Methods and kits for transcriptome signatures in the blood of transplant recipients using empirically derived algorithms that correlate with the presence of acute rejection in kidney biopsies

Abstract

Disclosed herein is a method of diagnostic tool for calculating the risk of acute rejection in renal allograft recipients.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63 / 382,919, filed November 9, 2022, which is incorporated herein by reference in its entirety.

[0002] The present disclosure relates to the field of molecular biology, and more particularly to the detection of RNA transcriptome molecular signatures. More specifically, the present disclosure relates to a method for generating a risk score correlating with the risk of acute rejection in renal allograft recipients. The method involves analyzing the blood of such patients by using an algorithm to determine the expression levels of an RNA signature set including 17 preselected RNA transcripts in order to identify the risk of acute rejection in renal allograft recipients and monitor and guide treatment. Differential expression analysis can be applied to the normalized expression read counts (i.e., gene read counts from next-generation sequencing (NGS) technology) values ​​of selected genes to derive a weighted cumulative risk score of acute rejection risk that can be calculated for each patient's blood sample. [Background technology]

[0003] For subjects with end-stage renal disease (ESKD), kidney transplantation is the treatment of choice (Abecassis et al., Clin J Am Soc Nephrol 3: 471-480, 2008). However, despite significant improvements in 1-year graft loss over the past decade, approximately 3% of renal allograft recipients restart dialysis or require retransplantation each year after transplantation. Late graft failure rates have remained virtually unchanged since the 1990s (Menon et al., J Am Soc Nephrol 28: 735-747, 2017).

[0004] Signs of renal allograft rejection primarily rely on monitoring methods such as proteinuria and serum creatinine. These criteria may lead to evaluation by biopsy. Currently, diagnosis of clinical acute rejection requires a renal allograft biopsy, most commonly performed when serum creatinine levels are elevated in the presence of renal injury, or, in cases of subclinical acute rejection, as part of a surveillance protocol. Biomarkers that correlate with or predict the presence of acute rejection are needed to support clinical management in a sensitive and minimally invasive manner. Clinical acute rejection (AR), i.e., acute rejection associated with decreased renal function, occurs in approximately 10% of transplanted kidneys (Eikmans et al., Front Med 5:358, 2019). Furthermore, up to one-third of recipients have evidence of acute rejection on surveillance biopsies within 12 months of transplantation, even in the absence of clinical decline in renal 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). Most cases of graft loss are due to chronic allograft injury or unexplained interstitial fibrosis and tubular atrophy. This has prompted research aimed at understanding and comparing the mechanisms underlying these late events, which include, among other things, alloantibody formation and recurrence of primary disease. Indeed, the lack of long-term improvement despite a dramatic reduction in acute rejection rates has called into question the assumption that acute rejection represents the primary determinant of long-term graft outcome.However, this hypothesis contrasts with evidence that acute rejection, both clinically overt and subclinical, with an inflammation / tubulitis score of 1 or higher based on renal biopsy, adversely affects long-term graft survival in patients receiving immunosuppressive therapy (Zhang et al., JCI Insight 4(11), 2019; Zhang et al., J Am Soc Nephrol 30(8):1481-1494, 2019). Therefore, AR remains one of the primary targets of post-transplant immunosuppressive therapy. Data regarding the impact of subclinical rejection and borderline rejection on graft outcomes are conflicting, and diagnosis is influenced by subjective reporting. Growing evidence suggests that subclinical inflammation adversely affects allografts, leading to the development of renal fibrosis and long-term decline in renal function (Rampersad et al., Am J Transplant 22:761-771, 2022).

[0005] One of the major problems with current immunosuppression protocols is that they are not tailored to the needs of individual patients. Most individuals receive a standardized immunosuppression protocol, resulting in some patients being exposed to over- or under-suppression and resulting in complications. Early identification of individuals at highest or lowest risk for acute rejection may allow for more targeted treatment to improve long-term outcomes and reduce risk (Cippa, et al., Clin J Am Soc Nephrol 10: 2213-2220, 2015).

[0006] This disclosure provides a novel set of transcriptome signatures that can be used to identify the risk of the presence of an acute rejection episode. The rigor of NGS assays generates performance characteristics, including accuracy and precision, that can better inform the medical management of kidney transplant patients in a more personalized and predictive manner, taking into account the full clinical continuum.

[0007] Tests for renal allograft rejection, such as serum creatinine or proteinuria, can have low sensitivity and, while they are late indicators of damage that rise as a warning of rejection, are not completely predictive. Such tests may involve or lead to the taking of a biopsy specimen from the patient. There is a need in the art for improved tests that do not require invasive biopsies and are more predictive of the risk of allograft rejection. Summary of the Invention

[0008] In one aspect, provided herein is a method of identifying a risk of a renal allograft recipient experiencing allograft rejection, the method comprising: (a) isolating RNA from a biological specimen from the renal allograft recipient; (b) measuring the expression levels of a preselected gene signature set in the specimen from the recipient, wherein the preselected gene set comprises the following genes: OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, and SOCS3; (c) normalizing the expression levels of the preselected gene signature set; (d) calculating a risk score from the normalized expression levels of the preselected gene signature set using an empirically derived algorithm; and (e) determining whether the recipient's risk score falls into a high-risk category or a low-risk category for allograft rejection.

[0009] In some embodiments, the algorithm in the calculating step uses the formula:

number

[0010] In some embodiments, the risk score ranges from 0 to 100, with a risk score of 51 to 100 indicating a high risk of experiencing allograft rejection. In some embodiments, the risk score ranges from 0 to 100, with a risk score of 0 to 50 indicating a low risk of experiencing allograft rejection.

[0011] In some embodiments, the preselected set of genes includes at least nine of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the preselected set of genes includes at least ten of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the preselected set of genes includes at least 11 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the preselected set of genes includes at least 12 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the preselected set of genes includes at least 13 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the preselected set of genes includes at least 14 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.In some embodiments, the preselected set of genes includes at least 15 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the preselected set of genes includes at least 16 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the preselected set of genes includes the genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

[0012] In some embodiments, the expression level is measured by a method selected from the group consisting of NanoString™, RNASeq NextSeq™, MiSEQ™, and quantitative polymerase chain reaction (qPCR).

[0013] In some embodiments, a method for selecting a renal allograft recipient for a treatment to reduce the risk of renal allograft rejection comprises: (a) isolating RNA from a blood sample from the renal allograft recipient; (b) measuring expression levels of a preselected gene signature set in the recipient's blood; (c) normalizing the expression levels of the preselected gene signature set; (d) calculating a risk score from the normalized expression levels of the preselected gene signature set using an empirically derived algorithm; (d) determining whether the recipient is at high risk or low risk for allograft rejection based on the risk score, which is communicated to a clinician as an interpretation; and (e) administering a treatment to prevent allograft rejection if the recipient is at high risk for allograft rejection.

[0014] A method is provided herein, wherein the preselected set of genes includes the genes OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, and SOCS3.

[0015] In some embodiments, the calculating step uses the formula:

number

[0016] In some embodiments, the risk score ranges from 0 to 100, with a risk score of 51 to 100 indicating a high risk of experiencing allograft rejection. In some embodiments, the risk score ranges from 0 to 100, with a risk score of 0 to 50 indicating a low risk of experiencing allograft rejection.

[0017] In some embodiments, the preselected set of genes includes at least nine of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the preselected set of genes includes at least ten of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the preselected set of genes includes at least 11 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the preselected set of genes includes at least 12 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the preselected set of genes includes at least 13 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the preselected set of genes includes at least 14 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.In some embodiments, the preselected set of genes includes at least 15 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the preselected set of genes includes at least 16 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the preselected set of genes includes the genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

[0018] In some embodiments, the expression level is measured by a method selected from the group consisting of NanoString™, RNASeq NextSeq™, MiSEQ™, and quantitative polymerase chain reaction (qPCR).

