WO2025093575A1

WO2025093575A1 - Pre-transplant blood based transcriptomic signatures for prediction of kidney transplant rejection

Info

Publication number: WO2025093575A1
Application number: PCT/EP2024/080626
Authority: WO
Inventors: Patricia Connolly; Nehal DOSHI; Michael J. Donovan
Original assignee: Verici Dx Plc
Current assignee: Verici Dx Plc
Priority date: 2023-10-30
Filing date: 2024-10-29
Publication date: 2025-05-08
Anticipated expiration: 2026-04-30

Abstract

Disclosed herein is a method for a diagnostic tool to calculate a renal allograft recipient's risk for early acute rejection.

Description

PRE-TRANSPLANT BLOOD BASED TRANSCRIPTOMIC SIGNATURES FOR PREDICTION OF

KIDNEY TRANSPLANT REJECTION

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims the benefit of U.S. Provisional Patent Application No. 63/594,266, filed October 30, 2023, which is incorporated herein by reference in its entirety.

FIELD

[0002] This disclosure relates to the field of molecular biology, and more particularly to detecting RNA transcriptomic molecular signatures. More particularly, this disclosure relates to methods for producing a risk score from pre-transplant blood which is correlated to a renal allograft recipient’s risk for early acute rejection following transplantation with a deceased donor kidney graft.

BACKGROUND

[0003] Kidney transplantation is the treatment of choice for subjects with end stage kidney disease (ESKD) (Barker et al. Cold Spring Harb Perspect Med. 2013 ;3(4)). However, despite remarkable improvements in 1-year graft loss over the last decade, in each subsequent year after transplant, approximately 3% of kidney allograft recipients return to dialysis or require re -transplantation. Rates of late graft failure are relatively unchanged since the 1990s (Pilch et al. Pharmacotherapy. 2021;41(l): 119-131).

[0004] One of the major issues with current immunosuppressive protocols is that they are not tailored to the individual patient needs. In clinical practice, immunosuppressive therapy is often decided based on broad clinical criteria including anti-HLA antibodies, race, prior transplantations, and recipient age. However, these indicators perform poorly in predicting individual risk for development of AR and do not pertain to the majority of patients (Lim et al., Transplant Rev 31 (1): 10-17, 2017). As a result, most patients receive a standardized immunosuppressive protocol resulting in some individuals being exposed to insufficient or excessive immunosuppression, leading to early acute rejection or complications associated with over-immunosuppression, respectively. These complications include diabetes, hypertension, heart disease, infections and malignancy (Menon et al., J Am Soc Nephrol 28: 735-747, 2017; Marcen et al., Drugs 12:69 (16):2227-2243, 2009). [0005] The number of patients receiving higher doses of immunosuppression around the time of a transplant continues to increase in an attempt to minimize rejection and protect the kidney (Lim et al., Transplant Rev 31 (1): 10-17, 2017). Early identification of individuals at highest risk of early acute rejection could allow more personalized or targeted therapies aimed at improving long-term outcomes. Evidence exists that the phenotype and function of the immune system in patients before kidney transplantation affects the risk for subsequent early acute rejection after transplantation, but no biomarker has previously been identified to quantify this risk.

[0006] Indication of kidney allograft rejection relies mainly on post-transplant monitoring methods such as proteinuria and serum creatinine. Biomarkers which predict early acute rejection are needed to support clinical management in a sensitive and less invasive manner. Clinical acute rejection (AR), i.e. acute rejection associated with a decline in kidney function, occurs in approximately 10% of transplanted kidneys (Eikmans et al., Front Med 5:358, 2019). In addition, up to one-third of recipients have evidence of acute rejection on surveillance biopsy in the first 12 months despite not having a clinical decline in their kidney function (subclinical acute rejection) (Cippa, et al., Clin J Am Soc Nephrol 10: 2213-2220, 2015; Nankivell et al., Am J Transplant 6: 2006-2012, 2006; Rush et al., Clin J Am Soc Nephrol 1: 138 -143, 2006; and Zhang et al., JCI Insight 4(11), 2019).

[0007] AR still represents one of the major targets for immunosuppressive therapy after transplantation. One of the main issues of current immunosuppressive protocols is the fact that they are not tailored to individual patient needs. Most patients receive a standardized immunosuppressive protocol resulting in some individuals being exposed to too much or too little immunosuppression with resultant complications. Pre-transplant identification of individuals at highest or lowest risk of early acute rejection could allow more targeted therapies aimed at reducing or better management of early acute rejection thus improving long-term outcomes and reducing long-term risk (Cippa, et al., Clin J Am Soc Nephrol 10: 2213-2220, 2015).

[0008] Provided herein are methods to address the unmet need of pre -transplant prediction of the risk of early acute kidney rejection in deceased donor transplant recipients subsequent to graft implantation. The methods comprise analyzing the blood of patients collected prior to the renal transplant surgery to receive a deceased donor implant. The analysis determines the expression level of an mRNA signature set comprising 15-29 preselected mRNAs in order to identify and tailor treatment of immunosuppressive regimens in such patients posttransplantation. A differential expression analysis can be applied to normalized expression read count (e.g. read counts of genes from next generation sequencing technology) values of selected genes to derive a weighted cumulative risk score for the risk of early acute rejection which can be obtained for each patient.

SUMMARY

[0009] In one aspect, provided herein is a method for identifying the risk that a renal allograft recipient is experiencing allograft rejection comprising the steps of (a) isolating RNA from a biological specimen from the renal allograft recipient; (b) determining the expression levels of a preselected gene signature set in the specimen of the recipient; wherein the preselected gene set comprises one or more of the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, and MY06; (c) normalizing the expression levels of the preselected gene signature set; (d) calculating a risk score using an empirically derived algorithm from normalized expression levels of the preselected gene signature set; (e) determining whether the recipient’s risk score falls within high or low risk category for allograft rejection based on the pre-defined cutpoint; and (f) reporting the recipient’s risk score.

[00010] In some embodiments, the algorithm in the calculating step is a linear regression analysis model that utilizes the formula

to determine the probability of allograft rejection, where

P(Y=1|X) is the probability of the outcome Y=1 (e.g., presence of early acute rejection) given the input features X.

X=(X1,X2, . . . ,Xp) represents the RNA biomarker expression levels.

[30 is the intercept term. 1, 2, . . . , p are the coefficients corresponding to the RNA biomarkers Xl,X2,...,Xp. e is the base of the natural logarithm.

[00011] In some embodiments, the risk score varies between 0 - 100 and with pre-defined cutpoint defining a low vs. a high risk of experiencing allograft early acute rejection, and wherein a risk score of 0-45 indicates a low risk of early acute rejection. In some embodiments, the risk score varies between 0 - 100 and with pre-defined cutpoint defining a low vs. a high risk of experiencing allograft early acute rejection, and wherein a risk score 46-100 indicates a high risk of early acute rejection.

