Methods and kits for transcriptomics features with empirical-based algorithms relating to presence of acute rejection during renal biopsy in graft recipient's blood

Abstract

Disclosed herein are methods for diagnostic tools to calculate the risk of acute rejection in a renal allograft recipient.

Description

[0001] Cross-reference with 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. Technical Field

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

[0003] Kidney transplantation is the preferred treatment for most patients with end-stage renal disease (ESKD) (Abecassis et al., Clin J Am Soc Nephrol 3:471-480, 2008). However, despite significant improvements in 1-year graft failure over the past decade, approximately 3% of allogeneic kidney transplant recipients return to dialysis or require repeat transplantation each year following transplantation. The rate of late graft failure has remained relatively unchanged since the 1990s (Menon et al., J Am Soc Nephrol 28:735–747, 2017).

[0004] Indications for renal allogeneic transplant rejection primarily rely on monitoring methods such as proteinuria and serum creatinine. These measures may lead to evaluation via biopsy. Currently, the diagnosis of clinical acute rejection requires a renal allogeneic transplant biopsy, most commonly triggered by elevated serum creatinine in the presence of renal injury, or collected as part of a surveillance protocol in cases of subclinical acute rejection. Biomarkers that correlate with or predict the presence of acute rejection are needed to support clinical management in a sensitive and less invasive manner. Clinical acute rejection (AR), which is 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 had evidence of acute rejection in surveillance biopsies during the first 12 months, despite not exhibiting clinical decline in their 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., JCIInsight 4(11), 2019). Chronic allogeneic transplant injury or unexplained interstitial fibrosis and tubular atrophy explain the majority of graft failure cases. This has spurred research aimed at understanding and contrasting the mechanisms responsible for these late events, including alloantibody formation and relapse of the primary disease. Indeed, the lack of long-term improvement despite a remarkable decrease in acute rejection rates has challenged the assumption that acute rejection represents a major determinant of long-term transplant outcomes. However, this hypothesis contrasts sharply with evidence that acute rejection, both clinical and subclinical, with a renal biopsy score of 1 or higher for inflammation / tubulitis negatively impacts long-term graft survival in patients receiving immunosuppressive regimens (Zhang et al., JCI Insight 4(11), 2019; Zhang et al., J Am SocNephrol 30(8):1481-1494, 2019). Therefore, acute rejection (AR) remains a primary target for post-transplant immunosuppressive therapy. Data on the impact of subclinical rejection and borderline changes on transplant outcomes are contradictory, and diagnoses are influenced by subjective reporting. A growing body of evidence suggests that subclinical inflammation negatively impacts allogeneic transplantation, along with 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 patients receive standardized immunosuppression protocols, resulting in some individuals being exposed to too much or too little immunosuppression, leading to complications as a result. Early identification of individuals at the highest or lowest risk of acute rejection could allow for more targeted therapies designed 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 transcriptomics signatures that can be used to identify individuals at risk of acute rejection. The rigor of NGS assays allows for the production of performance signatures, including accuracy and precision, which better inform the medical management of kidney transplant patients in a more personalized and predictive manner while forming a complete clinical continuum.

[0007] Tests for kidney allogeneic transplant rejection, such as serum creatinine or proteinuria, may be insensitive and are late indicators of damage, rising as a warning sign of rejection but not entirely predictive. Such tests may involve or result in obtaining a biopsy sample from the patient. There is a need in this field for improved tests that do not require invasive biopsies and are better predictive of the risk of allogeneic transplant rejection. Summary of the Invention

[0008] In one aspect, this paper provides a method for identifying the risk of allogeneic transplant rejection in kidney allogeneic transplant recipients, comprising the steps of: (a) isolating RNA from a biological sample from a kidney allogeneic transplant recipient; (b) determining the expression levels of a preselected set of gene signatures in the recipient's sample; wherein the preselected set of gene signatures includes genes OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, and SOCS3; (c) normalizing the expression levels of the preselected gene signatures; (d) calculating a risk score from the normalized expression levels of the preselected gene signatures using an empirically derived algorithm; and (e) determining whether the recipient's risk score belongs to a high-risk or low-risk category of allogeneic transplant rejection.

[0009] In some implementations, the algorithm in the calculation step is a logistic regression model, which utilizes the following formula: Where t is the risk score, β0 is the y-intercept feature of the logistic regression algorithm, β1 is the gene coefficient, and x1 is the gene expression, to determine the probability of allogeneic transplant rejection.

[0010] In some implementations, the risk score varies between 0 and 100, with a risk score of 51-100 indicating a high risk of experiencing allogeneic transplant rejection. In some implementations, the risk score varies between 0 and 100, with a risk score of 0-50 indicating a low risk of experiencing allogeneic transplant rejection.

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

[0012] In some implementations, the expression level is selected from NanoString. TM RNASeq NextSeq TM MiSEQ TM The method was determined using quantitative polymerase chain reaction (qPCR).

[0013] In another aspect, this paper provides a method for selecting kidney allogeneic transplant recipients for treatment to reduce the risk of kidney allogeneic transplant rejection, comprising: (a) isolating RNA from a blood sample from a kidney allogeneic transplant recipient; (b) determining the expression levels of a preselected set of gene signatures in the recipient's blood; (c) normalizing the expression levels of the preselected gene signatures; (d) calculating a risk score from the normalized expression levels of the preselected gene signatures using an empirically derived algorithm; (e) determining whether the recipient is at high or low risk of allogeneic transplant rejection based on the risk score delivered to a clinician as an interpretation; and (e) if the recipient is at high risk of allogeneic transplant rejection, administering treatment to prevent allogeneic transplant rejection, wherein the preselected gene set includes genes OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, and SOCS3.

[0014] In some implementations, the calculation step is a logistic regression model, which utilizes the following formula: Where t is the risk score, β0 is the y-intercept feature of the logistic regression algorithm, β1 is the gene coefficient, and x1 is the gene expression, to determine the probability of allogeneic transplant rejection.

[0015] In some implementations, the risk score varies between 0 and 100, with a risk score of 51-100 indicating a high risk of experiencing allogeneic transplant rejection. In some implementations, the risk score varies between 0 and 100, with a risk score of 0-50 indicating a low risk of experiencing allogeneic transplant rejection.

[0016] In some embodiments, the preselected gene set includes at least nine 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 preselected gene set includes at least ten 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 preselected gene set includes at least 11 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 preselected gene set includes at least 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 preselected gene set includes at least 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 preselected gene set includes at least 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 implementations, the preselected gene set includes at least 15 of the 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 gene set includes at least 16 genes selected from NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

[0017] In some implementations, the expression level is selected from NanoString. TM RNASeq NextSEQ TM MiSEQ TM The method was determined using quantitative polymerase chain reaction (qPCR).

