US20200407791A1

US20200407791A1 - Tissue molecular signatures of kidney transplant rejections

Info

Publication number: US20200407791A1
Application number: US16/751,523
Authority: US
Inventors: Daniel R. Salomon; Sunil Kurian; Michael M. Abecassis
Original assignee: Scripps Research Institute; Northwestern University
Current assignee: Scripps Research Institute; Northwestern University
Priority date: 2013-09-09
Filing date: 2020-01-24
Publication date: 2020-12-31
Also published as: CA2923700A1; AU2024200059A1; EP3044333A1; GB201603464D0; EP3044333A4; AU2014317834A1; AU2020277267A1; US20150167085A1; AU2020277267B2; US20170191128A1; US10870888B2; AU2024200059B2; WO2015035367A1; US20180371546A1; GB2532672A

Abstract

By a genome-wide gene analysis of expression profiles of known or putative gene sequences in kidney biopsy samples, the present inventors have identified a consensus set of gene expression-based molecular biomarkers for distinguishing kidney transplantation patients who have Acute Rejection (AR), Acute Dysfunction No Rejection (ADNR), Chronic Allograft Nephropathy (CAN), or Transplant Excellent/Normal (TX). These molecular biomarkers are useful for diagnosis, prognosis and monitoring of transplantation patients.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 15/313,215 (filed Nov. 22, 2016, now pending), which is a § 371 U.S. national phase filing of PCT International Patent Application No. PCT/US2015/032,195 (filed May 22, 2015, now expired), which claims the benefit of priority to U.S. application Ser. No. 14/481,167 (filed Sep. 9, 2014, now abandoned); to International Application No. PCT/US2014/054735 (filed Sep. 9, 2014, now expired); to U.S. Provisional Application No. 62/029,038 (filed Jul. 25, 2014, now expired); to U.S. Provisional Application No. 62/001,889 (filed May 22, 2014, now expired); to U.S. Provisional Application No. 62/001,902 (filed May 22, 2014, now expired); and to U.S. Provisional Application No. 62/001,909 (filed May 22, 2014, now expired). Each of the aforementioned priority applications is incorporated by reference herein in its entirety.

STATEMENT CONCERNING GOVERNMENT SUPPORT

This invention was made in part with the U.S. government support by the National Institutes of Health Grant No. AI063603. The U.S. Government therefore may have certain rights in the invention.

COPYRIGHT NOTIFICATION

Pursuant to 37 C.F.R. § 1.71(e), Applicants note that a portion of this disclosure contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.

BACKGROUND OF THE INVENTION

Kidney transplantation offers a significant improvement in life expectancy and quality of life for patients with end stage renal disease. Unfortunately, graft losses due to allograft dysfunction or other uncertain etiologies have greatly hampered the therapeutic potential of kidney transplantation. Currently, immune reactivity of kidney transplant recipients is estimated by monitoring the levels of immunosuppressive drugs, and by functional and/or histological evaluation of the allograft. Diagnosis of acute rejection relies on clinical data (e.g., patient signs and symptoms) and laboratory data such as tissue biopsy. The laboratory pathologist generally seeks three main histological signs: (1) infiltrating T cells, perhaps accompanied by infiltrating eosinophils, plasma cells, and neutrophils, particularly in telltale ratios, (2) structural compromise of tissue anatomy, varying by tissue type transplanted, and (3) injury to blood vessels.
There is a need in the art for alternative and more effective means that can diagnose and directly quantify the extent of the recipient's immune response towards the allograft. Such means would help clinicians to customize the prescription of immunosuppressive drugs to individual patients. The present invention addresses this and other unfulfilled needs in the art.

SUMMARY OF THE INVENTION

In one aspect, the invention provides methods of prognosing, detecting, diagnosing or monitoring a kidney transplant rejection or injury, or lack thereof in a subject. These methods may involve obtaining nucleic acids of interest, and then (a) determining expression levels in a subject of at least 5 genes selected from the genes listed in Table 7, Table 8, Table 9, Table 10, or Table 11; and (b) detecting, prognosing, diagnosing or monitoring from the expression levels of the genes an ongoing transplant rejection or injury, or lack thereof in the subject. In some embodiments, the nucleic acids of interest comprise mRNA extracted from a sample from a subject or nucleic acids derived from the mRNA extracted from the sample from the subject. In some embodiments, the nucleic acids are contacted with probes, wherein the probes are specific for the at least five genes. In some preferred embodiments, step (a) is performed on a biopsy sample of the subject, particularly a kidney biopsy of the subject.
Some of the methods are directed to subjects who have acute rejection (AR), acute dysfunction no rejection (ADNR), chronic allograft nephropathy (CAN), or well-functioning normal transplant (TX). In some of the methods, for each of the at least five genes, step (b) entails comparing the expression level of the gene in the subject to one or more reference expression levels of the gene associated with AR, ADNR, CAN, or TX. In some methods, step (b) further involves for each of the at least five genes assigning the expression level of the gene in the subject a value or other designation which can provide an indication whether the subject has AR, ADNR, CAN, or TX. In some of these methods, the expression level of each of the at least five genes is assigned a value on a normalized scale of values associated with a range of expression levels in kidney transplant patients with AR, ADNR, CAN, or TX. In some methods, the expression level of each of the at least five genes is assigned a value or other designation in order to provide an indication that the subject has or is at risk of AR, ADNR, CAN, has well-functioning normal transplant, or that the expression level is uninformative. In some methods, step (b) further entails combining the values or designations for each of the genes to provide a combined value or designation which can provide an indication whether the subject has or is at risk of AR, ADNR, CAN, or has well-functioning normal transplant (TX).
In some methods of the invention, the steps are repeated at different times on the subject. Some of these methods are directed to subjects who are receiving a drug. In some of these methods, a change in the combined value or designation over time provides an indication of the effectiveness of the drug. In some methods of the invention, the subject has undergone a kidney transplant within 1 month, 3 months, 1 year, 2 years, 3 years or 5 years of performing step (a). In some methods, step (a) is performed on at least 10, 20, 40, or 100 genes. Some of the methods further include changing the treatment regime of the subject responsive to the prognosing, detecting, diagnosing or monitoring step. In some of these methods, the subject has received a drug before performing the methods, and the change involves administering an additional drug or administering a higher dose of the same drug, or administering a lower dose of the same drug, or stopping administering the same drug.
Some methods of the invention utilize the genes listed in Table 7 for prognosing or diagnosing subjects who have AR, ADNR, CAN, or TX. Some other methods of the invention utilize the genes listed in Table 8 for prognosing or diagnosing subjects who have AR, ADNR, or TX. Still some other methods of the invention utilize the genes listed in Table 9 for prognosing or diagnosing subjects who have CAN or TX. Still some other methods of the invention utilize the genes listed in Table 10 for prognosing or diagnosing subjects who have AR or TX. Still some other methods of the invention utilize the genes listed in Table 11 for prognosing or diagnosing subjects who have CAN/IFTA or TX. In some methods, expression levels of the genes are determined at the mRNA level or at the protein level. In some methods, step (b) is performed by a computer.
In another aspect, the invention provides an array which contains 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 7, Table 8, Table 9, Table 10, or Table 11. Some of the arrays contain a plurality of mRNAs that are fewer than 1000 or fewer than 100 in number. In some arrays, the plurality of nucleic acid probes are attached to a planar support or to beads. In a related aspect, the invention provides an array which contains support or supports bearing a plurality of ligands that specifically bind to a plurality of proteins fewer than 5000 in number. Typically, the plurality of proteins include at least five proteins encoded by genes selected from Table 7, Table 8, Table 9, Table 10, or Table 11. Some of these arrays contain ligands that specifically bind to a plurality of proteins that are fewer than 1000 or fewer than 100 in number. In some arrays, the plurality of ligands are attached to a planar support or to beads. In some of the arrays, the ligands are different antibodies that bind to different proteins of the plurality of proteins.
In another aspect, the invention provides methods of expression analysis. The methods involve determining expression levels of up to 5000 genes in a sample from a subject having a kidney transplant. Typically, the genes include at least 5 genes selected from Table 7, Table 8, Table 9, Table 10, or Table 11. In some methods, the expression levels of up to 100 or 1000 genes are determined. In some methods, the expression levels are determined at the mRNA level or at the protein level. In some methods, the expression levels are determined by quantitative PCR or hybridization to an array or RNA sequencing.
In still another aspect, the invention provides methods of screening a compound for activity in inhibiting or treating a kidney transplant rejection or injury. The methods involve (a) administering the compound to a subject having or at risk of developing a kidney transplant rejection; (b) determining expression levels of at least five genes in the subject selected from Tables 1-11 and species variants thereof before and after administering the compound to the subject, (c) determining whether the compound has activity in inhibiting or treating the kidney transplant rejection from a change in expression levels of the genes after administering the compound. In some of these methods, the kidney transplant rejection or injury to be treated or inhibited is AR, ADNR, or CAN. In some methods, step (c) involves for each of the at least five changes assigning a value or designation depending on whether the change in the expression level of the gene relative to one or more reference levels indicating presence or absence of the kidney transplant rejection. Some of these methods further entail determining a combined value or designation for the at least five genes from the values or designations determined for each gene. Some of the methods employ subjects who are human or nonhuman animal models of the kidney transplant rejection.
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 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, the assay is a sequencing assay. In another embodiment, the assay is a RNA sequencing assay.
In some embodiments, the transplant recipient has a normal serum creatinine level. In some cases, the transplant recipient has an elevated serum creatinine level. In some cases, 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 example, the transplant recipient has a serum creatinine level less than 3 mg/dL, less than 2 mg/dL, less than 1.5 mg/dL, or less than 1.0 mg/dL.
A further understanding of the nature and advantages of the present invention may be realized by reference to the remaining portions of the specification and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

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, and transmitting reports over a computer network.

FIG. 3 shows a computer system for implementing the methods of the disclosure.

DETAILED DESCRIPTION

The present invention is predicated in part on the development of molecular classifiers that can diagnose kidney transplantation patients who are Acute Rejection (AR), Acute Dysfunction No Rejection (ADNR), Chronic Allograft Nephropathy/Chronic Rejection (CAN/IFTA; CR), or Transplant Excellent/Normal (TX). The molecular classifiers were identified using RNA from kidney biopsies of the patients. These classifiers were successfully validated in an independent cohort, underscoring their applicability in significantly different racial/ethnic backgrounds as well as significantly different drug regimens. These signatures also correlated as well or better with assays based on creatinine levels and histology-based predictions. As detailed herein, they provide molecular insights into disease pathogenesis and functional molecular pathways including possible new drug targets.
The invention accordingly provides molecular diagnostic assays based on the identified molecular classifiers and additional studies of the inventors. The assays are cost effective, labor-saving and completely objective as compared to conventional light histology, the current “gold standard”. It also provides a molecular score for phenotypes that are very difficult to accomplish with light histology. Thus, methods of the invention can be used as an alternative or complement to light histology in order to make more informed therapy decisions. They provide practical advantages of an automated, rapid molecular-based diagnostic over the current workflow for light histology involving pathologists making the interpretations. In addition to the diagnostic utilities, such assays could also provide a more in-depth understanding of the complex mechanisms of acute rejection, chronic injury, and tolerance in organ transplantation. This would allow the design of new and potentially more effective strategies for the minimization of immunosuppression, or even for the induction of immunological tolerance.
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 an invasive manner (110), such as via a biopsy, 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).
The following sections provide guidance for carrying out the methods of the invention.

I. Definitions

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this invention pertains. The following references provide one of skill with a general definition of many of the terms used in this invention: Academic Press Dictionary of Science and Technology, Morris (Ed.), Academic Press (1^sted., 1992); Illustrated Dictionary of Immunology, Cruse (Ed.), CRC Pr I LIc (2^nded., 2002); Oxford Dictionary of Biochemistry and Molecular Biology, Smith et al. (Eds.), Oxford University Press (revised ed., 2000); Encyclopaedic Dictionary of Chemistry, Kumar (Ed.), Anmol Publications Pvt. Ltd. (2002); Dictionary of Microbiology and Molecular Biology, Singleton et al. (Eds.), John Wiley & Sons (3^rded., 2002); Dictionary of Chemistry, Hunt (Ed.), Routledge (1^sted., 1999); Dictionary of Pharmaceutical Medicine, Nahler (Ed.), Springer-Verlag Telos (1994); Dictionary of Organic Chemistry, Kumar and Anandand (Eds.), Anmol Publications Pvt. Ltd. (2002); and A Dictionary of Biology (Oxford Paperback Reference), Martin and Hine (Eds.), Oxford University Press (4^thed., 2000). In addition, the following definitions are provided to assist the reader in the practice of the invention.
Transplantation is the transfer of tissues, cells or an organ from a donor into a recipient. If the donor and recipient as the same person, the graft is referred to as an autograft and as is usually the case between different individuals of the same species an allograft. Transfer of tissue between species is referred to as a xenograft. Unless otherwise noted, all graft samples described herein were allografts.
A biopsy is a specimen obtained from a living patient for diagnostic or prognostic evaluation. Kidney biopsies can be obtained with a needle.
An average value can refer to any of a mean, median or mode.
Chronic allograft nephropathy (CAN), also known as sclerosing/chronic allograft nephropathy, is the leading cause of kidney transplant failure and happens month to years after the transplant. It is characterized by a gradual decline in kidney function and, typically, accompanied by high blood pressure and hematuria. The histopathology is characterized by interstitial fibrosis, tubular atrophy, fibrotic intimal thickening of arteries and glomerulosclerosis.
A gene expression level is associated with a particular phenotype e.g., presence of acute graft rejection if the gene is differentially expressed in a patient having the phenotype relative to a patient lacking the phenotype to a statistically significant extent. Unless otherwise apparent from the context a gene expression level can be measured at the mRNA and/or protein level.
A target nucleic acids is a nucleic acid (often derived from a biological sample), to which a polynucleotide probe is designed to specifically hybridize. The probe can detect presence, absence and/or amount of the target. The term can refer to the specific subsequence of a larger nucleic acid to which the probe is directed or to the overall sequence (e.g., cDNA or mRNA) whose expression level is to be detected. The term can also refer to a nucleic acid that is analyzed by a method, including sequencing, PCR, or other method known in the art.
The term subject or patient can include human or non-human animals. Thus, the methods and described herein are applicable to both human and veterinary disease and animal models. Preferred subjects are “patients,” i.e., living humans that are receiving medical care for a disease or condition. This includes persons with no defined illness who are being investigated for signs of pathology. The term subject or patient can include transplant recipients or donors or healthy subjects. The methods can be particularly useful for human subjects who have undergone a kidney transplant although they can also be used for subjects who have gone other types of transplant (e.g., heart, liver, lung, stem cell, etc.). The subjects may be mammals or non-mammals. Preferably, the subject is a human but in some cases, the subject is a non-human mammal, such as a non-human primate (e.g., ape, monkey, chimpanzee), cat, dog, rabbit, goat, horse, cow, pig, rodent, mouse, SCID mouse, rat, guinea pig, or sheep. The subject may be male or female; the subject may be and, in some cases, the subject may be an infant, child, adolescent, teenager or adult. 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.
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 subject that is not receiving immunosuppressive therapy.
A transplant recipient may be a recipient of a solid organ or a fragment of a solid organ such as a kidney. 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. In some particular examples, the transplanted kidney may be a kidney differentiated in vitro from pluripotent stem cell(s) (e.g., induced pluripotent stem cells or embryonic stem cells).
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.
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 obtained by the methods disclosed herein, such as detection of subAR.
Diagnosis refers to methods of estimating or determining whether or not a patient is suffering from a given disease or condition or severity of the condition. Diagnosis does not require ability to determine the presence or absence of a particular disease with 100% accuracy, or even that a given course or outcome is more likely to occur than not. Instead, the “diagnosis” refers to an increased probability that a certain disease or condition is present in the subject compared to the probability before the diagnostic test was performed. Similarly, a prognosis signals an increased probability that a given course or outcome will occur in a patient relative to the probability before the prognostic test.
A probe or polynucleotide probe is a nucleic acid capable of binding to a target nucleic acid of complementary sequence through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation, thus forming a duplex structure. The probe binds or hybridizes to a “probe binding site.” A probe can include natural (e.g., A, G, C, U, or T) or modified bases (e.g., 7-deazaguanosine, inosine.). A probe can be an oligonucleotide which is a single-stranded DNA. Polynucleotide probes can be synthesized or produced from naturally occurring polynucleotides. In addition, the bases in a probe can be joined by a linkage other than a phosphodiester bond, so long as it does not interfere with hybridization. Thus, probes can include, for example, peptide nucleic acids in which the constituent bases are joined by peptide bonds rather than phosphodiester linkages (see, e.g., Nielsen et al., Science 254, 1497-1500 (1991)). Some probes can have leading and/or trailing sequences of noncomplementarity flanking a region of complementarity.
A perfectly matched probe has a sequence perfectly complementary to a particular target sequence. The probe is typically perfectly complementary to a portion (subsequence) of a target sequence. The term “mismatch probe” refers to probes whose sequence is deliberately selected not to be perfectly complementary to a particular target sequence.
The term “isolated,” “purified” or “substantially pure” means an object species (e.g., a nucleic acid sequence described herein or a polypeptide encoded thereby) has been at least partially separated from the components with which it is naturally associated.
Differential expression refers to a statistically significant difference in expression levels of a gene between two populations of samples (e.g., kidney tissue samples with and without a specific type of kidney graft rejection such as AR). The expression levels can differ for example by at least a factor of 1.5 or 2 between such populations of samples. Differential expression includes genes that are expressed in one population and are not expressed (at least at detectable levels) in the other populations. Unique expression refers to detectable expression in one population and undetectable expression (i.e., insignificantly different from background) in the other population using the same technique (e.g., as in the present example for detection).
Control populations for comparison with populations undergoing a graft rejection or injury (e.g., AR) are usually referred to as being without the rejection or injury. Unless otherwise indicated, such a control population also means subjects without acute kidney rejection and/or chronic rejection.
Hybridization reactions are preferably performed under stringent conditions in which probes or primers hybridize to their intended target with which they have perfect complementarity and not to or at least to a reduced extent to other targets. An example of stringent hybridization conditions are hybridization in 6×sodium chloride/sodium citrate (SSC) at about 45° C., followed by one or more washes in 0.2×SSC, 0.1% SDS at 50° C., 55° C., 60° C., and even more or 65° C.
Statistical significance means p<0.05, <0.01, or <0.005, or even <0.001 level.
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”.