[0019] In some embodiments, the treatment for preventing allograft rejection comprises one or more immunosuppressive therapies. [Brief explanation of the drawings]

[0020] [Figure 1] FIG. 1 shows a CONSORT diagram of the study enrollment of the clinical trial described in the Examples. [Figure 2]Figure 2 shows a process diagram from specimen receipt to risk score generation. 1) Test is ordered, blood specimen is collected and sent to the laboratory. 2) RNA is isolated, cDNA library is prepared, and RNASeq is performed. 3) Sequencing data is uploaded and QC is assessed. The file is input into a pipeline and proprietary algorithms that process the data and generate test results. 4) Laboratory director reviews assay and patient quality control and approves results for release. Abbreviations: QC: Quality Control [Figure 3A] Figures 3A and 3B show the clinical performance of the NGS17 genetic test versus the clinical model. Figure 3A shows that the clinical performance of the next-generation sequencing test (solid line) was superior to the clinical model (creatinine at biopsy, dashed line) as indicated by the AUC, and Figure 3B shows that the applied threshold of 50 identified patients most likely to experience transplant rejection. Abbreviations: AR: acute rejection; AUC: area under the curve; non-AR: non-acute rejection; NPV: negative predictive value; PPV: positive predictive value. [Figure 3B] Figures 3A and 3B show the clinical performance of the NGS17 genetic test versus the clinical model. Figure 3A shows that the clinical performance of the next-generation sequencing test (solid line) was superior to the clinical model (creatinine at biopsy, dashed line) as indicated by the AUC, and Figure 3B shows that the applied threshold of 50 identified patients most likely to experience transplant rejection. Abbreviations: AR: acute rejection; AUC: area under the curve; non-AR: non-acute rejection; NPV: negative predictive value; PPV: positive predictive value. DETAILED DESCRIPTION OF THE INVENTION

[0021] definition In accordance with the present disclosure there may be employed conventional molecular biology, proteomics, microbiology, recombinant DNA, immunology, cell biology, and other related techniques within the skill of the art.

[0022] As used herein, the "expression level" of an RNA disclosed herein generally refers to the mRNA expression level of a gene in a gene signature, or the measurable level of a gene in a gene signature measured in a sample, which 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-Seq, microarray, targeted gene expression sequencing (TRex), NanoString analysis, etc.

[0023] As used herein, "measuring the level of expression," "measuring the expression level," or "detecting the level of expression" refers to quantifying the amount of mRNA present in a sample, e.g., "measuring the expression level of a gene," and may or may not refer to normalized quantification. Detection of expression of a specific mRNA can be achieved using any method known in the art, such as those described herein. Typically, mRNA detection methods include sequence-specific detection, such as RNASeq or qRT-PCR. mRNA-specific primers and probes can be designed using nucleic acid sequences known in the art.

[0024] A "control" or "non-rejection case" is defined as a sample taken from a patient who has received an allograft transplant and whose biopsy shows a negative diagnosis of acute rejection. Alternatively, a "control" can be defined as a kit control referenced to a standardized source, such as Universal Human Reference RNA (UHR).

[0025] As used herein, "acute rejection" is defined as rejection of a transplant (e.g., a renal allograft) that occurs early after transplantation, e.g., within 0-6 months after transplantation. In some embodiments, early rejection occurs within 12 months of receiving the transplant. In some embodiments, acute rejection is clinical. In some embodiments, acute rejection is subclinical. In some embodiments, acute rejection is T cell-mediated. In some embodiments, acute rejection is antibody-mediated. In some embodiments, acute rejection is mediated by both T cells and antibodies. Acute rejection can be of any severity, including borderline rejection.

[0026] Gene signature The gene expression profiles disclosed herein provide a blood-based assay that can be easily performed on transplant patients longitudinally. Renal transplant patients may have frequent doctor visits following transplantation, with the intervals between visits gradually increasing over time. During these visits, the patient's renal function level and immunosuppression level are typically monitored. The gene signatures described herein can be used to monitor a patient's risk of acute rejection of the renal allograft. In some embodiments, the gene signatures described herein can be used to predict a patient's risk of developing acute rejection of the renal allograft within about 30 days from the time a clinical sample (e.g., biopsy) is collected.

[0027] We identified and validated an algorithm containing a blood-based 17-gene signature in allograft recipients that generates a risk score that correlates with the presence or absence of acute rejection as determined by histopathology on renal biopsy. Application of this gene set will improve the medical management of kidney transplant recipients in a more personalized manner with regard to immunosuppressive therapy.

[0028] The gene expression profiles disclosed herein can be performed during routine monitoring visits or in response to clinical indications requiring further investigation. A positive test result in the absence of a change in creatinine levels indicates subclinical inflammation and may lead to a decision to increase immunosuppressant therapy and / or discontinue immunosuppressant tapering, or to perform a biopsy. Repeated testing, e.g., using the gene signature described herein, may lead to subsequent reductions in immunosuppression. For example, if two subsequent tests indicate low risk, the prednisone dose would be reduced by 2.5 or 5 mg, or the target tacrolimus level would be lowered by 0.5 mg / dL. A high-risk test result in the presence of elevated creatinine levels suggests clinical acute rejection evidenced by kidney damage. In this case, the patient would be treated with either high-dose steroids or antilymphocyte agents, depending on the individual's overall immunological risk and the transplant center's management protocol.

[0029] A risk score can be calculated from the normalized expression levels of a pre-selected gene signature set using an empirically derived algorithm, which has the formula:

number

[0030] Gene expression may be normalized, for example, using variance stabilization transformation (VST). VST is a methodology well known in the art, which involves normalizing gene expression based on a fixed variance function (Zararsiz G, Goksuluk D, Korkmaz S, Eldem V, Zararsiz GE, Duru IP, Ozturk A. A comprehensive simulation study on classification of RNA-Seq data. PLoS One. 2017 Aug 23;12(8):e0182507. doi: 10.1371 / journal.pone.0182507. PMID: 28832679; PMCID: PMC5568128).

[0031] Genes may be up-regulated or down-regulated, and the model coefficient β may be positively correlated with acute rejection or negatively correlated with AR.

[0032] The regression model generates a probability score between 0 and 1, which is converted to a risk score (x100) between 0 and 100. The weighted cumulative score (r) can be used as each patient's risk score for acute rejection. The risk scores are defined over a reporting range of 0 to 100 and may be classified as low, medium, or high risk based on cutoff points from which a predictive value for a patient's risk of experiencing acute rejection is calculated. In some embodiments, a risk score of 51 or greater indicates a high risk for a patient experiencing acute rejection. In some embodiments, a risk score of 50 or less indicates a low risk for a patient experiencing acute rejection.

[0033] The preselected gene signature set can include the genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, DHCR24, or any combination or subset thereof. In some embodiments, the preselected gene signature set consists of NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

[0034] How to use In one aspect, the disclosure provides a method for identifying a renal allograft recipient's risk of experiencing clinical or subclinical acute rejection and subsequent graft loss, comprising: (a) isolating RNA from a blood sample; (b) synthesizing cDNA from the RNA and using it to sequence the transcriptome; (c) measuring the expression level of each of 17 genes in a gene signature set; (d) normalizing and weighting the expression counts from the 17 genes using an empirically derived logistic regression algorithm to calculate a risk score; and (e) determining whether the recipient is at high or low risk for acute rejection and subsequent allograft loss. The genes in the gene signature set may be selected from the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the genes in the gene signature are OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, and SOCS3.

[0035] According to the present disclosure, patients who have undergone kidney transplants can undergo the assays of the present disclosure as part of their post-transplant follow-up and monitoring. The method can include collecting peripheral blood, extracting RNA, and generating an RNA sequencing library of cDNA. In some embodiments, the assay includes performing transcriptome-wide RNA sequencing of some or all of the 17 specific signature genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the assay includes sequencing the genes: OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, and SOCS3.

[0036] The expression levels of all or some of the 17 genes can be measured, and an acute rejection risk algorithm can be applied to determine a risk assessment score for each individual patient. In some embodiments, if a patient's score exceeds a predetermined cutoff point, the patient is classified as high-risk, and the patient can be evaluated for immunosuppression administered in the same manner as used for high-risk patients, such as avoidance of immunosuppressants such as calcineurin inhibitors (CNIs), steroid withdrawal, and avoidance of mTOR inhibitors (such as sirolimus / temsirolimus or everolimus) or belatacept. In some embodiments, if the score is below a predetermined cutoff point, the patient is considered to be at low risk for AR, and the patient may be a candidate for steroid withdrawal or less aggressive treatment with mTOR inhibitors or belatacept.

[0037] The exact cutoff was determined based on RNA sequencing performed on the algorithm training cohort. It is noteworthy that when planning a patient's immunosuppression protocol, other clinical factors that predefine whether a candidate is high- or low-risk, such as age, serum creatinine, and the presence of anti-HLA antibodies, must be considered during testing. In some embodiments, a risk score of 51 to 100 indicates a high risk for the patient experiencing acute rejection. In some embodiments, a risk score of 0 to 50 indicates a low risk for the patient experiencing acute rejection.

[0038] In one aspect, the present disclosure relates to methods for accurately diagnosing subclinical and clinical rejection and accurately identifying allografts at risk for subsequent histological and functional decline, as well as allograft recipients at risk for allograft loss. If such high-risk allograft recipients are identified, the present disclosure includes methods for treating such patients. These methods include, but are not limited to, increasing the administration of immunosuppressants, i.e., calcineurin inhibitors (CNIs) such as cyclosporine or tacrolimus, or less fibrogenic immunosuppressants such as mycophenolate mofetil (MMF) and / or sirolimus. The primary class of immunosuppressants is 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 effective in such treatments. Immunosuppression can be achieved by a variety of drugs, including steroids, targeted antibodies, and CNIs such as tacrolimus.