[0018] In some implementations, treatment to prevent allogeneic transplant rejection includes one or more immunosuppressive therapies. Attached Figure Description

[0019] Figure 1 A CONSORT diagram illustrating the study enrollment of the clinical trial described in the examples is shown.

[0020] Figure 2 This diagram illustrates the process from sample receipt to risk score generation. 1) Order the test, collect a blood sample, and send it to the laboratory. 2) Isolate RNA; prepare a cDNA library and perform RNASeq. 3) Sequencing data is uploaded, and QC is assessed; files are entered into pipelines and proprietary algorithms to process the data and generate test results. 4) The laboratory supervisor reviews the assay and patient QC to approve the release of results. Abbreviation: QC, Quality Control Figure 3A and 3B The clinical performance of the NGS17-gene test was shown compared to the clinical model. Figure 3A The clinical performance of the next-generation sequencing test (solid line) is superior to that of the clinical model (creatinine at biopsy, dashed line), as confirmed by AUC, and Figure 3B The 50-threshold displayed by the application identified patients most likely to have 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 Implementation

[0021] definition According to this disclosure, conventional molecular biology, proteomics, microbiology, recombinant DNA, immunology, cell biology, and other related techniques within the scope of this art can be employed.

[0022] As used herein, the “expression level” of 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), for example, quantitative real-time PCR, “qRT-PCR”, RNA-seq, microarrays, directed gene expression sequencing (TRex), NanoString analysis, etc.

[0023] As used herein, "determine expression level," "detect expression level," or "determine expression level" in, for example, "determine gene expression level," specifies the amount of mRNA present in a sample, and may or may not refer to normalized quantification. The detection of specific mRNA expression can be achieved using any method known in the art as described herein. Generally, mRNA detection methods include sequence-specific detection, such as by RNASeq or qRT-PCR. mRNA-specific primers and probes can be designed using nucleic acid sequences known in the art.

[0024] "Control" or "non-rejection case" is defined as a sample obtained from an allogeneic transplant recipient with a biopsy that is interpreted as negative for acute rejection, or "control" may be defined as a kit control in which standardized material, such as universal human reference RNA (UHR), is referenced.

[0025] As used herein, “acute rejection” is defined as rejection of a transplant (e.g., allogeneic kidney transplant) that occurs early after transplantation, for example, within 0–6 months post-transplantation. In some embodiments, early rejection occurs within 12 months of transplantation. 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 grade, including borderline.

[0026] Genetic characteristics The gene expression profiles disclosed herein provide a blood-based assay that can be readily performed longitudinally in transplant patients. Kidney transplant patients may be frequently examined by their physicians post-transplant, with the time interval between visits gradually increasing over time. During these clinic visits, patients' renal function and levels of immunosuppression are typically monitored. The gene profiles described herein can be used to monitor a patient's risk of developing acute rejection of a kidney allogeneic transplant. In some embodiments, the gene profiles described herein can be used to predict a patient's risk of developing acute rejection of a kidney allogeneic transplant within approximately 30 days from the time a clinical sample (e.g., biopsy) is obtained.

[0027] The inventors have identified and validated an algorithmic, blood-based 17-gene signature in allogeneic transplant recipients that generates a risk score associated with the presence or absence of acute rejection, as identified by histopathology on a kidney biopsy. The application of this gene set provides information for improving the medical management of kidney transplant recipients in a more personalized manner, particularly regarding immunosuppressive therapy.

[0028] The gene expression profiles disclosed herein can be performed during routine clinical monitoring or in response to clinical indications requiring further investigation. A positive test in the absence of a change in creatinine levels will indicate subclinical inflammation and may lead to an increase in immunosuppression and / or a gradual reduction in immunosuppression or a decision to perform a biopsy. Repeat testing, for example, using the gene signatures described herein, can guide subsequent reductions in immunosuppression. For example, if two subsequent tests are low-risk, the prednisone dose may be reduced by 2.5 or 5 mg, or the target level of tacrolimus will be reduced by 0.5 mg / dL. If the test is high-risk in the presence of increased creatinine, this will indicate clinical acute rejection, such as that confirmed by kidney injury. In such cases, the patient will be treated with high-dose steroids or antilymphocyte agents depending on the individual's overall immune risk and the transplant center's management procedures.

[0029] Risk scores can be calculated from the normalized expression levels of a pre-selected set of gene features using empirically derived algorithms. The algorithm could be a logistic regression model, which utilizes the following formula: Where t is the risk score, β0 is the y-intercept feature of the logistic regression algorithm, and β1 is the coefficient of a particular selected gene as defined in the algorithm, and so on, counting each gene in the feature; similarly, x1 is the specific expression of a gene selected from the patient at the time of blood collection as determined by the testing method. This also continues upwards, where x2 is the expression level of the next gene in the feature (normalized gene count), and so on, until the expression of all 17 genes with all 17 correlation coefficients is included in the calculation of the results.

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

[0031] Genes may be upregulated or downregulated, and the model coefficient β may be positively correlated with acute rejection or negatively correlated with AR.

[0032] The regression model produces a probability score from 0 to 1, which is then transformed (x100) into a risk score from 0 to 100. The weighted cumulative score (r) can be used as a risk score for acute rejection for each patient. The risk score can then be explicitly defined as low, intermediate, or high risk based on a defined cutoff point, which is a predicted value calculated for the patient's risk of experiencing acute rejection across a reporting range of 0 to 100. In some embodiments, a risk score of 51 or higher indicates a high risk of acute rejection. In some embodiments, a risk score of 50 or lower indicates a low risk of acute rejection.

[0033] The preselected gene feature set may include 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 feature 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, this disclosure provides a method for identifying the risk of clinical or subclinical acute rejection and subsequent graft failure in a kidney allogeneic transplant recipient, comprising the steps of: (a) isolating RNA from a blood sample; (b) synthesizing cDNA from the RNA and using it for transcriptome sequencing; (c) determining the expression level of each of 17 genes in a genetic signature set; (d) normalizing the expression counts from the 17 genes and using them in a weighted manner using an empirically derived logistic regression algorithm to calculate a risk score; and (e) determining an interpretation regarding whether the recipient is at high or low risk of acute rejection and subsequent allogeneic graft failure. The genes in the genetic signature set can 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 implementations, the genes in the genetic signature are OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, and SOCS3.

[0035] According to this disclosure, patients who have undergone kidney transplantation can undergo the assays described herein as part of their post-transplant follow-up and monitoring. Methods may include obtaining peripheral blood, extracting RNA, and generating an RNA sequencing library of cDNA. In some embodiments, the assay includes RNA sequencing of the entire transcriptome, including some or all of the 17 specific characteristic 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 of the genes OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, and SOCS33.