II. Genes in Profiles

Table 7 lists 199 genes whose expression changes significantly in kidney biopsies between transplant patients undergoing acute rejection (AR), acute dysfunction no rejection (ADNR), chronic allograft nephropathy (CAN), or are transplant excellent (i.e., with normal functional transplant) (TX). The columns in the table have the following meanings: column 1 is a number assigned to a gene, column 2 is an Affymetrix number indicating a set of probes suitable for measuring expression of the gene, column 3 is a gene designation number, column 4 is a gene name (recognized names of HUGO or similar bodies are used when available), column 5 is a further description of the gene, column 6 is a measure of the statistical significance of change in gene expression between the above patient populations, and columns 7-10 respectively show mean expression levels of ADNR, AR, CAN, and TX patients. Expression profiles of genes selected from this list can be used to distinguish kidney transplant patients with one of the above-noted four graft conditions or phenotypes (4-way prediction).
Table 8 provides similar information on 197 genes that show differential expression between kidney transplant patients undergoing acute rejection (AR), acute dysfunction no rejection (ADNR), or are transplant excellent (TX). These are the most common phenotypes of kidney transplant during the early post-transplant period, while CAN is usually a late manifestation of graft injury which is progressive. Expression profiles of genes selected from this Table are thus useful for distinguishing kidney transplant patients who are undergoing acute rejection (AR), acute dysfunction no rejection (ADNR), or are transplant excellent (TX) (3-way prediction). Table 9 similarly lists information on 200 genes which show differential expression between transplant patients who have chronic allograft nephropathy (CAN) and patients who are transplant excellent (TX). Table 10 lists information on genes which show differential expression between transplant patients who have acute rejection (AR) and patients who are transplant excellent (TX). Table 11 lists information on genes which show differential expression between transplant patients who have chronic allograft nephropathy (CAN)/interstitial fibrosis and tubular atrophy (IF/TA) and patients who are transplant excellent (TX). Expression profiles of genes selected from this list are typically suitable for making 2-way diagnosis between patients with these two phenotypes (4-way prediction).
The genes referred to in the above tables are human genes. In some methods, species variants or homologs of these genes are used in a non-human animal model. Species variants are the genes in different species having greatest sequence identity and similarity in functional properties to one another. Many species variants of the above human genes are listed in the Swiss-Prot database.
Raw gene expression levels are comparable between different genes in the same sample but not necessarily between different samples. As noted above, values given for gene expression levels can be normalized so that values for particular genes are comparable within and between the populations being analyzed. The normalization eliminates or at least reduces to acceptable levels any sample to sample differences arising from factors other than graft rejection (e.g., differences in overall transcription levels of patients due to general state of health and differences in sample preparation or nucleic acid amplification between samples) and also technical variation among the samples being analyzed. The normalization effectively applies a correction factor to the measured expression levels from a given array such that a profile of many expression levels in the array are the same between different patient samples. Software for normalizing overall expression patterns between different samples is both commercially (e.g., Partek Genomics Suite from Partek Systems) and publically available (e.g., XRAY from Biotique Systems or BRB ArrayTools from the National Cancer Institute). After applying appropriate normalizing factors to the measured expression value of a particular gene in different samples, an average or mean value of the expression level is determined for the samples in a population. The average or mean values between different populations are then compared to determine whether expression level has changed significantly between the populations. The changes in expression level indicated for a given gene represent the relative expression level of that gene in samples from a population of individuals with a defined condition (e.g., transplant patients with AR of specified Banff stage) relative to samples from a control population (kidney transplant patients not undergoing rejection). Similar principles apply in normalizing gene expression levels at the mRNA and protein levels. Comparisons between populations are made at the same level (e.g., mRNA levels in one population are compared with mRNA levels in another population or protein levels in one population with protein levels in another population).

III. Subject Populations

The methods of the invention are suitable for detecting and distinguishing different types of graft rejections in kidney transplant patients. The methods are particularly 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, lungs, stem cell) or on non-humans who have undergone kidney or other transplant. As detailed herein, the methods can be employed to distinguish transplant patients who (1) have or are at risk of having acute rejection (AR), (2) have or are at risk of having acute dysfunction no rejection (ADNR), (3) have or are at risk of having chronic allograft nephropathy (CAN), or (4) have normal functioning transplant (TX). Other than patients for such a four way diagnosis, the subject population can also comprise only patients at early post-transplant period who are therefore likely to have AR, have acute dysfunction no rejection (ADNR), or are transplant excellent (TX). The patients are examined via a three-way analysis to identify one of these three graft phenotypes. Further, the subject population can also merely contain late post-transplant patients who likely either have chronic allograft nephropathy (CAN) or are transplant excellent (TX). Such a subject population can be examined with methods of the invention to diagnose or confirm that the patients have late-manifestation of graft injury.
Regardless of the specific subject population, 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 kidney 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 one of the noted phenotype 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 kidney transplant in combination with determining expression levels at one or more time points thereafter. Similar methods can be practiced in non-human species, in which cases, the expression levels measured are the species equivalent of the human genes referenced above.
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 (SubAR), etc. In some cases, the condition may be AR. In some cases, the condition may be ADNR. In some cases, the condition may be SubAR. 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 (SubAR) (also known as SCAR) is generally a condition that is histologically identified as acute rejection but without concurrent functional deterioration. For kidney transplant recipients, subclinical rejection (SubAR) 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 SubAR subject typically shows normal and/or stable serum creatinine levels. SubAR 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. SubAR may be subclassified by some into acute SubAR or a milder form called borderline SubAR (suspicious for acute rejection) based on the biopsy histology.
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 (SubAR). 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.

IV. Methods of Measuring Profiles

The preferred sample type for analysis with methods of the invention is a tissue biopsy, e.g., kidney biopsy for kidney transplant patients. In addition to biopsy samples, some other types of samples may also be used in the practice of the invention. These include, e.g., blood samples which can be whole blood or fractions thereof such as plasma or lymphocytes. Other samples that can be analyzed include urine, feces, and saliva. The samples are typically isolated from a subject and not returned to the subject. The analytes of interests in the samples can be analyzed with or without further processing of the sample, such as purification and amplification.
Expression profiles are preferably measured at the nucleic acid level, meaning that levels of mRNA or nucleic acid derived therefrom (e.g., cDNA or cRNA). An expression profile refers to the expression levels of a plurality of genes in a sample. A nucleic derived from mRNA means a nucleic acid synthesized using mRNA as a template. Methods of isolation and amplification of mRNA are well known in the art, e.g., as described in WO 97/10365, WO 97/27317, Chapter 3 of Laboratory Techniques in Biochemistry and Molecular Biology: Hybridization With Nucleic Acid Probes, Part I. Theory and Nucleic Acid Preparation, (P. Tijssen, ed.) Elsevier, N.Y. (1993). If mRNA or a nucleic acid therefrom is amplified, the amplification is performed under conditions that approximately preserve the relative proportions of mRNA in the original samples, such that the levels of the amplified nucleic acids can be used to establish phenotypic associations representative of the mRNAs.
A variety of approaches are available for determining mRNA levels including probe arrays and quantitative PCR. A number of distinct array formats are available. Some arrays, such as an Affymetrix GeneChip® array, have different probes occupying discrete known areas of a contiguous support. Other arrays, such as arrays from Illumina, have 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 such case, typically several match probes are included with perfect complementarity to a given target mRNA together, optionally together with mismatch probes differing from the match probes are a known number of oligonucleotides (Lockhart, et al., Nature Biotechnology 14:1675-1680 (1996); and Lipschutz, et al., Nature Genetics Supplement 21: 20-24, 1999). Other arrays including full length cDNA sequences with perfect or near perfect complementarity to a particular cDNA (Schena et al. (Science 270:467-470 (1995); and DeRisi et al. (Nature Genetics 14:457-460 (1996)). Such arrays can also include various control probes, such as a probe complementarity with a house keeping gene likely to be expressed in most samples. Regardless of the specifics of array design, an array contains one or more probes either perfectly complementary to a particular target mRNA or sufficiently complementarity to the target mRNA to distinguish it from other mRNAs in the sample, and 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. Nos. 5,578,832, and 5,631,734. The intensity of labeling of probes hybridizing to a particular mRNA or its amplification product provides a raw measure of expression level.
In other methods, expression levels are determined by so-called “real time amplification” methods also known as quantitative PCR or Taqman (see, e.g., U.S. Pat. No. 5,210,015 to Gelfand, U.S. Pat. No. 5,538,848 to Livak, et al., and U.S. Pat. No. 5,863,736 to Haaland, as well as Heid, C. A., et al., Genome Research, 6:986-994 (1996); Gibson, U. E. M, et al., Genome Research 6:995-1001 (1996); Holland, P. M., et al., Proc. Natl. Acad. Sci. USA 88:7276-7280, (1991); and Livak, K. J., et al., PCR Methods and Applications 357-362 (1995)). The basis for this method of monitoring the formation of amplification product is to measure continuously PCR product accumulation using a dual-labeled fluorogenic oligonucleotide probe. The probe used in such assays is typically a short (ca. 20-25 bases) polynucleotide that is labeled with two different fluorescent dyes. The 5′ terminus of the probe is typically attached to a reporter dye and the 3′ terminus is attached to a quenching dye The probe is designed to have at least substantial sequence complementarity with a site on the target mRNA or nucleic acid derived from. Upstream and downstream PCR primers that bind to flanking regions of the locus are also added to the reaction mixture. When the probe is intact, energy transfer between the two fluorophors occurs and the quencher quenches emission from the reporter. During the extension phase of PCR, the probe is cleaved by the 5′ nuclease activity of a nucleic acid polymerase such as Taq polymerase, thereby releasing the reporter from the polynucleotide-quencher and resulting in an increase of reporter emission intensity which can be measured by an appropriate detector. The recorded values can then be used to calculate the increase in normalized reporter emission intensity on a continuous basis and ultimately quantify the amount of the mRNA being amplified. mRNA levels can also be measured without amplification by hybridization to a probe, for example, using a branched nucleic acid probe, such as a QuantiGene® Reagent System from Panomics.
In other methods, expression levels are determined by sequencing methods. Sequencing methods may include: Next Generation sequencing, 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.
Alternatively or additionally, expression levels of genes can be determined at the protein level, meaning that levels of proteins encoded by the genes discussed above are measured. Several methods and devices are well known for determining levels of proteins including immunoassays such as described in e.g., U.S. Pat. Nos. 6,143,576; 6,113,855; 6,019,944; 5,985,579; 5,947,124; 5,939,272; 5,922,615; 5,885,527; 5,851,776; 5,824,799; 5,679,526; 5,525,524; and 5,480,792. These assays include various sandwich, competitive, or non-competitive assay formats, to generate a signal that is related to the presence or amount of an protein analyte of interest. Any suitable immunoassay may be utilized, for example, lateral flow, enzyme-linked immunoassays (ELISA), radioimmunoassays (RIAs), competitive binding assays, and the like. Numerous formats for antibody arrays have been described proposed employing antibodies. Such arrays typically include different antibodies having specificity for different proteins intended to be detected. For example, usually at least one hundred different antibodies are used to detect one hundred different protein targets, each antibody being specific for one target. Other ligands having specificity for a particular protein target can also be used, such as the synthetic antibodies disclosed in WO/2008/048970. Other compounds with a desired binding specificity can be selected from random libraries of peptides or small molecules. U.S. Pat. No. 5,922,615 describes a device that utilizes multiple discrete zones of immobilized antibodies on membranes to detect multiple target antigens in an array. U.S. Pat. Nos. 5,458,852, 6,019,944, 6,143,576. Microtiter plates or automation can be used to facilitate detection of large numbers of different proteins. Protein levels can also be determined by mass spectrometry as described in the examples.
The selection of genes for determination of expression levels depends on the particular application. In general, the genes are selected from one of the tables indicated above as appropriate for the application. In some methods, expression levels of at least 2, 3, 4, 5, 10, 20, 25, 50, 100, 150, or 200 (e.g., 10-100) genes shown in any of Table 7, 8, 9, 10, or 11 are determined. In some methods, expression levels of at least 2, 3, 4, 5, 10, 20, 25, 50, 100, 150 or all genes shown in Table 7 are determined. In some methods, expression levels of at least 2, 3, 4, 5, 10, 20, 25, 50, 100, 150 or all genes in Table 8 are determined. In some methods, expression levels of at least 2, 3, 4, 5, 10, 20, 25, 50, 100, 150 or all genes shown in Table 9 are determined. In some methods, expression levels of at least 2, 3, 4, 5, 10, 20, 25, 50, 100, 150 or all genes shown in Table 10 are determined. In some methods, expression levels of at least 2, 3, 4, 5, 10, 20, 25, 50, 100, 150 or all genes shown in Table 11 are determined. In some methods, genes from different tables are tested (e.g., at least 2, 3, 5, 10, 25, 50 or more genes from each of Table 7, Table 8, Table 9, Table 10, and/or Table 11). In some methods, genes are selected such that genes from several different pathways are represented (e.g., at least one gene from at least 2, 3, 5, or 10 pathways). The genes within a pathway tend to be expressed in a coordinated expression whereas genes from different pathways tend to be expressed more independently. Thus, changes in expression based on the aggregate changes of genes from different pathways can have greater statistical significance than aggregate changes of genes within a pathway. As noted above, expression levels can be measured at either mRNA levels or protein levels.
Expression levels of the present genes and/or proteins can be combined with or without determination of expression levels of any other genes or proteins of interest (e.g., genes or proteins associated with rejection of kidneys or other organs in WO 2007/104537, WO 2009/060035), Anglicheau et al., PNAS 106, 5330-5335 (2009)) and references, 16, 20, 21, 22, 23, 25, 26, 37 and 39. In some methods, the genes in the expression profiles to be measured do not include at least one or all of the genes known to be linked to graft rejection, e.g., genes described in Halloran et al., Am. J. Transplant. 2013, 13(11):2865-74; and Halloran et al., Am. J. Transplant. 2013, 13(9):2352-63.
Regardless of the format adopted, the present methods can (but need not) be practiced by detection expression levels of a relatively small number of genes or proteins compared with the whole genome level expression analysis described in the Examples. In some methods, the total number of genes whose expression levels are determined is less than 5000, 1000, 500, 200, 100, 50, 25, 10, 5 or 3. In some methods, the total number of genes whose expression level is determined is 100-1500, 100-250, 500-1500 or 750-1250. In some methods, the total number of proteins whose expression levels are determined is less than 5000, 1000, 500, 200, 100, 50, 25, 10, 5 or 3. In some methods, the total number of proteins whose expression level is determined is 100-1500, 100-250, 500-1500 or 750-1250. Correspondingly, when an array form is used for detection of expression levels, the array includes probes or probes sets for less than 5000, 1000, 500, 200, 100, 50, 25, 10, 5 or 3 genes. Thus, for example, an Affymetrix GeneChip® expression monitoring array contains a set of about 20-50 oligonucleotide probes (half match and half-mismatch) for monitoring each gene of interest. Such an array design would include less than 5000, 1000, 500, 200, 100, 50, 25, 10, 5 or 3 such probes sets for detecting less than 5000, 1000, 500, 200, 100, 50, 25, 10, 5 or 3 genes. By further example, an alternative array including one cDNA for each gene whose expression level is to be detected would contain less than 5000, 1000, 500, 200, 100, 50, 25, 10, 5 or 3 such cDNAs for analyzing less than 5000, 1000, 500, 200, 100, 50, 25, 10, 5 or 3 genes. By further example, an array containing a different antibody for each protein to be detected would containing less than 5000, 1000, 500, 200, 100, 50, 25, 10, 5 or 3 different antibodies for analyzing less than 5000, 1000, 500, 200, 100, 50, 25, 10, 5 or 3 gene products.
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 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. In some 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.
The total sample population may comprise samples obtained by 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 biopsy 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 biopsy may be greater than about 1%. The percent of the total sample population that is obtained by biopsy may be greater than about 5%. The percent of the total sample population that is obtained by biopsy may be greater than about 10%.

