FAM204A
TTF1	SLC5A8	TFR2	MRPS10	LRCH3	IGSF9B
1566426_at	R3HDM1	236615_at	CREB1	TBC1D16	232759_at
ARV1	LYZ	1554771_at	NUS1	IQSEC2	HSF2BP
PRKD3	241769_at	P2RY10	MOB1A	MROH7	REST
1557538_at	TRIM15	JKAMP	C16orf55	CDK12	GLYR1
CACNA2D4	232290_at	DCAF16	ANKFY1	230868_at	GPCPD1
C22orf43	AKAP13	CCDC50	FBXL5	USP7	WDR45B
SMCR8	GDE1	UBE3A	PLA2G7	RFC3	231351_at
236931_at	SLC30A5	236322_at	CNOT6L	FAM22F	217679_x_at
RAB1A	1565677_at	EIF4G2	CUX1	MDM4	PARP10
RAB9A	PKN1	IPO13	1564378_a_at	242527_at	RBBP5
YWHAE	ACTR2	PON2	215123_at	TRIP6	233570_at
DIS3	GRB10	1561834_a_at	AKT2	HAUS6	1570229_at
RAB1A	NADKD1	COG6	SLC5A5	MRPL50	CRYL1
213740_s_at	LRCH3	GLB1L3	XPO7	240399_at	KATNBL1
244019_at	LOC100132874	TMEM64	AP1S2	231934_at	PRSS3P2
1570439_at	RAB7L1	MARS2	NFKBIB	TRAK1	1-Sep
CACNA2D4	ORC5	RP2	MLL2	240238_at	1561195_at
GLUD2	RNF170	TGIF1	NOC4L	230630_at	LIN7C
BLOC1S6	MS4A6A	PPARA	RPRD1A	RBM22	240319_at
LINC00667	SLC35D2	233313_at	RBM7	PIGM	237575_at
SCAMP1	TUBD1	SMARCA4	TTLL9	TSHZ2	S100A14
MAP3K2	SPOPL	ADO	ATPBD4	SPAG9	ARL6IP6
IBA57	SWAP70	ADH5	VASN	SPPL3	ARF4
ZCCHC10	HIBADH	243673_at	KLC4	ABCB8	P4HA2
1560741_at	TIMM17A	MOSPD2	ABHD5	COL17A1	1559702_at
FAM43A	DOCK4	TAF9B	214740_at	230617_at	TMEM106B
ALS2CR8	C9orf53	YY1	PTP4A2	HADH	236125_at
233296_x_at	1569311_at	MESDC1	EPC1	ATF6	WTAP
INF2	231005_at	GON4L	MZT2B	1554089_s_at	IGDCC3
PRMT6	C1orf228	LARP4	RASGRF1	PLEKHA4	ENDOV
IL13RA1	NLGN3	217446_x_at	SETD5-AS1	200624_s_at	1556931_at
SF3A2	1558670_at	RPP14	220691_at	TMEM239	TP53AIP1
FBXL12	PAGR1	RNF113A	FBXL20	237171_at	242233_at
233554_at	RRAS2	1558237_x_at	SQSTM1	MANEA	SPEN
241867_at	PDE8A	PTGER1	TRAPPC10	CLDN16	C16orf52
OGFRL1	IFNGR1	1561871_at	1565762_at	LOC100506369	SYMPK
231191_at	UHRF1BP1L	LCMT2	STX17	C6orf120	ITSN2
1560082_at	240446_at	MED23	PRKRA	ANKRD13C	RFC1
RHEB	GBAS	215874_at	MED30	1556373_a_at	ZC3H7A
RHOA	MRPS10	TRAM1	RHAG	214658_at	TRIM23
RRN3P3	ANKS1B	ATPAF1	PPID	1561167_at	240505_at
ELMSAN1	202374_s_at	NR2C2	APP	235804_at	DYRK1B
ADAT1	244382_at	ZNF451	RERE	ASPHD1	TOR1AIP1
242320_at	MCL1	DENND6B	1560862_at	PTEN	TMEM203
1555194_at	ZNF430	FAM83G	239453_at	CCNT2	ASAH1
238108_at	SFRP1	NAA40	ERN2	237264_at	CUL4B
240315_at	ARNT	TSPAN3	240279_at	ERICH1	BZRAP1-AS1
SCOC	ZNF318	MLLT10	222371_at	TTC9C	MTSS1
C15orf57	ZFR	WDR4	PEBP1	IGIP	DNM1
1562067_at	MEF2A	234609_at	AP1G1	MGC70870	ZNF777
235862_at	COL7A1	NIPSNAP3A	TRIOBP	JAK2	RALGAPA1
UBE2D4	ZNRF2	ANKRD52	RNF213	221242_at	IMPAD1
240478_at	RFC3	TMEM251	HNRNPM	LUC7L	222315_at
239409_at	FAM110A	234590_x_at	EBPL	217549_at	CDKN2AIP
ZNF146	CRYBB3	NAP1L1	LOC142937	LOC283174	SCAMP1
RASA2	WDR19	242920_at	CEP57	234788_x_at	216584_at
CSNK1A1	RPS2	FAM103A1	239121_at	UBE2D1	238656_at
B3GNT2	ETNK1	STK38L	C6orf106	PTP4A1	CAT
LOC100131067	227505_at	DNAJC10	PPP1R12C	SMG5	LOC100505876
FDX1	C2orf43	ARFIP2	ETNK1	CEP350	EMC4
TADA3	NCK1	DEFB1	IRF8	233473_x_at	GNAQ
IKZF1	222378_at	TMEM55A	TMEM88	LMBRD2	228694_at
242827_x_at	ST3GAL6	SSBP2	ENOPH1	LOC100505555	RICTOR
DNAJC14	222375_at	MAU2	CEP68	FAM45A	NDFIP2
NEDD9	ENTHD2	OSBPL11	WDR48	HBP1	243473_at
CD47	1557270_at	PAFAH1B2	PRKCH	PSME3	ACYP2
GSDMB	TPGS1	NOL9	ZNF224	HNRNPUL1	MTFR1
FABP3	PHLPP2	POU5F1P4	EPN1	244732_at	TRIM4
DR1	CYP2A6	CHD9	TET3	LILRA2	ZAN
FLT1	ADAM10	HEATR5B	PPP1R8	ANTXR1	215986_at
PCBD2	202397_at	RPRD1A	USP9X	PATL1	SRP72
1562194_at	MAVS	CAMSAP1	CCL22	1562056_at	KLF4
TRAPPC1	PDIA6	1570408_at	WDR77	236438_at	DRAXIN
PRKCB	234201_x_at	239171_at	FAM175A	C9orf84	NUP188
APCDD1L-AS1	VAMP2	CCDC108	INPP4A	OTOR	VAV3
243249_at	239264_at	NLRP1	HIGD1A	MRPL19	YWHAZ
CHMP2B	IFNGR2	240165_at	MAVS	TGFB1	CHD9
ABCD3	HNRNPC	1554413_s_at	LYPLA1	ARHGAP27	233960_s_at
DSTYK	ARHGAP26	1566959_at	FAM99B	FAM131C	228812_at
CASP8	243568_at	GGT7	CAT	SETBP1	DLD
CUL4A	ATXN10	RPS15	RPS10P7	ELP2	SMIM15
233648_at	FOXP3	1565915_at	ETNK1	1557512_at	1557724_a_at
242558_at	217164_at	232584_at	SUMO2	TRMT11	NSMCE4A
1557562_at	222436_s_at	233783_at	TCOF1	227556_at	ENDOG
MAGI1	DNAJB11	SNX5	POGZ	ST8SIA4	EVI5L
UBTD2	237311_at	241460_at	RHOQ	PDLIM5	FAM63A
EIF4E3	SMAD7	MUC3B	DKFZP547L112	BTBD7	IL1R2
CFDP1	204347_at	1556336_at	EDIL3	239613_at	MCTP2
COG7	UCHL5	MOGAT2	GHDC	UFL1	238807_at
234645_at	MGA	242476_at	COMMD10	POLH	CADM1
243992_at	CSGALNACT1	NICN1	HAP1	237456_at	OBSCN
VAMP3	NR5A1	DDX42	TMEM48	229717_at	HNRNPH1
243280_at	SEC23A	DNAJC10	ANKZF1	C17orf70	NUCKS1
NAA50	C15orf57	1555996_s_at	MUC20	1566823_a_at	USP31
AVL9	SRSF5	241445_at	DDR1-AS1	CTSC	HDGFRP2
TOB2	CTDSPL2	240008_at	RCN2	244674_at	XRCC6
C5orf22	6-Mar	PRKAR1B	240538_at	242058_at	226252_at
236612_at	PCBP3	LOC374443	CHD2	INTS10	OXR1
1558710_at	1556055_at	231604_at	FASTKD3	SARDH	ALPI
RAB3IP	SPTLC2	B4GALT1	243286_at	SCLT1	CASP8
KLHL28	PCMT1	EXOC6	CPNE6	229575_at	6-Sep
CCNG2	TMTC4	LYRM4	ZDHHC13	NKTR	232626_at
ZNF207	SGK494	ERVK13-1	CD44	SEZ6L2	211910_at
PEBP1	243860_at	NECAP2	RECQL5	ACHE	DNASE1L3
ROCK1	244332_at	CCDC40	SULT4A1	TTC9B	NF1
242927_at	MBD2	237330_at	SEC23IP	SUPT6H	TTC5
241387_at	235613_at	KIAA0141	ID2	GPR65	222156_x_at
ZNF304	LOC100507283	FAM174A	LINC00661	FLNB	FAM181A
227384_s_at	RAB22A	241376_at	DRD2	ATP9A	ATF1
HOXB2	PDCD6IP	TOR3A	FYTTD1	PLAGL1	RHOQ
AGGF1	9-Sep	243454_at	MAPRE1	LRRC37A3	IL13RA1
HIPK3	ZBTB43	SLC20A2	POT1	LINS	CCNY
SERP1	235847_at	ZNF655	242768_at	1566166_at	CEP57
TOR1AIP1	VAMP4	KCP	243078_at	RICTOR	LOC729852
242824_at	1556865_at	ZNF398	RSBN1L	DCAF13	PEX3
IKBIP	GOLPH3	BCLAF1	UPRT	GRB10	ARID4B
CHD7	PEX12	243396_at	C19orf43	1560982_at	DCAF8
SETD5	KBTBD4	216567_at	PTER	PTP4A2	1557422_at
REPS1	MLLT11	COX2	239780_at	1562051_at	SNAP23
233323_at	FLJ31813	ATP11A	239451_at	CCNL1	OGFRL1
TTBK2	RAB6A	216448_at	CADM3	C17orf58	239164_at
237895_at	PTPN11	PDIA6	243512_x_at	GRIPAP1	207756_at
IL13RA1	CHMP1B	UBE2D3	ZBTB20	SIAH1	242732_at
DIO3	242457_at	APPBP2	239555_at	232788_at	NOL9
234091_at	CIAPIN1	MCTP2	236610_at	RTCA	TCF3
233228_at	PRRC2A	ZNF708	MIER3	NENF	SAE1
B4GALNT3	240775_at	MIA	SLC32A1	233922_at	ZNF280D
MED30	238712_at	XRCC1	215197_at	216850_at	C12orf5
243826_at	216465_at	RBM15	ADD1	SNX10	1556327_a_at
TANK	ZFYVE21	PDP1	230980_x_at	BTBD18	MMP28
232909_s_at	RQCD1	CD9	FAM184B	BRCC3	ACSS1
TNFRSF19	SCAF11	COMMD10	FGF18	FAHD2CP	ULK2
1569999_at	1562063_x_at	KCTD18	234449_at	243509_at	AGO1
1558418_at	SMARCC2	HEATR5A	LACTB2	TFB2M	TPI1
UBE4B	XAB2	NCOA2	1556778_at	PALM3	TPSAB1
239557_at	SLC25A40	TRAPPC13	KDELR2	242386_x_at	CCDC28A
232890_at	TSPAN12	CELF2	238559_at	ATP5S	CEP152
ITPR1	KLHL9	206088_at	ZNF235	DCTD	CDK9
MEAF6	217653_x_at	PRSS53	239619_at	REL	RAE1
239331_at	U2SURP	SLC22A31	GNL3L	TOMM22	PROK2
243808_at	RALGAPA2	POLK	216656_at	SSTR5-AS1	CCNY
ZNF580	TPM3	SEC24A	USP2	LRMP	233790_at
KLF9	244474_at	MFI2	CGGBP1	TTL	PLEKHG3
241788_x_at	LOC150381	RUNX1-IT1	SOAT1	ALG13	G3BP2
204006_s_at

3-class univariate F-test was done on the Discovery cohort (1000 random permutations and FDR<10%; BRB ArrayTools)
Number of significant genes by controlling the proportion of false positive genes: 2977 Sorted by p-value of the univariate test.

Class 1: ADNR; Class 2: AR; Class 3: TX.

With probability of 80% the first 2977 genes contain no more than 10% of false discoveries. Further extension of the list was halted because the list would contain more than 100 false discoveries The ‘Pairwise significant’ column shows pairs of classes with significantly different gene expression at alpha=0.01. Class labels in a pair are ordered (ascending) by their averaged gene expression.

TABLE 1c

The top 200 gene probeset used in the 3-Way AR, ADNR TX ANOVA Analysis (using the HT
HG-U133+ PM Array Plates)
3-Way AR, ADNR TX ANOVA Analysis

				p-value -
#	Probeset ID	Gene Symbol	Gene Title	phenotype

1	213718_PM_at	RBM4	RNA binding motif protein 4	5.39E−10
2	227878_PM_s_at	ALKBH7	alkB, alkylation repair homolog 7 (E. coli)	3.15E−08
3	214405_PM_at	214405_PM_at_EST1	EST1	3.83E−08
4	210792_PM_x_at	SIVA1	SIVA1, apoptosis-inducing factor	4.64E−08
5	214182_PM_at	214182_PM_at_EST2	EST2	6.38E−08
6	1554015_PM_a_at	CHD2	chromodomain helicase DNA binding	6.50E−08
			protein 2
7	225839_PM_at	RBM33	RNA binding motif protein 33	7.41E−08
8	1554014_PM_at	CHD2	chromodomain helicase DNA binding	7.41E−08
			protein 2
9	214263_PM_x_at	POLR2C	polymerase (RNA) II (DNA directed)	7.47E−08
			polypeptide C, 33 kDa
10	1556865_PM_at	1556865_PM_at_EST3	EST3	9.17E−08
11	203577_PM_at	GTF2H4	general transcription factor IIH,	9.44E−08
			polypeptide 4, 52 kDa
12	218861_PM_at	RNF25	ring finger protein 25	1.45E−07
13	206061_PM_s_at	DICER1	dicer 1, ribonuclease type III	1.53E−07
14	225377_PM_at	C9orf86	chromosome 9 open reading frame 86	1.58E−07
15	1553107_PM_s_at	C5orf24	chromosome 5 open reading frame 24	2.15E−07
16	1557246_PM_at	KIDINS220	kinase D-interacting substrate, 220 kDa	2.43E−07
17	224455_PM_s_at	ADPGK	ADP-dependent glucokinase	3.02E−07
18	201055_PM_s_at	HNRNPA0	heterogeneous nuclear ribonucleoprotein	3.07E−07
			A0
19	236237_PM_at	236237_PM_at_EST4	EST4	3.30E−07
20	211833_PM_s_at	BAX	BCL2-associated X protein	3.92E−07
21	1558111_PM_at	MBNL1	muscleblind-like (Drosophila)	3.93E−07
22	206113_PM_s_at	RAB5A	RAB5A, member RAS oncogene family	3.95E−07
23	202306_PM_at	POLR2G	polymerase (RNA) II (DNA directed)	4.83E−07
			polypeptide G
24	242268_PM_at	CELF2	CUGBP, Elav-like family member 2	5.87E−07
25	223332_PM_x_at	RNF126	ring finger protein 126	6.24E−07
26	1561909_PM_at	1561909_PM_at_EST5	EST5	6.36E−07
27	213940_PM_s_at	FNBP1	formin binding protein 1	6.60E−07
28	210655_PM_s_at	FOXO3 /// FOXO3B	forkhead box O3 /// forkhead box O3B	6.82E−07
			pseudogene
29	233303_PM_at	233303_PM_at_EST6	EST6	8.00E−07
30	244219_PM_at	244219_PM_at_EST7	EST7	9.55E−07
31	35156_PM_at	R3HCC1	R3H domain and coiled-coil containing 1	1.02E−06
32	215210_PM_s_at	DLST	dihydrolipoamide S-succinyltransferase	1.08E−06
			(E2 component of 2-oxo-glutarate
			complex)
33	1563431_PM_x_at	CALM3	Calmodulin 3 (phosphorylase kinase,	1.15E−06
			delta)
34	202858_PM_at	U2AF1	U2 small nuclear RNA auxiliary factor 1	1.18E−06
35	1555536_PM_at	ANTXR2	anthrax toxin receptor 2	1.21E−06
36	210313_PM_at	LILRA4	leukocyte immunoglobulin-like receptor,	1.22E−06
			subfamily A (with TM domain), member 4
37	216997_PM_x_at	TLE4	transducin-like enhancer of split 4	1.26E−06
			(E(sp1) homolog, Drosophila)
38	201072_PM_s_at	SMARCC1	SWI/SNF related, matrix associated,	1.28E−06
			actin dependent regulator of chromatin,
			subfamily c
39	223023_PM_at	BET1L	blocked early in transport 1 homolog (S. cerevisiae)-	1.36E−06
			like
40	201556_PM_s_at	VAMP2	vesicle-associated membrane protein 2	1.39E−06
			(synaptobrevin 2)
41	218385_PM_at	MRPS18A	mitochondrial ribosomal protein S18A	1.44E−06
42	1555420_PM_a_at	KLF7	Kruppel-like factor 7 (ubiquitous)	1.50E−06
43	242726_PM_at	242726_PM_at_EST8	EST8	1.72E−06
44	233595_PM_at	USP34	ubiquitin specific peptidase 34	1.79E−06
45	218218_PM_at	APPL2	adaptor protein, phosphotyrosine	1.80E−06
			interaction, PH domain and leucine
			zipper containing 2
46	240991_PM_at	240991_PM_at_EST9	EST9	1.95E−06
47	210763_PM_x_at	NCR3	natural cytotoxicity triggering receptor 3	2.05E−06
48	201009_PM_s_at	TXNIP	thioredoxin interacting protein	2.10E−06
49	221855_PM_at	SDHAF1	succinate dehydrogenase complex	2.19E−06
			assembly factor 1
50	241955_PM_at	HECTD1	HECT domain containing 1	2.37E−06
51	213872_PM_at	C6orf62	Chromosome 6 open reading frame 62	2.57E−06
52	243751_PM_at	243751_PM_at_EST10	EST10	2.60E−06
53	232908_PM_at	ATAD2B	ATPase family, AAA domain containing	2.64E−06
			2B
54	222413_PM_s_at	MLL3	myeloid/lymphoid or mixed-lineage	2.74E−06
			leukemia 3
55	217550_PM_at	ATF6	activating transcription factor 6	2.86E−06
56	223123_PM_s_at	C1orf128	chromosome 1 open reading frame 128	2.87E−06
57	202283_PM_at	SERPINF1	serpin peptidase inhibitor, clade F	2.87E−06
			(alpha-2 antiplasmin, pigment epithelium
			derived fa
58	200813_PM_s_at	PAFAH1B1	platelet-activating factor acetylhydrolase	3.10E−06
			1b, regulatory subunit 1 (45 kDa)
59	223312_PM_at	C2orf7	chromosome 2 open reading frame 7	3.30E−06
60	217399_PM_s_at	FOXO3 /// FOXO3B	forkhead box O3 /// forkhead box O3B	3.31E−06
			pseudogene
61	218571_PM_s_at	CHMP4A	chromatin modifying protein 4A	3.47E−06
62	228727_PM_at	ANXA11	annexin A11	3.73E−06
63	200055_PM_at	TAF10	TAF10 RNA polymerase II, TATA box	3.76E−06
			binding protein (TBP)-associated factor,
			30 kDa
64	242854_PM_x_at	DLEU2	deleted in lymphocytic leukemia 2 (non-	3.84E−06
			protein coding)
65	1562250_PM_at	1562250_PM_at_EST11	EST11	3.99E−06
66	208657_PM_s_at	SEPT9	septin 9	4.12E−06
67	201394_PM_s_at	RBM5	RNA binding motif protein 5	4.33E−06
68	200898_PM_s_at	MGEA5	meningioma expressed antigen 5	4.55E−06
			(hyaluronidase)
69	202871_PM_at	TRAF4	TNF receptor-associated factor 4	4.83E−06
70	1558527_PM_at	LOC100132707	hypothetical LOC100132707	4.85E−06
71	203479_PM_s_at	OTUD4	OTU domain containing 4	4.86E−06
72	219931_PM_s_at	KLHL12	kelch-like 12 (Drosophila)	4.88E−06
73	203496_PM_s_at	MED1	mediator complex subunit 1	4.99E−06
74	216112_PM_at	216112_PM_at_EST12	EST12	5.22E−06
75	1557418_PM_at	ACSL4	Acyl-CoA synthetase long-chain family	5.43E−06
			member 4
76	212113_PM_at	ATXN7L3B	ataxin 7-like 3B	5.67E−06
77	204246_PM_s_at	DCTN3	dynactin 3 (p22)	5.68E−06
78	235868_PM_at	MGEA5	Meningioma expressed antigen 5	5.70E−06
			(hyaluronidase)
79	232725_PM_s_at	MS4A6A	membrane-spanning 4-domains,	5.73E−06
			subfamily A, member 6A
80	212886_PM_at	CCDC69	coiled-coil domain containing 69	5.84E−06
81	226840_PM_at	H2AFY	H2A histone family, member Y	5.86E−06
82	226825_PM_s_at	TMEM165	transmembrane protein 165	5.96E−06
83	227924_PM_at	INO80D	INO80 complex subunit D	6.18E−06
84	238816_PM_at	PSEN1	presenilin 1	6.18E−06
85	224798_PM_s_at	C15orf17	chromosome 15 open reading frame 17	6.31E−06
86	243295_PM_at	RBM27	RNA binding motif protein 27	6.34E−06
87	207460_PM_at	GZMM	granzyme M (lymphocyte met-ase 1)	6.46E−06
88	242131_PM_at	ATP6	ATP synthase F0 subunit 6	6.56E−06
89	228637_PM_at	ZDHHC1	zinc finger, DHHC-type containing 1	6.80E−06
90	233575_PM_s_at	TLE4	transducin-like enhancer of split 4	7.08E−06
			(E(sp1) homolog, Drosophila)
91	215088_PM_s_at	SDHC	succinate dehydrogenase complex,	7.18E−06
			subunit C, integral membrane protein,
			15 kDa
92	209675_PM_s_at	HNRNPUL1	heterogeneous nuclear ribonucleoprotein	7.35E−06
			U-like 1
93	37462_PM_i_at	SF3A2	splicing factor 3a, subunit 2, 66 kDa	7.38E−06
94	236545_PM_at	236545_PM_at_EST13	EST13	7.42E−06
95	232846_PM_s_at	CDH23	cadherin-related 23	7.42E−06
96	242679_PM_at	LOC100506866	hypothetical LOC100506866	7.52E−06
97	229860_PM_x_at	C4orf48	chromosome 4 open reading frame 48	7.60E−06
98	243557_PM_at	243557_PM_at_EST14	EST14	7.62E−06
99	222638_PM_s_at	C6orf35	chromosome 6 open reading frame 35	7.67E−06
100	209477_PM_at	EMD	emerin	7.70E−06
101	213328_PM_at	NEK1	NIMA (never in mitosis gene a)-related	7.72E−06
			kinase 1
102	1555843_PM_at	HNRNPM	Heterogeneous nuclear ribonucleoprotein M	7.72E−06
103	241240_PM_at	241240_PM_at_EST15	EST15	7.74E−06
104	218600_PM_at	LIMD2	LIM domain containing 2	7.81E−06
105	212994_PM_at	THOC2	THO complex 2	7.84E−06
106	243046_PM_at	243046_PM_at_EST16	EST16	8.03E−06
107	211947_PM_s_at	BAT2L2	HLA-B associated transcript 2-like 2	8.04E−06
108	238800_PM_s_at	ZCCHC6	Zinc finger, CCHC domain containing 6	8.09E−06
109	228723_PM_at	228723_PM_at_EST17	EST17	8.11E−06
110	242695_PM_at	242695_PM_at_EST18	EST18	8.30E−06
111	216971_PM_s_at	PLEC	plectin	8.39E−06
112	220746_PM_s_at	UIMC1	ubiquitin interaction motif containing 1	8.44E−06
113	238840_PM_at	LRRFIP1	leucine rich repeat (in FLII) interacting	8.59E−06
			protein 1
114	1556055_PM_at	1556055_PM_at_EST19	EST19	8.74E−06
115	AFFX-	AFFX-	EST20	9.08E−06
	M27830_5_at	M27830_5_at_EST20
116	215248_PM_at	GRB10	growth factor receptor-bound protein 10	9.43E−06
117	211192_PM_s_at	CD84	CD84 molecule	1.01E−05
118	214383_PM_x_at	KLHDC3	kelch domain containing 3	1.04E−05
119	208478_PM_s_at	BAX	BCL2-associated X protein	1.08E−05
120	229422_PM_at	NRD1	nardilysin (N-arginine dibasic	1.12E−05
			convertase)
121	206636_PM_at	RASA2	RAS p21 protein activator 2	1.14E−05
122	1559589_PM_a_at	1559589_PM_a_at_EST21	EST21	1.16E−05
123	229676_PM_at	MTPAP	Mitochondrial poly(A) polymerase	1.18E−05
124	201369_PM_s_at	ZFP36L2	zinc finger protein 36, C3H type-like 2	1.19E−05
125	215535_PM_s_at	AGPAT1	1-acylglycerol-3-phosphate O-	1.25E−05
			acyltransferase 1 (lysophosphatidic acid
			acyltransferase,
126	212162_PM_at	KIDINS220	kinase D-interacting substrate, 220 kDa	1.26E−05
127	218893_PM_at	ISOC2	isochorismatase domain containing 2	1.26E−05
128	204334_PM_at	KLF7	Kruppel-like factor 7 (ubiquitous)	1.26E−05
129	221598_PM_s_at	MED27	mediator complex subunit 27	1.31E−05
130	221060_PM_s_at	TLR4	toll-like receptor 4	1.32E−05
131	224821_PM_at	ABHD14B	abhydrolase domain containing 14B	1.35E−05
132	244349_PM_at	244349_PM_at_EST22	EST22	1.38E−05
133	244418_PM_at	244418_PM_at_EST23	EST23	1.41E−05
134	225157_PM_at	MLXIP	MLX interacting protein	1.42E−05
135	228469_PM_at	PPID	Peptidylprolyl isomerase D	1.44E−05
136	224332_PM_s_at	MRPL43	mitochondrial ribosomal protein L43	1.47E−05
137	1553588_PM_at	ND3 /// SH3KBP1	NADH dehydrogenase, subunit 3	1.48E−05
			(complex I) /// SH3-domain kinase
			binding protein 1
138	238468_PM_at	TNRC6B	trinucleotide repeat containing 6B	1.49E−05
139	235727_PM_at	KLHL28	kelch-like 28 (Drosophila)	1.53E−05
140	218978_PM_s_at	SLC25A37	solute carrier family 25, member 37	1.59E−05
141	221214_PM_s_at	NELF	nasal embryonic LHRH factor	1.62E−05
142	204282_PM_s_at	FARS2	phenylalanyl-tRNA synthetase 2,	1.64E−05
			mitochondrial
143	236155_PM_at	ZCCHC6	Zinc finger, CCHC domain containing 6	1.65E−05
144	224806_PM_at	TRIM25	tripartite motif-containing 25	1.66E−05
145	202840_PM_at	TAF15	TAF15 RNA polymerase II, TATA box	1.67E−05
			binding protein (TBP)-associated factor,
			68 kDa
146	207339_PM_s_at	LTB	lymphotoxin beta (TNF superfamily,	1.68E−05
			member 3)
147	221995_PM_s_at	221995_PM_s_at_EST24	EST24	1.69E−05
148	242903_PM_at	IFNGR1	interferon gamma receptor 1	1.70E−05
149	228826_PM_at	228826_PM_at_EST25	EST25	1.70E−05
150	220231_PM_at	C7orf16	chromosome 7 open reading frame 16	1.71E−05
151	242861_PM_at	NEDD9	neural precursor cell expressed,	1.72E−05
			developmentally down-regulated 9
152	202785_PM_at	NDUFA7	NADH dehydrogenase (ubiquinone) 1	1.74E−05
			alpha subcomplex, 7, 14.5 kDa
153	205787_PM_x_at	ZC3H11A	zinc finger CCCH-type containing 11A	1.76E−05
154	1554333_PM_at	DNAJA4	DnaJ (Hsp40) homolog, subfamily A,	1.77E−05
			member 4
155	1563315_PM_s_at	ERICH1	glutamate-rich 1	1.82E−05
156	202101_PM_s_at	RALB	v-ral simian leukemia viral oncogene	1.82E−05
			homolog B (ras related; GTP binding
			protein)
157	210210_PM_at	MPZL1	myelin protein zero-like 1	1.84E−05
158	217234_PM_s_at	EZR	ezrin	1.85E−05
159	219222_PM_at	RBKS	ribokinase	1.86E−05
160	213161_PM_at	TMOD1 /// TSTD2	tropomodulin 1 /// thiosulfate	1.88E−05
			sulfurtransferase (rhodanese)-like
			domain containing 2
161	236497_PM_at	LOC729683	hypothetical protein LOC729683	1.91E−05
162	203111_PM_s_at	PTK2B	PTK2B protein tyrosine kinase 2 beta	1.93E−05
163	1554571_PM_at	APBB1IP	amyloid beta (A4) precursor protein-	1.97E−05
			binding, family B, member 1 interacting
			protein
164	212007_PM_at	UBXN4	UBX domain protein 4	1.98E−05
165	1569106_PM_s_at	SETD5	SET domain containing 5	1.98E−05
166	243032_PM_at	243032_PM_at_EST26	EST26	2.00E−05
167	216380_PM_x_at	216380_PM_x_at_EST27	EST27	2.00E−05
168	217958_PM_at	TRAPPC4	trafficking protein particle complex 4	2.10E−05
169	200884_PM_at	CKB	creatine kinase, brain	2.11E−05
170	208852_PM_s_at	CANX	calnexin	2.12E−05
171	1558624_PM_at	1558624_PM_at_EST28	EST28	2.19E−05
172	203489_PM_at	SIVA1	SIVA1, apoptosis-inducing factor	2.23E−05
173	240652_PM_at	240652_PM_at_EST29	EST29	2.25E−05
174	214639_PM_s_at	HOXA1	homeobox A1	2.37E−05
175	203257_PM_s_at	C11orf49	chromosome 11 open reading frame 49	2.45E−05
176	217507_PM_at	SLC11A1	solute carrier family 11 (proton-coupled	2.56E−05
			divalent metal ion transporters), member 1
177	223166_PM_x_at	C9orf86	chromosome 9 open reading frame 86	2.57E−05
178	206245_PM_s_at	IVNS1ABP	influenza virus NS1A binding protein	2.62E−05
179	223290_PM_at	PDXP /// SH3BP1	pyridoxal (pyridoxine, vitamin B6)	2.64E−05
			phosphatase /// SH3-domain binding
			protein 1
180	224732_PM_at	CHTF8	CTF8, chromosome transmission fidelity	2.69E−05
			factor 8 homolog (S. cerevisiae)
181	204560_PM_at	FKBP5	FK506 binding protein 5	2.75E−05
182	1556283_PM_s_at	FGFR1OP2	FGFR1 oncogene partner 2	2.75E−05
183	212451_PM_at	SECISBP2L	SECIS binding protein 2-like	2.76E−05
184	208750_PM_s_at	ARF1	ADP-ribosylation factor 1	2.81E−05
185	238987_PM_at	B4GALT1	UDP-Gal:betaGlcNAc beta 1,4-	2.82E−05
			galactosyltransferase, polypeptide 1
186	227211_PM_at	PHF19	PHD finger protein 19	2.84E−05
187	223960_PM_s_at	C16orf5	chromosome 16 open reading frame 5	2.86E−05
188	223009_PM_at	C11orf59	chromosome 11 open reading frame 59	2.88E−05
189	229713_PM_at	PIP4K2A	Phosphatidylinositol-5-phosphate 4-	2.96E−05
			kinase, type II, alpha
190	1555330_PM_at	GCLC	glutamate-cysteine ligase, catalytic	2.96E−05
			subunit
191	242288_PM_s_at	EMILIN2	elastin microfibril interfacer 2	2.97E−05
192	207492_PM_at	NGLY1	N-glycanase 1	2.98E−05
193	233292_PM_s_at	ANKHD1 /// ANKHD1-	ankyrin repeat and KH domain	3.00E−05
		EIF4EBP3	containing 1 /// ANKHD1-EIF4EBP3
			readthrough
194	1569600_PM_at	DLEU2	Deleted in lymphocytic leukemia 2 (non-	3.00E−05
			protein coding)
195	218387_PM_s_at	PGLS	6-phosphogluconolactonase	3.03E−05
196	239660_PM_at	RALGAPA2	Ral GTPase activating protein, alpha	3.07E−05
			subunit 2 (catalytic)
197	230733_PM_at	230733_PM_at_EST30	EST30	3.07E−05
198	1557804_PM_at	1557804_PM_at_EST31	EST31	3.11E−05
199	210969_PM_at	PKN2	protein kinase N2	3.12E−05
200	233937_PM_at	GGNBP2	gametogenetin binding protein 2	3.13E−05

TABLE 1d

The gene list of all 4132 genes analyzed in the 3-Way AR, ADNR TX ANOVA Analysis (using the
HT HG-U133+ PM Array Plates)