[0036] The expression levels of all or some of the 17 genes can be determined, and an acute rejection risk algorithm can be applied to determine an individual patient's risk assessment score. In some implementations, if a patient's score is above a predetermined cutoff point, the patient is classified as high-risk, in which case the patient may be evaluated for immunosuppression that will be managed in the same manner as high-risk patients, such as immunosuppressive drugs like calcium-dependent phosphatase inhibitors (CNIs), avoidance of steroid withdrawal, avoidance of mTOR inhibitors (such as sirolimus / temsirolimus or everolimus), or beracept. In some implementations, if the score is below a predetermined cutoff point, the patient is considered low-risk for AR, in which case the patient may be a candidate for steroid withdrawal or a less aggressive regimen using mTOR inhibitors or beracept.

[0037] Precise cutoffs have been determined based on RNA sequencing of the algorithm training cohort. It is noteworthy that when planning immunosuppressive therapy regimens for patients, assays should be performed against the backdrop of other clinical factors that pre-identify candidates as high-risk or low-risk, such as age, serum creatinine, and the presence of anti-HLA antibodies. In some implementations, a risk score of 51 to 100 indicates a high risk of acute rejection. In some implementations, a risk score of 0–50 indicates a low risk of acute rejection.

[0038] In one aspect, this disclosure relates to methods for the accurate diagnosis of subclinical and clinical rejection and for the accurate identification of allogeneic transplant recipients at risk of subsequent histological and functional decline and at risk of graft failure. When such high-risk allogeneic transplant recipients are identified, this disclosure includes methods for treating such patients. Methods include, but are not limited to, increased administration of immunosuppressive drugs, i.e., calcium-dependent phosphatase inhibitors (CNIs), such as cyclosporine or tacrolimus, or immunosuppressive drugs with less fibrosis, such as mycophenolate mofetil (MMF) and / or sirolimus. The primary class of immunosuppressants is calcium-dependent phosphatase inhibitors (CNIs). Steroids such as prednisone may also be used to treat patients at risk of graft failure or functional decline. Antiproliferative agents such as mycophenolate mofetil, mycophenolate sodium, and azathioprine may also be used in this treatment. Immunosuppression can be achieved using many different drugs, including steroids, targeted antibodies, and CNIs such as tacrolimus.

[0039] This disclosure is based, at least in part, on the identification of gene expression profiles in kidney allogeneic transplant recipients from living or deceased donors, which determines the risk of acute rejection as defined by histopathological phenotypes on kidney biopsies. Without wishing to be bound by theory, it is assumed that gene expression profiles predict both subclinical and clinical acute rejection. This allows clinicians to personalize immunosuppressive regimens, maximizing immunosuppression in those at high risk and minimizing it in those at reduced risk.

[0040] For immunosuppression, individuals at low risk (e.g., patients with a risk score of 0 to 50) may, depending on other immune factors, use reduced doses of MMF, steroid-free regimens, or "weaker" and less frequently used primary immunosuppressants such as rapamycin or sirolimus. Everolimus Or Belasip Treatment. This approach of reducing immunosuppression has been shown to decrease the risk of severe post-transplant infections and malignancies. Those skilled in the art will recognize that these agents are less potent (or weaker) than others because they are associated with a higher risk of early acute rejection.

[0041] "Stronger" immunosuppressants include CNIs, such as tacrolimus ( / AstagrafXL (Astellas Pharma Inc.), Envarsus (Veloxis Pharma Inc.) and Generics and cyclosporine and (Novartis AG) and its generic versions. Individuals at higher risk (e.g., patients with a risk score of 51 to 100) can be treated with this potent immunosuppressant.

[0042] Furthermore, if a gene expression profile identifies an individual as being at risk of acute rejection (e.g., a patient with a risk score of 51 to 100), then that patient may undergo more intensive monitoring of clinical laboratory results or gene expression profiles. In some embodiments, patients are monitored monthly using the methods described herein. In some embodiments, patients are monitored every other month using the methods described herein. In some embodiments, patients are monitored every three months using the methods described herein. In some embodiments, patients are monitored every four months using the methods described herein. In some embodiments, patients are monitored every six months using the methods described herein. In some embodiments, patients are monitored annually using the methods described herein. In some embodiments, patients are monitored twice a year using the methods described herein. In some embodiments, patients are monitored every two years using the methods described herein.

[0043] In some embodiments, this disclosure provides a method for calculating the risk of acute rejection in a kidney allogeneic transplant recipient, comprising the steps of: providing a blood sample from the kidney allogeneic transplant recipient, isolating RNA from the blood sample, synthesizing cDNA from the mRNA, and measuring the expression levels of a set of 17 member genes present in the blood sample using an algorithm. Non-limiting examples of methods for measuring expression levels include RNA-Seq, microarrays, directed RNA expression (TREx) sequencing (Illumina, Inc., San Diego, California), NanoString (… mRNA expression assays (NanoString Technologies, Inc., Seattle, Washington) or qRT-PCR. The results of gene feature set analysis are compared with predetermined cutoff points. These methods are also described in Examples 1-6 below.

[0044] The 17-member gene signature set used to practice the methods disclosed herein may 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, the 17-member gene signature set used to practice 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 implementations, the method disclosed herein analyzes 8, 9, 10, 11, 12, 13, 14, 15, 16 or 17 members from a set of 17 member genetic features.

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

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

[0047] In some implementations, any 10 genes from NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24 are analyzed in the methods described herein. In some implementations, two genes from OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, SOCS3, and NCAPD2, KIF3B, STK24, PARN, DLG5, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24 are analyzed.

[0048] In some implementations, any 11 genes from NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24 are analyzed in the methods described herein. In some implementations, three genes from OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, SOCS3, and NCAPD2, KIF3B, STK24, PARN, DLG5, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24 are analyzed.

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

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

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

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

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

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

[0055] In some embodiments of the methods disclosed herein, it is desirable to detect and quantify mRNAs present in a sample. Detection and quantification of RNA expression can be achieved by any of a variety of methods well known in the art. Using known RNA sequences, specific probes and primers can be designed, as appropriate, for use in the detection methods described below. NanoString, microarrays, RNASeq, or quantitative polymerase chain reaction (qPCR), such as real-time polymerase chain reaction (RT-PCR) or directed RNA sequencing (TREx), can be used in the methods disclosed herein. Nucleic acids, including RNA, and especially mRNA, can be isolated using any suitable technique known in the art. For example, phenol-based extraction is a common method for isolating RNA. Phenol-based reagents contain a combination of denaturants and RNase inhibitors for cell and tissue lysis and subsequent separation of RNA from contaminants. Furthermore, methods such as using TRIZOL... TM Or TRI REAGENT TM These extraction procedures can be used to purify all RNA, large and small, and are efficient methods for isolating total RNA from biological samples containing mRNAs. Extraction procedures such as those using the QIAGEN-ALL prep kit and the Promega Maxwell simply RNA kit are also anticipated.