[00012] In some embodiments, the expression levels are determined by a method selected from the group consisting of NanoString™, RNASeq NextSeq™, MiSEQ™ and quantitative polymerase chain reaction (qPCR).

[00013] In some embodiments, the preselected gene signature set comprises the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, and MY06. In some embodiments, the preselected gene signature set comprises the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, MY06, TAFA2, C8orf82, PDSS1, ZNF652, ATP13A2, SIVA1, M0N1A, C19orf44, DALRD3, NUDT5, ZNF683, MPI, NDUFB7, and Clorfl 16.

[00014] In another aspect, provided herein is a method for adjusting the immunosuppressive treatment for a renal allograft recipient comprising: (a) isolating RNA from a blood specimen from the renal allograft recipient; (b) determining the expression levels of a preselected gene signature set in the blood of the recipient; (c) normalizing the expression levels of the preselected gene signature set; (d) calculating a risk score using an empirically derived algorithm from normalized expression levels of the preselected gene signature set; (d) determining whether the recipient is at high risk or low risk for allograft rejection based on the risk score which is delivered to a clinician as an interpreted result; and € adjusting the recipient’s treatment to prevent allograft rejection based on the patient’s risk for allograft rejection, wherein the preselected gene set comprises the TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, and MY06.

[00015] In some embodiments, the algorithm in the calculating step is a shrinkage discriminate analysis model that utilizes the formula

to determine the probability of allograft rejection where P(Y=1 |X) is the probability of the outcome Y=1 (e.g., presence of early acute rejection) given the input features X.

X=(X1,X2, . . . ,Xp) represents the RNA biomarker expression levels. [30 is the intercept term. i, 2,. . . , p are the coefficients corresponding to the RNA biomarkers XI,X2,.. . ,Xp. e is the base of the natural logarithm.

[00016] In some embodiments, the risk score varies between 0 - 100 and with pre-defined cutpoint defining a low vs. a high risk of experiencing allograft rejection and wherein a risk score of 0-45 indicates a low risk of early acute rejection. In some embodiments, the risk score varies between 0 - 100 and with pre-defined cutpoint defining a low vs. a high risk of experiencing allograft early acute rejection, and wherein a risk score 46-100 indicates a high risk of early acute rejection.

[00017] In some embodiments, the expression levels are determined by a method selected from the group consisting of NanoString™, RNASeq NextSEQ™, MiSEQ™ and quantitative polymerase chain reaction (qPCR).

[00018] In some embodiments, the preselected gene signature set comprises the TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, MY06, TAFA2, C8orf82, PDSS1, ZNF652, ATP13A2, SIVA1, and M0N1A. In some embodiments, the preselected gene signature set comprises the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, MY06, TAFA2, C8orf82, PDSS1, ZNF652, ATP13A2, SIVA1, M0N1A, C19orf44, DALRD3, NUDT5, ZNF683, MPI, NDUFB7, and Clorfl 16.

[00019] In some embodiments, the adjusting of the treatment comprises increasing a patient’s immunosuppressive treatment if the patient is at a high risk of early acute rejection. In some embodiments, the adjusting of the treatment comprises decreasing a patient’s immunosuppressive treatment if the patient is at a low risk of early acute rejection.

BRIEF DESCRIPTION OF DRAWINGS

[00020] FIG. 1 illustrates the discovery of the 29 select genes and shows a combination method of Shrinkage Discriminant Analysis (SDA) with Recursive Feature Elimination (RFE) which showed optimal performance of the model at length of 29 genes., with a mean AUC of 71.99% in the discovery set (n=123)

[00021]

[00022] FIGs. 2A and 2B show the performance of the 29-gene signature in a validation cohort. FIG. 2A shows the AUC over time calculated as a continuous variable. The AUC at day 60 is 0.785, p<0.001., FIG. 2B shows the Receiver Operating Characteristic (ROC) curve at Day 60.

[00023] FIG. 3 shows the cutoff point and event chart of the 122 patients in the validation set when evaluated using the 29-gene signature. The hazard ratio in high vs low risk classification is 7.25, p=0.008.

[00024] Fig. 4 shows In-bag performance of the 29 gene signature in the training set after algorithm lock.

DETAILED DESCRIPTION

[00025] Provided herein are gene signatures and methods of using the same to predict the risk of transplant rejection in a deceased donor kidney transplant recipient.

Gene Signatures

[00026] The gene expression profiles disclosed herein provide a clinical assay that is performed using blood collected pre-transplant from patients just prior to surgery to receive an allograft from a deceased donor. Renal transplant patients are carefully examined when offered a kidney from a deceased donor for prompt availability for transplant and suitability of the offered graft. Following transplantation, patients may be examined by their physician frequently, with time intervals between visits gradually decreasing overtime. During these clinic visits, the patients’ renal function and immunosuppression levels are usually monitored.

[00027] The present inventors have identified and validated a blood based 29-gene RNA signature including algorithm in patients preparing for allograft transplant that produces a risk score which correlates to the future, within 60 days post-transplant, presence or absence of early acute rejection as identified histopathologically on kidney biopsy. Application of this gene set informs improvements in medical management of kidney transplant recipients in a more individualized manner with regard to immunosuppressive therapy and clinical risk management.

[00028] The gene expression profile disclosed herein can be performed on blood collected prior to the time of kidney transplant surgery with a deceased donor allograft. Demonstration of a positive test, categorized as high risk, could indicate probability of early acute rejection within the first 2 months post-transplant, and could lead to an increase in immunosuppression and/or reduction of the immunosuppression taper or decision to biopsy. If the test is high risk, the patient could be treated with either high dose steroids or anti-lymphocyte agents depending on the overall immunological risk of the individual and the transplant centers’ management procedures.

[00029] In contrast, if a test indicates a low risk of rejection, the patient may not require as much immunosuppression or as aggressive immunosuppression and the patient may receive a lower dose of immunosuppression and/or the immunosuppression may be tapered faster. Reduction in overimmunosuppression reduces risks of toxicity, infection, and malignancy. [00030] Without wishing to be bound by theory, it is hypothesized that the 29-gene signature provided herein is more sensitive than existing methods of predicting likelihood of rejection, such as human leukocyte antigen (HLA) matching or panel reactive antibody (PRA) levels. The gene signatures provided herein may therefore help identify patients that would be qualified as high risk by existing methods but are in fact of low risk. Such patients may be receiving more immunosuppression than necessary, or more aggressive therapy than necessary, and the gene signatures described herein can allow a physician to tailor the patient’s immunosuppressive therapy better and avoid excess immunosuppression.