RBM4	IFT52	TMSB4Y	SLAMF6
ALKBH7	240759_PM_at	FLT3LG	LOC100287401
214405_PM_at_EST1	EZR	MTMR2	CFLAR
SIVA1	DEXI	C8orf82	PIGV
214182_PM_at_EST2	SMARCC2	ELP4	RB1CC1
CHD2	ADPGK	TEX264	SLC25A37
RBM33	TACR1	STX3	CES2
CHD2	FLJ10661	208278_PM_s_at	CLYBL
POLR2C	ACSL4	VPS25	SDHD
1556865_PM_at_EST3	STAC3	ISCA1	TRAF3IP3
GTF2H4	PHF20	SNCA	MRPL2
RNF25	UBXN7	CCDC17	CISH
DICER1	SCFD1	CCDC51	FBXO9
C9orf86	1559391_PM_s_at	MTPAP	LOC284454
C5orf24	1570151_PM_at	1566958_PM_at	CUL4B
KIDINS220	MED13L	STRADB	PHTF1
ADPGK	XCL1	HEY1	TACC1
HNRNPA0	1563833_PM_at	LOC100129361	RBM8A
236237_PM_at_EST4	CPEB4	EHBP1	PCMT1
BAX	PFKFB2	TMEM223	LOC100507315 ///
			PPP2R5C
MBNL1	244357_PM_at	OGT	TNPO1
RAB5A	DAAM2	231005_PM_at	FXR2
POLR2G	CCDC57	C16orf5	SLC25A19
CELF2	CDKN1C	GNGT2	RAB34
RNF126	CTAGE5	IKZF1	IFT52
1561909_PM_at_EST5	PTEN /// PTENP1	242008_PM_at	LCP1
FNBP1	DENND1C	STON2	ZNF677
FOXO3 /// FOXO3B	TAGAP	CALM1	HCG22
233303_PM_at_EST6	ANXA3	217347_PM_at	1564155_PM_x_at
244219_PM_at_EST7	UBE2I	ID3	OBFC2B
R3HCC1	LOC728723	TGIF2	SRSF2
DLST	243695_PM_at	CELF2	SORBS1
CALM3	C15orf57	MAP3K13	WDR82
U2AF1	ALG8	SLC4A7	ALOX5
ANTXR2	PSMD7	CCDC97	TES
LILRA4	C9orf84	UBE2J2	CD46
TLE4	TSPAN18	PATL1	CALU
SMARCC1	PIAS2	DTX3	ARHGDIA
BET1L	RNF145	ZNF304	SDCCAG8
VAMP2	C19orf43	GDAP2	C7orf26
MRPS18A	208324_PM_at	227729_PM_at	SEMA4F
KLF7	FCAR	TMED2	TLR1
242726_PM_at_EST8	LTN1	C6orf125	222300_PM_at
USP34	PKN2	PGAP3	NUDT4
APPL2	TTLL3	ZMYND11	MLL3
240991_PM_at_EST9	NAA15	CYB5B	SVIL
NCR3	GNAQ	MBD6	IFT46
TXNIP	KLF6	241434_PM_at	ARIH2
SDHAF1	TOR1A	GHITM	NFYC
HECTD1	SLC27A5	244555_PM_at	RABL2A /// RABL2B
C6orf62	SLC2A11	CTSB	235661_PM_at
243751_PM_at_EST10	240939_PM_x_at	LYRM7	PTPRC
ATAD2B	LSMD1	DNASE1	243031_PM_at
MLL3	WAC	FECH	ESD
ATF6	LOC100128590	UBE2W	244642_PM_at
C1orf128	NSMAF	TLK1	EDF1
SERPINF1	VAV3	229483_PM_at	C1orf159
PAFAH1B1	239166_PM_at	MKLN1	CHP
C2orf7	ST8SIA4	DERL2	1558385_PM_at
FOXO3 /// FOXO3B	ACTR10	C11orf31	1557667_PM_at
CHMP4A	PXK	PHC3	PDE6B
ANXA11	DGUOK	PPM1L	MFAP1
TAF10	MTG1	LOC100505764	TMED4
DLEU2	1557538_PM_at	ZNF281	PPARA
1562250_PM_at_EST11	TXNL4B	JAK3	ARHGAP24
SEPT9	239723_PM_at	NME6	PSMD9
RBM5	MAPKAPK2	ZMAT3	LOC100507006
MGEA5	CXXC5	MADD	GLUD1
TRAF4	CHST2	SLC2A8	CCDC19
LOC100132707	GSTM1	PALLD	AKR7A3
OTUD4	210824_PM_at	LOC283392	SCLY
KLHL12	ADAM17	243105_PM_at	GCLC
MED1	244267_PM_at	FCAR	USP32
216112_PM_at_EST12	LOC339862	CNOT8	GSPT1
ACSL4	1561733_PM_at	1555373_PM_at	RUFY2
ATXN7L3B	PEX14	HNRNPL	RHEBL1
DCTN3	EXOSC1	LOC646014	FAF1
MGEA5	230354_PM_at	VAV3	WSB1
MS4A6A	ZBTB43	LRBA	DNAJC7
CCDC69	C8orf60	HGD	MGC16275
H2AFY	C15orf54	TADA2B	1556508_PM_s_at
TMEM165	CELF2	227223_PM_at	VAPA
INO80D	241722_PM_x_at	SLC25A33	RRAGC
PSEN1	LIMD1	LMBR1L	TDG
C15orf17	OTUB1	SPRED1	ENO1
RBM27	KCTD10	SEC24D	SIDT2
GZMM	1560443_PM_at	239848_PM_at	UMPS
ATP6	FBXO41	232867_PM_at	FLJ35816
ZDHHC1	DCAF6 /// ND4	SLC35C2	RPRD1A
TLE4	PEPD	ANGEL1	PPIB
SDHC	CERK	242320_PM_at	ALAD
HNRNPUL1	PTPMT1	DSTYK	C10orf54
SF3A2	FAM189B	ERGIC2	C16orf88
236545_PM_at_EST13	TMED1	QRSL1	EPB41
CDH23	UBL4A	KDM2A	PRDX2
LOC100506866	SLC25A37	MED6	MMAB
C4orf48	SUGT1	ATP5D	PRDX3
243557_PM_at_EST14	MPZL1	RNF130	FOXO3
C6orf35	GSTM2	TCOF1	1560798_PM_at
EMD	MLL5	ARHGEF2	ENY2
NEK1	C19orf25	REPIN1	PTEN
HNRNPM	C6orf108	MRPS27	ZNF879
241240_PM_at_EST15	FPR2	RNASET2	233223_PM_at
LIMD2	EXOSC9	C9orf70	AGAP10 /// AGAP4 ///
			AGAP9 /// BMS1P1 ///
			BMS1P5 /// LOC399753
THOC2	BMS1P1 /// BMS1P5	243671_PM_at	NUBPL
243046_PM_at_EST16	CCDC92	GFM2	C20orf4
BAT2L2	HIF1AN	ZNHIT1	BBS2
ZCCHC6	IDS	ZNF554	BTBD19
228723_PM_at_EST17	240108_PM_at	KIAA1609	230987_PM_at
242695_PM_at_EST18	BMP6	ZNF333	C1QBP
PLEC	AKR1B1	CD59	HIST1H3B
UIMC1	NDUFB8	UBXN1	C16orf88
LRRFIP1	CCDC69	LZTFL1	RANGRF
1556055_PM_at_EST19	LRRFIP2	C17orf79	DSTN
AFFX-	OXR1	PSMG2	ADCY3
M27830_5_at_EST20
GRB10	MED20	LOC151146	SSH2
CD84	ADNP2	C15orf26	ATP5A1
KLHDC3	ADORA2A /// SPECC1L	SPRED1	C20orf30
BAX	HERC4	ARAF	1557410_PM_at
NRD1	MAT2A	241865_PM_at	237517_PM_at
RASA2	C20orf196	244249_PM_at	MYEOV2
1559589_PM_a_at_EST21	ABTB1	243233_PM_at	226347_PM_at
MTPAP	ZBTB3	NAP1L4	YTHDF3
ZFP36L2	COX5B	ANKRD55	JMJD1C
AGPAT1	SELM	DAD1	ZNRD1
KIDINS220	DKC1	MCTP2	CAPNS2
ISOC2	PPP1R11	242134_PM_at	229635_PM_at
KLF7	SLC35B2	NDUFA3	1569362_PM_at
MED27	FAM159A	243013_PM_at	241838_PM_at
TLR4	LOC100505501	CDV3	VAV2
ABHD14B	213574_PM_s_at	SRD5A1	CYP2W1
244349_PM_at_EST22	COX5B	229327_PM_s_at	TBC1D14
244418_PM_at_EST23	SRGN	ERICH1	244781_PM_x_at
MLXIP	SF3A2	230154_PM_at	NUDC
PPID	CLPP	APOB48R	C12orf45
MRPL43	ATP6V1C1	ACSL1	PTEN
ND3 /// SH3KBP1	CARD16 /// CASP1	C5orf56	CAMTA1
TNRC6B	CNPY3	232784_PM_at	240798_PM_at
KLHL28	HIRA	CD44	TOMM22
SLC25A37	MBP	SNPH	237165_PM_at
NELF	SLC41A3	LPP	MRPL46
FARS2	NDUFA2	ARPC5L	CSRNP2
ZCCHC6	GPN2	238183_PM_at	JAK1
TRIM25	233094_PM_at	ZNF83	ZNF2
TAF15	IL32	10-Sep	HLA-C
LTB	241774_PM_at	BMPR2	RPL10L
221995_PM_s_at_EST24	1558371_PM_a_at	TMEM101	TUG1
IFNGR1	SMEK1	TRUB2	227121_PM_at
228826_PM_at_EST25	UBE4B	216568_PM_x_at	LOC100287911
C7orf16	LOC100287482	244860_PM_at	KBTBD4 /// PTPMT1
NEDD9	PRPF18	GNB1	USP37
NDUFA7	1556942_PM_at	H6PD	CTNNA1
ZC3H11A	COX8A	LARP7	LEPREL4
DNAJA4	NFAT5	1560102_PM_at	DNMT3A
ERICH1	CDKN1C	PHF20L1	CD81
RALB	236962_PM_at	CFLAR	1557987_PM_at
MPZL1	CCL28	NIPAL3	ZBTB20
EZR	1560026_PM_at	TSEN15	FGD2
RBKS	SURF2	MAEA	DTX1
TMOD1 /// TSTD2	COG2	EPRS	ST7
LOC729683	PYGM	SBNO1	RBM42
PTK2B	LOC401320	SNTB2	242556_PM_at
APBB1IP	ZNF341	TNPO2	TOR3A
UBXN4	235107_PM_at	WDR77	FHL1
SETD5	MDM4	HBA1 /// HBA2	MRPS22
243032_PM_at_EST26	ADK	MAST4	231205_PM_at
216380_PM_x_at_EST27	RBPJ	RPP25	1557810_PM_at
TRAPPC4	FAM162A	GKAP1	ZNF569
CKB	WDR11	C19orf42	UBE2F
CANX	240695_PM_at	INVS	LOC283485
1558624_PM_at_EST28	BIN2	CYP3A43	SCP2
SIVA1	242279_PM_at	NHP2L1	CSH2
240652_PM_at_EST29	USP4	PCCA	DVL2
HOXA1	C19orf20	SAMM50	CD164
C11orf49	240220_PM_at	ENTPD1	CFL1
SLC11A1	ZNF689	MED13L	INHBB
C9orf86	235999_PM_at	HLA-E	PAPOLA
IVNS1ABP	235743_PM_at	NT5C2	GSPT1
PDXP /// SH3BP1	238420_PM_at	SNX5	TUBGCP5
CHTF8	NUBP2	FBXL3	SPPL3
FKBP5	MSH6	PSMD4	PFDN6
FGFR1OP2	CFLAR	PSMF1	NASP
SECISBP2L	ACAD9	LOC100127983	HAVCR2
ARF1	LOC284454	DRAM1	CDKN2A
B4GALT1	PPM1K	CARD16	SLC40A1
PHF19	243149_PM_at	STAG3L4	PRKDC
C16orf5	SKP1	GTDC1	RRN3P2
C11orf59	MED13	RINT1	SLC8A1
PIP4K2A	SNORD89	CSTB	ILVBL
GCLC	235288_PM_at	244025_PM_at	241184_PM_x_at
EMILIN2	PCK2	COL1A1	GPR44
NGLY1	TXN2	TTC1	FAM195A
ANKHD1 /// ANKHD1-	KHSRP	JAK2	DENND3
EIF4EBP3
DLEU2	ENTPD1	UTRN	NOSIP
PGLS	C19orf60	TMEM126B	241466_PM_at
RALGAPA2	TOX4	240538_PM_at	NCRNA00182
230733_PM_at_EST30	CCDC13	ZNF638	CAB39
1557804_PM_at_EST31	DGUOK	FAM178A	215029_PM_at
PKN2	SLC22A15	ABTB1	PGBD4
GGNBP2	SDHC	KIAA1704 ///	FXN
		LOC100507773
SLC25A43	PIGW	ELF1	HEMGN
CHMP6	LOC100287911	G6PC3	1566473_PM_a_at
CSTF2T	216589_PM_at	GYPB	FXYD2
PELI3	C1orf93	CFLAR	C9orf30
DNASE1L3	PLXNA2	SURF4	ZNF532
ARPC5L	232354_PM_at	DUSP8	GPR97
ITSN2	MPZL1	ECHDC1	ISCA2
PURB	PKP4	POLH	DHRS3
240870_PM_at	APOBEC3G	KRTAP1-3	FBXW9
ORAI1	239238_PM_at	237655_PM_at	IKZF4
SYNCRIP	ALPK1	DYNLRB1	ARHGEF10L
NUTF2	NUMB	RPRD1A	SHPK
238918_PM_at	DNAJB12	C17orf104	237071_PM_at
CD58	UCK1	RAD50	237337_PM_at
DLEU2	ASPH	STX8	FCGR3A /// FCGR3B
SPTLC2	FUS	AARSD1	HIVEP3
B3GALT4	PRDM4	CRCP	LOC728153
239405_PM_at	MBD3	FBXO31	SYTL3
TSPAN14	GGCX	GRAP2	NUS1 /// NUS1P3
AVEN	UBA7	C18orf55	HBA1 /// HBA2
213879_PM_at	NDUFA13	OS9	KPNB1
DPH1 /// OVCA2	CFLAR	1565918_PM_a_at	RTN4IP1
NACC1	JAK1	SF3B5	IKZF1
MTMR14	SSR4	215212_PM_at	ZNF570
SLC9A8	SLC35B4	244341_PM_at	ACIN1
MRPL52	LARP4B	PECI	C12orf52
BRD2	AP2B1	CORO1B	216143_PM_at
ARF1	232744_PM_x_at	BACE2	LOC646470
RAD1	MGC16142	TMED8	ALAS2
234278_PM_at	TFB1M	TCOF1	CHMP4B
TECPR1	C12orf39	TBC1D12	SEC24D
9-Sep	STS	SYTL3	PTGDS
NUDT18	SUCLG1	239046_PM_at	DDX46
HBD	PLEKHA9	230350_PM_at	ARHGAP24
NARFL	ATXN7	ZNF493	CSF2RA
ARMCX6	TMEM55B	RPL36AL	SMARCE1
PPFIA1	MAN2C1	C15orf41	GOLGA2P2Y ///
			GOLGA2P3Y
RALGAPA2	MRPS17 /// ZNF713	ANKRD19	TPI1
MED13L	237185_PM_at	LOC144438	CD83
CELF2	DPH1 /// OVCA2	222330_PM_at	RALGAPA1
PELI1	235190_PM_at	CHMP7	PGAM1
MLXIP	KLHL8	CPAMD8	C7orf68
244638_PM_at	ABCG1	244454_PM_at	238544_PM_at
HIPK1	MTMR3	LOC100130175	C1orf135
FNDC3B	DCAF7	239850_PM_at	TMOD1
CCR9	ARF4	HDAC3	DLD
HIPK2	RUNX3	SLC15A4	PPP4R1L
TLR4	242106_PM_at	C12orf43	FAM105A
HIPK1	C11orf73	LOC100288939	244648_PM_at
SMG7	EIF4E2	AP1S1	FAM45A
LLGL1	DDX6	239379_PM_at	1562468_PM_at
APH1A	RABL3	FOXP2	PARP2
241458_PM_at	ILKAP	GRHPR	ESRRA
DICER1	RBM47	HAS3	CNTNAP3
C17orf49	MDP1	ZDHHC19	STYXL1
VNN3	NDUFAF3	ZFP3	ANKRD54
CDKN1C	TRAF3	B3GNT6	230395_PM_at
ZNF593	IRF8	ALG1	LOC100288618
SMARCA2	217572_PM_at	CYP4V2	GANAB
241388_PM_at	TMEM93	UBR5	228390_PM_at
IL1R2	EZR	GFM1	C22orf29
BRD2	GDF15	1565597_PM_at	DCAF6 /// ND4
TPST1	243158_PM_at	BAGE2 /// MLL3	BRCC3
GAR1	KIAA0141	OGT	SRSF3
PTPN1	FHL3	PLIN4	227762_PM_at
RNF126	SRSF9	231695_PM_at	KIAA1109
COQ6	BCL6	CBR3	ROPN1L
SDHAF1	232081_PM_at	CALR	C17orf44
ERLIN1	ARL6IP4	1561893_PM_at	LAGE3
SMAD4	LOC151438	NFKBIE	ALG2
ARHGAP26	VPS39	TMEM189	1569854_PM_at
241091_PM_at	C17orf90	SSX2IP	LOC729683
YY1AP1	CD7	SRP72	231652_PM_at
1562265_PM_at	TLR8	NDUFB10	STK24
MYLIP	SLC2A4RG	CDC42EP3	IPO4
HINT2	LFNG	MYST3	C9orf16
PHF20L1	SSBP4	MED7	LOC100507602
MCTP2	ECHDC1	1570639_PM_at	RNF115
1554948_PM_at	243837_PM_x_at	TMEM189-UBE2V1 ///	1556657_PM_at
		UBE2V1
TBC1D5	NUDT22	HNRNPH2	MLC1
CR1	CUEDC2	KLRC3	TBC1D25
EDF1	UBIAD1	PHKA2	FBLIM1
GSTA4	FAM134C	240803_PM_at	217152_PM_at
ZXDC	DCUN1D1	CFLAR	ALOX5
MPI	E2F2	USP48	VAMP3
PKP4	SIPA1L2	WDR8	DEFB122
PATZ1	ZNF561	H1FX	231934_PM_at
CCL5	FAM113B	KLHL36	214731_PM_at
GTF2H2	CDC42	GRPEL1	SUPT7L
PMM1	TNPO3	APITD1	TOX
AHNAK	DUSP7	NEK9	PTEN
SH3BGRL	1569528_PM_at	ZNF524	SAMM50
1560926_PM_at	RPL18A /// RPL18AP3	LPP	241508_PM_at
ASH1L	NDUFC2	MARCKSL1	TPI1
RBM3	SHISA6	NSFL1C	ZNF580
NPM3	ADAM10	GLYR1	1569727_PM_at
1561872_PM_at	ATL1	ATP11B	235959_PM_at
DGCR6L	NSFL1C	ZNF37BP	ASPH
TSC22D3	NVL	TBL1Y	RPRD2
POLR2C	COX2	YWHAZ	USP28
MIPEP	1560868_PM_s_at	PRPF19	239571_PM_at
FKBP5	C1orf128	1565894_PM_at	LAT /// SPNS1
POLR3K	MED19	RPS19	EIF4E3
TSC22D3	MXD4	1564107_PM_at	CDS2
CALM3	TMEM179B	RASSF4	CXorf40B
NUFIP2	243659_PM_at	ECHDC3	TMEM161B
NCR3	PREPL	N4BP2L1	MRPL42
UBE4B	PSEN1	OLAH	C22orf32
KCNK17	HIRIP3	HDAC4	PIK3CA
TMEM69	PRO2852	KCNJ15	HEY1
1557852_PM_at	MRPS24	PRKAR2A	TTBK2
RNASEH2C	NAF1	CYSLTR1	NAMPT
AGPAT1	241219_PM_at	1559020_PM_a_at	AHDC1
1565614_PM_at	241993_PM_x_at	TMEM48	LYST
MED27	TOM1L2	PHC1	1559119_PM_at
PRPF6	LYST	CABIN1	242611_PM_at
NDUFB6	242407_PM_at	OCIAD1	PATL1
SORL1	LIMK2	CCR2	FAM82A2
PATL1	IL2RB	WDR48	HSPA4
242480_PM_at	ADI1	UBE2G1	RHOF
221205_PM_at	C12orf76 ///	MLL4	240240_PM_at
	LOC100510175
UBE2D3	PNPLA4	ETV6	GABRR2
MBOAT2	HIST1H1T	CENPB	AFG3L2
ZNF576	GOSR2	EP400	RGS14
U2AF1	VSIG4	1560625_PM_s_at	SKP2
U2AF1	RPL23AP32	KDM5A	SH3BGRL
TKTL1	APC	CIAPIN1	BEX4
240960_PM_at	MRPL12	CEP170 /// CEP170P1	PNPO
243546_PM_at	SGSM3	CASP2	239759_PM_at
IL6ST	LRP10	ZNF696	TMEM229B
PON2	MMP8	CFLAR	1556043_PM_a_at
213945_PM_s_at	ICMT	PLSCR3	241441_PM_at
LOC339290	PACSIN2	PDE7B	FPGS
CCL5	DSTNP2	C5orf4	NR1I3
C11orf83	NFATC2IP	ELAC1	DR1
243072_PM_at	ATP5D	SAMSN1	POLA2
1559154_PM_at	FASTK	ZNF23	ENSA
215109_PM_at	ZBTB11	SUMO3	RGS10
C5orf41	PPP1R3B	MRPS12	PDE6D
EGLN2	239124_PM_at	ZDHHC24	PSMB5
1568852_PM_x_at	PLD4	GLTPD1	NDNL2
UGP2	NSUN4	RBMX2	CHD6
TMEM141	ARPC5L	ACRBP	RUVBL2
KLRAQ1	242926_PM_at	NHP2	MAP3K7
MGEA5	PEX7	GNAQ	242142_PM_at
C14orf118	226146_PM_at	C10orf46	240780_PM_at
KLHL8	HIC1	CRKL	LOC729082
RERE	ANKRD11	C6orf26 /// MSH5	241786_PM_at
CIAO1	TNFRSF21	FAM104A	ROBLD3
SEC61B	ZMYM6	TWISTNB	GSTO1
INPP5D	239901_PM_at	FAM50B	PDZD7
SRI	IL12RB1	TMEM201	EIF3F
PATZ1	KPNB1	SNX13	PSEN1
241590_PM_at	NCOA2	PTPN9	DUSP14
ADAM17	238988_PM_at	MMACHC	GPBP1L1
LAT2	THOC4	SAV1	CASP4
MUDENG	F2R	CUL4B	PTENP1
ANAPC11	FGD4	RBBP6	CLCN7
ASB8	ABCF2	232478_PM_at	DOCK11
TPCN2	DDX28	SFXN3	PECAM1
SNX27	ATG2B	GATAD2A	FLOT1
238733_PM_at	PTPMT1	MLYCD	243787_PM_at
ALDH9A1	PSME3	BAT4	TPM4
TBL1XR1	RASSF4	PLA2G12A	OLAH
FAM126B	ZNF598	C7orf28B /// CCZ1	233270_PM_x_at
ATF6B	NEDD9	TK2	CHAF1A
CHCHD10	230123_PM_at	UHMK1	MRFAP1
MYLIP	ARIH2	NCRNA00094	GATM
NENF	NDUFB9	WNK2	TXNIP
UQCR10	SMAD4	TAOK3	IL28RA
TGIF2	PIK3AP1	ALMS1	AVIL
BCL7B	MRPL14	HBA1 /// HBA2	ACTG1
EP400	GFER	CASP2	LOC645166
RFX3	TELO2	PFKFB2	MRPS10
NCR3	C9orf23	ENTPD1	ANKZF1
CHCHD8	RFTN1	SRSF5	PDGFA
RAB5C	215392_PM_at	SMARCA4	217521_PM_at
244772_PM_at	GUCY1A3	LOC401320	PHAX
222303_PM_at	CCDC123	238619_PM_at	TTLL3
SART3	PFDN1	PHTF2	PPIL4 /// ZC3H12D
WWC3	235716_PM_at	MKLN1	URM1
240231_PM_at	FAM173A	DHX40	TSFM
AKAP17A	PTGDS	GADD45GIP1	ST7L
ZFP91	229249_PM_at	233808_PM_at	235917_PM_at
239809_PM_at	TET3	KIF3B	SAAL1
235841_PM_at	PON2	1562669_PM_at	PDE6D
ATP5G3	242362_PM_at	STX7	HNRNPD
CCDC107	COX5A	LYRM4	ASCL2
GPX1	C20orf30	NUDT4 /// NUDT4P1	KREMEN1
RHOC	C19orf53	P2RY10	CD163
VPS13B	GTF3C5	TES	TMEM43
TNPO1	DEF8	PRDX1	TRGV5
IL1R2	SPTLC2	ITCH	C11orf31
DNMT3A	KLF12	CX3CR1	243229_PM_at
C17orf90	TROVE2	MOBKL2A	KIAA0748
C19orf52	RABGAP1L	NUCKS1	C8orf33
CYTH2	EMB /// EMBP1	C8orf42	FBXO9
MYLIP	CCNK	FBXL12	CMTM6
CORO1B	237330_PM_at	PID1	KLRB1
JMJD4	UBTF	TNFSF8	ANXA4
UGGT1	239474_PM_at	NOL7	TMEM64
YIF1A	ELF2	SLC29A3	HNRNPUL2
233010_PM_at	LOC550643	PIGP	241145_PM_at
CHD7	PDE4A	C5orf41	238672_PM_at
MBNL1	CUTA	HLA-E	PHF21A
TXNIP	NSFL1C	MDM2	232205_PM_at
DPP3	1569238_PM_a_at	IRAK3	MDM4
GLE1	DNAJC30	ELK1	TFAP2E
NCRNA00094	PLA2G12A	IDH3A	243236_PM_at
232307_PM_at	RPS15A	GMPPB	DUSP5
ELOVL5	CTSC	TFCP2	CD36
CNO	229548_PM_at	1557238_PM_s_at	DGCR14
CDV3	ABHD6	ZDHHC19	INF2
MDH2	RBMS1	232726_PM_at	ITGB2
RHOF	PSMB6	244356_PM_at	RIOK3
STK17B	SCFD2	HECW2	RALBP1
240271_PM_at	SMARCC2	1557633_PM_at	TBRG1
KLF9	CISH	243578_PM_at	CYHR1
237881_PM_at	ADAM10	DLEU2	BMP1
ANKHD1	GPBP1L1	LOC285830	PLBD1
DENND1B	UBE2D2	RPAIN	NOL3
NSFL1C	HIATL2	SP1	SFRS18
RAB6A	232963_PM_at	SLC2A4RG	BID
N4BP2L2-IT	233867_PM_at	242865_PM_at	MRI1
240867_PM_at	PRR14	DDX51	STS
COMMD5	1568903_PM_at	242232_PM_at	234578_PM_at
PSMD6	PPP1R7	233264_PM_at	SNX3
AKAP10	ARL2BP	ECE1	ARNTL
NCKAP1L	IKBKB	SF3A3	UTP20
IQGAP1	CYC1	NUP50	GPCPD1
BTF3	ZNF644	RSRC1	PRKAB2
C5orf22	NUDT4	CR1	1556518_PM_at
MAPRE2	AES	ALG14	233406_PM_at
236772_PM_s_at	ZC3H7B	TRBC1	PITRM1
240326_PM_at	LHFPL5	MAP3K2	TMEM64
LOC100128439	REPS1	RNF24	TRIOBP
7-Mar	EWSR1 /// FLI1	238902_PM_at	LFNG
RSBN1L	LOC100271836 ///	AGAP2	PAM
	LOC641298
ARRB1	DDX28	BAZ1A	MON1B
PAOX	N4BP2L2	236901_PM_at	NDUFB8 /// SEC31B
ZDHHC17	RASSF7	TCTN3	HIST1H4C
ZNF394	242461_PM_at	ACRBP	STX11
C22orf32	YWHAB	WDR61	235466_PM_s_at
PELI1	KIAA1267	UBA7	MRPL9
236558_PM_at	PABPC1 /// RLIM	SH2D2A	1562982_PM_at
TMED9	233315_PM_at	CSF2RA	ATF2
238320_PM_at	SMAP1	PRPF31	TIMM17A
NLRP1	LOC100190939	NF1	ZNF641
GPR137B	MED13	229668_PM_at	DNAJC4
NDUFA8	LOC100127983	CHCHD1	ASXL2
POLR2C	PREX1	TAOK2	FBXO16 /// ZNF395
FAM118B	PAIP2	MGMT	DPY19L4
ASAH1	232952_PM_at	1566201_PM_at	NBR1
MRPL10	STS	ATPBD4	237588_PM_at
TTYH2	UBE2D3	MFNG	NUDT16
HECA	MRPS25	HIPK3	FAM65B
COX3	LOC100133321	SPAG9	XIRP1
REST	PUSL1	243888_PM_at	BECN1
242126_PM_at	ZDHHC24	C10orf58	RNF126
ARRB1	234326_PM_at	RSRC2	ZNF207
RBBP9	WWC3	FAM86B2 /// FAM86C	DNAH1
		/// LOC645332 ///
		LOC729375
STX16	216756_PM_at	CD160	C10orf78
232095_PM_at	229670_PM_at	AK2	YY1
C19orf33	RAB30	LOC100128590	223860_PM_at
CISD3	CNOT2	PABPC3	CRADD
PFAS	MAPK7	UPF1	229264_PM_at
FLJ31306	WDR48	LAMA3	1568449_PM_at
TBC1D7	241692_PM_at	RASSF5	LARP1B
MDM2	PSMC2	1570165_PM_at	LSG1
GSTZ1	243178_PM_at	SAP30L	1563092_PM_at
222378_PM_at	C6orf129	FDX1	ADO
KLHDC10	1562289_PM_at	UBE2J1	NFATC1
PPFIA1	CALM1	PRSS33	PHPT1
C1orf122	MYO1C	MBD3	POP4
244026_PM_at	HSBP1	COX4NB	244536_PM_at
RANBP9	EFEMP2	DYRK1B	STOML1
241688_PM_at	UBE2E2	TUFM	SSH2
NDUFB10	233810_PM_x_at	PDLIM2	ZDHHC16
LIMS1	PDIA3	PSMD9	OAT
C17orf106	241595_PM_at	BCL7A	RAB34
242403_PM_at	229841_PM_at	PSMA5	SEC61G
GSK3B	RWDD2B	RAD52	LOC221442
KBTBD2	NDST2	VAV3	GNL2
ARF5	TFAM	TAAR8	PACS1
LCOR	235592_PM_at	227501_PM_at	CACNB1
238954_PM_at	C1orf50	ADAM22	MAP4K4
1565975_PM_at	MIA3	MCL1	LRRC14
WIBG	RPS4X /// RPS4XP6	BCL2L11	C11orf31
OPRL1	ZBTB7A	TRPM6	BCKDHB
C11orf31	237396_PM_at	215147_PM_at	LOC728903 ///
			MGC21881
241658_PM_at	USP8	NANP	NUMB
ZC3HAV1	E2F3	ONECUT2	PPP1R15A
SLC9A3R1	CYTH3	RHOH	MALAT1
YLPM1	EXOC6	PIK3R2	INO80D
LRRFIP1	MDM2	R3HDM1	SNX24
PRDX6	239775_PM_at	TMEM107	RBMX2
FOXO3 /// FOXO3B	C15orf63 /// SERF2	ZNF395	244022_PM_at
230970_PM_at	LCP2	ITM2A	TOMM22
ACAA2	CRY2	PHYH	FLJ12334
FXR2	GGA2	XPA	232113_PM_at
ILF3	CCL5	DDR2	VPS13B
ANKLE2	HOPX	SEC11A	CSHL1
1560271_PM_at	CPEB4	DVL1	MLL3
GNS	AGER	PREB	PSMA1
SRSF9	C1orf151	SUB1	MED21
AP1S1	FRYL	TSEN15	C20orf11
SPCS1	CMTM5	ECHDC1	CRYZL1
PADI4	C7orf30	227384_PM_s_at	ORC5
MEF2A	HIST1H4J	KCNE3	SLC16A3
IL21R	LOC100129195	ACTG1	MRAS
EDEM3	NAT6	SNORA28	N4BP2L2
C7orf30	216683_PM_at	WTAP	TSN
CHMP4A	SERPINB5	MED31	NUDC
MRPL52	TRD@	COMTD1	OCEL1
SRSF1	RBM43	JMJD4	LOC441461
ARFGEF1	FLOT2	SFRP1	223409_PM_at
MAN1A1	BOLA3	PPP3R1 /// WDR92	1557796_PM_at
VAPA	237442_PM_at	QDPR	LOC284009
TGFBR2	HLA-E	RPL23	MLANA
232472_PM_at	LOC648987	HMOX1	PTP4A3
GNB5	RICTOR	NINJ1	CX3CR1
MYLIP	ARID4B	C9orf119	TMEM53
GTF2A2	ANKRD57	PSMF1	PWP1
ZNF688	DNAJB6 /// TMEM135	RNF17	MRPL18
ATP6	PPTC7	CATSPERG	PTAR1
RNF14	MRPL16	222315_PM_at	DPY30 /// MEMO1
NCRNA00116	LSM2	KCNE3	SPATA2L
PIK3R5	COX5B	FNIP1	FAM82B
ARFGEF1	COX5B	ZNF80	BTF3
C1orf63	HSD17B10	ANKS1A	ZNF638
ATP2A3	COX19	ATP5SL	216782_PM_at
EIF4A2	CRK	239574_PM_at	SUGT1
ND2	NOP16	1555977_PM_at	MBP
PITPNC1	ZBTB40	RNF5	LOC100507192
SFSWAP	HIGD2A ///	236679_PM_x_at	KCTD20
	LOC100506614
FKBP15	CRIPAK	BLVRB	SCAMP1
ZNF238	C1orf128	232406_PM_at	B3GALT6
MAZ	QRSL1	LOC100134822 ///	TRAPPC4
		LOC100288069
NUPL1	TMEM107	RTN4IP1	SBF2
220691_PM_at	MTMR11	HIBADH	FANCF
CDKN1C	GLUL	C14orf167	GRPEL1
C9orf69	COPB1	CYP19A1	ATP8B1
SMAP2	RASA4 /// RASA4B ///	239570_PM_at	ZNF24
	RASA4P
ACSL1	FBXL21	VAPB	SPECC1
MBD4	RALGDS	SDHAF2	CHD1L
FCER1A	PTPRCAP	1556462_PM_a_at	POP5
241413_PM_at	NSUN4	242995_PM_at	1564154_PM_at
240094_PM_at	UROD	ACVR1B	LOC728392 /// NLRP1
DPM3	1560230_PM_at	C12orf57	SSNA1
236572_PM_at	FCAR	231111_PM_at	FRG1
ATP11A	1564733_PM_at	C1orf58	DCAF7
SASH3	NRD1	TPM4	237586_PM_at
MRPS18B	FAM110A	CPEB3	240123_PM_at
231258_PM_at	NUDT16	241936_PM_x_at	NUP37
PIAS2	MTMR10	QDPR	AIF1
LGALS3	XRCC5	CPD	PRKAR1A
2-Sep	DAB2	RSRC1	238064_PM_at
239557_PM_at	GTF3C6	CHCHD2	TEX264
IVD	DHX30	HBB	TMEM41A
236495_PM_at	SENP5	LPAR1	RAB4A
RBKS	SAMHD1	SHMT2	TAF9B
DPP3	SSR2	NID1	RBMS1
ABHD6	1556007_PM_s_at	239331_PM_at	239721_PM_at
SLC12A9	DYNLRB1	241106_PM_at	MBD4
1565889_PM_at	CCDC93	WDR61	RAB6A
OTUB1	C1orf77	ACACB	KIAA0319L
243663_PM_at	ZNF70	MRP63	MKKS
FAM96B	TNFAIP8L1	TNFRSF10A	PMVK
LOC100130175	EML2	SERBP1	STK36
LOC401320	MCTP2	CYB5R3	VNN2
CHD4	227505_PM_at	ZNF384	RBBP4
242612_PM_at	TSPAN32	1563075_PM_s_at	FAM192A
RPS26	214964_PM_at	EVL	IKBKE
PDCD2	236139_PM_at	LPIN2	216813_PM_at
NCRNA00275	QKI	ZNRD1	MRPL24
SLC2A8	WBP11	FLJ38717	ZNF75D
RLIM	EXOSC6	DND1	1566680_PM_at
MRPL20	DNAJC10	ACLY	242306_PM_at
STK38	PLCL2	CSN1S2AP	CSGALNACT1
242542_PM_at	BOLA2 /// BOLA2B	236528_PM_at	242457_PM_at
PIK3C3	236338_PM_at	ARHGAP26	SNW1
1560342_PM_at	MAPK1	SPG20	PPARA
TRA2A	243423_PM_at	C10orf93	RUFY3
UBE2B	240137_PM_at	236005_PM_at	SEC61A1
UBE2D3	244433_PM_at	COQ4	SUMO2
SRSF4	FBXO9	GTPBP8	242857_PM_at
PPP6R3	IL13RA1	LOC148413	NCOA2
TIGIT	FBXO38	1565913_PM_at	CANX
ASB2	IMMP2L	RIN3	ZNF561
RHOT1	242125_PM_at	C15orf28	ISYNA1
LAMP1	SEMA4D	239859_PM_x_at	VHL
ATP2A3	RTN4	242958_PM_x_at	KRI1
FAM13AOS	GDI2	FLJ44342	PDCD6IP
ZBTB4	ATXN7L1	240547_PM_at	DNAJC17
SELPLG	STK35	C9orf16	GDAP1L1
233369_PM_at	SRGAP2	GAS7	EIF4EBP1
FLJ39582	1557637_PM_at	ZNF148	215369_PM_at
233876_PM_at	FAM174B	TMEM5	GPRIN3
NSMAF	238888_PM_at	FAM126B	C17orf63
PPP1R2	ALG13	CSNK2B /// LY6G5B	STAU1
BTF3	USF2	ASAP2	ZNF439
1560706_PM_at	ADAMTS2	ZNF407	CSTB
236883_PM_at	CABIN1	GLI4	RBM47
FAM98B	TCP11L2	AKAP13	240497_PM_at
HP1BP3	RBM25	ZBTB16	TADA2B
CELF2	233473_PM_x_at	1557993_PM_at	MRPS6
COPG2	HIST1H1T	TTF1	HEATR6
ZNF148	SNCA	NUDT10	MLL3
DNAJC3	229879_PM_at	COQ4	MDH1
C17orf59	SLC24A6	GLT25D1	SELENBP1
POLR2I	PXK	IKZF2	SLC7A6OS
UBE2H	TAGAP	GCNT7	MTMR11
LPAR2	ATP5G1	GPATCH2	NFATC2
TNPO1	AKT2	NMT2	244373_PM_at
CCDC97	SLC8A1	ADHFE1	NOLC1
KCTD5	DIP2B	ZNF784	C3orf21
238563_PM_at	MKRN1	CYBASC3	SIPA1L2
RNF4	MAL	BAT2	HDDC3
HBB	ZKSCAN1	APP	SBK1
TMCO3	ATF7IP	241630_PM_at	PFDN2
239245_PM_at	C1orf174	MRPL45	DPY19L1
CDK5RAP1	TAGAP	TMEM161A	MYL6
FAM43A	LSS	240008_PM_at	ZNF703
ATP5S	1565701_PM_at	AGTRAP	RBM47
LRCH4	1560332_PM_at	234164_PM_at	CD226
ANKFY1	CAPZA2	241152_PM_at	ORM1
STK32C	COX6A1	HBA1 /// HBA2	1560246_PM_at
TMCO3	SNORA37	TMX4	242233_PM_at
IL21R	CSTF1	TNFSF4	ZNF398
LOC100506902 ///	EXPH5	243395_PM_at	PIKFYVE
ZNF717
NDUFB11	CD74	NOL7	LRP6
FAM193B	NPY2R	236683_PM_at	C9orf86
BCL6	ATP13A3	TNRC6B	SHISA5
C6orf106	GIGYF2	UFM1	MCTP2
C14orf138	MAP4K4	FH	LOC100128071
FAM65B	THAP7	ESYT1	PRSS23
1569237_PM_at	ACP1	LGALS1	C1orf212
231351_PM_at	SLC22A17	238652_PM_at	NDN
GATAD2B	DLEU2 /// DLEU2L	DIMT1L	TBC1D22A
NDUFS6	BBX	TRIM66	242471_PM_at
GRAP	ESD	MTPN	LOC146880
KLF6	FAM49B	SNRNP27	MYOC
SLC6A6	CARS	FAIM3	SMOX
GHITM	PLEC	TTC39C	MBD2
FLT3	ENTPD1	RBL2	JMJD6
1566257_PM_at	LOC285370	CHMP1A	ACTR2
FKBP2	PRDX6	UQCRB	1562383_PM_at
HPS1	CBX6	ERCC8	GPM6A
229673_PM_at	C1orf21	CCNB1IP1	LOC284751
SGCB	ORMDL3	CC2D1B	239045_PM_at
HGD	RHBDD2	241444_PM_at	TFCP2
229571_PM_at	LSG1	NRG1	237377_PM_at
KIF5B	DNAJC30	JAGN1	COPZ1
CSAD	ENTPD4	RAF1	ATP8B4
MBP	PCMT1	FAM181B	AMZ2P1
HBB	COX2	1561915_PM_at	PARL
CTSZ	TIMM13	SH3GLB1	PROSC
AFF4	CAPRIN1	TMF1	C1orf85
RBM6	TASP1	SSRP1	CISD1
PGLS	1558588_PM_at	IGF2R	COPA
FKBP5	ZCRB1	EPRS	ZBTB4
244048_PM_x_at	SNAP23	IGLON5	1558299_PM_at
2-Sep	FLJ33630	UQCRB	ZNF333
P4HB	ACPP	HUS1	SPOPL
PFDN6	CNST	STX16	DUSP28
ARL16	SLC8A1	LOC100287227	PDZD8
ABHD5	CDK3	237710_PM_at	GPR27
SCFD2	A1BG	RBM22	TMEM30A
ITPA	243217_PM_at	LOC100286909	RPL23A
236889_PM_at	1557688_PM_at	UBIAD1	C20orf196
236168_PM_at	SLC8A1	SNTB2	238243_PM_at
TMED3	ARHGEF40	CTBP1	NOP16
RARS2	TMEM134	EIF2B1	SNAP23
PALLD	PLA2G7	C12orf75	ZNF542
RALGAPA2	216614_PM_at	243222_PM_at	SSR1
RP2	SERINC3	CASC4	5-Mar
FAM162A	PRPF18	CTSO	C1orf151
PTGER4	RNF144B	ETFB	DDX10
ASPH	ZCCHC6	SOCS2	RBM3
SPTLC1	1564568_PM_at	220711_PM_at	NUDT3
MAP3K3	SAFB	TTC12	234227_PM_at
234151_PM_at	CD163	TRAPPC2 ///	UBL5
		TRAPPC2P1
PSMB10	CCNL2	ALS2	GUSBP1
INSIG1	DLEU2	COX17	1559452_PM_a_at
ASPH	FLJ14107	ABHD15	MSH6
1565598_PM_at	EML2	ZFYVE27	BAGE2 /// BAGE4
PEBP1	244474_PM_at	TNRC6B	MIDN
CXorf40A	NOP16	ADPRH	LONP2
239383_PM_at	CLPTM1	ANKRD36	PPM1L
AKIRIN2	LOC285949	IDH2	ZNF608
240146_PM_at	1563076_PM_x_at	TP53TG1	HAX1
BMP2K	CXorf40A /// CXorf40B	FCGR2C	GIMAP1
240417_PM_at	THAP11	FAM13A	ISCA1
SH2D1B	1565888_PM_at	1565852_PM_at	215204_PM_at
FBXO25	BNC2	JMJD8	C1orf25
GRB10	RNF214	TRIM46	RNASEH2B
1558877_PM_at	235493_PM_at	PDE8A	TCP1
KIAA1609	MUSTN1	MTMR3	RAP1GDS1
C2orf18	SRPK2	DNAJC8	GP5
CMTM8	COMMD7	CCNL1	228734_PM_at
RNPEPL1	CLCN3	TMEM204	UBB
TMEM160	C11orf48	WDR1	SMARCB1
GRB10	RBM15B	GPER	236592_PM_at
LOC100129907	227479_PM_at	C11orf31	NCRNA00202
DGCR6 /// DGCR6L	BMPR1B	TMEM191A	C5orf30
MRPS18A	CD163	APOOL	ARF1
PCGF3	CHN2	242235_PM_x_at	SLC41A3
DCTPP1	243338_PM_at	SBK1	FGFBP2
244732_PM_at	EPS15L1	240839_PM_at	ITCH
2-Sep	CHCHD5	231513_PM_at	PACSIN1
SERBP1	ZNF689	C1orf144	DUSP1
C19orf56	ARL6IP4	C22orf30	HAVCR1
ING4	ETFA	HMGN3	242527_PM_at
LSMD1	WDR61	TEX10	ATF6B
ADPRH	MRO	C1orf27	TRAF3IP3
C7orf68	ZNF169	FKBP1A	TGFBR3
CACNA2D4	MYST3	ATP6V1C1	ICAM4
EXOSC7	GATAD1	DLEU2	242075_PM_at
CCND3	RAB9A	1564077_PM_at	C5orf4
TLR2	FAM120C	PRNP	AMBRA1
TRABD	C7orf53	RPL15	AHCY
AKR7A2	RGS10	HCFC1R1	RNF14
231644_PM_at	RYBP	SERPINE2	UEVLD
ACP5	CD247	NFKBIA	RGS10
239845_PM_at	TRADD	232580_PM_x_at	ENO1
ARHGAP26	239274_PM_at	ME2	SLC16A6
MCAT	CBX5	TNF	GTPBP6 ///
			LOC100508214 ///
			LOC100510565
NUCKS1	STAT6	H2AFY	APOOL
LRCH4 /// SAP25	HEATR2	PSME1	KLF4
LOC100131015	GUSBP3	WHAMML2	MS4A7
CFLAR	243992_PM_at	FLJ38109	DRAP1
MIF4GD	UCKL1	TAP2	244607_PM_at
RBCK1	DYRK4	C6orf89	232615_PM_at
PTPRO	NGFRAP1	PSMA3	236961_PM_at
227082_PM_at	PPP2R5E	1557456_PM_a_at	1563487_PM_at
241974_PM_at	SNCA	1559133_PM_at	EGLN3
TMEM102	1555261_PM_at	IFNGR1	239793_PM_at
IL13RA1	PPP1R16B	ZNF397	RBBP7
LOC100506295	SPATA7	FAHD2A	PLDN
LOC100510649	HEXB	MPZL1	PSMG4
MRPS34	240145_PM_at	MOBKL2C	WIPF2
RPP21 /// TRIM39 ///	ABCB7	DPY19L1	ALG3
TRIM39R
KIF22	NAT8B	SRD5A1	ZSWIM1
241114_PM_s_at	AKAP10	SEMA4C	SSX2IP
STAT6	243625_PM_at	DCTN6	BSG
RNF113A	CRYL1	E2F2	242968_PM_at
RPP38	CDADC1	CDC42SE1	DNAJB4
1559362_PM_at	PBX2	240019_PM_at	PWP2
INO80B /// WBP1	SERINC3	244580_PM_at	243350_PM_at
NLRP1	TP53	227333_PM_at	SCP2
239804_PM_at	240984_PM_at	MEAF6	TOX2
CDKN1C	1556769_PM_a_at	FCF1 /// LOC100507758	QKI
		/// MAPK1IP1L
C19orf70	ADCK2	PNN	AIF1L
INSIG1	IMPACT	ZNF395	242901_PM_at
COG1	GLTP	243107_PM_at	240990_PM_at
EIF5	CMTM4	24211713_PM_at	RPL14
XCL1 /// XCL2	1563303_PM_at	GNL1	GTF3C1
ARHGAP27	LOC100509088	C2orf88	NCKAP1
SNHG7	TMEM189-UBE2V1 ///	HIST1H4J /// HIST1H4K	PPAPDC1B
	UBE2V1
LIMK1	HMGB3	TDRD3	ATXN10
BOLA1	ZFAND2B	TRIP12	SORT1
235028_PM_at	PTPRS	ANXA4	NCAPH2
SH3BP1	CLCN5	SNHG11	TANK
PSMB4	CHCHD3	C17orf58	SNRPA1
PHTF1	UQCRFS1	PTEN	OSBPL3
REM2	MED8	HRASLS5	BPGM
EXOC6	RPP30	KRT10	TNS1
CELF2	1558048_PM_x_at	SLC46A2	MRAS
FBXO38	CXXC5	1562314_PM_at	IVD
TIMM23 /// TIMM23B	SETBP1	SAC3D1	PRTG
DNAJA4	DNAJC10	TRD@	KLHL6
240638_PM_at	BZW1	SRP72	TTLL3
HIGD2A	ZDHHC2	215252_PM_at	RHOBTB2
228151_PM_at	237733_PM_at	ARHGDIA	MARK3
FBXO33	MBOAT2	224254_PM_x_at	C19orf60
1560386_PM_at	ETV3	215981_PM_at	CTSB
TMEM59	FASLG	SLC43A3	PRDM4
MADD	SEPT7P2	1570281_PM_at	1563210_PM_at
MDH2	CHAF1A	DCAF7	HNRNPUL2
1566959_PM_at	GSTK1	237341_PM_at	ARHGEF2
NBR1	RAB27B	ATP5O	DCPS
PHB	CXXC5	TSPYL1	DNAJA4
RC3H2	HCFC1R1	DIS3L	ADCK2
GTF2H2B	ZCCHC6	239780_PM_at	RPA1
PIGU	KCTD6	PPP1R15A	216342_PM_x_at
C16orf58	CSRP1	NAALADL1	DGKZ
IL21R	234218_PM_at	ITGAM	244688_PM_at
MRPL55	JTB	CYTH4	C1orf21
NPFF	FARS2	POLG	FASTKD3
6-Sep	237018_PM_at	DCAF12	241762_PM_at
237118_PM_at	PRKAR1A	RNF34	TWIST2
CXCL5	HLA-DPA1	C14orf128	TPI1
SLC25A26	HBA1 /// HBA2	A2LD1	239842_PM_x_at
1555303_PM_at	1556107_PM_at	LOC100505935	FCHSD1
234604_PM_at	SYF2	JMJD1C	243064_PM_at
SLC2A6	238552_PM_at	SH3GLB1	239358_PM_at
TRA2A	NFIC	NDUFB2	LANCL2
SSSCA1	215846_PM_at	DNAJC8	MDM2
IFNGR1	LEPROTL1	ZNF792	CD3D
HBG1 /// HBG2	COX1	DCAKD	TMPO
FAM162A	ZDHHC4	RPL23A	SDHC
SORL1	UXS1	ALPK1	SCD5
HERC4	NDUFB5	MFAP3L	SLC4A11
LPAR5	1558802_PM_at	RBM14	YWHAB
POP4	CCDC147	ZNF330	RPL28
C14orf179	6-Sep	238883_PM_at	DCXR
232527_PM_at	CLC	RAB6A /// RAB6C	6-Mar
PHF3	227571_PM_at	RAB8B	MAP3K7
COX5A	CFLAR	RQCD1	KPNA6
243858_PM_at	SDF2L1	ABR	RFC3
CLEC10A	CD3E	PLEK2	SNRPD3
234807_PM_x_at	UBN2	UFC1	242997_PM_at
CTBP1	MRPS9	TMX1	PPIE
9-Sep	216871_PM_at	241913_PM_at	HLA-DPA1
GZMB	MPZL1	233931_PM_at	GLT8D1
VPS24	MTMR4	232700_PM_at	GPR56
C1orf43	NUP98	ZNF638	C12orf29
IRS2	TRRAP	ZBTB20	HLA-G
PEX26	USE1	GUCY1A3	ITPR2
TP53RK	FKBP1A	VAMP3	METTL6
ZZEF1	238842_PM_at	MBP	MCL1
XPNPEP1	MAN2A2	WWOX	229569_PM_at
ATP6AP1	PTMA	AFFX-M27830_M_at	GTF2H5
TMED9	CDK10	1557878_PM_at	DNAJC3
PHF19	SAMSN1	NUB1	C3orf78
EIF5	SNX5	CBX3	HLA-A /// HLA-F ///
			HLA-J
TRAPPC3	AKAP8	233219_PM_at	ILF2
244633_PM_at	SERGEF	SLBP	TMED2
RIPK1	IGFL2	COX15	EPS8L2
243904_PM_at	NOB1	PSPC1	PLA2G6
1566166_PM_at	NME7	LRRC8C	ERLIN1
C2orf28	CELF1	1566001_PM_at	JTB
CD84	MRPL9	PXK	CCND2
MTA2	CD3G	1558401_PM_at	C11orf73
NEAT1	DHX35	CHRAC1	SSBP1
TSTD1	PDS5B	MGC16275	LRRFIP1
236752_PM_at	FBXL17	1557224_PM_at	241597_PM_at
MRPL49	SLC39A4 /// SLC39A7	242059_PM_at	LUC7L
PLAGL2	223964_PM_x_at	MTIF2	MRPL34
VIPAR	NDUFB7	PDHB	GUF1
GSTK1	LIMD1	ACSL3	217379_PM_at
241191_PM_at	THUMPD3	ICAM2	PRPSAP2
THRAP3	GNLY	EBLN2	COPS6
PTGDS	C12orf66	LSS	YSK4
SLC38A7	HSD17B1	1563629_PM_a_at	BCL2L1
RHOBTB3	JTB	TARSL2	CIRBP
LOC100507255	240217_PM_s_at	MBD6	1561644_PM_x_at
SRRM2	PDLIM4	QARS	SIPA1L3
TANK	237239_PM_at	240665_PM_at	1556492_PM_a_at
ZNF511	FASTK	LOC400099	239023_PM_at
REPS2	SRD5A1	OPA1	TMEM147
ND6	1558695_PM_at	IFT81	DKFZp667F0711
237456_PM_at	RICTOR	GATAD2A	ZNHIT2
PI4KB	IFI27L2	OLAH	CMTM3
KLF6	1559156_PM_at	RAI1	ELOVL5
SNX3	RASA2	MAGED2	GPSM3
FXC1	CYTH1	C12orf5	MOGS
244010_PM_at	RBP4	ARHGAP24	MGC16384
1562505_PM_at	215397_PM_x_at	RAB4A /// SPHAR	1558783_PM_at
237544_PM_at	232047_PM_at	ICT1	CDK11A /// CDK11B
C7orf26	1563277_PM_at	MED29	C14orf64
AP1S1	MTCH2	SLC25A38	CPT1A
SRPK1	TP53TG1	SPTLC2	COPS8
RNF216	HEXIM2	CCNDBP1	EPB41L5
CFLAR	SSH2	USP48	KPNA2
HBG1 /// HBG2	C12orf62	242380_PM_at	APLP2
233239_PM_at	KRT81	MRPL54	ARHGEF40
RAP2B	BAT4	225906_PM_at	PABPC1
1563958_PM_at	C19orf40	HELQ	RALGPS1
BCL6	GNLY	NOTCH4	DNAJB14
NAA10	CEP152	HBA1 /// HBA2	PSMA1
LSM12	242875_PM_at	IL1RAP	COTL1
240636_PM_at	239716_PM_at	SLC6A6	RPS19
NSMCE1	1559037_PM_a_at	CPT1A	TRIM41
HADH	PHF21A	USP25	243469_PM_at
PPTC7	PSMC5	216890_PM_at	INSR
242167_PM_at	IL13RA1	236417_PM_at	TCEB2
PHB2	GPA33	239923_PM_at	NCOA2
LPP	TMEM203	PODN	ETFDH
PEMT	BTBD6	N6AMT2	GAS7
MRPS12	PDE1C	242374_PM_at	227897_PM_at
NECAP2	UBN2	NDUFS3	222626_PM_at
GRB10	BAK1	FAM120A	FRMD8
ATP5G3	PTK7	LOC441454 ///	PPP1R3B
		LOC728026 /// PTMA ///
		PTMAP5
KPNB1	C12orf65	COX4I1	1565862_PM_a_at
CTNNB1	238712_PM_at	GGCX	243682_PM_at
ALKBH3	SPNS1	TTC39C	RECK
STAG2	DHX37	ZNF625	TCTN3
PNKD	236370_PM_at	C20orf103	CAST
FAM113A	240038_PM_at	VDAC3	TAB2
RAMP1	ZNF638	SLC25A12	METRN
236322_PM_at	IRS2	MRP63	TCEAL8
UBE2H	ANAPC5	BAT2L2	239479_PM_x_at
236944_PM_at	PDSS2	230868_PM_at	GLS2
CHD2	240103_PM_at	PPP2R2B	238812_PM_at
SPTLC2	LMO7	NEDD8	ZNF33A
PRRG3	MYST3	RANGRF	MPP5
FAR1	C7orf53	1561202_PM_at	FOXP1
MRPS11	ZNF207	TOMM40L	CDC14A
237072_PM_at	NOC4L	CBX6	MAPKAPK5
PAPD4	PTPLB	MVP	LOC100506245
CC2D1A	1556332_PM_at	LASS5	EIF2AK3
GIPC1	MYCL1	229255_PM_x_at	ACSL3
SELPLG	EXOSC7	LYPLAL1	HSCB
PICALM	ATP5F1	CDV3	MLKL
ZNF581	LOC100272228	LOC100505592	TBC1D9
NDUFA11	PRKCB	LDOC1L	LOC728825 /// SUMO2
ATXN10	TAX1BP3	GBF1	LOC100130522
SIT1	LSM12	240176_PM_at	RSU1
GSTM1	STXBP3	TRADD	RNF24
ZNF207	RPS19BP1	220912_PM_at	242868_PM_at
RASGRP2	PLEKHJ1	RPL36	1558154_PM_at
ST20	SELK	HSD17B8	SUGP1
1557551_PM_at	243305_PM_at	SEL1L	220809_PM_at
CANX	MXD1	FCGR2C	PSMA1
PRPF4B	CDYL	PHF15	ZNRF1
NCRNA00152	ASAP1	234150_PM_at	RRS1
LRCH4 /// SAP25	RABGGTB	COBLL1	PDE5A
IL2RA	ESR2	STYXL1	GZF1
SUDS3	C14orf109	SRSF6	TIMM9
TFB1M	EPC1	1556944_PM_at	213979_PM_s_at
MIAT	PDCD7	DDX24	TTC27
STX16	SSTR2	AVIL	BHLHE40
PRKAB1	1569477_PM_at	SEC14L2	FGD4
UFSP2	CNPY2	PRO0471	RPL15
SAMD4B	DDX42	LOC553103	NLRP1
1566825_PM_at	PDHB	CHD4	240761_PM_at
APC	COX4I1	KCNJ2	BZW2
GDPD3	EEF1D	C7orf41	1556195_PM_a_at
HBG1 /// HBG2	LOC339352	PTPN2	BCAS2
CD300A	226252_PM_at	239048_PM_at	SOS2
SCUBE3	PARK7	232595_PM_at	NCRNA00107
PALLD	CPT2	RFK	GAS7
SIPA1L2	USP40	ACAP2	DTHD1
ZNF615	SNRNP25	PGGT1B	CDK2AP1
PICALM	TSC22D1	IAH1	ARPP19
HBB	GGNBP2	TTC12	ABCA5
241613_PM_at	LONP2	ABHD5	TROVE2
LOC729013	WDYHV1	ROD1	PSMB4
PRKCD	TCP11L1	CHFR	PGS1
UGCG	IL13RA1	1556646_PM_at	C16orf13
213048_PM_s_at	243931_PM_at	230599_PM_at	C4orf45
1560474_PM_at	FLJ38717	PEX11A	NUDT19
NOTCH2	229370_PM_at	TRBC2	241445_PM_at
LOC100128439	KIAA0141	SEMA4F	LOC115110
SRGAP2P1	SLC6A13	ARPP21	1560738_PM_at
ROCK1	ACP1	NFATC2	CDC16
GDE1	SYNGR2	RNPC3	RHOB
DOCK8	ZNF207	POC1B	ERGIC1
CEP72	CUL2	GORASP2	OR2W3
WHAMML1 ///	RASGRP2	239603_PM_x_at	C1orf220
WHAMML2
TMSB4X /// TMSL3	ATG13	DHX37	235680_PM_at
NFYA	C3orf37	TWF1	UBE3C
239709_PM_at	PHAX	232685_PM_at	NACA
237626_PM_at	KDELR1	LOC100507596	SLC6A6
SH3BP2	243089_PM_at	239597_PM_at	RIOK2
235894_PM_at	243482_PM_at	BCL10	241860_PM_at
SSBP4	CCDC50	WIPF2	C10orf76
PRELID1	CD68	230324_PM_at	1561128_PM_at
MDM2	HYLS1	232330_PM_at	LIG3
RNF181	CACNA2D3	TBC1D8	MRPS31
CUL4B	ZNF43	MGAT2	243249_PM_at
DOLK	LOC91548	NXT1	ZFAND5
TOR1AIP2	1566501_PM_at	TNRC6B	DERA
CNIH4	SNRPA1	PER1	TMEM159
MAX	PNPO	TMEM43	SCGB1C1
244592_PM_at	IL6ST	224989_PM_at	ANO6
SMARCAL1	C15orf63	TM6SF2	233506_PM_at
237554_PM_at	DNAJB11	FNTA	UNKL
NDUFB4	MESDC1	AP4M1	GAB2
ACBD6	PPP1R14B	MRC2	1570621_PM_at
238743_PM_at	WTAP	FNDC3B	SRGAP2
RQCD1	HSPC157	ADAMTS1	LOC151657
RBBP6	TRA2A	DSC2	PTPRE
C17orf77	C14orf119	234753_PM_x_at	MPV17
TRIB3	PPP1R16B	1558418_PM_at	C17orf108
236781_PM_at	SLC25A5	YY1	240154_PM_at
C15orf63 /// SERF2	MEX3D	VEGFB	PRMT1
C6orf226	DDOST	AMBRA1	216490_PM_x_at
COX6B1	KLHL22	CCT6A	CSNK1A1
ARID2	232344_PM_at	C4orf21	HDDC2
CD97	VNN3	MGA	MRPL33
LRCH4	CLEC4E	SAP30	ORMDL2
APBA3	ANKRD57	C20orf72	TNFSF12-TNFSF13 ///
			TNFSF13
FBXO41	ERICH1 /// FLJ00290	IQCK	RFX7
242772_PM_x_at	SRSF2IP	VIL1	ZNF397
C10orf76	DPYSL5	244502_PM_at	PTPRC
IK /// TMCO6	ANPEP	ATF6B /// TNXB	PARP8
ATP6AP2	ADAM17	214848_PM_at	C18orf21
LOC727820	SRD5A3	PREX1	ZEB2
PIK3CD	CCDC12	LSM14B	STK35
AIP	SRC	NIP7	244548_PM_at
UBAC2	242440_PM_at	PPA2	ASAH1
MED23	USP42	PLIN5	ZNF552
EI24	MIB2	NAMPT	CAND1
NME3	GDF10	RAPGEF2	LOC647979
1559663_PM_at	RNPC3	SNX2	KIAA2026
237264_PM_at	CCDC154	WIBG	SLC12A6
WAC	DPH2	ZBED1	C11orf10
C17orf37	P4HB	TRO	GRIN2B
1560622_PM_at	CSNK1G2	TPSAB1	HNRNPA3 ///
			HNRNPA3P1
232876_PM_at	ATXN7	SNX20	BAZ2A
1566966_PM_at	PITPNM1	KRTAP9-2	CISH
ZNF362	PDLIM1	C17orf63	NSMAF
239555_PM_at	KIAA1143	SUPT4H1	LETM1
RASSF7	KLF6	PRKAA1	235596_PM_at
C17orf81	235685_PM_at	TRIM23	ADORA3
224082_PM_at	SORD	ATP11B	RSBN1
PALB2	MALAT1	ACTG1	233354_PM_at
239819_PM_at	TMEM107	PIGC	STAG2
LOC200772	PER1	GRAMD1A	CD300LB
PCBP2	NBR1	SNUPN	1564996_PM_at
BTF3	RIBC1	FLJ44342	PHF1
LOC221442	234255_PM_at	SMU1	SEMA7A
239893_PM_at	IFT27	LOC100134822	1557772_PM_at
WDR1	IMPAD1	229206_PM_at	WDFY3
SMYD3	IL17RA /// LOC150166	IDH3A	242016_PM_at
242384_PM_at	LOC100190986	FCHO2	231039_PM_at
MGEA5	SNRK	GHITM	RNF213
CTDNEP1	ASTE1	ELP3	EML4
210598_PM_at	KIAA1659	SUSD1	HIPK3
1562898_PM_at	231471_PM_at	STYXL1	C14orf1
ALKBH7	RAC2	URB1	242688_PM_at
XAB2	C9orf89	1556645_PM_s_at	CD44
DUSP23	ZNF416	EXOSC3	WBSCR16
PRKAR1A	ZNF599	TRD@	237683_PM_s_at
PDSS1	CCNH	CDYL	SLC14A1
SAP18	FAM190B	ITPKC	GNAS
CIRBP	SLC16A3	NUDT2	C4orf23
MED19	229968_PM_at	XRCC6BP1	DUSP10
LOC644613	RPRD1A	241501_PM_at	1564886_PM_at
PDK3	WSB1	C5orf20	MBD1
RBM5	USP15	RNMTL1	KLHDC7B