[0039] The present invention is based, at least in part, on the identification of gene expression profiles expressed in recipients of renal allografts from living or deceased donors, which determine the probability of acute rejection risk as defined by histopathological phenotype in renal biopsies. Without being bound by theory, it is hypothesized that gene expression profiles can predict not only clinical acute rejection, but also subclinical acute rejection. This allows clinicians to individualize their approach to immunosuppressive therapy, maximizing immunosuppression in high-risk patients and reducing immunosuppression in low-risk patients.

[0040] Regarding immunosuppression, low-risk individuals (e.g., patients with a risk score of 0-50) can be treated with reduced MMF doses, steroid-free regimens, or "weaker," less frequently used first-line immunosuppressants such as rapamycin, sirolimus (Rapamune®), everolimus (Zortress®), and belatacept (Nulojix®), depending on other immunological factors. Utilizing this approach to reduce immunosuppression has been shown to reduce the risk of serious infections and malignancies after transplantation. It is recognized by those skilled in the art that these agents are less effective (or less potent) than other agents due to a higher risk of early acute rejection.

[0041] "More potent" immunosuppressants include CNIs such as tacrolimus (Prograf®, Advagraf® / Astagraf XL (Astellas Pharma), Envarsus XR® (Veloxis Pharma), and generic versions of Prograf®) and cyclosporine (Neoral®, Sandimmune® (Novartis), and generic versions). High-risk individuals (e.g., patients with a risk score of 51-100) may be treated with these more potent immunosuppressants.

[0042] Additionally, if the gene expression profile identifies an individual at risk for acute rejection (e.g., the patient has a risk score between 51 and 100), the patient may undergo more intensive monitoring of laboratory test results or gene expression profile. In some embodiments, the patient is monitored monthly using the methods described herein. In some embodiments, the patient is monitored every other month using the methods described herein. In some embodiments, the patient is monitored every three months using the methods described herein. In some embodiments, the patient is monitored every four months using the methods described herein. In some embodiments, the patient is monitored every six months using the methods described herein. In some embodiments, the patient is monitored annually using the methods described herein. In some embodiments, the patient is monitored twice a year using the methods described herein. In some embodiments, the patient is monitored every two years using the methods described herein.

[0043] In some embodiments, the present disclosure provides a method for calculating the risk of a renal allograft recipient experiencing acute rejection, the method comprising: preparing a blood sample from the renal allograft recipient; isolating RNA from the blood sample; synthesizing cDNA from the mRNA; and measuring the expression level of a 17-member gene signature set using an algorithm present in the blood sample. Non-limiting examples of methods for measuring expression levels include RNA-Seq, microarrays, targeted RNA expression (TREx) sequencing (Illumina, San Diego, CA), NanoString (nCounter® mRNA Expression Assay, NanoString Technologies, Seattle, WA), or qRT-PCR. The results of the gene signature set analysis are compared to a predefined cutoff point. These methods are also described in Examples 1-6 below.

[0044] A 17-member gene signature set for use in practicing the methods disclosed herein can include the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, DHCR24, and any combination or subgroup thereof. In some embodiments, a 17-member gene signature set for use in practicing the methods disclosed herein consists of NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, 8, 9, 10, 11, 12, 13, 14, 15, 16, or 17 members of a 17-member gene signature set are analyzed in the methods disclosed herein.

[0045] In some embodiments, any eight of the following genes are analyzed using the methods described herein: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the genes analyzed are OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, and SOCS3.

[0046] In some embodiments, any nine of the following genes are analyzed using the methods described herein: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the genes analyzed are OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, SOCS3, and one of NCAPD2, KIF3B, STK24, PARN, DLG5, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

[0047] In some embodiments, any ten of the following genes are analyzed using the methods described herein: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the following genes are analyzed: OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, SOCS3, and two of NCAPD2, KIF3B, STK24, PARN, DLG5, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

[0048] In some embodiments, any eleven genes are analyzed using the methods described herein: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the genes analyzed are OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, SOCS3, and three of NCAPD2, KIF3B, STK24, PARN, DLG5, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

[0049] In some embodiments, any 12 of the following genes are analyzed using the methods described herein: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the following four of the following genes are analyzed: OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, SOCS3, and NCAPD2, KIF3B, STK24, PARN, DLG5, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

[0050] In some embodiments, any 13 genes, NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24, are analyzed using the methods described herein. In some embodiments, the genes analyzed are OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, SOCS3, and five of NCAPD2, KIF3B, STK24, PARN, DLG5, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

[0051] In some embodiments, any 14 genes are analyzed using the methods described herein: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the genes analyzed are OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, SOCS3, and six genes are analyzed: NCAPD2, KIF3B, STK24, PARN, DLG5, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

[0052] In some embodiments, any 15 genes are analyzed using the methods described herein: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the genes analyzed are any seven of OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, SOCS3, and NCAPD2, KIF3B, STK24, PARN, DLG5, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

[0053] In some embodiments, any 16 genes are analyzed using the methods described herein: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the genes analyzed are any 8 genes selected from OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, SOCS3, and NCAPD2, KIF3B, STK24, PARN, DLG5, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

[0054] In some embodiments, each of the following 17 genes is analyzed using the methods described herein: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

[0055] In some embodiments of the methods disclosed herein, it is desirable to detect and quantify mRNA present in a sample. Detection and quantification of RNA expression can be achieved by any of a number of methods known in the art. The known sequence of the RNA can be used to design specific probes and primers for use in the detection methods described below, if desired. The methods disclosed herein can employ either NanoString, microarrays, RNASeq, or quantitative polymerase chain reaction (qPCR), such as real-time polymerase chain reaction (RT-PCR) or targeted RNA sequencing (TREx). Nucleic acids, including RNA, particularly mRNA, can be isolated using any suitable technique known in the art. For example, phenol-based extraction is a commonly used method for isolating RNA. Phenol-based reagents contain a combination of denaturants and RNase inhibitors to disrupt cells and tissues and then separate the RNA from contaminants. Furthermore, extraction procedures such as TRIZOL™ or TRI REAGENT™ can be used to purify all RNA, both large and small, and are efficient methods for isolating total RNA from biological samples containing mRNA. Extraction procedures such as those using QIAGEN's AllPrep Kit or Promega's Maxwell SimplyRNA Kit are also contemplated.

[0056] In some embodiments, the use of quantitative RT-PCR is desirable. Quantitative RT-PCR is a modified polymerase chain reaction used to rapidly measure the amount of nucleic acids. qRT-PCR is widely used to determine the presence or absence of a gene sequence in a sample and, if present, its copy number or relative copy amount compared to a reference sequence in the sample. Any PCR method capable of determining the expression of nucleic acid molecules, including mRNA, is within the scope of this disclosure. There are several variations of qRT-PCR known to those skilled in the art. In some embodiments, mRNA expression profiles can be determined using the nCounter® Analysis System (NanoString Technologies, Seattle, WA). NanoString Technologies' nCounter® Analysis System simultaneously profiles hundreds of mRNA, microRNA, or DNA targets with high sensitivity and precision. Target molecules are digitally detected in this system. The NanoString Analysis System uses molecular "barcodes" and single-molecule imaging to detect and count hundreds of unique transcripts in a single reaction. The NanoString Analysis protocol does not include an amplification step.

[0057] In a typical embodiment, a central clinical laboratory receives the blood sample and requisition from the ordering clinician, measures the expression values, and calculates a risk score, which is returned along with an interpretation to the ordering clinician, who evaluates the patient's complete clinical situation, including the calculated acute rejection risk score, and uses this information in the patient's medical management.

[0058] In another embodiment, the assay is performed as described above in a clinical laboratory using a kit, and the results are calculated through a web-based portal that provides access to bioinformatics pipelines and algorithms, and then transmitted electronically back to the ordering clinician.

[0059] In a specific embodiment, there is provided a method for identifying a renal allograft recipient's risk of experiencing allograft rejection, comprising: (a) isolating RNA from a biological sample (e.g., blood, tissue, or urine) from the renal allograft recipient; (b) synthesizing cDNA from the RNA and sequencing the cDNA, and then measuring the expression level of a preselected gene signature set in the recipient sample, wherein the preselected gene signature set includes at least the following genes: OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, and SOCS3; (c) normalizing the expression levels of said preselected gene signature set; (d) calculating a risk score from the normalized expression levels of said preselected gene signature set using an empirically derived algorithm; and (e) determining whether the recipient's risk score falls into a high-risk category or a low-risk category for allograft rejection based on predefined cut points.

[0060] In some embodiments, the method further comprises the step (f) of reporting the subject's risk score. In some embodiments, the method further comprises the step (g) of determining whether to administer immunosuppressive therapy to the recipient.