[00031] The risk score may be calculated using an empirically derived algorithm from normalized expression levels of the preselected gene signature set. The algorithm may be a logistic regression model for a binary outcome that utilizes the formula:

where:

X=(X1,X2, . . . ,Xp) represents the RNA biomarker expression levels.

[00032] Gene expression may be normalized, for example, using a normalization method that calculates equivalent value for one million full-length transcripts sequenced, TPM (transcripts per million). TPM is a methodology well known in the art and is a key step to allow comparison for multiple samples from different experiments. See Theory Biosci. 2012 Dec;131(4):281-5 for more details. [00033] Genes may be up or down regulated, and model coefficients can be positively correlated to EAR or negatively correlated to EAR.

[00034] The regression model generates probability scores between zero and one which are then converted (xlOO) to risk scores from zero to 100. The weighted cumulative score (r) can be used as a risk score for early acute rejection for each patient. The risk score may then be categorically defined as low or high risk based on a defined cut-off point within the reporting range of 0 to 100 and for which there is a calculated prognostic risk score for the patient experiencing an early acute rejection. In some embodiments, the cutpoint at 45.7, such that a risk score of 0-.45.7 indicates a low risk of early acute rejection and a risk score of 45.8-100 indicates a high risk of early acute rejection.

[00035] The preselected gene signature set may comprise the genes SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82 (also called MGC70857), SIVA1 (also called CD27BP), SSBP4, EGR3 (also called PILOT), MY06 (also called DFNA22, DFNB37, or KIAA0389), DALRD3 (also called FLJ10496), CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR (also called IL-1R8 or TIR8), TAFA2 (also called FAM19A2), LAMC1 (also called LAMB2), PDSS1, NUDT5, PEX10 (also called RNF69), ATP6V1D (also called ATP6M), MPI, C19orf44 (also called FLJ21742) ,TERF2IP (also called RAP1), ALDH6A1 (also called MMSDH), Clorfl 16 (also called SARG), or any combination or subset thereof. In some embodiments, the preselected gene signature set comprises the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, and MY06 In some embodiments, the preselected gene signature set consists of TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, and MY06.

[00036] In some embodiments, the preselected gene signature set comprises the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, MY06, TAFA2, C8orf82, PDSS1, ZNF652, ATP13A2, SIVA1, and M0N1A. In some embodiments, the preselected gene signature set consists of the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, MY06, TAFA2, C8orf82, PDSS1, ZNF652, ATP13A2, SIVA1, and M0N1A.

[00037] In some embodiments, the preselected gene signature set comprises the genes SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16. In some embodiments, the preselected gene signature set consists of the genes SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16.

[00038] In some embodiments, the preselected gene signature set comprises any 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 or 29 genes of SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16.

Methods of Use

[00039] Pursuant to the present disclosure, a patient who is preparing for renal transplant may have the assay of the present disclosure performed as part of their pre-transplant workup. The methods may comprise peripheral blood being taken, RNA being extracted, and RNA sequencing library of cDNA being generated. In some embodiments, this assay comprises performing RNA sequencing of the whole transcriptome, including the specific 29 signature genes.

[00040] Expression levels may be determined for some or all of the 29 genes selected from SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16, and the early acute rejection risk algorithm may be applied to determine the risk assessment score for individual patients. The “expression level” of RNA generally refers to the mRNA expression level of the genes in the gene signature, or the measurable level of the genes in the gene signature measured in a sample. Detecting expression of the specific mRNAs can be achieved using any method known in the art as described herein. For example, expression levels of mRNA can be determined by any suitable method known in the art, such as, but not limited to polymerase chain reaction (PCR), e.g., quantitative real-time PCR, “qRT- PCR”, RNA-sequencing, microarray, targeted gene expression sequencing (TRex), NanoString analysis, etc.. mRNA specific primers and probes can be designed using nucleic acid sequences, which are known in the art. [00041] In some embodiments, if the patient’s score is above a pre-defined cut-off point, the patient is categorized as being at high risk of early acute rejection. In some embodiments, the cutpoint is at 45, such that a risk score of 0-45. indicates a low risk of early acute rejection and a risk score of 46-100 indicates a high risk of early acute rejection.

[00042] An “early acute rejection” generally refers to a rejection of a transplant (e.g., an allogenic renal transplant) that occurs early after the transplant, e.g., 60 days after the transplant. In some embodiments, the early rejection occurs within 2 months of receiving the transplant. In some embodiments, the early acute rejection is clinical. In some embodiments, the early acute rejection is subclinical. In some embodiments, the early acute rejection is T- cell-mediated. In some embodiments, the early acute rejection is antibody-mediated. In some embodiments, the early acute rejection is mediated by both T cells and antibodies. An acute rejection may be of any grade, except borderline.

[00043] If a patient is identified as being at high risk of early acute rejection, the patient may be evaluated to receive immunosuppression that will be managed in the same manner employed for a high risk patient e.g., immunosuppressive drugs such as calcineurin inhibitors (CNI’s), avoidance of steroid withdrawal, avoidance of mTOR inhibitors (such as Sirolimus/Temsirolimus or Everolimus) or Belatacept. In some embodiments, if the score is below a pre-defined cut-off point, the patient is regarded as being at low risk for early acute rejection, in which case the patient may be a candidate for steroid withdrawal, or less aggressive regimens with mTOR inhibitors or Belatacept. In some embodiments, the cutpoint at 45, such that a risk score of 0-.45 indicates a low risk of early acute rejection and a risk score of 46-100 indicates a high risk of early acute rejection.

[00044] Of note, the assays provided herein are believed to be better predictors of early acute rejection than other clinical factors that would predefine a candidate as high (or low) risk e.g. age, high (>30%) panel reactive antibodies (PRA), the presence of anti-HLA- antibodies, and number of HLA mismatches. Thus, a patient may be identified as having a high risk of early acute rejection by number of HLA mismatches or PRA, but identified as having a low risk of early acute rejection by the methods described herein.

[00045] In one aspect, the present disclosure is directed to methods that accurately predict subclinical and clinical early acute rejection.

[00046] When such high risk allograft recipients (e.g., recipients having a risk score between 46 and 100) are identified, the present disclosure includes methods for treating such patients. The methods include, without limitation, increased administration of immunosuppressive drugs, i.e. a calcineurin inhibitor (CNI), such as cyclosporine or tacrolimus, or a less fibrogenic immunosuppressive drug such as mycophenolate mofetil (MMF) and/or sirolimus. The main class of immunosuppressants are the calcineurin inhibitors (CNIs). Steroids such as prednisone may also be administered to treat patients at risk for graft loss or functional decline. Antiproliferative agents such as Mycophenolate Mofetil, Mycophenolate Sodium and Azathioprine may also be useful in such treatments. Immunosuppression can be achieved with many different drugs, including steroids, targeted antibodies and CNIs such as tacrolimus.