TABLE 2

Inclusion/Exclusion criteria

General	1) Adult kidney transplant (age >18 years): first or multiple trans-
Inclusion	plants, high or low risk, cadaver or living donor organ recipients.
Criteria	2) Any cause of end-stage renal disease except as described in Exclusions.
	3) Consent to allow gene expression and proteomic studies to be done on samples.
	4) Meeting clinical and biopsy criteria specified below for Groups 1-3.
General	1) Combined organ recipients: kidney/pancreas, kidney/islet, heart/
Exclusion	kidney and liver/kidney.
Criteria	2) A recipient of two kidneys simultaneously unless the organs are
	both adult and considered normal organs (rationale is to avoid
	inclusion of pediatric en bloc or dual adult transplants with borderline
	organs).
	3) Any technical situation or medical problem such as a known bleeding
	disorder in which protocol biopsies would not be acceptable for safety
	reasons in the best judgment of the clinical investigators.
	4) Patients with active immune-related disorders such as rheumatoid
	arthritis, SLE, scleroderma and multiple sclerosis.
	5) Patients with acute viral or bacterial infections at the time of biopsy.
	6) Patients with chronic active hepatitis or HIV.
	7) CAN, that at the time of identification are in the best judgment of the
	clinicians too far along in the process or progressing to rapidly to make
	it likely that they will still have a functioning transplant a year later.
	8) Patients enrolled in another research study that in the best judgment
	of the clinical center investigator involves such a radical departure from
	standard therapy that the patient would not be representative of the
	groups under study in the Program Project.
Acute Rejection	1) Clinical presentation with acute kidney transplant dysfunction
(AR) Specific	at any timepost transplant
Inclusion	a. Biopsy-proven AR with tubulointerstitial cellular rejection with
Criteria	or without acute vascular rejection
Acute Rejection	1) Evidence of concomitant acute infection
(AR) Specific	a. CMV
Exclusion	b. BK nephritis
Criteria	c. Bacterial pyelonephritis
	d. Other
	2) Evidence of anatomical obstruction or vascular compromise
	3) If the best judgment of the clinical team prior to the biopsy
	is that the acute decrease in kidney function is due to dehydration,
	drug effect (i.e. ACE inhibitor) or calcineurin inhibitor excess
	4) If the biopsy is read as drug hypersensitivity (i.e. sulfa-
	mediated interstitial nephritis)
	5) Evidence of hemolytic uremic syndrome
Well-	1) Patient between 12 and 24 months post transplant
functioning	2) Stable renal function defined as at least three creatinine
Transplant/No	levels over a three month period that do not change more than 20%
Rejection (TX)	and without any pattern of a gradual increasing creatinine.
Specific	3) No history of rejection or acute transplant dysfunction by
Inclusion	clinical criteria or previous biopsy
Criteria	4) Serum creatinines <1.5 mg/dL for women, <1.6 mg/dL for men
	5) They must also have a calculated or measured creatinine
	clearance >45 ml/minute
	6) They must have well controlled blood pressure defined according
	to the JNC 7 guidelines of <140/90 (JNC7 Express, The Seventh Report
	of the Noint National Committee on Prevention, Detection, Evaluation
	and Treatment of High Blood Pressure, NIH Publication No. 03-5233,
	December 2003)
Well-	1) Patient less than one month after steroid withdrawal
functioning	2) Patients with diabetes (Type I or II, poorly controlled)
Transplant/No	3) Evidence of concomitant acute infection
Rejection (TX)	a. CMV
Specific	b. BK nephritis
Exclusion	c. Bacterial pyelonephritis
Criteria

*A special note regarding why noncompliance is not an exclusion criterion is important to emphasize. Noncompliance is not a primary issue in determining gene expression and proteomics profiles associated with molecular pathways of transplant immunity and tissue injury/repair.

TABLE 3

Clinical characteristics for the 148 study samples.

	Multivariate	Multivariate
	Analysis^¶	Analysis^¶
All Study Samples	Significance	Significance

	TX	AR	ADNR	Significance*	(Phenotypes)	(Phenotypes/Cohorts)

Subject Numbers	45	64	39	—	—	—
Recipient Age ± SD^§	50.1 ± 14.5	44.9 ± 14.3	49.7 ± 14.6	NS{circumflex over ( )}	NS	NS
(Years)
% Female Recipients	34.8	23.8	20.5	NS	NS	NS
% Recipient African	6.8	12.7	12.8	NS	NS	NS
American
% Pre-tx Type II	25.0	17.5	21.6	NS	NS	NS
Diabetes
% PRA > 20	29.4	11.3	11.5	NS	NS	NS
HU Mismatch ± SD	4.2 ± 2.1	4.3 ± 1.6	3.7 ± 2.1	NS	NS	NS
% Deceased Donor	43.5	65.1	53.8	NS	NS	NS
Donor Age ± SD	40.3 ± 14.5	38.0 ± 14.3	46.5 ± 14.6	NS	NS	NS
(Years)
% Female Donors	37.0	50.8	46.2	NS	NS	NS
% Donor African	3.2	4.9	13.3	NS	NS	NS
American
% Delayed Graft	19.0	34.4	29.0	NS	NS	NS
Function
% Induction	63.0	84.1	82.1	NS	NS	NS
Serum Creatinine ±	1.5 ± 0.5	3.2 ± 2.8	2.7 ± 1.8	TX vs. AR = 0.00001	TX vs. AR = 0.04	TX vs. AR vs.
SD (mg/dL)				TX vs. ADNR = 0.0002	TX vs. ADNR = 0.01	ADNR = 0.00002
				AR vs. ADNR = NS		AR vs. ADNR = NS
Time to Biopsy ± SD	512 ± 1359	751 ± 1127	760 ± 972	NS	NS	NS
(Days)
Biopsy ≦ 365 days	27 (54.2%)	38 (49.0%)	23 (52.4%)	NS	NS	NS
(%)
Biopsy > 366 days (%)	19 (45.8%)	32 (51.0%)	18 (47.6%)	NS	NS	NS
% Calcineurin	89.7	94.0	81.1	NS	NS	NS
Inhibitors
% Mycophenolic	78.3	85.7	84.6	NS	NS	NS
Acid Derivatives
% Oral Steroids	26.1	65.1	74A	TX vs. AR = 0.001	TX vs. ADNR = 0.04	NS
				TX vs. ADNR = 0.001
C4d Positive	0/13 (0%)	12/36 (33.3%)	1/20 (5%)	NS	NS	NS
Staining (%)^§

*Significance for at comparisons were determined with paired Students t-test for pair-wise comparisons of data with Standard Deviations and for dichotomous data comparisons by Chi-Square.
^¶A multivariate logistic regression model was used with a Wald test correction. In the first analysis (Phenotypes) we used all 148 samples and in the second analysis (Phenotypes/Cohorts) we did the analysis for each randomized set of 2 cohorts (Discovery and Validation).
{circumflex over ( )}NS = not significant (p ≧ 0.05)
^§Subjects with biopsy-positive staining for C4d and total number of subjects whose biopsies were stained for C4d with (%).

TABLE 4

Diagnostic metrics for the 3-way Nearest Centroid classifiers for AR, ADNR and TX in Discovery and Validation Cohorts

		%	%			Positive	Negative				Positive	Negative
		Predictive	Predictive			Predic-	Predic-				Predic-	Predic-
		Accuracy	Accuracy	Sensi-	Speci-	tive	tive		Sensi-	Spec-	tive	tive
		(Discovery	(Validation	tivity	ficity	Value	Value		tivity	ficity	Value	Value
Method	Classifies	Cohort)	Cohort)	(%)	(X)	(%)	(%)	AUC	(%)	(%)	(%)	(%)	AUC

200	TX vs. AR	92%	83%	87%	96%	95%	89%	0.917	73%	92%	89%	79%	0.837
Classi-	TX vs.	91%	82%	95%	90%	91%	95%	0.913	89%	76%	76%	89%	0.817
fiers	ADNR
	AR vs.	92%	90%	87%	100%	100%	86%	0.933	89%	92%	89%	92%	0.893
	ADNR
100	TX vs. AR	91%	83%	87%	93%	91%	90%	0.903	76%	88%	84%	82%	0.825
Classi-	TX vs.	98%	81%	95%	100%	100%	95%	0.975	84%	79%	80%	83%	0.814
fiers	ADNR
	AR vs.	98%	90%	95%	100%	100%	97%	0.980	88%	92%	88%	92%	0.900
	ADNR
50	TX vs. AR	92%	94%	88%	96%	95%	90%	0.923	88%	91%	89%	89%	0.891
Classi-	TX vs.	94%	95%	92%	98%	98%	90%	0.944	92%	90%	88%	89%	0.897
fiers	ADNR
	AR vs.	97%	93%	95%	97%	100%	97%	0.969	89%	91%	89%	89%	0.893
	ADNR
25	TX vs. AR	89%	92%	81%	96%	95%	84%	0.890	88%	90%	90%	89%	0.894
Classi-	TX vs.	95%	95%	95%	95%	95%	95%	0.948	92%	92%	89%	89%	0.898
fiers	ADNR
	AR vs.	96%	91%	95%	96%	95%	96%	0.955	85%	90%	89%	88%	0.882
	ADNR

TABLE 5

Diagnostic metrics for the 3-way DLDA and SVM classifiers for AR, ADNR and TX in Discovery and Validation Cohorts

%

Positive

Negative

Positive

Negative

Predictive

Predic-

Accuracy

Sensi-

Speci-

tive

Sensi-

Speci-

tive

(Discovery

(Validation

tivity

ficity

Value

tivity

ficity

Value

Method

Classifies

Cohort)

(%)

AUC

(%)

AUC

200
Classi-
fiers
DLDA	TX vs. AR	90%	84%	87%	93%	91%	89%	0.896	76%	92%	89%	81%	0.845
	TX vs.	95%	82%	95%	94%	95%	95%	0.945	89%	76%	76%	89%	0.825
	ADNR
	AR vs.	92%	84%	83%	100%	100%	86%	0.923	84%	92%	89%	88%	0.880
	ADNR
SVM	TX vs. AR	100%	83%	100%	100%	100%	100%	1.000	82%	86%	81%	86%	0.833
	TX vs.	100%	96%	100%	100%	100%	100%	1.000	100%	95%	95%	100%	0.954
	ADNR
	AR vs.	100%	95%	100%	100%	100%	100%	1.000	95%	96%	95%	96%	0.954
	ADNR
100											84%	82%	0.825
Classi-
fiers
DLDA	TX vs. AR	91%	83%	88%	93%	91%	90%	0.905	76%	88%	80%	83%	0.815
	TX vs.	97%	82%	95%	100%	100%	95%	0.970	84%	79%
	ADNR
	AR vs.	98%	90%	95%	100%	100%	97%	0.980	88%	92%	88%	92%	0.900
	ADNR
SVM	TX vs. AR	97%	87%	88%	100%	100%	91%	0.971	83%	92%	90%	85%	0.874
	TX vs.	98%	86%	96%	100%	100%	95%	0.988	86%	88%	90%	83%	0.867
	ADNR
	AR vs.	100%	87%	100%	100%	100%	100%	1.000	83%	92%	88%	88%	0.875
	ADNR
50
Classi-
fiers
DLDA	TX vs. AR	93%	83%	88%	97%	96%	90%	0.927	78%	88%	86%	81%	0.832
	TX vs.	96%	84%	92%	100%	100%	90%	0.955	82%	87%	90%	76%	0.836
	ADNR
	AR vs.	98%	85%	95%	100%	100%	97%	0.979	81%	88%	81%	88%	0.845
	ADNR
SVM	TX vs. AR	95%	83%	88%	100%	100%	91%	0.946	77%	88%	87%	79%	0.827
	TX vs.	98%	92%	96%	100%	100%	95%	0.976	87%	100%	100%	81%	0.921
	ADNR
	AR vs.	100%	85%	100%	100%	100%	100%	1.000	87%	85%	77%	92%	0.852
	ADNR
25
Classi-
fiers
DLDA	TX vs. AR	88%	92%	83%	93%	90%	88%	0.884	88%	85%	78%	90%	0.852
	TX vs.	92%	93%	95%	90%	90%	95%	0.924	92%	88%	85%	81%	0.864
	ADNR
	AR vs.	100%	91%	100%	100%	100%	100%	1.000	85%	87%	88%	76%	0.841
	ADNR
SVM	TX vs. AR	95%	92%	92%	97%	96%	94%	0.945	84%	87%	88%	84%	0.857
	TX vs.	96%	100%	96%	95%	96%	95%	0.955	100%	85%	83%	81%	0.874
	ADNR
	AR vs.	100%	100%	100%	100%	100%	100%	1.000	100%	86%	82%	81%	0.873
	ADNR

DLDA—Diagonal linear Discriminant Analysis
SVM—Support Vector Machines

TABLE 6

Optimism-corrected Area Under the Curves (AUC's) comparing two
methods for creating and validating 3-Way classifiers for AR vs.
ADNR vs. TX that demonstrates they provide equivalent results.

Discovery Cohort-based 200 probeset classifier *

				Opti-
				mism
				Cor-
		Original	Opti-	rected
Method	Classifies	AUC	mism	AUC

Nearest Centroid	AR, TX, ADNR	0.8500	0.0262	0.8238
Diagonal Linear	AR, TX, ADNR	0.8441	0.0110	0.8331
Discriminant
Analysis
Support Vector	AR, TX, ADNR	0.8603	0.0172	0.8431
Machines

Full study sample-based 200 probeset classifier *

				Opti-
				mism
		Original		Cor-
		AUC	Opti-	rected
Method	Classifies	(Bootstrapping)	mism	AUC

Nearest Centroid	AR, TX, ADNR	0.8641	0.0122	0.8519
Diagonal Linear	AR, TX, ADNR	0.8590	0.0036	0.8554
Discriminant
Analysis
Support Vector	AR, TX, ADNR	0.8669	0.0005	0.8664
Machines

* 153/200 (77%) of the discovery cohort-based classifier probesets were in the Top 500 of the full study sample-based 200 probeset classifier. Similarly, 141/200 (71%) of the full study sample-based 200 probeset classifier was in the top 500 probesets of the discovery cohort-based classifier.

Example 2

Materials and Methods

This Example describes some of the materials and methods employed in identification of differentially expressed genes in SCAR.
The discovery set of samples consisted of the following biopsy-documented peripheral blood samples. 69 PAXgene whole blood samples were collected from kidney transplant patients. The samples that were analyzed comprised 3 different phenotypes: (1) Acute Rejection (AR; n=21); (2) Sub-Clinical Acute Rejection (SCAR; n=23); and (3) Transplant Excellent (TX; n=25). Specifically, SCAR was defined by a protocol biopsy done on a patient with totally stable kidney function and the light histology revealed unexpected evidence of acute rejection (16 “Borderline”, 7 Banff 1A). The SCAR samples consisted of 3 month and 1 year protocol biopsies, whereas the TXs were predominantly 3 month protocol biopsies. All the AR biopsies were “for cause” where clinical indications like a rise in serum creatinine prompted the need for a biopsy. All patients were induced with Thymoglobulin.
All samples were processed on the Affymetrix HG-U133 PM only peg microarrays. To eliminate low expressed signals we used a signal filter cut-off that was data dependent, and therefore expression signals<Log₂3.74 (median signals on all arrays) in all samples were eliminated leaving us with 48734 probe sets from a total of 54721 probe sets. We performed a 3-way ANOVA analysis of AR vs. ADNR vs. TX. This yielded over 6000 differentially expressed probesets at a p-value<0.001. Even when a False Discovery rate cut-off of (FDR<10%), was used it gave us over 2700 probesets. Therefore for the purpose of a diagnostic signature we used the top 200 differentially expressed probe sets (Table 8) to build predictive models that could differentiate the three classes. We used three different predictive algorithms, namely Diagonal Linear Discriminant Analysis (DLDA), Nearest Centroid (NC) and Support Vector Machines (SVM) to build the predictive models. We ran the predictive models using two different methodologies and calculated the Area Under the Curve (AUC). SVM, DLDA and NC picked classifier sets of 200, 192 and 188 probesets as the best classifiers. Since there was very little difference in the AUC's we decided to use all 200 probesets as classifiers for all methods. We also demonstrated that these results were not the consequence of statistical over-fitting by using the replacement method of Harrell to perform a version of 1000-test cross-validation. Table 7 shows the performance of these classifier sets using both one-level cross validation as well as the Optimism Corrected Bootstrapping (1000 data sets).
An important point here is that in real clinical practice the challenge is actually not to distinguish SCAR from AR because by definition only AR presents with a significant increase in baseline serum creatinine. The real challenge is to take a patient with normal and stable creatinine and diagnose the hidden SCAR without having to depend on invasive and expensive protocol biopsies that cannot be done frequently in any case. Though we have already successfully done this using our 3-way analysis, we also tested a 2-way prediction of SCAR vs. TX. The point was to further validate that a phenotype as potentially subtle clinically as SCAR can be truly distinguished from TX. At a p-value<0.001, there were 33 probesets whose expression signals highly differentiated SCAR and TX, a result in marked contrast with the >2500 probesets differentially expressed between AR vs. TX at that same p-value. However, when these 33 probesets (Table 9) were used in NC to predict SCAR and TX creating a 2-way classifier, the predictive accuracies with a one-level cross-validation was 96% and with the Harrell 1000 test optimism correction it was 94%. Thus, we are confident that we can distinguish SCAR, TX and AR by peripheral blood gene expression profiling using this proof of principle data set.

TABLE 7

Blood Expression Profiling of Kidney Transplants: 3-Way Classifier AR vs. SCAR vs. TX.

							Postive	Negative
			AUC	Predictive			Predictive	Predictive
			after	Accuracy	Sensitivity	Specificity	Value	Value
Algorithm	Predictors	Comparison	Thresholding	(%)	(%)	(%)	(%)	(%)

Nearest Centroid	200	SCAR vs. TX	1.000	100	100	100	100	100
Nearest Centroid	200	SCAR vs. AR	0.953	95	92	100	100	90
Nearest Centroid	200	AR vs. TX	0.932	93	96	90	92	95

It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred methods and materials are described.
All publications, GenBank sequences, ATCC deposits, patents and patent applications cited herein are hereby expressly incorporated by reference in their entirety and for all purposes as if each is individually so denoted. Improvements in kidney transplantation have resulted in significant reductions in clinical acute rejection (AR) (8-14%) (Meier-Kriesche et al. 2004, Am J Transplant, 4(3): 378-383). However, histological AR without evidence of kidney dysfunction (i.e. subclinical AR) occurs in >15% of protocol biopsies done within the first year. Without a protocol biopsy, patients with subclinical AR would be treated as excellent functioning transplants (TX). Biopsy studies also document significant rates of progressive interstitial fibrosis and tubular atrophy in >50% of protocol biopsies starting as early as one year post transplant.

TABLE 8

200 Probeset classifer for distinguishing AR, SCAR and TX based on a 3-way ANOVA

						AR -	SCAR -	TX -
				p-value	stepup p-value	Mean	Mean	Mean
#	Probeset ID	Gene Symbol	Gene Title	(Phenotype)	(Phenotype)	Signal	Signal	Signal

1	238108_PM_at	—	—	1.70E−10	8.27E−06	73.3	45.4	44.4
2	243524_PM_at	—	—	3.98E−10	9.70E−06	72.3	41.3	37.7
3	1558831_PM_x_at	—	—	5.11E−09	8.30E−05	48.1	30.8	31.4
4	229858_PM_at	—	—	7.49E−09	8.31E−05	576.2	359.3	348.4
5	236685_PM_at	—	—	8.53E−09	8.31E−05	409.1	213.3	211.0
6	213546_PM_at	DKFZP586I1420	hypothetical protein	3.52E−08	2.60E−04	619.2	453.7	446.0
			DKFZp586I1420
7	231958_PM_at	C3orf31	Chromosome 3 open	4.35E−08	2.60E−04	22.8	20.1	16.4
			reading frame 31
8	210275_PM_s_at	ZFAND5	zinc finger, AN1-type	4.96E−08	2.60E−04	1045.9	1513.6	1553.8
			domain 5
9	244341_PM_at	—	—	5.75E−08	2.60E−04	398.3	270.7	262.8
10	1558822_PM_at	—	—	5.84E−08	2.60E−04	108.6	62.9	56.8
11	242175_PM_at	—	—	5.87E−08	2.60E−04	69.1	37.2	40.0
12	222357_PM_at	ZBTB20	zinc finger and BTB	6.97E−08	2.83E−04	237.4	127.4	109.8
			domain containing
			20
13	206288_PM_at	PGGT1B	protein	9.42E−08	3.53E−04	20.8	34.7	34.2
			geranylgeranyltransferase
			type I, beta subunit
14	222306_PM_at	—	—	1.03E−07	3.59E−04	23.3	15.8	16.0
15	1569601_PM_at	—	—	1.67E−07	4.80E−04	49.5	34.1	29.7
16	235138_PM_at	—	—	1.69E−07	4.80E−04	1169.9	780.0	829.7
17	240452_PM_at	GSPT1	G1 to S phase	1.74E−07	4.80E−04	97.7	54.4	48.6
			transition 1
18	243003_PM_at	—	—	1.77E−07	4.80E−04	92.8	52.5	51.3
19	218109_PM_s_at	MFSD1	major facilitator	1.90E−07	4.87E−04	1464.0	1881.0	1886.4
			superfamily domain
			containing 1
20	241681_PM_at	—	—	2.00E−07	4.87E−04	1565.7	845.7	794.6
21	243878_PM_at	—	—	2.19E−07	5.08E−04	76.1	39.7	39.5
22	233296_PM_x_at	—	—	2.33E−07	5.17E−04	347.7	251.5	244.7
23	243318_PM_at	DCAF8	DDB1 and CUL4	2.52E−07	5.34E−04	326.2	229.5	230.2
			associated factor 8
24	236354_PM_at	—	—	3.23E−07	6.39E−04	47.1	31.2	27.8
25	243768_PM_at	—	—	3.35E−07	6.39E−04	1142.0	730.6	768.5
26	238558_PM_at	—	—	3.65E−07	6.39E−04	728.5	409.4	358.4
27	237825_PM_x_at	—	—	3.66E−07	6.39E−04	34.2	20.9	19.9
28	244414_PM_at	—	—	3.67E−07	6.39E−04	548.7	275.2	284.0
29	215221_PM_at	—	—	4.06E−07	6.83E−04	327.2	176.7	171.9
30	235912_PM_at	—	—	4.46E−07	7.25E−04	114.1	71.4	59.5
31	239348_PM_at	—	—	4.87E−07	7.54E−04	20.1	14.5	13.4
32	240499_PM_at	—	—	5.06E−07	7.54E−04	271.4	180.1	150.2
33	208054_PM_at	HERC4	hect domain and RLD	5.11E−07	7.54E−04	114.9	57.6	60.0
			4
34	240263_PM_at	—	—	5.46E−07	7.81E−04	120.9	78.7	66.6
35	241303_PM_x_at	—	—	5.78E−07	7.81E−04	334.5	250.3	261.5
36	233692_PM_at	—	—	5.92E−07	7.81E−04	22.4	15.5	15.0
37	243561_PM_at	—	—	5.93E−07	7.81E−04	341.1	215.1	207.3
38	232778_PM_at	—	—	6.91E−07	8.86E−04	46.5	31.0	28.5
39	237632_PM_at	—	—	7.09E−07	8.86E−04	108.8	61.0	57.6
40	233690_PM_at	—	—	7.30E−07	8.89E−04	351.1	222.7	178.1
41	220221_PM_at	VPS13D	vacuolar protein	7.50E−07	8.89E−04	93.5	60.0	59.9
			sorting 13 homolog D
			(S. cerevisiae)
42	242877_PM_at	—	—	7.72E−07	8.89E−04	173.8	108.1	104.0
43	218155_PM_x_at	TSR1	TSR1, 20S rRNA	7.86E−07	8.89E−04	217.2	165.6	164.7
			accumulation,
			homolog
			(S. cerevisiae)
44	239603_PM_x_at	—	—	8.24E−07	8.89E−04	120.9	75.5	81.1
45	242859_PM_at	—	—	8.48E−07	8.89E−04	221.1	135.4	138.3
46	240866_PM_at	—	—	8.54E−07	8.89E−04	65.7	33.8	35.2
47	239661_PM_at	—	—	8.72E−07	8.89E−04	100.5	48.3	45.2
48	224493_PM_x_at	C18orf45	chromosome 18	8.77E−07	8.89E−04	101.8	78.0	89.7
			open reading frame
			45
49	1569202_PM_x_at	—	—	8.98E−07	8.89E−04	23.3	18.5	16.6
50	1560474_PM_at	—	—	9.12E−07	8.89E−04	25.2	17.8	18.5
51	232511_PM_at	—	—	9.48E−07	9.06E−04	77.2	46.1	49.9
52	228119_PM_at	LRCH3	leucine-rich repeats	1.01E−06	9.51E−04	117.2	84.2	76.1
			and calponin
			homology (CH)
			domain containing 3
53	228545_PM_at	ZNF148	zinc finger protein	1.17E−06	9.99E−04	789.9	571.1	579.7
			148
54	232779_PM_at	—	—	1.17E−06	9.99E−04	36.7	26.0	20.7
55	239005_PM_at	FLJ39739	Hypothetical	1.18E−06	9.99E−04	339.1	203.7	177.7
			FLJ39739
56	244478_PM_at	LRRC37A3	leucine rich repeat	1.20E−06	9.99E−04	15.7	12.6	12.7
			containing 37,
			member A3
57	244535_PM_at	—	—	1.28E−06	9.99E−04	261.5	139.5	137.8
58	1562673_PM_at	—	—	1.28E−06	9.99E−04	77.4	46.5	51.8
59	240601_PM_at	—	—	1.29E−06	9.99E−04	212.6	107.7	97.7
60	239533_PM_at	GPR155	G protein-coupled	1.30E−06	9.99E−04	656.3	396.7	500.1
			receptor 155
61	222358_PM_x_at	—	—	1.32E−06	9.99E−04	355.2	263.1	273.7
62	214707_PM_x_at	ALMS1	Alstrom syndrome 1	1.32E−06	9.99E−04	340.2	255.9	266.0
63	236435_PM_at	—	—	1.32E−06	9.99E−04	144.0	92.6	91.1
64	232333_PM_at	—	—	1.33E−06	9.99E−04	487.7	243.7	244.3
65	222366_PM_at	—	—	1.33E−06	9.99E−04	289.1	186.1	192.8
66	215611_PM_at	TCF12	transcription factor	1.38E−06	1.02E−03	45.5	32.4	30.8
			12
67	1558002_PM_at	STRAP	Serine/threonine	1.40E−06	1.02E−03	199.6	146.7	139.7
			kinase receptor
			associated protein
68	239716_PM_at	—	—	1.43E−06	1.02E−03	77.6	49.5	45.5
69	239091_PM_at	—	—	1.45E−06	1.02E−03	76.9	44.0	45.0
70	238883_PM_at	—	—	1.68E−06	1.15E−03	857.1	475.5	495.1
71	235615_PM_at	PGGT1B	protein	1.72E−06	1.15E−03	127.0	235.0	245.6
			geranylgeranyltransferase
			type I, beta subunit
72	204055_PM_s_at	CTAGE5	CTAGE family,	1.77E−06	1.15E−03	178.8	115.2	105.9
			member 5
73	239757_PM_at	ZFAND6	Zinc finger, AN1-type	1.81E−06	1.15E−03	769.6	483.3	481.9
			domain 6
74	1558409_PM_at	—	—	1.82E−06	1.15E−03	14.8	10.9	11.8
75	242688_PM_at	—	—	1.85E−06	1.15E−03	610.5	338.4	363.4
76	242377_PM_x_at	THUMPD3	THUMP domain	1.87E−06	1.15E−03	95.5	79.0	81.3
			containing 3
77	242650_PM_at	—	—	1.88E−06	1.15E−03	86.0	55.5	47.4
78	243589_PM_at	KIAA1267 ///	KIAA1267 ///	1.89E−06	1.15E−03	377.8	220.3	210.4
		LOC100294337	hypothetical
			LOC100294337
79	227384_PM_s_at	—	—	1.90E−06	1.15E−03	3257.0	2255.5	2139.7
80	237864_PM_at	—	—	1.91E−06	1.15E−03	121.0	69.2	73.4
81	243490_PM_at	—	—	1.92E−06	1.15E−03	24.6	17.5	16.5
82	244383_PM_at	—	—	1.96E−06	1.17E−03	141.7	93.0	77.5
83	215908_PM_at	—	—	2.06E−06	1.19E−03	98.5	67.9	67.5
84	230651_PM_at	—	—	2.09E−06	1.19E−03	125.9	74.3	71.5
85	1561195_PM_at	—	—	2.14E−06	1.19E−03	86.6	45.1	43.9
86	239268_PM_at	NDUFS1	NADH dehydrogenase	2.14E−06	1.19E−03	14.0	12.0	11.3
			(ubiquinone) Fe—S
			protein 1, 75 kDa
			(NADH-coenzyme Q
			reductase)
87	236431_PM_at	SR140	U2-associated SR140	2.16E−06	1.19E−03	69.4	47.9	43.9
			protein
88	236978_PM_at	—	—	2.19E−06	1.19E−03	142.4	88.6	88.1
89	1562957_PM_at	—	—	2.21E−06	1.19E−03	268.3	181.8	165.4
90	238913_PM_at	—	—	2.21E−06	1.19E−03	30.9	20.2	20.1
91	239646_PM_at	—	—	2.23E−06	1.19E−03	100.3	63.1	60.8
92	235701_PM_at	—	—	2.34E−06	1.24E−03	133.2	66.1	60.0
93	235601_PM_at	—	—	2.37E−06	1.24E−03	121.9	75.5	79.0
94	230918_PM_at	—	—	2.42E−06	1.25E−03	170.4	114.5	94.4
95	219112_PM_at	FNIP1 ///	folliculin interacting	2.49E−06	1.28E−03	568.2	400.2	393.4
		RAPGEF6	protein 1 /// Rap
			guanine nucleotide
			exchange factor
			(GEF) 6
96	202228_PM_s_at	NPTN	neuroplastin	2.52E−06	1.28E−03	1017.7	1331.5	1366.4
97	242839_PM_at	—	—	2.78E−06	1.39E−03	17.9	14.0	13.6
98	244778_PM_x_at	—	—	2.85E−06	1.42E−03	105.1	68.0	65.9
99	237388_PM_at	—	—	2.91E−06	1.42E−03	59.3	38.0	33.0
100	202770_PM_s_at	CCNG2	cyclin G2	2.92E−06	1.42E−03	142.2	269.0	270.0
101	240008_PM_at	—	—	2.96E−06	1.42E−03	96.2	65.6	56.2
102	1557718_PM_at	PPP2R5C	protein phosphatase	2.97E−06	1.42E−03	615.2	399.8	399.7
			2, regulatory subunit
			B′, gamma
103	215528_PM_at	—	—	3.01E−06	1.42E−03	126.8	62.6	69.0
104	204689_PM_at	HHEX	hematopoietically	3.08E−06	1.44E−03	381.0	499.9	567.9
			expressed homeobox
105	213718_PM_at	RBM4	RNA binding motif	3.21E−06	1.46E−03	199.3	140.6	132.2
			protein 4
106	243233_PM_at	—	—	3.22E−06	1.46E−03	582.3	343.0	337.1
107	239597_PM_at	—	—	3.23E−06	1.46E−03	1142.9	706.	720.8
108	232890_PM_at	—	—	3.24E−06	1.46E−03	218.0	148.7	139.9
109	232883_PM_at	—	—	3.42E−06	1.53E−03	127.5	79.0	73.1
110	241391_PM_at	—	—	3.67E−06	1.62E−03	103.8	51.9	48.3
111	244197_PM_x_at	—	—	3.71E−06	1.62E−03	558.0	397.3	418.8
112	205434_PM_s_at	AAK1	AP2 associated	3.75E−06	1.62E−03	495.2	339.9	301.2
			kinase 1
113	235725_PM_at	SMAD4	SMAD family	3.75E−06	1.62E−03	147.1	102.1	112.0
			member 4
114	203137_PM_at	WTAP	Wilms tumor 1	3.89E−06	1.66E−03	424.1	609.4	555.8
			associated protein
115	231075_PM_x_at	RAPH1	Ras association	3.91E−06	1.66E−03	30.4	19.3	18.2
			(RalGDS/AF-6) and
			pleckstrin homology
			domains 1
116	236043_PM_at	LOC100130175	hypothetical protein	3.98E−06	1.67E−03	220.6	146.2	146.5
			LOC100130175
117	238299_PM_at	—	—	4.09E−06	1.70E−03	217.1	130.4	130.3
118	243667_PM_at	—	—	4.12E−06	1.70E−03	314.5	225.3	232.8
119	223937_PM_at	FOXP1	forkhead box P1	4.20E−06	1.72E−03	147.7	85.5	90.9
120	238666_PM_at	—	—	4.25E−06	1.72E−03	219.1	148.3	145.5
121	1554771_PM_at	—	—	4.28E−06	1.72E−03	67.2	41.5	40.8
122	202379_PM_s_at	NKTR	natural killer-tumor	4.34E−06	1.73E−03	1498.2	1170.6	1042.6
			recognition sequence
123	244695_PM_at	GHRLOS	ghrelin opposite	4.56E−06	1.79E−03	78.0	53.0	52.5
			strand (non-protein
			coding)
124	239393_PM_at	—	—	4.58E−06	1.79E−03	852.0	554.2	591.7
125	242920_PM_at	—	—	4.60E−06	1.79E−03	392.8	220.9	251.8
126	242405_PM_at	—	—	4.66E−06	1.80E−03	415.8	193.8	207.4
127	1556432_PM_at	—	—	4.69E−06	1.80E−03	61.5	43.1	38.1
128	1570299_PM_at	—	—	4.77E−06	1.81E−03	27.0	18.0	19.8
129	225198_PM_at	VAPA	VAMP (vesicle-	4.85E−06	1.83E−03	192.0	258.3	273.9
			associated
			membrane protein)-
			associated protein A,
			33 kDa
130	230702_PM_at	—	—	4.94E−06	1.85E−03	28.2	18.4	17.5
131	240262_PM_at	—	—	5.07E−06	1.88E−03	46.9	22.8	28.0
132	232216_PM_at	YME1L1	YME1-like 1	5.14E−06	1.89E−03	208.6	146.6	130.1
			(S. cerevisiae)
133	225171_PM_at	ARHGAP18	Rho GTPase	5.16E−06	1.89E−03	65.9	109.1	121.5
			activating protein 18
134	243992_PM_at	—	—	5.28E−06	1.92E−03	187.1	116.0	125.6
135	227082_PM_at	—	—	5.45E−06	1.96E−03	203.8	140.4	123.0
136	239948_PM_at	NUP153	nucleoporin 153 kDa	5.50E−06	1.96E−03	39.6	26.5	27.8
137	221905_PM_at	CYLD	cylindromatosis	5.51E−06	1.96E−03	433.0	316.8	315.1
			(turban tumor
			syndrome)
138	242578_PM_x_at	SLC22A3	Solute carrier family	5.56E−06	1.96E−03	148.4	109.2	120.1
			22 (extraneuronal
			monoamine
			transporter),
			member 3
139	1569238_PM_a_at	—	—	5.73E−06	1.99E−03	71.0	33.0	36.1
140	201453_PM_x_at	RHEB	Ras homolog	5.76E−06	1.99E−03	453.3	600.0	599.0
			enriched in brain
141	236802_PM_at	—	—	5.76E−06	1.99E−03	47.9	29.1	29.6
142	232615_PM_at	—	—	5.82E−06	1.99E−03	4068.5	3073.4	2907.4
143	237179_PM_at	PCMTD2	protein-L-	5.84E−06	1.99E−03	48.7	30.2	26.8
			isoaspartate (D-
			aspartate) O-
			methyltransferase
			domain containing 2
144	203255_PM_at	FBXO11	F-box protein 11	5.98E−06	2.02E−03	748.3	529.4	539.6
145	212989_PM_at	SGMS1	sphingomyelin	6.04E−06	2.03E−03	57.2	93.1	107.9
			synthase 1
146	236754_PM_at	PPP1R2	protein phosphatase	6.17E−06	2.05E−03	505.3	380.7	370.1
			1, regulatory
			(inhibitor) subunit 2
147	1559496_PM_at	PPA2	pyrophosphatase	6.24E−06	2.05E−03	68.8	39.7	39.3
			(inorganic) 2
148	236494_PM_x_at	—	—	6.26E−06	2.05E−03	135.0	91.1	82.9
149	237554_PM_at	—	—	6.30E−06	2.05E−03	53.4	31.5	30.1
150	243469_PM_at	—	—	6.37E−06	2.05E−03	635.2	308.1	341.5
151	240155_PM_x_at	ZNF493 ///	zinc finger protein	6.45E−06	2.05E−03	483.9	299.9	316.6
		ZNF738	493 /// zinc finger
			protein 738
152	222442_PM_s_at	ARL8B	ADP-ribosylation	6.47E−06	2.05E−03	201.5	292.	268.3
			factor-like 8B
153	240307_PM_at	—	—	6.48E−06	2.05E−03	55.4	36.8	33.1
154	200864_PM_s_at	RAB11A	RAB11A, member	6.50E−06	2.05E−03	142.1	210.9	233.0
			RAS oncogene family
155	235757_PM_at	—	—	6.53E−06	2.05E−03	261.4	185.2	158.9
156	222351_PM_at	PPP2R1B	protein phosphatase	6.58E−06	2.06E−03	75.8	51.1	45.4
			2, regulatory subunit
			A, beta
157	222788_PM_s_at	RSBN1	round spermatid	6.63E−06	2.06E−03	389.9	302.7	288.2
			basic protein 1
158	239815_PM_at	—	—	6.70E−06	2.06E−03	216.9	171.4	159.5
159	219392_PM_x_at	PRR11	proline rich 11	6.77E−06	2.07E−03	1065.3	827.5	913.2
160	240458_PM_at	—	—	6.80E−06	2.07E−03	414.3	244.6	242.0
161	235879_PM_at	MBNL1	Muscleblind-like	6.88E−06	2.08E−03	1709.2	1165.5	1098.0
			(Drosophila)
162	230529_PM_at	HECA	headcase homolog	7.08E−06	2.13E−03	585.1	364.3	418.4
			(Drosophila)
163	1562063_PM_x_at	KIAA1245 ///	KIAA1245 ///	7.35E−06	2.20E−03	350.4	238.8	260.8
		NBPF1 ///	neuroblastoma
		NBPF10 ///	breakpoint family,
		NBPF11 ///	member 1 ///
		NBPF12 ///	neuroblastoma
		NBPF24 ///	breakpoint fam
		NBPF8 ///
		NBPF9
164	202769_PM_at	CCNG2	cyclin G2	7.42E−06	2.20E−03	697.1	1164.0	1264.6
165	1556493_PM_a_at	KDM4C	lysine (K)-specific	7.64E−06	2.24E−03	81.4	49.0	44.5
			demethylase 4C
166	216509_PM_x_at	MLLT10	myeloid/lymphoid or	7.64E−06	2.24E−03	22.4	17.9	19.3
			mixed-lineage
			leukemia (trithorax
			homolog, Drosophila);
			translocate
167	223697_PM_x_at	C9orf64	chromosome 9 open	7.70E−06	2.25E−03	1013.6	771.2	836.8
			reading frame 64
168	235999_PM_at	—	—	7.77E−06	2.25E−03	227.6	174.1	182.1
169	244766_PM_at	LOC100271836 ///	SMG1 homolog,	8.03E−06	2.31E−03	133.4	99.4	87.5
		LOC440354 ///	phosphatidylinositol
		LOC595101 ///	3-kinase-related
		LOC641298 ///	kinase pseudogene ///
		SMG1	PI-3-kinase-r
170	230332_PM_at	ZCCHC7	Zinc finger, CCHC	8.07E−06	2.31E−03	467.4	265.1	263.2
			domain containing 7
171	235308_PM_at	ZBTB20	zinc finger and BTB	8.17E−06	2.32E−03	256.7	184.2	167.3
			domain containing
			20
172	242492_PM_at	CLNS1A	Chloride channel,	8.19E−06	2.32E−03	128.5	82.8	79.2
			nucleotide-sensitive,
			1A
173	215898_PM_at	TTLL5	tubulin tyrosine	8.24E−06	2.32E−03	20.9	14.0	13.8
			ligase-like family,
			member 5
174	244840_PM_x_at	DOCK4	dedicator of	8.65E−06	2.42E−03	43.1	16.5	21.5
			cytokinesis 4
175	220235_PM_s_at	C1orf103	chromosome 1 open	8.72E−06	2.43E−03	88.4	130.5	143.3
			reading frame 103
176	229467_PM_at	PCBP2	Poly(rC) binding	8.80E−06	2.44E−03	186.5	125.4	135.8
			protein 2
177	232527_PM_at	—	—	8.99E−06	2.48E−03	667.4	453.9	461.3
178	243286_PM_at	—	—	9.24E−06	2.53E−03	142.6	98.2	87.2
179	215628_PM_x_at	—	—	9.28E−06	2.53E−03	49.6	36.3	39.4
180	1556412_PM_at	—	—	9.45E−06	2.56E−03	34.9	24.7	23.8
181	204786_PM_s_at	IFNAR2	interferon (alpha,	9.64E−06	2.59E−03	795.6	573.0	639.2
			beta and omega)
			receptor 2
182	234258_PM_at	—	—	9.73E−06	2.60E−03	27.4	17.8	20.3
183	233274_PM_at	—	—	9.76E−06	2.60E−03	109.9	77.5	79.4
184	239784_PM_at	—	—	9.82E−06	2.60E−03	137.0	80.1	70.1
185	242498_PM_x_at	—	—	1.01E−05	2.65E−03	59.2	40.4	38.9
186	231351_PM_at	—	—	1.02E−05	2.67E−03	124.8	70.8	60.6
187	222368_PM_at	—	—	1.03E−05	2.67E−03	89.9	54.5	44.3
188	236524_PM_at	—	—	1.03E−05	2.67E−03	313.2	234.7	214.2
189	243834_PM_at	TNRC6A	trinucleotide repeat	1.04E−05	2.67E−03	211.8	145.1	146.9
			containing 6A
190	239167_PM_at	—	—	1.04E−05	2.67E−03	287.4	150.2	160.3
191	239238_PM_at	—	—	1.05E−05	2.67E−03	136.0	81.6	92.0
192	237194_PM_at	—	—	1.05E−05	2.67E−03	57.2	34.4	27.9
193	242772_PM_x_at	—	—	1.06E−05	2.67E−03	299.2	185.2	189.4
194	243827_PM_at	—	—	1.06E−05	2.67E−03	115.9	50.1	56.4
195	1552536_PM_at	VTI1A	vesicle transport	1.10E−05	2.75E−03	61.7	35.1	34.6
			through interaction
			with t-SNAREs
			homolog 1A (yeast)
196	243696_PM_at	KIAA0562	KIAA0562	1.12E−05	2.77E−03	19.0	14.8	15.0
197	233648_PM_at	—	—	1.12E−05	2.77E−03	33.9	21.0	24.1
198	225858_PM_s_at	XIAP	X-linked inhibitor of	1.16E−05	2.85E−03	1020.7	760.3	772.6
			apoptosis
199	238736_PM_at	REV3L	REV3-like, catalytic	1.19E−05	2.91E−03	214.2	135.8	151.6
			subunit of DNA
			polymerase zeta
			(yeast)
200	221192_PM_x_at	MFSD11	major facilitator	1.20E−05	2.92E−03	100.4	74.5	81.2
			superfamily domain
			containing 11