[0061] The methods described herein for identifying a renal allograft recipient's risk of experiencing allograft rejection can include analyzing eight or more of the following genes: OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, and SOCS3. In some embodiments, the methods include analyzing 12 genes (e.g., OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, SOCS3, DLG5, HLA-DPA1, NCAPD2, and DHCR24). In some embodiments, the method comprises analyzing 12 genes selected from NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the method comprises analyzing 13 genes selected from NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the method comprises analyzing 14 genes selected from NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the method comprises analyzing 15 genes selected from NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.In some embodiments, the method comprises analyzing 16 genes selected from NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. In some embodiments, the method comprises analyzing 17 genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

[0062] Biological samples (e.g., blood or biopsies) for measuring expression levels of genes in the gene signatures provided herein can be collected at an appropriate time post-transplant, hi some embodiments, the samples are collected at 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 8 months, 9 months, 10 months, 11 months, 12 months, 13 months, 14 months, 15 months, 16 months, 17 months, 18 months, 2 years, 3 years, 4 years, 5 years, or more post-transplant.

[0063] In some embodiments, repeated samples are taken to monitor the patient's risk of developing an acute rejection episode. In some embodiments, repeated samples are taken to monitor the patient's response to treatment. Monitoring response to treatment can be used, for example, when a patient has been treated for a previously identified acute rejection episode, but it is not known whether the rejection has resolved without another biopsy. The methods provided herein can be used to determine whether a previously identified acute reaction is likely to have resolved. If the patient continues to be at high risk for rejection, further or more aggressive treatment may be required.

[0064] In some embodiments, samples are taken monthly. In some embodiments, samples are taken every other month. In some embodiments, samples are taken every three months. In some embodiments, samples are taken every four months. In some embodiments, samples are taken every six months. In some embodiments, samples are taken annually. In some embodiments, samples are taken twice a year. In some embodiments, samples are taken every two years. Without wishing to be bound by theory, it is hypothesized that Tutivia can predict the risk of acute rejection occurring approximately 30 days before and after biopsy.

[0065] In another aspect, provided herein is a method for identifying the risk of a renal allograft recipient experiencing allograft rejection, comprising transcriptome sequencing performed with GOCAR as described in Example 6 below. In some embodiments, a first selection of candidate genes is obtained using differential gene expression analysis using weighted gene co-expression network analysis (WCGNA) (e.g., Langfelder et al., 2008. BMC Bioinformatics. 9, 599, incorporated herein by reference in its entirety) and DEseq2 (e.g., Love, et al., 2014 Genome Biol. 15, 550, incorporated herein by reference in its entirety), and is supplemented with genes from a previous study based on the same cohort (see Zhang et al., J Am Soc Nephrol 30(8):1481-1494, 2019, incorporated herein by reference in its entirety), as well as 263 genes identified in a meta-analysis across different cohorts. In such embodiments, the initial gene set can be further refined by selecting genes most associated with the outcome using boruta_py, a Python implementation of the Boruta feature selection algorithm (see Kursa et al., 2010. J. Stat. Softw. 36, 1-13, incorporated herein by reference in its entirety). In some embodiments, a logistic regression model is built using Optuna for hyperparameter optimization with 5-fold cross-validation across the parameter search (see Akiba et al., 2019. Doi:10.48550 / arXiv.1907.10902, incorporated herein by reference in its entirety).

[0066] A non-limiting example of the use of gene signature sets in predicting the risk of acute rejection is described below using a 17-gene signature set. In some embodiments, the methods disclosed herein comprise the following four steps: 1) Training Set: In a group of kidney transplant patients for whom outcome is known based on the histopathological phenotype of the renal biopsy, blood samples are collected on or around the date of the for-cause or protocol-based renal biopsy. The training set contains well-characterized relevant data, including demographics, relevant clinical data, medications and dosages, and histopathological results. Gene expression levels in the training set are used to derive a gene signature that comprises the algorithm for calculating the risk score of the test. 2) Gene Expression Measurement: The expression levels of the 17 genes in blood samples from kidney transplant patients in the training set are measured using any of several well-known techniques. Expression measurements using RNASeq, TREx, NanoString, microarray, or qPCR technologies are described in Examples 2, 3, and 4 below. Expression levels are expressed differently depending on the technology applied. For example, TREx uses the number of sequence reads mapped to a gene; qPCR uses the CT (threshold cycle) value; and NanoString uses the number of transcripts. 3) Establishment of acute rejection risk score and cutoff value: Differential expression analysis is performed to calculate P-values ​​for gene features of interest at expression levels and read lengths. P-values ​​reflect gene features that may have significant implications for the outcome of histopathologically defined acute rejection. Subset analysis and normalization are used to support the selection of the final gene set and establish the final risk score algorithm. 4) Once the final gene set and algorithm are defined by the training set, an independent validation set of kidney transplant patients is tested to determine the performance of the gene set and algorithm in an independent population. The results of the validation set are used to establish the clinical effectiveness of the gene set and algorithm. Based on the risk score, predictive statistics are obtained, such as the predicted area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the true positive rate versus false positive rate at various threshold settings. Using ROC analysis, the area under the curve, sensitivity / specificity, positive predictive value (PPV), and negative predictive value (NPV) can be calculated to determine the cutoff or optimal model and measure overall predictive accuracy. An optimal risk score cutoff that best distinguishes between high and low risk of acute rejection is established. Two distinct groups are expected to be defined by distinct cutoffs: If a patient belongs to the high-risk group, the biopsy is likely to reveal acute rejection, and the test result is interpreted as positive. If a patient belongs to the low-risk group, the biopsy is unlikely to reveal acute rejection, and the test is interpreted as negative. 5) Clinical Trial: The clinical laboratory measures the expression levels of the gene signature set in new patients with unknown acute rejection risk using the same technology as used for the validation set. A risk score is calculated and compared to the cutpoints to determine the acute rejection risk score classification. The clinical laboratory sends the test results to the ordering clinician.

[0067] The expression levels and / or reference expression levels can be stored in a suitable and secure data storage medium (e.g., a database), which can be interfaced with other suitable relevant systems, such as a patient billing system, a laboratory freezer inventory system, or a laboratory information system.

[0068] "Recording" refers to a process of storing information on a computer-readable medium, using any method known in the art. Any convenient data storage structure can be selected based on the means for accessing the stored information. A variety of data processor programs and formats can be used for storage, such as word processing text, files, database formats, etc.

[0069] 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 disclosure. One of ordinary skill in the art will readily recognize that any number of available computer-based systems are suitable for use with the present disclosure. Data storage means can include any article of manufacture containing a record of current information as described above, or memory access means capable of accessing such an article of manufacture.

[0070] kit In another aspect, the disclosure provides a kit for identifying renal allograft recipients at risk for acute rejection, comprising, in one or more separate containers, primer pairs for a gene signature set: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24, a buffer, a housekeeping gene panel, primers for the housekeeping gene panel, a positive control, a negative control, and instructions for use.

[0071] In certain embodiments, a kit for determining the risk of the presence of acute rejection in a renal allograft recipient is provided.

[0072] The kit may include primers for the 17-member gene signature set, optionally a housekeeping gene panel for the TREx and NanoString assays (e.g., as described in Example 6), primers for housekeeping genes for the qPCR assays, and control probes.

[0073] The kit may further include one or more RNA extraction reagents and / or cDNA synthesis reagents. In other embodiments, the kit may include one or more containers into which the biological agents are placed, preferably appropriately dispensed. The kit may also include printed instructions on how to use the materials of the kit.

[0074] The components of the kit may be packaged either in aqueous media or in lyophilized form. The kit may also include one or more pharmaceutically acceptable excipients, diluents, and / or carriers. Non-limiting examples of pharmaceutically acceptable excipients, diluents, and / or carriers include RNAase-free water, distilled water, buffered water, saline, PBS, reaction buffer, labeling buffer, washing buffer, and hybridization buffer.

