WO2017100259A1

WO2017100259A1 - Pretransplant prediction of post-transplant acute rejection

Info

Publication number: WO2017100259A1
Application number: PCT/US2016/065286
Authority: WO
Inventors: Barbara MURPHY
Original assignee: Icahn School Of Medicine At Mount Sinai
Priority date: 2015-12-08
Filing date: 2016-12-07
Publication date: 2017-06-15

Abstract

Disclosed herein are gene signature sets expressed by kidney allograft recipients prior to transplant that determine the risk for acute rejection (AR) post-transplant and methods of using the gene signature sets for identifying renal allograft recipients at risk for acute rejection. Also disclosed herein are kits for use in the invention which comprise primer pairs for the gene signature sets.

Description

PRETRANSPLANT PREDICTION OF POST-TRANSPLANT

ACUTE REJECTION

GOVERNMENT GRANT CLAUSE

This invention was made with government support under Grant No. 1U01AI070107 awarded by the National Institute of Allergy and Infectious Diseases/National Institutes of Health (NIH/NIAID). The government has certain rights in the invention.

TECHNICAL FIELD

This invention relates to the field of molecular biology, and more particularly to detecting mRNA molecular signatures. More particularly, this invention relates to methods for diagnosing a renal allograft recipient's risk for developing acute rejection and allograft loss. The methods comprise analyzing the blood of renal allograft recipients by detenmning the expression level of an mRNA signature set comprising at least 7 preselected mRNAs in order to identify and treat such patients pre- and posttransplantation. A logistic regression fitting model can be applied to normalized expression read count (e.g. read counts of genes from next generation sequencing technology) values to derive a statistical model from which a probability score for the risk of acute rejection and allograft loss can be calculated for each patient

BACKGROUND

Acute rejection occurs to some degree in all transplants, except between identical twins, unless immunosuppression is achieved (usually through drugs). Acute rejection begins as early as one week after transplant, the risk being highest in the first three months, though it can occur months to years later. Highly vascular tissues such as kidney often host the earliest signs— particularly at endothelial cells lining blood vessels— though it eventually occurs in roughly 10 to 20% of kidney allograft recipients.

A single episode of acute rejection can be recognized and promptly treated, usually preventing organ failure, but recurrent episodes or subclinical rejection leads to chronic rejection. Therefore, what is needed in the art are materials and methods for identifying kidney allograft recipients that are at risk for acute rejection as early as possible and preferably before transplantation.

SUMMARY

Disclosed herein are gene expression profiles expressed by kidney allograft recipients prior to transplant that determine the risk for acute rejection (AR) post-transplant.

In one aspect, the present invention provides a method for identifying a renal allograft recipient at risk of developing acute rejection comprising the steps of (a) providing a pre- transplant blood specimen from the renal allograft recipient; (b) determining the expression levels of genes in a gene signature set in the blood specimen of the recipient; (c) comparing the expression levels of the genes in the gene signature set with tile expression levels of the genes in the gene signature set in a control, and (d) determining the recipient will be at risk for acute rejection and allograft rejection if the expression level of one or more genes in the gene signature set in the specimen is altered from the expression level of the same one or more genes in the gene signature set in the control.

In another aspect, the results of the assay are applied to a penalized logistic regression fitting model

is the probability of AR, β*ΐ is penalized coefficiency and gi is the expression value of gene i) that can be used to compute a probability score for acute rejection for each patient If the probability score of the patient is higher than the probability score of the control, then the patient is at risk for acute rejection.

In another aspect, the present invention provides a kit for identifying renal allograft recipients that will be at risk for acute rejection comprising in one or more separate containers primer pairs for the gene signature set: GBP5, PDZK1IP1, AFAP1, FLVCR1- AS1, TRAV12-2, TPX2 and CPNE5, buffers, positive and negative controls and instructions for use.

In yet another aspect, the present invention provides a kit for identifying renal allograft recipients that will be at risk for acute rejection comprising in one or more separate containers primer pairs for the gene signature set: OSBPL10, ΓΠΗ4, GBP5, PDZK1IP1, GPER1, PCBP3, CLEC17A, AFAP1, ZNF665, FLVCR1-AS1, TRAV12-2, HLA-K, TPX2, C19orf77, LIN7B and CPNE5, buffers, positive and negative controls and instructions for use.

In yet another aspect, the present invention provides a method for identifying a renal allograft recipient at risk of acute rejection of the allograft before transplantation comprising the steps of (a) obtaining a blood specimen from the renal allograft recipient; (b) isolating mRNA from the blood specimen; (c) synthesizing cDNA from the mKNA;( d) determining the expression levels of a gene signature set in said recipient's blood; and (e) diagnosing the allograft recipient as being at risk for acute rejection and allograft loss if the expression level of one or more genes in the gene signature set in the allograft recipient's blood specimen is altered compared to the expression level of the same one or more genes in the control blood specimen and (f) treating the recipient identified as being at risk for acute rejection or allograft loss with Induction therapy.

In yet a still further aspect, the present invention provides a method for selecting a renal allograft patient for Induction therapy prior to transplantation to reduce the risk of renal acute rejection or allograft loss which comprises comparing the expression level of a gene signature set obtained from the patient with the expression level of the gene signature set in a control sample obtained from an allograft recipient that did not suffer acute rejection, and selecting the patient for treatment with Induction therapy if the expression level of one or more genes in the gene signature set from the patient is altered compared to the expression level of one or more genes in the gene signature set in the control, and admiriistering Induction therapy to said patient. These and other aspects of the present invention will be apparent to those of ordinary skill in the art in light of the present specification and appended claims.

DETAILED DESCRIPTION

Definitions:

As used herein, an allograft recipient who is at "high risk" for acute rejection of the allograft and allograft loss is significantly more likely to reject the allograft, without intervention, than a subject who is at "low risk."