[0056] In some embodiments, the use of quantitative RT-PCR is desirable. Quantitative RT-PCR is a modification of polymerase chain reaction (qRT) methods for the rapid measurement of nucleic acid quantities. qRT-PCR is commonly used to determine the presence of a gene sequence in a sample and, if present, to determine the copy number or the amount of copy number relative to a reference sequence in the sample. Any PCR method that can determine the expression of nucleic acid molecules, including mRNA, falls within the scope of this disclosure. Several variations of qRT-PCR methods are well known to those skilled in the art. In some embodiments, quantitative RT-PCR can be used... Analysis System (NanoString) Seattle, WA) to determine mRNA expression profiles. (Source: NanoString Technologies) The analysis system provides high sensitivity and accuracy for the simultaneous profiling of hundreds of mRNAs, microRNAs, or DNA targets. In this system, target molecules are detected digitally. The NanoString analysis system uses molecular "barcodes" and single-molecule imaging to detect and count hundreds of unique transcripts in a single reaction. The NanoString analysis protocol does not include any amplification steps.

[0057] In a typical implementation, the central clinical laboratory will determine the expression values ​​and calculate a risk score upon receiving the blood sample and the request from the ordering clinician. The risk score, with interpretation, will be returned to the ordering clinician, who will evaluate the patient's complete clinical background, including the calculated acute rejection risk score, and will utilize this information in the patient's medical management.

[0058] In an alternative implementation, the assay will be performed in a clinical laboratory using the kit as described above, and the results will be computed via a web-based portal with access to bioinformatics pipelines and algorithms, and then returned electronically to the ordering clinician.

[0059] In a specific implementation plan, this paper provides a method for identifying the risk of allogeneic transplant rejection in kidney allogeneic transplant recipients, comprising the following steps: (a) Isolating RNA from biological samples (e.g., blood, tissue, or urine) from the said kidney allogeneic transplant recipient; (b) Synthesize cDNA from the RNA and sequence the cDNA, then determine the expression levels of a preselected set of gene signatures in the recipient sample; wherein the preselected set of gene signatures includes at least the genes OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, and SOCS3. (c) Normalize the expression levels of the preselected gene feature set; (d) Using an empirically derived algorithm, calculate a risk score from the normalized expression levels of the preselected gene feature set; and (e) Determine whether the recipient’s risk score belongs to the high-risk or low-risk category of allogeneic transplant rejection based on predetermined cutpoints.

[0060] In some embodiments, the method further includes step (f) risk scoring of the reporting subject. In some embodiments, the method further includes step (g) determining whether to administer immunosuppressant treatment to the recipient.

[0061] The methods described herein for identifying the risk of allogeneic transplant rejection in kidney allogeneic transplant recipients may include analyzing more than eight 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 methods include 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 implementations, the method includes 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 implementations, the method includes 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 implementations, the method includes 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 implementations, the method includes 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 implementations, the method includes 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 biopsy) can be obtained at an appropriate time after transplantation to determine the expression levels of genes in the genetic signatures provided herein. In some embodiments, samples are obtained at one, two, three, four, five, six, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, or eighteen months after transplantation.

[0063] In some implementations, duplicate samples are obtained to monitor a patient's risk of acute rejection. In other implementations, duplicate samples are obtained to monitor a patient's response to treatment. For example, monitoring the response to treatment can be used when a patient has undergone treatment for a previously identified acute rejection, but the resolution of the rejection is unknown to the patient without a further biopsy. The methods provided herein can be used to determine whether a previously identified acute response has likely resolved. If the patient remains at a high risk of rejection, further or more aggressive treatment may be necessary.

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

[0065] In another aspect, this paper provides a method for identifying the risk of allogeneic transplant rejection in kidney allogeneic transplant recipients, including transcriptomics sequencing in GOCAR as described in Example 6 below. In some embodiments, the initial selection of candidate genes was obtained using weighted co-expression network analysis (WCGNA) (see, e.g., Langfelder et al., 2008. BMC Bioinformatics. 9, 599, which is incorporated herein by reference in its entirety) and differential gene expression analysis using DEseq2 (see, e.g., Love et al., 2014 Genome Biol. 15, 550, which is incorporated herein by reference in its entirety), supplemented by genes from previous studies based on the same cohort (see Zhang et al., J Am Soc Nephrol 30(8):1481-1494, 2019, which is incorporated herein by reference in its entirety), as well as 263 genes identified in meta-analyses across different cohorts. In this implementation, the Python implementation of the Boruta feature selection algorithm, boruta_py (see Kursa et al., 2010. J.Stat.Softw.36, 1-13, which is incorporated herein by reference in its entirety), can be used to further filter the initial gene set by selecting the genes most relevant to the results. In some implementations, a logistic regression model is constructed using Optuna (see Akiba et al., 2019. Doi:10.48550 / arXiv.1907.10902, which is incorporated herein by reference in its entirety) for hyperparameter optimization, which has 5-fold cross-validation throughout the parameter search.

[0066] The following describes non-limiting examples of the use of gene signature sets in predicting the risk of acute rejection, using 17 gene signature sets. In some implementations, the methods disclosed herein include the following four steps: 1) Training Set: A group of kidney transplant patients with known outcomes based on renal biopsy histopathological phenotypes will have blood samples collected on or near the date of a for-cause or protocol-based kidney biopsy. The training set will contain adequately characterized relevant data, including demographic data, relevant clinical data, drug therapies and dosages, and histopathological outcomes. Gene expression levels in the training set will be used to derive the genetic characteristics used in the risk score calculation for the test, including the algorithm.

[0067] 2) Measuring Gene Expression: The expression levels of 17 genes from blood samples of kidney transplant patients in the training set will be measured using any of several well-known techniques. Examples 2, 3, and 4 below describe the use of RNASeq, TREx, NanoString, microarray, or qPCR techniques for measuring expression. Expression levels are expressed differently depending on the technique applied. For example, TREx uses a count of sequence reads located at the gene. qPCR uses CT (Threshold cycle) values, and NanoString uses a count of transcripts.