V. Analysis of Expression Levels

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.
The individual expression levels, whether absolute or relative, can be converted into values or other designations providing an indication of presence or risk of a kidney transplant rejection or injury by comparison with one or more reference points. For different phenotypes of graft injuries (e.g., AR, ADNR, CAN; or TX), different gene sets are typically used in the analysis. For example, chronic allograft nephropathy (CAN) can be determined with gene sets selected from Table 9 or Table 7. Acute rejection (AR) and acute dysfunction no rejection (ADNR) can be determined with genes selected from Table 8 or Table 7. Acute rejection (AR) and chronic allograft nephropathy/interstitial fibrosis and tubular atrophy (IF/TA) can be determined with genes selected from Table 10 or Table 11.
For kidney transplant with each of the phenotypes noted above, the reference points can include a measure of an average or mean expression level of a gene in subjects having had a kidney transplant with the specific phenotype. The reference points can also include a scale of values found in kidney transplant patients including patients having that phenotype. The reference points can also or alternatively include a reference value in the subject before kidney transplant, or a reference value in a population of patients who have not undergone kidney transplant. Such reference points can be expressed in terms of absolute or relative concentrations of gene products as for measured values in a sample.
For comparison between a measured expression level and reference level(s), the measured level sometimes needs to be normalized for comparison with the reference level(s) or vice versa. The normalization serves to eliminate or at least minimize changes in expression level unrelated to the specific kidney transplant injury or phenotype (e.g., from differences in overall health of the patient or sample preparation). Normalization can be performed by determining what factor is needed to equalize a profile of expression levels measured from different genes in a sample with expression levels of these genes in a set of reference samples from which the reference levels were determined. Commercial software is available for performing such normalizations between different sets of expression levels.
Comparison of the measured expression level of a gene with one or more of the above reference points provides a value (i.e., numerical) or other designation (e.g., symbol or word(s)) of presence or susceptibility to a kidney transplant injury. In some methods, a binary system is used; that is a measured expression level of a gene is assigned a value or other designation indicating presence or susceptibility to a kidney transplant injury or lack thereof without regard to degree. For example, the expression level can be assigned a value of 1 to indicate presence or susceptibility to an injury and −1 to indicate absence or lack of susceptibility to the injury. Such assignment can be based on whether the measured expression level is closer to an average or mean level in kidney transplant patients having or not having a specific injury phenotype. In other methods, a ternary system is used in which an expression level is assigned a value or other designation indicating presence or susceptibility to a specific injury phenotype or lack thereof or that the expression level is uninformative. Such assignment can be based on whether the expression level is closer to the average or mean level in kidney transplant patient undergoing the specific injury, closer to an average or mean level in kidney transplant patients lacking the injury or intermediate between such levels. For example, the expression level can be assigned a value of +1, −1 or 0 depending on whether it is closer to the average or mean level in patients undergoing the injury, is closer to the average or mean level in patients not undergoing the injury or is intermediate. In other methods, a particular expression level is assigned a value on a scale, where the upper level is a measure of the highest expression level found in kidney transplant patients and the lowest level of the scale is a measure of the lowest expression level found in kidney transplant patients at a defined time point at which patients may be susceptible to a grant rejection or injury (e.g., one year post transplant). Preferably, such a scale is normalized scale (e.g., from 0-1) such that the same scale can be used for different genes. Optionally, the value of a measured expression level on such a scale is indicated as being positive or negative depending on whether the upper level of the scale associates with presence or susceptibility to the injury or lack thereof. It does not matter whether a positive or negative sign is used for an injury phenotype or lack thereof as long as the usage is consistent for different genes.
Values or other designation can also be assigned based on a change in expression level of a gene relative to a previous measurement of the expression level of gene in the same patient. Here as elsewhere expression level of a gene can be measured at the protein or nucleic acid level. Such a change can be characterized as being toward, away from or neutral with respect to average or mean expression levels of the gene in kidney transplant patients undergoing or not undergoing a grant rejection or injury. For example, a gene whose expression level changes toward an average or mean expression level in kidney transplant patients undergoing a graft injury can be assigned a value of 1, and a gene whose express level changes way from an average or mean expression level in kidney transplant patients undergoing the injury and toward an average or mean expression level in kidney transplant patients not undergoing the injury can be assigned a value −1. Of course, more sophisticated systems of assigning values are possible based on the magnitude of changes in expression of a gene in a patient.
Having determined values or other designations of expression levels of individual genes providing an indication of presence or susceptibility to a kidney graft injury or lack thereof, the values or designations are combined to provide an aggregate value for all of the genes being analyzed. If each gene is assigned a score of +1 if its expression level indicates presence or susceptibility to a graft injury and −1 if its expression level indicates absence or lack of susceptibility to the injury and optionally zero if uninformative, the different values can be combined by addition. The same approach can be used if each gene is assigned a value on the same normalized scale and assigned as being positive or negative depending whether the upper point of the scale is associate with presence or susceptibility to a specific kidney grant injury or lack thereof. In some cases, the signal intensity for each gene is obtained and used to compute a score. The score may be obtained by adding up the values for the upregulated genes to obtain an upregulated gene value and adding up the values of the downregulated genes to obtain a downregulated gene value; the downregulated gene value may be compared with the upregulated value (e.g., by calculating a ratio) to determine the score. Other methods of combining values for individual markers of disease into a composite value that can be used as a single marker are described in US20040126767 and WO/2004/059293. In some cases, the score may be used to evaluate severity of a transplant condition, such as by comparing the score with a score normally associated with kidney graft injury. In some cases, the score may be used to monitor a subject transplant recipient over time. In such case, scores at a plurality of timepoints maybe compared in order to assess the relative condition of the subject. For example, if the subject's score rises over time, that may indicate that the subject has kidney graft injury and that his or her condition is worsening over time.
Sample Data
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
Classifiers
The methods include using a trained classifier or algorithm to analyze sample data, particularly to detect subAR. In some instances, the expression levels from sample are used to develop or train an algorithm or classifier provided herein. In some instances, gene expression levels are measured in a sample from a transplant recipient (or a healthy or transplant excellent control) 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., subAR).
Training of multi-dimensional classifiers (e.g., algorithms) may be performed using numerous samples. For example, training of the multi-dimensional classifier may be performed using 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. In some cases, training of the multi-dimensional classifier may be performed using at least about 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350, 400, 450, 500 or more samples. In some cases, training of the multi-dimensional classifier may be performed using 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.
Further disclosed herein are classifier sets and methods of producing one or more classifier sets. The classifier set may comprise one or more genes, particularly genes from Tables 7, 8, or 9. In some cases, 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, 50, 100, 150, 200, 300 or more genes from Tables 7, 8, 9, 10, or 11. 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 subAR and normal transplant function (TX). 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 AR, subAR, and TX. In some cases, the classifier may work by applying two or more classifiers sequentially. For example, the first classifier may classify AR+subAR and TX, which results in a set of samples that are classified either as (1) TX or (2) AR or subAR. In some cases, a second classifier capable of distinguishing between AR and subAR is applied to the samples classified as having AR or subAR in order to detect the subAR samples.
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 with subAR. 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.
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.
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 subAR, AR, TX, subAR+AR, or other condition.
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 subAR. In some instances, the output of the algorithm can be the risk of the subject of having a condition, such as subAR. 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 subAR.
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_i* represents the expression level of gene i;
x* represents a vector of expression levels for all p genes to be used for classification drawn from the sample to be classified;
x _k′ may be a shrunken centroid calculated from a training data and a shrinkage factor;
x _ik′ may be a component of x _k′ corresponding 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)}_k(x*) may 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:
$C (x^{*}) = \underset{k \in {1, K}}{\arg \max} {\hat{p}}_{k} (x^{*}) .$

VI. Diagnosis, Prognosis and Monitoring

The above described methods can provide a value or other designation for a patient which indicates whether the aggregate measured expression levels in a patient is more like kidney transplant patients with one of the graft injury phenotypes noted above (e.g., AR, ADNR, CAN, or TX). Such a value provides an indication that the patient either has or is at enhanced risk of developing a specific graft injury, or conversely does not have or is at reduced risk of having that specific graft injury phenotype. Risk is a relative term in which risk of one patient is compared with risk of other patients either qualitatively or quantitatively. For example, the value of one patient can be compared with a scale of values for a population of patients having undergone kidney transplant to determine whether the patient's risk relative to that of other patients. In general, diagnosis is the determination of the present condition of a patient (e.g., presence or absence of a graft injury) and prognosis is developing future course of the patient (e.g., risk of developing kidney transplant rejection or injury in the future or likelihood of improvement in response to treatment); however, the analyses contemplated by these terms may overlap or even be the same. For example, the present methods alone do not necessarily distinguish between presence and enhanced risk of a kidney transplant injury. However, these possibilities can be distinguished by additional testing.
If a patient is indicated as having or being at enhanced risk of a kidney transplant injury, the physician can subject the patient to additional testing including performing a kidney biopsy examination, or performing other analyses such as creatinine, BUN or glomerular filtration rate at increased frequency. Additionally or alternatively, the physician can change the treatment regime being administered to the patient. This includes administration of steroid boluses and the addition of other drugs to the maintenance therapy, or the administration of antilymphocyte antibodies in case of resistance to the primary line of therapy. In some embodiments, the 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). In the case of HCV recurrence, the patients may be additionally administered drugs to counter the viral infection, e.g., interferons, ribavirin, and protease inhibitors. 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 graft injury, 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 methods, expression levels are determined at intervals in a particular patient (i.e., monitoring). Such methods can provide a series of values changing over time indicating whether the aggregate expression levels in a particular patient are more like the expression levels in patients undergoing a specific kidney transplant rejection/injury or not undergoing the rejection/injury. Movement in value toward or away from the graft injury can provide an indication whether an existing immunosuppressive regime is working, whether the immunosuppressive regime should be changed or whether a biopsy or increased monitoring by other markers rate should be performed.

VII. Drug Screening

The expression profiles associated with a kidney transplant rejection/injury or lack thereof provided by the invention are useful in screening drugs, either in clinical trials or in animal models of the injury. 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 kidney transplant patients, usually in comparison with a control population administered a placebo.
The changes in expression levels of genes can be analyzed in individual patients and across a treated or control population. Analysis at the level of an individual patient provides an indication of the overall status of the patient at the end of the trial (i.e., whether gene expression profile indicates presence or enhanced susceptibility to a kidney transplant rejection/injury) and/or an indication whether that profile has changed toward or away from such indication in the course of the trial. Results for individual patients can be aggregated for a population allowing comparison between treated and control population. Mannon et al., Kidney International (1999) 55, 1935-1944. In this case, the expression levels of genes detected are the species variants or homologs of the human genes referenced above in whatever species of non-human animal on which tests are being conducted. Although the average or mean expression levels of human genes determined in human kidney transplant patients undergoing or not undergoing a specific transplant rejection/injury are not necessarily directly comparable to those of homolog genes in an animal model, the human values can nevertheless be used to provide an indication whether a change in expression level of a non-human homolog is in a direction toward or away from an injury or susceptibility thereto. The expression profile of individual animals in a trial can provide an indication of the status of the animal at the end of the trial with respect to presence or susceptibility to the injury and/or change in such status during the trial. Results from individual animals can be aggregated across a population and treated and control populations compared. Average changes in the expression levels of genes can then be compared between the two populations.

VIII. Computer Implemented Methods

Expression levels can be analyzed and associated with status of a subject (e.g., presence or susceptibility to a kidney transplant injury) in a digital computer. Optionally, such a computer is directly linked to a scanner or the like receiving experimentally determined signals related to expression levels. Alternatively, expression levels can be input by other means. The computer can be programmed to convert raw signals into expression levels (absolute or relative), compare measured expression levels with one or more reference expression levels, or a scale of such values, as described above. The computer can also be programmed to assign values or other designations to expression levels based on the comparison with one or more reference expression levels, and to aggregate such values or designations for multiple genes in an expression profile. The computer can also be programmed to output a value or other designation providing an indication of presence or susceptibility to a rejection as well as any of the raw or intermediate data used in determining such a value or designation.
A typical computer (see U.S. Pat. No. 6,785,613 FIGS. 4 and 5) includes a bus which interconnects major subsystems such as a central processor, a system memory, an input/output controller, an external device such as a printer via a parallel port, a display screen via a display adapter, a serial port, a keyboard, a fixed disk drive and a floppy disk drive operative to receive a floppy disk. Many other devices can be connected such as a scanner via I/O controller, a mouse connected to serial port or a network interface. The computer contains computer readable media holding codes to allow the computer to perform a variety of functions. These functions include controlling automated apparatus, receiving input and delivering output as described above. The automated apparatus can include a robotic arm for delivering reagents for determining expression levels, as well as small vessels, e.g., microtiter wells for performing the expression analysis.
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.

EXAMPLES

The following examples are offered to illustrate, but not to limit the present invention.

Example 1. 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 1 shows the performance of the 4 way AR, ADNR, CAN, TX NC classifier using such a data driven threshold. 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 2 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 3 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. Similarly the locked 3 way classifier performs equally well on the Brazilian cohort with 98% accuracy (Table 4). 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 5 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. This locked classifier performs equally well on the Brazilian cohort with 95% accuracy (Table 6). Again we show that our 2 way CAN, TX classifier also validates on the external data set.
In another example of the 2-way classifier similar to the 2-way CAN/TX classifier described earlier, Table 10 shows the top 400 probe sets of 2-Way Classifier AR vs. TX and Table 11 shows the top 400 probe sets of 2-Way Classifier CAN/IFTA vs. TX.
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
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 (SubAR). 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, SubAR, 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, SubAR 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, SubAR 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, SubAR, 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, SubAR, 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 ADNR from CAN a kidney transplant recipient.
Predicting a Condition of a Transplant Recipient
In some embodiments, the methods provided herein can predict AR prior to actual onset of the conditions. In some instances, the methods provided herein can predict AR, IFTA, CAN, ADNR, SubAR 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, SubAR 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, SubAR 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, CCI779, 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]-pi- peridin-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.
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. 3 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.
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 1

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

Validation Cohort

				Predictive			Postive	Negative
				Accuracy	Sensitivity	Specificity	Predictive	Predictive
Algorithm	Predictors	Comparison	AUC	(%)	(%)	(%)	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 2