In accordance with the present invention, there may be employed conventional molecular biology, microbiology, recombinant DNA, immunology, cell biology and other related techniques within the skill of the art See, e.g., Sambrook et al., (2001) Molecular Cloning: A Laboratory Manual. 3rd ed. Cold Spring Harbor Laboratory Press: Cold Spring Harbor, N.Y.; Sambrook et al., (1989) Molecular Cloning: A Laboratory Manual. 2nd ed. Cold Spring Harbor Laboratory Press: Cold Spring Harbor, N.Y.; Ausubel et al., eds. (200S) Current Protocols in Molecular Biology. John Wiley and Sons, Inc.: Hoboken, N.J.; Bonifacino et al., eds. (2005) Current Protocols in Cell Biology. John Wiley and Sons, Inc.: Hoboken, N.J.; Coligan et al., eds. (2005) Current Protocols in Immunology, John Wiley and Sons, Inc.: Hoboken, N.J.; Coico et al., eds. (2005) Current Protocols in Microbiology, John Wiley and Sons, Inc.: Hoboken, N.J.; Coligan et al., eds. (2005) Current Protocols in Protein Science, John Wiley and Sons, Inc.: Hoboken, N.J.; Enna et al., eds. (2005) Current Protocols in Pharmacology John Wiley and Sons, Inc.: Hoboken, N.J.; Hames et al., eds. (1999) Protein Expression: A Practical Approach. Oxford University Press: Oxford; Freshney (2000) Culture of Animal Cells: A Manual of Basic Technique. 4th ed. Wiley-Liss; among others. The Current Protocols listed above are updated several times every year

As used herein, the "expression level" of an mRNA disclosed herein means the mRNA expression level of the marker, or the measurable level of the marker in a sample, which can be determined by any suitable method known in the art, such as, but not limited to Northern blot, polymerase chain reaction (PCR), e.g., quantitative real-time PCR, "qRT- PCR", MiSEQ, Nanostring analysis, etc.

As used herein, the term "about" or "approximately" usually means within an acceptable error range for the type of value and method of measurement For example, it can mean within 20%, more preferably within 10%, and most preferably still within 5% of a given value or range. Alternatively, especially in biological systems, the term "about" means within about a log (i.e., an order of magnitude) preferably within a factor of two of a given value.

The terms "decrease", "decreased", "reduced", "reduction" or "down-regulated" are all used herein generally to mean a decrease by a statistically significant amount. However, for avoidance of doubt, "reduced", "reduction", "down-regulated" "decreased" or "decrease" means a decrease by at least 10% as compared to a reference level, for example a decrease by at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90% or up to and including a 100% decrease (i.e. absent level as compared to a reference sample), or any decrease between 10-100% as compared to a reference level, or at least about a 0.5-fold, or at least about a 1.0-fold, or at least about a 1.2-fold, or at least about a 1.5-fold, or at least about a 2-fold, or at least about a 3-fold, or at least about a 4-fold, or at least about a 5-fold or at least about a 10-fold decrease, or any decrease between 1.0-fold and 10-fold or greater as compared to a reference level.

The terms "increased", "increase" or "up-regulated" are all used herein to generally mean an increase by a statistically significant amount; for the avoidance of any doubt, the terms "increased" or "increase" means an increase of at least 10% as compared to a reference level, for example an increase of at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90% or up to and including a 100% increase or any increase between 10-100% as compared to a reference level, or at least about a 0.5-fold, or at least about a 1.0-fold, or at least about a 1.2-fold, or at least about a 1.5-fold, or at least about a 2-fold, or at least about a 3-fold, or at least about a 4-fold, or at least about a 5-fold or at least about a 10-fold increase, or any increase between 1.0-fold and 10-fold or greater as compared to a reference level.

As used herein, "detennining the level of expression," "determining the expression level" or "detecting the level of express", as in, for example, "determining the expression level of a gene" refers to quantifying the amount of mRNA present in a sample. Detecting expression of the specific mRNAs, can be achieved using any method known in the art as described herein. Typically, mRNA detection methods involve sequence specific detection, such as by RT-PCR, mRNA specific primers and probes can be designed using nucleic acid sequences, which are known in the art

As used herein, an "altered" level of expression of a mRNA compared to reference level or control level is an at least 0.5-fold (e.g., at least 1- 2-; 3-; 4-; 5-; 6-; 7-; 8-; 9-; 10-; 15- ; 20-; 30-; 40-; 50-; 75-; 100-; 200-; 500-; 1,000-; 2000-; 5,000-; or 10,000-fold) altered level of expression of the mRNA. It is understood that the alteration can be an increase or a decrease. Alternatively, altered expression level is defined as an increase in the risk probability score using parameters in the logistic regression model established from a training patient group, comparing the probability score to the cutoff derived from the training set.

As used herein "combination therapy" means the treatment of a subject in need of treatment with a certain composition or drug in which the subject is treated or given one or more other compositions or drugs for the disease in conjunction with the first and/or in conjunction with one or more other therapies, such as, e.g., an immunosuppressive therapy or other anti-rejection therapy. Such combination therapy can be sequential therapy wherein the patient is treated first with one treatment modality (e.g., drug or therapy), and then the other (e.g., drug or therapy), and so on, or all drugs and/or therapies can be administered simultaneously. In either case, these drugs and/or therapies are said to be "co- administered." It is to be understood that "co-administered'' does not necessarily mean that the drugs and/or therapies are administered in a combined form (i.e., they may be administered separately or together to the same or different sites at the same or different times).