[0068] 3) Establishing Acute Rejection Risk Score and Cutoff Point: Differential expression analysis will be performed to calculate p-values ​​for gene features with desired expression levels and read lengths. The p-values ​​reflect gene features that may be significant for histopathologically defined acute rejection outcomes. Subset analysis and regularization will be used to support the selection of the final gene set and to set the final risk scoring algorithm.

[0069] 4) Once the final gene set and algorithm are defined using the training set, an independent validation set of kidney transplant patients is examined to determine the performance of the gene set and algorithm on an independent population. The validation set results are used to establish the effectiveness of the gene set + algorithm in clinical performance. Based on risk scores, predictive statistics of the true positive rate versus false positive rate at various threshold settings are obtained, such as the predicted AUC (area under the curve) of the ROC (Receiver Operating Characteristic) curve. ROC analysis can be used to determine the cutoff point or optimal model and measure overall predictive accuracy by calculating the area under the curve, sensitivity / specificity, positive predictive value (PPV), and negative predictive value (NPV). An optimal risk score cutoff point is established, which best distinguishes between high and low risk of acute rejection. Clear cutoff points are expected to exist for entering both groups because if patients are in the high-risk group, they have a high probability of having acute rejection at biopsy time, and the test is interpreted as positive. If patients are in the low-risk group, they have a low probability of having acute rejection at biopsy time, and the test is interpreted as negative.

[0070] 5) Clinical Testing: In a clinical laboratory, the expression levels of a set of genetic features from new patients with unknown acute rejection risk are measured using the same techniques used for the validation set. A risk score is calculated and compared to a cutoff point to determine the acute rejection risk classification. The clinical laboratory will then send the test results to the ordering clinician.

[0071] Expression levels and / or reference expression levels can be stored in a suitable and secure data storage medium (e.g., a database). The database can interface with other appropriate and relevant systems, such as a patient billing system, a laboratory freezer inventory system, or a laboratory information system.

[0072] "Recorded" refers to the process of storing information on a computer-readable medium using any method known in the art. Any convenient data storage structure can be chosen based on the method used to access the stored information. A variety of data processing programs and formats can be used for storage, such as word processing text files, database formats, etc.

[0073] As used herein, "computer-based system" refers to hardware devices, software devices, and data storage devices used for analyzing information contained in this disclosure. Those skilled in the art will readily recognize any number of available computer-based systems suitable for use with respect to this disclosure. Data storage devices may include any article of manufacture containing records of current information as described above, or memory access devices that can access such article of manufacture.

[0074] Reagent test kit In another aspect, this disclosure provides a kit for identifying kidney allogeneic transplant recipients at risk of acute rejection, comprising, in one or more separate containers, primer pairs for the following genetic signatures: NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24, buffer, housekeeping gene panel, primers for the housekeeping gene panel, positive control, negative control, and instructions for use.

[0075] In some implementations, a kit is provided for determining the risk of acute rejection in kidney allogeneic transplant recipients.

[0076] The kit may include primers for a 17-member gene signature set, optional housekeeping gene groups for TREx and NanoString assays (e.g., as described in Example 6), primers for housekeeping genes for qPCR assays, and control probes.

[0077] The kit may further include one or more RNA extraction reagents and / or reagents for cDNA synthesis. In other embodiments, the kit may include one or more containers in which biological agents are placed, preferably appropriately aliquoted. The kit may also include printed instructions for use of the kit materials.

[0078] The kit components may be packaged in aqueous media or 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 RNase-free water, distilled water, buffered water, physiological saline, PBS, reaction buffer, labeling buffer, wash buffer, and hybridization buffer.

[0079] The kits disclosed herein can take many forms. Generally, the kit will include reagents suitable for determining the expression levels of gene sets in a sample (e.g., those disclosed herein). Optionally, the kit may include one or more control samples. Furthermore, in some cases, the kit will include written information providing a reference (e.g., predetermined values), wherein a comparison of gene expression levels in the subject with said reference (predetermined values) indicates a clinical status.

[0080] Example The invention is further described below in the embodiments, which are intended to further describe the invention without limiting its scope.

[0081] Example 1: RNA sequencing assay: Identification of the 17-gene set and its application in predicting AR.

[0082] The RNA sequencing assay kit includes: 1) Illumina TruSeq mRNA library preparation kit (Prep Kit) 2) TruSeq RNA Single Indexes Set 3) Promega Maxwell Simply RNA Kit for extracting high-quality total RNA Methods for RNA sequencing and data processing: Total RNA was extracted from whole blood collected from kidney transplant recipients within the first 6 months post-transplantation using the Maxwell Simply RNA Kit. CDNA libraries encoding transcriptomes were generated using the Illumina TruSeq mRNA library preparation kit. Indexed libraries were sequenced on an Illumina NextSeq 2000 or NextSeq 550Dx sequencer. Reads of good quality were first trimmed, filtering out rRNA and HBB reads, and the remaining reads were aligned to a human reference database. The resulting transcripts were then counted to normalized expression levels. These normalized count matrices were then used to calculate an acute rejection risk score using the 17-gene feature algorithm. The QC results were converted to a 0-100 scale and reported as the final acute rejection risk score. The final acute rejection risk score was further categorized into high or low acute rejection risk categories based on predetermined cutoff points.

[0083] Example 2: Targeted RNA Expression (TREx) Assay 1) Customized assay kit (primer set for 17 gene groups and housekeeping gene groups (Example 6) and reagents) 2) RNA Sample Preparation Kit v2 3) TruSeq RNA Single Index Set 4) QIAGEN for extracting high-quality total RNA Reagent test kit Targeted expression of TREx experiments: QIAGEN will be used The kit extracts total RNA. It will be used... RNA Sample Preparation Kit v2 generates sequencing libraries by following the manufacturer's protocol: In short, firstly, polyadenylated (polyA) mRNA is purified and fragmented from total RNA. First-strand cDNA synthesis is performed using random hexamer primers and reverse transcriptase, followed by second-strand cDNA synthesis. After an end-repair process that converts the protruding ends of the cDNAs to blunt ends, multiple indexing adapters are added to the ends of the double-stranded cDNA. PCR is performed using primer pairs specific to gene groups and housekeeping genes to enrich the targets. Finally, the indexed library is validated, normalized, and merged for sequencing on a NextSeq2000 sequencer.

[0084] TREx data processing: Raw RNA-seq data generated using the NextSeq sequencer (Illumina) will be processed using the following procedure: First, high-quality reads will be aligned against several human reference databases using the BWA1 alignment algorithm, including the hg19 human genome, exons, splicing junctions, and contaminated databases, including ribosomal and mitochondrial RNA sequences. After filtering and locating reads to contaminated databases, reads that uniquely align to the desired amplicon (i.e., PCR product from paired primers) region with a maximum of two mismatches will then be counted as the expression level of the corresponding gene and further normalized based on housekeeping gene expression.