[00047] The present disclosure is at least in part based on the identification of gene expression profiles expressed in a candidate for kidney allograft transplant from deceased donors, that determine the risk for the probability of early acute rejection as defined by histopathology phenotype on kidney biopsy. Without wishing to be bound by theory, it is hypothesized that the gene expression profile is predictive of subclinical as well as clinical early acute rejection. This gives the clinician the ability to personalize the approach to the immunosuppression regimen, thereby maximizing immunosuppression in those at high risk and lowering immunosuppression in those with decreased risk.

[00048] For immunosuppression, an individual at lower risk (e.g., a patient having a risk factor of 0-45) can, depending on other immunological factors, be treated with a reduced dose of MMF, a steroid-free regimen or with a “weaker” less frequently used primary immunosuppressant, such as Rapamycin Sirolimus (Rapamune®), Everolimus (Zortress®) or Belatacept (Nulojix®). Utilizing this approach in lowering immunosuppression has been shown to reduce the risk of serious post-transplant infections and malignancies. These agents are recognized by those of ordinary skill in the art as less potent (or weaker) than other agents, because they are associated with a higher risk of early acute rejection. In some embodiments, a patient with a low risk score of early acute rejection is already being treated with one or more immunosuppressive therapies and the method comprises decreasing the patient’s immunosuppression. In some embodiments, a patient with a low risk score of early acute rejection is already being treated with one or more immunosuppressive therapies and the method comprises administering to the patient a lower dose of one or more of the immunosuppressive therapies. In some embodiments, a patient with a low risk score of early acute rejection is already being treated with one or more immunosuppressive therapies and the method comprises stopping the administration of one or more of the immunosuppressive therapies of the patient’s existing regimen. In some embodiments, a patient with a low risk score of early acute rejection is already being treated with one or more immunosuppressive therapies and the method comprises accelerating the tapering of one or more of the immunosuppressive therapies of the patient’s existing regimen. Reduction in immunosuppressive dosing is desirable, because the reduction can reduce risk of infection and malignancy as well as reduce probability of CNI toxicity.

[00049] “Stronger” immunosuppressive agents include CNI’s, such as tacrolimus (Prograf®, Advagraf® / Astagraf XL (Astellas Pharma Inc.) , Envarsus XR® (Veloxis Pharma Inc.) and generics of Prograf® and cyclosporine (Neoral® and Sandimmune® (Novartis AG) and generics thereof. An individual at higher risk (e.g., a patient with a risk score between 45.7 and 100) may be treated with such stronger immunosuppressive agents. In some embodiments, a patient with a high risk score of acute early rejection is already being treated with one or more immunosuppressive therapies and the method comprises further increasing the patient’s immunosuppression. In some embodiments, a patient with a high risk score of acute early rejection is already being treated with one or more immunosuppressive therapies and the method comprises administering to the patient a higher dose of one or more of the immunosuppressive therapies. In some embodiments, a patient with a high risk score of acute early rejection is already being treated with one or more immunosuppressive therapies and the method comprises adding one or more immunosuppressive therapies to the patient’s existing regimen. In some embodiments, a patient with a high risk score of early acute rejection is already being treated with one or more immunosuppressive therapies and the method comprises slowing the tapering of one or more of the immunosuppressive therapies of the patient’s existing regimen.

[00050] In addition, if the gene expression profde identifies an individual as at risk for early acute rejection, the patient may be subjected to more intensive monitoring of clinical laboratory results including gene expression profiles.

[00051] In some embodiments, the present disclosure provides methods of calculating risk that a kidney allograft candidate for deceased donor transplant recipient is probable to experience early acute rejection comprising the steps of providing a blood specimen from a kidney allograft candidate recipient prior to transplant surgery, isolating RNA from the blood specimen, synthesizing cDNA from the mRNA, and measuring the expression levels of a 29 member gene signature set with algorithm present in the blood specimen. Non-limiting examples of methods of measuring expression levels include RNA-Seq, microarray, targeted RNA expression (TREx) sequencing (Illumina, Inc. San Diego California), NanoString (nCounter® mRNA Expression Assay-NanoString Technologies, Inc. Seattle Washington) or qRT-PCR. The results of the gene signature set analysis are compared to a pre-defined cut-off point. [00052] In contrast, if a test indicates a low risk of rejection, the patient may not require as much immunosuppression or as aggressive immunosuppression and the patient may receive a lower dose of immunosuppression and/or the immunosuppression may be tapered faster. [00053] Without wishing to be bound by theory, it is hypothesized that the gene signature provided herein is more sensitive than existing methods of predicting rejection, such as human leukocyte antigen (HLA) tissue typing or rates of panel reactive antibody (PRA) levels

[00054] In some embodiments, the methods described herein are suitable for predicting the risk of the a transplant candidate experiencing an early acute rejection of a donor organ (e.g., a renal transplant) within about 60 days of receiving the transplant. In some embodiments, the methods described herein are suitable for predicting the risk of the a transplant candidate experiencing an early acute rejection of a donor organ (e.g., a renal transplant) within about 70 days of receiving the transplant. In some embodiments, the methods described herein are suitable for predicting the risk of the a transplant candidate experiencing an early acute rejection of a donor organ (e.g., a renal transplant) within about 80 days of receiving the transplant. In some embodiments, the methods described herein are suitable for predicting the risk of the a transplant candidate experiencing an early acute rejection of a donor organ (e.g., a renal transplant) within about 90 days of receiving the transplant.

[00055] In a specific embodiment, provided herein is a method for identifying the risk that a candidate renal allograft recipient will experience allograft rejection comprising the steps of:

(a) isolating RNA from a biological specimen (e.g., blood, tissue, or urine) from the renal allograft candidate collected prior to the transplant surgery.

(b) synthesizing cDNA from the RNA and sequencing the cDNA, then determining the expression levels of a preselected gene signature set in the specimen of the transplant candidate; wherein the preselected gene set comprises at least the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, and MY06;

(c) normalizing the expression levels of the preselected gene signature set;

(d) calculating a risk score using an empirically derived algorithm from normalized expression levels of the preselected gene signature set; and

(e) determining whether the recipient’s risk score falls within high or low risk category for allograft rejection based on the defined cutpoints. [00056] In some embodiments, the method further comprises step (f) reporting the subject’s risk score. In some embodiments, the method further comprises step (g) determining adjusting the recipient’s immunosuppressant treatment to the recipient.