The term "pharmaceutically acceptable derivative" as used herein means any pharmaceutically acceptable salt, solvate or prodrug, e.g., ester, of a compound of the invention, which upon administration to the recipient is capable of providing (directly or indirectly) a compound of the invention, or an active metabolite or residue thereof. Such derivatives are recognizable to those skilled in the art, without undue experimentation. Nevertheless, reference is made to the teaching of Burger's Medicinal Chemistry and Drug Discovery, 5th Edition, Vol. 1 : Principles and Practice, which is incorporated herein by reference to the extent of teaching such derivatives. Pharmaceutically acceptable derivatives include salts, solvates, esters, carbamates, and/or phosphate esters.

As used herein the terms "therapeutically effective" and "effective amount", used interchangeably, applied to a dose or amount refer to a quantity of a composition, compound or pharmaceutical formulation that is sufficient to result in a desired activity upon administration to a subject in need thereof. Within the context of the present invention, the term "therapeutically effective" refers to that quantity of a composition, compound or pharmaceutical formulation that is sufficient to reduce or eliminate at least one symptom of a disease or condition specified herein, e.g., acute rejection and/or allograft rejection When a combination of active ingredients is administered, the effective amount of the combination may or may not include amounts of each ingredient that would have been effective if administered individually. The dosage of the therapeutic formulation will vary, depending upon the nature of the disease or condition, the patient's medical history, the frequency of adnu^'nistration, the manner of administration, the clearance of the agent from the host, and the like. The initial dose may be larger, followed by smaller maintenance doses. The dose may be administered, e.g., weekly, biweekly, daily, semi-weekly, etc., to maintain an effective dosage level.

"Control" is defined as a sample obtained from a patient that received an allograft transplant that is not suffering from acute rejection.

"Induction therapy" is defined herein as administration of an immunosuppressive agent to a patient at high risk of acute rejection begun before or preferably on the day of the transplant. The agent is a biologic agent, either a lymphocyte-depleting agent or an interleukin 2 receptor antagonist (IL2-RA) as described below. The purpose of induction therapy is to deplete or modulate T-cell responses in patients at high risk for acute rejection at the time of transplantation and reduce the risk for acute rejection.

The present invention is based on the identification of gene expression profiles expressed by a kidney allograft recipient prior to transplant that determine the risk for acute rejection post-transplant. The gene expression profile is predictive of subclinical as well as clinical acute rejection post-transplantation. This gives the clinician the ability to personalize the approach to the immunosuppression regimen at the time of the transplant, thereby maximizing immunosuppression in those at high risk and lowering immunosuppression in those with decreased risk. Specifically, induction therapy using, for example myroglobulin/Anti-Thymocyte Globulin (ATG), an anti-IL-2R blocker or

Campath-1H (Alemtuzmab, an anti-CDS2 monoclonal antibody directed against an antigen present on the surface of T lymphocytes) will be administered to those at high risk for acute rejection and avoided in those with low risk. Induction therapy is started the day of the transplant. For maintenance immunosuppression, an individual at lower risk can, depending on other immunological factors, be treated with a weaker immunosuppressant, such as Rapamycin or Belatacept These are recognized by those of ordinary skill in the art as less potent because they are associated with a high acute rejection rate. "Stronger" immunosuppressive agents include calcineurin inhibitors, such as tacrolimus (Prograf® and Advagraf® / Astagraf XL (Astellas Pharma Inc.) and generics of Prograf® and cyclosporine (Neoral® and Sandimmune® (Novartis AG) and generics.

In addition, decisions regarding the donor recipient combination may be guided so as to minimize other controllable immunological factors. For example, transplantations could be avoided based on donor recipient combinations in which the recipient had antibodies directed against the donor HLA or if there were HLA-DR mismatches. However, mis is not absolute, especially in the case of HLA-DR mismatches. In this embodiment transplantation may be avoided if a patient required desensitization, i.e., plasmapheresis and intravenous immunoglobulin (IVIG) administration for donor specific antibodies. However, this would mostly apply to living donor allografts. In this embodiment, avoiding donor-recipient mismatches can result in significant cost savings.

In addition, if the gene expression profile identifies an individual as higher risk for acute rejection post-transplant this patient may be subject to more intensive monitoring of clinical labs or gene expression profiles for diagnosis of subclinical acute rejection.

The present invention provides methods for identifying kidney allograft recipients at risk for acute rejection and graft loss prior to transplantation comprising the steps of providing a blood specimen from a kidney allograft recipient before transplantation, isolating mRNA from the blood specimen, synthesizing cDNA from the mRNA, and measuring the expression levels of at least one gene from a gene panel comprising a selected gene signature set present in the blood specimen. Non-limiting examples of methods of measuring expression levels include the MiSEQ sequence system (Illumina, Inc. San Diego California), Nanostring (nCounter® miRNA Expression Assay- Nanostring Technologies, Inc. Seattle Washington) or qPCR. The results of the gene signature set analysis are compared to a control. The greater the alteration in patient's expression level compared to the control, the greater the risk of acute rejection. These methods are described in Examples 1-3 below. In another embodiment, the results of the assay are applied to a penalized logistic regression fitting model where

is the probability of AR, β*ί is penalized coefficiency and gi is the expression value of gene i) that can be used to compute a probability score for acute rejection for each patient. If the probability score of the patient is higher than the probability score of the control, then the patient is at risk for acute rejection.

Peripheral blood signatures using gene signature sets comprising at least 7 and up to 16 preselected genes have been identified. These preselected gene signature sets can be used to accurately identify kidney allograft recipients at risk for acute rejection and subsequent graft rejection before the transplant surgery.

In one preferred embodiment a 16 member gene signature set is used. In another preferred embodiment a seven member signature set is used.