TABLE 9

33 probesets that differentiate SCAR and TX at p-value < 0.001 in PAXGene blood tubes

				FFold -
	Gene		p-value	Change		SCAR -	TX-
Probeset ID	Symbol	Gene Title	(Phenotype)	(SCAR vs. TX)	ID	Mean	Mean

1553094_PM_at	TAC4	tachykinin 4	0.000375027	−1.1	1553094_PM_at	8.7	9.6
		(hemokinin)
1553352_PM_x_at	ERVWE1	endogenous retroviral	0.000494742	−1.26	1553352_PM_x_at	15.5	19.6
		family W, env(C7),
		member 1
1553644_PM_at	C14orf49	chromosome 14 open	0.000868817	−1.16	1553644_PM_at	10.1	11.7
		reading frame 49
1556178_PM_x_at	TAF8	TAF8 RNA	0.000431074	1.24	1556178_PM_x_at	39.2	31.7
		polymerase II, TATA
		box binding protein
		(TBP)-associated
		factor, 43 kDa
1559687_PM_at	TMEM221	transmembrane	8.09E−05	−1.16	1559687_PM_at	13.0	15.1
		protein 221
1562492_PM_at	LOC340090	hypothetical	0.00081096	−1.1	1562492_PM_at	8.8	9.7
		LOC340090
1563204_PM_at	ZNF627	Zinc finger protein	0.000784254	−1.15	1563204_PM_at	10.6	12.2
		627
1570124_PM_at	—	—	0.000824814	−1.14	1570124_PM_at	10.6	12.2
204681_PM_s_at	RAPGEF5	Rap guanine	0.000717727	−1.18	204681_PM_s_at	9.6	11.3
		nucleotide exchange
		factor (GEF) 5
206154_PM_at	RLBP1	retinaldehyde binding	0.000211941	−1.13	206154_PM_at	11.0	12.4
		protein 1
209053_PM_s_at	WHSC1	Wolf-Hirschhorn	0.000772412	1.23	209053_PM_s_at	15.1	12.3
		syndrome candidate 1
209228_PM_x_at	TUSC3	tumor suppressor	0.000954529	−1.13	209228_PM_x_at	8.9	10.1
		candidate 3
211701_PM_s_at	TRO	trophinin	0.000684486	−1.13	211701_PM_s_at	10.0	11.3
213369_PM_at	CDHR1	cadherin-related	0.000556648	−1.14	213369_PM_at	10.8	12.3
		family member 1
215110_PM_at	MBL1P	mannose-binding	0.000989176	−1.13	215110_PM_at	9.2	10.4
		lectin (protein A) 1,
		pseudogene
215232_PM_at	ARHGAP44	Rho GTPase	0.000332776	−1.18	215232_PM_at	11.1	13.1
		activating protein 44
217158_PM_at	LOC442421	hypothetical	2.98E−05	1.18	217158_PM_at	14.2	12.0
		LOC442421 ///
		prostaglandin E2
		receptor EP4 subtype-
		like
218365_PM_s_at	DARS2	aspartyl-tRNA	0.000716035	1.18	218365_PM_s_at	17.2	14.5
		synthetase 2,
		mitochondrial
219695_PM_at	SMPD3	sphingomyelin	0.000377151	−1.47	219695_PM_at	12.0	17.6
		phosphodiesterase 3,
		neutral membrane
		(neutral
		sphingomyelinase II)
220603_PM_s_at	MCTP2	multiple C2 domains,	0.000933412	−1.38	220603_PM_s_at	338.5	465.8
		transmembrane 2
224963_PM_at	SLC26A2	solute carrier family	0.000961242	1.47	224963_PM_at	94.3	64.0
		26 (sulfate
		transporter), member
		2
226729_PM_at	USP37	ubiquitin specific	0.000891038	1.24	226729_PM_at	32.9	26.6
		peptidase 37
228226_PM_s_at	ZNF775	zinc finger protein 775	0.000589512	1.2	228226_PM_s_at	20.5	17.1
230608_PM_at	C1orf182	chromosome 1 open	0.000153478	−1.18	230608_PM_at	15.9	18.8
		reading frame 182
230756_PM_at	ZNF683	zinc finger protein 683	0.00044751	1.52	230756_PM_at	26.7	17.6
231757_PM_at	TAS2R5	taste receptor, type 2,	0.000869775	−1.12	231757_PM_at	9.3	10.4
		member 5
231958_PM_at	C3orf31	Chromosome 3 open	4.09E−05	1.22	231958_PM_at	20.1	16.4
		reading frame 31
237290_PM_at	—	—	0.000948318	−1.22	237290_PM_at	10.3	12.5
237806_PM_s_at	LOC729296	hypothetical	0.00092234	−1.18	237806_PM_s_at	10.2	12.0
		LOC729296
238459_PM_x_at	SPATA6	spermatogenesis	0.000116525	−1.15	238459_PM_x_at	9.2	10.5
		associated 6
241331_PM_at	SKAP2	Src kinase associated	0.000821476	−1.39	241331_PM_at	16.4	22.9
		phosphoprotein 2
241368_PM_at	PLIN5	perilipin 5	0.000406066	−1.61	241368_PM_at	84.5	136.3
241543_PM_at	—	—	0.000478221	−1.17	241543_PM_at	9.4	11.0

Example 3

Differentially Expressed Genes Associated with Kidney Transplant Rejections

This Example describes global analysis of gene expressions in kidney transplant patients with different types of rejections or injuries.
A total of biopsy-documented 274 kidney biopsy samples from the Transplant Genomics Collaborative Group (TGCG) were processed on the Affymetrix HG-U133 PM only peg microarrays. The 274 samples that were analyzed comprised of 4 different phenotypes: Acute Rejection (AR; n=75); Acute Dysfunction No Rejection (ADNR; n=39); Chronic Allograft Nephropathy (CAN; n=61); and Transplant Excellent (TX; n=99).
Signal Filters: To eliminate low expressed signals we used a signal filter cut-off that was data driven, and expression signals<Log 2 4.23 in all samples were eliminated leaving us with 48882 probe sets from a total of 54721 probe sets.
4-Way AR/ADNR/CAN/TX Classifier:
We first did a 4 way comparison of the AR, ADNR, CAN and TX samples. The samples comprised of four different classes a 4-way ANOVA analysis yielded more than 10,000 differentially expressed genes even at a stringent p value cut-off of <0.001. Since we were trying to discover a signature that could differentiate these four classes we used only the top 200 differentially expressed probe sets to build predictive models. We ran the Nearest Centroid (NC) algorithm to build the predictive models. When we used the top 200 differentially expressed probe sets between all four phenotypes, the best predictor model was based on 199 probe sets.
Nearest Centroid (NC) classification takes the gene expression profile of a new sample, and compares it to each of the existing class centroids. The class whose centroid that it is closest to, in squared distance, is the predicted class for that new sample. It also provides the centroid distances for each sample to each of the possible phenotypes being tested. In other words, in a 2-way classifier like AR vs. TX, the tool provides the “best” classification and provides the centroid distances to the two possible outcomes: TX and AR.
We observed in multiple datasets that there are 4 classes of predictions made. First, are correctly classified as TX by both biopsy and NC. Second, are correctly classified as AR by both biopsy and NC. Third, are truly misclassified samples. In other words, the biopsy says one thing and the molecular profile another. In these cases, the centroid distances for the given classifications are dramatically different, making the molecular classification very straightforward and simply not consistent with the biopsy phenotype assigned. Whether this is because the gold standard biopsy classification is wrong or the molecular classification is wrong is impossible to know at this point.
However, there is a fourth class that we call “mixed” classifications. In these cases supposedly “misclassified” samples by molecular profile show a nearest centroid distance that is not very different when compared to that of the “correct” classification based on the biopsy. In other words, the nearest centroid distances of most of these misclassified “mixed” samples are actually very close to the correct biopsy classification. However, because NC has no rules set to deal with the mixed situation it simply calls the sample by the nominally higher centroid distance.
The fact is that most standard implementations of class prediction algorithms currently available treat all classes as dichotomous variables (yes/no diagnostically). They are not designed to deal with the reality of medicine that molecular phenotypes of clinical samples can actually represent a continuous range of molecular scores based on the expression signal intensities with complex implications for the diagnoses. Thus, “mixed” cases where the centroid distances are only slightly higher for TX than AR is still classified as a TX, even if the AR distances are only slightly less. In this case, where there is a mixture of TX and AR by expression, it is obvious that the case is actually an AR for a transplant clinician, not a TX. Perhaps just a milder form of AR and this is the reason for using thresholding.
Thus, we set a threshold for the centroid distances. The threshold is driven by the data. The threshold equals the mean difference NC provides in centroid distances for the two possible classifications (i.e. AR vs. TX) for all correctly classified samples in the data set (e.g. classes 1 and 2 of the 4 possible outcomes of classification). This means that for the “mixed” class of samples, if a biopsy-documented sample was misclassified by molecular profiling, but the misclassification was within the range of the mean calculated centroid distances of the true classifications in the rest of the data, then that sample would not be considered as a misclassified sample.
Table 10a shows the performance of the 4 way AR, ADNR, CAN, TX NC classifier using such a data driven threshold. Table 10b shows the top 200 probeset used for the 4 way AR, ADNR, CAN, TX NC classifier. So, using the top 200 differentially expressed probesets from a 4-way AR, ADNR, CAN and TX ANOVA with a Nearest Centroid classifier, we are able to molecularly classify the 4 phenotypes at 97% accuracy. Smaller classifier sets did not afford any significant increase in the predictive accuracies. To validate this data we applied this classification to an externally collected data set. These were samples collected at the University of Sao Paolo in Brazil. A total of 80 biopsy-documented kidney biopsy samples were processed on the same Affymetrix HG-U133 PM only peg microarrays. These 80 samples that were analyzed comprised of the same 4 different phenotypes: AR (n=23); ADNR (n=11); CAN (n=29); and TX (n=17).
We performed the classification based on the “locked “NC predictor (meaning that none of the thresholding parameters were changed. Table 11 shows the performance of our locked 4 way AR, ADNR, CAN, TX NC classifier in the Brazilian cohort. So, using the top 200 differentially expressed probesets from a 4-way AR, ADNR, CAN and TX ANOVA with a “locked” Nearest Centroid classifier we are able to molecularly classify the 4 phenotypes with similar accuracy in an independently and externally collected validation set. This validates our molecular classifier of the biopsy on an independent external data set. It also demonstrates that the classifier is not subject to influence based on significant racial differences represented in the Brazilian population.
3-Way AR/ADNR/TX Classifier:
Similarly, we did a 3 way comparison of the AR, ADNR and TX samples since these are the most common phenotypes encountered during the early post-transplant period with CAN usually being a late manifestation of graft injury which is progressive. The samples comprised of these 3 different classes, and a 4-way ANOVA analysis again yielded more than 10,000 differentially expressed genes, so we used only the top 200 differentially expressed probe sets to build predictive models. We ran the Nearest Centroid (NC) algorithm to build the predictive models. When we used the top 200 differentially expressed probe sets between all four phenotypes the best predictor model was based on 197 probe sets.
Table 12a shows the performance of the 3 way AR, ADNR, TX NC classifier with which we are able to molecularly classify the 3 phenotypes at 98% accuracy in the TGCG cohort. Table 12b shows the top 200 probeset used for the 3 way AR, ADNR, TX NC classifier in the TGCG cohort. Similarly the locked 3 way classifier performs equally well on the Brazilian cohort with 98% accuracy (Table 13). Therefore, our 3 way classifier also validates on the external data set.
2-Way CAN/TX Classifier:
Finally we also did a 2 way comparison of the CAN and TX samples. The samples comprised of these 2 classes with an ANOVA analysis again yielded ˜11,000 differentially expressed genes, so we used only the top 200 differentially expressed probe sets to build predictive models. We ran the Nearest Centroid (NC) algorithm to build the predictive models. When we used the top 200 differentially expressed probe sets the best predictor model was based on all 200 probe sets. Table 14a shows the performance of the 2 way CAN, TX NC classifier with which we are able to molecularly classify the 4 phenotypes at 97% accuracy in the TGCG cohort. Table 14b shows the top 200 probeset used for the 2 way CAN, TX NC classifier in the TGCG cohort. This locked classifier performs equally well on the Brazilian cohort with 95% accuracy (Table 15). Again we show that our 2 way CAN, TX classifier also validates on the external data set.
It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred methods and materials are described.
All publications, GenBank sequences, ATCC deposits, patents and patent applications cited herein are hereby expressly incorporated by reference in their entirety and for all purposes as if each is individually so denoted.

TABLE 10a

Biopsy Expression Profiling of Kidney Transplants: 4-Way
Classifier AR vs. ADNR vs. CAN vs. TX (TGCG Samples)

Validation Cohort

				Predictive			Postive	Negative
				Accuracy			Predictive	Predictive
Algorithm	Predictors	Comparison	AUC	(%)	Sensitivity (%)	Specificity (%)	Value (%)	Value (%)

Nearest Centroid	199	AR vs. TX	0.957	95	96	96	94	97
Nearest Centroid	199	ADNR vs. TX	0.977	97	94	100	100	97
Nearest Centroid	199	CAN vs. TX	0.992	99	98	100	100	99

TABLE 10b

Biopsy Expression Profiling of Kidney Transplants: 4-Way Classifier AR vs. ADNR vs. CAN vs. TX (TGCG Samples)

					p-value
					(Final	ADN
	Probeset	Entrez	Gene		Pheno-	R -	AR -	CAN -	TX -
#	ID	Gene	Symbol	Gene Title	type)	Mean	Mean	Mean	Mean

1	204446_PM_s_at	240	ALOX5	arachidonate 5-lipoxygenase	2.82E−34	91.9	323.9	216.7	54.7
2	202207_PM_at	10123	ARL4C	ADP-ribosylation factor-	1.31E−32	106.9	258.6	190.4	57.2
				like 4C
3	204698_PM_at	3669	ISG20	interferon stimulated	1.50E−31	41.5	165.1	96.1	27.6
				exonuclease gene 20 kDa
4	225701_PM_at	80709	AKNA	AT-hook transcription	1.75E−31	37.7	102.8	73.2	29.0
				factor
5	207651_PM_at	29909	GPR171	G protein-coupled receptor	6.30E−31	25.8	89.9	57.0	20.9
				171
6	204205_PM_at	60489	APOBEC3G	apolipoprotein B mRNA	1.27E−30	95.4	289.4	192.0	78.7
				editing enzyme, catalytic
				polypeptide-like 3G
7	208948_PM_s_at	6780	STAU1	staufen, RNA binding	1.37E−30	1807.9	1531.8	1766.0	2467.4
				protein, homolog 1
				(Drosophila)
8	217733_PM_s_at	9168	TMSB10	thymosin beta 10	2.38E−30	4414.7	6331.3	5555.2	3529.0
9	205831_PM_at	914	CD2	CD2 molecule	2.73E−30	40.4	162.5	100.9	33.9
10	209083_PM_at	11151	CORO1A	coronin, actin binding	5.57E−30	46.9	163.8	107.1	34.3
				protein, 1A
11	210915_PM_x_at	28638	TRBC2	T cell receptor beta constant	5.60E−30	39.7	230.7	129.7	37.5
				2
12	211368_PM_s_at	834	CASP1	caspase 1, apoptosis-related	6.21E−30	102.6	274.3	191.4	81.8
				cysteine peptidase
				(interleukin 1, beta,
				convertase)
13	201042_PM_at	7052	TGM2	transglutaminase 2 (C	6.28E−30	131.8	236.5	172.6	80.1
				polypeptide, protein-
				glutamine-gamma-
				glutamyltransferase)
14	227353_PM_at	147138	TMC8	transmembrane channel-like	7.76E−30	19.8	64.2	42.7	16.6
				8
15	1555852_PM_at	100507463	LOC100507463	hypothetical	8.29E−30	78.9	202.6	154.2	70.9
				LOC100507463
16	226878_PM_at	3111	HLA-	major histocompatibility	1.63E−29	102.0	288.9	201.4	94.3
			DOA	complex, class II, DO alpha
17	238327_PM_at	440836	ODF3B	outer dense fiber of sperm	1.74E−29	32.8	81.4	58.5	26.1
				tails 3B
18	229437_PM_at	114614	MIR155HG	MIR155 host gene (non-	1.78E−29	15.4	50.4	28.5	12.9
				protein coding)
19	33304_PM_at	3669	ISG20	interferon stimulated	2.40E−29	33.2	101.3	63.4	22.1
				exonuclease gene 20 kDa
20	226621_PM_at	9180	OSMR	oncostatin M receptor	2.42E−29	545.6	804.5	682.9	312.1
21	1553906_PM_s_at	221472	FGD2	FYVE, RhoGEF and PH	2.43E−29	104.6	321.0	219.3	71.9
				domain containing 2
22	1405_PM_i_at	6352	CCL5	chemokine (C-C motif)	2.54E−29	68.0	295.7	195.6	54.6
				ligand 5
23	226219_PM_at	257106	ARHGAP30	Rho GTPase activating	2.92E−29	46.4	127.9	91.8	37.5
				protein 30
24	204891_PM_s_at	3932	LCK	lymphocyte-specific protein	3.79E−29	19.3	74.2	43.4	17.8
				tyrosine kinase
25	210538_PM_s_at	330	BIRC3	baculoviral IAP repeat-	5.06E−29	106.7	276.7	199.1	84.6
				containing 3
26	202644_PM_s_at	7128	TNFAIP3	tumor necrosis factor, alpha-	5.47E−29	169.8	380.4	278.2	136.6
				induced protein 3
27	227346_PM_at	10320	IKZF1	IKAROS family zinc finger	7.07E−29	24.9	79.7	53.3	19.8
				1 (Ikaros)
28	202957_PM_at	3059	HCLS1	hematopoietic cell-specific	8.26E−29	119.2	299.5	229.9	82.2
				Lyn substrate 1
29	202307_PM_s_at	6890	TAP1	transporter 1, ATP-binding	1.01E−28	172.4	420.6	280.0	141.0
				cassette, sub-family B
				(MDR/TAP)
30	202748_PM_at	2634	GBP2	guanylate binding protein 2,	1.10E−28	196.7	473.0	306.7	141.0
				interferon-inducible
31	211796_PM_s_at	28638 ///	TRBC1 ///	T cell receptor beta constant	1.31E−28	69.2	431.5	250.2	63.6
		28639	TRBC2	1 /// T cell receptor beta
				constant 2
32	213160_PM_at	1794	DOCK2	dedicator of cytokinesis 2	1.36E−28	33.7	92.6	66.0	27.8
33	211656_PM_x_at	100133583 ///	HLA-	major histocompatibility	1.63E−28	211.3	630.2	459.8	208.2
		3119	DQB1 ///	complex, class II, DQ beta 1 ///
			LOC100133583	HLA class II
				histocompatibili
34	223322_PM_at	83593	RASSF5	Ras association	1.68E−28	41.5	114.4	79.3	39.2
				(RalGDS/AF-6) domain
				family member 5
35	205488_PM_at	3001	GZMA	granzyme A (granzyme 1,	1.72E−28	37.3	164.8	102.3	33.4
				cytotoxic T-lymphocyte-
				associated serine esterase 3)
36	213603_PM_s_at	5880	RAC2	ras-related C3 botulinum	1.87E−28	113.9	366.5	250.3	86.5
				toxin substrate 2 (rho
				family, small GTP binding
				protein Rac2)
37	229390_PM_at	441168	FAM26F	family with sequence	1.94E−28	103.8	520.0	272.4	75.9
				similarity 26, member F
38	206804_PM_at	917	CD3G	CD3g molecule, gamma	1.99E−28	19.7	60.6	36.4	17.3
				(CD3-TCR complex)
39	209795_PM_at	969	CD69	CD69 molecule	2.06E−28	17.6	57.6	40.6	15.2
40	219574_PM_at	55016	1-Mar	membrane-associated ring	2.07E−28	51.5	126.0	87.5	36.2
				finger (C3HC4) 1
41	207320_PM_x_at	6780	STAU1	staufen, RNA binding	2.21E−28	1425.1	1194.6	1383.2	1945.3
				protein, homolog 1
				(Drosophila)
42	218983_PM_at	51279	C1RL	complement component 1, r	2.97E−28	167.1	244.5	206.9	99.4
				subcomponent-like
43	206011_PM_at	834	CASP1	caspase 1, apoptosis-related	3.23E−28	74.5	198.0	146.2	60.7
				cysteine peptidase
				(interleukin 1, beta,
				convertase)
44	213539_PM_at	915	CD3D	CD3d molecule, delta	5.42E−28	70.8	335.1	168.1	60.0
				(CD3-TCR complex)
45	213193_PM_x_at	28639	TRBC1	T cell receptor beta constant	5.69E−28	95.3	490.9	286.5	92.1
				1
46	232543_PM_x_at	64333	ARHGAP9	Rho GTPase activating	6.79E−28	31.7	99.1	62.3	25.8
				protein 9
47	200986_PM_at	710	SERPING1	serpin peptidase inhibitor,	7.42E−28	442.7	731.5	590.2	305.3
				clade G (C1 inhibitor),
				member 1
48	213037_PM_x_at	6780	STAU1	staufen, RNA binding	9.35E−28	1699.0	1466.8	1670.1	2264.9
				protein, homolog 1
				(Drosophila)
49	204670_PM_x_at	3123 ///	HLA-	major histocompatibility	1.03E−27	2461.9	4344.6	3694.9	2262.0
		3126	DRB1 ///	complex, class II, DR beta 1 ///
			HLA-	major histocompatibility
			DRB4	comp
50	217028_PM_at	7852	CXCR4	chemokine (C-X-C motif)	1.52E−27	100.8	304.4	208.4	75.3
				receptor 4
51	203761_PM_at	6503	SLA	Src-like-adaptor	1.61E−27	69.6	179.2	138.4	51.4
52	201137_PM_s_at	3115	HLA-	major histocompatibility	1.95E−27	1579.1	3863.7	3151.7	1475.9
			DPB1	complex, class II, DP beta 1
53	205269_PM_at	3937	LCP2	lymphocyte cytosolic	2.16E−27	30.2	92.2	58.9	22.9
				protein 2 (SH2 domain
				containing leukocyte protein
				of 76 kDa)
54	205821_PM_at	22914	KLRK1	killer cell lectin-like	2.56E−27	30.3	111.5	72.5	31.3
				receptor subfamily K,
				member 1
55	204655_PM_at	6352	CCL5	chemokine (C-C motif)	3.28E−27	77.5	339.4	223.4	66.8
				ligand 5
56	226474_PM_at	84166	NLRC5	NLR family, CARD domain	3.54E−27	64.4	173.5	129.8	55.1
				containing 5
57	212503_PM_s_at	22982	DIP2C	DIP2 disco-interacting	3.69E−27	559.7	389.2	502.0	755.0
				protein 2 homolog C
				(Drosophila)
58	213857_PM_s_at	961	CD47	CD47 molecule	4.33E−27	589.6	858.0	703.2	481.2
59	206118_PM_at	6775	STAT4	signal transducer and	4.58E−27	21.0	49.5	37.7	18.1
				activator of transcription 4
60	227344_PM_at	10320	IKZF1	IKAROS family zinc finger	5.87E−27	17.8	40.0	28.5	14.9
				1 (Ikaros)
61	230550_PM_at	64231	MS4A6A	membrane-spanning 4-	5.98E−27	44.8	124.3	88.0	30.9
				domains, subfamily A,
				member 6A
62	235529_PM_x_at	25939	SAMHD1	SAM domain and HD	6.56E−27	189.3	379.9	289.1	128.0
				domain 1
63	205758_PM_at	925	CD8A	CD8a molecule	7.28E−27	24.2	105.8	60.3	22.2
64	211366_PM_x_at	834	CASP1	caspase 1, apoptosis-related	7.37E−27	115.3	261.0	186.0	87.1
				cysteine peptidase
				(interleukin 1, beta,
				convertase)
65	209606_PM_at	9595	CYTIP	cytohesin 1 interacting	7.48E−27	41.4	114.3	79.0	32.9
				protein
66	201721_PM_s_at	7805	LAPTM5	lysosomal protein	8.04E−27	396.5	934.6	661.3	249.4
				transmembrane 5
67	204774_PM_at	2123	EVI2A	ecotropic viral integration	8.14E−27	63.6	168.5	114.7	44.9
				site 2A
68	215005_PM_at	54550	NECAB2	N-terminal EF-hand calcium	8.32E−27	36.7	23.4	30.9	65.7
				binding protein 2
69	229937_PM_x_at	10859	LILRB1	Leukocyte immunoglobulin-	8.33E−27	23.5	79.9	50.0	18.5
				like receptor, subfamily B
				(with TM and ITIM
				domains), member
70	209515_PM_s_at	5873	RAB27A	RAB27A, member RAS	8.93E−27	127.3	192.2	160.5	85.2
				oncogene family
71	242916_PM_at	11064	CEP110	centrosomal protein 110 kDa	8.98E−27	30.8	68.1	51.2	26.2
72	205270_PM_s_at	3937	LCP2	lymphocyte cytosolic	9.04E−27	56.8	162.6	104.4	44.6
				protein 2 (SH2 domain
				containing leukocyte protein
				of 76 kDa)
73	214022_PM_s_at	8519	IFITM1	interferon induced	9.31E−27	799.1	1514.7	1236.6	683.3
				transmembrane protein 1 (9-27)
74	1552703_PM_s_at	114769 ///	CARD16 ///	caspase recruitment domain	1.01E−26	64.6	167.8	120.6	54.9
		834	CASP1	family, member 16 ///
				caspase 1, apoptosis-related
				cysteine
75	202720_PM_at	26136	TES	testis derived transcript (3	1.05E−26	285.4	379.0	357.9	204.2
				LIM domains)
76	202659_PM_at	5699	PSMB10	proteasome (prosome,	1.10E−26	180.7	355.6	250.3	151.5
				macropain) subunit, beta
				type, 10
77	236295_PM_s_at	197358	NLRC3	NLR family, CARD domain	1.19E−26	19.0	52.5	37.0	18.6
				containing 3
78	229041_PM_s_at	—	—	—	1.31E−26	36.5	132.1	84.7	32.4
79	205798_PM_at	3575	IL7R	interleukin 7 receptor	1.32E−26	44.1	136.8	106.3	33.4
80	209970_PM_x_at	834	CASP1	caspase 1, apoptosis-related	1.36E−26	116.0	266.6	181.6	88.7
				cysteine peptidase
				(interleukin 1, beta,
				convertase)
81	204336_PM_s_at	10287	RGS19	regulator of G-protein	1.54E−26	95.2	187.7	135.7	67.0
				signaling 19
82	204912_PM_at	3587	IL10RA	interleukin 10 receptor,	1.61E−26	57.0	178.7	117.2	46.1
				alpha
83	227184_PM_at	5724	PTAFR	platelet-activating factor	1.70E−26	89.8	191.4	134.4	62.7
				receptor
84	209969_PM_s_at	6772	STAT1	signal transducer and	1.82E−26	395.8	1114.5	664.6	320.8
				activator of transcription 1,
				91 kDa
85	232617_PM_at	1520	CTSS	cathepsin S	1.88E−26	209.6	537.9	392.2	154.8
86	224451_PM_x_at	64333	ARHGAP9	Rho GTPase activating	1.94E−26	34.2	103.4	71.8	29.4
				protein 9
87	209670_PM_at	28755	TRAC	T cell receptor alpha	2.06E−26	37.9	149.9	96.2	38.5
				constant
88	1559584_PM_a_at	283897	C16orf54	chromosome 16 open	2.22E−26	31.3	95.8	71.5	26.1
				reading frame 54
89	208306_PM_x_at	3123	HLA-	Major histocompatibility	2.29E−26	2417.5	4278.5	3695.8	2255.0
			DRB1	complex, class II, DR beta 1
90	229383_PM_at	55016	1-Mar	membrane-associated ring	2.36E−26	33.8	88.0	52.1	22.9
				finger (C3HC4) 1
91	235735_PM_at	—	—	—	2.46E−26	13.0	34.9	24.5	11.2
92	203416_PM_at	963	CD53	CD53 molecule	2.56E−26	215.9	603.0	422.7	157.8
93	212504_PM_at	22982	DIP2C	DIP2 disco-interacting	3.21E−26	334.5	227.2	289.4	452.5
				protein 2 homolog C
				(Drosophila)
94	204279_PM_at	5698	PSMB9	proteasome (prosome,	3.45E−26	241.6	637.4	419.4	211.3
				macropain) subunit, beta
				type, 9 (large
				multifunctional peptidase
95	235964_PM_x_at	25939	SAMHD1	SAM domain and HD	3.60E−26	172.9	345.9	270.1	117.5
				domain 1
96	213566_PM_at	6039	RNASE6	ribonuclease, RNase A	3.84E−26	180.9	482.0	341.1	134.3
				family, k6
97	221698_PM_s_at	64581	CLEC7A	C-type lectin domain family	4.00E−26	61.5	164.6	112.8	49.7
				7, member A
98	227125_PM_at	3455	IFNAR2	interferon (alpha, beta and	4.03E−26	70.0	126.2	96.7	55.8
				omega) receptor 2
99	226525_PM_at	9262	STK17B	serine/threonine kinase 17b	4.14E−26	146.8	338.7	259.1	107.6
100	221666_PM_s_at	29108	PYCARD	PYD and CARD domain	4.95E−26	60.7	132.8	95.5	44.7
				containing
101	209774_PM_x_at	2920	CXCL2	chemokine (C-X-C motif)	5.73E−26	24.9	52.9	38.2	15.5
				ligand 2
102	206082_PM_at	10866	HCP5	HLA complex P5	5.98E−26	76.2	185.1	129.0	66.6
103	229391_PM_s_at	441168	FAM26F	family with sequence	6.03E−26	98.6	379.7	212.1	73.7
				similarity 26, member F
104	229295_PM_at	150166 ///	IL17RA ///	interleukin 17 receptor A ///	6.13E−26	76.4	131.8	98.4	50.0
		23765	LOC150166	hypothetical protein
				LOC150166
105	202901_PM_x_at	1520	CTSS	cathepsin S	6.32E−26	67.8	180.5	130.9	45.1
106	226991_PM_at	4773	NFATC2	nuclear factor of activated	6.49E−26	37.9	87.0	66.7	30.3
				T-cells, cytoplasmic,
				calcineurin-dependent 2
107	223280_PM_x_at	64231	MS4A6A	membrane-spanning 4-	6.72E−26	269.0	711.8	451.2	199.5
				domains, subfamily A,
				member 6A
108	201601_PM_x_at	8519	IFITM1	interferon induced	7.27E−26	1471.3	2543.5	2202.5	1251.1
				transmembrane protein 1 (9-27)
109	1552701_PM_a_at	114769	CARD16	caspase recruitment domain	7.33E−26	143.8	413.6	273.3	119.8
				family, member 16
110	229625_PM_at	115362	GBP5	guanylate binding protein 5	7.80E−26	29.3	133.3	68.6	24.0
111	38149_PM_at	9938	ARHGAP25	Rho GTPase activating	9.83E−26	51.3	108.9	83.2	43.2
				protein 25
112	203932_PM_at	3109	HLA-	major histocompatibility	1.03E−25	422.4	853.9	633.5	376.0
			DMB	complex, class II, DM beta
113	228964_PM_at	639	PRDM1	PR domain containing 1,	1.15E−25	21.2	52.1	41.5	17.0
				with ZNF domain
114	225799_PM_at	112597 ///	LOC541471 ///	hypothetical LOC541471 ///	1.23E−25	230.5	444.3	339.2	172.0
		541471	NCRNA00152	non-protein coding RNA
				152
115	204118_PM_at	962	CD48	CD48 molecule	1.34E−25	82.9	341.5	212.4	65.2
116	211742_PM_s_at	2124	EV12B	ecotropic viral integration	1.36E−25	73.9	236.5	166.2	53.2
				site 2B
117	213416_PM_at	3676	ITGA4	integrin, alpha 4 (antigen	1.47E−25	26.3	78.1	50.8	22.8
				CD49D, alpha 4 subunit of
				VLA-4 receptor)
118	211991_PM_s_at	3113	HLA-	major histocompatibility	1.50E−25	1455.0	3605.4	2837.2	1462.9
			DPA1	complex, class II, DP alpha 1
119	232024_PM_at	26157	GIMAP2	GTPase, IMAP family	1.57E−25	90.2	197.7	146.7	72.5
				member 2
120	205159_PM_at	1439	CSF2RB	colony stimulating factor 2	1.73E−25	33.7	107.5	70.4	26.3
				receptor, beta, low-affinity
				(granulocyte-macrophage)
121	228471_PM_at	91526	ANKRD44	ankyrin repeat domain 44	1.79E−25	106.1	230.3	184.6	86.5
122	203332_PM_s_at	3635	INPP5D	inositol polyphosphate-5-	1.88E−25	27.9	60.5	42.6	24.0
				phosphatase, 145 kDa
123	223502_PM_s_at	10673	TNFSF13B	tumor necrosis factor	2.02E−25	73.0	244.3	145.5	60.0
				(ligand) superfamily,
				member 13b
124	229723_PM_at	117289	TAGAP	T-cell activation	2.07E−25	29.2	82.9	55.9	26.2
				RhoGTPase activating
				protein
125	206978_PM_at	729230	CCR2	chemokine (C-C motif)	2.17E−25	32.1	100.7	68.6	27.3
				receptor 2
126	1555832_PM_s_at	1316	KLF6	Kruppel-like factor 6	2.31E−25	899.4	1076.8	1003.3	575.1
127	211990_PM_at	3113	HLA-	major histocompatibility	2.53E−25	2990.7	5949.1	5139.9	3176.3
			DPA1	complex, class II, DP alpha
				1
128	202018_PM_s_at	4057	LTF	lactotransferrin	2.90E−25	392.3	1332.4	624.5	117.7
129	210644_PM_s_at	3903	LAIR1	leukocyte-associated	2.90E−25	29.7	74.6	45.3	21.2
				immunoglobulin-like
				receptor 1
130	222294_PM_s_at	5873	RAB27A	RAB27A, member RAS	3.13E−25	198.7	309.1	263.0	146.4
				oncogene family
131	238668_PM_at	—	—	—	3.29E−25	18.2	49.2	33.6	14.5
132	213975_PM_s_at	4069	LYZ	lysozyme	3.31E−25	458.4	1626.0	1089.7	338.1
133	204220_PM_at	9535	GMFG	glia maturation factor,	3.46E−25	147.0	339.3	241.4	128.9
				gamma
134	243366_PM_s_at	—	—	—	3.46E−25	24.7	72.1	52.5	22.0
135	221932_PM_s_at	51218	GLRX5	glutaredoxin 5	3.64E−25	1351.5	1145.8	1218.1	1599.3
136	225415_PM_at	151636	DTX3L	deltex 3-like (Drosophila)	3.77E−25	230.2	376.4	290.8	166.9
137	205466_PM_s_at	9957	HS3ST1	heparan sulfate	4.15E−25	73.6	123.8	96.0	42.1
				(glucosamine) 3-O-
				sulfotransferase 1
138	200904_PM_at	3133	HLA-E	major histocompatibility	4.20E−25	1142.5	1795.2	1607.7	994.7
				complex, class I, E
139	228442_PM_at	4773	NFATC2	nuclear factor of activated	4.48E−25	39.0	84.8	62.8	32.0
				T-cells, cytoplasmic,
				calcineurin-dependent 2
140	204923_PM_at	54440	SASH3	SAM and SH3 domain	4.49E−25	25.4	68.2	47.6	21.7
				containing 3
141	223640_PM_at	10870	HCST	hematopoietic cell signal	4.52E−25	91.0	234.0	158.3	72.7
				transducer
142	211582_PM_x_at	7940	LST1	leukocyte specific transcript	4.53E−25	57.5	183.8	121.2	49.4
				1
143	219014_PM_at	51316	PLAC8	placenta-specific 8	5.94E−25	38.8	164.1	88.6	30.7
144	210895_PM_s_at	942	CD86	CD86 molecule	6.21E−25	32.3	85.0	52.6	21.6
145	AFFX-	6772	STAT1	signal transducer and	6.81E−25	642.1	1295.1	907.8	539.6
	HUMISGF3A/			activator of transcription 1,
	M97935_3_at			91 kDa
146	201315_PM_x_at	10581	IFITM2	interferon induced	6.87E−25	2690.9	3712.1	3303.7	2175.3
				transmembrane protein 2 (1-8D)
147	228532_PM_at	128346	C1orf162	chromosome 1 open reading	7.07E−25	82.6	217.7	140.2	60.0
				frame 162
148	202376_PM_at	12	SERPINA3	serpin peptidase inhibitor,	7.13E−25	186.2	387.1	210.2	51.7
				clade A (alpha-1
				antiproteinase, antitrypsin),
				member 3
149	212587_PM_s_at	5788	PTPRC	protein tyrosine	7.18E−25	114.8	398.6	265.7	90.3
				phosphatase, receptor type,
				C
150	223218_PM_s_at	64332	NFKBIZ	nuclear factor of kappa light	7.26E−25	222.6	497.9	399.9	159.1
				polypeptide gene enhancer
				in B-cells inhibitor, zeta
151	224356_PM_x_at	64231	MS4A6A	membrane-spanning 4-	7.33E−25	150.6	399.6	249.6	111.2
				domains, subfamily A,
				member 6A
152	206420_PM_at	10261	IGSF6	immunoglobulin	7.58E−25	45.1	131.5	74.3	32.7
				superfamily, member 6
153	225764_PM_at	2120	ETV6	ets variant 6	7.66E−25	92.6	133.0	112.8	77.0
154	1555756_PM_a_at	64581	CLEC7A	C-type lectin domain family	7.74E−25	16.6	45.4	28.9	13.2
				7, member A
155	226218_PM_at	3575	IL7R	interleukin 7 receptor	8.14E−25	55.6	197.0	147.1	41.4
156	209198_PM_s_at	23208	SYT11	synaptotagmin XI	8.28E−25	30.0	45.3	41.8	22.8
157	202803_PM_s_at	3689	ITGB2	integrin, beta 2	9.57E−25	100.5	253.0	182.6	65.2
				(complement component 3
				receptor 3 and 4 subunit)
158	215049_PM_x_at	9332	CD163	CD163 molecule	9.85E−25	232.8	481.3	344.5	112.9
159	202953_PM_at	713	C1QB	complement component 1, q	9.99E−25	215.8	638.4	401.1	142.5
				subcomponent, B chain
160	208091_PM_s_at	81552	VOPP1	vesicular, overexpressed in	1.02E−24	495.5	713.9	578.1	409.7
				cancer, prosurvival protein 1
161	201288_PM_at	397	ARHGDIB	Rho GDP dissociation	1.13E−24	354.9	686.8	542.2	308.1
				inhibitor (GDI) beta
162	213733_PM_at	4542	MYO1F	myosin IF	1.27E−24	26.8	52.7	39.4	20.9
163	212588_PM_at	5788	PTPRC	protein tyrosine	1.41E−24	94.4	321.0	217.7	76.4
				phosphatase, receptor type,
				C
164	242907_PM_at	—	—	—	1.49E−24	59.3	165.1	99.7	39.8
165	209619_PM_at	972	CD74	CD74 molecule, major	1.55E−24	989.0	1864.7	1502.3	864.9
				histocompatibility complex,
				class II invariant chain
166	239237_PM_at	—	—	—	1.75E−24	15.9	34.9	25.3	14.5
167	217022_PM_s_at	100126583 ///	IGHA1 ///	immunoglobulin heavy	1.80E−24	77.7	592.5	494.6	49.4
		3493 ///	IGHA2 ///	constant alpha 1 ///
		3494	LOC100126583	immunoglobulin heavy
				constant alpha 2 (A2m ma
168	201859_PM_at	5552	SRGN	serglycin	1.82E−24	1237.9	2171.9	1747.0	981.8
169	243418_PM_at	—	—	—	1.88E−24	56.3	31.1	49.8	104.8
170	202531_PM_at	3659	IRF1	interferon regulatory factor	1.93E−24	92.9	226.0	154.5	77.0
				1
171	208966_PM_x_at	3428	IFI16	interferon, gamma-inducible	1.98E−24	406.7	760.4	644.9	312.6
				protein 16
172	1555759_PM_a_at	6352	CCL5	chemokine (C-C motif)	2.02E−24	81.4	350.8	233.2	68.3
				ligand 5
173	202643_PM_s_at	7128	TNFAIP3	tumor necrosis factor, alpha-	2.11E−24	43.7	92.8	68.1	34.8
				induced protein 3
174	223922_PM_x_at	64231	MS4A6A	membrane-spanning 4-	2.22E−24	289.2	656.8	424.1	214.5
				domains, subfamily A,
				member 6A
175	209374_PM_s_at	3507	IGHM	immunoglobulin heavy	2.26E−24	61.8	437.0	301.0	45.0
				constant mu
176	227677_PM_at	3718	JAK3	Janus kinase 3	2.29E−24	18.6	51.7	32.0	15.5
177	221840_PM_at	5791	PTPRE	protein tyrosine	2.38E−24	71.0	133.2	102.5	51.8
				phosphatase, receptor type,
				E
178	200887_PM_s_at	6772	STAT1	signal transducer and	2.47E−24	1141.7	2278.6	1602.9	972.9
				activator of transcription 1,
				91 kDa
179	221875_PM_x_at	3134	HLA-F	major histocompatibility	2.72E−24	1365.3	2400.6	1971.8	1213.0
				complex, class I, F
180	206513_PM_at	9447	AIM2	absent in melanoma 2	2.87E−24	17.2	50.7	30.5	13.9
181	214574_PM_x_at	7940	LST1	leukocyte specific transcript 1	2.95E−24	74.1	222.8	142.0	61.7
182	231776_PM_at	8320	EOMES	eomesodermin	3.07E−24	24.0	63.9	43.5	22.4
183	205639_PM_at	313	AOAH	acyloxyacyl hydrolase	4.03E−24	30.3	72.6	45.3	25.2
				(neutrophil)
184	201762_PM_s_at	5721	PSME2	proteasome (prosome,	4.45E−24	1251.6	1825.1	1423.4	1091.0
				macropain) activator subunit
				2 (PA28 beta)
185	217986_PM_s_at	11177	BAZ1A	bromodomain adjacent to	4.79E−24	87.5	145.2	116.4	62.5
				zinc finger domain, 1A
186	235229_PM_at	—	—	—	4.84E−24	50.9	210.6	135.0	41.9
187	204924_PM_at	7097	TLR2	toll-like receptor 2	4.84E−24	96.8	162.0	116.6	66.6
188	202208_PM_s_at	10123	ARL4C	ADP-ribosylation factor-	4.89E−24	54.0	99.6	77.0	42.2
				like 4C
189	227072_PM_at	25914	RTTN	rotatin	5.01E−24	101.1	74.5	83.6	132.9
190	202206_PM_at	10123	ARL4C	ADP-ribosylation factor-	5.08E−24	60.8	128.5	96.1	36.0
				like 4C
191	204563_PM_at	6402	SELL	selectin L	5.11E−24	40.7	134.7	76.1	31.7
192	219386_PM_s_at	56833	SLAMF8	SLAM family member 8	5.17E−24	28.2	92.1	52.2	19.4
193	218232_PM_at	712	C1QA	complement component 1, q	5.88E−24	128.8	287.1	197.0	85.8
				subcomponent, A chain
194	232311_PM_at	567	B2M	Beta-2-microglobulin	6.06E−24	42.3	118.6	83.7	35.2
195	219684_PM_at	64108	RTP4	receptor (chemosensory)	6.09E−24	63.1	129.3	93.7	50.4
				transporter protein 4
196	204057_PM_at	3394	IRF8	interferon regulatory factor	6.59E−24	89.8	184.8	134.9	71.4
				8
197	208296_PM_x_at	25816	TNFAIP8	tumor necrosis factor, alpha-	6.65E−24	136.9	242.5	195.1	109.6
				induced protein 8
198	204122_PM_at	7305	TYROBP	TYRO protein tyrosine	6.73E−24	190.5	473.4	332.8	143.3
				kinase binding protein
199	224927_PM_at	170954	KIAA1949	KIAA1949	6.87E−24	98.8	213.6	160.2	74.7