[00057] In another aspect, provided herein is a method for adjusting the immunosuppressive treatment for a renal allograft recipient comprising:

(a) isolating RNA from a blood specimen from the renal allograft recipient;

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

(c) normalizing the expression levels of the preselected gene signature set

(d) calculating a risk score using an empirically derived algorithm from normalized expression levels of the preselected gene signature set

(d) determining whether the recipient is at high risk or low risk for allograft rejection based on the risk score which is delivered to a clinician as an interpreted result; and

(e) adjusting the recipient’s treatment to prevent allograft rejection based on the patient’s risk for allograft rejection, wherein the preselected gene set comprises the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, and MY06.

[00058] The gene signatures provided herein may therefore help identify patients that would be qualified as high risk by existing methods but are in fact of low risk. Such patients may be receiving more immunosuppression than necessary, or more aggressive therapy than necessary, and the gene signatures described herein can allow a physician to tailor the patient’s immunosuppressive therapy better and avoid excess immunosuppression.

[00059] The gene signature set for use in practicing the methods disclosed herein may comprise the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, Clorfl l6, and any combination or subgroup thereof.

[00060] In some embodiments, the gene signature for use in the methods described herein may comprise any 5 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl l6. [00061] In some embodiments, the gene signature for use in the methods described herein may comprise any 6 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl l6.

[00062] In some embodiments, the gene signature for use in the methods described herein may comprise any 7 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl l6.

[00063] In some embodiments, the gene signature for use in the methods described herein may comprise any 8 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl l6.

[00064] In some embodiments, the gene signature for use in the methods described herein may comprise any 9 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl l6.

[00065] In some embodiments, the gene signature for use in the methods described herein may comprise any 10 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl l6.

[00066] In some embodiments, the gene signature for use in the methods described herein may comprise any 11 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl l6.

[00067] In some embodiments, the gene signature for use in the methods described herein may comprise any 12 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16.

[00068] In some embodiments, the gene signature for use in the methods described herein may comprise any 13 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl l6.

[00069] In some embodiments, the gene signature for use in the methods described herein may comprise any 14 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl l6.

[00070] In some embodiments, the gene signature for use in the methods described herein may comprise any 15 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16. In some embodiments, the gene signature for use in the methods described herein comprises the genes The preselected gene signature set may comprise the genes SIVA1, DALRD3, SSBP4, MY06, SLC25A35, M0N1A, C8orf82, ATP6V1D, CLN5, LAMC1, C19orf44, NUDT5, PEX10, SIGIRR, and NDUFB7.

[00071] In some embodiments, the gene signature for use in the methods described herein may comprise any 16 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16. In some embodiments, the gene signature for use in the methods described herein comprises the genes The preselected gene signature set may comprise the genes SIVA1, DALRD3, SSBP4, MY06, SLC25A35, M0N1A, C8orf82, ATP6V1D, CLN5, LAMC1, C19orf44, NUDT5, PEX10, SIGIRR, and NDUFB7 and any one of METRNL, RAPGEFL1, ZNF652, ATP13A2, EGR3, CD320, ZNF683, C4BPA, TAFA2, PDSS1, MPI, TERF2IP, ALDH6A1, and Clorfl 16.

[00072] In some embodiments, the gene signature for use in the methods described herein may comprise any 17 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16. In some embodiments, the gene signature for use in the methods described herein comprises the genes The preselected gene signature set may comprise the genes SIVA1, DALRD3, SSBP4, MY06, SLC25A35, M0N1A, C8orf82, ATP6V1D, CLN5, LAMC1, C19orf44, NUDT5, PEX10, SIGIRR, and NDUFB7 and any two of METRNL, RAPGEFL1, ZNF652, ATP13A2, EGR3, CD320, ZNF683, C4BPA, TAFA2, PDSS1, MPI, TERF2IP, ALDH6A1, and Clorfl 16.

[00073] In some embodiments, the gene signature for use in the methods described herein may comprise any 18 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16. In some embodiments, the gene signature for use in the methods described herein comprises the genes The preselected gene signature set may comprise the genes SIVA1, DALRD3, SSBP4, MY06, SLC25A35, M0N1A, C8orf82, ATP6V1D, CLN5, LAMC1, C19orf44, NUDT5, PEX10, SIGIRR, and NDUFB7 and any three of METRNL, RAPGEFL1, ZNF652, ATP13A2, EGR3, CD320, ZNF683, C4BPA, TAFA2, PDSS1, MPI, TERF2IP, ALDH6A1, and Clorfl 16.

[00074] In some embodiments, the gene signature for use in the methods described herein may comprise any 19 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16. In some embodiments, the gene signature for use in the methods described herein comprises the genes The preselected gene signature set may comprise the genes SIVA1, DALRD3, SSBP4, MY06, SLC25A35, M0N1A, C8orf82, ATP6V1D, CLN5, LAMC1, C19orf44, NUDT5, PEX10, SIGIRR, and NDUFB7 and any four of METRNL, RAPGEFL1, ZNF652, ATP13A2, EGR3, CD320, ZNF683, C4BPA, TAFA2, PDSS1, MPI, TERF2IP, ALDH6A1, and Clorfl 16.

[00075] In some embodiments, the gene signature for use in the methods described herein may comprise any 20 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16. In some embodiments, the gene signature for use in the methods described herein comprises the genes The preselected gene signature set may comprise the genes SIVA1, DALRD3, SSBP4, MY06, SLC25A35, M0N1A, C8orf82, ATP6V1D, CLN5, LAMC1, C19orf44, NUDT5, PEX10, SIGIRR, and NDUFB7 and any five of METRNL, RAPGEFL1, ZNF652, ATP13A2, EGR3, CD320, ZNF683, C4BPA, TAFA2, PDSS1, MPI, TERF2IP, ALDH6A1, and Clorfl l6.

[00076] In some embodiments, the gene signature for use in the methods described herein may comprise any 21 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16. In some embodiments, the gene signature for use in the methods described herein comprises the genes The preselected gene signature set may comprise the genes SIVA1, DALRD3, SSBP4, MY06, SLC25A35, M0N1A, C8orf82, ATP6V1D, CLN5, LAMC1, C19orf44, NUDT5, PEX10, SIGIRR, and NDUFB7 and any six of METRNL, RAPGEFL1, ZNF652, ATP13A2, EGR3, CD320, ZNF683, C4BPA, TAFA2, PDSS1, MPI, TERF2IP, ALDH6A1, and Clorfl 16.

[00077] In some embodiments, the gene signature for use in the methods described herein may comprise any 22 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16. In some embodiments, the gene signature for use in the methods described herein comprises the genes The preselected gene signature set may comprise the genes SIVA1, DALRD3, SSBP4, MY06, SLC25A35, M0N1A, C8orf82, ATP6V1D, CLN5, LAMC1, C19orf44, NUDT5, PEX10, SIGIRR, and NDUFB7 and any seven of METRNL, RAPGEFL1, ZNF652, ATP13A2, EGR3, CD320, ZNF683, C4BPA, TAFA2, PDSS1, MPI, TERF2IP, ALDH6A1, and Clorfl 16.