The 16 member gene signature set for use in practicing the methods disclosed herein comprises the following genes: OSBPL10, ΠΤΗ4, GBP5, PDZK1IP1, GPER1, PCBP3, CLEC17A, AFAP1, ZNF665, FLVCR1-AS1, TRAV12-2, HLA-K, TPX2, C19orf77, LIN7B and CPNE5.

In another preferred embodiment a 7 member gene signature set is used. The seven member gene signature set is a subset of the 16 member gene signature set.

The 7 member gene signature set for use in practicing the methods disclosed herein comprises the following genes: GBP5, PDZKIIPI, AFAP1, FLVCR1-AS1, TRAV12-2, TPX2 and CPNE5. As described below, it has been found that the addition of clinical factors further enhanced the predictive value of the gene signature set. In a particularly preferred embodiment, adding the quantity of circulating HLA class I antibodies to the equation significantly increased performance of the assay. The amount of HLA class I antibodies can be determined by using the well-known Luminex-based HLA antibody screening technology (commercially available kits are LABScreen™ and LABType™ assays provided by One Lambda Inc., Canoga Park, CA, USA, and Lifecodes™ assays by Tepnel Corp., Stamford, CT, USA).

In some of the methods disclosed herein, it is desirable to detect and quantify mRNAs present in a sample. Detection and quantification of RNA expression can be achieved by any one of a number of methods well known in the art. Using the known sequences for RNA family members, specific probes and primers can be designed for use in the detection methods described below as appropriate. Any one of Nanostring, RNAseq, or quantitative Polymerase Chain Reactions (qPCR) such as Real Time Polymerase Chain Reactions (RT-PCR) or MiSEQ can be used in the methods disclosed herein. In some cases, detection and quantification of RNA expression requires isolation of nucleic acid from a sample, such as a cell or tissue sample. Nucleic acids, including RNA and specifically mRNA, can be isolated using any suitable technique known in the art For example, phenol-based extraction is a common method for isolation of RNA. Phenol- based reagents contain a combination of denaturants and RNAase inhibitors for cell and tissue disruption and subsequent separation of RNA from contaminants. In addition, extraction procedures such as those using TRIZOL™ or TRI REAGENT™, will purify all RNAs, large and small, and are efficient methods for isolating total RNA from biological samples that contain mRNAs. Extraction procedures such as those using QIAGEN-ALL prep kit are also contemplated.

In some embodiments, use of quantitative RT-PCR is desirable. Quantitative RT-PCR is a modification of the polymerase chain reaction used to rapidly measure the quantity of a product of polymerase chain reaction. qRT-PCR is commonly used for the purpose of deterrnining whether a genetic sequence is present in a sample, and if it is present, the number of copies in the sample. Any method of PCR that can determine the expression of a nucleic acid molecule, including a mRNA, falls within the scope of the present disclosure. There are several variations of the qRT-PCR method which are well known to those of ordinary skill in the art.

In some embodiments, the mRNA expression profile can be determined using an nCounter® analysis system (Nano String Technologies®, Seattle, WA). The nCounter® Analysis System from NanoString Technologies profile hundreds of mRNAs, microRNAs, or DNA targets simultaneously with high sensitivity and precision. Target molecules are detected digitally. The NanoString analysis system uses molecular "barcodes" and single-molecule imaging to detect and count hundreds of unique transcripts in a single reaction. The protocol does not include any amplification steps. This is a preferred method for rapid detection of the expression levels of mRNAs.

In another embodiment the invention provides kits for determining whether a patient that will receive a kidney allograft is at risk for acute rejection of the allograft comprising in one or more containers primer pairs for the 16 member gene signature set, positive and negative controls, buffers and instructions for use.

In another preferred embodiment the invention provides a kit for determining whether a patient that will receive a kidney allograft is at risk for acute rejection of the allograft comprising in one or more containers primer pairs for the 7 member gene signature set, positive and negative controls, buffers and instructions for use.

In a typical embodiment, a clinical lab will obtain the expression value using the patient's sample and send it to the patient's doctor. The doctor will then communicate this value to his web based service provider. The service provider will enter that value in the bioinformatics system which already has the penalized co-efficiency for each gene of the preselected gene signature set and the cutoff from the logistic regression model from the training set. The bioinformatics system will use this information to calculate the probability score for the patient. The calculated score will reflect the patient's AR status.

The overall procedure of application of the gene signature set in predicting the risk for AR is described below using the sixteen- and seven-member gene signature set.

1) Selecting a training group: A group of kidney transplant patients with balanced AR and no AR (control) cases (total number N=~100) will be carefully selected. The training group will have well-characterized demographics, clinical data, and pathological results which have been reviewed by at least two pathologists.

2) Measuring expression of the genes: Expression levels of the 7 and 16 genes from the blood samples pre -transplant of each patient in the training group can be measured using any technique, and preferably by MiSEQ, RT-PCR or Nanostring technology. Use of the Nanostring techniques is described in Example 2 below.

3) Establishing a regression model and cutoff: A penalized logistic regression fitting model using the logistf R package (a statistical package available from r-project.org) will be then applied on expression values of the 7 and 16 genes to derive the statistical model from which the β* value will be derived for each gene and the probability score of acute rejection for each patient will be calculated from the following equation:

where is the probability of AR,

is penalized coefficiency and gi is the read count of gene i.

Based on the probability score, the prediction statistics such as prediction AUC (area under the curve) of ROC (Receive operating characteristic) curve of the true positive rate versus the false positive rate, sensitivity/specificity, the positive predictive values (PPV) and the negative predictive values (NPV) will be determined. At a given specificity (90%), a probability score cutoff will be established which best detects the presence of acute rejection. It is expected that there will be a clear cutoff into two groups in that if a patient is in the top group they have a high likelihood of having acute rej ection and the test is determined to be positive but if they are in the bottom group they have a very low likelihood of having acute rejection and the test is determined to be negative.