[0075] The kits of the present disclosure can take a variety of forms. Typically, the kits include reagents suitable for measuring the expression levels of a set of genes (e.g., as disclosed herein) in a sample. Optionally, the kits may also include one or more control samples. Additionally, the kits optionally include written information providing a reference value (e.g., a predetermined value) such that a comparison of the subject's gene expression level with the reference value (predetermined value) is indicative of a clinical condition. [Example]

[0076] The present invention is further described below in examples which are intended to further illustrate the invention without limiting the scope of the invention. Example 1: RNA sequencing assay: Identification of a 17-gene set and its application for AR prediction. The RNA sequencing assay kit includes: 1)Illumina TruSeq mRNA Library Prep Kit 2)TruSeq RNA Single Indexes Set 3) Promega's Maxwell SimplyRNA Kit for high-quality total RNA extraction

[0077] RNA sequencing and data processing methods: Total RNA was extracted from whole blood collected from kidney transplant recipients within 6 months of transplantation using the Maxwell SimplyRNA Kit. Coding transcriptome cDNA libraries were generated using the Illumina TruSeq mRNA Library Prep Kit. Indexed libraries were sequenced on an Illumina NextSeq2000 or NextSeq550Dx sequencer. First, high-quality reads were trimmed, rRNA and HBB reads were excluded, and the remaining reads were aligned to a human reference database. The resulting transcripts were counted as normalized expression levels. These normalized count matrices were then used to calculate an acute rejection risk score using a 17-gene signature algorithm. Results that passed QC were converted to a 0-100 scale and reported as the final acute rejection risk score. The final acute rejection risk score was further classified into high and low acute rejection risk categories based on predefined cutoff points. Example 2: Target RNA Expression (TREx) Assay 1) Custom Assay Kit (primer sets and reagents for the 17-gene panel and housekeeping gene panel (Example 6)) 2) Illumina® TruSeq® RNA Sample Preparation Kit v2 3)TruSeq RNA Single Indexes Set 4) QIAGEN RNeasy® Kit for high-quality total RNA extraction

[0078] Targeted Expression TREx Experiments: Total RNA was extracted using the QIAGEN RNeasy® kit. Sequencing libraries were generated using the Illumina® TruSeq® RNA Sample Preparation Kit v2 according to the manufacturer's protocol. Briefly, poly(A)-containing mRNA was first purified from total RNA and fragmented. First-strand cDNA synthesis was performed using random hexamer primers and reverse transcriptase, followed by second-strand cDNA synthesis. After an end-repair process that converts overhangs to blunt cDNA ends, multiple index adapters were added to the ends of the double-stranded cDNA. PCR was performed to enrich targets using primer pairs specific to a gene panel and housekeeping genes. The indexed libraries were then validated, normalized, and pooled for sequencing on a NextSeq2000 sequencer.

[0079] TREx Data Processing: Raw RNA-seq data generated by a NextSeq sequencer (Illumina) are processed as follows: High-quality reads are first aligned to multiple human reference databases, such as the hg19 human genome, exon, splice junction, and contamination databases, using the BWA1 alignment algorithm. After filtering reads mapped to the contamination database, uniquely aligned reads with up to two mismatches to the amplicon (i.e., PCR product from paired primers) region of interest are counted as the expression level of the corresponding gene, and further normalized based on the expression of housekeeping genes. Example 3: NanoString Assay 1) Custom CodeSet (a barcoded probe set for a 17-gene panel, a housekeeping gene panel (Example 6), and a negative control provided by NanoString). 2) nCounter® Master Kit, including nCounter Cartridge, nCounter Plate Pack, and nCounter Prep Pack 3) QIAGEN RNeasy® Kit for high-quality total RNA extraction

[0080] NanoString Experiment: Total RNA is extracted using the QIAGEN RNeasy® Kit according to the manufacturer's protocol. Barcode probes are annealed to the total RNA in solution at 65°C using the master kit. Capture probes capture the target, which is immobilized for data collection. After hybridization, the sample is transferred to the nCounter PreStation, and the probe / target is immobilized on the nCounter Cartridge. The probes are then counted by the nCounter Digital Analyzer.

[0081] mRNA transcriptomics data analysis The raw count data from the NanoString analyzer is processed as follows: The raw count data is first normalized to the number of housekeeping genes, and mRNAs with counts lower than the median + 3 standard deviations of the negative control counts are excluded. Because the use of different reagent lots introduces variability in the data, the counts of each mRNA for different reagent lots are calibrated by multiplying them by a factor that is the ratio of the average counts of samples in the different reagent lots. The calibrated counts from various experimental batches are further adjusted using the ComBat package. Example 4: qPCR assay 1) Primer container (17 tubes containing one qPCR assay for each of the 17 genes, including the 17-gene panel and two housekeeping genes (ACTB and GAPDH) and a control probe (18S ribosomal RNA). These assays were obtained from Life Technologies. 2) TaqMan® Universal Master Mix II: qPCR reaction reagent 3) TaqMan® Array 96-well plates. 4) Agilent AffinityScript qPCR cDNA Synthesis Kit: Highest efficiency in RNA-to-cDNA conversion and fully optimized for real-time quantitative PCR (qPCR) applications.

[0082] Total RNA was extracted from allograft biopsy samples using an AllPrep Kit (QIAGEN, Valencia, CA, USA). cDNA was synthesized using an AffinityScript RT Kit (Agilent, Santa Clara, CA, USA) with oligo-dT primers. TaqMan qPCR assays for the 17-gene signature set, two housekeeping genes (ACTB, GAPDH), and 18S ribosomal RNA were purchased from ABI Life Technology (Grand Island, NY). qPCR laboratory processing was performed on the cDNA using the TaqMan® Universal Mix, and PCR reactions were monitored and acquired using the system. Samples were run in triplicate. Threshold cycle (CT) values ​​were also generated for the predictor gene set and the two housekeeping genes. The ΔCT value for each gene was calculated by subtracting the mean CT value of the housekeeping genes from the CT value of each gene. Example 5: RNA transcriptome sequencing assay kit: Identification of a 17-gene set and its application for AR prediction. The RNA sequencing assay kit includes: 1) Illumina® RNA Prep with Enrichment (L) Kit 2) IDT® for Illumina® RNA UD Indexes Set C, Ligation 3) Illumina NextSeq 1000 / 2000 P2 Reagents (200 cycles) 3) QIAGEN PAXgene Blood RNA Kit(50)

[0083] RNA sequencing and data processing methods: Total RNA was extracted from whole blood collected from kidney transplant recipients within 6 months of transplantation using the QIAGEN PAXgene Blood RNA Kit. A coding transcriptome cDNA library was generated using the Illumina® RNA Prep with Enrichment Kit. Indexed libraries were sequenced on an Illumina NextSeq2000 or NextSeq550Dx sequencer. High-quality reads were first trimmed, rRNA and HBB reads were excluded, and the remaining reads were aligned to a human reference database. The resulting transcripts were counted as normalized expression levels. These normalized count matrices were then used to calculate an acute rejection risk score using a 17-gene signature algorithm. Results that passed QC were converted to a 0-100 scale and reported as the final acute rejection risk score. The final acute rejection risk score was further classified into high and low acute rejection risk categories based on predefined cutoff points. Example 6: RNA transcriptome sequencing assay: Identification of a 17-gene set and its application for AR prediction. This example presents data from a non-randomized, prospective, observational international study (NCT04727788) to validate the ability of genomic testing to predict the risk of renal clinical and subclinical AR and chronic allograft injury.

[0084] method Participants and study design Thirteen research sites adhering to the Declaration of Helsinki were included in the validation set. Participants were enrolled in this study from March 2021 to January 2023. The study was approved by Advarra's IRB (Pro00049177). The Australian Chronic Allograft Failure (AUSCAD) study subjects were also included. Participants were living-donor or deceased-donor kidney transplant recipients aged 18 to 80 years who were able to provide signed informed consent. The CONSORT diagram is shown in Figure 1. Recipients of multiorgan transplants (excluding kidney and pancreas transplants), patients participating in therapeutic clinical trials for transplant rejection, patients with active HIV or hepatitis C infection, and pregnant patients were excluded. This observational study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.

[0085] Study Procedures and Specimen Collection Study participants were evaluated at pretransplant visits, and detailed demographic, medical, and transplant history, including donor clinical characteristics, was obtained. After transplantation, participants were asked to return at 1, 3, 6, 12, and 24 months posttransplant to provide medication updates and collect laboratory, clinical, and pathological data. At 3 and 12 months, core biopsies of the allograft were performed according to institutional protocols, either as protocol-mandated or standard surveillance procedures. Additionally, unscheduled visits for clinically indicated biopsies according to institutional protocols were included. Blood samples were collected at all posttransplant visits. Peripheral blood samples were collected into two PAXgene® tubes at all protocol and unscheduled visits. At protocol biopsy visits, blood was collected within an average of 0 days of the associated biopsy date. For 23 patients, blood was collected within 31 days of biopsy, following institutional protocols for conducting research blood tests after biopsy due to COVID-19 and related visit restrictions.

[0086] Given that the goal was to use blood RNA signatures to predict biopsy histology and risk of rejection, the timing of kidney biopsy and blood collection is important for the current correlation study and subsequent evaluation of clinical utility. Blood samples collected from allograft recipients at time points corresponding to the biopsy were sent to the laboratory for testing.