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 3

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 4

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 5

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

Validation Cohort

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

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

TABLE 6

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

Validation Cohort

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

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

TABLE 7

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

					p-value
		Entrez			(Final	ADNR-	AR-	CAN-	TX-
#	Probeset ID	Gene	Gene Symbol	Gene Title	Phenotype)	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-like 4C	1.31E−32	106.9	258.6	190.4	57.2
3	204698_PM_at	3669	ISG20	interferon stimulated exonuclease	1.50E−31	41.5	165.1	96.1	27.6
				gene 20 kDa
4	225701_PM_at	80709	AKNA	AT-hook transcription factor	1.75E−31	37.7	102.8	73.2	29.0
5	207651_PM_at	29909	GPR171	G protein-coupled receptor 171	6.30E−31	25.8	89.9	57.0	20.9
6	204205_PM_at	60489	APOBEC3G	apolipoprotein B mRNA editing	1.27E−30	95.4	289.4	192.0	78.7
				enzyme, catalytic polypeptide-like 3G
7	208948_PM_s_at	6780	STAU1	staufen, RNA binding protein, homolog	1.37E−30	1807.9	1531.8	1766.0	2467.4
				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 protein, 1A	5.57E−30	46.9	163.8	107.1	34.3
11	210915_PM_x_at	28638	TRBC2	T cell receptor beta constant 2	5.60E−30	39.7	230.7	129.7	37.5
12	211368_PM_s_at	834	CASP1	caspase 1, apoptosis-related cysteine	6.21E−30	102.6	274.3	191.4	81.8
				peptidase (interleukin 1, beta,
				convertase)
13	201042_PM_at	7052	TGM2	transglutaminase 2 (C polypeptide,	6.28E−30	131.8	236.5	172.6	80.1
				protein-glutamine-gamma-
				glutamyltransferase)
14	227353_PM_at	147138	TMC8	transmembrane channel-like 8	7.76E−30	19.8	64.2	42.7	16.6
15	1555852_P_M_at	100507463	LOC10050746	hypothetical LOC100507463	8.29E−30	78.9	202.6	154.2	70.9
			3
16	226878_PM_at	3111	HLA-DOA	major histocompatibility complex, class	1.63E−29	102.0	288.9	201.4	94.3
				II, DO alpha
17	238327_PM_at	440836	ODF3B	outer dense fiber of sperm tails 3B	1.74E−29	32.8	81.4	58.5	26.1
18	229437_PM_at	114614	MIR155HG	MIR155 host gene (non-protein coding)	1.78E−29	15.4	50.4	28.5	12.9
19	33304_PM_at	3669	ISG20	interferon stimulated exonuclease	2.40E−29	33.2	101.3	63.4	22.1
				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 domain	2.43E−29	104.6	321.0	219.3	71.9
				containing 2
22	1405_PM_i_at	6352	CCL5	chemokine (C-C motif) ligand 5	2.54E−29	68.0	295.7	195.6	54.6
23	226219_PM_at	257106	ARHGAP30	Rho GTPase activating protein 30	2.92E−29	46.4	127.9	91.8	37.5
24	204891_PM_s_at	3932	LCK	lymphocyte-specific protein tyrosine	3.79E−29	19.3	74.2	43.4	17.8
				kinase
25	210538_PM_s_at	330	BIRC3	baculoviral IAP repeat-containing 3	5.06E−29	106.7	276.7	199.1	84.6
26	202644_PM_s_at	7128	TNFAIP3	tumor necrosis factor, alpha-induced	5.47E−29	169.8	380.4	278.2	136.6
				protein 3
27	227346_PM_at	10320	IKZF1	IKAROS family zinc finger 1 (Ikaros)	7.07E−29	24.9	79.7	53.3	19.8
28	202957_PM_at	3059	HCLS1	hematopoietic cell-specific Lyn	8.26E−29	119.2	299.5	229.9	82.2
				substrate 1
29	202307_PM_s_at	6890	TAP1	transporter 1, ATP-binding cassette,	1.01E−28	172.4	420.6	280.0	141.0
				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 /// T	1.31E−28	69.2	431.5	250.2	63.6
		28639	TRBC2	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-DQB1 ///	major histocompatibility complex, class	1.63E−28	211.3	630.2	459.8	208.2
		/// 3119	LOC10013358	II, DQ beta 1 /// HLA class II
			3	histocompatibili
34	223322_PM_at	83593	RASSF5	Ras association (RaIGDS/AF-6) domain	1.68E−28	41.5	114.4	79.3	39.2
				family member 5
35	205488_PM_at	3001	GZMA	granzyme A (granzyme 1, cytotoxic 1-	1.72E−28	37.3	164.8	102.3	33.4
				lymphocyte-associated serine esterase
				3)
36	213603_PM_s_at	5880	RAC2	ras-related C3 botulinum toxin	1.87E−28	113.9	366.5	250.3	86.5
				substrate 2 (rho family, small GTP
				binding protein Rac2)
37	229390_PM_at	441168	FAM26F	family with sequence similarity 26,	1.94E−28	103.8	520.0	272.4	75.9
				member F
38	206804_PM_at	917	CD3G	CD3g molecule, gamma (CD3-TCR	1.99E−28	19.7	60.6	36.4	17.3
				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 finger	2.07E−28	51.5	126.0	87.5	36.2
				(C3HC4) 1
41	207320_PM_x_at	6780	STAU1	staufen, RNA binding protein, homolog	2.21E−28	1425.1	1194.6	1383.2	1945.3
				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 cysteine	3.23E−28	74.5	198.0	146.2	60.7
				peptidase (interleukin 1, beta,
				convertase)
44	213539_PM_at	915	CD3D	CD3d molecule, delta (CD3-TCR	5.42E−28	70.8	335.1	168.1	60.0
				complex)
45	213193_PM_x_at	28639	TRBC1	T cell receptor beta constant 1	5.69E−28	95.3	490.9	286.5	92.1
46	232543_PM_x_at	64333	ARHGAP9	Rho GTPase activating protein 9	6.79E−28	31.7	99.1	62.3	25.8
47	200986_PM_at	710	SERPING1	serpin peptidase inhibitor, clade G (C1	7.42E−28	442.7	731.5	590.2	305.3
				inhibitor), member 1
48	213037_PM_x_at	6780	STAU1	staufen, RNA binding protein, homolog	9.35E−28	1699.0	1466.8	1670.1	2264.9
				1 (Drosophila)
49	204670_PM_x_at	3123 ///	HLA-DRB1 ///	major histocompatibility complex, class	1.03E−27	2461.9	4344.6	3694.9	2262.0
		3126	HLA-DRB4	II, DR beta 1 /// major
				histocompatibility comp
50	217028_PM_at	7852	CXCR4	chemokine (C-X-C motif) receptor 4	1.52E−27	100.8	304.4	208.4	75.3
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-DPB1	major histocompatibility complex, class	1.95E−27	1579.1	3863.7	3151.7	1475.9
				II, DP beta 1
53	205269_PM_at	3937	LCP2	lymphocyte cytosolic protein 2 (SH2	2.16E−27	30.2	92.2	58.9	22.9
				domain containing leukocyte protein of
				76 kDa)
54	205821_PM_at	22914	KLRK1	killer cell lectin-like receptor subfamily	2.56E−27	30.3	111.5	72.5	31.3
				K, member 1
55	204655_PM_at	6352	CCL5	chemokine (C-C motif) ligand 5	3.28E−27	77.5	339.4	223.4	66.8
56	226474_PM_at	84166	NLRC5	NLR family, CARD domain containing 5	3.54E−27	64.4	173.5	129.8	55.1
57	212503_PM_s_at	22982	DIP2C	DIP2 disco-interacting protein 2	3.69E−27	559.7	389.2	502.0	755.0
				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 activator of	4.58E−27	21.0	49.5	37.7	18.1
				transcription 4
60	227344_PM_at	10320	IKZF1	IKAROS family zinc finger 1 (Ikaros)	5.87E−27	17.8	40.0	28.5	14.9
61	230550_PM_at	64231	MS4A6A	membrane-spanning 4-domains,	5.98E−27	44.8	124.3	88.0	30.9
				subfamily A, member 6A
62	235529_PM_x_at	25939	SAMHD1	SAM domain and HD domain 1	6.56E−27	189.3	379.9	289.1	128.0
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 cysteine	7.37E−27	115.3	261.0	186.0	87.1
				peptidase (interleukin 1, beta,
				convertase)
65	209606_PM_at	9595	CYTIP	cytohesin 1 interacting protein	7.48E−27	41.4	114.3	79.0	32.9
66	201721_PM_s_at	7805	LAPTM5	lysosomal protein transmembrane 5	8.04E−27	396.5	934.6	661.3	249.4
67	204774_PM_at	2123	EVI2A	ecotropic viral integration site 2A	8.14E−27	63.6	168.5	114.7	44.9
68	215005_PM_at	54550	NECAB2	N-terminal EF-hand calcium binding	8.32E−27	36.7	23.4	30.9	65.7
				protein 2
69	229937_PM_x_at	10859	LILRB1	Leukocyte immunoglobulin-like	8.33E−27	23.5	79.9	50.0	18.5
				receptor, subfamily B (with TM and
				ITIM domains), member
70	209515_PM_s_at	5873	RAB27A	RAB27A, member RAS oncogene family	8.93E−27	127.3	192.2	160.5	85.2
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 protein 2 (SH2	9.04E−27	56.8	162.6	104.4	44.6
				domain containing leukocyte protein of
				76 kDa)
73	214022_PM_s_at	8519	IFITM1	interferon induced transmembrane	9.31E−27	799.1	1514.7	1236.6	683.3
				protein 1 (9-27)
74	1552703_PM_s_at	114769 ///	CARD16 ///	caspase recruitment domain family,	1.01E−26	64.6	167.8	120.6	54.9
		834	CASP1	member 16 /// caspase 1, apoptosis-
				related cysteine
75	202720_PM_at	26136	TES	testis derived transcript (3 LIM	1.05E−26	285.4	379.0	357.9	204.2
				domains)
76	202659_PM_at	5699	PSMB10	proteasome (prosome, macropain)	1.10E−26	180.7	355.6	250.3	151.5
				subunit, beta type, 10
77	236295_PM_s_at	197358	NLRC3	NLR family, CARD domain containing 3	1.19E−26	19.0	52.5	37.0	18.6
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 cysteine	1.36E−26	116.0	266.6	181.6	88.7
				peptidase (interleukin 1, beta,
				convertase)
81	204336_PM_s_at	10287	RGS19	regulator of G-protein signaling 19	1.54E−26	95.2	187.7	135.7	67.0
82	204912_PM_at	3587	IL10RA	interleukin 10 receptor, alpha	1.61E−26	57.0	178.7	117.2	46.1
83	227184_PM_at	5724	PTAFR	platelet-activating factor receptor	1.70E−26	89.8	191.4	134.4	62.7
84	209969_PM_s_at	6772	STAT1	signal transducer and activator of	1.82E−26	395.8	1114.5	664.6	320.8
				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 protein 9	1.94E−26	34.2	103.4	71.8	29.4
87	209670_PM_at	28755	TRAC	T cell receptor alpha constant	2.06E−26	37.9	149.9	96.2	38.5
88	1559584_PM_a_at	283897	C16orf54	chromosome 16 open reading frame	2.22E−26	31.3	95.8	71.5	26.1
				54
89	208306_PM_x_at	3123	HLA-DRB1	Major histocompatibility complex, class	2.29E−26	2417.5	4278.5	3695.8	2255.0
				II, DR beta 1
90	229383_PM_at	55016	1-Mar	membrane-associated ring finger	2.36E−26	33.8	88.0	52.1	22.9
				(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 protein 2	3.21E−26	334.5	227.2	289.4	452.5
				homolog C (Drosophila)
94	204279_PM_at	5698	PSMB9	proteasome (prosome, macropain)	3.45E−26	241.6	637.4	419.4	211.3
				subunit, beta type, 9 (large
				multifunctional peptidase
95	235964_PM_x_at	25939	SAMHD1	SAM domain and HD domain 1	3.60E−26	172.9	345.9	270.1	117.5
96	213566_PM_at	6039	RNASE6	ribonuclease, RNase A family, k6	3.84E−26	180.9	482.0	341.1	134.3
97	221698_PM_s_at	64581	CLEC7A	C-type lectin domain family 7, member	4.00E−26	61.5	164.6	112.8	49.7
				A
98	227125_PM_at	3455	IFNAR2	interferon (alpha, beta and omega)	4.03E−26	70.0	126.2	96.7	55.8
				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 containing	4.95E−26	60.7	132.8	95.5	44.7
101	209774_PM_x_at	2920	CXCL2	chemokine (C-X-C motif) ligand 2	5.73E−26	24.9	52.9	38.2	15.5
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 similarity 26,	6.03E−26	98.6	379.7	212.1	73.7
				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 T-cells,	6.49E−26	37.9	87.0	66.7	30.3
				cytoplasmic, calcineurin-dependent 2
107	223280_PM_x_at	64231	MS4A6A	membrane-spanning 4-domains,	6.72E−26	269.0	711.8	451.2	199.5
				subfamily A, member 6A
108	201601_PM_x_at	8519	IFITM1	interferon induced transmembrane	7.27E−26	1471.3	2543.5	2202.5	1251.1
				protein 1 (9-27)
109	1552701_PM_a_at	114769	CARD16	caspase recruitment domain family,	7.33E−26	143.8	413.6	273.3	119.8
				member 16
110	229625_PM_at	115362	GBPS	guanylate binding protein 5	7.80E−26	29.3	133.3	68.6	24.0
111	38149_PM_at	9938	ARHGAP25	Rho GTPase activating protein 25	9.83E−26	51.3	108.9	83.2	43.2
112	203932_PM_at	3109	HLA-DMB	major histocompatibility complex, class	1.03E−25	422.4	853.9	633.5	376.0
				II, DM beta
113	228964_PM_at	639	PRDM1	PR domain containing 1, with ZNF	1.15E−25	21.2	52.1	41.5	17.0
				domain
114	225799_PM_at	112597 ///	LOC541471	hypothetical LOC541471 /// non-	1.23E−25	230.5	444.3	339.2	172.0
		541471	///	protein coding RNA 152
			NCRNA00152
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	EVI2B	ecotropic viral integration site 2B	1.36E−25	73.9	236.5	166.2	53.2
117	213416_PM_at	3676	ITGA4	integrin, alpha 4 (antigen CD49D, alpha	1.47E−25	26.3	78.1	50.8	22.8
				4 subunit of VLA-4 receptor)
118	211991_PM_s_at	3113	HLA-DPA1	major histocompatibility complex, class	1.50E−25	1455.0	3605.4	2837.2	1462.9
				II, DP alpha 1
119	232024_PM_at	26157	GIMAP2	GTPase, IMAP family member 2	1.57E−25	90.2	197.7	146.7	72.5
120	205159_PM_at	1439	CSF2RB	colony stimulating factor 2 receptor,	1.73E−25	33.7	107.5	70.4	26.3
				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-phosphatase,	1.88E−25	27.9	60.5	42.6	24.0
				145 kDa
123	223502_PM_s_at	10673	INFSF13B	tumor necrosis factor (ligand)	2.02E−25	73.0	244.3	145.5	60.0
				superfamily, member 13b
124	229723_PM_at	117289	TAGAP	T-cell activation RhoGTPase activating	2.07E−25	29.2	82.9	55.9	26.2
				protein
125	206978_PM_at	729230	CCR2	chemokine (C-C motif) receptor 2	2.17E−25	32.1	100.7	68.6	27.3
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-DPA1	major histocompatibility complex, class	2.53E−25	2990.7	5949.1	5139.9	3176.3
				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 immunoglobulin-	2.90E−25	29.7	74.6	45.3	21.2
				like receptor 1
130	222294_PM_s_at	5873	RAB27A	RAB27A, member RAS oncogene family	3.13E−25	198.7	309.1	263.0	146.4
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, gamma	3.46E−25	147.0	339.3	241.4	128.9
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 (glucosamine) 3-O-	4.15E−25	73.6	123.8	96.0	42.1
				sulfotransferase 1
138	200904_PM_at	3133	HLA-E	major histocompatibility complex, class	4.20E−25	1142.5	1795.2	1607.7	994.7
				I, E
139	228442_PM_at	4773	NFATC2	nuclear factor of activated T-cells,	4.48E−25	39.0	84.8	62.8	32.0
				cytoplasmic, calcineurin-dependent 2
140	204923_PM_at	54440	SASH3	SAM and SH3 domain containing 3	4.49E−25	25.4	68.2	47.6	21.7
141	223640_PM_at	10870	HCST	hematopoietic cell signal transducer	4.52E−25	91.0	234.0	158.3	72.7
142	211582_PM_x_at	7940	LST1	leukocyte specific transcript 1	4.53E−25	57.5	183.8	121.2	49.4
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 activator of	6.81E−25	642.1	1295.1	907.8	539.6
	HUMISGF3A/			transcription 1, 91 kDa
	M97935_3_at
146	201315_PM_x_at	10581	IFITM2	interferon induced transmembrane	6.87E−25	2690.9	3712.1	3303.7	2175.3
				protein 2 (1-8D)
147	228532_PM_at	128346	C1orf162	chromosome 1 open reading frame	7.07E−25	82.6	217.7	140.2	60.0
				162
148	202376_PM_at	12	SERPINA3	serpin peptidase inhibitor, clade A	7.13E−25	186.2	387.1	210.2	51.7
				(alpha-1 antiproteinase, antitrypsin),
				member 3
149	212587_PM_s_at	5788	PTPRC	protein tyrosine phosphatase, receptor	7.18E−25	114.8	398.6	265.7	90.3
				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-domains,	7.33E−25	150.6	399.6	249.6	111.2
				subfamily A, member 6A
152	206420_PM_at	10261	IGSF6	immunoglobulin superfamily, member	7.58E−25	45.1	131.5	74.3	32.7
				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, member	7.74E−25	16.6	45.4	28.9	13.2
				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 (complement	9.57E−25	100.5	253.0	182.6	65.2
				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 cancer,	1.02E−24	495.5	713.9	578.1	409.7
				prosurvival protein 1
161	201288_PM_at	397	ARHGDIB	Rho GDP dissociation inhibitor (GDI)	1.13E−24	354.9	686.8	542.2	308.1
				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 phosphatase, receptor	1.41E−24	94.4	321.0	217.7	76.4
				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 constant alpha	1.80E−24	77.7	592.5	494.6	49.4
		/// 3493 ///	IGHA2 ///	1 /// immunoglobulin heavy constant
		3494	LOC10012658	alpha 2 (A2m ma
			3
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	1.93E−24	92.9	226.0	154.5	77.0
171	208966_PM_x_at	3428	IFI16	interferon, gamma-inducible protein	1.98E−24	406.7	760.4	644.9	312.6
				16
172	1555759_PM_a_at	6352	CCL5	chemokine (C-C motif) ligand 5	2.02E−24	81.4	350.8	233.2	68.3
173	202643_PM_s_at	7128	TNFAIP3	tumor necrosis factor, alpha-induced	2.11E−24	43.7	92.8	68.1	34.8
				protein 3
174	223922_PM_x_at	64231	MS4A6A	membrane-spanning 4-domains,	2.22E−24	289.2	656.8	424.1	214.5
				subfamily A, member 6A
175	209374_PM_s_at	3507	IGHM	immunoglobulin heavy constant mu	2.26E−24	61.8	437.0	301.0	45.0
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 phosphatase, receptor	2.38E−24	71.0	133.2	102.5	51.8
				type, E
178	200887_PM_s_at	6772	STAT1	signal transducer and activator of	2.47E−24	1141.7	2278.6	1602.9	972.9
				transcription 1, 91 kDa
179	221875_PM_x_at	3134	HLA-F	major histocompatibility complex, class	2.72E−24	1365.3	2400.6	1971.8	1213.0
				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 (neutrophil)	4.03E−24	30.3	72.6	45.3	25.2
184	201762_PM_s_at	5721	PSME2	proteasome (prosome, macropain)	4.45E−24	1251.6	1825.1	1423.4	1091.0
				activator subunit 2 (PA28 beta)
185	217986_PM_s_at	11177	BAZ1A	bromodomain adjacent to zinc finger	4.79E−24	87.5	145.2	116.4	62.5
				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-like 4C	4.89E−24	54.0	99.6	77.0	42.2
189	227072_PM_at	25914	RUN	rotatin	5.01E−24	101.1	74.5	83.6	132.9
190	202206_PM_at	10123	ARL4C	ADP-ribosylation factor-like 4C	5.08E−24	60.8	128.5	96.1	36.0
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) transporter	6.09E−24	63.1	129.3	93.7	50.4
				protein 4
196	204057_PM_at	3394	IRF8	interferon regulatory factor 8	6.59E−24	89.8	184.8	134.9	71.4
197	208296_PM_x_at	25816	TNFAIP8	tumor necrosis factor, alpha-induced	6.65E−24	136.9	242.5	195.1	109.6
				protein 8
198	204122_PM_at	7305	TYROBP	TYRO protein tyrosine kinase binding	6.73E−24	190.5	473.4	332.8	143.3
				protein
199	224927_PM_at	170954	KIAA1949	KIAA1949	6.87E−24	98.8	213.6	160.2	74.7