[0085] Example 3: NanoString Measurement 1) Custom CodeSet (17 gene groups, a housekeeping gene group (Example 6), and a barcoded probe set of negative controls provided by NanoString).

[0086] 2) Includes nCounter Cartridge, nCounter Plate Pack, and nCounter Prep Pack Master kit.

[0087] 3) QIAGEN for extracting high-quality total RNA Reagent test kit NanoString Experiment: QIAGEN will be used The kit extracts total RNA following the manufacturer's instructions; barcode probes are annealed with the total RNA in solution at 65°C using the master kit. Capture probes capture the target to be immobilized for data collection. After hybridization, the sample is transferred to the nCounter Pre Station, and the probe / target is immobilized on the nCounter Cartridge. The probes are then counted using the nCounter digital analyzer.

[0088] mRNA transcriptomics data analysis Raw count data from the NanoString analyzer will be processed as follows: First, the raw count data will be normalized relative to the housekeeping gene count, and mRNAs with counts lower than the median plus 3 standard deviations below the negative control count will be filtered out. Due to data variation caused by using different reagent batches, the count for each mRNA from each different reagent batch will be calibrated by multiplying by a factor that accounts for the ratio of the average count of samples from different reagent batches. The calibrated counts from different experimental batches will be further adjusted using the ComBat package.

[0089] Example 4: qPCR assay 1) Primer containers (17 tubes, one qPCR assay per tube for each of the 17 genes, comprising a 17-gene group and two housekeeping genes (ACTB and GAPDH) as well as a control probe (18S ribosomal RNA). These assays were obtained from Life Technologies.

[0090] 2) General primary mixture ( Universal Master Mix II: Reagent for qPCR reactions 3) Array of 96-hole flat plates ( ARRAY 96-WELLPLATE).

[0091] 4) Agilent AffinityScript qPCR cDNA Synthesis Kit: for the highest efficiency in converting RNA to cDNA, and fully optimized for real-time quantitative PCR (qPCR) applications.

[0092] Total RNA will be extracted from allogeneic biopsy samples using the ALLprep kit (QIAGEN-ALLprep kit, Valencia, CA, USA). cDNA will be synthesized using the AffinityScript RT kit (Agilent Inc., Santa Clara, CA) with oligodoxythymidine primers. TaqMan qPCR assays for 17-gene signatures, two housekeeping genes (ACTB, GAPDH), and 18S ribosomal RNA will be purchased from ABI Life Technology (Grand Island, NY). The qPCR laboratory procedure will use… A universal mixture is used to process cDNAs, and the PCR reaction is monitored and obtained using a system. Samples are measured in triplicate. A predicted gene set and threshold cycle (CT) values ​​for two housekeeping genes are generated. The ΔCT value for each gene is calculated by subtracting the average CT value of the housekeeping genes from the CT value of each gene.

[0093] Example 5: RNA transcriptomics sequencing assay kit: Identification of 17-gene set and its application in predicting AR.

[0094] The RNA sequencing assay kit includes: 1) RNA Preparation and Enrichment (Prep with Enrichment), (L) Kit 2) for RNA UD Index Set C ( for RNA UD Indexes SetC), link 3) Illumina NextSeq 1000 / 2000P2 reagent (200 cycles) 3) QIAGEN PAXgene Blood RNA Kit (50) Methods for RNA sequencing and data processing: Total RNA was extracted from whole blood collected from kidney transplant recipients within the first 6 months post-transplantation using the QIAGEN PAXgene Blood RNA Kit. RNA preparation and enrichment kits generated cDNA libraries encoding transcriptomes. Indexed libraries were sequenced on an Illumina NextSeq 2000 or NextSeq 550Dx sequencer. First, reads of good quality were trimmed, rRNA and HBB reads were filtered out, and the remaining reads were aligned to a human reference database. The resulting transcripts were then counted to normalized expression levels. These normalized count matrices were then used to calculate an acute rejection risk score using the 17-gene feature algorithm. The QC results were converted to a 0-100 scale and reported as the final acute rejection risk score. The final acute rejection risk score was further categorized into high or low acute rejection risk categories based on predetermined cutoff points.

[0095] Example 6: RNA transcriptomics sequencing: Identification of the 17-gene set and its application in predicting AR.

[0096] This example demonstrates data from a non-randomized, prospective, observational international study (NCT04727788) to validate the ability of genomic testing to predict the risk of clinical and subclinical renal allogeneic transplantation injury (AR) and chronic allogeneic transplantation damage.

[0097] method Participants and Research Design The validation set included thirteen research sites that followed the Declaration of Helsinki. Participants were enrolled in the study between March 2021 and January 2023. The study was approved by the Advara IRB, Pro00049177. It also included the subjects of the Australian Chronic Allograft Dysfunction (AUSCAD) study. Participants were included if they were living donors or deceased donor kidney transplant recipients aged 18 to ≤80 years and were able to provide signed informed consent. Figure 1 A CONSORT diagram is provided. Recipients of multi-organ transplants who are actively HIV-positive or have hepatitis C+, or who are pregnant, are excluded, as are patients undergoing kidney-pancreas transplants or participating in therapeutic clinical trials involving transplant rejection. The observational studies described follow the reporting guidelines of Strengthening the Reporting of Observational Studies in Epidemiology (STROBE).

[0098] Research procedures and sample collection Study participants were evaluated at their pre-transplantation visit, where detailed demographic, medical, and transplant history, including donor clinical characteristics, was obtained. Post-transplant, participants were required to return at 1, 3, 6, 12, and 24 months for updates on drug therapy and collection of laboratory, clinical, and pathological data. At 3 and 12 months, core biopsies of the allogeneic graft were obtained from treatment protocol mandated or standard surveillance procedures, according to site protocols. This also included unscheduled visits for clinically indicated biopsies, according to local site protocols. Blood samples were collected during all post-transplantation visits. Peripheral blood was collected for two RNA samples at all procedural and unscheduled visits. In tubes. At the time of procedural biopsy visit, blood was collected within a median of 0 days from the date of the relevant biopsy. Blood was obtained from 23 patients within 31 days after the biopsy, partly due to COVID and related visit restrictions, as well as site guidance requirements for obtaining study blood after the biopsy.

[0099] Given the intention to use blood RNA signatures to predict the risk of biopsy histology and rejection, the timing of blood collection and kidney biopsy is important for current relevance studies and subsequent clinical utility evaluation. Blood samples collected from allogeneic transplant recipients at the time points corresponding to the biopsy were sent to the laboratory for testing.