[0087] All diagnostic renal biopsies were first evaluated by the respective local pathologist and then sent digitally (including all hematoxylin-and-eosin (H&E) and special stains, and C4d immunohistochemistry, when available) to the central pathology laboratory. Glass slides were sent to the central pathology laboratory for approximately 5% of patients. A secondary central pathology data review was independently obtained for approximately 15% of patients. The use of secondary review was part of the initial study plan to adjudicate difficult discrepant cases, such as borderline histology, C4d interpretation, or use of Banff criteria other than the 2019 standard. Especially for borderline classifications, both the local pathology diagnosis and the central pathology diagnosis were evaluated, taking into account the subjective, semiquantitative nature of the histologic (immunocytologic / morphologic) phenotype. Therefore, there was built-in room for an additional pathologist to reach consensus in case of discrepancies (see Friedewald et al., Am J Transplant. 2019;19(1):98-109). All biopsies were evaluated according to the 2019 Banff classification criteria (Loupy et al. Am J Transplant. 2020;20(9)).

[0088] HLA typing was performed according to the protocols of each participating medical institution and / or organ procurement organization. HLA typing results are reported in the study and adjusted to assess the number of relevant discrepancies. H&E, periodic acid-Schiff (PAS), and immunohistochemistry for C4d and SV40 for polyomavirus-associated nephropathy (PVAN) were performed via digital images or stained slides using standard diagnostic criteria for acute and chronic rejection, histopathological features of calcineurin inhibitor toxicity, and other conditions potentially affecting the allograft. Acute cellular and antibody-mediated renal rejection were determined using the 2019 Banff classification criteria (Friedewald 2019, see above), while chronic injury was diagnosed as inflammation within the IFTA area and scored according to the Chronic Allograft Damage Index (CADI) and the 2019 Banff guidance. Chronic active (CA) ABMR was defined according to the Banff system criteria (Friedewald 2019, see above). To reduce inherent bias, the study staff, laboratories, central pathology laboratory, and clinician investigators were blinded to the results.

[0089] Primary Objectives and Study Endpoints The primary objective was to validate the prognostic performance of a peripheral blood gene expression signature ("Tutivia") to predict risk of acute rejection through correlation with histopathology of surveillance or cause-of-care kidney biopsies. The primary outcome was evidence of clinical or subclinical rejection on kidney biopsy histopathology within 6 months post-transplant.

[0090] 17-gene signature analysis Total RNA was extracted from peripheral blood using Promega's Maxwell SimplyRNA Kit. Indexed transcriptome cDNA libraries were generated using the Illumina Stranded mRNA Library Prep Ligation Kit according to the manufacturer's instructions. The indexed libraries were sequenced on an Illumina NextSeq2000. High-quality reads were first trimmed by removing rRNA and HBB reads and then aligned to the human reference genome database. Resulting counts were normalized before calculating the acute rejection risk score using a predefined 17-gene algorithm. All data processing was performed using a validated data processing and prediction pipeline. Results that passed predefined quality control (QC) criteria were converted to a 0-100 scale and reported as the final acute rejection risk score. The process from sample receipt to Tutivia risk score generation is detailed in Figure 2.

[0091] The Tutivia algorithm incorporates quantitative measures of normalized individual gene transcription, which are differentially weighted and assigned values ​​to calculate a final risk score. The final Tutivia 17-gene algorithm was derived from blood samples from the GoCar (Zhang et al., J Am Soc Nephrol. 2019;30(8):1481-1494) cohort, which served as a training set. The GoCar samples were resequenced as defined in the Methods section above, confirming the original findings. A novel, unbiased, unsupervised bioinformatics discovery and exploration process was employed on over 11,000 genes, resulting in the current 17-gene signature. This test development process identified only two genes from the original signature (Annexin A5 and TSC22D1) (Zhang, 2019, see above), further establishing the uniqueness of the Tutivia gene set and algorithm. A complete list of the 17 genes in the Tutivia gene signature, including ensemble ID / name, assumed role, and associated references, is provided in Table 1. Each citation listed in Table 1 is incorporated herein by reference in its entirety. [Table 1-1] [Table 1-2] [Table 1-3] [Table 1-4]

[0092] Of the 17 genes, seven are involved in maintaining renal function (including cell division and metabolism), four are linked to immune pathways such as antigen processing, apoptosis, and T- and B-cell activation, and six are part of various cytokine cascades that influence macrophage, neutrophil, and NK cell activation, as well as both antibody- and T-cell-based rejection and cytolysis. Some of these genes are directly involved in the dynamic processing of RNA and protein status within all cell types, which directly relates to immune pathways seen at the "static" level in biopsies. The final acute rejection risk score was further classified into high- or low-AR risk categories based on a predefined score cutoff of 50.

[0093] statistical analysis Characteristics of study participants with and without rejection were compared using the Wilcoxon rank-sum test (for continuous values) or the chi-square test (for categorical values). Tutivia's ability to predict rejection was assessed by the area under the receiver operating characteristic curve (AUROC). The primary analysis was a comparison of Tutivia with a pre-specified clinical benchmark model (creatinine at biopsy) (see Gielis et al. Nephrol Dial Transplant. 2020;35(4):714-721). The secondary analysis combined the clinical model using Tutiva with serum creatinine at biopsy using AUROC. Of note, the current benchmark model for serum creatinine at biopsy was not applied to subjects with specific adverse events (AEs) (i.e., delayed graft function, BK viremia, acute kidney injury, and acute allograft dysfunction) before the culprit biopsy. There was consensus that because AEs were affecting creatinine values, serum creatinine values ​​in these subjects were unreliable (i.e., benchmark values ​​were missing). To reflect this, random values ​​were assigned for serum creatinine in these subjects, achieving a benchmark AUC of 0.5. All AUROC model measures were calculated using bootstrapping with 500 replicates for optimistic correction. Because AUROC reflects discrimination, all covariates were simply modeled as linear, and no variable selection procedures were performed. The primary analysis was a comparison of two statistical prediction models, which was performed using previously developed methods (see Riley et al. Stat Med. 2019;38(7):1276-1296). A sample size of 151 subjects provided 90% power to detect a 5% improvement in Nagelkerke's R-squared for the Tutivia trial relative to the benchmark model predicting clinical and subclinical rejection.

[0094] We used the power method described by Riley et al., which has been shown to be more accurate than the traditional 10 events per predictor rule (Riley et al., BMJ. 2020;368). An online calculator for this calculation is available at https: / / riskcalc.org / samplesize / . All statistical analyses were performed using R Studio version 4.1.3 (R Foundation for Statistical Computing). A two-sided p-value of <0.05 was considered statistically significant.

[0095] result Validation cohort transcript profiles and renal biopsy characteristics The current study is part of an ongoing international, nonrandomized, observational trial aimed at validating a genomic test to predict the risk of clinical and subclinical AR in renal allografts. As shown in Table 2, there were 151 participants from five countries (United States, France, Italy, Spain, and Australia). The mean age at transplant in this cohort was 53 years, the majority were male (64%), and 79% were first-time transplant recipients. The mean time to acute rejection after biopsy was 57 days, and the overall rejection rate was 31% (n = 47). The population self-identified as 72% White and approximately 21% Black (see Bureau, Racial and Ethnic Diversity in the United States: 2010 Census and 2020 Census. Accessed December 9, 2022). Individual patient characteristics were also evaluated for their importance in predicting rejection risk. There were no restrictions on on-site immunosuppressive therapy (patients received ATG / thymoglobulin and steroids (73 patients), interleukin-2 receptor subunit alpha (IL2RA) and steroids (49 patients), no IL2RA and steroids (1 patient), alemtuzumab and steroids (24 patients), steroids only (2 patients), or ATG / thymoglobulin, IL2RA, and steroids (2 patients)). The majority of 151 patients (n = 128, 85%) had blood drawn within 1 month of the biopsy date, and 15% (n = 23) had blood drawn between 1 and 31 days after biopsy. Importantly, all 151 patients received some type of induction therapy; 48% (n = 73) received ATG / thymoglobulin and steroids, 33% (n = 49) received IL2RA and steroids, and 19% (n = 29) received other combination therapy.

[0096] As shown in Table 2, the median donor age was 46 years, with 51 living donors and 100 deceased donors, of which 52 were identified as standard criteria donors (SCD), 17 as extended criteria donors (ECD), and 31 as donors after cardiac death (DCD). Four participants (2.6%) had ABO blood type incompatibility, and 16 (11%) showed positive (>30%) panel reactive antibodies (PRA) for both HLA class I and II at enrollment. Seventy-three patients (48%) had four or more HLA mismatches for A, B, DRB1, and DQB1.