TABLE 8

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

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

1	242956_PM_at	3417	IDH1	Isocitrate dehydrogenase 1 (NADP+), soluble	2.95E−22	32.7	29.9	53.6
2	208948_PM_s_at	6780	STAU1	staufen, RNA binding protein, homolog 1	1.56E−29	1807.9	1531.8	2467.4
				(Drosophila)
3	213037_PM_x_at	6780	STAU1	staufen, RNA binding protein, homolog 1	4.77E−27	1699.0	1466.8	2264.9
				(Drosophila)
4	207320_PM_x_at	6780	STAU1	staufen, RNA binding protein, homolog 1	6.17E−28	1425.1	1194.6	1945.3
				(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, clade A (alpha-1	1.05E−25	186.2	387.1	51.7
				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 1, r subcomponent-	9.46E−25	167.1	244.5	99.4
				like
9	215005_PM_at	54550	NECAB2	N-terminal EF-hand calcium binding protein 2	1.95E−25	36.7	23.4	65.7
10	202720_PM_at	26136	TES	testis derived transcript (3 LIM domains)	4.32E−24	285.4	379.0	204.2
11	240320_PM_at	100131781	C14orf164	chromosome 14 open reading frame 164	1.64E−23	204.9	84.2	550.0
12	243418_PM_at	—	—	—	1.81E−24	56.3	31.1	104.8
13	205466_PM_s_at	9957	HS3ST1	heparan sulfate (glucosamine) 3-O-	5.64E−24	73.6	123.8	42.1
				sulfotransferase 1
14	201042_PM_at	7052	TGM2	transglutaminase 2 (C polypeptide, protein-	3.87E−28	131.8	236.5	80.1
				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 protein 2 homolog C	6.62E−27	559.7	389.2	755.0
				(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 oncogene family	2.11E−23	127.3	192.2	85.2
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-like 4C	1.83E−29	106.9	258.6	57.2
21	227697_PM_at	9021	SOCS3	suppressor of cytokine signaling 3	2.93E−23	35.9	69.1	19.9
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 (colonic epithelium-	2.71E−22	187.7	274.0	131.7
				enriched)
24	212504_PM_at	22982	DIP2C	DIP2 disco-interacting protein 2 homolog C	2.57E−25	334.5	227.2	452.5
				(Drosophila)
25	200986_PM_at	710	SERPING1	serpin peptidase inhibitor, clade G (C1	2.88E−26	442.7	731.5	305.3
				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 /// hypothetical	6.40E−25	76.4	131.8	50.0
		23765	LOC150166	protein LOC150166
28	231358_PM_at	83876	MRO	maestro	1.11E−22	199.7	81.2	422.1
29	201666_PM_at	7076	TIMP1	TIMP metallopeptidase inhibitor 1	1.54E−22	1035.3	1879.4	648.0
30	209774_PM_x_at	2920	CXCL2	chemokine (C-X-C motif) ligand 2	1.50E−26	24.9	52.9	15.5
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, member 10 (aromatic	2.55E−22	156.6	93.7	229.6
				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-like 4C	1.16E−22	60.8	128.5	36.0
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 domain 1	4.49E−24	189.3	379.9	128.0
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) polymerase family, member	1.88E−22	47.9	79.2	35.7
				8
40	201721_PM_s_at	7805	LAPTM5	lysosomal protein transmembrane 5	3.72E−24	396.5	934.6	249.4
41	204336_PM_s_at	10287	RGS19	regulator of G-protein signaling 19	3.52E−25	95.2	187.7	67.0
42	235964_PM_x_at	25939	SAMHD1	SAM domain and HD domain 1	2.45E−23	172.9	345.9	117.5
43	208091_PM_s_at	81552	VOPP1	vesicular, overexpressed in cancer, prosurvival	9.47E−25	495.5	713.9	409.7
				protein 1
44	204446_PM_s_at	240	ALOX5	arachidonate 5-lipoxygenase	6.25E−31	91.9	323.9	54.7
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 protein 2 homolog C	2.57E−22	66.4	35.7	92.5
				(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 receptor	1.73E−24	89.8	191.4	62.7
50	221840_PM_at	5791	PTPRE	protein tyrosine phosphatase, receptor type, E	8.52E−23	71.0	133.2	51.8
51	225799_PM_at	112597 ///	LOC541471	hypothetical LOC541471 /// non-protein	1.18E−23	230.5	444.3	172.0
		541471	///	coding RNA 152
			NCRNA00152
52	202957_PM_at	3059	HCLS1	hematopoietic cell-specific Lyn substrate 1	1.94E−25	119.2	299.5	82.2
53	229383_PM_at	55016	1-Mar	membrane-associated ring finger (C3HC4) 1	8.09E−25	33.8	88.0	22.9
54	33304_PM_at	3669	ISG20	interferon stimulated exonuclease gene 20 kDa	2.83E−27	33.2	101.3	22.1
55	222062_PM_at	9466	IL27RA	interleukin 27 receptor, alpha	1.79E−22	34.9	65.8	26.4
56	219574_PM_at	55016	1-Mar	membrane-associated ring finger (C3HC4) 1	6.65E−25	51.5	126.0	36.2
57	202748_PM_at	2634	GBP2	guanylate binding protein 2, interferon-	7.51E−26	196.7	473.0	141.0
				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-like 4C	1.24E−23	54.0	99.6	42.2
60	221666_PM_s_at	29108	PYCARD	PYD and CARD domain containing	5.55E−24	60.7	132.8	44.7
61	227125_PM_at	3455	IFNAR2	interferon (alpha, beta and omega) receptor 2	3.57E−24	70.0	126.2	55.8
62	226525_PM_at	9262	STK17B	serine/threonine kinase 17b	3.43E−24	146.8	338.7	107.6
63	210644_PM_s_at	3903	LAIR1	leukocyte-associated immunoglobulin-like	2.96E−24	29.7	74.6	21.2
				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 domain containing 2	1.67E−26	104.6	321.0	71.9
67	223922_PM_x_at	64231	MS4A6A	membrane-spanning 4-domains, subfamily A,	8.68E−24	289.2	656.8	214.5
				member 6A
68	230550_PM_at	64231	MS4A6A	membrane-spanning 4-domains, subfamily A,	7.63E−24	44.8	124.3	30.9
				member 6A
69	202953_PM_at	713	C1QB	complement component 1, q subcomponent, B	2.79E−22	215.8	638.4	142.5
				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 site 2A	5.10E−24	63.6	168.5	44.9
72	211366_PM_x_at	834	CASP1	caspase 1, apoptosis-related cysteine	3.40E−24	115.3	261.0	87.1
				peptidase (interleukin 1, beta, convertase)
73	204698_PM_at	3669	ISG20	interferon stimulated exonuclease gene 20 kDa	1.27E−28	41.5	165.1	27.6
74	201762_PM_s_at	5721	PSME2	proteasome (prosome, macropain) activator	2.31E−22	1251.6	1825.1	1091.0
				subunit 2 (PA28 beta)
75	204470_PM_at	2919	CXCL1	chemokine (C-X-C motif) ligand 1 (melanoma	2.81E−24	22.9	63.7	16.2
				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-related cysteine	4.40E−25	116.0	266.6	88.7
				peptidase (interleukin 1, beta, convertase)
78	228532_PM_at	128346	C1orf162	chromosome 1 open reading frame 162	1.57E−22	82.6	217.7	60.0
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-domains, subfamily A,	4.29E−23	159.2	397.6	118.7
				member 6A
82	223280_PM_x_at	64231	MS4A6A	membrane-spanning 4-domains, subfamily A,	2.53E−24	269.0	711.8	199.5
				member 6A
83	225701_PM_at	80709	AKNA	AT-hook transcription factor	2.87E−29	37.7	102.8	29.0
84	224356_PM_x_at	64231	MS4A6A	membrane-spanning 4-domains, subfamily A,	1.56E−23	150.6	399.6	111.2
				member 6A
85	202643_PM_s_at	7128	TNFAIP3	tumor necrosis factor, alpha-induced protein 3	9.92E−24	43.7	92.8	34.8
86	202644_PM_s_at	7128	TNFAIP3	tumor necrosis factor, alpha-induced protein 3	2.12E−27	169.8	380.4	136.6
87	213566_PM_at	6039	RNASE6	ribonuclease, RNase A family, k6	1.55E−23	180.9	482.0	134.3
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 superfamily, member 6	5.65E−23	45.1	131.5	32.7
92	217028_PM_at	7852	CXCR4	chemokine (C-X-C motif) receptor 4	6.84E−27	100.8	304.4	75.3
93	232024_PM_at	26157	GIMAP2	GTPase, IMAP family member 2	2.94E−24	90.2	197.7	72.5
94	238327_PM_at	440836	ODF3B	outer dense fiber of sperm tails 3B	1.75E−27	32.8	81.4	26.1
95	209083_PM_at	11151	CORO1A	coronin, actin binding protein, 1A	2.78E−27	46.9	163.8	34.3
96	232724_PM_at	64231	MS4A6A	membrane-spanning 4-domains, subfamily A,	6.28E−23	24.8	47.6	20.7
				member 6A
97	211742_PM_s_at	2124	EVI2B	ecotropic viral integration site 2B	1.56E−22	73.9	236.5	53.2
98	202659_PM_at	5699	PSMB10	proteasome (prosome, macropain) subunit,	2.29E−24	180.7	355.6	151.5
				beta type, 10
99	226991_PM_at	4773	NFATC2	nuclear factor of activated T-cells, cytoplasmic,	2.42E−23	37.9	87.0	30.3
				calcineurin-dependent 2
100	210538_PM_s_at	330	BIRC3	baculoviral IAP repeat-containing 3	5.40E−26	106.7	276.7	84.6
101	205269_PM_at	3937	LCP2	lymphocyte cytosolic protein 2 (SH2 domain	7.35E−25	30.2	92.2	22.9
				containing leukocyte protein of 76 kDa)
102	211368_PM_s_at	834	CASP1	caspase 1, apoptosis-related cysteine	9.43E−27	102.6	274.3	81.8
				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 T-cells, cytoplasmic,	5.58E−23	39.0	84.8	32.0
				calcineurin-dependent 2
105	213603_PM_s_at	5880	RAC2	ras-related C3 botulinum toxin substrate 2 (rho	3.38E−25	113.9	366.5	86.5
				family, small GTP binding protein Rac2)
106	228964_PM_at	639	PRDM1	PR domain containing 1, with ZNF domain	1.42E−23	21.2	52.1	17.0
107	209606_PM_at	9595	CYTIP	cytohesin 1 interacting protein	1.66E−26	41.4	114.3	32.9
108	214022_PM_s_at	8519	IFITM1	interferon induced transmembrane protein 1	1.61E−23	799.1	1514.7	683.3
				(9-27)
109	202307_PM_s_at	6890	TAP1	transporter 1, ATP-binding cassette, sub-family	8.61E−26	172.4	420.6	141.0
				B (MDR/TAP)
110	204882_PM_at	9938	ARHGAP25	Rho GTPase activating protein 25	8.14E−23	51.5	107.3	43.0
111	227344_PM_at	10320	IKZF1	IKAROS family zinc finger 1 (Ikaros)	5.30E−26	17.8	40.0	14.9
112	205270_PM_s_at	3937	LCP2	lymphocyte cytosolic protein 2 (SH2 domain	3.75E−24	56.8	162.6	44.6
				containing leukocyte protein of 76 kDa)
113	223640_PM_at	10870	HCST	hematopoietic cell signal transducer	5.55E−23	91.0	234.0	72.7
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 protein 30	1.87E−26	46.4	127.9	37.5
116	38149_PM_at	9938	ARHGAP25	Rho GTPase activating protein 25	1.20E−23	51.3	108.9	43.2
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 activator of transcription	1.27E−22	1141.7	2278.6	972.9
				1, 91 kDa
120	1555756_PM_a_at	64581	CLEC7A	C-type lectin domain family 7, member A	2.11E−22	16.6	45.4	13.2
121	205039_PM_s_at	10320	IKZF1	IKAROS family zinc finger 1 (Ikaros)	6.73E−23	29.1	65.9	24.2
122	206011_PM_at	834	CASP1	caspase 1, apoptosis-related cysteine	6.34E−25	74.5	198.0	60.7
				peptidase (interleukin 1, beta, convertase)
123	221698_PM_s_at	64581	CLEC7A	C-type lectin domain family 7, member A	9.79E−24	61.5	164.6	49.7
124	227346_PM_at	10320	IKZF1	IKAROS family zinc finger 1 (Ikaros)	1.51E−26	24.9	79.7	19.8
125	230499_PM_at	—	—	—	8.55E−23	29.7	68.7	24.7
126	229391_PM_s_at	441168	FAM26F	family with sequence similarity 26, member F	2.74E−23	98.6	379.7	73.7
127	205159_PM_at	1439	CSF2RB	colony stimulating factor 2 receptor, beta, low-	1.74E−23	33.7	107.5	26.3
				affinity (granulocyte-macrophage)
128	205639_PM_at	313	AOAH	acyloxyacyl hydrolase (neutrophil)	4.43E−23	30.3	72.6	25.2
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 inhibitor (GDI) beta	1.92E−22	354.9	686.8	308.1
131	209969_PM_s_at	6772	STAT1	signal transducer and activator of transcription	5.49E−24	395.8	1114.5	320.8
				1, 91 kDa
132	229390_PM_at	441168	FAM26F	family with sequence similarity 26, member F	3.91E−25	103.8	520.0	75.9
133	242916_PM_at	11064	CEP110	centrosomal protein 110 kDa	1.99E−23	30.8	68.1	26.2
134	207651_PM_at	29909	GPR171	G protein-coupled receptor 171	1.90E−29	25.8	89.9	20.9
135	229937_PM_x_at	10859	LILRB1	Leukocyte immunoglobulin-like receptor,	1.67E−24	23.5	79.9	18.5
				subfamily B (with TM and ITIM domains),
				member
136	232543_PM_x_at	64333	ARHGAP9	Rho GTPase activating protein 9	3.66E−26	31.7	99.1	25.8
137	203332_PM_s_at	3635	INPP5D	inositol polyphosphate-5-phosphatase, 145 kDa	3.66E−24	27.9	60.5	24.0
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, alpha	1.28E−24	57.0	178.7	46.1
140	204205_PM_at	60489	APOBEC3G	apolipoprotein B mRNA editing enzyme,	6.40E−27	95.4	289.4	78.7
				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 activator of transcription	8.28E−26	21.0	49.5	18.1
				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-like 8	6.49E−29	19.8	64.2	16.6
146	1552701_PM_a_at	114769	CARD16	caspase recruitment domain family, member	2.52E−23	143.8	413.6	119.8
				16
147	1552703_PM_s_at	114769 ///	CARD16 ///	caspase recruitment domain family, member	7.57E−24	64.6	167.8	54.9
		834	CASP1	16 /// caspase 1, apoptosis-related cysteine
148	229437_PM_at	114614	MIR155HG	MIR155 host gene (non-protein coding)	3.90E−27	15.4	50.4	12.9
149	204319_PM_s_at	6001	RGS10	regulator of G-protein signaling 10	6.54E−24	159.7	360.4	139.4
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 reading frame 54	2.86E−24	31.3	95.8	26.1
152	212588_PM_at	5788	PTPRC	protein tyrosine phosphatase, receptor type, C	2.46E−22	94.4	321.0	76.4
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 complex, class II, DM	6.25E−23	422.4	853.9	376.0
				beta
156	223502_PM_s_at	10673	INFSF13B	tumor necrosis factor (ligand) superfamily,	2.62E−23	73.0	244.3	60.0
				member 13b
157	1405_PM_i_at	6352	CCL5	chemokine (C-C motif) ligand 5	2.22E−25	68.0	295.7	54.6
158	226474_PM_at	84166	NLRC5	NLR family, CARD domain containing 5	1.33E−23	64.4	173.5	55.1
159	204220_PM_at	9535	GMFG	glia maturation factor, gamma	1.56E−23	147.0	339.3	128.9
160	204923_PM_at	54440	SASH3	SAM and SH3 domain containing 3	4.56E−23	25.4	68.2	21.7
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-DRB1 ///	major histocompatibility complex, class II, DR	1.31E−23	2461.9	4344.6	2262.0
		3126	HLA-DRB4	beta 1 /// major 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) receptor 2	4.46E−23	32.1	100.7	27.3
166	224451_PM_x_at	64333	ARHGAP9	Rho GTPase activating protein 9	4.23E−24	34.2	103.4	29.4
167	204279_PM_at	5698	PSMB9	proteasome (prosome, macropain) subunit,	2.81E−23	241.6	637.4	211.3
				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	GBPS	guanylate binding protein 5	5.85E−24	29.3	133.3	24.0
170	213416_PM_at	3676	ITGA4	integrin, alpha 4 (antigen CD49D, alpha 4	1.19E−23	26.3	78.1	22.8
				subunit of VLA-4 receptor)
171	206804_PM_at	917	CD3G	CD3g molecule, gamma (CD3-TCR complex)	1.50E−27	19.7	60.6	17.3
172	222895_PM_s_at	64919	BCL11B	B-cell CLL/lymphoma 11B (zinc finger protein)	7.01E−23	22.0	64.5	19.1
173	211582_PM_x_at	7940	LST1	leukocyte specific transcript 1	2.13E−22	57.5	183.8	49.4
174	1555852_PM_at	100507463	LOC10050746	hypothetical LOC100507463	8.92E−26	78.9	202.6	70.9
			3
175	213539_PM_at	915	CD3D	CD3d molecule, delta (CD3-TCR complex)	9.01E−27	70.8	335.1	60.0
176	239237_PM_at	—	—	—	9.82E−24	15.9	34.9	14.5
177	229723_PM_at	117289	TAGAP	T-cell activation RhoGTPase activating protein	5.21E−24	29.2	82.9	26.2
178	204655_PM_at	6352	CCL5	chemokine (C-C motif) ligand 5	7.63E−24	77.5	339.4	66.8
179	229041_PM_s_at	—	—	—	1.64E−24	36.5	132.1	32.4
180	205267_PM_at	5450	POU2AF1	POU class 2 associating factor 1	4.47E−23	17.9	86.8	15.7
181	226878_PM_at	3111	HLA-DOA	major histocompatibility complex, class II, DO	2.59E−25	102.0	288.9	94.3
				alpha
182	205488_PM_at	3001	GZMA	granzyme A (granzyme 1, cytotoxic T-	4.14E−25	37.3	164.8	33.4
				lymphocyte-associated serine esterase 3)
183	201137_PM_s_at	3115	HLA-DPB1	major histocompatibility complex, class II, DP	2.17E−22	1579.1	3863.7	1475.9
				beta 1
184	204891_PM_s_at	3932	LCK	lymphocyte-specific protein tyrosine kinase	7.95E−28	19.3	74.2	17.8
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 kinase	7.40E−23	16.7	44.4	15.7
187	223322_PM_at	83593	RASSF5	Ras association (RaIGDS/AF-6) domain family	5.21E−26	41.5	114.4	39.2
				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 constant 1 /// T cell	4.38E−25	69.2	431.5	63.6
		28639	TRBC2	receptor beta constant 2
191	210915_PM_x_at	28638	TRBC2	T cell receptor beta constant 2	1.91E−27	39.7	230.7	37.5
192	205821_PM_at	22914	KLRK1	killer cell lectin-like receptor subfamily K,	1.18E−25	30.3	111.5	31.3
				member 1
193	236295_PM_s_at	197358	NLRC3	NLR family, CARD domain containing 3	6.22E−26	19.0	52.5	18.6
194	213193_PM_x_at	28639	TRBC1	T cell receptor beta constant 1	4.22E−25	95.3	490.9	92.1
195	211656_PM_x_at	100133583	HLA-DQB1 ///	major histocompatibility complex, class II, DQ	5.98E−25	211.3	630.2	208.2
		/// 3119	LOC10013358	beta 1 /// HLA class II histocompatibili
			3
196	209670_PM_at	28755	TRAC	T cell receptor alpha constant	1.44E−24	37.9	149.9	38.5
197	213888_PM_s_at	80342	TRAF3IP3	TRAF3 interacting protein 3	1.66E−22	30.8	101.9	30.5