TABLE 11

Biopsy Expression Profiling of Kidney Transplants: 4-Way
Classifier AR vs. ADNR vs. CAN vs. TX (Brazilian Samples)

Validation Cohort

Nearest Centroid	199	AR vs. TX	0.976	98	100	95	95	100
Nearest Centroid	199	ADNR vs. TX	1.000	100	100	100	100	100
Nearest Centroid	199	CAN vs. TX	1.000	100	100	100	100	100

TABLE 12a

Biopsy Expression Profiling of Kidney Transplants: 3-Way Classifier AR vs. ADNR vs. TX (TGCG Samples)

Validation Cohort

Nearest Centroid	197	AR vs. TX	0.979	98	96	100	100	96
Nearest Centroid	197	ADNR vs. TX	0.987	99	97	100	100	98
Nearest Centroid	197	AR vs. ADNR	0.968	97	100	93	95	100

TABLE 12b

Biopsy Expression Profiling of Kidney Transplants: 3-Way Classifier AR vs. ADNR vs. TX (TGCG Samples)

					p-value
		Entrez	Gene		(Final	ADN
#	Probeset ID	Gene	Symbol	Gene Title	Phenotype)	R-Mean	AR-Mean	TX-Mean

1	242956_PM_at	3417	IDH1	Isocitrate dehydrogenase	2.95E−22	32.7	29.9	53.6
				1 (NADP+), soluble
2	208948_PM_s_at	6780	STAU1	staufen, RNA binding	1.56E−29	1807.9	1531.8	2467.4
				protein, homolog 1
				(Drosophila)
3	213037_PM_x_at	6780	STAU1	staufen, RNA binding	4.77E−27	1699.0	1466.8	2264.9
				protein, homolog 1
				(Drosophila)
4	207320_PM_x_at	6780	STAU1	staufen, RNA binding	6.17E−28	1425.1	1194.6	1945.3
				protein, homolog 1
				(Drosophila)
5	1555832_PM_s_at	1316	KLF6	Kruppel-like factor 6	5.82E−23	899.4	1076.8	575.1
6	202376_PM_at	12	SERPINA3	serpin peptidase inhibitor,	1.05E−25	186.2	387.1	51.7
				clade A (alpha- 1
				antiproteinase,
				antitrypsin), member 3
7	226621_PM_at	9180	OSMR	oncostatin M receptor	1.28E−27	545.6	804.5	312.1
8	218983_PM_at	51279	C1RL	complement component	9.46E−25	167.1	244.5	99.4
				1, r subcomponent-like
9	215005_PM_at	54550	NECAB2	N-terminal EF-hand	1.95E−25	36.7	23.4	65.7
				calcium binding protein 2
10	202720_PM_at	26136	TES	testis derived transcript (3	4.32E−24	285.4	379.0	204.2
				LIM domains)
11	240320_PM_at	100131781	C14orfl64	chromosome 14 open	1.64E−23	204.9	84.2	550.0
				reading frame 164
12	243418_PM_at	—	—	—	1.81E−24	56.3	31.1	104.8
13	205466_PM_s_at	9957	HS3ST1	heparan sulfate	5.64E−24	73.6	123.8	42.1
				(glucosamine) 3-O-
				sulfotransferase 1
14	201042_PM_at	7052	TGM2	transglutaminase 2 (C	3.87E−28	131.8	236.5	80.1
				polypeptide, protein-
				glutamine-gamma-
				glutamyltransferase)
15	202018_PM_s_at	4057	LTF	lactotransferrin	4.00E−25	392.3	1332.4	117.7
16	212503_PM_s_at	22982	DIP2C	DIP2 disco-interacting	6.62E−27	559.7	389.2	755.0
				protein 2 homolog C
				(Drosophila)
17	215049_PM_x_at	9332	CD163	CD163 molecule	4.65E−23	232.8	481.3	112.9
18	209515_PM_s_at	5873	RAB27A	RAB27A, member RAS	2.11E−23	127.3	192.2	85.2
				oncogene family
19	221932_PM_s_at	51218	GLRX5	glutaredoxin 5	2.08E−22	1351.5	1145.8	1599.3
20	202207_PM_at	10123	ARL4C	ADP-ribosylation factor-	1.83E−29	106.9	258.6	57.2
				like 4C
21	227697_PM_at	9021	SOCS3	suppressor of cytokine	2.93E−23	35.9	69.1	19.9
				signaling 3
22	227072_PM_at	25914	RTTN	rotatin	4.26E−23	101.1	74.5	132.9
23	201136_PM_at	5355	PLP2	proteolipid protein 2	2.71E−22	187.7	274.0	131.7
				(colonic epithelium-
				enriched)
24	212504_PM_at	22982	DIP2C	DIP2 disco-interacting	2.57E−25	334.5	227.2	452.5
				protein 2 homolog C
				(Drosophila)
25	200986_PM_at	710	SERPING1	serpin peptidase inhibitor,	2.88E−26	442.7	731.5	305.3
				clade G (C1 inhibitor),
				member 1
26	203233_PM_at	3566	IL4R	interleukin 4 receptor	6.30E−23	97.6	138.5	72.0
27	229295_PM_at	150166 ///	IL17RA ///	interleukin 17 receptor A ///	6.40E−25	76.4	131.8	50.0
		23765	LOC150166	hypothetical protein
				LOC150166
28	231358_PM_at	83876	MRO	maestro	1.11E−22	199.7	81.2	422.1
29	201666_PM_at	7076	TIMP1	T1MP metallopeptidase	1.54E−22	1035.3	1879.4	648.0
				inhibitor 1
30	209774_PM_x_at	2920	CXCL2	chemokine (C-X-C motif)	1.50E−26	24.9	52.9	15.5
				ligand 2
31	217733_PM_s_at	9168	TMSB10	thymosin beta 10	9.34E−27	4414.7	6331.3	3529.0
32	222939_PM_s_at	117247	SLC16A10	solute carrier family 16,	2.55E−22	156.6	93.7	229.6
				member 10 (aromatic
				amino acid transporter)
33	204924_PM_at	7097	TLR2	toll-like receptor 2	2.11E−22	96.8	162.0	66.6
34	225415_PM_at	151636	DTX3L	deltex 3-like (Drosophila)	3.04E−24	230.2	376.4	166.9
35	202206_PM_at	10123	ARL4C	ADP-ribosylation factor-	1.16E−22	60.8	128.5	36.0
				like 4C
36	213857_PM_s_at	961	CD47	CD47 molecule	2.56E−27	589.6	858.0	481.2
37	235529_PM_x_at	25939	SAMHD1	SAM domain and HD	4.49E−24	189.3	379.9	128.0
				domain 1
38	206693_PM_at	3574	IL7	interleukin 7	3.12E−22	37.3	57.0	28.9
39	219033_PM_at	79668	PARP8	poly (ADP-ribose)	1.88E−22	47.9	79.2	35.7
				polymerase family,
				member 8
40	201721_PM_s_at	7805	LAPTM5	lysosomal protein	3.72E−24	396.5	934.6	249.4
				transmembrane 5
41	204336_PM_s_at	10287	RGS19	regulator of G-protein	3.52E−25	95.2	187.7	67.0
				signaling 19
42	235964_PM_x_at	25939	SAMHD1	SAM domain and HD	2.45E−23	172.9	345.9	117.5
				domain 1
43	208091_PM_s_at	81552	VOPP1	vesicular, overexpressed	9.47E−25	495.5	713.9	409.7
				in cancer, prosurvival
				protein 1
44	204446_PM_s_at	240	ALOX5	arachidonate 5-	6.25E−31	91.9	323.9	54.7
				lipoxygenase
45	212703_PM_at	83660	TLN2	talin 2	6.13E−23	270.8	159.7	357.5
46	213414_PM_s_at	6223	RPS19	ribosomal protein S19	1.57E−22	4508.2	5432.1	4081.7
47	1565681_PM_s_at	22982	DIP2C	DIP2 disco-interacting	2.57E−22	66.4	35.7	92.5
				protein 2 homolog C
				(Drosophila)
48	225764_PM_at	2120	ETV6	ets variant 6	2.63E−23	92.6	133.0	77.0
49	227184_PM_at	5724	PTAFR	platelet-activating factor	1.73E−24	89.8	191.4	62.7
				receptor
50	221840_PM_at	5791	PTPRE	protein tyrosine	8.52E−23	71.0	133.2	51.8
				phosphatase, receptor
				type, E
51	225799_PM_at	112597 ///	LOC541471 ///	hypothetical LOC541471 ///	1.18E−23	230.5	444.3	172.0
		541471	NCRNA00152	non-protein coding
				RNA 152
52	202957_PM_at	3059	HCLS1	hematopoietic cell-	1.94E−25	119.2	299.5	82.2
				specific Lyn substrate 1
53	229383_PM_at	55016	1-Mar	membrane-associated ring	8.09E−25	33.8	88.0	22.9
				finger (C3HC4) 1
54	33304_PM_at	3669	ISG20	interferon stimulated	2.83E−27	33.2	101.3	22.1
				exonuclease gene 20 kDa
55	222062_PM_at	9466	IL27RA	interleukin 27 receptor,	1.79E−22	34.9	65.8	26.4
				alpha
56	219574_PM_at	55016	1-Mar	membrane-associated ring	6.65E−25	51.5	126.0	36.2
				finger (C3HC4) 1
57	202748_PM_at	2634	GBP2	guanylate binding protein	7.51E−26	196.7	473.0	141.0
				2, interferon-inducible
58	210895_PM_s_at	942	CD86	CD86 molecule	3.80E−23	32.3	85.0	21.6
59	202208_PM_s_at	10123	ARL4C	ADP-ribosylation factor-	1.24E−23	54.0	99.6	42.2
				like 4C
60	221666_PM_s_at	29108	PYCARD	PYD and CARD domain	5.55E−24	60.7	132.8	44.7
				containing
61	227125_PM_at	3455	IFNAR2	interferon (alpha, beta and	3.57E−24	70.0	126.2	55.8
				omega) receptor 2
62	226525_PM_at	9262	STK17B	serine/threonine kinase	3.43E−24	146.8	338.7	107.6
				17b
63	210644_PM_s_at	3903	LAIR1	leukocyte-associated	2.96E−24	29.7	74.6	21.2
				immunoglobulin-like
				receptor 1
64	230391_PM_at	8832	CD84	CD84 molecule	1.89E−22	47.9	130.1	32.5
65	242907_PM_at	—	—	—	2.54E−22	59.3	165.1	39.8
66	1553906_PM_s_at	221472	FGD2	FYVE, RhoGEF and PH	1.67E−26	104.6	321.0	71.9
				domain containing 2
67	223922_PM_x_at	64231	MS4A6A	membrane-spanning 4-	8.68E−24	289.2	656.8	214.5
				domains, subfamily A,
				member 6A
68	230550_PM_at	64231	MS4A6A	membrane-spanning 4-	7.63 E−24	44.8	124.3	30.9
				domains, subfamily A,
				member 6A
69	202953_PM_at	713	C1QB	complement component	2.79E−22	215.8	638.4	142.5
				1, q subcomponent, B
				chain
70	213733_PM_at	4542	MYO1F	myosin IF	2.25E−23	26.8	52.7	20.9
71	204774_PM_at	2123	EVI2A	ecotropic viral integration	5.10E−24	63.6	168.5	44.9
				site 2A
72	211366_PM_x_at	834	CASP1	caspase 1, apoptosis-	3.40E−24	115.3	261.0	87.1
				related cysteine peptidase
				(interleukin 1, beta,
				convertase)
73	204698_PM_at	3669	ISG20	interferon stimulated	1.27E−28	41.5	165.1	27.6
				exonuclease gene 20 kDa
74	201762_PM_s_at	5721	PSME2	proteasome (prosome,	2.31E−22	1251.6	1825.1	1091.0
				macropain) activator
				subunit 2 (PA28 beta)
75	204470_PM_at	2919	CXCL1	chemokine (C-X-C motif)	2.81E−24	22.9	63.7	16.2
				ligand 1 (melanoma
				growth stimulating
				activity, alpha)
76	242827_PM_x_at	—	—	—	9.00E−23	22.1	52.3	16.3
77	209970_PM_x_at	834	CASP1	caspase 1, apoptosis-	4.40E−25	116.0	266.6	88.7
				related cysteine peptidase
				(interleukin 1, beta,
				convertase)
78	228532_PM_at	128346	C1orf162	chromosome 1 open	1.57E−22	82.6	217.7	60.0
				reading frame 162
79	232617_PM_at	1520	CTSS	cathepsin S	2.83E−23	209.6	537.9	154.8
80	203761_PM_at	6503	SLA	Src-like-adaptor	3.80E−23	69.6	179.2	51.4
81	219666_PM_at	64231	MS4A6A	membrane-spanning 4-	4.29E−23	159.2	397.6	118.7
				domains, subfamily A,
				member 6A
82	223280_PM_x_at	64231	MS4A6A	membrane-spanning 4-	2.53E−24	269.0	711.8	199.5
				domains, subfamily A,
				member 6A
83	225701_PM_at	80709	AKNA	AT-hook transcription	2.87E−29	37.7	102.8	29.0
				factor
84	224356_PM_x_at	64231	MS4A6A	membrane-spanning 4-	1.56E−23	150.6	399.6	111.2
				domains, subfamily A,
				member 6A
85	202643_PM_s_at	7128	TNFAIP3	tumor necrosis factor,	9.92E−24	43.7	92.8	34.8
				alpha-induced protein 3
86	202644_PM_s_at	7128	TNFAIP3	tumor necrosis factor,	2.12E−27	169.8	380.4	136.6
				alpha-induced protein 3
87	213566_PM_at	6039	RNASE6	ribonuclease, RNase A	1.55E−23	180.9	482.0	134.3
				family, k6
88	219386_PM_s_at	56833	SLAMF8	SLAM family member 8	1.22E−22	28.2	92.1	19.4
89	203416_PM_at	963	CD53	CD53 molecule	3.13E−23	215.9	603.0	157.8
90	200003_PM_s_at	6158	RPL28	ribosomal protein L28	1.73E−22	4375.2	5531.2	4069.9
91	206420_PM_at	10261	IGSF6	immunoglobulin	5.65E−23	45.1	131.5	32.7
				superfamily, member 6
92	217028_PM_at	7852	CXCR4	chemokine (C-X-C motif)	6.84E−27	100.8	304.4	75.3
				receptor 4
93	232024_PM_at	26157	GIMAP2	GTPase, IMAP family	2.94E−24	90.2	197.7	72.5
				member 2
94	238327_PM_at	440836	ODF3B	outer dense fiber of sperm	1.75E−27	32.8	81.4	26.1
				tails 3B
95	209083_PM_at	11151	CORO1A	coronin, actin binding	2.78E−27	46.9	163.8	34.3
				protein, 1A
96	232724_PM_at	64231	MS4A6A	membrane-spanning 4-	6.28E−23	24.8	47.6	20.7
				domains, subfamily A,
				member 6A
97	211742_PM_s_at	2124	EVI2B	ecotropic viral integration	1.56E−22	73.9	236.5	53.2
				site 2B
98	202659_PM_at	5699	PSMB10	proteasome (prosome,	2.29E−24	180.7	355.6	151.5
				macropain) subunit, beta
				type, 10
99	226991_PM_at	4773	NFATC2	nuclear factor of activated	2.42E−23	37.9	87.0	30.3
				T-cells, cytoplasmic,
				calcineurin-dependent 2
100	210538_PM_s_at	330	BIRC3	baculoviral IAP repeat-	5.40E−26	106.7	276.7	84.6
				containing 3
101	205269_PM_at	3937	LCP2	lymphocyte cytosolic	7.35E−25	30.2	92.2	22.9
				protein 2 (SH2 domain
				containing leukocyte
				protein of 76 kDa)
102	211368_PM_s_at	834	CASP1	caspase 1, apoptosis-	9.43E−27	102.6	274.3	81.8
				related cysteine peptidase
				(interleukin 1, beta,
				convertase)
103	205798_PM_at	3575	IL7R	interleukin 7 receptor	1.57E−24	44.1	136.8	33.4
104	228442_PM_at	4773	NFATC2	nuclear factor of activated	5.58E−23	39.0	84.8	32.0
				T-cells, cytoplasmic,
				calcineurin-dependent 2
105	213603_PM_s_at	5880	RAC2	ras-related C3 botulinum	3.38E−25	113.9	366.5	86.5
				toxin substrate 2 (rho
				family, small GTP
				binding protein Rac2)
106	228964_PM_at	639	PRDM1	PR domain containing 1,	1.42E−23	21.2	52.1	17.0
				with ZNF domain
107	209606_PM_at	9595	CYTIP	cytohesin 1 interacting	1.66E−26	41.4	114.3	32.9
				protein
108	214022_PM_s_at	8519	IFITM1	interferon induced	1.61E−23	799.1	1514.7	683.3
				transmembrane protein 1
				(9-27)
109	202307_PM_s_at	6890	TAP1	transporter 1, ATP-	8.61E−26	172.4	420.6	141.0
				binding cassette, sub-
				family B (MDR/TAP)
110	204882_PM_at	9938	ARHGAP25	Rho GTPase activating	8.14E−23	51.5	107.3	43.0
				protein 25
111	227344_PM_at	10320	IKZF1	IKAROS family zinc	5.30E−26	17.8	40.0	14.9
				finger 1 (Ikaros)
112	205270_PM_s_at	3937	LCP2	lymphocyte cytosolic	3.75E−24	56.8	162.6	44.6
				protein 2 (SH2 domain
				containing leukocyte
				protein of 76 kDa)
113	223640_PM_at	10870	HCST	hematopoietic cell signal	5.55E−23	91.0	234.0	72.7
				transducer
114	226218_PM_at	3575	IL7R	interleukin 7 receptor	2.15E−23	55.6	197.0	41.4
115	226219_PM_at	257106	ARHGAP30	Rho GTPase activating	1.87E−26	46.4	127.9	37.5
				protein 30
116	38149_PM_at	9938	ARHGAP25	Rho GTPase activating	1.20E−23	51.3	108.9	43.2
				protein 25
117	213975_PM_s_at	4069	LYZ	lysozyme	2.70E−22	458.4	1626.0	338.1
118	238668_PM_at	—	—	—	1.35E−23	18.2	49.2	14.5
119	200887_PM_s_at	6772	STAT1	signal transducer and	1.27E−22	1141.7	2278.6	972.9
				activator of transcription
				1, 91 kDa
120	1555756_PM_a_at	64581	CLEC7A	C-type lectin domain	2.11E−22	16.6	45.4	13.2
				family 7, member A
121	205039_PM_s_at	10320	IKZF1	IKAROS family zinc	6.73E−23	29.1	65.9	24.2
				finger 1 (Ikaros)
122	206011_PM_at	834	CASP1	caspase 1, apoptosis-	6.34E−25	74.5	198.0	60.7
				related cysteine peptidase
				(interleukin 1, beta,
				convertase)
123	221698_PM_s_at	64581	CLEC7A	C-type lectin domain	9.79E−24	61.5	164.6	49.7
				family 7, member A
124	227346_PM_at	10320	IKZF1	IKAROS family zinc	1.51E−26	24.9	79.7	19.8
				finger 1 (Ikaros)
125	230499_PM_at	—	—	—	8.55E−23	29.7	68.7	24.7
126	229391_PM_s_at	441168	FAM26F	family with sequence	2.74E−23	98.6	379.7	73.7
				similarity 26, member F
127	205159_PM_at	1439	CSF2RB	colony stimulating factor	1.74E−23	33.7	107.5	26.3
				2 receptor, beta, low-
				affinity (granulocyte-
				macrophage)
128	205639_PM_at	313	AOAH	acyloxyacyl hydrolase	4.43E−23	30.3	72.6	25.2
				(neutrophil)
129	204563_PM_at	6402	SELL	selectin L	1.00E−23	40.7	134.7	31.7
130	201288_PM_at	397	ARHGDIB	Rho GDP dissociation	1.92E−22	354.9	686.8	308.1
				inhibitor (GDI) beta
131	209969_PM_s_at	6772	STAT1	signal transducer and	5.49E−24	395.8	1114.5	320.8
				activator of transcription
				1, 91 kDa
132	229390_PM_at	441168	FAM26F	family with sequence	3.91E−25	103.8	520.0	75.9
				similarity 26, member F
133	242916_PM_at	11064	CEP110	centrosomal protein	1.99E−23	30.8	68.1	26.2
				110 kDa
134	207651_PM_at	29909	GPR171	G protein-coupled	1.90E−29	25.8	89.9	20.9
				receptor 171
135	229937_PM_x_at	10859	LILRB1	Leukocyte	1.67E−24	23.5	79.9	18.5
				immunoglobulin-like
				receptor, subfamily B
				(with TM and ITIM
				domains), member
136	232543_PM_x_at	64333	ARHGAP9	Rho GTPase activating	3.66E−26	31.7	99.1	25.8
				protein 9
137	203332_PM_s_at	3635	INPP5D	inositol polyphosphate-5-	3.66E−24	27.9	60.5	24.0
				phosphatase, 145 kDa
138	213160_PM_at	1794	DOCK2	dedicator of cytokinesis 2	1.03E−24	33.7	92.6	27.8
139	204912_PM_at	3587	IL10RA	interleukin 10 receptor,	1.28E−24	57.0	178.7	46.1
				alpha
140	204205_PM_at	60489	APOBEC3G	apolipoprotein B mRNA	6.40E−27	95.4	289.4	78.7
				editing enzyme, catalytic
				polypeptide-like 3G
141	206513_PM_at	9447	AIM2	absent in melanoma 2	9.67E−23	17.2	50.7	13.9
142	203741_PM_s_at	113	ADCY7	adenylate cyclase 7	2.34E−22	23.6	59.3	19.7
143	206118_PM_at	6775	STAT4	signal transducer and	8.28E−26	21.0	49.5	18.1
				activator of transcription
				4
144	227677_PM_at	3718	JAK3	Janus kinase 3	6.48E−24	18.6	51.7	15.5
145	227353_PM_at	147138	TMC8	transmembrane channel-	6.49E−29	19.8	64.2	16.6
				like 8
146	1552701_PM_a_at	114769	CARD16	caspase recruitment	2.52E−23	143.8	413.6	119.8
				domain family, member
				16
147	1552703_PM_s_at	114769 ///	CARD16 ///	caspase recruitment	7.57E−24	64.6	167.8	54.9
		834	CASP1	domain family, member
				16 /// caspase 1,
				apoptosis-related cysteine
148	229437_PM_at	114614	MIR155HG	MIR155 host gene (non-	3.90E−27	15.4	50.4	12.9
				protein coding)
149	204319_PM_s_at	6001	RGS10	regulator of G-protein	6.54E−24	159.7	360.4	139.4
				signaling 10
150	204118_PM_at	962	CD48	CD48 molecule	3.85E−23	82.9	341.5	65.2
151	1559584_PM_a_at	283897	C16orf54	chromosome 16 open	2.86E−24	31.3	95.8	26.1
				reading frame 54
152	212588_PM_at	5788	PTPRC	protein tyrosine	2.46E−22	94.4	321.0	76.4
				phosphatase, receptor
				type, C
153	219014_PM_at	51316	PLAC8	placenta-specific 8	2.03E−23	38.8	164.1	30.7
154	235735_PM_at	—	—	—	1.39E−25	13.0	34.9	11.2
155	203932_PM_at	3109	HLA-DMB	major histocompatibility	6.25E−23	422.4	853.9	376.0
				complex, class II, DM
				beta
156	223502_PM_s_at	10673	TNFSF13B	tumor necrosis factor	2.62E−23	73.0	244.3	60.0
				(ligand) superfamily,
				member 13b
157	1405_PM_i_at	6352	CCL5	chemokine (C-C motif)	2.22E−25	68.0	295.7	54.6
				ligand 5
158	226474_PM_at	84166	NLRC5	NLR family, CARD	1.33E−23	64.4	173.5	55.1
				domain containing 5
159	204220_PM_at	9535	GMFG	glia maturation factor,	1.56E−23	147.0	339.3	128.9
				gamma
160	204923_PM_at	54440	SASH3	SAM and SH3 domain	4.56E−23	25.4	68.2	21.7
				containing 3
161	206082_PM_at	10866	HCP5	HLA complex P5	1.23E−23	76.2	185.1	66.6
162	204670_PM_x_at	3123 ///	HLA-	major histocompatibility	1.31E−23	2461.9	4344.6	2262.0
		3126	DRB1 ///	complex, class II, DR beta
			HLA-	1 /// major
			DRB4	histocompatibility comp
163	228869_PM_at	124460	SNX20	sorting nexin 20	2.59E−22	25.7	67.3	22.2
164	205831_PM_at	914	CD2	CD2 molecule	2.30E−27	40.4	162.5	33.9
165	206978_PM_at	729230	CCR2	chemokine (C-C motif)	4.46E−23	32.1	100.7	27.3
				receptor 2
166	224451_PM_x_at	64333	ARHGAP9	Rho GTPase activating	4.23E−24	34.2	103.4	29.4
				protein 9
167	204279_PM_at	5698	PSMB9	proteasome (prosome,	2.81E−23	241.6	637.4	211.3
				macropain) subunit, beta
				type, 9 (large
				multifunctional peptidase
168	209795_PM_at	969	CD69	CD69 molecule	5.81E−27	17.6	57.6	15.2
169	229625_PM_at	115362	GBP5	guanylate binding protein	5.85E−24	29.3	133.3	24.0
				5
170	213416_PM_at	3676	ITGA4	integrin, alpha 4 (antigen	1.19E−23	26.3	78.1	22.8
				CD49D, alpha 4 subunit
				of VLA-4 receptor)
171	206804_PM_at	917	CD3G	CD3g molecule, gamma	1.50E−27	19.7	60.6	17.3
				(CD3-TCR complex)
172	222895_PM_s_at	64919	BCL11B	B-cell CLL/lymphoma	7.01E−23	22.0	64.5	19.1
				11B (zinc finger protein)
173	211582_PM_x_at	7940	LST1	leukocyte specific	2.13E−22	57.5	183.8	49.4
				transcript 1
174	1555852_PM_at	100507463	LOC100507463	hypothetical	8.92E−26	78.9	202.6	70.9
				LOC100507463
175	213539_PM_at	915	CD3D	CD3d molecule, delta	9.01E−27	70.8	335.1	60.0
				(CD3-TCR complex)
176	239237_PM_at	—	—	—	9.82E−24	15.9	34.9	14.5
177	229723_PM_at	117289	TAGAP	T-cell activation	5.21E−24	29.2	82.9	26.2
				RhoGTPase activating
				protein
178	204655_PM_at	6352	CCL5	chemokine (C-C motif)	7.63E−24	77.5	339.4	66.8
				ligand 5
179	229041_PM_s_at	—	—	—	1.64E−24	36.5	132.1	32.4
180	205267_PM_at	5450	POU2AF1	POU class 2 associating	4.47E−23	17.9	86.8	15.7
				factor 1
181	226878_PM_at	3111	HLA-DOA	major histocompatibility	2.59E−25	102.0	288.9	94.3
				complex, class II, DO
				alpha
182	205488_PM_at	3001	GZMA	granzyme A (granzyme 1,	4.14E−25	37.3	164.8	33.4
				cytotoxic T-lymphocyte-
				associated serine esterase
				3)
183	201137_PM_s_at	3115	HLA-	major histocompatibility	2.17E−22	1579.1	3863.7	1475.9
			DPB1	complex, class II, DP beta
				1
184	204891_PM_s_at	3932	LCK	lymphocyte-specific	7.95E−28	19.3	74.2	17.8
				protein tyrosine kinase
185	231776_PM_at	8320	EOMES	eomesodermin	1.71E−23	24.0	63.9	22.4
186	211339_PM_s_at	3702	ITK	IL2-inducible T-cell	7.40E−23	16.7	44.4	15.7
				kinase
187	223322_PM_at	83593	RASSF5	Ras association	5.21E−26	41.5	114.4	39.2
				(RalGDS/AF-6) domain
				family member 5
188	205758_PM_at	925	CD8A	CD8a molecule	2.80E−25	24.2	105.8	22.2
189	231124_PM_x_at	4063	LY9	lymphocyte antigen 9	2.81E−23	16.7	45.8	15.7
190	211796_PM_s_at	28638 ///	TRBC1 ///	T cell receptor beta	4.38E−25	69.2	431.5	63.6
		28639	TRBC2	constant 1 /// T cell
				receptor beta constant 2
191	210915_PM_x_at	28638	TRBC2	T cell receptor beta	1.91E−27	39.7	230.7	37.5
				constant 2
192	205821_PM_at	22914	KLRK1	killer cell lectin-like	1.18E−25	30.3	111.5	31.3
				receptor subfamily K,
				member 1
193	236295_PM_s_at	197358	NLRC3	NLR family, CARD	6.22E−26	19.0	52.5	18.6
				domain containing 3
194	213193_PM_x_at	28639	TRBC1	T cell receptor beta	4.22E−25	95.3	490.9	92.1
				constant 1
195	211656_PM_x_at	100133583 ///	HLA-	major histocompatibility	5.98E−25	211.3	630.2	208.2
		3119	DQB1 ///	complex, class II, DQ
			LOC100133583	beta 1 /// HLA class II
				histocompatibili
196	209670_PM_at	28755	TRAC	T cell receptor alpha	1.44E−24	37.9	149.9	38.5
				constant
197	213888_PM_s_at	80342	TRAF3IP3	TRAF3 interacting	1.66E−22	30.8	101.9	30.5
				protein 3

TABLE 13

Biopsy Expression Profiling of Kidney Transplants: 3-Way Classifier AR vs. ADNR vs. TX (Brazilian Samples)

Validation Cohort

Nearest Centroid	197	AR vs. TX	0.976	98	100	95	95	100
Nearest Centroid	197	ADNR vs. TX	1.000	100	100	100	100	100
Nearest Centroid	197	AR vs. ADNR	0.962	97	100	91	95	100

TABLE 14a

Biopsy Expression Profiling of Kidney Transplants: 2-Way Classifier CAN vs. TX (TGCG Samples)

Validation Cohort

				Predictive			Postive	Negative
				Accuracy			Predictive	Predictive
Algorithm	Predictors	Comparison	AUC	(%)	Sensitivity (%)	Specificity (%)	Value (%)	Value (%)

Nearest Centroid	200	AR vs. TX	0.965	97	96	97	96	97

TABLE 14b

Biopsy Expression Profiling of Kidney Transplants: 2-Way Classifier CAN vs. TX

					p-value
		Entrez	Gene		(Final	CAN -	TX -
#	Probeset ID	Gene	Symbol	Gene Title	Phenotype)	Mean	Mean