The alternative is that patients will be broken into tertiles based on their probability score determined as above. In this case if the patient is in (1) the top tertile they have a high likelihood of having acute rejection and the test is determined to be positive; (2) the second tertile or intermediate group their risk cannot be accurately determined; and (3) the bottom tertile they have a very low likelihood of having acute rejection and the test is determined to be negative.

The coefficiency (β* value) and the cutoff derived from the training group will be entered and stored into a web-based bioinformatics computer system which can be accessed from clinical lab/doctor office via the internet.

4) Diagnosis: The expression levels of the gene signature sets will be measured by the same technology used for the training set in the clinical lab. By using a web-based bioinformatics system, the probability score will be calculated by summarizing the expression value (gi) of the 7 and/or 16 genes multiplied by their β* values which are derived from the training set. The probability score will be compared to the cutoff to determine the AR status. An increase in the probability score in the patient relative to the probability score in a control indicates that the patient is at an increased risk for acute rejection. A clinical lab will send the testing results to the doctor.

Expression levels and/or reference expression levels may be stored in a suitable data storage medium (e.g., a database) and are, thus, also available for future diagnoses. This also allows efficiently diagnosing prevalence for a disease because suitable reference results can be identified in the database once it has been confirmed (in the future) that the subject from which the corresponding reference sample was obtained did experience acute rejection.

As used herein a "database" comprises data collected (e.g., analyte and/or reference level information and /or patient information) on a suitable storage medium. Moreover, the database may further comprise a database management system. The database management system is, preferably, a network-based, hierarchical or object-oriented database management system. More preferably, the database will be implemented as a distributed (federal) system, e.g. as a Client-Server-System. More preferably, the database is structured as to allow a search algorithm to compare a test data set with the data sets comprised by the data collection. Specifically, by using such an algorithm, the database can be searched for similar or identical data sets being indicative of renal allograft rejection risk. Thus, if an identical or similar data set can be identified in the data collection, the test data set will be associated with renal allograft rejection risk. Consequently, the information obtained from the data collection can be used to diagnose an allograft recipient's risk for allograft loss or based on a test data set obtained from a subject. More preferably, the data collection comprises characteristic values of all analytes comprised by any one of the groups recited above.

The invention further provides for the communication of assay results or diagnoses or both to technicians, physicians or patients for example. In certain embodiments, computers will be used to communicate assay results or diagnoses or both to interested parties, e.g., physicians and their patients.

In some embodiments, the method disclosed herein further comprises modifying the recipient's clinical record to identify the recipient as being at risk for developing acute rejection and/or allograft loss. The clinical record may be stored in any suitable data storage medium (e.g., a computer readable medium). In some embodiments of the invention, a diagnosis based on the methods provided herein is communicated to the allograft recipient as soon as possible after the diagnosis is obtained. The diagnosis may be communicated to the recipient by the recipient's treating physician. Alternatively, the diagnosis may be sent to a recipient by e-mail or communicated to the subject by phone. The diagnosis may be sent to a recipient in the form of a report. A computer may be used to communicate the diagnosis by e-mail or phone. In certain embodiments, the message containing results of a diagnostic test may be generated and delivered automatically to the recipient using a combination of computer hardware and software which will be familiar to artisans skilled in telecommunications.

Aspects of the present invention include computer program products for identifying a subject who has not undergone a renal allograft and is at risk for acute rejection, wherein the computer program product, when loaded onto a computer, is configured to employ a gene expression result from a sample derived from the subject to determining whether a subject who will undergo a renal allograft is at risk for acute rejection wherein the gene expression result comprises expression data for at least one gene signature set.

Also provided are reference expression profiles for a phenotype mat is one of: (a) low risk for acute rejection; or (b) high risk for acute rejection; wherein the expression profile is recorded on a computer readable medium that is accessible by a user, e.g., in a user readable format. In certain embodiments, the expression profile is a profile for a phenotype that is low risk. In certain embodiments, the expression profile is a profile for a phenotype that is high risk.

The expression profiles and databases thereof may be provided in a variety of media to facilitate their use. "Media" refers to a manufacture that contains the expression profile information of the present invention. The databases of the present invention can be recorded on computer readable media, e.g. any medium that can be read and accessed directly by a user employing a computer. Such media include, but are not limited to: magnetic storage media, such as floppy discs, hard disc storage medium, and magnetic tape; optical storage media such as CD-ROM; electrical storage media such as RAM and ROM; and hybrids of these categories such as magnetic/optical storage media. One of ordinary skill in the art can readily appreciate how any of the presently known computer readable mediums can be used to create a manufacture comprising a recording of the present database information.

"Recorded" refers to a process for storing information on computer readable medium, using any such methods known in the art. Any convenient data storage structure may be chosen, based on the means used to access the stored information. A variety of data processor programs and formats can be used for storage, e.g. word processing text file, database format, etc. Thus, the subject expression profile databases are accessible by a user, i.e., the database files are saved in a user-readable format (e.g., a computer readable format, where a user controls the computer).

As used herein, "a computer-based system" refers to the hardware means, software means, and data storage means used to analyze the information of the present invention. The minimum hardware of the computer-based systems of the present invention comprises a central processing unit (CPU), input means, output means, and data storage means. A skilled artisan can readily appreciate that any one of the currently available computer-based system are suitable for use in the present invention. The data storage means may comprise any manufacture comprising a recording of the present information as described above, or a memory access means that can access such a manufacture.

Table 1 shows the results obtained using the assay of the present invention.