TABLE 9

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

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

1	204698_PM_at	3669	ISG20	interferon stimulated exonuclease gene 20 kDa	1.93E−19	96.1	27.6
2	33304_PM_at	3669	ISG20	interferon stimulated exonuclease gene 20 kDa	2.02E−19	63.4	22.1
3	217022_PM_s_at	100126583	IGHA1 ///	immunoglobulin heavy constant alpha 1 ///	4.31E−19	494.6	49.4
		/// 3493 ///	IGHA2 ///	immunoglobulin heavy constant alpha 2 (A2m ma
		3494	LOC10012658
			3
4	202957_PM_at	3059	HCLS1	hematopoietic cell-specific Lyn substrate 1	5.13E−19	229.9	82.2
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, with ZNF domain	2.37E−18	41.5	17.0
9	201042_PM_at	7052	TGM2	transglutaminase 2 (C polypeptide, protein-glutamine-	3.13E−18	172.6	80.1
				gamma-glutamyltransferase)
10	226219_PM_at	257106	ARHGAP30	Rho GTPase activating protein 30	7.21E−18	91.8	37.5
11	225701_PM_at	80709	AKNA	AT-hook transcription factor	7.27E−18	73.2	29.0
12	202207_PM_at	10123	ARL4C	ADP-ribosylation factor-like 4C	7.98E−18	190.4	57.2
13	219574_PM_at	55016	MAR1	membrane-associated ring finger (C3HC4) 1	8.98E−18	87.5	36.2
14	209083_PM_at	12-Jul	CORO1A	coronin, actin binding protein, 1A	1.06E−17	107.1	34.3
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) ligand 5	2.19E−17	195.6	54.6
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 1 (Ikaros)	2.92E−17	53.3	19.8
19	204205_PM_at	60489	APOBEC3G	apolipoprotein B mRNA editing enzyme, catalytic	2.92E−17	192.0	78.7
				polypeptide-like 3G
20	218322_PM_s_at	51703	ACSL5	acyl-CoA synthetase long-chain family member 5	3.15E−17	84.5	48.1
21	238327_PM_at	440836	ODF3B	outer dense fiber of sperm tails 3B	3.42E−17	58.5	26.1
22	218983_PM_at	51279	C1RL	complement component 1, r subcomponent-like	4.33E−17	206.9	99.4
23	210538_PM_s_at	330	BIRC3	baculoviral IAP repeat-containing 3	4.49E−17	199.1	84.6
24	207651_PM_at	29909	GPR171	G protein-coupled receptor 171	5.48E−17	57.0	20.9
25	201601_PM_x_at	8519	IFITM1	interferon induced transmembrane protein 1 (9-27)	6.07E−17	2202.5	1251.1
26	226878_PM_at	3111	HLA-DOA	major histocompatibility complex, class II, DO alpha	6.12E−17	201.4	94.3
27	1555756_PM_a_at	64581	CLEC7A	C-type lectin domain family 7, member A	6.22E−17	28.9	13.2
28	1559584_PM_a_at	283897	C16orf54	chromosome 16 open reading frame 54	6.73E−17	71.5	26.1
29	209795_PM_at	969	CD69	CD69 molecule	9.46E−17	40.6	15.2
30	230550_PM_at	64231	MS4A6A	membrane-spanning 4-domains, subfamily A, member 6A	1.20E−16	88.0	30.9
31	1553906_PM_s_at	221472	FGD2	FYVE, RhoGEF and PH domain containing 2	1.34E−16	219.3	71.9
32	205798_PM_at	3575	IL7R	interleukin 7 receptor	1.55E−16	106.3	33.4
33	1555852_PM_at	100507463	LOC10050746	hypothetical LOC100507463	1.81E−16	154.2	70.9
			3
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 cysteine peptidase	1.85E−16	191.4	81.8
				(interleukin 1, beta, convertase)
36	226474_PM_at	84166	NLRC5	NLR family, CARD domain containing 5	1.85E−16	129.8	55.1
37	201137_PM_s_at	3115	HLA-DPB1	major histocompatibility complex, class II, DP beta 1	1.90E−16	3151.7	1475.9
38	210785_PM_s_at	9473	C1orf38	chromosome 1 open reading frame 38	2.07E−16	39.9	16.4
39	215121_PM_x_at	100290481	IGLC7 ///	immunoglobulin lambda constant 7 /// immunoglobulin	2.13E−16	1546.8	250.4
		/// 28823	IGLV1-44 ///	lambda variable 1-44 /// immunoglob
		/// 28834	LOC10029048
			1
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 4, 40 kDa	2.65E−16	39.5	19.2
43	202720_PM_at	26136	TES	testis derived transcript (3 LIM domains)	2.68E−16	357.9	204.2
44	220005_PM_at	53829	P2RY13	purinergic receptor P2Y, G-protein coupled, 13	2.72E−16	29.6	14.8
45	200904_PM_at	3133	HLA-E	major histocompatibility complex, class I, E	2.73E−16	1607.7	994.7
46	222294_PM_s_at	5873	RAB27A	RAB27A, member RAS oncogene family	2.91E−16	263.0	146.4
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 1 (Ikaros)	3.39E−16	28.5	14.9
49	209374_PM_s_at	3507	IGHM	immunoglobulin heavy constant mu	3.73E−16	301.0	45.0
50	202307_PM_s_at	6890	TAP1	transporter 1, ATP-binding cassette, sub-family B	4.84E−16	280.0	141.0
				(MDR/TAP)
51	223218_PM_s_at	64332	NFKBIZ	nuclear factor of kappa light polypeptide gene enhancer in	5.05E−16	399.9	159.1
				B-cells inhibitor, zeta
52	229437_PM_at	114614	MIR155HG	MIR155 host gene (non-protein coding)	5.85E−16	28.5	12.9
53	213603_PM_s_at	5880	RAC2	ras-related C3 botulinum toxin substrate 2 (rho family,	5.98E−16	250.3	86.5
				small GTP binding protein Rac2)
54	214669_PM_x_at	3514 ///	IGK@ ///	immunoglobulin kappa locus /// immunoglobulin kappa	6.32E−16	3449.1	587.1
		50802	IGKC	constant
55	211430_PM_s_at	28396 ///	IGHG1 ///	immunoglobulin heavy constant gamma 1 (G1m marker)	6.39E−16	2177.7	266.9
		3500 ///	IGHM ///	/// immunoglobulin heavy constant mu
		3507	IGHV4-31
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 locus	7.54E−16	2387.0	343.2
58	227353_PM_at	147138	TMC8	transmembrane channel-like 8	8.01E−16	42.7	16.6
59	200986_PM_at	710	SERPING1	serpin peptidase inhibitor, clade G (C1 inhibitor), member	8.10E−16	590.2	305.3
				1
60	212203_PM_x_at	10410	IFITM3	interferon induced transmembrane protein 3 (1-8U)	8.17E−16	4050.1	2773.8
61	221651_PM_x_at	3514 ///	IGK@ ///	immunoglobulin kappa locus /// immunoglobulin kappa	9.60E−16	3750.2	621.2
		50802	IGKC	constant
62	214836_PM_x_at	28299 ///	IGK@ ///	immunoglobulin kappa locus /// immunoglobulin kappa	9.72E−16	544.2	109.1
		3514 ///	IGKC ///	constant /// immunoglobulin kappa v
		50802	IGKV1-5
63	1552703_PM_s_at	114769 ///	CARD16 ///	caspase recruitment domain family, member 16 ///	1.12E−15	120.6	54.9
		834	CASP1	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 constant 7 /// immunoglobulin	1.16E−15	1453.0	248.1
		28834	IGLV1-44	lambda variable 1-44
66	222939_PM_s_at	117247	SLC16A10	solute carrier family 16, member 10 (aromatic amino acid	1.22E−15	115.1	229.6
				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 domain 1	1.26E−15	270.1	117.5
69	205159_PM_at	1439	CSF2RB	colony stimulating factor 2 receptor, beta, low-affinity	1.28E−15	70.4	26.3
				(granulocyte-macrophage)
70	224451_PM_x_at	64333	ARHGAP9	Rho GTPase activating protein 9	1.34E−15	71.8	29.4
71	214677_PM_x_at	100287927	IGL@ ///	Immunoglobulin lambda locus /// Hypothetical protein	1.35E−15	2903.1	433.7
		/// 3535	LOC10028792	LOC100287927
			7
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 protein 25	1.46E−15	83.2	43.2
74	221671_PM_x_at	3514 ///	IGK@ ///	immunoglobulin kappa locus /// immunoglobulin kappa	1.57E−15	3722.5	642.9
		50802	IGKC	constant
75	214022_PM_s_at	8519	IFITM1	interferon induced transmembrane protein 1 (9-27)	1.59E−15	1236.6	683.3
76	223217_PM_s_at	64332	NFKBIZ	nuclear factor of kappa light polypeptide gene enhancer in	1.61E−15	196.6	79.7
				B-cells inhibitor, zeta
77	206118_PM_at	6775	STAT4	signal transducer and activator of transcription 4	1.67E−15	37.7	18.1
78	221666_PM_s_at	29108	PYCARD	PYD and CARD domain containing	1.82E−15	95.5	44.7
79	207375_PM_s_at	3601	IL15RA	interleukin 15 receptor, alpha	1.94E−15	51.2	28.2
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 locus /// immunoglobulin kappa	2.18E−15	3866.2	670.5
		50802	IGKC	constant
83	36711_PM_at	23764	MAFF	v-maf musculoaponeurotic fibrosarcoma oncogene	2.26E−15	113.0	40.7
				homolog F (avian)
84	227125_PM_at	3455	IFNAR2	interferon (alpha, beta and omega) receptor 2	2.27E−15	96.7	55.8
85	235735_PM_at	—	—	—	2.58E−15	24.5	11.2
86	209515_PM_s_at	5873	RAB27A	RAB27A, member RAS oncogene family	2.61E−15	160.5	85.2
87	204670_PM_x_at	3123 ///	HLA-DRB1 ///	major histocompatibility complex, class II, DR beta 1 ///	2.61E−15	3694.9	2262.0
		3126	HLA-DRB4	major histocompatibility comp
88	205269_PM_at	3937	LCP2	lymphocyte cytosolic protein 2 (SH2 domain containing	2.85E−15	58.9	22.9
				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 /// hypothetical protein	3.02E−15	98.4	50.0
		23765	LOC150166	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) ligand 2	3.45E−15	38.2	15.5
93	211656_PM_x_at	100133583	HLA-DQB1 ///	major histocompatibility complex, class II, DQ beta 1 ///	3.51E−15	459.8	208.2
		/// 3119	LOC10013358	HLA class II histocompatibili
			3
94	206011_PM_at	834	CASP1	caspase 1, apoptosis-related cysteine peptidase	3.56E−15	146.2	60.7
				(interleukin 1, beta, convertase)
95	202803_PM_s_at	3689	ITGB2	integrin, beta 2 (complement component 3 receptor 3 and	3.68E−15	182.6	65.2
				4 subunit)
96	221698_PM_s_at	64581	CLEC7A	C-type lectin domain family 7, member A	3.69E−15	112.8	49.7
97	229937_PM_x_at	10859	LILRB1	Leukocyte immunoglobulin-like receptor, subfamily B	3.75E−15	50.0	18.5
				(with TM and ITIM domains), member
98	235529_PM_x_at	25939	SAMHD1	SAM domain and HD domain 1	3.99E−15	289.1	128.0
99	223322_PM_at	83593	RASSF5	Ras association (RaIGDS/AF-6) domain family member 5	4.03E−15	79.3	39.2
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 transmembrane 5	4.83E−15	661.3	249.4
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) receptor 2	5.01E−15	68.6	27.3
104	244353_PM_s_at	154091	SLC2A12	solute carrier family 2 (facilitated glucose transporter),	5.72E−15	51.6	100.5
				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 (sodium phosphate), member 3	6.40E−15	95.6	206.6
107	225636_PM_at	6773	STAT2	signal transducer and activator of transcription 2, 113 kDa	6.63E−15	711.5	485.3
108	229390_PM_at	441168	FAM26F	family with sequence similarity 26, member F	6.73E−15	272.4	75.9
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) receptor 4	7.40E−15	208.4	75.3
112	204655_PM_at	6352	CCL5	chemokine (C-C motif) ligand 5	8.57E−15	223.4	66.8
113	227184_PM_at	5724	PTAFR	platelet-activating factor receptor	8.78E−15	134.4	62.7
114	202748_PM_at	2634	GBP2	guanylate binding protein 2, interferon-inducible	8.91E−15	306.7	141.0
115	226991_PM_at	4773	NFATC2	nuclear factor of activated T-cells, cytoplasmic,	9.05E−15	66.7	30.3
				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 receptor	9.57E−15	42.7	22.1
118	238668_PM_at	—	—	—	9.84E−15	33.6	14.5
119	204923_PM_at	54440	SASH3	SAM and SH3 domain containing 3	9.93E−15	47.6	21.7
120	230036_PM_at	219285	SAMD9L	sterile alpha motif domain containing 9-like	1.02E−14	128.0	72.7
121	211742_PM_s_at	2124	EVI2B	ecotropic viral integration site 2B	1.03E−14	166.2	53.2
122	236782_PM_at	154075	SAMD3	sterile alpha motif domain containing 3	1.11E−14	23.3	13.3
123	232543_PM_x_at	64333	ARHGAP9	Rho GTPase activating protein 9	1.13E−14	62.3	25.8
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-like polypeptide 1 ///	1.22E−14	187.5	52.1
		91316 ///	IGLL3P ///	immunoglobulin lambda-like polypeptide 3,
		91353	LOC91316
126	208306_PM_x_at	3123	HLA-DRB1	Major histocompatibility complex, class II, DR beta 1	1.25E−14	3695.8	2255.0
127	217235_PM_x_at	28816	IGLV2-11	immunoglobulin lambda variable 2-11	1.29E−14	196.8	37.8
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 cysteine peptidase	1.35E−14	186.0	87.1
				(interleukin 1, beta, convertase)
131	200797_PM_s_at	4170	MCL1	myeloid cell leukemia sequence 1 (BCL2-related)	1.38E−14	793.7	575.9
132	31845_PM_at	2000	ELF4	E74-like factor 4 (ets domain transcription factor)	1.40E−14	60.7	34.3
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 similarity 26, member F	1.49E−14	212.1	73.7
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 locus /// immunoglobulin kappa	1.61E−14	131.1	32.6
		/// 28875	IGKC ///	constant /// immunoglobulin kappa v
		/// 3514 ///	IGKV3D-15 ///
		50802	LOC10051004
			4
137	205488_PM_at	3001	GZMA	granzyme A (granzyme 1, cytotoxic T-lymphocyte-	1.82E−14	102.3	33.4
				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 site 2A	1.95E−14	114.7	44.9
140	204336_PM_s_at	10287	RGS19	regulator of G-protein signaling 19	2.01E−14	135.7	67.0
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 T-cells, cytoplasmic,	2.06E−14	62.8	32.0
				calcineurin-dependent 2
143	206804_PM_at	917	CD3G	CD3g molecule, gamma (CD3-TCR complex)	2.18E−14	36.4	17.3
144	201315_PM_x_at	10581	IFITM2	interferon induced transmembrane protein 2 (1-8D)	2.21E−14	3303.7	2175.3
145	203561_PM_at	2212	FCGR2A	Fc fragment of IgG, low affinity IIa, receptor (CD32)	2.22E−14	66.4	29.2
146	219117_PM_s_at	51303	FKBP11	FK506 binding protein 11, 19 kDa	2.31E−14	341.3	192.9
147	242827_PM_x_at	—	—	—	2.37E−14	38.9	16.3
148	214768_PM_x_at	28299 ///	IGK@ ///	immunoglobulin kappa locus /// immunoglobulin kappa	2.38E−14	116.7	21.1
		3514 ///	IGKC ///	constant /// immunoglobulin kappa v
		50802	IGKV1-5
149	227253_PM_at	1356	CP	ceruloplasmin (ferroxidase)	2.49E−14	44.7	22.0
150	209619_PM_at	972	CD74	CD74 molecule, major histocompatibility complex,	2.51E−14	1502.3	864.9
				class II invariant chain
151	208966_PM_x_at	3428	IF116	interferon, gamma-inducible protein 16	2.65E−14	644.9	312.6
152	239237_PM_at	—	—	—	2.79E−14	25.3	14.5
153	213566_PM_at	6039	RNASE6	ribonuclease, RNase A family, k6	2.82E−14	341.1	134.3
154	201288_PM_at	397	ARHGDIB	Rho GDP dissociation inhibitor (GDI) beta	2.86E−14	542.2	308.1
155	209606_PM_at	9595	CYTIP	cytohesin 1 interacting protein	2.90E−14	79.0	32.9
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 subcomponent, B chain	3.00E−14	401.1	142.5
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 protein 2 (SH2 domain containing	3.12E−14	104.4	44.6
				leukocyte protein of 76 kDa)
160	223658_PM_at	9424	KCNK6	potassium channel, subfamily K, member 6	3.18E−14	35.9	22.0
161	202637_PM_s_at	3383	ICAM1	intercellular adhesion molecule 1	3.18E−14	89.1	45.7
162	202935_PM_s_at	6662	SOX9	SRY (sex determining region Y)-box 9	3.18E−14	117.0	46.1
163	217986_PM_s_at	11177	BAZ1A	bromodomain adjacent to zinc finger domain, 1A	3.21E−14	116.4	62.5
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-domains, subfamily A, member 7	3.38E−14	346.0	128.3
166	1552701_PM_a_at	114769	CARD16	caspase recruitment domain family, member 16	3.60E−14	273.3	119.8
167	226659_PM_at	50619	DEF6	differentially expressed in FDCP 6 homolog (mouse)	3.63E−14	35.2	22.2
168	213502_PM_x_at	91316	LOC91316	glucuronidase, beta/immunoglobulin lambda-like	3.63E−14	1214.7	419.3
				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 tyrosine kinase	3.74E−14	43.4	17.8
171	224252_PM_s_at	53827	FXYD5	FXYD domain containing ion transport regulators	3.76E−14	73.8	32.5
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 ion transport regulators	4.52E−14	89.2	39.1
176	218232_PM_at	712	C1QA	complement component 1, q subcomponent, A chain	4.63E−14	197.0	85.8
177	202208_PM_s_at	10123	ARL4C	ADP-ribosylation factor-like 4C	4.63E−14	77.0	42.2
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 domain 4B	5.05E−14	20.6	12.1
180	208948_PM_s_at	6780	STAU1	staufen, RNA binding protein, homolog 1 (Drosophila)	5.23E−14	1766.0	2467.4
181	211645_PM_x_at	—	—	—	5.24E−14	166.7	27.4
182	236295_PM_s_at	197358	NLRC3	NLR family, CARD domain containing 3	5.28E−14	37.0	18.6
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 reading frame 71	6.12E−14	175.8	280.8
187	204912_PM_at	3587	IL1ORA	interleukin 10 receptor, alpha	6.25E−14	117.2	46.1
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 protein)	6.77E−14	85.6	40.8
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 gene 12 (non-protein coding)	7.00E−14	174.8	93.2
192	202644_PM_s_at	7128	TNFAIP3	tumor necrosis factor, alpha-induced protein 3	7.11E−14	278.2	136.6
193	211796_PM_s_at	28638 ///	TRBC1 ///	T cell receptor beta constant 1 /// T cell receptor beta	7.13E−14	250.2	63.6
		28639	TRBC2	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 constant /// immunoglobulin	7.51E−14	266.3	50.9
		28904 ///	IGKV1-5 ///	kappa variable 1-5 /// immunoglobulin
		3514 ///	IGKV1D-8 ///
		652493 ///	LOC652493
		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 (glucosamine) 3-O-sulfotransferase 1	7.84E−14	96.0	42.1
198	203332_PM_s_at	3635	INPP5D	inositol polyphosphate-5-phosphatase, 145 kDa	7.89E−14	42.6	24.0
199	64064_PM_at	55340	GIMAP5	GTPase, IMAP family member 5	7.98E−14	170.6	111.4
200	211644_PM_x_at	3514 ///	IGK@ ///	immunoglobulin kappa locus /// immunoglobulin kappa	8.04E−14	246.9	47.5
		50802	IGKC	constant