1	204698_PM_at	3669	ISG20	interferon stimulated	1.93E−19	96.1	27.6
				exonuclease gene 20 kDa
2	33304_PM_at	3669	ISG20	interferon stimulated	2.02E−19	63.4	22.1
				exonuclease gene 20 kDa
3	217022_PM_s_at	100126583 ///	IGHA1 ///	immunoglobulin heavy	4.31E−19	494.6	49.4
		3493 ///	IGHA2 ///	constant alpha 1 ///
		3494	LOC100126583	immunoglobulin heavy
				constant alpha 2 (A2m ma
4	202957_PM_at	3059	HCLS1	hematopoietic cell-specific	5.13E−19	229.9	82.2
				Lyn substrate 1
5	203761_PM_at	6503	SLA	Src-like-adaptor	1.10E−18	138.4	51.4
6	204446_PM_s_at	240	ALOX5	arachidonate 5-lipoxygenase	1.36E−18	216.7	54.7
7	209198_PM_s_at	23208	SYT11	synaptotagmin XI	1.93E−18	41.8	22.8
8	228964_PM_at	639	PRDM1	PR domain containing 1,	2.37E−18	41.5	17.0
				with ZNF domain
9	201042_PM_at	7052	TGM2	transglutaminase 2 (C	3.13E−18	172.6	80.1
				polypeptide, protein-
				glutamine-gamma-
				glutamyltransferase)
10	226219_PM_at	257106	ARHGAP30	Rho GTPase activating	7.21E−18	91.8	37.5
				protein 30
11	225701_PM_at	80709	AKNA	AT-hook transcription	7.27E−18	73.2	29.0
				factor
12	202207_PM_at	10123	ARL4C	ADP-ribosylation factor-	7.98E−18	190.4	57.2
				like 4C
13	219574_PM_at	55016	MAR1	membrane-associated ring	8.98E−18	87.5	36.2
				finger (C3HC4) 1
14	209083_PM_at	12-Jul	CORO1A	coronin, actin binding	1.06E−17	107.1	34.3
				protein, 1A
15	226621_PM_at	9180	OSMR	oncostatin M receptor	1.85E−17	682.9	312.1
16	1405_PM_i_at	6352	CCL5	chemokine (C-C motif)	2.19E−17	195.6	54.6
				ligand 5
17	213160_PM_at	1794	DOCK2	dedicator of cytokinesis 2	2.75E−17	66.0	27.8
18	227346_PM_at	10320	IKZF1	IKAROS family zinc finger	2.92E−17	53.3	19.8
				1 (Ikaros)
19	204205_PM_at	60489	APOBEC3G	apolipoprotein B mRNA	2.92E−17	192.0	78.7
				editing enzyme, catalytic
				polypeptide-like 3G
20	218322_PM_s_at	51703	ACSL5	acyl-CoA synthetase long-	3.15E−17	84.5	48.1
				chain family member 5
21	238327_PM_at	440836	ODF3B	outer dense fiber of sperm	3.42E−17	58.5	26.1
				tails 3B
22	218983_PM_at	51279	C1RL	complement component 1, r	4.33E−17	206.9	99.4
				subcomponent-like
23	210538_PM_s_at	330	BIRC3	baculoviral IAP repeat-	4.49E−17	199.1	84.6
				containing 3
24	207651_PM_at	29909	GPR171	G protein-coupled receptor	5.48E−17	57.0	20.9
				171
25	201601_PM_x_at	8519	IFITM1	interferon induced	6.07E−17	2202.5	1251.1
				transmembrane protein 1 (9-
				27)
26	226878_PM_at	3111	HLA-	major histocompatibility	6.12E−17	201.4	94.3
			DOA	complex, class II, DO alpha
27	1555756_PM_a_at	64581	CLEC7A	C-type lectin domain family	6.22E−17	28.9	13.2
				7, member A
28	1559584_PM_a_at	283897	C16orf54	chromosome 16 open	6.73E−17	71.5	26.1
				reading frame 54
29	209795_PM_at	969	CD69	CD69 molecule	9.46E−17	40.6	15.2
30	230550_PM_at	64231	MS4A6A	membrane-spanning 4-	1.20E−16	88.0	30.9
				domains, subfamily A,
				member 6A
31	1553906_PM_s_at	221472	FGD2	FYVE, RhoGEF and PH	1.34E−16	219.3	71.9
				domain containing 2
32	205798_PM_at	3575	IL7R	interleukin 7 receptor	1.55E−16	106.3	33.4
33	1555852_PM_at	100507463	LOC100507463	hypothetical	1.81E−16	154.2	70.9
				LOC100507463
34	224916_PM_at	340061	TMEM173	transmembrane protein 173	1.84E−16	67.8	40.0
35	211368_PM_s_at	834	CASP1	caspase 1, apoptosis-related	1.85E−16	191.4	81.8
				cysteine peptidase
				(interleukin 1, beta,
				convertase)
36	226474_PM_at	84166	NLRC5	NLR family, CARD domain	1.85E−16	129.8	55.1
				containing 5
37	201137_PM_s_at	3115	HLA-	major histocompatibility	1.90E−16	3151.7	1475.9
			DPB1	complex, class II, DP beta 1
38	210785_PM_s_at	9473	C1orf38	chromosome 1 open reading	2.07E−16	39.9	16.4
				frame 38
39	215121_PM_x_at	100290481 ///	IGLC7 ///	immunoglobulin lambda	2.13E−16	1546.8	250.4
		28823 ///	IGLV1-44 ///	constant 7 ///
		28834	LOC100290481	immunoglobulin lambda
				variable 1-44 ///
				immunoglob
40	1555832_PM_s_at	1316	KLF6	Kruppel-like factor 6	2.35E−16	1003.3	575.1
41	221932_PM_s_at	51218	GLRX5	glutaredoxin 5	2.49E−16	1218.1	1599.3
42	207677_PM_s_at	4689	NCF4	neutrophil cytosolic factor	2.65E−16	39.5	19.2
				4, 40 kDa
43	202720_PM_at	26136	TES	testis derived transcript (3	2.68E−16	357.9	204.2
				LIM domains)
44	220005_PM_at	53829	P2RY13	purinergic receptor P2Y, G-	2.72E−16	29.6	14.8
				protein coupled, 13
45	200904_PM_at	3133	HLA-E	major histocompatibility	2.73E−16	1607.7	994.7
				complex, class I, E
46	222294_PM_s_at	5873	RAB27A	RAB27A, member RAS	2.91E−16	263.0	146.4
				oncogene family
47	205831_PM_at	914	CD2	CD2 molecule	3.32E−16	100.9	33.9
48	227344_PM_at	10320	IKZF1	IKAROS family zinc finger	3.39E−16	28.5	14.9
				1 (Ikaros)
49	209374_PM_s_at	3507	IGHM	immunoglobulin heavy	3.73E−16	301.0	45.0
				constant mu
50	202307_PM_s_at	6890	TAP1	transporter 1, ATP-binding	4.84E−16	280.0	141.0
				cassette, sub-family B
				(MDR/TAP)
51	223218_PM_s_at	64332	NFKBIZ	nuclear factor of kappa light	5.05E−16	399.9	159.1
				polypeptide gene enhancer
				in B-cells inhibitor, zeta
52	229437_PM_at	114614	MIR155HG	MIR155 host gene (non-	5.85E−16	28.5	12.9
				protein coding)
53	213603_PM_s_at	5880	RAC2	ras-related C3 botulinum	5.98E−16	250.3	86.5
				toxin substrate 2 (rho
				family, small GTP binding
				protein Rac2)
54	214669_PM_x_at	3514 ///	IGK@ ///	immunoglobulin kappa	6.32E−16	3449.1	587.1
		50802	IGKC	locus /// immunoglobulin
				kappa constant
55	211430_PM_s_at	28396 ///	IGHG1 ///	immunoglobulin heavy	6.39E−16	2177.7	266.9
		3500 ///	IGHM ///	constant gamma 1 (G1m
		3507	IGHV4-31	marker) /// immunoglobulin
				heavy constant mu
56	228471_PM_at	91526	ANKRD44	ankyrin repeat domain 44	6.42E−16	184.6	86.5
57	209138_PM_x_at	3535	IGL@	Immunoglobulin lambda	7.54E−16	2387.0	343.2
				locus
58	227353_PM_at	147138	TMC8	transmembrane channel-like	8.01E−16	42.7	16.6
				8
59	200986_PM_at	710	SERPING1	serpin peptidase inhibitor,	8.10E−16	590.2	305.3
				clade G (C1 inhibitor),
				member 1
60	212203_PM_x_at	10410	IFITM3	interferon induced	8.17E−16	4050.1	2773.8
				transmembrane protein 3 (1-
				8U)
61	221651_PM_x_at	3514 ///	1GK@ ///	immunoglobulin kappa	9.60E−16	3750.2	621.2
		50802	IGKC	locus /// immunoglobulin
				kappa constant
62	214836_PM_x_at	28299 ///	IGK@ ///	immunoglobulin kappa	9.72E−16	544.2	109.1
		3514 ///	IGKC ///	locus /// immunoglobulin
		50802	IGKV1-5	kappa constant ///
				immunoglobulin kappa v
63	1552703_PM_s_at	114769 ///	CARD16 ///	caspase recruitment domain	1.12E−15	120.6	54.9
		834	CASP1	family, member 16 ///
				caspase 1, apoptosis-related
				cysteine
64	202901_PM_x_at	1520	CTSS	cathepsin S	1.13E−15	130.9	45.1
65	215379_PM_x_at	28823 ///	IGLC7 ///	immunoglobulin lambda	1.16E−15	1453.0	248.1
		28834	IGLV1-44	constant 7 ///
				immunoglobulin lambda
				variable 1-44
66	222939_PM_s_at	117247	SLC16A10	solute carrier family 16,	1.22E−15	115.1	229.6
				member 10 (aromatic amino
				acid transporter)
67	232617_PM_at	1520	CTSS	cathepsin S	1.22E−15	392.2	154.8
68	235964_PM_x_at	25939	SAMHD1	SAM domain and HD	1.26E−15	270.1	117.5
				domain 1
69	205159_PM_at	1439	CSF2RB	colony stimulating factor 2	1.28E−15	70.4	26.3
				receptor, beta, low-affinity
				(granulocyte-macrophage)
70	224451_PM_x_at	64333	ARHGAP9	Rho GTPase activating	1.34E−15	71.8	29.4
				protein 9
71	214677_PM_x_at	100287	IGL@ ///	Immunoglobulin lambda	1.35E−15	2903.1	433.7
		927 ///	LOC100287927	locus /// Hypothetical
		3535		protein LOC100287927
72	217733_PM_s_at	9168	TMSB10	thymosin beta 10	1.37E−15	5555.2	3529.0
73	38149_PM_at	9938	ARHGAP25	Rho GTPase activating	1.46E−15	83.2	43.2
				protein 25
74	221671_PM_x_at	3514 ///	IGK@ ///	immunoglobulin kappa	1.57E−15	3722.5	642.9
		50802	IGKC	locus /// immunoglobulin
				kappa constant
75	214022_PM_s_at	8519	IFITM1	interferon induced	1.59E−15	1236.6	683.3
				transmembrane protein 1 (9-
				27)
76	223217_PM_s_at	64332	NFKBIZ	nuclear factor of kappa light	1.61E−15	196.6	79.7
				polypeptide gene enhancer
				in B-cells inhibitor, zeta
77	206118_PM_at	6775	STAT4	signal transducer and	1.67E−15	37.7	18.1
				activator of transcription 4
78	221666_PM_s_at	29108	PYCARD	PYD and CARD domain	1.82E−15	95.5	44.7
				containing
79	207375_PM_s_at	3601	IL15RA	interleukin 15 receptor,	1.94E−15	51.2	28.2
				alpha
80	209197_PM_at	23208	SYT11	synaptotagmin XI	2.02E−15	38.2	24.9
81	243366_PM_s_at	—	—	—	2.05E−15	52.5	22.0
82	224795_PM_x_at	3514 ///	IGK@ ///	immunoglobulin kappa	2.18E−15	3866.2	670.5
		50802	IGKC	locus /// immunoglobulin
				kappa constant
83	36711_PM_at	23764	MAFF	v-maf musculoaponeurotic	2.26E−15	113.0	40.7
				fibrosarcoma oncogene
				homolog F (avian)
84	227125_PM_at	3455	IFNAR2	interferon (alpha, beta and	2.27E−15	96.7	55.8
				omega) receptor 2
85	235735_PM_at	—	—	—	2.58E−15	24.5	11.2
86	209515_PM_s_at	5873	RAB27A	RAB27A, member RAS	2.61E−15	160.5	85.2
				oncogene family
87	204670_PM_x_at	3123 ///	HLA-	major histocompatibility	2.61E−15	3694.9	2262.0
		3126	DRB1 ///	complex, class II, DR beta 1 ///
			HLA-	major histocompatibility
			DRB4	comp
88	205269_PM_at	3937	LCP2	lymphocyte cytosolic	2.85E−15	58.9	22.9
				protein 2 (SH2 domain
				containing leukocyte protein
				of 76 kDa)
89	226525_PM_at	9262	STK17B	serine/threonine kinase 17b	3.00E−15	259.1	107.6
90	229295_PM_at	150166 ///	IL17RA ///	interleukin 17 receptor A ///	3.02E−15	98.4	50.0
		23765	LOC150166	hypothetical protein
				LOC150166
91	206513_PM_at	9447	AIM2	absent in melanoma 2	3.18E−15	30.5	13.9
92	209774_PM_x_at	2920	CXCL2	chemokine (C-X-C motif)	3.45E−15	38.2	15.5
				ligand 2
93	211656_PM_x_at	100133583 ///	HLA-	major histocompatibility	3.51E−15	459.8	208.2
		3119	DQB1 ///	complex, class II, DQ beta 1 ///
			LOC100133583	HLA class II
				histocompatibili
94	206011_PM_at	834	CASP1	caspase 1, apoptosis-related	3.56E−15	146.2	60.7
				cysteine peptidase
				(interleukin 1, beta,
				convertase)
95	202803_PM_s_at	3689	ITGB2	integrin, beta 2	3.68E−15	182.6	65.2
				(complement component 3
				receptor 3 and 4 subunit)
96	221698_PM_s_at	64581	CLEC7A	C-type lectin domain family	3.69E−15	112.8	49.7
				7, member A
97	229937_PM_x_at	10859	LILRB1	Leukocyte immunoglobulin-	3.75E−15	50.0	18.5
				like receptor, subfamily B
				(with TM and ITIM
				domains), member
98	235529_PM_x_at	25939	SAMHD1	SAM domain and HD	3.99E−15	289.1	128.0
				domain 1
99	223322_PM_at	83593	RASSF5	Ras association	4.03E−15	79.3	39.2
				(RalGDS/AF-6) domain
				family member 5
100	211980_PM_at	1282	COL4A1	collagen, type IV, alpha 1	4.75E−15	1295.8	774.7
101	201721_PM_s_at	7805	LAPTM5	lysosomal protein	4.83E−15	661.3	249.4
				transmembrane 5
102	242916_PM_at	11064	CEP110	centrosomal protein 110 kDa	4.89E−15	51.2	26.2
103	206978_PM_at	729230	CCR2	chemokine (C-C motif)	5.01E−15	68.6	27.3
				receptor 2
104	244353_PM_s_at	154091	SLC2A12	solute carrier family 2	5.72E−15	51.6	100.5
				(facilitated glucose
				transporter), member 12
105	215049_PM_x_at	9332	CD163	CD163 molecule	6.21E−15	344.5	112.9
106	1552510_PM_at	142680	SLC34A3	solute carrier family 34	6.40E−15	95.6	206.6
				(sodium phosphate),
				member 3
107	225636_PM_at	6773	STAT2	signal transducer and	6.63E−15	711.5	485.3
				activator of transcription 2,
				113 kDa
108	229390_PM_at	441168	FAM26F	family with sequence	6.73E−15	272.4	75.9
				similarity 26, member F
109	235229_PM_at	—	—	—	6.90E−15	135.0	41.9
110	226218_PM_at	3575	IL7R	interleukin 7 receptor	7.22E−15	147.1	41.4
111	217028_PM_at	7852	CXCR4	chemokine (C-X-C motif)	7.40E−15	208.4	75.3
				receptor 4
112	204655_PM_at	6352	CCL5	chemokine (C-C motif)	8.57E−15	223.4	66.8
				ligand 5
113	227184_PM_at	5724	PTAFR	platelet-activating factor	8.78E−15	134.4	62.7
				receptor
114	202748_PM_at	2634	GBP2	guanylate binding protein 2,	8.91E−15	306.7	141.0
				interferon-inducible
115	226991_PM_at	4773	NFATC2	nuclear factor of activated	9.05E−15	66.7	30.3
				T-cells, cytoplasmic,
				calcineurin-dependent 2
116	216565_PM_x_at	—	—	—	9.49E−15	1224.6	779.1
117	203104_PM_at	1436	CSF1R	colony stimulating factor 1	9.57E−15	42.7	22.1
				receptor
118	238668_PM_at	—	—	—	9.84E−15	33.6	14.5
119	204923_PM_at	54440	SASH3	SAM and SH3 domain	9.93E−15	47.6	21.7
				containing 3
120	230036_PM_at	219285	SAMD9L	sterile alpha motif domain	1.02E−14	128.0	72.7
				containing 9-like
121	211742_PM_s_at	2124	EVI2B	ecotropic viral integration	1.03E−14	166.2	53.2
				site 2B
122	236782_PM_at	154075	SAMD3	sterile alpha motif domain	1.11E−14	23.3	13.3
				containing 3
123	232543_PM_x_at	64333	ARHGAP9	Rho GTPase activating	1.13E−14	62.3	25.8
				protein 9
124	231124_PM_x_at	4063	LY9	lymphocyte antigen 9	1.18E−14	33.7	15.7
125	215946_PM_x_at	3543 ///	IGLL1 ///	immunoglobulin lambda-	1.22E−14	187.5	52.1
		91316 ///	IGLL3P ///	like polypeptide 1 ///
		91353	LOC91316	immunoglobulin lambda-
				like polypeptide 3,
126	208306_PM_x_at	3123	HLA-	Major histocompatibility	1.25E−14	3695.8	2255.0
			DRB1	complex, class II, DR beta 1
127	217235_PM_x_at	28816	IGLV2-11	immunoglobulin lambda	1.29E−14	196.8	37.8
				variable 2-11
128	209546_PM_s_at	8542	APOL1	apolipoprotein L, 1	1.33E−14	206.8	114.1
129	203416_PM_at	963	CD53	CD53 molecule	1.34E−14	422.7	157.8
130	211366_PM_x_at	834	CASP1	caspase 1, apoptosis-related	1.35E−14	186.0	87.1
				cysteine peptidase
				(interleukin 1, beta,
				convertase)
131	200797_PM_s_at	4170	MCL1	myeloid cell leukemia	1.38E−14	793.7	575.9
				sequence 1 (BCL2-related)
132	31845_PM_at	2000	ELF4	E74-like factor 4 (ets	1.40E−14	60.7	34.3
				domain transcription factor)
133	221841_PM_s_at	9314	KLF4	Kruppel-like factor 4 (gut)	1.48E−14	132.3	65.2
134	229391_PM_s_at	441168	FAM26F	family with sequence	1.49E−14	212.1	73.7
				similarity 26, member F
135	203645_PM_s_at	9332	CD163	CD163 molecule	1.51E−14	274.9	85.0
136	211643_PM_x_at	100510044 ///	IGK@ ///	immunoglobulin kappa	1.61E−14	131.1	32.6
		28875 ///	IGKC ///	locus /// immunoglobulin
		3514 ///	IGKV3D-15 ///	kappa constant ///
		50802	LOC100510044	immunoglobulin kappa v
137	205488_PM_at	3001	GZMA	granzyme A (granzyme 1,	1.82E−14	102.3	33.4
				cytotoxic T-lymphocyte-
				associated serine esterase 3)
138	201464_PM_x_at	3725	JUN	jun proto-oncogene	1.90E−14	424.7	244.5
139	204774_PM_at	2123	EVI2A	ecotropic viral integration	1.95E−14	114.7	44.9
				site 2A
140	204336_PM_s_at	10287	RGS19	regulator of G-protein	2.01E−14	135.7	67.0
				signaling 19
141	244654_PM_at	64005	MYO1G	myosin IG	2.03E−14	26.8	14.9
142	228442_PM_at	4773	NFATC2	nuclear factor of activated	2.06E−14	62.8	32.0
				T-cells, cytoplasmic,
				calcineurin-dependent 2
143	206804_PM_at	917	CD3G	CD3g molecule, gamma	2.18E−14	36.4	17.3
				(CD3-TCR complex)
144	201315_PM_x_at	10581	IFITM2	interferon induced	2.21E−14	3303.7	2175.3
				transmembrane protein 2 (1-
				8D)
145	203561_PM_at	2212	FCGR2A	Fc fragment of IgG, low	2.22E−14	66.4	29.2
				affinity IIa, receptor (CD32)
146	219117_PM_s_at	51303	FKBP11	FK506 binding protein 11,	2.31E−14	341.3	192.9
				19 kDa
147	242827_PM_x_at	—	—	—	2.37E−14	38.9	16.3
148	214768_PM_x_at	28299 ///	IGK@ ///	immunoglobulin kappa	2.38E−14	116.7	21.1
		3514 ///	IGKC ///	locus /// immunoglobulin
		50802	IGKV1-5	kappa constant ///
				immunoglobulin kappa v
149	227253_PM_at	1356	CP	ceruloplasmin (ferroxidase)	2.49E−14	44.7	22.0
150	209619_PM_at	972	CD74	CD74 molecule, major	2.51E−14	1502.3	864.9
				histocompatibility complex,
				class II invariant chain
151	208966_PM_x_at	3428	IFI16	interferon, gamma-inducible	2.65E−14	644.9	312.6
				protein 16
152	239237_PM_at	—	—	—	2.79E−14	25.3	14.5
153	213566_PM_at	6039	RNASE6	ribonuclease, RNase A	2.82E−14	341.1	134.3
				family, k6
154	201288_PM_at	397	ARHGDIB	Rho GDP dissociation	2.86E−14	542.2	308.1
				inhibitor (GDI) beta
155	209606_PM_at	9595	CYTIP	cytohesin 1 interacting	2.90E−14	79.0	32.9
				protein
156	205758_PM_at	925	CD8A	CD8a molecule	2.91E−14	60.3	22.2
157	202953_PM_at	713	C1QB	complement component 1, q	3.00E−14	401.1	142.5
				subcomponent, B chain
158	203233_PM_at	3566	IL4R	interleukin 4 receptor	3.06E−14	116.7	72.0
159	205270_PM_s_at	3937	LCP2	lymphocyte cytosolic	3.12E−14	104.4	44.6
				protein 2 (SH2 domain
				containing leukocyte protein
				of 76 kDa)
160	223658_PM_at	9424	KCNK6	potassium channel,	3.18E−14	35.9	22.0
				subfamily K, member 6
161	202637_PM_s_at	3383	ICAM1	intercellular adhesion	3.18E−14	89.1	45.7
				molecule 1
162	202935_PM_s_at	6662	SOX9	SRY (sex determining	3.18E−14	117.0	46.1
				region Y)-box 9
163	217986_PM_s_at	11177	BAZ1A	bromodomain adjacent to	3.21E−14	116.4	62.5
				zinc finger domain, 1A
164	210915_PM_x_at	28638	TRBC2	T cell receptor beta constant 2	3.27E−14	129.7	37.5
165	223343_PM_at	58475	MS4A7	membrane-spanning 4-	3.38E−14	346.0	128.3
				domains, subfamily A,
				member 7
166	1552701_PM_a_at	114769	CARD16	caspase recruitment domain	3.60E−14	273.3	119.8
				family, member 16
167	226659_PM_at	50619	DEF6	differentially expressed in	3.63E−14	35.2	22.2
				FDCP 6 homolog (mouse)
168	213502_PM_x_at	91316	LOC91316	glucuronidase,	3.63E−14	1214.7	419.3
				beta/immunoglobulin
				lambda-like polypeptide 1
				pseudogene
169	219332_PM_at	79778	MICALL2	MICAL-like 2	3.71E−14	68.7	44.4
170	204891_PM_s_at	3932	LCK	lymphocyte-specific protein	3.74E−14	43.4	17.8
				tyrosine kinase
171	224252_PM_s_at	53827	FXYD5	FXYD domain containing	3.76E−14	73.8	32.5
				ion transport regulator 5
172	242878_PM_at	—	—	—	3.90E−14	53.2	30.1
173	224709_PM_s_at	56990	CDC42SE2	CDC42 small effector 2	4.07E−14	1266.2	935.7
174	40420_PM_at	6793	STK10	serine/threonine kinase 10	4.32E−14	42.0	24.4
175	218084_PM_x_at	53827	FXYD5	FXYD domain containing	4.52E−14	89.2	39.1
				ion transport regulator 5
176	218232_PM_at	712	C1QA	complement component 1, q	4.63E−14	197.0	85.8
				subcomponent, A chain
177	202208_PM_s_at	10123	ARL4C	ADP-ribosylation factor-	4.63E−14	77.0	42.2
				like 4C
178	220146_PM_at	51284	TLR7	toll-like receptor 7	4.93E−14	31.6	17.8
179	228752_PM_at	84766	EFCAB4B	EF-hand calcium binding	5.05E−14	20.6	12.1
				domain 4B
180	208948_PM_s_at	6780	STAU1	staufen, RNA binding	5.23E−14	1766.0	2467.4
				protein, homolog 1
				(Drosophila)
181	211645_PM_x_at	—	—	—	5.24E−14	166.7	27.4
182	236295_PM_s_at	197358	NLRC3	NLR family, CARD domain	5.28E−14	37.0	18.6
				containing 3
183	224927_PM_at	170954	KIAA1949	KIAA1949	5.44E−14	160.2	74.7
184	225258_PM_at	54751	FBLIM1	filamin binding LIM protein 1	6.03E−14	228.7	125.4
185	202898_PM_at	9672	SDC3	syndecan 3	6.07E−14	64.8	32.0
186	218789_PM_s_at	54494	C11orf71	chromosome 11 open	6.12E−14	175.8	280.8
				reading frame 71
187	204912_PM_at	3587	IL10RA	interleukin 10 receptor,	6.25E−14	117.2	46.1
				alpha
188	211582_PM_x_at	7940	LST1	leukocyte specific transcript 1	6.48E−14	121.2	49.4
189	214617_PM_at	5551	PRF1	perforin 1 (pore forming	6.77E−14	85.6	40.8
				protein)
190	231887_PM_s_at	27143	KIAA1274	KIAA1274	7.00E−14	45.6	30.0
191	223773_PM_s_at	85028	SNHG12	small nucleolar RNA host	7.00E−14	174.8	93.2
				gene 12 (non-protein
				coding)
192	202644_PM_s_at	7128	TNFAIP3	tumor necrosis factor, alpha-	7.11E−14	278.2	136.6
				induced protein 3
193	211796_PM_s_at	28638 ///	TRBC1 ///	T cell receptor beta constant	7.13E−14	250.2	63.6
		28639	TRBC2	1 /// T cell receptor beta
				constant 2
194	206254_PM_at	1950	EGF	epidermal growth factor	7.38E−14	176.6	551.3
195	216207_PM_x_at	28299 ///	IGKC ///	immunoglobulin kappa	7.51E−14	266.3	50.9
		28904 ///	IGKV1-5 ///	constant /// immunoglobulin
		3514 ///	IGKV1D-8 ///	kappa variable 1-5 ///
		652493 ///	LOC652493 ///	immunoglobulin
		652694	LOC652694
196	232311_PM_at	567	B2M	Beta-2-microglobulin	7.73E−14	83.7	35.2
197	205466_PM_s_at	9957	HS3ST1	heparan sulfate	7.84E−14	96.0	42.1
				(glucosamine) 3-O-
				sulfotransferase 1
198	203332_PM_s_at	3635	INPP5D	inositol polyphosphate-5-	7.89E−14	42.6	24.0
				phosphatase, 145 kDa
199	64064_PM_at	55340	GIMAP5	GTPase, IMAP family	7.98E−14	170.6	111.4
				member 5
200	211644_PM_x_at	3514 ///	IGK@ ///	immunoglobulin kappa	8.04E−14	246.9	47.5
		50802	IGKC	locus /// immunoglobulin
				kappa constant

TABLE 15

Biopsy Expression Profiling of Kidney Transplants: 2-Way Classifier CAN vs. TX (Brazilian Samples)

Validation Cohort

				Predictive			Postive	Negative
				Accuracy			Predictive	Predictive
Algorithm	Predictors	Comparison	AUC	(%)	Sensitivity (%)	Specificity (%)	Value (%)	Value (%)

Nearest Centroid	200	AR vs. TX	0.954	95	95	96	95	96

Example 4

Expression Signatures to Distinguish Liver Transplant Injuries

Biomarker profiles diagnostic of specific types of graft injury post-liver transplantation (LT), such as acute rejection (AR), hepatitis C virus recurrence (HCV-R), and other causes (acute dysfunction no rejection/recurrence; ADNR) could enhance the diagnosis and management of recipients. Our aim was to identify diagnostic genomic (mRNA) signatures of these clinical phenotypes in the peripheral blood and allograft tissue.
Patient Populations: The study population consisted of 114 biopsy-documented Liver PAXgene whole blood samples comprised of 5 different phenotypes: AR (n=25), ADNR (n=16), HCV(n=36), HCV+AR (n=13), and TX (n=24).
Gene Expression Profiling and Analysis: All samples were processed on the Affymetrix HG-U133 PM only peg microarrays. To eliminate low expressed signals we used a signal filter cut-off that was data dependent, and therefore expression signals<Log 2 4.23 (median signals on all arrays) in all samples were eliminated leaving us with 48882 probe sets from a total of 54721 probe sets. The first comparison performed was a 3-way ANOVA analysis of AR vs. ADNR vs. TX. This yielded 263 differentially expressed probesets at a False Discovery rate (FDR<10%). We used these 263 probesets to build predictive models that could differentiate the three classes. We used the Nearest Centroid (NC) algorithm to build the predictive models. We ran the predictive models using two different methodologies and calculated the Area Under the Curve (AUC). First we did a one-level cross validation, where the data is first divided into 10 random partitions. At each iteration, 1/10 of the data is held out for testing while the remaining 9/10 of the data is used to fit the parameters of the model. This can be used to obtain an estimate of prediction accuracy for a single model. Then we modeled an algorithm for estimating the optimism, or over-fitting, in predictive models based on using bootstrapped datasets to repeatedly quantify the degree of over-fitting in the model building process using sampling with replacement. This optimism corrected AUC value is a nearly unbiased estimate of the expected values of the optimism that would be obtained in external validation (we used 1000 randomly created data sets). Table 16a shows the optimism corrected AUCs for the 263 probesets that were used to predict the accuracies for distinguishing between AR, ADNR and TX in Liver PAXgene samples. Table 16b shows the 263 probesets used for distinguishing between AR, ADNR and TX in Liver PAXgene samples.
It is clear from the above Table 16a that the 263 probeset classifier was able to distinguish the three phenotypes with very high predictive accuracy. The NC classifier had a sensitivity of 83%, specificity of 93%, and positive predictive value of 95% and a negative predictive value of 78% for the AR vs. ADNR comparison. It is important to note that these values did not change after the optimism correction where we simulated 1000 data sets showing that these are really robust signatures.
The next comparison we performed was a 3-way ANOVA of AR vs. HCV vs. HCV+AR which yielded 147 differentially expressed probesets at a p value<0.001. We chose to use this set of predictors because at an FDR<10% we had only 18 predictors, which could possibly be due to the smaller sample size of the HCV+AR (n=13) or a smaller set of differentially expressed genes in one of the phenotypes. However, since this was a discovery set to test the proof of principle whether there were signatures that could distinguish samples that had an admixture of HCV and AR from the pure AR and the pure HCV populations, we ran the predictive algorithms on the 147 predictors. Table 17a shows the AUCs for the 147 probesets that were used to predict the accuracies for distinguishing between AR, HCV and HCV+AR in Liver PAXgene samples. Table 17b shows the 147 probesets used for distinguishing between AR, HCV and HCV+AR in Liver PAXgene samples.
The NC classifier had a sensitivity of 87%, specificity of 97%, and positive predictive value of 95% and a negative predictive value of 92% for the AR vs HCV comparison using the optimism correction where we simulated 1000 data sets giving us confidence that the simulations that were done to mimic a real clinical situation did not alter the robustness of this set of predictors.
For the biopsies, again, we performed a 3-way ANOVA of AR vs. HCV vs. HCV+AR that yielded 320 differentially expressed probesets at an FDR<10%. We specifically did this because at a p-value<0.001 there were over 950 probesets. We ran the predictive models on this set of classifiers in the same way mentioned for the PAXgene samples. Table 18a shows the AUCs for the one-level cross validation and the optimism correction for the classifier set comprised of 320 probesets that were used to predict the accuracies for distinguishing between AR, HCV and HCV+AR in Liver biopsies. Table 18b shows the 320 probesets that used for distinguishing AR vs. HCV vs. HCV+AR in Liver biopsies.
In summary, for both the blood and the biopsy samples from liver transplant subjects we have classifier sets that can distinguish AR, HCV and HCV+AR with AUCs between 0.79-0.83 in blood and 0.69-0.83 in the biopsies. We also have a signature from whole blood that can distinguish AR, ADNR and TX samples with AUC's ranging from 0.87-0.92.
It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred methods and materials are described.
All publications, GenBank sequences, ATCC deposits, patents and patent applications cited herein are hereby expressly incorporated by reference in their entirety and for all purposes as if each is individually so denoted.

TABLE 16a

AUCs for the 263 probes to predict AR, ADNR and TX in Liver whole blood samples.

							Postive	Negative
				Predictive			Predictive	Predictive
Algorithm	Predictors	Comparison	AUC	Accuracy (%)	Sensitivity (%)	Specificity (%)	Value (%)	Value (%)

Nearest Centroid	263	AR vs. ADNR	0.882	88	83	93	95	78
Nearest Centroid	263	AR vs. TX	0.943	95	95	95	95	95
Nearest Centroid	263	ADNR vs. TX	0.883	88	93	83	78	95

TABLE 16b

The 263 probesets for distinguishing between AR, ADNR and TX in Liver PAXgene samples

				p-value	ADNR -	AR -	TX -
#	Probeset ID	Gene Symbol	Gene Title	(Phenotype)	Mean	Mean	Mean