[0097] Patient characteristics The characteristics of the 151 patients are listed in Table 2 below. [Table 2-1] [Table 2-2]

[0098] All subjects underwent at least one surveillance or justification renal biopsy within 6 months after transplantation, which was histologically assessed for rejection by a central pathologist using the 2019 Banff classification criteria (Loupy et al. Am J Transplant. 2020;20(9):2318-2331). One hundred seven (71%) surveillance (protocol) biopsies and 44 (29%) justification (clinically indicated) biopsies were included. A comparison of central and institutional pathology findings is shown in Table 3. [Table 3]

[0099] As shown in Table 3, central pathologists classified approximately 50% more biopsies as showing evidence of rejection than institutional pathologists. Given the subjectivity of the diagnostic process, reviewing all cases by a single expert pathologist ensured a level of consistency in diagnostic interpretation, which is crucial for the correlational study design described in this report. Of the 47 allograft rejections, 20 (42%) were in the surveillance group, with a mean time to rejection of 97.5 days (range, 78–133 days), and 27 (58%) were in the just-cause group, with a mean time to rejection of 21 days (range, 6–175 days). The mean time to rejection by biopsy was 58 days (range, 6–175 days). Of the 47 ARs, 11 were classified as borderline TCMR, 13 as TCMR-IA or higher, 12 as ABMR, and 11 as mixed rejection. Of the 23 patients who had blood drawn after biopsy, 18 (78%) had justified biopsies and 5 had surveillance biopsies. Of these, 8 were classified by the in-center pathologist as rejection, including 7 justified (4 TCMR, 1 ABMR, 1 mixed), and 1 surveillance biopsy (mixed).

[0100] The 31% rejection rate is likely a result of adding surveillance biopsies to justification biopsies and including borderline cases in the rejection group. Supporting this observation, several studies have previously reported high rejection rates of 29% to 46% in surveillance biopsies performed within 6 months after transplantation, including one study in which a protocol biopsy was performed on day 8 after transplantation (Shapiro et al., Am J Transplant. 2001;1(1):47-50; Nankivell et al., Am J Transplant. 2006;6(9):2006-2012; Cippa et al., Clin J Am Soc Nephrol. 2015;10(12):2213-2220; Zhang et al., JCI Insight. 2019;4(11); Crespo et al., Transplantation. 2017;101(9):2102-2110).

[0101] 3.2. Performance of the 17-gene Tutivia assay The 17-gene assay was evaluated using a receiver operating curve (ROC) with an AUC of 0.69 (95% CI: 59.7–78.3), compared with an AUC of 0.51 (95% CI: 42.9–60.0) for a baseline clinical model of creatinine at biopsy (p=0.009). This demonstrated that Tutivia is a continuous predictor for distinguishing between rejection and non-rejection (Figure 3A). It is also noteworthy that even when combined with a baseline clinical model of creatinine at biopsy (see Gielis et al., Nephrol Dial Transplant. 2020;35(4):714–721) (AUC=0.68 (95% CI: 59.2–77.2)), the 17-gene assay remained an independent predictor of transplant risk. Applying a predetermined cutoff value for rejection, setting a score of ≤50 for low risk and >50 for high risk, 40 patients were classified as high-risk (26.5%) and 111 as low-risk (73.5%). Of the 111 low-risk patients, 88 were free of AR, 7 had borderline AR, and 24 of the 40 high-risk patients had AR confirmed by the 2019 Banff classification criteria. The NPV was 79%, PPV was 60%, and odds ratio was 5.74 (Figure 3B, Table 4). Of the 23 patients whose blood was drawn after biopsy (median 15 days), only 8 patients demonstrated acute rejection on in-center pathology; 4 of the 8 (50%) had blood drawn within 10 days. Despite 7 of the 8 patients receiving some form of immunosuppressive therapy on or shortly after biopsy, the Tutivia assay correctly classified all 8 patients as having rejection. This indicates no significant effect on the Tutivia signature (i.e., no change in risk category). [Table 4]

[0102] 3.3. Clinical subgroup analysis In 151 patients, 35 (23%) clinically indicated biopsies were performed within 60 days of transplantation. Of these 35 early biopsies, 24 (69%) demonstrated AR and 20 (83%) had a high-risk Tutivia score, suggesting that Tutivia may play a role as an early predictor of AR. When evaluating Tutivia's performance by type of clinical rejection confirmed by cause-specific biopsy, the PPV was 0.75 (95% CI 0.57-0.87) and the NPV was 0.63 (0.39-0.82) (Table 3). Table 5 shows additional performance indicators, including sensitivity and specificity for both justification and surveillance biopsies (see Friedewald et al., Am J Transplant. 2019;19(1):98-109; Bloom et al., J Am Soc Nephrol. 2017;28(7):2221-2232; Halloran et al., Transplantation. 2022;106(12):2435; Bixler and Kleiboeker, Donor-derived cell-free DNA: clinical applications for the diagnosis of rejection. Published online 2020; Lee et al., Semantic Scholar. Transplant. Published online 2023; and Oellerich et al., Am J Transplant. 2019;19(11):3087-3099).

[0103] Although it is difficult to compare Tutivia with other commercially available tests for predicting allograft rejection (due to assay type, study design, Banff endpoints, and prevalence), Tutivia's PPV of 0.75 and sensitivity of 0.78 were the highest for predicting just-cause rejection among all tests listed in Table 5. Of the other commercially available gene expression tests listed in Table 5, only TruGraf (Friedewald et al., Am J Transplant. 2019;19(1):98-109) is notable for being contraindicated for the first 90 days. TruGraf was designed and validated to eliminate the need for biopsy in quiescent patients, making it completely different from Tutivia. Furthermore, the current version of TruGraf includes 120 genes in its algorithm, none of which overlap with Tutivia. This is not unexpected, given that TruGraf was developed using microarray technology for surveillance-only biopsies from quiescent kidneys with stable renal function as a rule-out test. In contrast, Tutiva uses RNA sequencing and was developed as an "all-comers" test regardless of clinical status. Gene discovery in biomarker development is heavily influenced by the design, training cohort, and clinical definition of rejection. For example, TruGraf classified a tubulitis score of i0 on t2 or t3 as borderline (Park et al., Clin J Am Soc Nephrol. 2021;16(10):1539-1551). This differs from the Banff classification criteria, but Tutivia adheres to the 2019 Banff classification criteria. In contrast, in subclinical acute rejection (i.e., surveillance biopsy), Tutivia had a PPV of 0.25 (95% CI: 0.09-0.53), sensitivity of 0.15 (0.05, 0.36), NPV of 0.82 (95% CI: 0.73-0.89), and specificity of 0.90 (0.81, 0.94). Additional efforts are underway to improve Tutivia's rejection prediction in the surveillance biopsy setting, but in its current form, the test is very good at ruling out rejection in the subclinical setting.Other studies have reported similar challenges (see Table 5) (Friedewald et al., Am J Transplant. 2019;19(1):98-109; Halloran et al., Transplantation. 2022;106(12):2435; Bixler & Kleiboeke, Donor-derived cell-free DNA: clinical applications for the diagnosis of rejection. Published online 2020; Lee et al., Transplant. Published online 2023; and Verissimo Veronese et al., Clin Transplant. 2005;19(4):518-521). [Table 5]

[0104] Renal biopsies of patients with positive SV40 staining were evaluated for PVAN, but current biomarker tests can make it difficult to distinguish BK virus from rejection. Six patients (4%) had biopsies that were positive for PVAN. Compared with the SV40-negative group (including both rejection and non-rejection patients), positive SV40 staining was highly correlated with a low-risk Tutivia outcome (C = 0.78).

[0105] 4. Consideration In this multicenter, international, prospective study, the prognostic performance of Tutivia to predict the risk of acute rejection was validated through correlation with surveillance or clinically indicated renal biopsies as determined by the 2019 Banff guidelines (see Nankivell et al., N Engl J Med. 2010;363(15):1451-1462; Verissimo Veronese et al., Clin Transplant. 2005;19(4):518-521).

[0106] Results showed that 83% of early indication (clinical for-reason) biopsies diagnosed with rejection characterized by the 2019 Banff classification criteria had a high-risk Tutivia score, highlighting its exceptional discrimination in predicting early clinical acute rejection with a PPV of 75% and NPV of 63%. As shown in Table 3, in surveillance biopsies, Tutivia performed very well in ruling out rejection with an NPV of 82% and specificity of 90%, but performed suboptimally in identifying acute rejection with a PPV of 25% and sensitivity of 15%. Furthermore, the generalized acute rejection prediction of an NPV of 79% and a PPV of 60%, regardless of biopsy type, supports broader clinical use of the Tutivia assay. This signature is particularly important because it has been validated in a prospective, correlational, real-world evidence study of "all-comers," providing clinically useful information at both ends of the rejection spectrum.

[0107] The GoCAR study (see Zhang et al., J Am Soc Nephrol. 2019;30(8):1481-1494) provided the first feasibility evidence that peripheral blood transcriptomes can successfully identify individuals at high risk for acute rejection and future graft loss 3 months after transplantation. Serial surveillance biopsies can characterize the current immune response and potentially provide important information to guide clinical treatment decisions, but they are time-consuming, expensive, and invasive, and often carry an increased risk of secondary complications. Therefore, a non-invasive clinical bioassay that provides assessment of the graft without the need for a biopsy would be highly advantageous (see Menon et al., J Am Soc Nephrol. 2017;28(3):735-747; Eikmans et al., Front Med. 2019;6(JAN):358; and Naesens et al., J Am Soc Nephrol. 2018;29(1):24-34).