In Table 1, ROC (Receiver operating characteristics, i.e. the curve of true positive rate versus false positive rate), serisitivity/spetificity, the positive predictive values (PPV) and negative predictive values (NPV) and accuracy (ACC) were determined. Results obtained with the 16 member gene are set forth on column 2. Results obtained with the 7 member gene are set forth on column 5. FP/FN, false positive/false negative, HLA MM, HLA mismatches. It was determined that the addition of clinical factors further enhanced the predictive value of the gene signature set. Addition of data which indicated the amount of HLA class I antibodies (column 3) significantly increased performance beyond the gene signature set alone or the gene signature set combined with the number of HLA-Class I mismatches determined by HLA typing of the donor and recipient (column 4). HLA typing can be done by flow cytometry, RT-PCR and RNA-Seq. (Reference: Methods. 2012 Apr;56(4):471-6. doi: 10.10I6/j.ymeth.2012.03.025. Epub 2012 Mar 28. HLA techniques: typing and antibody detection in the laboratory of immunogenetics. Bontadini A.)

Model 2 minimized the number of genes to seven. While the performance of this gene signature set is less than that of Model 1, when combined with the number of HLA Class I antibodies it was very effective at predicting acute rejection.

Kits

In certain embodiments, kits are provided for determining a renal allograft recipient's risk for acute rejection and allograft loss.

The kits comprise primers for the 16 member gene signature set as set forth in Example 6 below (for Nanostring assays), primers for 2 housekeeping genes, (beta actin (ACTB) and glyceraldehyde 3-phosphate dehydrogenase, GAPDH), and a control probe, 18S ribosomal RNA (for qPCR assays).

In an alternative embodiment, the kits comprise primers for the 7 member gene signature set as set forth in Example 6 below (for Nanostring assays), primers for 2 housekeeping genes, (beta actin (ACTB) and glyceraldehyde 3-phosphate dehydrogenase, GAPDH), and a control probe, 18S ribosomal RNA (for qPCR assays).

A kit can further comprise one or more mRNA extraction reagents and/or reagents for cDNA synthesis. In other embodiments, the kit can comprise, one or more containers into which the biological agents are placed and, preferably, suitably aliquotted. The components of the kits may be packaged either in aqueous media or in lyophilized form. The kits may also comprise one or more pharmaceutically acceptable excipients, diluents, and/or carriers. Non-limiting examples of pharmaceutically acceptable excipients, diluents, and/or carriers include KNAase-free water, distilled water, buffered water, physiological saline, PBS, Ringer's solution, dextrose solution, reaction buffers, labeling buffers, washing buffers, and hybridization buffers.

The kits of the invention can take on a variety of forms. Typically, a kit will include reagents suitable for determining gene set expression levels (e.g., those disclosed herein) in a sample. Optionally, the kits may contain one or more control samples. Also, the kits, in some cases, will include written information (indicia) providing a reference (e.g., predetermined values), wherein a comparison between the gene expression levels in the subject and the reference (predetermined values) is indicative of a clinical status.

The present invention is described further below in Examples which are intended to further describe the invention without limiting the scope thereof.

Example 1: MiSEO Assay

1) Custom Assay (barcoded probesets for the 16 gene panel including a housekeeping gene panel)

2) Illumina® TruSeq® RNA Sample Preparation Kit v2

3) QIAGEN RNeasy® Kit for extraction of high quality total RNA

MiSEQ Experiments

The total RNA will be extracted using QIAGEN RNeasy® Kit. The sequencing library will be generated using the Illumina® TruSeq® RNA Sample Preparation Kit v2 by following the manufacturer's protocol: briefly, polyA-containihg mRNA will be first purified and fragmented from the total RNA. The first- strand cDNA synthesis will be performed using random hexamer primers and reverse transcriptase and followed by the second strand cDNA synthesis. After the endrepair process, which converts the overhangs into blunt ends of cDNAs, multiple indexing adapters will be added to the end of the double stranded cDNA and PCR will be performed to enrich the targets using the primer pairs specific for the gene panel and housekeeping genes. Finally, the indexed libraries will be validated, normalized and pooled for sequencing on the MiSEQ sequencer.

MiSEQ data processing

The raw RNAseq data generated by the MiSEQ sequencer will be processed by the following procedure: The reads with good quality will be first aligned to several human reference databases including hgl9 human genome, exon, splicing junction and contamination database including ribosome and mitochondria RNA sequences using the BWA1 alignment algorithm. After filtering reads that mapped to the contamination database, the reads that are uniquely aligned with a maximal 2 mis-matches to the desired amplicon (i.e. PCR product from the paired primers) regions will be then counted as expression level for the corresponding gene and further subjected to normalization based on the expression of the housekeeping genes.

Example 2: Nanostring Assay

1) Custom CodeSet (barcoded probesets for the 16 gene panel including 3 house-keeping genes and negative controls provided by Nanostring).

2) nCounter® Master Kit including nCounter Cartridge, nCounter Plate Pack and nCounter Prep Pack.

3) QIAGEN RNeasy® Kit for extraction of high quality total RNA Nanostring Experiments:

Total RNA will be extracted using QIAGEN RNeasy® Kit by following the manufacturer's protocol; Barcode probes will be annealed to the total RNA in solution at 65°C with the master kit. The capture probe will capture the target to be immobilized for data. After hybridization, the sample will be transferred to the nCounter Pre Station and the probe/target will be immobilized on the nCounter Cartridge. The probes are then counted by the nCounter Digital Analyzer. mRNA Transcriptomic Data analysis

The raw count data from the Nanostring analyzer will be processed using the following procedure: the raw count data will be first normalized to the count of the housekeeping genes and the mRNAs with counts lower than the median plus 3 standard deviation of the counts of the negative controls will be filtered out. Due to data variation arising from the reagent lot, the count for each mRNA from different reagent lots will be calibrated by multiplying a factor of the ratio of the averaged counts of the samples on different reagent lots. The calibrated counts from different experimental batches will be further adjusted using the ComBat package.