1	215415_PM_s_at	LYST	lysosomal trafficking	3.79E−07	32.3	25.8	43.6
			regulator
2	241038_PM_at	—	—	4.79E−07	16.1	21.0	16.4
3	230776_PM_at	—	—	2.10E−06	10.4	13.7	10.2
4	212805_PM_at	PRUNE2	prune homolog 2	4.09E−06	15.8	15.2	33.9
			(Drosophila)
5	215090_PM_x_at	LOC440434	aminopeptidase puromycin	7.28E−06	164.6	141.0	208.0
			sensitive pseudogene
6	243625_PM_at	—	—	7.64E−06	31.2	20.8	29.9
7	232222_PM_at	C18orf49	chromosome 18 open	8.85E−06	33.7	35.7	42.4
			reading frame 49
8	235341_PM_at	DNAJC3	DnaJ (Hsp40) homolog,	1.06E−05	21.8	22.1	35.0
			subfamily C, member 3
9	1557733_PM_a_at	—	—	1.21E−05	83.8	116.0	81.2
10	212906_PM_at	GRAMD1B	GRAM domain containing	1.26E−05	52.7	51.0	45.7
			1B
11	1555874_PM_x_at	MGC21881	hypothetical locus	1.53E−05	20.5	20.0	19.3
			MGC21881
12	227645_PM_at	PIK3R5	phosphoinositide-3-kinase,	1.66E−05	948.4	824.5	1013.0
			regulatory subunit 5
13	235744_PM_at	PPTC7	PTC7 protein phosphatase	1.73E−05	21.3	18.0	25.7
			homolog (S. cerevisiae)
14	1553873_PM_at	KLHL34	kelch-like 34 (Drosophila)	1.89E−05	11.1	12.1	9.9
15	218408_PM_at	TIMM10	translocase of inner	2.16E−05	125.9	137.7	99.4
			mitochondrial membrane 10
			homolog (yeast)
16	227486_PM_at	NT5E	5′-nucleotidase, ecto (CD73)	2.46E−05	14.7	18.6	15.6
17	231798_PM_at	NOG	noggin	2.49E−05	17.0	25.9	15.1
18	205920_PM_at	SLC6A6	solute carrier family 6	2.53E−05	25.9	25.0	39.3
			(neurotransmitter
			transporter, taurine),
			member 6
19	222435_PM_s_at	UBE2J1	ubiquitin-conjugating	2.63E−05	212.6	292.4	324.0
			enzyme E2, J1 (UBC6
			homolog, yeast)
20	207737_PM_at	—	—	2.89E−05	8.2	8.5	8.6
21	209644_PM_x_at	CDKN2A	cyclin-dependent kinase	2.91E−05	13.7	13.9	11.5
			inhibitor 2A (melanoma,
			p16, inhibits CDK4)
22	241661_PM_at	JMJD1C	jumonji domain containing	2.99E−05	18.4	21.9	34.8
			1C
23	202086_PM_at	MX1	myxovirus (influenza virus)	3.04E−05	562.6	496.4	643.9
			resistance 1, interferon-
			inducible protein p78
			(mouse)
24	243819_PM_at	—	—	3.11E−05	766.7	495.1	661.8
25	210524_PM_x_at	—	—	3.12E−05	154.5	209.2	138.6
26	217714_PM_x_at	STMN1	stathmin 1	3.39E−05	22.3	28.5	20.4
27	219659_PM_at	ATP8A2	ATPase, aminophospholipid	3.65E−05	10.4	10.8	9.8
			transporter, class I, type 8A,
			member 2
28	219915_PM_s_at	SLC16A10	solute carrier family 16,	3.70E−05	19.4	21.8	15.8
			member 10 (aromatic amino
			acid transporter)
29	214039_PM_s_at	LAPTM4B	lysosomal protein	3.81E−05	70.4	104.0	74.2
			transmembrane 4 beta
30	214107_PM_x_at	LOC440434	aminopeptidase puromycin	4.27E−05	182.8	155.0	224.7
			sensitive pseudogene
31	225408_PM_at	MBP	myelin basic protein	4.54E−05	34.1	32.6	47.9
32	1552623_PM_at	HSH2D	hematopoietic SH2 domain	4.93E−05	373.7	323.9	401.3
			containing
33	206974_PM_at	CXCR6	chemokine (C-X-C motif)	5.33E−05	24.6	31.0	22.9
			receptor 6
34	203764_PM_at	DLGAP5	discs, large (Drosophila)	5.41E−05	9.3	10.9	8.6
			homolog-associated protein
			5
35	213915_PM_at	NKG7	natural killer cell group 7	5.73E−05	2603.1	1807.7	1663.1
			sequence
36	1570597_PM_at	—	—	5.86E−05	8.3	7.8	7.5
37	228290_PM_at	PLK1S1	Polo-like kinase 1 substrate	6.00E−05	47.2	35.6	45.8
			1
38	230753_PM_at	PATL2	protein associated with	6.11E−05	169.0	123.0	131.6
			topoisomerase II homolog 2
			(yeast)
39	202016_PM_at	MEST	mesoderm specific	6.25E−05	18.3	27.5	17.3
			transcript homolog (mouse)
40	212730_PM_at	SYNM	synemin, intermediate	6.30E−05	16.7	19.5	14.4
			filament protein
41	209203_PM_s_at	BICD2	bicaudal D homolog 2	6.50E−05	197.8	177.0	256.6
			(Drosophila)
42	1554397_PM_s_at	UEVLD	UEV and lactate/malate	6.59E−05	20.8	17.7	25.2
			dehyrogenase domains
43	217963_PM_s_at	NGFRAP1	nerve growth factor receptor	7.61E−05	505.9	713.1	555.7
			(TNFRSF16) associated
			protein 1
44	201656_PM_at	ITGA6	integrin, alpha 6	7.75E−05	87.4	112.6	84.1
45	1553685_PM_s_at	SP1	Sp1 transcription factor	7.83E−05	27.4	27.3	41.3
46	236717_PM_at	FAM179A	family with sequence	8.00E−05	55.1	39.8	42.1
			similarity 179, member A
47	240913_PM_at	FGFR2	fibroblast growth factor	8.33E−05	9.2	9.6	10.2
			receptor 2
48	243756_PM_at	—	—	8.47E−05	7.9	8.5	7.4
49	222036_PM_s_at	MCM4	minichromosome	8.52E−05	29.5	35.1	25.4
			maintenance complex
			component 4
50	202644_PM_s_at	TNFAIP3	tumor necrosis factor, alpha-	8.57E−05	516.0	564.5	475.8
			induced protein 3
51	229625_PM_at	GBP5	guanylate binding protein 5	9.23E−05	801.9	1014.7	680.8
52	235545_PM_at	DEPDC1	DEP domain containing 1	9.83E−05	8.0	8.7	8.3
53	204641_PM_at	NEK2	NIMA (never in mitosis	0.000100269	10.2	12.5	10.0
			gene a)-related kinase 2
54	213931_PM_at	ID2 /// ID2B	inhibitor of DNA binding 2,	0.000101645	562.9	504.9	384.6
			dominant negative helix-
			loop-helix protein ///
			inhibitor of
55	216125_PM_s_at	RANBP9	RAN binding protein 9	0.000102366	35.4	37.0	50.3
56	205660_PM_at	OASL	2′-5′-oligoadenylate	0.000102776	470.5	394.6	493.4
			synthetase-like
57	222816_PM_s_at	ZCCHC2	zinc finger, CCHC domain	0.000105861	301.3	308.7	320.8
			containing 2
58	1554696_PM_s_at	TYMS	thymidylate synthetase	0.000110478	11.1	16.2	11.2
59	232229_PM_at	SETX	senataxin	0.000113076	44.2	34.5	48.7
60	204929_PM_s_at	VAMP5	vesicle-associated	0.000113182	152.8	197.8	153.6
			membrane protein 5
			(myobrevin)
61	203819_PM_s_at	IGF2BP3	insulin-like growth factor 2	0.000113349	45.4	75.4	51.1
			mRNA binding protein 3
62	210164_PM_at	GZMB	granzyme B (granzyme 2,	0.000113466	955.2	749.5	797.1
			cytotoxic T-lymphocyte-
			associated serine esterase 1)
63	202589_PM_at	TYMS	thymidylate synthetase	0.000113758	50.0	85.8	44.4
64	240507_PM_at	—	—	0.000116854	8.8	8.4	8.2
65	204475_PM_at	MMP1	matrix metallopeptidase 1	0.000116902	9.2	15.4	9.6
			(interstitial collagenase)
66	222625_PM_s_at	NDE1	nudE nuclear distribution	0.000119388	60.6	55.3	72.2
			gene E homolog 1
			(A. nidulans)
67	1562697_PM_at	LOC339988	hypothetical LOC339988	0.000125343	145.2	97.8	105.4
68	218662_PM_s_at	NCAPG	non-SMC condensin I	0.000129807	11.5	14.8	10.7
			complex, subunit G
69	201212_PM_at	LGMN	legumain	0.000129933	15.4	18.9	14.2
70	236191_PM_at	—	—	0.000133129	83.4	71.0	76.6
71	33736_PM_at	STOML1	stomatin (EPB72)-like 1	0.000137232	44.9	47.9	37.4
72	221695_PM_s_at	MAP3K2	mitogen-activated protein	0.000139287	76.4	76.8	130.8
			kinase kinase kinase 2
73	241692_PM_at	—	—	0.000142595	57.5	44.8	61.8
74	218741_PM_at	CENPM	centromere protein M	0.000142617	13.5	15.9	12.3
75	220684_PM_at	TBX21	T-box 21	0.00014693	272.6	169.0	182.2
76	233700_PM_at	—	—	0.000148072	125.7	74.1	156.3
77	217336_PM_at	RPS10 ///	ribosomal protein S10 ///	0.000149318	76.4	93.5	63.0
		RPS10P7	ribosomal protein S10
			pseudogene 7
78	224391_PM_s_at	SIAE	sialic acid acetylesterase	0.000152602	28.8	42.0	33.8
79	201220_PM_x_at	CTBP2	C-terminal binding protein 2	0.000155512	1316.8	1225.6	1516.2
80	204589_PM_at	NUAK1	NUAK family, SNF1-like	0.00015593	13.1	10.1	9.6
			kinase, 1
81	1565254_PM_s_at	ELL	elongation factor RNA	0.000157726	29.2	24.5	40.4
			polymerase II
82	243362_PM_s_at	LOC641518	hypothetical LOC641518	0.000159096	14.3	21.1	13.5
83	219288_PM_at	C3orf14	chromosome 3 open reading	0.000162164	31.1	43.4	28.0
			frame 14
84	210797_PM_s_at	OASL	2′-5′-oligoadenylate	0.000167239	268.3	219.6	304.2
			synthetase-like
85	243917_PM_at	CLIC5	chloride intracellular	0.00017077	10.9	9.6	10.5
			channel 5
86	237538_PM_at	—	—	0.000176359	18.4	21.3	18.0
87	207926_PM_at	GP5	glycoprotein V (platelet)	0.000178057	17.3	19.3	15.7
88	204103_PM_at	CCL4	chemokine (C-C motif)	0.000178791	338.5	265.9	235.5
			ligand 4
89	212843_PM_at	NCAM1	neural cell adhesion	0.000180762	28.7	25.8	33.5
			molecule 1
90	213629_PM_x_at	MT1F	metallothionein 1F	0.000186273	268.3	348.4	234.3
91	212687_PM_at	LIMS1	LIM and senescent cell	0.000188224	859.6	1115.2	837.3
			antigen-like domains 1
92	242898_PM_at	EIF2AK2	eukaryotic translation	0.000189906	82.5	66.4	81.2
			initiation factor 2-alpha
			kinase 2
93	208228_PM_s_at	FGFR2	fibroblast growth factor	0.000194281	8.9	11.1	8.7
			receptor 2
94	219386_PM_s_at	SLAMF8	SLAM family member 8	0.000195762	18.6	23.0	16.5
95	201470_PM_at	GSTO1	glutathione S-transferase	0.000200503	1623.3	1902.3	1495.5
			omega 1
96	204326_PM_x_at	MT1X	metallothionein 1X	0.000202494	370.5	471.8	313.0
97	213996_PM_at	YPEL1	yippee-like 1 (Drosophila)	0.00020959	48.9	37.9	40.4
98	203820_PM_s_at	IGF2BP3	insulin-like growth factor 2	0.000210022	21.8	35.5	23.2
			mRNA binding protein 3
99	218599_PM_at	REC8	REC8 homolog (yeast)	0.000216761	42.6	43.3	41.1
100	216836_PM_s_at	ERBB2	v-erb-b2 erythroblastic	0.000217714	14.6	12.0	12.9
			leukemia viral oncogene
			homolog 2,
			neuro/glioblastoma derived o
101	213258_PM_at	TFPI	tissue factor pathway	0.000218458	13.6	24.6	14.2
			inhibitor (lipoprotein-
			associated coagulation
			inhibitor)
102	212859_PM_x_at	MT1E	metallothionein 1E	0.000218994	166.9	238.1	134.5
103	214617_PM_at	PRF1	perforin 1 (pore forming	0.000222846	1169.2	822.3	896.0
			protein)
104	38918_PM_at	SOX13	SRY (sex determining	0.000223958	14.1	10.9	11.8
			region Y)-box 13
105	209969_PM_s_at	STAT1	signal transducer and	0.00022534	1707.4	1874.3	1574.4
			activator of transcription 1,
			91 kDa
106	205909_PM_at	POLE2	polymerase (DNA directed),	0.000226803	14.0	16.0	12.7
			epsilon 2 (p59 subunit)
107	205612_PM_at	MMRN1	multimerin 1	0.000227425	10.3	15.5	11.1
108	218400_PM_at	OAS3	2′-5′-oligoadenylate	0.000231476	142.6	125.9	170.8
			synthetase 3, 100 kDa
109	202503_PM_s_at	KIAA0101	KIAA0101	0.00023183	34.4	65.8	25.5
110	225636_PM_at	STAT2	signal transducer and	0.000234463	1425.0	1422.9	1335.1
			activator of transcription 2,
			113 kDa
111	226579_PM_at	—	—	0.000234844	97.7	81.1	104.6
112	1555764_PM_s_at	TIMM10	translocase of inner	0.000235756	195.6	204.3	158.7
			mitochondrial membrane 10
			homolog (yeast)
113	218429_PM_s_at	C19orf66	chromosome 19 open	0.00024094	569.9	524.1	527.4
			reading frame 66
114	242155_PM_x_at	RFFL	ring finger and FYVE-like	0.000244391	62.8	46.7	72.0
			domain containing 1
115	1556643_PM_at	FAM125A	Family with sequence	0.000244814	173.2	181.8	181.2
			similarity 125, member A
116	201957_PM_at	PPP1R12B	protein phosphatase 1,	0.000246874	93.3	63.9	107.9
			regulatory (inhibitor)
			subunit 12B
117	219716_PM_at	APOL6	apolipoprotein L, 6	0.000248621	86.0	95.2	79.1
118	1554206_PM_at	TMLHE	trimethyllysine hydroxylase,	0.00026882	45.3	41.0	53.4
			epsilon
119	207795_PM_s_at	KLRD1	killer cell lectin-like	0.000271145	294.6	201.8	192.5
			receptor subfamily D,
			member 1
120	210756_PM_s_at	NOTCH2	notch 2	0.000271193	94.0	99.4	142.6
121	219815_PM_at	GAL3ST4	galactose-3-O-	0.00027183	17.3	19.9	16.4
			sulfotransferase 4
122	230405_PM_at	C5orf56	chromosome 5 open reading	0.000279441	569.5	563.2	521.9
			frame 56
123	228617_PM_at	XAF1	XIAP associated factor 1	0.000279625	1098.8	1162.1	1043.0
124	240733_PM_at	—	—	0.000281133	87.3	54.9	81.2
125	209773_PM_s_at	RRM2	ribonucleotide reductase M2	0.000281144	48.7	88.2	40.4
126	215236_PM_s_at	PICALM	phosphatidylinositol binding	0.000284863	61.6	65.8	113.8
			clathrin assembly protein
127	229534_PM_at	ACOT4	acyl-CoA thioesterase 4	0.000286097	17.1	13.2	12.6
128	215177_PM_s_at	ITGA6	integrin, alpha 6	0.000287492	35.2	44.2	34.0
129	210321_PM_at	GZMH	granzyme H (cathepsin G-	0.000293732	1168.2	616.6	532.0
			like 2, protein h-CCPX)
130	206194_PM_at	HOXC4	homeobox C4	0.000307767	20.0	17.1	15.1
131	214115_PM_at	VAMP5	Vesicle-associated	0.000308837	11.8	13.2	12.2
			membrane protein 5
			(myobrevin)
132	211102_PM_s_at	LILRA2	leukocyte immunoglobulin-	0.000310388	94.3	78.0	129.0
			like receptor, subfamily A
			(with TM domain), member 2
133	201818_PM_at	LPCAT1	lysophosphatidylcholine	0.000311597	662.1	517.3	651.3
			acyltransferase 1
134	53720_PM_at	C19orf66	chromosome 19 open	0.000311821	358.7	323.7	319.7
			reading frame 66
135	221648_PM_s_at	LOC100507192	hypothetical	0.000312201	68.4	96.2	56.1
			LOC100507192
136	236899_PM_at	—	—	0.000318309	9.8	10.5	8.8
137	220467_PM_at	—	—	0.000319714	205.5	124.9	201.6
138	218638_PM_s_at	SPON2	spondin 2, extracellular	0.000320682	168.2	109.2	137.0
			matrix protein
139	211287_PM_x_at	CSF2RA	colony stimulating factor 2	0.00032758	173.0	150.9	224.0
			receptor, alpha, low-affinity
			(granulocyte-macrophage)
140	222058_PM_at	—	—	0.000332098	82.7	61.0	101.6
141	224428_PM_s_at	CDCA7	cell division cycle	0.000332781	22.9	31.5	19.6
			associated 7
142	228675_PM_at	LOC100131733	hypothetical	0.000346627	15.2	17.6	14.5
			LOC100131733
143	221248_PM_s_at	WHSC1L1	Wolf-Hirschhorn syndrome	0.000354663	25.6	26.9	33.0
			candidate 1-like 1
144	227697_PM_at	SOCS3	suppressor of cytokine	0.000354764	103.6	192.4	128.8
			signaling 3
145	240661_PM_at	LOC284475	hypothetical protein	0.000355764	79.3	53.9	89.5
			LOC284475
146	204886_PM_at	PLK4	polo-like kinase 4	0.000357085	8.9	11.8	8.9
147	216834_PM_at	RGS1	regulator of G-protein	0.00035762	12.4	19.6	11.4
			signaling 1
148	234089_PM_at	—	—	0.000359586	10.5	10.1	11.2
149	236817_PM_at	ADAT2	adenosine deaminase,	0.000362076	15.6	14.3	12.0
			tRNA-specific 2, TAD2
			homolog (S. cerevisiae)
150	225349_PM_at	ZNF496	zinc finger protein 496	0.000363116	11.7	12.0	10.4
151	219863_PM_at	HERC5	hect domain and RLD 5	0.000365254	621.1	630.8	687.7
152	221985_PM_at	KLHL24	kelch-like 24 (Drosophila)	0.000374117	183.6	184.7	216.9
153	1552977_PM_a_at	CNPY3	canopy 3 homolog	0.000378983	351.3	319.3	381.7
			(zebrafish)
154	1552667_PM_a_at	SH2D3C	SH2 domain containing 3C	0.000380655	67.1	55.5	82.8
155	223502_PM_s_at	TNFSF13B	tumor necrosis factor	0.000387301	2713.6	3366.3	2999.3
			(ligand) superfamily,
			member 13b
156	235139_PM_at	GNGT2	guanine nucleotide binding	0.000389019	41.8	35.8	38.6
			protein (G protein), gamma
			transducing activity
			polypeptide
157	239979_PM_at	—	—	0.000389245	361.6	375.0	282.8
158	211882_PM_x_at	FUT6	fucosyltransferase 6 (alpha	0.000392613	11.1	11.6	10.6
			(1,3) fucosyltransferase)
159	1562698_PM_x_at	LOC339988	hypothetical LOC339988	0.000394736	156.3	108.5	117.0
160	201890_PM_at	RRM2	ribonucleotide reductase M2	0.000397796	23.6	42.5	21.7
161	243349_PM_at	KIAA1324	KIAA1324	0.000399335	15.4	12.8	20.2
162	243947_PM_s_at	—	—	0.000399873	8.4	9.6	8.9
163	205483_PM_s_at	ISG15	ISG15 ubiquitin-like	0.000409282	1223.6	1139.6	1175.7
			modifier
164	202705_PM_at	CCNB2	cyclin B2	0.000409541	14.7	20.9	13.8
165	210835_PM_s_at	CTBP2	C-terminal binding protein 2	0.000419387	992.3	926.1	1150.4
166	210554_PM_s_at	CTBP2	C-terminal binding protein 2	0.000429433	1296.5	1198.0	1519.5
167	207085_PM_x_at	CSF2RA	colony stimulating factor 2	0.000439275	204.5	190.0	290.3
			receptor, alpha, low-affinity
			(granulocyte-macrophage)
168	204205_PM_at	APOBEC3G	apolipoprotein B mRNA	0.000443208	1115.8	988.8	941.4
			editing enzyme, catalytic
			polypeptide-like 3G
169	227394_PM_at	NCAM1	neural cell adhesion	0.000443447	19.1	19.4	25.3
			molecule 1
170	1568943_PM_at	INPP5D	inositol polyphosphate-5-	0.000450045	127.3	87.7	114.0
			phosphatase, 145 kDa
171	213932_PM_x_at	HLA-A	major histocompatibility	0.00045661	9270.0	9080.1	9711.9
			complex, class I, A
172	226202_PM_at	ZNF398	zinc finger protein 398	0.000457538	84.5	78.4	98.3
173	233675_PM_s_at	LOC374491	TPTE and PTEN	0.000457898	8.8	8.1	8.5
			homologous inositol lipid
			phosphatase pseudogene
174	220711_PM_at	—	—	0.000458552	197.6	162.7	209.0
175	1552646_PM_at	IL11RA	interleukin 11 receptor,	0.000463237	18.9	15.9	19.6
			alpha
176	227055_PM_at	METTL7B	methyltransferase like 7B	0.000464226	11.1	15.0	11.8
177	223980_PM_s_at	SP110	SP110 nuclear body protein	0.000471467	1330.9	1224.3	1367.3
178	242367_PM_at	—	—	0.000471796	9.1	10.5	9.6
179	218543_PM_s_at	PARP12	poly (ADP-ribose)	0.000476879	513.8	485.7	475.7
			polymerase family, member
			12
180	204972_PM_at	OAS2	2′-5′-oligoadenylate	0.000480934	228.5	215.8	218.7
			synthetase 2, 69/71 kDa
181	205746_PM_s_at	ADAM17	ADAM metallopeptidase	0.000480965	39.0	47.0	60.4
			domain 17
182	1570645_PM_at	—	—	0.000482948	9.3	9.1	8.4
183	211286_PM_x_at	CSF2RA	colony stimulating factor 2	0.000484313	261.3	244.7	345.6
			receptor, alpha, low-affinity
			(granulocyte-macrophage)
184	1557545_PM_s_at	RNF165	ring finger protein 165	0.000489377	17.4	15.4	18.3
185	236545_PM_at	—	—	0.000491065	479.3	367.8	526.2
186	228280_PM_at	ZC3HAV1L	zinc finger CCCH-type,	0.000495768	25.3	36.4	23.7
			antiviral 1-like
187	239798_PM_at	—	—	0.000505865	43.9	63.7	48.8
188	208055_PM_s_at	HERC4	hect domain and RLD 4	0.000507283	37.6	34.8	45.8
189	225692_PM_at	CAMTA1	calmodulin binding	0.000515621	244.8	308.6	245.1
			transcription activator 1
190	210986_PM_s_at	TPM1	tropomyosin 1 (alpha)	0.000532739	344.0	379.1	391.9
191	205929_PM_at	GPA33	glycoprotein A33	0.00053619	18.3	21.8	16.7
			(transmembrane)
192	242234_PM_at	XAF1	XIAP associated factor 1	0.000537429	123.1	133.1	114.9
193	206113_PM_s_at	RAB5A	RAB5A, member RAS	0.000543933	77.5	73.0	111.4
			oncogene family
194	242520_PM_s_at	C1orf228	chromosome 1 open reading	0.000547685	30.4	42.5	29.4
			frame 228
195	229203_PM_at	B4GALNT3	beta-1,4-N-acetyl-	0.000549855	9.1	9.0	9.7
			galactosaminyl transferase 3
196	201601_PM_x_at	IFITM1	interferon induced	0.000554665	6566.1	7035.7	7016.0
			transmembrane protein 1 (9-27)
197	221024_PM_s_at	SLC2A10	solute carrier family 2	0.000559418	8.3	9.7	8.6
			(facilitated glucose
			transporter), member 10
198	204439_PM_at	IFI44L	interferon-induced protein	0.000570113	343.5	312.4	337.1
			44-like
199	215894_PM_at	PTGDR	prostaglandin D2 receptor	0.000571076	343.8	191.2	233.7
			(DP)
200	230846_PM_at	AKAP5	A kinase (PRKA) anchor	0.000572655	10.7	10.9	9.6
			protein 5
201	210340_PM_s_at	CSF2RA	colony stimulating factor 2	0.000572912	154.2	146.3	200.8
			receptor, alpha, low-affinity
			(granulocyte-macrophage)
202	237240_PM_at	—	—	0.000573343	9.4	10.7	9.4
203	223836_PM_at	FGFBP2	fibroblast growth factor	0.000574294	792.6	432.4	438.4
			binding protein 2
204	233743_PM_x_at	S1PR5	sphingosine-1-phosphate	0.000577598	9.3	8.6	9.6
			receptor 5
205	229254_PM_at	MFSD4	major facilitator superfamily	0.000581119	9.4	11.0	9.3
			domain containing 4
206	243674_PM_at	LOC100240735 ///	hypothetical	0.00058123	14.5	12.9	12.1
		LOC401522	LOC100240735 ///
			hypothetical LOC401522
207	208116_PM_s_at	MAN1A1	mannosidase, alpha, class	0.000581644	34.4	39.1	55.0
			1A, member 1
208	222246_PM_at	—	—	0.000584363	15.9	13.9	17.9
209	212659_PM_s_at	IL1RN	interleukin 1 receptor	0.000592065	87.2	94.5	116.3
			antagonist
210	204070_PM_at	RARRES3	retinoic acid receptor	0.000597748	771.6	780.7	613.7
			responder (tazarotene
			induced) 3
211	219364_PM_at	DHX58	DEXH (Asp-Glu-X-His)	0.000599299	92.7	85.2	85.3
			box polypeptide 58
212	204747_PM_at	IFIT3	interferon-induced protein	0.000601375	603.1	576.7	586.2
			with tetratricopeptide
			repeats 3
213	240258_PM_at	ENO1	enolase 1, (alpha)	0.000601726	9.0	9.3	10.5
214	210724_PM_at	EMR3	egf-like module containing,	0.000609884	622.3	437.3	795.3
			mucin-like, hormone
			receptor-like 3
215	204211_PM_x_at	EIF2AK2	eukaryotic translation	0.000611116	168.3	139.2	179.6
			initiation factor 2-alpha
			kinase 2
216	234975_PM_at	GSPT1	G1 to S phase transition 1	0.000615027	16.6	16.3	21.4
217	228145_PM_s_at	ZNF398	zinc finger protein 398	0.000620533	373.0	329.5	374.3
218	201565_PM_s_at	ID2	inhibitor of DNA binding 2,	0.000627734	1946.2	1798.1	1652.9
			dominant negative helix-
			loop-helix protein
219	226906_PM_s_at	ARHGAP9	Rho GTPase activating	0.000630617	636.2	516.2	741.5
			protein 9
220	228412_PM_at	LOC643072	hypothetical LOC643072	0.00064178	213.5	186.6	282.7
221	233957_PM_at	—	—	0.000644277	33.2	24.7	40.1
222	221277_PM_s_at	PUS3	pseudouridylate synthase 3	0.000649375	86.6	99.3	77.8
223	203911_PM_at	RAP1GAP	RAP1 GTPase activating	0.000658389	106.6	40.1	116.1
			protein
224	219352_PM_at	HERC6	hect domain and RLD 6	0.000659313	94.6	87.2	81.8
225	204994_PM_at	MX2	myxovirus (influenza virus)	0.000663904	1279.3	1147.0	1329.9
			resistance 2 (mouse)
226	227499_PM_at	FZD3	frizzled homolog 3	0.00066528	11.7	11.0	9.8
			(Drosophila)
227	222930_PM_s_at	AGMAT	agmatine ureohydrolase	0.000665618	12.9	14.9	11.4
			(agmatinase)
228	204575_PM_s_at	MMP19	matrix metallopeptidase 19	0.000668161	9.6	9.3	9.9
229	221038_PM_at	—	—	0.000671518	8.7	8.2	9.3
230	233425_PM_at	—	—	0.000676591	76.4	70.6	77.9
231	228972_PM_at	LOC100306951	hypothetical	0.000679857	77.8	84.0	60.0
			LOC100306951
232	1560999_PM_a_at	—	—	0.000680202	9.8	10.6	10.7
233	225931_PM_s_at	RNF213	ring finger protein 213	0.000685818	339.7	313.2	333.3
234	1559110_PM_at	—	—	0.000686358	11.7	11.5	13.4
235	207538_PM_at	IL4	interleukin 4	0.000697306	8.3	9.5	8.7
236	210358_PM_x_at	GATA2	GATA binding protein 2	0.000702179	22.8	30.8	16.8
237	236341_PM_at	CTLA4	cytotoxic T-lymphocyte-	0.000706875	16.5	22.3	16.8
			associated protein 4
238	227416_PM_s_at	ZCRB1	zinc finger CCHC-type and	0.000708438	388.0	422.6	338.2
			RNA binding motif 1
239	210788_PM_s_at	DHRS7	dehydrogenase/reductase	0.000719333	1649.6	1559.9	1912.3
			(SDR family) member 7
240	213287_PM_s_at	KRT10	keratin 10	0.000721676	557.8	585.1	439.3
241	204026_PM_s_at	ZWINT	ZW10 interactor	0.000724993	23.3	31.1	19.9
242	239223_PM_s_at	FBXL20	F-box and leucine-rich	0.00073241	106.8	75.0	115.9
			repeat protein 20
243	234196_PM_at	—	—	0.000742539	140.6	81.3	162.4
244	214931_PM_s_at	SRPK2	SRSF protein kinase 2	0.00074767	30.0	30.9	45.3
245	216907_PM_x_at	KIR3DL1 ///	killer cell immunoglobulin-	0.000748056	18.8	12.6	13.8
		KIR3DL2 ///	like receptor, three domains,
		LOC727787	long cytoplasmic tail, 1 /// k
246	243802_PM_at	DNAH12	dynein, axonemal, heavy	0.000751054	8.8	9.9	8.4
			chain 12
247	212070_PM_at	GPR56	G protein-coupled receptor	0.000760168	338.8	177.5	198.1
			56
248	239185_PM_at	ABCA9	ATP-binding cassette, sub-	0.000767347	8.3	9.0	9.8
			family A (ABC1), member
			9
249	229597_PM_s_at	WDFY4	WDFY family member 4	0.000769378	128.9	96.6	148.4
250	216243_PM_s_at	IL1RN	interleukin 1 receptor	0.000770819	131.4	134.1	180.7
			antagonist
251	206991_PM_s_at	CCR5	chemokine (C-C motif)	0.000771059	128.5	128.6	110.5
			receptor 5
252	219385_PM_at	SLAMF8	SLAM family member 8	0.000789607	13.8	13.2	11.3
253	240438_PM_at	—	—	0.000801737	10.8	10.4	11.4
254	226303_PM_at	PGM5	phosphoglucomutase 5	0.000802853	11.9	12.6	24.2
255	205875_PM_s_at	TREX1	three prime repair	0.000804871	254.9	251.6	237.6
			exonuclease 1
256	1566201_PM_at	—	—	0.000809569	10.4	9.0	10.2
257	211230_PM_s_at	PIK3CD	phosphoinositide-3-kinase,	0.000812288	20.4	20.3	24.6
			catalytic, delta polypeptide
258	202566_PM_s_at	SVIL	supervillin	0.000819718	43.9	41.0	67.5
259	244846_PM_at	—	—	0.000821386	75.0	55.1	84.9
260	208436_PM_s_at	IRF7	interferon regulatory factor 7	0.000826426	264.0	262.4	281.2
261	242020_PM_s_at	ZBP1	Z-DNA binding protein 1	0.000828174	87.9	83.1	102.5
262	203779_PM_s_at	MPZL2	myelin protein zero-like 2	0.000830222	10.4	10.0	12.9
263	212458_PM_at	SPRED2	sprouty-related, EVH1	0.000833211	11.5	11.4	13.4
			domain containing 2

TABLE 17a

AUCs for the 147 probes to predict AR, HCV and AR + HCV in Liver whole blood samples.

							Postive	Negative
				Predictive			Predictive	Predictive
Algorithm	Predictors	Comparison	AUC	Accuracy (%)	Sensitivity (%)	Specificity (%)	Value (%)	Value (%)

Nearest Centroid	147	AR vs. HCV	0.952	96	87	97	95	92
Nearest Centroid	147	AR vs. HCV + AR	0.821	82	91	92	95	85
Nearest Centroid	147	HCV vs. HCV + AR	0.944	94	92	97	92	97

TABLE 17b

The 147 probesets for distinguishing between AR, HCV and HCV + AR in Liver PAXgene samples

							HCV +
				p-value	AR -	HCV -	AR -
#	Probeset ID	Gene Symbol	Gene Title	(Phenotype)	Mean	Mean	Mean

1	241038_PM_at	—	—	4.76E−08	21.0	13.2	13.9
2	207737_PM_at	—	—	5.33E−06	8.5	8.4	10.2
3	1557733_PM_a_at	—	—	6.19E−06	116.0	50.8	64.5
4	228290_PM_at	PLK1S1	Polo-like kinase 1 substrate 1	7.97E−06	35.6	48.1	48.5
5	231798_PM_at	NOG	noggin	8.34E−06	25.9	12.6	9.4
6	214039_PM_s_at	LAPTM4B	lysosomal protein	9.49E−06	104.0	58.3	68.5
			transmembrane 4 beta
7	241692_PM_at	—	—	9.61E−06	44.8	65.1	78.4
8	230776_PM_at	—	—	1.21E−05	13.7	10.4	9.5
9	217963_PM_s_at	NGFRAP1	nerve growth factor receptor	1.56E−05	713.1	461.2	506.6
			(TNFRSF16) associated
			protein 1
10	243917_PM_at	CLIC5	chloride intracellular channel 5	1.67E−05	9.6	10.9	11.6
11	219915_PM_s_at	SLC16A10	solute carrier family 16,	1.77E−05	21.8	13.2	12.5
			member 10 (aromatic amino
			acid transporter)
12	1553873_PM_at	KLHL34	kelch-like 34 (Drosophila)	1.85E−05	12.1	9.6	9.1
13	227645_PM_at	PIK3R5	phosphoinositide-3-kinase,	2.12E−05	824.5	1003.6	1021.4
			regulatory subunit 5
14	1552623_PM_at	HSH2D	hematopoietic SH2 domain	2.54E−05	323.9	497.5	445.4
			containing
15	227486_PM_at	NT5E	5′-nucleotidase, ecto (CD73)	2.66E−05	18.6	13.4	12.2
16	219659_PM_at	ATP8A2	ATPase, aminophospholipid	4.00E−05	10.8	9.0	8.9
			transporter, class I, type 8A,
			member 2
17	1555874_PM_x_at	MGC21881	hypothetical locus	4.16E−05	20.0	21.0	31.4
			MGC21881
18	202086_PM_at	MX1	myxovirus (influenza virus)	4.52E−05	496.4	1253.1	1074.1
			resistance 1, interferon-
			inducible protein p78 (mouse)
19	233675_PM_s_at	LOC374491	TPTE and PTEN homologous	4.85E−05	8.1	8.2	9.9
			inositol lipid phosphatase
			pseudogene

20	219815_PM_at	GAL3ST4	galactose-3-O-	5.37E−05	19.9	17.0	14.3
			sulfotransferase 4
21	242898_PM_at	EIF2AK2	eukaryotic translation	6.06E−05	66.4	116.6	108.7
			initiation factor 2-alpha
			kinase 2
22	215177_PM_s_at	ITGA6	integrin, alpha 6	6.39E−05	44.2	26.9	23.9
23	236717_PM_at	FAM179A	family with sequence	6.43E−05	39.8	51.3	73.3
			similarity 179, member A
24	242520_PM_s_at	C1orf228	chromosome 1 open reading	6.67E−05	42.5	29.1	26.4
			frame 228
25	207926_PM_at	GP5	glycoprotein V (platelet)	7.03E−05	19.3	14.7	16.0
26	211882_PM_x_at	FUT6	fucosyltransferase 6 (alpha	8.11E−05	11.6	9.8	10.7
			(1,3) fucosyltransferase)
27	201656_PM_at	ITGA6	integrin, alpha 6	8.91E−05	112.6	69.0	70.7
28	233743_PM_x_at	S1PR5	sphingosine-1-phosphate	9.26E−05	8.6	10.1	9.2
			receptor 5
29	210797_PM_s_at	OASL	2′-5′-oligoadenylate	9.28E−05	219.6	497.2	446.0
			synthetase-like
30	243819_PM_at	—	—	9.55E−05	495.1	699.2	769.8
31	209728_PM_at	HLA-DRB4 ///	major histocompatibility	0.000102206	33.8	403.5	55.2
		LOC100509582	complex, class II, DR beta 4
			/// HLA class II
			histocompatibili
32	218638_PM_s_at	SPON2	spondin 2, extracellular	0.000103572	109.2	215.7	187.9
			matrix protein
33	224293_PM_at	TTTY10	testis-specific transcript, Y-	0.000103782	8.7	11.1	10.2
			linked 10 (non-protein
			coding)
34	205660_PM_at	OASL	2′-5′-oligoadenylate	0.000105267	394.6	852.0	878.1
			synthetase-like
35	230753_PM_at	PATL2	protein associated with	0.00010873	123.0	168.6	225.2
			topoisomerase II homolog 2
			(yeast)
36	243362_PM_s_at	LOC641518	hypothetical LOC641518	0.000114355	21.1	13.1	11.2
37	213996_PM_at	YPEL1	yippee-like 1 (Drosophila)	0.00012688	37.9	55.8	59.5
38	232222_PM_at	C18orf49	chromosome 18 open reading	0.000129064	35.7	65.1	53.0
			frame 49
39	205612_PM_at	MMRN1	multimerin 1	0.000142028	15.5	9.9	11.2
40	214791_PM_at	SP140L	SP140 nuclear body protein-	0.000150108	223.4	278.8	285.8
			like
41	240507_PM_at	—	—	0.000152167	8.4	9.5	8.1
42	203819_PM_s_at	IGF2BP3	insulin-like growth factor 2	0.000174054	75.4	45.9	62.4
			mRNA binding protein 3
43	219288_PM_at	C3orf14	chromosome 3 open reading	0.000204911	43.4	29.2	51.0
			frame 14
44	214376_PM_at	—	—	0.000213039	8.9	9.6	8.1
45	1568609_PM_s_at	FAM91A2 ///	family with sequence	0.000218802	378.6	472.7	427.1
		FLJ39739 ///	similarity 91, member A2 ///
		LOC100286793	hypothetical FLJ39739 ///
		///	hypothetica
		LOC728855 ///
		LOC728875
46	207538_PM_at	IL4	interleukin 4	0.000226354	9.5	8.3	8.9
47	243947_PM_s_at	—	—	0.000227289	9.6	8.4	8.6
48	204211_PM_x_at	EIF2AK2	eukaryotic translation	0.000227971	139.2	222.0	225.5
			initiation factor 2-alpha
			kinase 2
49	221648_PM_s_at	LOC100507192	hypothetical LOC100507192	0.000230544	96.2	62.4	62.1
50	202016_PM_at	MEST	mesoderm specific transcript	0.000244181	27.5	17.0	19.3
			homolog (mouse)
51	220684_PM_at	TBX21	T-box 21	0.000260563	169.0	279.9	309.1
52	219018_PM_s_at	CCDC85C	coiled-coil domain containing	0.000261452	14.9	17.1	17.1
			85C
53	204575_PM_s_at	MMP19	matrix metallopeptidase 19	0.00026222	9.3	9.3	11.3
54	1568943_PM_at	INPP5D	inositol polyphosphate-5-	0.000265939	87.7	143.4	133.5
			phosphatase, 145 kDa
55	220467_PM_at	—	—	0.000269919	124.9	215.2	206.0
56	207324_PM_s_at	DSC1	desmocollin 1	0.000280239	14.5	11.3	10.3
57	218400_PM_at	OAS3	2′-5′-oligoadenylate	0.000288454	125.9	316.7	299.6
			synthetase 3, 100 kDa
58	214617_PM_at	PRF1	perforin 1 (pore forming	0.000292417	822.3	1327.9	1415.4
			protein)
59	239798_PM_at	—	—	0.000294263	63.7	39.1	35.3
60	242020_PM_s_at	ZBP1	Z-DNA binding protein 1	0.000303843	83.1	145.8	128.5
61	201786_PM_s_at	ADAR	adenosine deaminase, RNA-	0.000305042	2680.0	3340.9	3194.2
			specific
62	234974_PM_at	GALM	galactose mutarotase (aldose	0.000308107	63.1	88.8	93.7
			1-epimerase)
63	233121_PM_at	—	—	0.000308702	17.8	23.8	19.4
64	1557545_PM_s_at	RNF165	ring finger protein 165	0.000308992	15.4	24.2	22.1
65	229203_PM_at	B4GALNT3	beta-1,4-N-acetyl-	0.000309508	9.0	10.1	8.6
			galactosaminyl transferase 3
66	210164_PM_at	GZMB	granzyme B (granzyme 2,	0.000322925	749.5	1241.7	1374.7
			cytotoxic T-lymphocyte-
			associated serine esterase 1)
67	222468_PM_at	KIAA0319L	KIAA0319-like	0.000327428	286.7	396.3	401.1
68	223272_PM_s_at	C1orf57	chromosome 1 open reading	0.000342477	69.0	54.6	77.4
			frame 57
69	240913_PM_at	FGFR2	fibroblast growth factor	0.00035107	9.6	10.6	11.7
			receptor 2
70	230854_PM_at	BCAR4	breast cancer anti-estrogen	0.000352682	10.2	10.2	8.9
			resistance 4
71	1562697_PM_at	LOC339988	hypothetical LOC339988	0.000360155	97.8	151.3	142.0
72	222732_PM_at	TRIM39	tripartite motif-containing 39	0.000372812	115.6	135.8	115.4
73	227917_PM_at	FAM85A ///	family with sequence	0.000373226	206.8	154.1	154.9
		FAM85B	similarity 85, member A ///
			family with sequence
			similarity 85, me
74	212687_PM_at	LIMS1	LIM and senescent cell	0.000383722	1115.2	824.0	913.2
			antigen-like domains 1
75	216836_PM_s_at	ERBB2	v-erb-b2 erythroblastic	0.000384613	12.0	16.3	14.3
			leukemia viral oncogene
			homolog 2,
			neuro/glioblastoma derived o
76	236191_PM_at	—	—	0.000389259	71.0	95.0	114.3
77	213932_PM_x_at	HLA-A	major histocompatibility	0.000391535	9080.1	10344.2	10116.9
			complex, class I, A
78	229254_PM_at	MFSD4	major facilitator superfamily	0.000393739	11.0	9.0	9.5
			domain containing 4
79	212843_PM_at	NCAM1	neural cell adhesion molecule 1	0.000401596	25.8	50.2	37.7
80	235256_PM_s_at	GALM	galactose mutarotase (aldose	0.000417617	58.0	79.8	90.2
			1-epimerase)
81	1566201_PM_at	—	—	0.000420058	9.0	10.3	8.8
82	204994_PM_at	MX2	myxovirus (influenza virus)	0.000438751	1147.0	1669.1	1518.5
			resistance 2 (mouse)
83	237240_PM_at	—	—	0.000440008	10.7	9.2	9.1
84	232478_PM_at	—	—	0.000447263	51.3	96.8	71.5
85	211410_PM_x_at	KIR2DL5A	killer cell immunoglobulin-	0.00045859	24.8	31.7	39.0
			like receptor, two domains,
			long cytoplasmic tail, 5A
86	1569551_PM_at	—	—	0.00045899	12.7	17.5	17.9
87	222816_PM_s_at	ZCCHC2	zinc finger, CCHC domain	0.00046029	308.7	502.0	404.6
			containing 2
88	1557071_PM_s_at	NUB1	negative regulator of	0.000481473	108.5	144.0	155.3
			ubiquitin-like proteins 1
89	219737_PM_s_at	PCDH9	protocadherin 9	0.000485253	37.9	76.4	66.9
90	230563_PM_at	RASGEF1A	RasGEF domain family,	0.000488148	86.8	121.7	139.4
			member 1A
91	1560080_PM_at	—	—	0.000488309	9.9	11.0	12.2
92	243756_PM_at	—	—	0.000488867	8.5	7.5	8.2
93	212730_PM_at	SYNM	synemin, intermediate	0.000521028	19.5	15.7	27.7
			filament protein
94	1552977_PM_a_at	CNPY3	canopy 3 homolog (zebrafish)	0.000521239	319.3	395.2	261.4
95	218657_PM_at	RAPGEFL1	Rap guanine nucleotide	0.000529963	10.4	11.9	11.5
			exchange factor (GEF)-like 1
96	228139_PM_at	RIPK3	receptor-interacting serine-	0.000530418	87.8	107.4	102.7
			threonine kinase 3
97	38918_PM_at	SOX13	SRY (sex determining region	0.000534735	10.9	13.1	13.1
			Y)-box 13
98	207795_PM_s_at	KLRD1	killer cell lectin-like receptor	0.000538523	201.8	309.8	336.1
			subfamily D, member 1
99	212906_PM_at	GRAMD1B	GRAM domain containing 1B	0.000540879	51.0	58.3	78.1
100	1561098_PM_at	LOC641365	hypothetical LOC641365	0.000541122	8.7	8.5	10.1
101	209593_PM_s_at	TOR1B	torsin family 1, member B	0.000542383	271.7	392.9	408.3
			(torsin B)
102	223980_PM_s_at	SP110	SP110 nuclear body protein	0.000543351	1224.3	1606.9	1561.2
103	1554206_PM_at	TMLHE	trimethyllysine hydroxylase,	0.000545869	41.0	50.6	46.5
			epsilon
104	240438_PM_at	—	—	0.000555441	10.4	12.0	13.1
105	212190_PM_at	SERPINE2	serpin peptidase inhibitor,	0.00055869	25.8	18.3	21.4
			clade E (nexin, plasminogen
			activator inhibitor type 1), me
106	202081_PM_at	IER2	immediate early response 2	0.000568285	1831.1	2155.1	1935.4
107	234089_PM_at	—	—	0.000585869	10.1	12.4	11.9
108	235139_PM_at	GNGT2	guanine nucleotide binding	0.000604705	35.8	50.6	51.5
			protein (G protein), gamma
			transducing activity
			polypeptide
109	235545_PM_at	DEPDC1	DEP domain containing 1	0.00060962	8.7	8.4	10.0
110	242096_PM_at	—	—	0.000618307	8.6	8.7	10.3
111	1553042_PM_a_at	NFKBID	nuclear factor of kappa light	0.000619863	14.9	17.7	16.0
			polypeptide gene enhancer in
			B-cells inhibitor, delta
112	209368_PM_at	EPHX2	epoxide hydrolase 2,	0.000625958	33.6	25.2	22.3
			cytoplasmic
113	1553681_PM_a_at	PRF1	perforin 1 (pore forming	0.000629562	181.7	312.5	312.3
			protein)
114	223836_PM_at	FGFBP2	fibroblast growth factor	0.000647084	432.4	739.7	788.9
			binding protein 2
115	210812_PM_at	XRCC4	X-ray repair complementing	0.000674811	13.2	15.5	16.5
			defective repair in Chinese
			hamster cells 4
116	230846_PM_at	AKAP5	A kinase (PRKA) anchor	0.000678814	10.9	9.3	11.2
			protein 5
117	214567_PM_s_at	XCL1 ///	chemokine (C motif) ligand 1	0.000680647	211.0	338.8	347.2
		XCL2	/// chemokine (C motif)
			ligand 2
118	237221_PM_at	—	—	0.00069712	9.9	8.7	9.5
119	232793_PM_at	—	—	0.000698404	10.2	12.5	13.0
120	239479_PM_x_at	—	—	0.000700142	28.1	18.0	20.6
121	1558836_PM_at	—	—	0.000706412	33.2	53.1	45.7
122	1562698_PM_x_at	LOC339988	hypothetical LOC339988	0.000710123	108.5	165.5	158.7
123	1552646_PM_at	IL11RA	interleukin 11 receptor, alpha	0.000716149	15.9	19.4	16.3
124	236220_PM_at	—	—	0.000735209	9.9	8.3	7.7
125	211379_PM_x_at	B3GALNT1	beta-1,3-N-	0.00074606	8.9	8.2	9.7
			acetylgalactosaminyltransferase
			1 (globoside blood group)
126	222830_PM_at	GRHL1	grainyhead-like 1	0.000766774	14.7	10.5	10.4
			(Drosophila)
127	210948_PM_s_at	LEF1	lymphoid enhancer-binding	0.000768363	54.2	36.2	33.1
			factor 1
128	244798_PM_at	LOC100507492	hypothetical LOC100507492	0.000800826	48.3	32.0	26.6
129	226666_PM_at	DAAM1	dishevelled associated	0.000828238	64.3	50.3	47.8
			activator of morphogenesis 1
130	229378_PM_at	STOX1	storkhead box 1	0.000836722	10.2	8.5	9.6
131	206366_PM_x_at	XCL1	chemokine (C motif) ligand 1	0.000839844	194.1	306.8	324.9
132	214115_PM_at	VAMP5	Vesicle-associated membrane	0.000866755	13.2	12.1	16.6
			protein 5 (myobrevin)
133	201212_PM_at	LGMN	legumain	0.00087505	18.9	15.9	13.1
134	204863_PM_s_at	IL6ST	interleukin 6 signal transducer	0.000897042	147.6	107.1	111.1
			(gp130, oncostatin M
			receptor)
135	232229_PM_at	SETX	senataxin	0.000906105	34.5	45.3	36.9
136	1555407_PM_s_at	FGD3	FYVE, RhoGEF and PH	0.00091116	88.7	103.2	67.0
			domain containing 3
137	223127_PM_s_at	C1orf21	chromosome 1 open reading	0.000923068	9.1	10.3	11.0
			frame 21
138	202458_PM_at	PRSS23	protease, serine, 23	0.000924141	38.8	74.1	79.3
139	210606_PM_x_at	KLRD1	killer cell lectin-like receptor	0.000931313	289.8	421.9	473.0
			subfamily D, member 1
140	212444_PM_at	—	—	0.000935909	10.2	11.6	10.2
141	240893_PM_at	—	—	0.000940973	8.6	9.7	10.3
142	219474_PM_at	C3orf52	chromosome 3 open reading	0.000948853	8.9	10.0	10.2
			frame 52
143	235087_PM_at	UNKL	unkempt homolog	0.000967141	10.3	9.8	8.3
			(Drosophila)-like
144	216907_PM_x_at	KIR3DL1 ///	killer cell immunoglobulin-	0.000987803	12.6	16.1	19.1
		KIR3DL2 ///	like receptor, three domains,
		LOC727787	long cytoplasmic tail, 1 /// k
145	238402_PM_s_at	FLJ35220	hypothetical protein	0.000990348	17.2	19.9	15.3
			FLJ35220
146	239273_PM_s_at	MMP28	matrix metallopeptidase 28	0.000993809	11.7	9.0	8.7
147	215894_PM_at	PTGDR	prostaglandin D2 receptor	0.000994157	191.2	329.4	283.2
			(DP)

TABLE 18a

AUCs for the 320 probes to predict AR, ADNR and TX in Liver biopsy samples.