[00078] In some embodiments, the gene signature for use in the methods described herein may comprise any 23 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16. In some embodiments, the gene signature for use in the methods described herein comprises the genes The preselected gene signature set may comprise the genes SIVA1, DALRD3, SSBP4, MY06, SLC25A35, M0N1A, C8orf82, ATP6V1D, CLN5, LAMC1, C19orf44, NUDT5, PEX10, SIGIRR, and NDUFB7 and any eight of METRNL, RAPGEFL1, ZNF652, ATP13A2, EGR3, CD320, ZNF683, C4BPA, TAFA2, PDSS1, MPI, TERF2IP, ALDH6A1, and Clorfl 16. [00079] In some embodiments, the gene signature for use in the methods described herein may comprise any 24 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16. In some embodiments, the gene signature for use in the methods described herein comprises the genes The preselected gene signature set may comprise the genes SIVA1, DALRD3, SSBP4, MY06, SLC25A35, M0N1A, C8orf82, ATP6V1D, CLN5, LAMC1, C19orf44, NUDT5, PEX10, SIGIRR, and NDUFB7 and any nine of METRNL, RAPGEFL1, ZNF652, ATP13A2, EGR3, CD320, ZNF683, C4BPA, TAFA2, PDSS1, MPI, TERF2IP, ALDH6A1, and Clorfl 16.

[00080] In some embodiments, the gene signature for use in the methods described herein may comprise any 25 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16. In some embodiments, the gene signature for use in the methods described herein comprises the genes The preselected gene signature set may comprise the genes SIVA1, DALRD3, SSBP4, MY06, SLC25A35, M0N1A, C8orf82, ATP6V1D, CLN5, LAMC1, C19orf44, NUDT5, PEX10, SIGIRR, and NDUFB7 and any ten of METRNL, RAPGEFL1, ZNF652, ATP13A2, EGR3, CD320, ZNF683, C4BPA, TAFA2, PDSS1, MPI, TERF2IP, ALDH6A1, and Clorfl 16.

[00081] In some embodiments, the gene signature for use in the methods described herein may comprise any 26 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16. In some embodiments, the gene signature for use in the methods described herein comprises the genes The preselected gene signature set may comprise the genes SIVA1, DALRD3, SSBP4, MY06, SLC25A35, M0N1A, C8orf82, ATP6V1D, CLN5, LAMC1, C19orf44, NUDT5, PEX10, SIGIRR, and NDUFB7 and any 11 of METRNL, RAPGEFL1, ZNF652, ATP13A2, EGR3, CD320, ZNF683, C4BPA, TAFA2, PDSS1, MPI, TERF2IP, ALDH6A1, and Clorfl 16.

[00082] In some embodiments, the gene signature for use in the methods described herein may comprise any 27 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16. In some embodiments, the gene signature for use in the methods described herein comprises the genes The preselected gene signature set may comprise the genes SIVA1, DALRD3, SSBP4, MY06, SLC25A35, M0N1A, C8orf82, ATP6V1D, CLN5, LAMC1, C19orf44, NUDT5, PEX10, SIGIRR, and NDUFB7 and any 12 of METRNL, RAPGEFL1, ZNF652, ATP13A2, EGR3, CD320, ZNF683, C4BPA, TAFA2, PDSS1, MPI, TERF2IP, ALDH6A1, and Clorfl 16.

[00083] In some embodiments, the gene signature for use in the methods described herein may comprise any 28 of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16. In some embodiments, the gene signature for use in the methods described herein comprises the genes The preselected gene signature set may comprise the genes SIVA1, DALRD3, SSBP4, MY06, SLC25A35, M0N1A, C8orf82, ATP6V1D, CLN5, LAMC1, C19orf44, NUDT5, PEX10, SIGIRR, and NDUFB7 and any 13 of METRNL, RAPGEFL1, ZNF652, ATP13A2, EGR3, CD320, ZNF683, C4BPA, TAFA2, PDSS1, MPI, TERF2IP, ALDH6A1, and Clorfl 16.

[00084] In some embodiments, the gene signature for use in the methods described herein comprises each of the following genes: SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16.

[00085] In some embodiment, the 29-gene signature set for use in practicing the methods disclosed herein consists of SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16.

[00086] In some embodiments, the preselected gene signature set comprises the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, and MY06. In some embodiments, the preselected gene signature set consists of the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, and MY06. In some embodiments, the preselected gene signature set comprises the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, MY06, TAFA2, C8orf82, PDSS1, ZNF652, ATP13A2, SIVA1, and M0N1A. In some embodiments, the preselected gene signature set consists of the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, MY06, TAFA2, C8orf82, PDSS1, ZNF652, ATP13A2, SIVA1, and M0N1A. In some embodiments, the preselected gene signature set comprises the genes SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl l6. In some embodiments, the preselected gene signature set consists of the genes SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl l6.

[00087] In some embodiments 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 targets, specific probes and primers can be designed for use in the detection methods described below as appropriate. Any one of NanoString, microarray, RNA Sequencing, or quantitative Polymerase Chain Reactions (qPCR) such as Real Time Polymerase Chain Reactions (RT-PCR) or Targeted RNA sequencing (TREx) can be used in the methods disclosed herein. Nucleic acids, including RNA and specifically mRNA, can be isolated using any suitable technique known in the art. Extraction procedures such as those using TRIZOL™ or TRI REAGENT™, may be used to 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 the QIAGEN-ALL prep kit and Promega Maxwell simplyRNA kit are also contemplated.

[00088] In some embodiments, use of quantitative RT-PCR is desirable. Quantitative RT- PCR is a modification of the polymerase chain reaction method used to rapidly measure the quantity of a nucleic acid. qRT-PCR is commonly used for the purpose of determining whether a genetic sequence is present in a sample, and if it is present, the number of copies or the relative quantity of copies compared to a reference sequence in the sample. Any method of PCRthat can determine the expression of a nucleic acid molecule, including an mRNA, falls within the scope of the present disclosure. There are several variations of the qRT-PCR method that 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 (NanoString Technologies®, Seattle, WA). The nCounter® Analysis System from NanoString Technologies profiles hundreds of mRNAs, microRNAs, or DNA targets simultaneously with high sensitivity and precision. In this system, target molecules are detected digitally. The NanoString analysis system uses molecular “barcodes” and singlemolecule imaging to detect and count hundreds of unique transcripts in a single reaction. The NanoString analysis protocol does not include any amplification steps.

[00089] In a typical embodiment, the central clinical laboratory will determine the expression values and calculate the risk score upon receipt of blood sample and requisition from an ordering clinician. The risk score along with interpretation will be returned to the ordering clinician who will evaluate the full clinical context for the patient, including the calculated AR risk score and will utilize this information in medical management for the patient.