Example 3: qPCR Assay

1) Primer container (19 tubes with one qPCR assay per tube for 16 genes, which include the 7 gene-panel and 2 housekeeping genes (ACTB and GAPDH) and the control probe (18S ribosomal RNA). The assays are obtained from LifeTech.

2) TaqMan® Universal Master Mix Π: reagents for qPCR reactions

3) TaqMan® ARRAY 96-WELL PLATE (6x16)

4) Agilent AffinityScript QPCR cDNA Synthesis Kit: for the highest efficiency of converting RNA to cDNA and fully optimized for real-time quantitative PCR (QPCR) applications. Total RNA will be extracted from the allograft biopsy samples using the ALLprep kit (QIAGEN-ALLprep kit, Valencia, CA USA). cDNA will be synthesized using the AffinityScript RT kit with oligo dT primers (Agilent Inc. Santa Clara, CA). TaqMan qPCR assays for the 16 gene signature set, 2 housekeeping genes (ACTB, GAPDH) and 18S ribosomal RNA will be purchased from ABI Life Technology (Grand Island, NY). qPCR experiments will be performed on cDNAs using the TAQMAN universal mix and PCR reactions will be monitored and acquired using an ABI7900HT system. Samples will be measured in triplicate. Cycle Times (CT) values for the prediction gene set as well as the 2 housekeeping genes will be generated. The ACT value of each gene will be computed by subtracting the average CT value for the housekeeping genes from the CT value of each gene.

Example 4: Identification of gene signature sets.

In the Example below the following materials and methods were used.

Whole blood obtained at the baseline visit from 113 allograft recipients. Baseline, means prior to kidney transplantation and therefore, prior to exposure to donor antigens. Each whole blood had a corresponding 3 months and 12 months post-transplant renal allograft biopsy from the same patient Each biopsy was scored centrally by two blinded pathologists. In addition, the results of biopsies that were clinically indicated were also included. Acute rejection (AR) was defined by the Banff classification (Solez, K. et al. Am J Transplant 8, 753-60 (2008). The endpoint was all AR any time post-transplant within the 2 year follow-up time of the study, both clinical and subclinical AR including boarderline. mRNA was extracted from whole blood samples and hybridized on the RNA-Seq platform from illumina®. The paired end sequencing tags for each sample were mapped to the human genome assembly (GRCh37/hgl9) using Tophat ⁴ (two mismatches in the alignment allowed) and Binary Alignment/Map (BAM) (Bioinformatics. 2009 Aug IS; 25(16): 2078-2079) files were generated. Duplicate reads were subsequently removed using Picard Tools fhttp ://picard.sourceforge.net). Using the Ensemble database as the gene model, read counts for 56635 transcripts were derived from the output BAM files with HTseq (European Molecular Biology Lab (EMBL), Heidelberg, Germany) a python package for the analysis of high-throughput sequencing data. Normalization and batch correction for the RNA-Seq data were performed to ensure that the data sets were comparable and to increase the signal to noise intensity. DESeq (European Molecular Biology Lab (EMBL) identified the significantly differentiated expressed signatures between AR (AR and borderline, n=48) vs. non AR (n=65).

The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used with the Corrected Akaike Information Criterion (AICC) statistic (equation 1) ⁵ for feature selection from all significantly expressed genes for AR prediction. LASSO provides stability and robustness statistics that generate sparse models and thus can be used for LI feature selection. LASSO seeks not only a well-fit model but also a "simple" one that will avoid the large variation that comes with estimating complex models ⁶ . Logistic regression was then used for odds ratio estimates for each gene/predictor for AR. All clinical confounders including recipient and donor characteristics were adjusted in the model. All genes for AR derived from the model were not confounded by clinical variables.

Statistical analyses were carried out using SAS 9.3.2 (SAS institute, Cary, NC) and R 2.15.1⁷. P<0.05 was considered statistically significant.

Example 5: Use of the assay of the present invention to identify patients at risk for

AR

Whole blood was obtained at the baseline visit (prior to transplant) from 113 recipients, a cohort of the GoCAR study. mRNA was extracted from whole blood samples and hybridized on the RNA-Seq platform from Ulumina®. The expression of the 16 gene signature set was obtained using Nanostring and assigned a probability score for the risk of acute rejection based on predetermined levels from a reference population. Based on the probability score for the 16 gene signature set in combination with the total number of anti-HLA Class I antibodies in the recipients' blood, 52 patients at high risk for acute rejection post-transplant were identified.

In a long term follow up study, only 4 out of the 52 patients did not go on-to develop acute rejection and 1 patient that was determined to be at low risk did develop acute rejection.

REFERENCES:

1. Mengel, M. et al. Banff 2011 Meeting report: new concepts in antibody-mediated rejection. Am J Transplant 12, 563-70 (2012).

2. Yilmaz, S. et al. Protocol core needle biopsy and histologic Chronic Allograft Damage Index (CADI) as surrogate end point for long-term graft survival in multicenter studies. J Am Soc Nephrol 14, 773-9 (2003).

3. Solez, K. et al Banff 07 classification of renal allograft pathology: updates and future directions. Am J Transplant 8, 753-60 (2008).

4. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7, 562-78 (2012).

5. Clifford M. Hurvich, C.-L.T. A CORRECTED AKADCE INFORMATION CRITERION FOR VECTOR AUTOREGRESSIVE MODEL SELECTION. Journal of Time Series Analysis 14, 271-279 (2008).

6. Tibshirani, R. Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society Series B, 58, 267-288 (1996).

7. R. Ihaka, R.G. R: A Language for Data Analysis and Graphics. Journal of computational and graphical statistics 5(1996).

A number of embodiments of the invention have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. Accordingly, other embodiments are within the scope of the following claims.