TABLE 10

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

	Fold
AR vs TX	Changes	p-values

MAPT	−3.0916	2.99053E−20
GATSL3 /// TBC1D10A	−2.11118	3.16645E−17
LOC100288617	−2.04457	1.22069E−16
ACSM2A /// ACSM2B	−1.95127	1.57754E−16
AKR7A3	−2.05229	1.75126E−16
FTCD	−2.71817	4.97004E−16
GPHN	−1.81578	1.17623E−15
SLC13A2	−2.18768	3.58849E−15
DNAJC6	−1.76929	5.20867E−15
SLC17A3	−1.84587	6.8169E−15
RGN	−2.37098	7.84126E−15
RDH12	−4.26697	9.20572E−15
CRYAA	−2.74677	1.32872E−14
SLC7A13	−4.66793	1.86514E−14
ALB	−6.82725	1.89192E−14
HAGH	−1.81366	7.33148E−14
ALDH6A1	−2.20224	8.91056E−14
HBA1 /// HBA2	−1.88978	9.34894E−14
CLYBL	−1.8789	9.81129E−14
FLJ42875	−1.97495	1.45096E−13
FBP1	−2.28393	2.00607E−13
FABP1	−2.86164	2.18592E−13
SLC2A5	−2.17033	2.79078E−13
ETNK2	−1.92877	3.8779E−13
ADH6	−2.40152	4.1841E−13
PEPD	−1.97852	4.63261E−13
PPAPDC1A	−2.02799	6.60672E−13
CLCNKA /// CLCNKB	−1.89433	7.0432E−13
LPPR1	−2.46608	8.09001E−13
PNPLA3	−2.89596	8.8556E−13
GSTA1	−1.92437	1.16843E−12
DHDH	−3.08428	1.29848E−12
HPD	−3.98241	1.30435E−12
HIBADH	−1.89283	1.4274E−12
EPHX2	−2.11633	1.98928E−12
MAF	−1.76205	2.0964E−12
TMEM178	−1.78847	2.20622E−12
LOC642891	−1.73984	2.80875E−12
LOC285733	−1.95994	3.23545E−12
FMO1	−2.25513	4.50736E−12
GGT6	−2.66101	4.6681E−12
FLJ22763	−1.93955	5.14404E−12
SLC6A13	−2.24946	5.34043E−12
PTGER3	−1.81112	5.52121E−12
SLC30A8	−1.84747	6.11346E−12
IGSF11	−2.21766	6.61316E−12
CYP3A7	−2.91581	6.87921E−12
LRRC43	−1.9031	8.14997E−12
APOH	−5.57453	8.27826E−12
FAM83D	−2.32876	8.74785E−12
ASPA	−2.31579	8.80911E−12
PROZ	−2.78599	9.23614E−12
TNNC1	−1.79223	1.01194E−11
PRODH2	−3.38674	1.25579E−11
C15orf59	−1.77893	1.64304E−11
GLYCTK	−1.82955	1.70772E−11
KMO	−2.36048	1.9935E−11
KNG1	−2.63283	2.20875E−11
LDHD	−2.57191	2.27083E−11
CDHR5	−2.63356	2.2819E−11
HAAO	−1.84903	2.42843E−11
ACSF2	−2.00048	2.48828E−11
SLC37A4	−2.01845	2.87337E−11
TINAG	−1.8544	2.91111E−11
PBLD	−1.91883	3.1387E−11
CYP46A1	−1.93139	3.14729E−11
CNDP1	−2.3083	3.26491E−11
SLC12A6	−2.31688	3.56291E−11
AFM	−6.66098	3.67152E−11
C10orf72	−1.76232	3.70569E−11
CLCNKB	−1.96378	3.83317E−11
SLC6A19	−3.99223	3.8873E−11
ATP6V0D2	−2.33798	4.04264E−11
RHBG	−1.80807	4.19595E−11
PM20D1	−2.51866	4.26226E−11
ACADSB	−2.10959	4.46915E−11
CLDN8	−2.16632	4.88256E−11
DEPDC7	−1.85669	5.00337E−11
C2orf54	−1.7955	5.39601E−11
FAM151A	−5.32405	5.61188E−11
SLC22A11	−2.10205	5.65313E−11
RALYL	−2.47687	5.78764E−11
ACSM5	−1.88278	5.80564E−11
TMEM207	−2.95205	6.13829E−11
RUNDC3B	−2.06687	6.42529E−11
ANGPTL3	−2.56539	7.90237E−11
LOC388588	−2.00381	9.24089E−11
APOM	−2.50604	9.25689E−11
KCNJ15	−2.03561	9.83258E−11
ZYG11A	−2.40225	1.04754E−10
AGXT	−2.6006	1.17202E−10
UPB1	−2.7529	1.20683E−10
CAPN3	−2.1059	1.23158E−10
ENTPD5	−1.95549	1.28898E−10
METTL7B	−2.18054	1.2938E−10
KHK	−2.60696	1.31642E−10
LOC284578	−1.90116	1.38269E−10
PDZD3	−1.83091	1.46276E−10
SERPINA5	−1.74937	1.6048E−10
TM4SF5	−2.64814	1.71797E−10
NT5E	−1.77876	1.82759E−10
CTH	−1.95197	1.84254E−10
AQP11	−2.0271	2.48812E−10
SLC34A3	−2.04588	2.84241E−10
DMGDH	−2.55618	3.18184E−10
SORD	−2.24476	3.30972E−10
DPEP1	−3.05594	3.61209E−10
LOC153328	−1.8068	3.80205E−10
FOLH1	−2.32575	3.80509E−10
PLCXD3	−1.76665	3.89412E−10
OSTBETA	−2.43601	3.93207E−10
ABCC2	−2.53512	3.97325E−10
ECHDC3	−1.73755	3.97793E−10
CRYL1	−1.82953	4.00197E−10
SLC13A3	−2.13984	4.04239E−10
SLC16A10	−2.44322	4.7162E−10
C9orf66	−3.0274	5.03177E−10
GPR98	−4.26595	6.258E−10
HAO2	−3.23686	6.39362E−10
CLPTM1	−2.00927	6.66131E−10
ACP5	−2.79078	6.66908E−10
ISOC2	−1.8934	6.90662E−10
FOLH1B	−2.76374	7.11947E−10
TLN2	−2.37363	7.32503E−10
GALM	−1.91896	7.36792E−10
OSTalpha	−2.78066	7.52226E−10
SLC5A11	−2.75896	8.19688E−10
EPHX1	−2.00145	8.56848E−10
GAS2	−1.80474	9.28707E−10
NOX4	−2.05807	1.01236E−09
FGF1	−1.82212	1.24912E−09
FCAMR	−2.07507	1.27892E−09
LOC100130691	−2.59795	1.29088E−09
ARSE	−1.80351	1.31681E−09
RETSAT	−1.76392	1.36913E−09
SLC22A7	−2.26476	1.51477E−09
C7orf10	−2.15451	1.6297E−09
SERPINA6	−3.41598	1.63356E−09
CPNE6	−1.8087	1.66697E−09
MAP7D2	−1.74996	1.92036E−09
SEPP1	−1.90167	2.17198E−09
AGXT2L1	−3.23817	2.32327E−09
C4orf12	−2.14021	2.3661E−09
RAB11FIP3	−2.44555	2.48943E−09
GK /// GK3P	−1.83394	2.70788E−09
PC	−2.6032	2.83833E−09
PANK1	−2.15352	3.13166E−09
NAGS	−2.18509	3.13645E−09
MME	−2.67889	3.20928E−09
SUSD2	−2.3654	3.35377E−09
SLC3A2	−2.00345	3.38775E−09
AMDHD1	−2.76004	3.69681E−09
MTTP	−2.43293	3.87705E−09
PHYH	−1.84295	3.99573E−09
CYP17A1	−2.01122	4.11531E−09
BPHL	−2.37599	4.15467E−09
ASB9	−2.91835	4.72846E−09
CYP2B6 /// CYP2B7P1	−3.27876	4.9278E−09
HADH	−1.95557	5.18111E−09
AQP7	−2.04383	5.46752E−09
SLC10A2	−2.73836	5.7561E−09
ZMYND12	−2.1429	5.83754E−09
HIBCH	−1.80435	5.87555E−09
CYP4F3	−2.53506	5.96184E−09
MGAM	−2.1997	6.22815E−09
CYP4F2 /// CYP4F3	−3.3825	6.27466E−09
SLC22A12	−2.11852	7.58307E−09
QPRT	−1.87851	7.79844E−09
THY1	−2.21775	7.97833E−09
SNTA1	−1.9336	9.67285E−09
ACOT1 /// ACOT2	−1.78472	9.85738E−09
SLC2A2	−1.89882	1.00173E−08
MYH8	−2.11404	1.02047E−08
SLC6A18	−1.86164	1.08202E−08
PRAP1	−2.21358	1.09557E−08
MRO	−4.28065	1.13633E−08
EHHADH	−1.77338	1.14067E−08
DIO1	−3.00979	1.33367E−08
TTC36	−2.36726	1.36035E−08
PSAT1	−2.38335	1.3782E−08
ATP6V1G3	−2.23634	1.38236E−08
ACE2	−1.79037	1.42874E−08
GJB1	−1.96896	1.44201E−08
SLC22A6	−2.86071	1.46506E−08
TRIM50	−2.72359	1.48482E−08
SOST	−3.04185	1.52557E−08
ESPL1	−2.15374	1.5528E−08
CALB1	−2.65581	1.57872E−08
AZGP1	−2.47351	1.60913E−08
PXMP2	−1.95647	1.66904E−08
TMEM120A	−1.83373	1.84556E−08
KLK1	−4.86671	1.85761E−08
SLC22A8	−3.51523	1.87712E−08
FUT3	−1.8837	1.93982E−08
SLC7A8	−1.91659	1.99023E−08
PDK2	−1.97685	2.13903E−08
ANXA9	−2.33358	2.23676E−08
P4HA2	−1.94378	2.2935E−08
SLC34A1	−2.68387	2.29418E−08
ACY3	−2.02646	2.33713E−08
C2orf40	−2.555	2.37292E−08
FUT6	−1.92525	2.41426E−08
ACOT7	−1.95494	2.45886E−08
PNPLA1	−1.78439	2.48072E−08
ABAT	−1.82921	2.80329E−08
UPP2	−3.15846	3.23662E−08
MOSC2	−2.32105	3.32422E−08
LOC389332	−1.76102	3.35953E−08
ALDH1B1	−1.77365	3.4294E−08
GPC5	−2.35239	3.47079E−08
AMN	−2.01448	3.76136E−08
SLC22A13	−1.95166	3.79569E−08
CALML3	−2.53824	3.9564E−08
PDXP /// SH3BP1	−1.90447	3.96765E−08
RHCG	−2.56004	4.20488E−08
VEPH1	−1.98345	4.37645E−08
GC	−1.79578	4.8255E−08
RNF186	−2.27866	5.26723E−08
GAL3ST1	−1.97099	5.32145E−08
GLYAT	−2.45554	5.56573E−08
BPI	−1.8412	5.56594E−08
REEP6	−2.30623	5.70126E−08
ACOT4	−1.9105	6.45152E−08
MUC13	−2.12245	6.98563E−08
NPR3	−2.54362	7.01278E−08
RAB11FIP5	−1.7414	7.17374E−08
SLC12A3	−2.79439	7.49512E−08
EGF	−3.16114	8.28225E−08
HMGCS2	−2.1871	8.66021E−08
SLC5A10	−2.39748	8.98765E−08
TUBAL3	−2.16082	9.26757E−08
LOC145837	−2.93781	9.38032E−08
SLC7A9	−2.43122	9.7596E−08
LOC727944	−2.81085	9.79329E−08
CYP4F2	−3.50762	1.00843E−07
SLC5A2	−1.91024	1.07955E−07
DHDPSL	−1.82011	1.08169E−07
MIOX	−3.2511	1.10411E−07
CPN2	−2.12523	1.1914E−07
SLC7A7	−2.02602	1.22723E−07
CYP4A11 /// CYP4A22	−1.98138	1.28529E−07
G6PC	−3.25106	1.28591E−07
DDC	−2.62111	1.28799E−07
RGS7	−1.76457	1.30087E−07
PRNP	−1.77477	1.35141E−07
SLC4A9	−1.95261	1.3642E−07
PAH	−3.20056	1.43122E−07
ESRRG	−1.73711	1.52263E−07
SLC26A9	−1.74242	1.53483E−07
LOC644242	−1.91415	1.67473E−07
CTXN3	−4.3921	1.68953E−07
UGT1A8 /// UGT1A9	−3.14494	1.86107E−07
PCK2	−2.70246	1.87667E−07
ABP1	−2.82479	2.01634E−07
HNF4G	−1.92184	2.02132E−07
CYP3A4	−2.29038	2.02506E−07
C21orf33	−1.76567	2.1446E−07
CUBN	−2.30227	2.21237E−07
FMO4	−2.66707	2.24563E−07
GPD1	−2.0302	2.35214E−07
APOC3	−2.11763	2.36005E−07
GK3P	−2.07563	2.40413E−07
GK	−2.20019	2.40894E−07
ENPP6	−2.67481	2.45316E−07
ASS1	−2.1157	2.62889E−07
DAO	−3.44454	2.65842E−07
HPGD	−2.38564	2.72233E−07
GCHFR	−1.75487	2.94943E−07
TMEM174	−3.55663	2.96489E−07
CHDH	−2.24862	2.99682E−07
CLEC18A /// CLEC18B /// CLEC18C	−2.16625	3.19031E−07
UMOD	−2.26429	3.21104E−07
HRG	−2.98761	3.27498E−07
TSKU	−1.73688	3.29833E−07
HSD17B14	−2.18571	3.50438E−07
SLC23A3	−2.59008	3.53828E−07
ITLN1	−1.87748	3.67115E−07
CAMK2G	−1.73622	4.07919E−07
GLYATL1	−2.38225	4.39935E−07
UGT2B28	−1.99594	5.11969E−07
A2LD1	−2.47322	5.71176E−07
ALAD	−1.88239	5.71331E−07
HSD11B2	−2.18597	5.81499E−07
GDPD3	−2.23762	6.32071E−07
CSDC2	−1.98441	6.72915E−07
AQP3	−1.94567	7.02888E−07
PLG	−3.86142	7.96637E−07
PGPEP1	−1.89438	8.15391E−07
LPA /// PLG	−4.61775	8.82027E−07
PAQR7	−1.85889	9.01772E−07
SLC27A2	−1.92807	9.02555E−07
C18orf56	−2.04976	9.15996E−07
SLC23A1	−2.80195	9.52247E−07
ZDHHC9	−1.73908	1.08396E−06
C12orf64	−2.19674	1.10586E−06
SLC25A10	−2.41468	1.18238E−06
ANK2	−1.90934	1.20132E−06
USP2	−2.02703	1.21673E−06
COLEC11	−2.43455	1.36731E−06
PKLR	−2.17875	1.37898E−06
GPT2	−2.34268	1.46992E−06
CDHR2	−1.91669	1.4866E−06
ACOX2	−2.2757	1.49911E−06
XPNPEP2	−2.99801	1.50402E−06
HEPACAM2	−2.2925	1.60973E−06
UBE2QL1	−2.15896	1.63642E−06
SLC39A5	−2.74063	1.69299E−06
TMEM106A	−2.02395	1.69787E−06
ATP6V0E2	−1.78673	1.86877E−06
AK3L1	−1.93512	1.90809E−06
AP1M2	−1.9461	2.03528E−06
ARSF	−2.25573	2.07251E−06
AGXT2	−2.22933	2.16372E−06
DAK	−2.20229	2.36754E−06
SLC2A12	−1.77017	2.43975E−06
ALDOB	−1.76925	2.55745E−06
ALDH4A1	−2.03136	2.70075E−06
SFXN2	−2.60877	3.45507E−06
DIP2C	−2.05583	3.64214E−06
CTSL2	−2.52968	3.71723E−06
SLC28A2	−1.93371	3.82506E−06
FUZ	−1.79456	3.95111E−06
C19orf69	−2.09232	4.14502E−06
REN	−1.85222	5.09502E−06
C12orf59	−1.75089	5.13955E−06
TMEM132E	−2.18984	5.33005E−06
ZGPAT	−2.44576	5.34764E−06
PLEKHA5	−1.96129	5.89183E−06
GRAMD1C	−1.74771	6.11458E−06
DUSP9	−1.91589	6.38564E−06
SLC22A4	−1.89514	6.66544E−06
SORCS1	−1.85441	7.11084E−06
STC2	−1.81501	7.21338E−06
LEAP2	−2.38133	7.40637E−06
KCNN2	−2.25018	7.51697E−06
IDH1	−1.8165	7.62782E−06
NAT8B	−2.42914	7.8931E−06
SLC5A9	−1.75921	9.82367E−06
TM7SF2	−1.92027	9.949E−06
PLA2G12B	−2.13901	1.09806E−05
CLRN3	−1.91704	1.10718E−05
PCYT2	−1.80892	1.18832E−05
SLC4A1	−2.48783	1.19999E−05
GSTA3	−2.10371	1.22206E−05
PVALB	−2.29588	1.2453E−05
ATP6V1C2	−1.98979	1.29667E−05
PIK3C2G	−1.76675	1.29855E−05
DHRS11	−1.74961	1.35061E−05
NEDD4L	−1.89373	1.41229E−05
AQP2	−2.03409	1.4403E−05
SLC5A12	−2.19859	1.46257E−05
AS3MT	−1.83726	1.71006E−05
AHCY	−1.8639	1.78963E−05
PIPOX	−2.48991	1.79528E−05
CEL	−1.82327	1.812E−05
DCXR	−2.16471	1.92539E−05
CYP4A11	−1.86722	2.10549E−05
C4orf31	−1.82811	2.58079E−05
CYP4A22	−2.07108	2.87185E−05
GLTPD2	−2.11301	3.06781E−05
MAP7	−2.19683	3.85037E−05
TMEM86A	−1.89253	4.99658E−05
SLC25A1	−1.82031	5.0373E−05
DPP4	−2.22087	5.57627E−05
IYD	−2.08101	5.9404E−05
UGT1A1 /// UGT1A10 /// UGT1A3 ///	−1.8015	6.24555E−05
UGT1A4 /// UGT1A5 /// UGT1A6 ///
UGT1A7 /// UGT1A8 /// UGT1A9
A1CF	−2.20926	6.47761E−05
C5orf23	−1.93642	7.84432E−05
NECAB2	−3.13495	8.16616E−05
RNLS	−1.98084	8.33272E−05
DNMT3L	−2.65364	8.93726E−05
NPHS2	−1.90039	9.36204E−05
RAB17	−1.79062	0.000102017
ABCC6 /// LOC100292715	−1.9274	0.000110765
MUC15	−1.84539	0.000111654
PPP1R16A	−1.89951	0.000117374
KCNK5	−1.76687	0.000126376
LOC100287428	−1.84499	0.000134038
APOE	−1.85436	0.000138448
GIPC2	−1.94676	0.00014941
C11orf54	−1.75689	0.000156408
SLC17A5	−1.74223	0.000163743
KL	−1.8958	0.000167359
CYP8B1	−2.25808	0.000167576
DPYS	−2.08317	0.000176199
SH3GL2	−1.91695	0.000186793
DHRS4 /// DHRS4L2	−2.07083	0.000208985
ALLC	−1.78998	0.000234174
PTGR1	−1.75022	0.000261226
CRHBP	−2.11391	0.00027295
C9orf103	−1.88471	0.00056122
SLC28A1	−1.77452	0.001183781
SLC16A9	−1.89967	0.001212523
ABHD6	−1.8011	0.001402915
BHMT	−1.86117	0.003092475
CRYM	−1.84582	0.004771269
RENBP	−1.85157	0.00698823
ABCC6P1	−1.87408	0.008304558
L2HGDH	−1.79573	0.025300732
ACSM3	−1.79192	0.029160166