							Postive	Negative
				Predictive			Predictive	Predictive
Algorithm	Predictors	Comparison	AUC	Accuracy (%)	Sensitivity (%)	Specificity (%)	Value (%)	Value (%)

Nearest Centroid	320	AR vs. HCV	0.937	94	84	100	100	89
Nearest Centroid	320	AR vs. HCV + AR	1.000	100	100	100	100	100
Nearest Centroid	320	HCV vs. HCV + AR	0.829	82	82	89	75	92

TABLE 18b

The 320 probesets that distinguish AR vs. HCV vs. HCV + AR in Liver Biopsies

							HCV +
		Gene		p-value	AR -	HCV -	AR -
#	Probeset ID	Symbol	Gene Title	(Phenotype)	Mean	Mean	Mean

1	219863_PM_at	HERC5	hect domain and RLD 5	1.53E−14	250.4	1254.7	1620.1
2	205660_PM_at	OASL	2′-5′-oligoadenylate	3.30E−14	128.1	1273.7	1760.9
			synthetase-like
3	210797_PM_s_at	OASL	2′-5′-oligoadenylate	4.03E−14	62.0	719.3	915.2
			synthetase-like
4	214453_PM_s_at	IFI44	interferon-induced protein 44	3.98E−13	342.2	1646.7	1979.2
5	218986_PM_s_at	DDX60	DEAD (Asp-Glu-Ala-Asp)	5.09E−12	352.2	1253.2	1403.0
			box polypeptide 60
6	202869_PM_at	OAS1	2′,5′-oligoadenylate synthetase	4.47E−11	508.0	1648.7	1582.5
			1, 40/46 kDa
7	226702_PM_at	CMPK2	cytidine monophosphate	5.23E−11	257.3	1119.1	1522.6
			(UMP-CMP) kinase 2,
			mitochondrial
8	203153_PM_at	IFIT1	interferon-induced protein	5.31E−11	704.0	2803.7	3292.9
			with tetratricopeptide repeats 1
9	202086_PM_at	MX1	myxovirus (influenza virus)	5.53E−11	272.4	1420.9	1836.8
			resistance 1, interferon-
			inducible protein p78 (mouse)
10	242625_PM_at	RSAD2	radical S-adenosyl methionine	9.62E−11	56.2	389.2	478.2
			domain containing 2
11	213797_PM_at	RSAD2	radical S-adenosyl methionine	1.43E−10	91.4	619.3	744.7
			domain containing 2
12	204972_PM_at	OAS2	2′-5′-oligoadenylate	2.07E−10	88.7	402.1	536.1
			synthetase 2, 69/71 kDa
13	219352_PM_at	HERC6	hect domain and RLD 6	2.52E−10	49.5	206.7	272.8
14	205483_PM_s_at	ISG15	ISG15 ubiquitin-like modifier	3.68E−10	629.9	3181.1	4608.0
15	205552_PM_s_at	OAS1	2′,5′-oligoadenylate synthetase	4.08E−10	224.7	868.7	921.2
			1, 40/46 kDa
16	204415_PM_at	IFI6	interferon, alpha-inducible	5.83E−10	787.8	4291.7	5465.6
			protein 6
17	205569_PM_at	LAMP3	lysosomal-associated	6.80E−10	21.8	91.3	126.2
			membrane protein 3
18	219209_PM_at	IFIH1	interferon induced with	8.15E−10	562.3	1246.9	1352.7
			helicase C domain 1
19	218400_PM_at	OAS3	2′-5′-oligoadenylate	2.85E−09	87.9	265.2	364.5
			synthetase 3, 100 kDa
20	229450_PM_at	IFIT3	interferon-induced protein	4.69E−09	1236.3	2855.3	3291.7
			with tetratricopeptide repeats 3
21	226757_PM_at	IFIT2	interferon-induced protein	5.35E−09	442.3	1083.2	1461.9
			with tetratricopeptide repeats 2
22	204439_PM_at	IFI44L	interferon-induced protein 44-	5.77E−09	146.3	794.4	1053.5
			like
23	227609_PM_at	EPSTI1	epithelial stromal interaction 1	1.03E−08	396.9	1079.8	1370.3
			(breast)
24	204747_PM_at	IFIT3	interferon-induced protein	1.59E−08	228.3	698.1	892.7
			with tetratricopeptide repeats 3
25	217502_PM_at	IFIT2	interferon-induced protein	1.85E−08	222.9	575.1	745.9
			with tetratricopeptide repeats 2
26	228607_PM_at	OAS2	2′-5′-oligoadenylate	2.16E−08	60.9	182.0	225.6
			synthetase 2, 69/71 kDa
27	224870_PM_at	KIAA0114	KIAA0114	2.48E−08	156.5	81.8	66.0
28	202411_PM_at	IFI27	interferon, alpha-inducible	4.25E−08	1259.4	5620.8	5634.1
			protein 27
29	223220_PM_s_at	PARP9	poly (ADP-ribose)	4.48E−08	561.7	1084.4	1143.1
			polymerase family, member 9
30	208436_PM_s_at	IRF7	interferon regulatory factor 7	4.57E−08	58.9	102.9	126.9
31	219211_PM_at	USP18	ubiquitin specific peptidase 18	6.39E−08	51.0	183.6	196.1
32	206133_PM_at	XAF1	XIAP associated factor 1	7.00E−08	463.9	1129.2	1327.1
33	202446_PM_s_at	PLSCR1	phospholipid scramblase 1	1.12E−07	737.8	1317.7	1419.8
34	235276_PM_at	EPSTI1	epithelial stromal interaction 1	1.58E−07	93.5	244.2	279.9
			(breast)
35	219684_PM_at	RTP4	receptor (chemosensory)	1.64E−07	189.5	416.3	541.7
			transporter protein 4
36	222986_PM_s_at	SHISA5	shisa homolog 5 (Xenopus	1.68E−07	415.0	586.9	681.4
			laevis)
37	223298_PM_s_at	NT5C3	5′-nucleotidase, cytosolic III	2.06E−07	247.6	443.4	474.7
38	228275_PM_at	—	—	2.24E−07	71.6	159.3	138.9
39	228617_PM_at	XAF1	XIAP associated factor 1	2.28E−07	678.3	1412.3	1728.5
40	214022_PM_s_at	IFITM1	interferon induced	2.37E−07	1455.1	2809.3	3537.2
			transmembrane protein 1 (9-27)
41	214059_PM_at	IFI44	Interferon-induced protein 44	2.61E−07	37.1	158.8	182.5
42	206553_PM_at	OAS2	2′-5′-oligoadenylate	2.92E−07	18.9	45.6	53.1
			synthetase 2, 69/71 kDa
43	214290_PM_s_at	HIST2H2AA3	histone cluster 2, H2aa3 ///	3.50E−07	563.4	1151.2	1224.7
		///	histone cluster 2, H2aa4
		HIST2H2AA4
44	1554079_PM_at	GALNTL4	UDP-N-acetyl-alpha-D-	3.58E−07	69.9	142.6	109.0
			galactosamine:polypeptide N-
			acetylgalactosaminyltransferase-
			like 4
45	202430_PM_s_at	PLSCR1	phospholipid scramblase 1	3.85E−07	665.7	1162.8	1214.5
46	218280_PM_x_at	HIST2H2AA3	histone cluster 2, H2aa3 ///	5.32E−07	299.7	635.3	721.7
		///	histone cluster 2, H2aa4
		HIST2H2AA4
47	202708_PM_s_at	HIST2H2BE	histone cluster 2, H2be	7.04E−07	62.4	112.2	115.4
48	222134_PM_at	DDO	D-aspartate oxidase	7.37E−07	76.0	134.9	118.4
49	215071_PM_s_at	HIST1H2AC	histone cluster 1, H2ac	9.11E−07	502.4	1009.1	1019.0
50	209417_PM_s_at	IFI35	interferon-induced protein 35	9.12E−07	145.5	258.9	323.5
51	218543_PM_s_at	PARP12	poly (ADP-ribose)	9.29E−07	172.3	280.3	366.3
			polymerase family, member
			12
52	202864_PM_s_at	SP100	SP100 nuclear antigen	1.09E−06	372.5	604.2	651.9
53	217719_PM_at	EIF3L	eukaryotic translation	1.15E−06	4864.0	3779.0	3600.0
			initiation factor 3, subunit L
54	230314_PM_at	—	—	1.29E−06	36.0	62.5	59.5
55	202863_PM_at	SP100	SP100 nuclear antigen	1.37E−06	500.0	751.3	815.8
56	236798_PM_at	—	—	1.38E−06	143.1	307.0	276.8
57	233555_PM_s_at	SULF2	sulfatase 2	1.38E−06	47.0	133.4	119.0
58	236717_PM_at	FAM179A	family with sequence	1.44E−06	16.5	16.1	24.2
			similarity 179, member A
59	228531_PM_at	SAMD9	sterile alpha motif domain	1.54E−06	143.0	280.3	351.7
			containing 9
60	209911_PM_x_at	HIST1H2BD	histone cluster 1, H2bd	1.69E−06	543.7	999.9	1020.2
61	238039_PM_at	LOC728769	hypothetical LOC728769	1.77E−06	62.8	95.5	97.2
62	222067_PM_x_at	HIST1H2BD	histone cluster 1, H2bd	1.78E−06	378.1	651.6	661.4
63	201601_PM_x_at	IFITM1	interferon induced	2.00E−06	1852.8	2956.0	3664.5
			transmembrane protein 1 (9-27)
64	213361_PM_at	TDRD7	tudor domain containing 7	2.09E−06	158.5	314.1	328.6
65	224998_PM_at	CMTM4	CKLF-like MARVEL	2.15E−06	42.6	30.0	22.3
			transmembrane domain
			containing 4
66	222793_PM_at	DDX58	DEAD (Asp-Glu-Ala-Asp)	2.41E−06	93.9	231.9	223.1
			box polypeptide 58
67	225076_PM_s_at	ZNFX1	zinc finger, NFX1-type	2.55E−06	185.0	286.0	359.1
			containing 1
68	236381_PM_s_at	WDR8	WD repeat domain 8	2.68E−06	41.6	61.5	64.8
69	202365_PM_at	UNC119B	unc-119 homolog B (C. elegans)	2.72E−06	383.4	272.7	241.0
70	215690_PM_x_at	GPAA1	glycosylphosphatidylinositol	2.75E−06	141.0	103.7	107.5
			anchor attachment protein 1
			homolog (yeast)
71	211799_PM_x_at	HLA-C	major histocompatibility	2.77E−06	912.3	1446.0	1649.4
			complex, class I, C
72	218943_PM_s_at	DDX58	DEAD (Asp-Glu-Ala-Asp)	2.87E−06	153.9	310.7	350.7
			box polypeptide 58
73	235686_PM_at	C2orf60	chromosome 2 open reading	3.32E−06	17.2	23.2	20.1
			frame 60
74	236193_PM_at	LOC100506979	hypothetical LOC100506979	3.96E−06	24.5	48.1	51.2
75	221767_PM_x_at	HDLBP	high density lipoprotein	4.00E−06	1690.9	1301.2	1248.4
			binding protein
76	225796_PM_at	PXK	PX domain containing	4.08E−06	99.2	168.1	154.9
			serine/threonine kinase
77	209762_PM_x_at	SP110	SP110 nuclear body protein	4.68E−06	150.5	242.3	282.0
78	211060_PM_x_at	GPAA1	glycosylphosphatidylinositol	4.74E−06	153.1	113.3	116.8
			anchor attachment protein 1
			homolog (yeast)
79	218019_PM_s_at	PDXK	pyridoxal (pyridoxine,	4.95E−06	304.5	210.8	198.6
			vitamin B6) kinase
80	219364_PM_at	DHX58	DEXH (Asp-Glu-X-His) box	5.46E−06	71.5	111.2	113.0
			polypeptide 58
81	203281_PM_s_at	UBA7	ubiquitin-like modifier	6.79E−06	80.2	108.2	131.0
			activating enzyme 7
82	200923_PM_at	LGALS3BP	lectin, galactoside-binding,	6.99E−06	193.1	401.5	427.4
			soluble, 3 binding protein
83	208527_PM_x_at	HIST1H2BE	histone cluster 1, H2be	7.54E−06	307.7	529.7	495.4
84	219479_PM_at	KDELC1	KDEL (Lys-Asp-Glu-Leu)	7.81E−06	74.1	131.5	110.6
			containing 1
85	200950_PM_at	ARPC1A	actin related protein 2/3	1.00E−05	1015.8	862.8	782.0
			complex, subunit 1A, 41 kDa
86	213294_PM_at	EIF2AK2	eukaryotic translation	1.02E−05	390.4	690.7	651.6
			initiation factor 2-alpha kinase 2
87	205943_PM_at	TDO2	tryptophan 2,3-dioxygenase	1.06E−05	7808.6	10534.7	10492.0
88	217969_PM_at	C11orf2	chromosome 11 open reading	1.21E−05	302.6	235.0	214.8
			frame 2
89	1552370_PM_at	C4orf33	chromosome 4 open reading	1.24E−05	58.4	124.5	97.2
			frame 33
90	211911_PM_x_at	HLA-B	major histocompatibility	1.34E−05	4602.1	6756.7	7737.3
			complex, class I, B
91	232563_PM_at	ZNF684	zinc finger protein 684	1.36E−05	131.9	236.2	231.8
92	203882_PM_at	IRF9	interferon regulatory factor 9	1.43E−05	564.0	780.1	892.0
93	225991_PM_at	TMEM41A	transmembrane protein 41A	1.45E−05	122.5	202.1	179.6
94	239988_PM_at	—	—	1.53E−05	11.5	15.4	16.1
95	244434_PM_at	GPR82	G protein-coupled receptor 82	1.55E−05	18.5	32.5	37.0
96	201489_PM_at	PPIF	peptidylprolyl isomerase F	1.58E−05	541.7	899.5	672.9
97	221476_PM_s_at	RPL15	ribosomal protein L15	1.58E−05	3438.3	2988.5	2742.8
98	244398_PM_x_at	ZNF684	zinc finger protein 684	1.65E−05	57.2	96.9	108.5
99	208628_PM_s_at	YBX1	Y box binding protein 1	1.66E−05	4555.5	3911.6	4365.0
100	211710_PM_x_at	RPL4	ribosomal protein L4	1.73E−05	5893.1	4853.3	4955.4
101	229741_PM_at	MAVS	mitochondrial antiviral	1.78E−05	65.2	44.6	34.4
			signaling protein
102	206386_PM_at	SERPINA7	serpin peptidase inhibitor,	1.90E−05	3080.8	4251.6	4377.2
			clade A (alpha-1
			antiproteinase, antitrypsin),
			member 7
103	213293_PM_s_at	TRIM22	tripartite motif-containing 22	1.92E−05	1122.0	1829.2	2293.2
104	200089_PM_s_at	RPL4	ribosomal protein L4	1.93E−05	3387.5	2736.6	2823.9
105	235037_PM_at	TMEM41A	transmembrane protein 41A	1.96E−05	134.7	218.5	192.9
106	226459_PM_at	PIK3AP1	phosphoinositide-3-kinase	2.10E−05	2152.4	2747.6	2929.7
			adaptor protein 1
107	200023_PM_s_at	EIF3F	eukaryotic translation	2.16E−05	1764.9	1467.2	1365.3
			initiation factor 3, subunit F
108	205161_PM_s_at	PEX11A	peroxisomal biogenesis factor	2.17E−05	51.9	87.3	76.9
			11 alpha
109	225291_PM_at	PNPT1	polyribonucleotide	2.18E−05	287.0	469.1	455.0
			nucleotidyltransferase 1
110	220445_PM_s_at	CSAG2 ///	CSAG family, member 2 ///	2.24E−05	16.3	91.2	120.9
		CSAG3	CSAG family, member 3
111	226229_PM_s_at	SSU72	SSU72 RNA polymerase II	2.24E−05	50.4	36.7	32.3
			CTD phosphatase homolog
			(S. cerevisiae)
112	207418_PM_s_at	DDO	D-aspartate oxidase	2.48E−05	35.2	57.0	50.7
113	201786_PM_s_at	ADAR	adenosine deaminase, RNA-	2.59E−05	1401.5	1867.9	1907.8
			specific
114	224724_PM_at	SULF2	sulfatase 2	2.61E−05	303.6	540.1	553.9
115	201618_PM_x_at	GPAA1	glycosylphosphatidylinositol	2.63E−05	131.2	98.1	97.5
			anchor attachment protein 1
			homolog (yeast)
116	201154_PM_x_at	RPL4	ribosomal protein L4	2.78E−05	3580.5	2915.6	2996.2
117	200094_PM_s_at	EEF2	eukaryotic translation	3.08E−05	3991.6	3248.5	3061.1
			elongation factor 2
118	208424_PM_s_at	CIAPIN1	cytokine induced apoptosis	3.17E−05	66.7	94.8	94.8
			inhibitor 1
119	204102_PM_s_at	EEF2	eukaryotic translation	3.23E−05	3680.8	3102.7	2853.6
			elongation factor 2
120	203595_PM_s_at	IFIT5	interferon-induced protein	3.44E−05	266.9	445.8	450.9
			with tetratricopeptide repeats 5
121	228152_PM_s_at	DDX60L	DEAD (Asp-Glu-Ala-Asp)	3.52E−05	136.1	280.8	304.5
			box polypeptide 60-like
122	201490_PM_s_at	PPIF	peptidylprolyl isomerase F	3.64E−05	209.2	443.5	251.4
123	217933_PM_s_at	LAP3	leucine aminopeptidase 3	3.81E−05	3145.6	3985.6	4629.9
124	203596_PM_s_at	IFIT5	interferon-induced protein	3.93E−05	195.9	315.8	339.0
			with tetratricopeptide repeats 5
125	220104_PM_at	ZC3HAV1	zinc finger CCCH-type,	4.25E−05	23.3	53.1	57.7
			antiviral 1
126	213080_PM_x_at	RPL5	ribosomal protein L5	4.28E−05	6986.7	6018.3	5938.6
127	208729_PM_x_at	HLA-B	major histocompatibility	4.58E−05	4720.9	6572.7	7534.4
			complex, class I, B
128	32541_PM_at	PPP3CC	protein phosphatase 3,	4.71E−05	63.3	79.7	81.3
			catalytic subunit, gamma
			isozyme
129	216231_PM_s_at	B2M	beta-2-microglobulin	4.79E−05	13087.7	14063.7	14511.1
130	206082_PM_at	HCP5	HLA complex P5	4.91E−05	129.7	205.7	300.9
131	213275_PM_x_at	CTSB	cathepsin B	4.93E−05	2626.4	2001.3	2331.0
132	200643_PM_at	HDLBP	high density lipoprotein	5.04E−05	404.4	317.8	304.4
			binding protein
133	235309_PM_at	RPS15A	ribosomal protein S15a	5.08E−05	98.5	77.4	55.3
134	209761_PM_s_at	SP110	SP110 nuclear body protein	5.33E−05	84.2	145.6	156.0
135	230753_PM_at	PATL2	protein associated with	5.55E−05	42.8	52.1	68.4
			topoisomerase II homolog 2
			(yeast)
136	225369_PM_at	ESAM	endothelial cell adhesion	5.72E−05	14.9	13.1	11.9
			molecule
137	219255_PM_x_at	IL17RB	interleukin 17 receptor B	5.88E−05	334.9	607.9	568.7
138	208392_PM_x_at	SP110	SP110 nuclear body protein	6.05E−05	60.2	96.1	115.5
139	221044_PM_s_at	TRIM34 ///	tripartite motif-containing 34	6.07E−05	47.0	65.1	70.9
		TRIM6-	/// TRIM6-TRIM34
		TRIM34	readthrough
140	1554375_PM_a_at	NR1H4	nuclear receptor subfamily 1,	6.23E−05	585.8	913.1	791.8
			group H, member 4
141	210218_PM_s_at	SP100	SP100 nuclear antigen	6.41E−05	129.0	207.4	222.0
142	206340_PM_at	NR1H4	nuclear receptor subfamily 1,	6.67E−05	983.3	1344.6	1278.4
			group H, member 4
143	222868_PM_s_at	IL18BP	interleukin 18 binding protein	7.04E−05	72.0	45.4	90.9
144	204211_PM_x_at	EIF2AK2	eukaryotic translation	7.04E−05	144.8	215.9	229.8
			initiation factor 2-alpha kinase 2
145	231702_PM_at	TDO2	Tryptophan 2,3-dioxygenase	7.09E−05	57.9	101.7	83.6
146	204906_PM_at	RPS6KA2	ribosomal protein S6 kinase,	7.10E−05	40.1	28.3	28.7
			90 kDa, polypeptide 2
147	218192_PM_at	IP6K2	inositol hexakisphosphate	7.15E−05	84.0	112.5	112.7
			kinase 2
148	211528_PM_x_at	HLA-G	major histocompatibility	7.45E−05	1608.7	2230.0	2613.2
			complex, class I, G
149	208546_PM_x_at	HIST1H2BB	histone cluster 1, H2bb ///	7.82E−05	65.3	131.7	112.0
		///	histone cluster 1, H2bc ///
		HIST1H2BC	histone cluster 1, H2bd /// his
		///
		HIST1H2BD
		///
		HIST1H2BE
		///
		HIST1H2BG
		///
		HIST1H2BH
		///
		HIST1H2BI
150	204483_PM_at	ENO3	enolase 3 (beta, muscle)	7.85E−05	547.8	1183.9	891.4
151	203148_PM_s_at	TRIM14	tripartite motif-containing 14	7.97E−05	590.8	803.6	862.4
152	1557120_PM_at	EEF1A1	Eukaryotic translation	8.14E−05	20.5	17.4	17.4
			elongation factor 1 alpha 1
153	203067_PM_at	PDHX	pyruvate dehydrogenase	8.21E−05	322.0	457.6	413.2
			complex, component X
154	224156_PM_x_at	IL17RB	interleukin 17 receptor B	8.48E−05	426.4	755.4	699.9
155	203073_PM_at	COG2	component of oligomeric	9.64E−05	73.6	100.2	96.2
			golgi complex 2
156	211937_PM_at	EIF4B	eukaryotic translation	9.68E−05	823.8	617.5	549.7
			initiation factor 4B
157	229804_PM_x_at	CBWD2	COBW domain containing 2	9.69E−05	170.0	225.0	229.1
158	225009_PM_at	CMTM4	CKLF-like MARVEL	0.00010207	54.0	40.5	32.3
			transmembrane domain
			containing 4
159	221305_PM_s_at	UGT1A8 ///	UDP glucuronosyltransferase	0.000109701	214.8	526.8	346.9
		UGT1A9	1 family, polypeptide A8 ///
			UDP glucuronosyltransferase 1
160	1557820_PM_at	AFG3L2	AFG3 ATPase family gene 3-	0.000112458	1037.9	1315.0	1232.5
			like 2 (S. cerevisiae)
161	237627_PM_at	LOC100506318	hypothetical LOC100506318	0.000115046	29.2	22.6	19.1
162	205819_PM_at	MARCO	macrophage receptor with	0.000115755	625.3	467.4	904.8
			collagenous structure
163	215313_PM_x_at	HLA-A ///	major histocompatibility	0.000116881	6193.5	8266.5	9636.7
		LOC100507703	complex, class I, A /// HLA
			class I histocompatibility
			antigen
164	226950_PM_at	ACVRL1	activin A receptor type II-like 1	0.000118584	28.2	25.1	35.5
165	213716_PM_s_at	SECTM1	secreted and transmembrane 1	0.000118874	44.7	32.0	50.6
166	207468_PM_s_at	SFRP5	secreted frizzled-related	0.000121583	19.6	25.5	20.2
			protein 5
167	218674_PM_at	C5orf44	chromosome 5 open reading	0.000124195	60.4	97.9	77.7
			frame 44
168	219691_PM_at	SAMD9	sterile alpha motif domain	0.000126093	29.6	49.5	53.9
			containing 9
169	230795_PM_at	—	—	0.00012691	115.4	188.1	164.2
170	200941_PM_at	HSBP1	heat shock factor binding	0.000127149	559.2	643.2	623.6
			protein 1
171	230174_PM_at	LYPLAL1	lysophospholipase-like 1	0.000127616	476.3	597.5	471.3
172	214459_PM_x_at	HLA-C	major histocompatibility	0.000131095	4931.4	6208.3	6855.4
			complex, class I, C
173	228971_PM_at	LOC100505759	hypothetical LOC100505759	0.000131603	210.7	139.7	91.6
174	217073_PM_x_at	APOA1	apolipoprotein A-I	0.000135801	12423.2	13707.0	13369.3
175	203964_PM_at	NMI	N-myc (and STAT) interactor	0.000138824	641.8	820.4	930.9
176	1556988_PM_s_at	CHD1L	chromodomain helicase DNA	0.000142541	164.4	241.1	226.9
			binding protein 1-like
177	214890_PM_s_at	FAM149A	family with sequence	0.000144828	534.0	444.9	342.4
			similarity 149, member A
178	209115_PM_at	UBA3	ubiquitin-like modifier	0.000144924	456.2	532.0	555.8
			activating enzyme 3
179	212284_PM_x_at	TPT1	tumor protein, translationally-	0.000146465	15764.0	14965.0	14750.6
			controlled 1
180	1552274_PM_at	PXK	PX domain containing	0.000150376	24.9	37.1	43.1
			serine/threonine kinase
181	214889_PM_at	FAM149A	family with sequence	0.00015075	295.1	236.6	152.6
			similarity 149, member A
182	213287_PM_s_at	KRT10	keratin 10	0.000151197	644.2	551.6	509.4
183	213051_PM_at	ZC3HAV1	zinc finger CCCH-type,	0.000152213	635.3	963.0	917.5
			antiviral 1
184	219731_PM_at	CC2D2B	Coiled-coil and C2 domain	0.000152224	37.5	50.5	50.5
			containing 2B
185	206211_PM_at	SELE	selectin E	0.000156449	76.0	35.1	22.8
186	217436_PM_x_at	HLA-A ///	major histocompatibility	0.000159936	972.4	1408.3	1820.7
		HLA-F ///	complex, class I, A /// major
		HLA-J	histocompatibility complex,
			clas
187	203970_PM_s_at	PEX3	peroxisomal biogenesis factor 3	0.000164079	387.4	540.4	434.7
188	1556643_PM_at	FAM125A	Family with sequence	0.000170998	68.0	107.1	95.8
			similarity 125, member A
189	211529_PM_x_at	HLA-G	major histocompatibility	0.000174559	2166.9	3107.2	3708.7
			complex, class I, G
190	223187_PM_s_at	ORMDL1	ORM1-like 1 (S. cerevisiae)	0.000182187	784.3	918.4	945.5
191	1566249_PM_at	—	—	0.000182326	15.1	12.7	12.3
192	218111_PM_s_at	CMAS	cytidine monophosphate N-	0.000182338	242.6	418.6	310.9
			acetylneuraminic acid
			synthetase
193	224361_PM_s_at	IL17RB	interleukin 17 receptor B	0.000183121	231.0	460.8	431.4
194	217807_PM_s_at	GLTSCR2	glioma tumor suppressor	0.000185926	3262.6	2650.0	2523.4
			candidate region gene 2
195	222571_PM_at	ST6GALNAC6	ST6 (alpha-N-acetyl-	0.00018814	31.7	24.2	25.0
			neuraminyl-2,3-beta-
			galactosyl-1,3)-N-
			acetylgalactosaminide alpha-2
196	208012_PM_x_at	SP110	SP110 nuclear body protein	0.000189717	245.7	344.1	397.9
197	208579_PM_x_at	H2BFS	H2B histone family, member S	0.000192843	352.8	581.2	525.7
198	204309_PM_at	CYP11A1	cytochrome P450, family 11,	0.000193276	17.5	27.3	29.2
			subfamily A, polypeptide 1
199	211956_PM_s_at	EIF1	eukaryotic translation	0.000193297	6954.0	6412.9	6189.5
			initiation factor 1
200	214455_PM_at	HIST1H2BC	histone cluster 1, H2bc	0.000196036	49.9	104.4	101.5
201	232140_PM_at	—	—	0.00019705	25.3	32.7	30.9
202	214054_PM_at	DOK2	docking protein 2, 56 kDa	0.000197843	28.6	25.1	39.9
203	210606_PM_x_at	KLRD1	killer cell lectin-like receptor	0.000201652	59.7	46.6	94.1
			subfamily D, member 1
204	211943_PM_x_at	TPT1	tumor protein, translationally-	0.000202842	12849.6	11913.9	11804.6
			controlled 1
205	205506_PM_at	VIL1	villin 1	0.000209043	67.1	28.6	21.7
206	210514_PM_x_at	HLA-G	major histocompatibility	0.000214822	715.2	976.4	1100.2
			complex, class I, G
207	235885_PM_at	P2RY12	purinergic receptor P2Y, G-	0.000216727	21.1	30.2	49.1
			protein coupled, 12
208	212997_PM_s_at	TLK2	tousled-like kinase 2	0.000217726	86.1	108.5	119.7
209	211976_PM_at	—	—	0.000218277	145.9	115.9	104.8
210	231718_PM_at	SLU7	SLU7 splicing factor homolog	0.000221207	185.0	205.3	234.8
			(S. cerevisiae)
211	225634_PM_at	ZC3HAV1	zinc finger CCCH-type,	0.000224661	388.3	511.6	490.5
			antiviral 1
212	205936_PM_s_at	HK3	hexokinase 3 (white cell)	0.000231343	22.5	19.2	30.2
213	203912_PM_s_at	DNASE1L1	deoxyribonuclease I-like 1	0.000231815	171.2	151.3	183.8
214	224603_PM_at	—	—	0.000232518	562.4	449.5	405.8
215	218085_PM_at	CHMP5	chromatin modifying protein 5	0.000232702	484.6	584.5	634.2
216	204821_PM_at	BTN3A3	butyrophilin, subfamily 3,	0.000235674	245.0	335.6	401.3
			member A3
217	217819_PM_at	GOLGA7	golgin A7	0.000242192	845.3	1004.2	967.8
218	200629_PM_at	WARS	tryptophanyl-tRNA synthetase	0.000244656	423.1	279.6	508.5
219	206342_PM_x_at	IDS	iduronate 2-sulfatase	0.000246177	122.3	88.8	95.0
220	1560023_PM_x_at	—	—	0.000247892	14.4	12.5	12.6
221	213706_PM_at	GPD1	glycerol-3-phosphate	0.000254153	124.3	227.8	162.9
			dehydrogenase 1 (soluble)
222	204312_PM_x_at	CREB1	cAMP responsive element	0.000257352	28.9	41.8	34.8
			binding protein 1
223	230036_PM_at	SAMD9L	sterile alpha motif domain	0.000265574	54.8	75.0	115.7
			containing 9-like
224	222730_PM_s_at	ZDHHC2	zinc finger, DHHC-type	0.000270517	96.7	66.7	58.1
			containing 2
225	224225_PM_s_at	ETV7	ets variant 7	0.000274744	32.8	55.4	71.0
226	1294_PM_at	UBA7	ubiquitin-like modifier	0.000290256	94.7	122.9	138.8
			activating enzyme 7
227	211075_PM_s_at	CD47	CD47 molecule	0.000296663	767.0	998.4	1061.6
228	228091_PM_at	STX17	syntaxin 17	0.000298819	94.3	134.9	110.7
229	205821_PM_at	KLRK1	killer cell lectin-like receptor	0.000299152	95.2	73.8	156.4
			subfamily K, member 1
230	1563075_PM_s_at	—	—	0.000300425	41.4	63.6	82.2
231	224701_PM_at	PARP14	poly (ADP-ribose)	0.000301162	367.5	538.6	589.3
			polymerase family, member
			14
232	209300_PM_s_at	NECAP1	NECAP endocytosis	0.000304084	184.5	246.0	246.0
			associated 1
233	200937_PM_s_at	RPL5	ribosomal protein L5	0.00030872	3893.3	3346.0	3136.1
234	208523_PM_x_at	HIST1H2BI	histone cluster 1, H2bi	0.000310294	79.8	114.5	115.8
235	210657_PM_s_at	4-Sep	septin 4	0.000314978	122.1	78.4	61.6
236	239979_PM_at	—	—	0.000315949	40.3	78.8	114.4
237	208941_PM_s_at	SEPHS1	selenophosphate synthetase 1	0.000316337	291.7	228.3	213.0
238	201649_PM_at	UBE2L6	ubiquitin-conjugating enzyme	0.000320318	928.3	1228.3	1623.0
			E2L 6
239	211927_PM_x_at	EEF1G	eukaryotic translation	0.000325197	5122.7	4241.7	4215.5
			elongation factor 1 gamma
240	225458_PM_at	LOC25845	hypothetical LOC25845	0.000337719	93.6	131.5	110.8
241	208490_PM_x_at	HIST1H2BF	histone cluster 1, H2bf	0.000339692	61.0	96.3	97.7
242	201322_PM_at	ATP5B	ATP synthase, H+	0.000342076	2068.5	2566.2	2543.7
			transporting, mitochondrial F1
			complex, beta polypeptide
243	221978_PM_at	HLA-F	major histocompatibility	0.00034635	49.8	69.5	100.6
			complex, class I, F
244	204031_PM_s_at	PCBP2	poly(rC) binding protein 2	0.000351625	2377.6	2049.5	1911.5
245	243624_PM_at	PIAS2	Protein inhibitor of activated	0.000352892	17.7	15.4	14.1
			STAT, 2
246	212998_PM_x_at	HLA-DQB1	major histocompatibility	0.000359233	570.2	339.6	742.5
		///	complex, class II, DQ beta 1
		LOC100133583	/// HLA class II
			histocompatibili
247	204875_PM_s_at	GMDS	GDP-mannose 4,6-	0.00035965	73.9	41.2	45.5
			dehydratase
248	225721_PM_at	SYNPO2	synaptopodin 2	0.000362084	69.1	43.3	32.1
249	229696_PM_at	FECH	ferrochelatase	0.000362327	42.6	34.1	28.8
250	208812_PM_x_at	HLA-C	major histocompatibility	0.000365707	7906.3	9602.6	10311.7
			complex, class I, C
251	211666_PM_x_at	RPL3	ribosomal protein L3	0.000376419	4594.1	4006.1	3490.3
252	219948_PM_x_at	UGT2A3	UDP glucuronosyltransferase	0.000376972	219.5	454.5	350.3
			2 family, polypeptide A3
253	204158_PM_s_at	TCIRG1	T-cell, immune regulator 1,	0.000384367	217.8	197.5	311.3
			ATPase, H+ transporting,
			lysosomal V0 subunit A3
254	209846_PM_s_at	BTN3A2	butyrophilin, subfamily 3,	0.000386605	424.5	612.5	703.0
			member A2
255	243225_PM_at	LOC283481	hypothetical LOC283481	0.000388527	62.6	42.2	39.2
256	1554676_PM_at	SRGN	serglycin	0.000399135	11.6	12.7	15.0
257	202748_PM_at	GBP2	guanylate binding protein 2,	0.000406447	393.4	258.6	446.1
			interferon-inducible
258	238654_PM_at	VSIG10L	V-set and immunoglobulin	0.000411449	15.7	19.5	19.7
			domain containing 10 like
259	218949_PM_s_at	QRSL1	glutaminyl-tRNA synthase	0.000413577	154.7	217.8	188.1
			(glutamine-hydrolyzing)-like 1
260	230306_PM_at	VPS26B	vacuolar protein sorting 26	0.000420436	80.8	66.4	59.0
			homolog B (S. pombe)
261	204450_PM_x_at	APOA1	apolipoprotein A-I	0.000427479	11811.2	13302.5	13014.4
262	213932_PM_x_at	HLA-A	major histocompatibility	0.000435087	7218.3	9083.8	10346.9
			complex, class I, A
263	201641_PM_at	BST2	bone marrow stromal cell	0.000438494	217.2	396.5	401.8
			antigen 2
264	1552275_PM_s_at	PXK	PX domain containing	0.000438718	24.7	38.6	34.4
			serine/threonine kinase
265	210633_PM_x_at	KRT10	keratin 10	0.000438865	535.9	466.6	443.1
266	217874_PM_at	SUCLG1	succinate-CoA ligase, alpha	0.000441648	2582.3	3199.8	3034.6
			subunit
267	223192_PM_at	SLC25A28	solute carrier family 25,	0.000456748	157.1	178.0	220.5
			member 28
268	204820_PM_s_at	BTN3A2 ///	butyrophilin, subfamily 3,	0.000457313	1264.5	1537.9	1932.9
		BTN3A3	member A2 /// butyrophilin,
			subfamily 3, member A3
269	32069_PM_at	N4BP1	NEDD4 binding protein 1	0.00045791	320.7	400.4	402.0
270	208870_PM_x_at	ATP5C1	ATP synthase, H+	0.000464012	3210.8	3791.7	3616.3
			transporting, mitochondrial F1
			complex, gamma polypeptide 1
271	207104_PM_x_at	LILRB1	leukocyte immunoglobulin-	0.000468733	52.9	52.0	80.6
			like receptor, subfamily B
			(with TM and ITIM domains),
			member
272	209035_PM_at	MDK	midkine (neurite growth-	0.000469597	18.5	25.2	30.3
			promoting factor 2)
273	230307_PM_at	LOC100129794	similar to hCG1804255	0.000471715	17.3	14.8	13.5
274	225255_PM_at	MRPL35	mitochondrial ribosomal	0.000478299	44.4	59.0	49.3
			protein L35
275	229625_PM_at	GBP5	guanylate binding protein 5	0.000478593	243.9	147.4	393.5
276	209140_PM_x_at	HLA-B	major histocompatibility	0.000478945	8305.0	10032.9	11493.8
			complex, class I, B
277	210905_PM_x_at	POU5F1P4	POU class 5 homeobox 1	0.000492713	11.9	13.7	13.9
			pseudogene 4
278	218480_PM_at	AGBL5	ATP/GTP binding protein-like 5	0.000494707	23.8	20.7	18.1
279	209253_PM_at	SORBS3	sorbin and SH3 domain	0.000495796	97.5	86.2	78.2
			containing 3
280	207801_PM_s_at	RNF10	ring finger protein 10	0.000508149	374.0	297.5	327.3
281	212539_PM_at	CHD1L	chromodomain helicase DNA	0.000509089	482.2	677.2	613.0
			binding protein 1-like
282	224492_PM_s_at	ZNF627	zinc finger protein 627	0.000513422	127.6	168.3	125.0
283	1557186_PM_s_at	TPCN1	two pore segment channel 1	0.000513966	26.5	21.5	22.4
284	203610_PM_s_at	TRIM38	tripartite motif-containing 38	0.000514783	100.5	139.2	156.0
285	211530_PM_x_at	HLA-G	major histocompatibility	0.000525417	1034.7	1429.2	1621.6
			complex, class I, G
286	201421_PM_s_at	WDR77	WD repeat domain 77	0.000527341	114.5	143.9	133.4
287	200617_PM_at	MLEC	malectin	0.000529672	244.8	174.2	147.7
288	1555982_PM_at	ZFYVE16	zinc finger, FYVE domain	0.000550743	27.5	35.4	27.8
			containing 16
289	211345_PM_x_at	EEF1G	eukaryotic translation	0.000555581	4011.7	3333.0	3247.8
			elongation factor 1 gamma
290	1555202_PM_a_at	RPRD1A	regulation of nuclear pre-	0.000561763	14.0	17.2	14.3
			mRNA domain containing 1A
291	218304_PM_s_at	OSBPL11	oxysterol binding protein-like	0.000565559	230.5	347.9	328.7
			11
292	219464_PM_at	CA14	carbonic anhydrase XIV	0.000570778	64.9	43.5	32.6
293	204278_PM_s_at	EBAG9	estrogen receptor binding site	0.000570888	482.5	591.0	510.6
			associated, antigen, 9
294	218298_PM_s_at	C14orf159	chromosome 14 open reading	0.000571869	411.1	515.6	573.0
			frame 159
295	213675_PM_at	—	—	0.000576321	39.1	27.4	25.2
296	1555097_PM_a_at	PTGFR	prostaglandin F receptor (FP)	0.000581257	11.0	12.8	14.0
297	209056_PM_s_at	CDC5L	CDC5 cell division cycle 5-	0.000582594	552.0	682.3	659.9
			like (S. pombe)
298	208912_PM_s_at	CNP	2′,3′-cyclic nucleotide 3′	0.00058579	308.8	415.8	392.9
			phosphodiesterase
299	227018_PM_at	DPP8	dipeptidyl-peptidase 8	0.000587266	29.6	38.2	41.9
300	224650_PM_at	MAL2	mal, T-cell differentiation	0.000592979	600.4	812.5	665.3
			protein 2
301	217492_PM_s_at	PTEN ///	phosphatase and tensin	0.000601775	545.5	511.2	426.0
		PTENP1	homolog /// phosphatase and
			tensin homolog pseudogene 1
302	211654_PM_x_at	HLA-DQB1	major histocompatibility	0.000608592	538.8	350.2	744.4
			complex, class II, DQ beta 1
303	220312_PM_at	FAM83E	family with sequence	0.000609835	16.0	13.9	13.7
			similarity 83, member E
304	228230_PM_at	PRIC285	peroxisomal proliferator-	0.00061118	42.0	55.4	57.6
			activated receptor A
			interacting complex 285
305	215171_PM_s_at	TIMM17A	translocase of inner	0.000624663	1432.1	1905.5	1715.4
			mitochondrial membrane 17
			homolog A (yeast)
306	228912_PM_at	VIL1	villin 1	0.000630544	53.0	29.5	27.6
307	203047_PM_at	STK10	serine/threonine kinase 10	0.000638877	41.0	39.1	54.7
308	232617_PM_at	CTSS	cathepsin S	0.000640978	1192.9	1083.0	1561.2
309	236219_PM_at	TMEM20	transmembrane protein 20	0.000648505	20.5	38.9	36.1
310	240681_PM_at	—	—	0.000649144	140.6	202.3	192.8
311	1553317_PM_s_at	GPR82	G protein-coupled receptor 82	0.000667359	13.3	20.1	21.2
312	212869_PM_x_at	TPT1	tumor protein, translationally-	0.000669242	14240.7	13447.2	13475.2
			controlled 1
313	219356_PM_s_at	CHMP5	chromatin modifying protein 5	0.000670413	1104.5	1310.4	1322.9
314	1552555_PM_at	PRSS36	protease, serine, 36	0.000676354	14.2	12.9	11.8
315	203147_PM_s_at	TRIM14	tripartite motif-containing 14	0.000676359	334.8	419.3	475.4
316	43511_PM_s_at	—	—	0.000678774	70.7	60.9	80.0
317	221821_PM_s_at	C12orf41	chromosome 12 open reading	0.000683679	180.0	213.8	206.9
			frame 41
318	218909_PM_at	RPS6KC1	ribosomal protein S6 kinase,	0.000686673	105.8	155.8	151.5
			52 kDa, polypeptide 1
319	232724_PM_at	MS4A6A	membrane-spanning 4-	0.000686877	106.7	108.3	160.4
			domains, subfamily A,
			member 6A
320	218164_PM_at	SPATA20	spermatogenesis associated 20	0.000693114	181.5	130.4	156.0

Claims

What is claimed is:

1. A method of detecting or predicting a condition of a transplant recipient, the method comprising:

a. obtaining a sample, wherein the sample comprises one or more gene expression products from the transplant recipient;

b. performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and

c. detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is a classifier capable of distinguishing between at least two conditions that are not normal conditions, wherein one of the at least two conditions is an acute rejection, and wherein one of the at least two conditions is a transplant dysfunction with no rejection.

2. (canceled)

3. A method of detecting or predicting a condition of a transplant recipient, the method comprising:

b. performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is a three-way classifier capable of distinguishing between at least three conditions, and wherein one of the at least three conditions is transplant rejection.

4-10. (canceled)

11. The method of claim 1, wherein the gene expression products are RNA.

12. The method of claim 1, wherein the gene expression products are polypeptides.

13. The method of claim 1, wherein the gene expression products are DNA complements of RNA expression products from the transplant recipient.

14. The method of claim 1, wherein the algorithm is a trained algorithm.

15. The method of claim 14, wherein the trained algorithm is trained with gene expression data from biological samples from at least three different cohorts.

16. The method of claim 14, wherein the trained algorithm comprises a linear classifier.

17. The method of claim 14, wherein the linear classifier comprises one or more linear discriminant analysis, Fisher's linear discriminant, Naïve Bayes classifier, Logistic regression, Perceptron, Support vector machine (SVM) or a combination thereof.

18. The method of claim 1, wherein the algorithm comprises a Diagonal Linear Discriminant Analysis (DLDA) algorithm.

19. The method of claim 1, wherein the algorithm comprises a Nearest Centroid algorithm.

20. The method of claim 1, wherein the algorithm comprises a Random Forest algorithm or statistical bootstrapping.

21. The method of claim 1, wherein the algorithm comprises a Prediction Analysis of Microarrays (PAM) algorithm.

22. The method of claim 1, wherein the algorithm is not validated by a cohort-based analysis of an entire cohort.

23. The method of claim 1, wherein the algorithm is validated by a combined analysis with an unknown phenotype and a subset of a cohort with known phenotypes.

24. The method of claim 1, wherein the one or more gene expression products comprises five or more gene expression products with different sequences.

25. The method of claim 24, wherein the five or more gene expression products correspond to less than 200 genes listed in Table 1a or 1c.

26. The method of claim 15, wherein the biological samples are differentially classified based on one or more clinical features.

27. The method of claim 26, wherein the one or more clinical features comprise status or outcome of a transplanted organ.

28. The method of claim 1, wherein the classifier is a three-way classifier which is generated, in part, by comparing two or more gene expression profiles from two or more control samples.

29-30. (canceled)

31. The method of claim 28, wherein the two or more gene expression profiles from the two or more control samples are normalized by frozen multichip average (fRMA).

32-33. (canceled)

34. The method of claim 1, wherein the sample is a blood sample or is derived from a blood sample.

35. The method of claim 34, wherein the blood sample is a peripheral blood sample.

36. The method of claim 35, wherein the blood sample is a whole blood sample.

37. The method of claim 1, wherein the sample does not comprise tissue from a biopsy of a transplanted organ of the transplant recipient.

38. The method of claim 1, wherein the sample is not derived from tissue from a biopsy of a transplanted organ of the transplant recipient.

39. The method of claim 1, wherein the assay is a microarray, SAGE, blotting, RT-PCR, sequencing and/or quantitative PCR assay.

40. The method of claim 1, wherein the assay is a microarray assay.

41. The method of claim 40, wherein the microarray assay comprises the use of an Affymetrix Human Genome U133 Plus 2.0 GeneChip or an HT HG-U133+PM Array Plate.

42. The method of claim 1, wherein the assay is a sequencing assay.

43. The method of claim 42, wherein the assay is a RNA sequencing assay.

44. The method of claim 42, wherein the assay comprises a DNA sequencing assay.

45. The method of claim 42, wherein the assay comprises a NextGen sequencing assay or massively parallel sequencing assay.

46. The method of claim 1, wherein the gene expression products correspond to five or more genes listed in Table 1a or 1c.

47. The method of claim 1, wherein the method has an error rate of less than about 10%.

48. The method of claim 1, wherein the method has an accuracy of at least about 70%.

49. The method of claim 1, wherein the method has a sensitivity of at least about 80%.

50. The method of claim 1, wherein the method has a specificity of at least about 80%.

51. The method of claim 1, wherein the transplant recipient is a recipient of an organ or tissue.

52. The method of claim 1, wherein the transplant recipient has a serum creatinine level of at least 1.5 mg/dL.

53. The method of claim 1, wherein the transplant recipient has a serum creatinine level of at least 3 mg/dL.

54. The method of claim 1, wherein the transplant recipient is a recipient of a transplanted organ, and the organ is an eye, lung, kidney, heart, liver, pancreas, intestines, or a combination thereof.

55. The method of claim 1, wherein the transplant recipient is a kidney transplant recipient.

56. The method of claim 1, wherein the transplant recipient is a liver transplant recipient.

57. The method of claim 1, further comprising providing or terminating a treatment for the transplant recipient based on the detected or predicted condition of the transplant recipient.

58. A method of detecting, diagnosing, predicting or monitoring a status or outcome of a transplant in a transplant recipient, the method comprising:

a. determining a level of expression of one or more genes in a sample from a transplant recipient, wherein the level of expression is determined by RNA sequencing; and

b. diagnosing, predicting or monitoring a status or outcome of a transplant in the transplant recipient.

59. A method comprising the steps of:

a. determining a level of expression of one or more genes in a sample from a transplant recipient;

b. normalizing the expression level data from step (a) using a frozen robust multichip average (fRMA) algorithm to produce normalized expression level data;

c. producing one or more classifiers based on the normalized expression level data from step (b); and

d. detecting, diagnosing, predicting or monitoring a status or outcome of a transplant in the transplant recipient based on the one or more classifiers from step (c).

60-61. (canceled)

62. The method of claim 1, wherein the method has a negative predictive value of greater than 70%.

63-66. (canceled)

67. A non-transitory computer-readable storage media comprising:

a. a database, in a computer memory, of one or more clinical features of two or more control samples, wherein

i. the two or more control samples are from two or more transplant recipients; and

ii. the two or more control samples are differentially classified based on a classification system comprising three or more different classes;

b. a first software module configured to compare the one or more clinical features of the two or more control samples; and

c. a second software module configured to produce a classifier set based on the comparison of the one or more clinical features.

68-71. (canceled)

72. A system comprising:

a. a digital processing device comprising an operating system configured to perform executable instructions and a memory device;

b. a computer program including instructions executable by the digital processing device to classify a sample from a transplant recipient comprising:

i. a software module configured to receive a gene expression profile of one or more genes from the sample from the transplant recipient;

ii. a software module configured to analyze the gene expression profile from the transplant recipient; and

iii. a software module configured to classify the sample from the transplant recipient based on a classification system comprising three or more classes.

73-76. (canceled)