It is further to be understood that all values are approximate, and are provided for description. Patents, patent applications, publications, product descriptions, and protocols are cited throughout this application, the disclosures of which are incorporated herein by reference in their entireties for all purposes.

Claims

WHAT IS CLAIMED IS:

1. A method for identifying a renal allograft recipient at risk of developing acute rejection comprising the steps of

(a) providing a pre-transplant blood specimen from the renal allograft recipient;

(b) determining the expression levels of a gene signature set in the blood specimen of the recipient;

(c) comparing the expression levels of the gene signature set with the expression levels of the gene signature set in a control, and

(d) determining the recipient will be at risk for allograft rejection if the expression level of one or more genes in the gene signature set in the specimen is altered from the expression level of the same one or more genes in the gene signature set in the control.

2. The method of claim 1 wherein the alteration comprises an increase or a decrease in the expression level of one or more genes in the gene signature set in the specimen compared to the same one or more genes in the gene signature set in the control.

3. The method of claim 1 wherein the alteration comprises an increase and a decrease in the expression level of one or more genes in the gene signature set in the specimen compared to the same one or more genes in the gene signature set in the control

4. The method of claim 1 wherein the gene signature set comprises at least one gene selected from the group consisting of: OSBPL10, ΙΊΤΗ4, GBP5, PDZK1IP1, GPER1, PCBP3, CLEC17A, AFAP1, ZNF665, FLVCR1-AS1, TRAV12-2, HLA-K, TPX2, C19orf77, LIN7B and CPNE5.

5. The method of claim 1 wherein the gene signature set comprises at least one gene selected from the group consisting of: GBP5, PDZK1IP1, AFAP1, FLVCR1-AS1, TRAV12-2, TPX2 and CPNE5.

6. The method of claim 1 wherein the determining step comprises applying the expression levels determined in the patient's sample to a penalized logistic regression fitting model

is the probability of rejection β*, i is the penalized coefficiency and gi is the read count of gene i to determine the probability of allograft rejection.

7. The method of claim 1 further comprising determining the amount of HLA Class I antibodies in the allograft recipient's blood.

8. The method of claim 1 wherein the expression levels are determined by a method selected from the group consisting of Nano string, MiSEQ and quantitative polymerase chain reaction (qPCR).

9. A method for identifying a renal allograft recipient at risk of acute rejection of the allograft before transplantation comprising the steps of

(a) obtaining a blood specimen from the renal allograft recipient;

(b) isolating mRNA from the blood specimen;

(c) synthesizing cDNA from the mRNA;

(d) determining the expression levels of a gene signature set in said recipient's blood;

(e) diagnosing the allograft recipient as being at risk for acute rejection and allograft loss if the expression level of one or more genes in the gene signature set in the allograft recipient's blood specimen is altered compared to the expression level of the same one or more genes in the control blood specimen, and treating the recipient identified as being at risk for acute rejection or allograft loss with Induction therapy.

10. The method of claim 9 wherein said Induction therapy comprises administering a therapeutically effective amount of Anti-thymocyte globulin or Campath-IH.

11. The method of claim 9 wherein the gene signature set comprises at least the genes OSBPL10, ΓΠΗ4, GBP5, PDZK1IP1, GPER1, PCBP3, CLEC17A, AFAP1, ZNF665, FLVCR1-AS1, TRAV12-2, HLA-K, TPX2, C19orf77, LIN7B and CPNE5.

12. The method of claim 9 wherein the gene signature set comprises at least the genes: GBP5, PDZK1IP1, AFAP1, FLVCR1-AS1, TRAV12-2, TPX2 and CPNE5.

13. The method of claim 9 wherein the expression levels are determined by a method selected from the group consisting of Nanostring, MiSEQ and quantitative polymerase chain reaction (qPCR).

14. A kit for identifying renal allograft recipients at risk for acute rejection and allograft loss comprising in one or more separate containers primer pairs for the gene signature set: GBP5, PDZK1IP1, AFAPl, FLVCR1-AS1, TRAV12-2, TPX2 and CPNE, buffers, positive and negative controls and instructions for use.

15. A kit for identifying renal allograft recipients at risk for acute rejection and allograft loss comprising in one or more separate containers primer pairs for the gene signature set: OSBPL10, ΓΠΗ4, GBP5, PDZK1IP1, GPER1, PCBP3, CLEC17A, AFAPl, ZNF665, FLVCR1-AS1, TRAV12-2, HLA-K, TPX2, C19orf77, LIN7B and CPNE5, buffers, positive and negative controls and instructions for use.

16. The kit of claim 14 or IS further comprising housekeeping genes and primers for the housekeeping genes.

17. The method of claim 9 further comprising calculating the probability score of acute rejection for said patient using the equation log (p(x))/(l-p(x)) = β*0+β*^1+ p*igi+.... + β*9^9)_> wherein p(x) is the probability of rejection β*, i is the penalized coefficiency and gi is the read count of gene i, to determine the probability of acute rejection.

18. The method of claim 17 wherein the probability score is determined using a computer based system.

19. The method of claim 17 wherein the probability score is used to detennine the cutoff value.

20. A method for selecting a renal allograft patient for Induction therapy prior to transplantation to reduce the risk of renal acute rejection or allograft loss which comprises

comparing the expression level of a gene signature set obtained from the patient with the expression level of a signature gene set in a control sample obtained from an allograft recipient that did not suffer acute rejection, and selecting the patient for treatment with Induction therapy if the expression level of one or more genes in the gene signature set from the patient is altered compared to the expression level of one or more of the genes in the gene signature set in the control, and

administering Induction therapy to said patient

21. The method of claim 20 wherein said Induction therapy comprises administering a therapeutically effective amount of Anti-thymocyte globulin or Campath-1H.