[0100] All diagnostic renal biopsy samples were first evaluated by pathologists at their respective local sites and then sent digitally (including all hematoxylin and eosin stained biopsies (H&E) and any special stains and C4D immunohistochemistry, when available) for central pathological review. Slides from approximately 5% of patients were sent for central pathological review. A second central pathological data review was independently obtained from approximately 15% of patients. The use of the second review was part of an initial study program to adjudicate cases with challenging inconsistencies, including marginal histology, C4D interpretation, or the use of non--2019 BANFF criteria. Local and central pathological diagnoses were evaluated in light of the subjective, semi-quantitative nature of histological (immunocytic / morphological) phenotypic analysis, particularly for marginal classifications. Thus, when inconsistencies exist, it is inherently permissible for another pathologist to reach a consensus (see Friedewald et al., Am J Transplant. 2019; 19(1):98-109). All biopsies were evaluated using the 2019 BANFF criteria (Loupy et al., Am J Transplant. 2020; 20(9)).

[0101] HLA typing was performed according to the protocols of each local site and / or organ procurement organization. Results of HLA typing were reported in the study and coordinated for assessing the number of relevant mismatches. H&E, periodate-Schiff (PAS), and C4d and SV40 immunohistochemistry for polyomavirus-associated nephropathy (PVAN) were performed via digital imaging or stained slides using standard diagnostic criteria for acute and chronic rejection, histopathological features of calcium-dependent phosphatase inhibitor toxicity, and other conditions that may affect allogeneic transplantation. Acute cell and antibody-mediated renal rejection was identified using the 2019 Banff criteria (see Friedewald 2019, above), while chronic injury was diagnosed as inflammation within the IFTA region and scored using the Chronic Allogeneic Transplant Injury Index (CADI) and the Banff 2019 guidelines. Chronic active (CA) ABMR was defined according to the BANFF system criteria (see Friedewald 2019, above). Results were not seen by researchers, laboratory, central pathologists, and clinicians to reduce inherent bias.

[0102] Main objectives and research endpoints The primary objective was to validate the prognostic performance of peripheral blood gene expression characteristics (“Tutivia”) in relation to histopathology from surveillance or for any reason renal biopsy. The primary outcome was evidence of clinical or subclinical rejection on renal biopsy histopathology within 6 months post-transplantation.

[0103] 17-Gene Characterization Analysis Total RNA was extracted from peripheral blood using the Promega Maxwell Simply RNA Kit. Following the manufacturer's instructions, an indexed transcriptome cDNA library was generated using the Illumina Stranded mRNA Library Prep Ligation Kit. The indexed library was sequenced on an Illumina NextSeq2000. Good quality reads were trimmed and rRNA and HBB reads were removed before alignment with the human reference genome database. The 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 passed pre-specified quality control (QC) criteria were converted to a 0-100 scale and reported as the final acute rejection risk score. The process from sample reception to Tutivia risk score generation is described in [details omitted]. Figure 2 The details are as follows.

[0104] The Tutivia algorithm incorporates normalized quantitative measurements of individual gene transcripts, which are differentially weighted and assigned values ​​that can be used to calculate the final risk score. The final Tutivia 17-gene algorithm was derived from blood samples from the GoCar cohort (see Zhang et al. J Am Soc Nephrol. 2019; 30(8):1481-1494), which served as the training set. The GoCar samples were re-sequenced as defined in the Methods section above, confirming the original findings. A novel, unbiased, unsupervised bioinformatics discovery query process was employed on >11,000 genes, resulting in the current 17 gene signatures. During the test development process, only two genes from the original signatures (Annexin A5 and TSC22D1) (see Zhang, 2019, above) were identified, further establishing the uniqueness of the Tutivia gene set and algorithm. A complete list of the 17 genes in the Tutivia gene signatures, including ensemble ID / name, putative role, and relevant references, is shown in Table 1. Each citation listed in Table 1 is incorporated into this paper as a whole for reference.

[0105] Table 1: Genes in RNA characteristics.

[0106] Of the 17 genes, 7 are related to maintaining kidney function (including cell division and metabolism), 4 are related to immune pathways such as antigen processing, T-cell and B-cell apoptosis and activation, and 6 are part of a variety of cytokine cascades affecting macrophage, neutrophil, and NK cell activation, as well as antibody- and T-cell-based rejection and cell lysis. Some of these genes are directly associated with high-level dynamic processing of RNA and protein states across all cell types, which is directly related to immune pathways observed at the 'static' level in biopsies. Based on a predetermined 50-point scoring cutoff, the final acute rejection risk score was further categorized into high or low-AR risk categories.

[0107] Statistical analysis The 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). The ability of Tutivia 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, namely creatinine at biopsy (see Gielis et al., Nephrol Dial Transplant. 2020; 35(4):714-721). The secondary analysis was a combination of the clinical model and AUROC in the case of Tutivia compared with serum creatinine at biopsy. It is noteworthy that the current benchmark model of serum creatinine at biopsy is not applied to subjects who had specific adverse events (AEs) prior to the biopsy for any reason (i.e., delayed graft function, BK viremia, acute kidney injury, and acute allogeneic graft dysfunction). It is generally agreed that these subjects do not have reliable serum creatinine levels (i.e., a lack of benchmark) due to the impact of AEs on creatinine values. To reflect this, random values ​​were attributed to serum creatinine of these subjects to obtain a baseline AUC of 0.5. All AUROC model measurements were calculated using bootstrapping, utilizing 500 replicates to correct for optimism. Given that AUROC reflects discriminability, all covariates were simply modeled as linear, and no variable selection procedure was performed. In cases where the primary analysis was a comparison of two statistical prediction models, previously developed methods (see Riley et al. Stat Med. 2019; 38(7):1276-1296) were used to power the study. The sample size of 151 subjects provided 90% power in predicting clinical and subclinical rejection, detecting a 5% improvement in the R-squared of Nagelkerke's test at Tutivia compared to the baseline model.

[0108] The powering method described by Riley et al. was used. It has been shown to be more accurate than the traditional rule of 10 events per predictor (Riley et al., BMJ. 2020; 368). An online calculator for the calculations is available at https: / / riskcalc.org / samplesize / . All statistical analyses were performed using RStudio, version 4.1.3 (R Foundation for Statistical Computing). A two-sided p < 0.05 was considered statistically significant.