TABLE 11

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

	Fold
IFTA vs TX	Changes	p-values

TMSB10	1.51781	3.98249E−09
S100A11	1.56822	1.38989E−08
SLCO2A1	1.63966	3.44566E−08
HLA-DRB1 /// HLA-DRB4	1.58546	4.3499E−08
HLA-DRB1	1.55271	5.38233E−08
SLC2A12	−1.70211	5.46662E−08
VCAM1	1.65026	6.48536E−08
UPP1	1.63064	1.16852E−07
LDLRAD3	1.65949	1.26498E−07
SH2B3	1.54593	1.36597E−07
EGF	−2.89482	1.71342E−07
MLL3	1.65249	2.14515E−07
TRIM50	−2.12508	2.24723E−07
AKNA	1.53574	2.41689E−07
GIMAP5	1.56499	2.67192E−07
HLA-DMB	1.57184	2.90879E−07
CKLF	1.55882	4.12568E−07
SLC34A3	−1.63066	5.51514E−07
HS3ST1	1.53641	5.56865E−07
MARCKSL1	1.61621	5.84424E−07
SLC16A7	−1.62101	7.39903E−07
B2M	1.50123	9.50899E−07
TSPAN13	1.53438	9.8449E−07
PARVG	1.58576	1.07603E−06
TUBA1A	1.62753	1.08009E−06
GUCY1A3	1.50036	1.08029E−06
HLA-F	1.52472	1.176E−06
PARP12	1.51868	1.20425E−06
GAB3	1.51079	1.3961E−06
ELF4	1.63538	1.52125E−06
FAM83D	−1.89217	1.53199E−06
PARP14	1.56897	1.53554E−06
LCP2	1.6147	1.54053E−06
SAMD9L	1.61164	1.55377E−06
ASNS	1.64299	1.56911E−06
RNF213	1.52854	1.57479E−06
FERMT3	1.57985	1.59445E−06
APOL1	1.57279	1.60354E−06
TNFAIP8L2	1.51502	1.70532E−06
PSMB10	1.54422	1.75542E−06
PMEPA1	1.66003	2.04838E−06
RAB31	1.6387	2.08849E−06
PRR24	1.54645	2.08952E−06
SMPDL3A	−1.50869	2.10279E−06
RRAS	1.51587	2.26269E−06
PVALB	−2.2572	2.72451E−06
PSMB8	1.56268	2.72636E−06
TM4SF5	−1.80583	2.77734E−06
A2LD1	−1.77136	2.85983E−06
NUAK1	1.53844	2.92207E−06
MCAM	1.6564	3.11631E−06
RNLS	−1.5429	3.14475E−06
ASB9	−1.79403	3.1805E−06
IL15RA	1.60325	3.26002E−06
LOC645895 /// MYBL1	−1.52279	3.36189E−06
MST4	1.57651	3.39448E−06
COL4A2	1.61415	3.54382E−06
NPHS1	−1.77154	3.58126E−06
KCNN2	−1.95169	3.61421E−06
NECAB2	−1.83644	3.61683E−06
GGT5	1.5766	4.21195E−06
EFCAB4B	1.52589	4.27242E−06
TLR2	1.58142	4.27452E−06
C21orf63	1.53537	4.30722E−06
GLTPD2	−1.71616	5.05996E−06
TMPRSS2	−1.52104	5.44787E−06
MRO	−2.43891	5.4527E−06
MBOAT1	1.54611	5.70031E−06
KLK1	−2.708	6.20862E−06
IDH1	−1.50367	6.55865E−06
ARRDC2	1.66068	6.57954E−06
UBE2QL1	−1.76895	6.76469E−06
ALB	−4.11767	6.82617E−06
ZYG11A	−1.92637	6.83448E−06
SAMD3	1.58295	7.06467E−06
GPR98	−2.62258	7.19656E−06
MLKL	1.56773	7.21744E−06
SLC2A5	−1.91328	7.25168E−06
KLF4	1.56399	7.33393E−06
ITPRIPL2	1.55643	7.34185E−06
APOL6	1.55686	7.34937E−06
LOC153328	−1.54066	7.35108E−06
SPATA17	−1.58696	7.36371E−06
IRF7	1.57063	7.78387E−06
CYP3A4	−1.68477	8.04902E−06
CIITA	1.59697	8.05175E−06
PRKCDBP	1.52369	8.25806E−06
CTSL2	−2.2103	9.30206E−06
C12orf35	1.61211	9.52663E−06
GATSL3 /// TBC1D10A	−1.59015	1.02924E−05
CCR5	1.61292	1.04302E−05
LOC91316	1.55332	1.09782E−05
LHFP	1.61605	1.10787E−05
ITGAL	1.65885	1.22092E−05
SLC12A3	−2.04892	1.22155E−05
PLAUR	1.60949	1.23043E−05
PPAP2C	1.63507	1.23102E−05
IER5	1.52273	1.23557E−05
CYP3A7	−2.0504	1.28185E−05
SLC4A1	−1.81494	1.34578E−05
EBF1	1.52946	1.35965E−05
IL16	1.51006	1.50211E−05
HRG	−1.90476	1.51876E−05
PRKCB	1.59664	1.56904E−05
SYCE1L	1.61979	1.58566E−05
PARP8	1.63188	1.59697E−05
FAM65B	1.56435	1.69584E−05
PTGER3	−1.89681	1.7065E−05
FIBIN	1.62824	1.74203E−05
RNF182	1.50841	1.82387E−05
ANGPTL3	−2.04101	1.82518E−05
PHACTR3	1.51835	1.84497E−05
FKBP11	1.64574	1.84525E−05
CARD6	1.50388	1.84747E−05
CD1D	1.59602	1.85397E−05
GMFG	1.64971	1.85486E−05
AFM	−3.09543	1.90192E−05
LAYN	1.50713	1.91771E−05
BIN2	1.5738	2.00422E−05
HADH	−1.53494	2.01328E−05
CLDN3	1.58139	2.01595E−05
IL8	1.6304	2.06309E−05
CHST15	1.57068	2.08254E−05
HLA-DOB	1.57448	2.08412E−05
ALDH3A2	−1.51338	2.24457E−05
G6PC	−2.06203	2.31973E−05
LOC153684	1.51481	2.35676E−05
BTN3A3	1.58385	2.36092E−05
LOC96610	1.54288	2.37697E−05
PRSS2 /// PRSS3	−1.55684	2.39309E−05
AADAT	−1.50725	2.45144E−05
ATP6V1C2	−1.87663	2.51529E−05
FAM84A	1.63054	2.72254E−05
MYADM	1.57406	2.72605E−05
LOC727944	−2.1898	2.74724E−05
MOSC2	−1.62916	2.95301E−05
SLCO3A1	1.55284	2.95888E−05
RSPH1	1.64647	2.97493E−05
C1orf192	1.60412	2.99924E−05
NNT	−1.64647	3.17285E−05
SEL1L3	1.61562	3.17683E−05
C9orf103	−1.61381	3.20544E−05
CD74	1.62637	3.27408E−05
JUN	1.63578	3.27609E−05
RGS7	−1.53976	3.27856E−05
AOAH	1.58066	3.28623E−05
FAM24B	−1.53027	3.30462E−05
NCKAP1L	1.59544	3.3182E−05
DCDC2	1.52668	3.46016E−05
PLCL1	−1.5323	3.47883E−05
SERPINA6	−1.72919	3.5546E−05
CPNE6	−1.52074	3.67948E−05
LPCAT1	1.62907	3.68576E−05
ADM	−1.53891	3.74019E−05
ELMOD1	−1.64162	3.79781E−05
MSL1	1.56313	3.84376E−05
PLA2G12B	−1.7228	3.89562E−05
TUBB6	1.61007	4.06407E−05
CDC42EP5	1.61246	4.08613E−05
TLN2	−1.51459	4.21746E−05
TAP2	1.59627	4.28878E−05
EMP3	1.52713	4.48564E−05
VEGFA	−1.50122	4.7167E−05
SIRPG	1.57626	5.08523E−05
HCK	1.62706	5.08921E−05
ALAD	−1.51752	5.34836E−05
GK3P	−1.67708	5.42274E−05
CCR2	1.5253	5.72632E−05
GSTA3	−1.5681	5.78607E−05
FYB	1.55028	5.88739E−05
ACADSB	−1.50559	6.10124E−05
FOLH1B	−1.9831	6.11789E−05
CALML3	−1.94826	6.21615E−05
LOC100289727 /// NCF1 /// NCF1B ///	1.58313	6.39151E−05
NCF1C
PIGR	1.50262	6.4456E−05
SLC16A10	−1.85159	6.50594E−05
ANXA3	1.66158	6.59979E−05
GK	−1.88206	6.68014E−05
MX1	1.59966	6.80462E−05
SORCS1	−1.61133	6.84525E−05
DNMT3L	−1.9539	7.10165E−05
ARHGAP28	−1.56725	7.16985E−05
APOH	−2.43413	7.25988E−05
RHCG	−1.86366	7.39257E−05
C15orf59	−1.54017	7.44282E−05
MX2	1.65683	7.78195E−05
IFI27	1.51629	7.82297E−05
FOLH1	−1.96392	8.12122E−05
TMEM207	−2.29284	8.39494E−05
GPR65	1.63506	9.08757E−05
CAMK1D /// LOC283070	1.52061	9.22035E−05
HDAC9	1.56346	9.31902E−05
WT1	−1.52969	9.37583E−05
CMPK2	1.60157	9.46544E−05
SERPINH1	1.65676	9.67787E−05
CAPN6	1.62011	0.00010357
FLNA	1.50644	0.00010568
PRSS3	−1.52475	0.000108476
DKK3	1.51087	0.000117202
RUNX3	1.54606	0.000128211
LOC100132891	1.5901	0.000128519
AQP11	−1.57835	0.00013752
LOC388588	−1.5056	0.000138318
HS6ST2	−1.80972	0.000140022
GABBR1	1.58018	0.000140037
CRTAM	1.52125	0.000140069
SUSD2	−1.51355	0.000142327
DPP4	−1.67197	0.000145216
SFXN2	−1.6002	0.000145357
C12orf64	−2.01929	0.000155923
PROZ	−1.80974	0.000157604
FCGR3B	1.55481	0.00015864
ACSL4	1.50161	0.000161869
DUSP5	1.54175	0.000163447
GPR18	1.60959	0.000166523
ESPL1	−1.7324	0.000171456
ATP6V1G3	−1.77982	0.000172096
DNAJC3	1.58767	0.00017247
S100A9	1.54746	0.000178741
CD27	1.64479	0.000180879
MUC1	1.63077	0.000186774
GALNT3	1.51347	0.000188942
MTHFD2	1.57956	0.00019287
TTC36	−1.52589	0.000195258
P2RY8	1.53511	0.000195337
EPB41L3	−1.52238	0.00020416
ANK2	−1.64784	0.000204429
LOC100131781	−2.9471	0.000204454
ABCC2	−1.68245	0.000207988
HEPACAM2	−1.84159	0.000209517
NTM	1.51173	0.000212033
KLRC1 /// KLRC2	1.63195	0.000220071
KRT19	1.55043	0.000225436
KYNU	−1.8178	0.000228908
VEPH1	−1.62722	0.000229217
SPTLC1	−1.64623	0.000231038
TARP	1.62228	0.000243027
ARSF	−1.50712	0.000250355
PNPLA3	−1.62818	0.000251061
AMDHD1	−1.6455	0.000254162
TBC1D10C	1.5601	0.000256812
SELP	1.52502	0.000258767
RCSD1	1.57125	0.000261223
TMEM178	−1.58308	0.000263195
SLC6A19	−1.82272	0.000275501
P2RY14	1.59513	0.000279279
APOC3	−1.53424	0.000300535
ACOX2	−1.62014	0.000301637
FHL2	1.52656	0.000311563
ALOX5AP	1.60741	0.000314792
C15orf48	1.65767	0.000314854
C10orf128	1.59052	0.000317657
PRELP	1.60495	0.000320644
CELF2	1.52413	0.000324376
ATP6V0D2	−1.75411	0.000325699
MAP7	−1.79697	0.000334732
SLC23A3	−1.54687	0.000334785
NPR3	−1.79863	0.000368992
CRISPLD2	1.53183	0.000389441
RALYL	−1.74543	0.00039422
CTH	−1.55662	0.000396559
SLC4A9	−1.52135	0.000411691
GEM	1.54158	0.000427531
SELE	1.57885	0.00043229
WNT5A	1.52321	0.000435362
ARPC3	1.66106	0.000439626
CYBB	1.51692	0.00045065
RGS1	1.56699	0.000457779
PRNP	−2.36306	0.000488807
IGSF11	−1.63702	0.000509764
GK /// GK3P	−1.55526	0.000515407
LOC1518	−1.51858	0.000515767
SLC5A11	−1.60913	0.000521049
RMND1	−1.55838	0.000540732
HPGD	−1.78558	0.00054091
DHDH	−1.71486	0.000544292
NAT8B	−1.55811	0.000549575
SAR1B	−1.64073	0.000558645
C4orf31	−1.85662	0.000571234
RDH12	−2.01518	0.000572799
SAMSN1	1.60145	0.000591216
C1S	1.50415	0.000607667
FLJ42875	−1.60547	0.000608022
CCDC3	1.57889	0.000623944
DDN	−1.50715	0.000626257
GNB5	−1.55044	0.000634195
SOST	−2.11137	0.000644151
LPPR1	−1.82253	0.000649757
CFH	1.6367	0.000669139
MME	−1.64596	0.000674042
LOC100287237	1.56531	0.000764834
SRGN	1.54084	0.00076549
KNG1	−1.62639	0.000769626
AGXT2L1	−2.02162	0.00077628
GBP1	1.60205	0.000778436
SPON1	1.56225	0.000788929
CFH /// CFHR1	1.52603	0.000790894
LY75	1.61206	0.000791747
HIBADH	−1.79186	0.000811983
MAPT	−1.60132	0.000852554
TACSTD2	1.52097	0.000874577
PTTG1	1.51809	0.00091356
BASP1	1.54985	0.000923589
GPRC5A	1.52408	0.000927775
PRKX /// PRKY	1.54463	0.000970822
MTTP	−1.69593	0.000995856
TNKS2	1.51761	0.001002234
NR1D2	1.55627	0.001020233
PRODH2	−1.74304	0.001020309
FAM134C	1.5638	0.001050607
C1orf186	1.60148	0.001110359
MYL9	1.55261	0.001139867
TPD52	−1.70752	0.001169747
FAM151A	−1.97601	0.001205059
ZGPAT	−1.57946	0.001246363
ACOT7	−1.528	0.001258945
FH	−1.68361	0.00125963
SORD	−1.56419	0.001267712
PAH	−1.90254	0.001272156
MUC13	−1.69478	0.001290676
NUDT6	−1.5431	0.001345321
SLC28A2	−1.52768	0.001385666
OSTalpha	−1.60696	0.001399651
TMEM174	−1.65713	0.001454055
TNC	1.60863	0.001486039
PBLD	−1.55582	0.001556271
AGXT	−1.75973	0.001590847
DDAH1	−1.6755	0.001592385
CHI3L1	−1.65385	0.001602868
FOS	1.59586	0.001625845
BAX	1.60078	0.001631275
CES1	1.63045	0.001669311
TYRP1	−1.82362	0.001697884
SLC10A2	−1.73028	0.001716763
TDO2	1.63563	0.001726221
NEURL1B	1.56331	0.001726312
CTXN3	−1.79282	0.001743693
DMGDH	−1.51241	0.001749636
NFAT5	1.59118	0.001753736
TUBAL3	−1.51042	0.001804062
IGFBP6	1.58017	0.001901916
DIO1	−1.59848	0.001910455
SLC23A1	−1.52549	0.001946197
THY1	−1.53788	0.001996268
PSAT1	−1.69764	0.002002125
DAO	−1.60066	0.002066006
PM20D1	−1.71127	0.002086353
CCL3 /// CCL3L1 /// CCL3L3	1.51353	0.002087392
KMO	−1.60343	0.002283512
IFI44L	1.64407	0.0023117
CRYAA	−1.80094	0.002325131
OSTBETA	−1.58545	0.002365191
LOC100288332 ///	1.62547	0.002388231
LOC100288583 /// NPIPL3
PLG	−1.78244	0.002461498
LPL	−1.66864	0.00246504
CCR7	1.51168	0.002557407
LRRC28	−1.56336	0.002620124
RRAD	1.53482	0.002741312
MUC15	−1.59237	0.002741823
IFI6	1.61934	0.002829231
DDC	−1.7423	0.002861605
CRHBP	−1.99859	0.002945064
CYP2B6 /// CYP2B7P1	−1.75265	0.002949884
DNAJC12	−1.64575	0.00299662
HIRA	1.51289	0.003063139
CCL20	1.60658	0.003165424
LOC145837	−1.7347	0.003171816
FGF1	−1.64515	0.003348491
ARHGDIB	1.51724	0.003359731
GPHN	−1.54106	0.003390414
C9orf66	−1.51569	0.003831676
PTPRO	−1.52077	0.004034566
UPP2	−1.74057	0.00418584
GSN	−1.6508	0.00438635
SLC34A1	−1.53144	0.004434079
PCOLCE2	−1.58643	0.004936761
NELL2	1.50929	0.005435169
IFT57	−1.60347	0.005534224
ASPN	−1.54129	0.005627179
GPC5	−1.62746	0.006502554
TAC1	1.62375	0.00651702
VAMP3	−1.58292	0.006552971
LPA /// PLG	−1.76434	0.006796348
ADAMDEC1	1.50784	0.006940972
CXCL13	1.55288	0.007006212
GPR34	1.58029	0.007177428
SLC7A13	−2.2753	0.007781262
NCRNA00182	1.65305	0.009416748
CNN1	1.5022	0.009933538
SRPX	1.60599	0.010174115
ITLN1	−1.80647	0.01035312
CYP4F2 /// CYP4F3	−1.58922	0.01326078
UGT1A8 /// UGT1A9	−1.69177	0.018037058
CMYA5	−1.51105	0.020198567
OGN	−1.60803	0.022700207
PDGFRL	−1.53918	0.02458871
LEFTY1	1.66065	0.031306922
MYH8	−1.53175	0.038086016
TNNC1	−1.86111	0.042271383
MFAP5	−1.61249	0.043993201

Claims

We claim:

1. A method of prognosing, detecting, diagnosing or monitoring a kidney transplant rejection or injury, or lack thereof in a subject, comprising:

(a) obtaining nucleic acids of interest, wherein the nucleic acids of interest comprise mRNA extracted from a sample from a subject or nucleic acids derived from the mRNA extracted from the sample from the subject;

(b) detecting expression levels in the subject of the at least five genes selected from the at least one of Tables 7, 8, 9, 10, and 11, using the nucleic acids of interest obtained in step (a); and

(c) prognosing, detecting, diagnosing or monitoring the kidney transplant rejection or injury, or lack thereof in the subject from the expression levels detected in step (c).

2. The method of claim 1, further comprising contacting the nucleic acids of interest with probes, wherein the probes are specific for the at least five genes selected in step (b).

3. The method of claim 1, wherein the sample from the subject is a biopsy sample.

4. The method of claim 1, wherein the subject has acute rejection (AR), acute dysfunction no rejection (ADNR), chronic allograft nephropathy (CAN), or well-functioning normal transplant (TX).

5. The method of claim 1, wherein for each of the at least five genes, step (c) comprises comparing the expression level of the gene in the subject to one or more reference expression levels of the gene associated with AR, ADNR, CAN, or TX.

6. The method of claim 5, wherein step (c) further comprises for each of the at least five genes assigning the expression level of the gene in the subject a value or other designation providing an indication whether the subject has AR, ADNR, CAN, or TX.

7. The method of claim 6, wherein the expression level of each of the at least five genes is assigned a value on a normalized scale of values associated with a range of expression levels in kidney transplant patients with AR, ADNR, CAN, or TX.

8. The method of claim 7, wherein the expression level of each of the at least five genes is assigned a value or other designation providing an indication that the subject has or is at risk of AR, ADNR, CAN, has well-functioning normal transplant, or that the expression level is uninformative.

9. The method of claim 6, wherein step (c) further comprises combining the values or designations for each of the genes to provide a combined value or designation providing an indication whether the subject has or is at risk of AR, ADNR, CAN, or has TX.

10. The method of claim 9, wherein the method is repeated at different times on the subject.

11. The method of claim 9, wherein the subject is receiving a drug, and a change in the combined value or designation over time provides an indication of the effectiveness of the drug.

12. The method of claim 1, wherein the subject has undergone a kidney transplant within 1 month, 3 months, 1 year, 2 years, 3 years or 5 years of performing step (a).

13. The method of claim 1, wherein step (b) is performed on at least 10, 20, 40, or 100 genes.

14. The method of claim 1, further comprising changing the treatment regime of the subject responsive to the prognosing, detecting, diagnosing or monitoring step.

15. The method of claim 14, wherein the subject has received a drug before performing the methods, and the changing the treatment regime comprises administering an additional drug, administering a higher dose of the same drug, administering a lower dose of the same drug or stopping administering the same drug.

16. The method of claim 1, wherein the subject is prognosed or diagnosed to have AR, have ADNR, have CAN, or have TX, and wherein the at least five genes are selected from the at least one of Tables 7, 8, 9, 10, and 11.

17. The method of claim 1, wherein expression levels are determined at the mRNA level or at the protein level.

18. The method of claim 1, wherein step (c) is performed by a computer.

19. An array, comprising a support or supports bearing a plurality of nucleic acid probes complementary to a plurality of mRNAs fewer than 5000 in number, wherein the plurality of mRNAs includes mRNAs expressed by at least five genes selected from the at least one of Tables 7, 8, 9, 10 and 11.

20. A method of expression analysis, comprising determining expression levels of up to 5000 genes in a sample from a subject having a kidney transplant, wherein the genes include at least five genes selected from the at least one of Tables 7, 8, 9, 10, and 11.