[00090] In an alternate embodiment, the assay will be performed as described above in a clinical laboratory but using a kit, and the results will be calculated through a web-based portal with access to the bioinformatic pipeline and algorithm and then returned electronically to the ordering clinician.

[00091] The biological specimen (e.g., blood) to determine the expression level of the genes in the gene signature provided herein may be taken at a suitable time before transplantation.

Kits

[00092] In certain embodiments, kits are provided for determining a renal allograft recipient’s risk of the presence of early acute rejection. In some embodiments, the kit is a kit comprising the regents necessary for RNA sequencing of the genes SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, Clorfl 16, or any combination of subgroup thereof.

[00093] The kits may comprise primers for the gene signature set optionally a housekeeping gene panel for TREx and NanoString assays, primers for housekeeping genes for qPCR assays and a control probe.

[00094] A kit can further comprise one or more RNA 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 aliquoted. The kit may also contain printed instructions for use of the kit materials.

[00095] 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 including RNAase-free water, distilled water, buffered water, physiological saline, PBS, reaction buffers, labeling buffers, washing buffers, and hybridization buffers.

[00096] The kits of the disclosure 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. In addition, the kits, in some cases, will include written information 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.

EXAMPLES

[00097] The present invention is described further below in Examples which are intended to further describe the invention without limiting the scope thereof.

Example 1: Development and Validation of a 29-gene Signature to Predict Acute Allograft Rejection

[00098] Demographic and clinical features of both a kidney donor and recipient are utilized to estimate risk and likelihood of graft failure. Unfortunately, many of these features have performed poorly at predicting likelihood of early acute rejection (EAR). Clinically and analytically validated biomarkers are needed to assist in guiding medical management in the early months following transplant. This example shows the development and validation of a novel, next-generation sequencing (NGS) biomarker assay which interrogates the immunologic profile of pre- transplant patients receiving a cadaveric donor kidney. By applying algorithm-enabled clinical decision trees, an RNA targeted NGS feature set predicting the likelihood of EAR was developed.

Methods

RNA Sequencing for Training Cohort

[00099] RNA sequencing procedures were carried out as previously described (see, e.g., Bestard et al., Prospective observational study to validate a Next Generation Sequencing blood RNA signature to predict early kidney transplant rejection. Am J Transplant. 2024 Mar;24(3):436-447, or as per manufacturers’ instructions. The training cohort consisted of 123 kidney transplant recipients.

RNA Sequencing Data Conversion and Normalization

[000100] The RNA sequencing raw data FASTQ files were processed to obtain raw read counts. The raw FASTQ files were trimmed to remove any adapter sequences using SeqPurge version 2022_l l-72-g9164a905 followed by alignment with bowtie2 2.3.5.1 to filter for any reads mapping to the rRNA or HBB genes. The filtered reads were then subject to alignment with STAR v2.6. la , to generate transcript quantification data for each sample. The transcript counts were aggregated to gene counts and normalized to Total Per Million (TPM) counts using stringtie v 1.3.4d to account for variations in library size. .

Pre-Filtering

[000101] To focus on biologically relevant genes, a pre-filtering step was employed. Genes categorized as non-coding were removed from the dataset. Additionally, genes with a length less than 400 base pairs were removed. Low-expressed genes were identified as those genes with mean raw count of less than 10 or max raw count of less than 50. These genes were removed further from the analysis.

[000102] The 29 genes analyzed were SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl l6.

The 15 genes with strongest predictive value were TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, MY06, and PEX10.

[000103] The 22 genes with the strongest predictive value were TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, MY06, PEX10, TAFA2, C8orf82, PDSS1, ZNF652, ATP13A2, SIVA1, and

M0N1A.

Clinical Validation

[000104] The predictive model developed through the training phase was subjected to clinical validation. Clinical trial participants from 11 participating sites of an all-comers, prospective, multicenter international study ((NCT04727788) contributed the patient samples and clinical data for the discovery (n=123) or validation set (n=122) . The validation set, which was completely independent of the patients used in gene discovery or the model training, were employed to assess the model's accuracy, sensitivity, and specificity in predicting graft rejection outcome. Further, the final logistic regression risk prediction model was locked and the statistical analyses were conducted to a pre-approved Statistical Analysis Plan (SAP). The performance of the final, locked logistic regression risk prediction model was rigorously evaluated to determine its clinical performance in predicting early acute rejection outcomes.

[000105] All participants had blood collected prior to transplant surgery. Protocol design included patients being followed at 1, 3, 6 and 12 month visits and included a protocol biopsy at 3 and 12 months as well as anytime for-cause; all biopsies were read centrally. Clinical endpoint of EAR was defined by kidney biopsy histopathology according to BANFF criteria which included TCMR 1A or higher, AB MR or mixed AR. RNA from peripheral blood from a 123-member discovery and training set of kidney transplant recipients was sequenced using Illumina NextSeq2000 technology to 12.5M read-depth. This cohort data was analyzed for genes of interest exhibiting differential expression and correlating to outcome of early acute rejection as defined by central pathology from renal biopsy. From this analysis, a 29 gene signature was identified, and a machine-learning logistic regression (LR) algorithm was developed and locked. The algorithm produces a risk score, called Clarava™, which is interpreted as high or low risk classification correlated to the outcome of early acute rejection in the first 60 days post transplant. The algorithm was then applied to an independent set of 122 deceased donor (DD) kidney transplant recipients as the validation set.

[000106] Groups were compared using the Wilcoxon Rank Sum Test. Competing risks regression was used to model time until event endpoints such as early acute rejection. Timedependent area under the receiver operating characteristic curve analysis was used to measure the performance of Clarava™.

[000107] This methodological approach ensures that data processing, pre-filtering, differential gene expression analysis, feature selection, and model training are conducted systematically and reproducibly, paving the way for reliable clinical validation and potential application in organ transplant medicine. Results

[000108] The gene signature uses a time-based model, and over time, patients move to one of four competing endpoints: (1) rejection (antibody-mediated, T-cell mediated, or mixed), (2) death or graft loss, (3) no rejection, or (4) borderline/suspicious). The top performance of the gene signature tested herein was in the first two months post-transplant. The data shown is at 60 days cutpoint.

[000109] Performance assessing the accuracy of prognostic risk classification was done by comparison to the current gold standard of histopathologic findings by renal pathology according to current BANFF criteria of either a protocol or for-cause kidney biopsy taken within the first 60 days post implantation in patients receiving a DD transplant.

[000110] Clinical and demographic details of the validation patient set are shown in Table 1.