[0108] These data provide evidence that Tutiva is a useful assay for identifying and potentially monitoring both low- and high-risk kidney transplant recipients in a variety of clinical scenarios. Because the current study design is prospective and includes "all-comers" adult kidney transplant recipients from multiple centers worldwide, the results will not be biased by patient selection criteria or a lack of diversity. Furthermore, the investigators and central pathologists were blinded to all study results to ensure there was no bias in evaluating all patients' kidney biopsies. This study is also unique in that, rather than enrolling only patients who underwent clinically indicated (justified) biopsies after transplantation, the majority of patients underwent planned surveillance biopsies independent of suspected rejection.

[0109] A recent review article details the importance of noninvasive "liquid biopsy" approaches for predicting and monitoring transplant rejection, particularly in kidney transplant patients (Benincasa et al., Hum Immunol. 2023;84(2):89-97). In this article, the authors introduce the field of "transplantomics" and emphasize the need for "network" machine learning approaches to decipher and clarify the application of "omics" to clinical rejection. Tutivia employed machine learning strategies for gene identification and broad applicability to validate a generalizable signature equivalent to a gene expression profile for predicting early AR. The diverse gene panel represents specific cell-based mechanisms of protein processing and receptor biology, directly linked to classical immune regulation, supporting the aforementioned complex "network."

[0110] Furthermore, a fully independent post-hoc evaluation of the Tutivia assay for predicting subacute or clinical rejection was recently published from a prospective, randomized, treatment trial of 21 patients (see Tawhari et al., Front Immunol. 2022;13). This study reported an NPV of 0.92 (95% CI: 0.63-98.60), PPV of 0.70 (95% CI: 0.45-0.87), and AUC of 0.83, further supporting the generalizability of this approach and demonstrating the promising performance of the Tutivia assay as a tool for predicting the likelihood of rejection.

[0111] Serum creatinine has historically been the most widely utilized test for assessing renal function and remains the gold standard in clinical practice as a predictor of acute kidney injury (Aldea et al., Front Pediatr. 2022;10:841). Current research demonstrates that Tutivia, acting as a biomarker, significantly improves serum creatinine measurements in identifying acute kidney rejection. Furthermore, when monitoring and surveillance biopsies are clinically indicated, Tutivia is effective in ruling out rejection. Investigations are ongoing to determine the molecular factors behind diagnosed tissue-based (acute) rejection and their relationship, if any, to long-term graft survival. Overall, Tutiva provides more accurate prediction of acute rejection, representing an improvement over current standard clinical care alone. Another key finding was Tutivia's performance in blood samples collected after biopsy in eight patients. Seven of these patients had justified biopsies, and all were receiving different types and durations of treatment (except for one subclinical biopsy patient). All were identified as high-risk by the Tutivia gene signature. Thus, the limited time frame between biopsy and blood draw, combined with the reported variability in treatment, supports the stability and robustness of the Tutivia gene signature in the acute setting.

[0112] Even more promising is the correlation between patients with BK nephropathy and a low Tutivia score, which allows for differentiation between acute graft rejection and virus-related processes. Due to the lack of a biomarker that identifies BK-associated inflammation, further investigation of patients with BK nephropathy is required to further confirm these initial observations, even though they are clinically interesting and relevant to our field.

[0113] conclusion This study provides clinical validation of Tutivia as a non-invasive and accurate predictor of acute rejection (early rejection) beyond the current standard of care. The implementation of this blood-based transcriptome signature assay will enable clinicians to monitor patient health after kidney transplantation using a non-invasive baseline and prospective continuous approach.

[0114] Although several embodiments of the present invention have been described, it will be understood that various modifications can be made without departing from the spirit and scope of the invention. Accordingly, other embodiments are within the scope of the following claims.

[0115] Further, it should be understood that all values ​​are approximate and are presented for illustrative purposes. Patents, patent applications, publications, product descriptions, and protocols are cited throughout this application, the disclosures of which are incorporated herein by reference for all purposes.

Claims

1. 1. A method for identifying a renal allograft recipient's risk of experiencing allograft rejection, comprising: (a) isolating RNA from a biological specimen from said renal allograft recipient; (b) measuring the expression level of a preselected gene signature set in the recipient sample, wherein the preselected gene set includes the following genes: OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, and SOCS3; (c) normalizing the expression levels of said preselected gene signature set; (d) calculating a risk score from the normalized expression levels of said preselected gene signature set using an empirically derived algorithm; and (e) determining whether the recipient's risk score falls into a high-risk category or a low-risk category for allograft rejection.

2. The algorithm in the calculating step uses the formula: [Equation 1] This is a logistic regression model that uses where t is the risk score and β ０ is the y-intercept characteristic of the logistic regression algorithm, and β １ is the coefficient of the gene, and x １ The method of claim 1, wherein is the expression of the gene.

3. 3. The method of claim 1 or 2, wherein the risk score varies between 0 and 100, with a risk score of 51 to 100 indicating an increased risk of experiencing allograft rejection.

4. 4. The method of any one of claims 1 to 3, wherein the risk score varies between 0 and 100, with a risk score of 0 to 50 indicating a low risk of experiencing allograft rejection.

5. 5. The method of claim 1, wherein the preselected set of genes includes at least nine of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

6. 5. The method of claim 1, wherein the preselected set of genes includes at least 10 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

7. 5. The method of claim 1, wherein the preselected set of genes includes at least 11 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

8. 5. The method of claim 1, wherein the preselected set of genes includes at least 12 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

9. 5. The method of claim 1, wherein the preselected set of genes includes at least 13 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

10. 5. The method of claim 1, wherein the preselected set of genes includes at least 14 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

11. 5. The method of claim 1, wherein the preselected set of genes includes at least 15 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

12. 5. The method of claim 1, wherein the preselected set of genes includes at least 16 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

13. 5. The method of claim 1, wherein the preselected set of genes includes the genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

14. 14. The method of any one of claims 1 to 13, wherein the expression level is measured by a method selected from the group consisting of NanoString™, RNASeq NextSeq™, MiSEQ™, and quantitative polymerase chain reaction (qPCR).

15. 1. A method for selecting a renal allograft recipient for treatment to reduce the risk of renal allograft rejection, comprising: (a) isolating RNA from a blood sample from said renal allograft recipient; (b) measuring the expression level of a preselected gene signature set in the blood of said recipient; (c) normalizing the expression levels of said preselected gene signature set; (d) calculating a risk score from the normalized expression levels of said preselected gene signature set using an empirically derived algorithm; (d) determining whether the recipient is at high or low risk for allograft rejection based on the risk score, which is sent to a clinician as an interpretation; and (e) administering a treatment to prevent allograft rejection if the recipient is at high risk of allograft rejection; The method of claim 1, wherein the preselected set of genes includes the genes OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, and SOCS3.

16. The algorithm in the calculating step uses the formula: [Equation 2] where t is the risk score and β ０ is the y-intercept characteristic of the logistic regression algorithm, and β １ is the coefficient of the gene, and x １ The method of claim 15, wherein is the expression of the gene.

17. The method of claim 15 or 16, wherein the risk score varies between 0 and 100, with a risk score of 51 to 100 indicating an increased risk of experiencing allograft rejection.

18. 18. The method of any one of claims 15 to 17, wherein the risk score varies between 0 and 100, with a risk score of 0 to 50 indicating a low risk of experiencing allograft rejection.

19. 18. The method of any one of claims 15 to 17, wherein the preselected set of genes includes at least nine of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

20. 18. The method of any one of claims 15 to 17, wherein the preselected set of genes includes at least 10 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

21. 18. The method of any one of claims 15 to 17, wherein the preselected set of genes includes at least 11 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

22. 18. The method of any one of claims 15 to 17, wherein the preselected set of genes includes at least 12 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

23. 18. The method of any one of claims 15 to 17, wherein the preselected set of genes includes at least 13 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

24. 18. The method of any one of claims 15 to 17, wherein the preselected set of genes includes at least 14 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

25. 18. The method of any one of claims 15 to 17, wherein the preselected set of genes includes at least 15 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

26. 18. The method of any one of claims 15 to 17, wherein the preselected set of genes includes at least 16 of the following genes: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

27. 18. The method of any one of claims 15 to 17, wherein the preselected set of genes includes the genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

28. 28. The method of any one of claims 15 to 27, wherein the expression level is measured by a method selected from the group consisting of NanoString™, RNASeq NextSeq™, MiSEQ™, and quantitative polymerase chain reaction (qPCR).

29. 29. The method of any one of claims 15 to 28, wherein the treatment for preventing allograft rejection comprises one or more immunosuppressive therapies.

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