Table 1 : Validation Patient Set Clinical and Demographic Information

[000111] The training set cohort performance of the assay is shown in FIG. 1. [000112] The performance of the assay in the validation cohort is shown in FIGs. 2A and 2B. The assay performance results in risk classification of low risk or high-risk correlated to defined outcome of EAR on histopathology in the first 60 days following transplant producing an AUC of 0.785, p < 0.001. The sensitivity was 0.67, and the specificity was 0.78; the diagnostic odds ratio was 7.25, p=0.008 in high vs low risk classification. The high odds ratio demonstrates that the binary classification into high vs. low risk categories is significant for stratification, thus supporting clinical management. The cut-off value is illustrated in FIG. 3

[000113] . The number of HLA mismatches (A, B, DRB and DQB) as well as high PRA, defined as >30% in class I or class II, were analyzed in the validation set using a competing risks regression model. Within the validation cohort, using a competing risks regression model, there is no evidence of an association between number of HLA mismatches (A, B, DRB and DQB) and early acute rejection by 60 days (P=l .0).

[000114] Using a Cox regression model, there is no evidence of an association between number of class I or class II PRA>30 and early acute rejection by 60 days (P=0.4).

[000115] This unique design of correlating a pre-transplant blood-based transcriptomic signature + algorithm with a post-transplant rejection phenotype based on histopathology in the kidney biopsy represents a level of evidence and advancement that currently does not exist in biomarker transplant biology.

[000116] These data show that the correlation of a blood-based NGS signature with kidney biopsy histopathology provides a diverse and robust platform of enriched clinical evidence not traditionally reported in kidney transplant. A pre-transplant immunologic risk assessment tool has the potential to yield a novel, more precise level of evidence for clinicians and their patients at a critical time in their pre-surgical preparedness.

Claims

CLAIMS:

1. A method for identifying the risk that a renal allograft recipient is experiencing allograft rejection comprising the steps of:

(a) isolating RNA from a biological specimen from the renal allograft recipient;

(b) determining the expression levels of a preselected gene signature set in the specimen of the recipient; wherein the preselected gene set comprises the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, and MY06;

(c) normalizing the expression levels of the preselected gene signature set;

(d) calculating a risk score using an empirically derived algorithm from normalized expression levels of the preselected gene signature set;

(e) determining whether the recipient’s risk score falls within high or low risk category for allograft rejection based on the pre-defined cutpoint; and

(f) reporting the recipient’s risk score.

2. The method of claim, wherein the algorithm in the calculating step is a logistic regression analysis model that utilizes the formula

to determine the probability of allograft rejection, where

X=(X1,X2, . . . ,Xp) represents the RNA biomarker expression levels.

PO is the intercept term. 1, 2, . . . , p are the coefficients corresponding to the RNA biomarkers Xl,X2,...,Xp. e is the base of the natural logarithm.

3. The method of claim 1 or 2, wherein the risk score varies between 0 - 100 and with pre-defined cutpoint defining a low vs. a high risk of experiencing allograft early acute rejection, and wherein a risk score of 0-45 indicates a low risk of early acute rejection.

4. The method of any one of claims 1-3, wherein the risk score varies between 0 - 100 and with pre-defined cutpoint defining a low vs. a high risk of experiencing allograft early acute rejection, and wherein a risk score 46-100 indicates a high risk of early acute rejection.

5. The method of any one of claims 1 to 4, wherein the expression levels are determined by a method selected from the group consisting of Nano String™, RNASeq NextSeq™, MiSEQ™ and quantitative polymerase chain reaction (qPCR).

6. The method of any one of claims 1-5, wherein the preselected gene signature set comprises the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, MY06, TAFA2, C8orf82, PDSS1, ZNF652, ATP13A2, SIVA1, and M0N1A.

7. The method of any one of claims 1-5, wherein the preselected gene signature set comprises the genes SLC25A35, METRNL, RAPGEFL1, M0N1A, ZNF652, ATP13A2, C8orf82, SIVA1, SSBP4, EGR3, MY06, DALRD3, CD320, ZNF683, CLN5, NDUFB7, C4BPA, SIGIRR, TAFA2, LAMC1, PDSS1, NUDT5, PEX10, ATP6V1D, MPI, C19orf44, TERF2IP, ALDH6A1, and Clorfl 16.

8. A method for adjusting the immunosuppressive treatment for a renal allograft recipient comprising:

(a) isolating RNA from a blood specimen from the renal allograft recipient;

(c) normalizing the expression levels of the preselected gene signature set

(e) adjusting the recipient’s treatment to prevent allograft rejection based on the patient’s risk for allograft rejection, wherein the preselected gene set comprises the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, and MYO6.

9. The method of claim 8, wherein the algorithm in the calculating step is a logistic regression model that utilizes the formula

to determine the probability of allograft rejection. where P(Y=1 |X) is the probability of the outcome Y=1 (e.g., presence of early acute rejection) given the input features X.

X=(X1,X2, . . . ,Xp) represents the RNA biomarker expression levels.

P0 is the intercept term. 1, 2, . . . , p are the coefficients corresponding to the RNA biomarkers Xl,X2,...,Xp. e is the base of the natural logarithm.

10. The method of claim 8 or 9, wherein the risk score varies between 0 - 100 and with pre-defined cutpoint defining a low vs. a high risk of experiencing allograft rejection and wherein a risk score of 0-45 indicates a low risk of early acute rejection.

11. The method of any one of claims 8-10, wherein the risk score varies between 0 - 100 and with pre-defined cutpoint defining a low vs. a high risk of experiencing allograft early acute rejection, and wherein a risk score 46-100 indicates a high risk of early acute rejection.

12. The method of any one of claims 8-11, wherein the expression levels are determined by a method selected from the group consisting of Nano String™, RNASeq NextSEQ™, MiSEQ™ and quantitative polymerase chain reaction (qPCR).

13. The method of any one of claims 8-12, wherein the preselected gene signature set comprises the genes. TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, MYO6, TAFA2, C8orf82, PDSS1, ZNF652, ATP13A2, SIVA1, and MON1A.

14. The method of any one of claims 8-12, wherein the preselected gene signature set comprises the genes TERF2IP, ALDH6A1, SIGIRR, LAMC1, C4BPA, CLN5, ATP6V1D, PEX10, CD320, RAPGEFL1, SSBP4, EGR3, METRNL, SLC25A35, MY06, TAFA2, C8orf82, PDSS1, ZNF652, ATP13A2, SIVA1, M0N1A, C19orf44, DALRD3, NUDT5, ZNF683, MPI, NDUFB7, and Clorfl 16.

15. The method of any one of claims 8-14, wherein the adjusting of the treatment comprises increasing a patient’s immunosuppressive treatment if the patient is at a high risk of early acute rejection.

16. The method of any one of claims 8-14, wherein the adjusting of the treatment comprises decreasing a patient’s immunosuppressive treatment if the patient is at a low risk of early acute rejection.