[0109] result Validation cohort transcript profiles and renal biopsy features The current study is part of an ongoing global, non-randomized observational trial to validate genomic tests for predicting clinical and subclinical rejection risk in kidney allogeneic transplantation. As identified in Table 2, there were 151 participants from five countries (United States, France, Italy, Spain, and Australia). The median age at transplantation in the cohort was 53 years, predominantly male (64%), and 79% were first-time transplant recipients. The median time to biopsy for acute rejection was 57 days, with an overall rejection rate of 31% (n = 47). Race within the cohort was self-determined, with 72% being white and nearly 21% being Black (see Bureau, Racial and Ethnic Diversity in the United States: 2010 Census and 2020 Census. Accessed December 9, 2022). The importance of individual patient characteristics in predicting rejection risk was also assessed. There were no restrictions on the site-specific immunosuppressive regimen (patients received ATG / anti-thymoglobulin with steroids (73 patients), interleukin-2 receptor subunit α (IL2RA) with steroids (49 patients), IL2RA without steroids (1 patient), alemtuzumab with steroids (24 patients), steroids only (2 patients), or ATG / anti-thymoglobulin & IL2RA with steroids (2 patients)). The majority of the 151 patients (n=128, 85%) had their blood collected within one month prior to the biopsy date, and 15% (n=23 patients) had their blood collected 1 to 31 days after the biopsy. Importantly, all 151 patients received some form of induction therapy, including 48% (n=73) using ATG / antithymocyte globulin with steroids, 33% (n=49) using IL2RA with steroids, and 19% (n=29) using other combinations.

[0110] As indicated in Table 2, the median donor age was 46 years, with 51 living donors and 100 deceased donors. Of the deceased donors, 52 were identified as standard donors (SCD), 17 as extended standard donors (ECD), and 31 as post-cardiac death donors (DCD). Four participants (2.6%) were ABO incompatible, and 16 (11%) had positive (>30%) population reactive antibodies (PRA) against both HLA class I and II at enrollment. 73 patients (48%) had >4 HLA mismatches at A, B, DRB1, and DQB1.

[0111] Patient characteristics The characteristics of the 151 patients are listed in Table 2 below.

[0112] Table 2: Patient Characteristics Within 6 months post-transplantation, all subjects underwent at least one surveillance or ex post-transplant renal biopsy, which was histologically evaluated for rejection by the central pathologist using the BANFF 2019 criteria (Loupy et al., Am J Transplant. 2020; 20(9):2318-2331); including 107 (71%) surveillance (procedural) and 44 (29%) ex post-transplant (clinically indicated) biopsies. A comparison of the central pathological diagnoses with those of the local pathologist is presented in Table 3.

[0113] Table 3: Biopsy pathology between local and central pathology diagnoses (central pathologist diagnoses based on BANFF 2019 criteria) As shown in Table 3, biopsies demonstrating evidence of rejection were classified by central pathologists approximately 50% more than those by local pathologists. Given the subjectivity of the diagnostic process, utilizing a single expert pathologist to review all cases to provide a degree of consistency in diagnostic interpretation is crucial for the relevant trial design described in this report. Of the 47 allogeneic transplant rejections, 20 (42%) were in the surveillance group, with a median time to rejection of 97.5 days (78–133), while 27 (58%) in the cause-of-care group showed a median time to rejection of 21 days (6–175). The median time to rejection for any biopsy was 58 days (6–175). Of the 47 ARs, 11 were borderline TCMR, 13 were TCMR-IA or higher, 12 were ABMR, and 11 were classified as mixed rejections. Of the 23 patients for whom blood was drawn after biopsy, 18 (78%) had biopsies for cause of care, and 5 were surveillance biopsies. Of these, 8 cases were classified as rejection by local pathology, including 7 cases for various reasons (4 cases by TCMR, 1 case by ABMR, and 1 case by a mixed result) and 1 case by surveillance biopsy (mixed result).

[0114] The 31% rejection rate is most likely the result of adding surveillance biopsies in cases of biopsies with cause and including marginal types in the rejection group. To support these observations, several studies have previously reported high rejection rates ranging from 29% to 46% in surveillance biopsies within 6 months post-transplantation, including one publication with a procedural biopsy performed 8 days post-transplantation (Shapiro et al., Am J Transplant. 2001; 1(1):47-50) (see Shapiro et al., Am J Transplant. 2001; 1(1):47-50; Nankivell et al., Am J Transplant. 2006; 6(9):2006-2012; Cippà et al., Clin J Am Soc Nephrol. 2015; 10(12):2213-2220; Zhang et al., JCIinsight. 2019; 4(11); Crespo et al., Transplantation. 2017; 101(9):2102-2110).

[0115] 3.2.17-Gene Tutivia Assay Performance The 17-gene assay was evaluated using receiver operating characteristic (ROC) curves, comparing it to a clinical model with baseline creatinine at biopsy, which had an AUC of 0.51 (95% CI 42.9–60.0) and an AUC of 0.69 (95% CI 59.7–78.3), p = 0.009. This demonstrates Tutivia as a continuous predictor for distinguishing between rejection and non-rejection. Figure 3A It is also noteworthy that even when combined with a clinical model of baseline 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 remains an independent predictor of transplant risk. Forty patients were classified as high-risk (26.5%) and 111 patients as low-risk (73.5%), using predetermined cutoffs for rejection as low risk ≤50 and high risk >50. Of the 111 low-risk patients, 88 had no AR, 7 had borderline AR, and 24 of the 40 high-risk patients had BANFF 2019-confirmed ARs, translating to 79% NPV and 60% PPV with an odds ratio of 5.74. Figure 3B(See Table 4). Of the 23 patients whose blood was drawn after biopsy (median 15 days), only 8 showed acute rejection by local pathology, and 4 of those 8 (50%) had blood drawn within 10 days. The Tutivia assay correctly classified all 8 as rejection, although 7 of the 8 patients received some form of immunosuppressive therapy on or shortly after the biopsy date, suggesting no significant effect on the Tutivia characteristic (i.e., no change in risk category).

[0116] Table 4: Performance of Tutivia with model cutoff points to classify patients into high-risk and low-risk groups based on correlation with surveillance or renal biopsy for any reason.

[0117] 3.3. Clinical Subgroup Analysis Of the 151 patients, 35 (23%) underwent clinically indicated (etiological) biopsies before 60 days post-transplantation. Of these 35 early biopsies, 24 (69%) showed acute rejection (AR), and 20 (83%) had high-risk Tutivia scores, suggesting the role of Tutivia as an early predictor of AR. Tutivia performance, evaluated according to clinical rejection type, was determined in etiological biopsies to have a PPV of 0.75 (95% CI 0.57–0.87) and an NPV of 0.63 (0.39–0.82) (Table 3). Table 5 provides additional performance metrics, including sensitivity and specificity for both cause-of-care biopsies 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. 2020 online publication; Lee et al., Semantic Scholar. Transplant. 2023 online publication; and Oellerich et al., Am J Transplant. 2019; 19(11):3087-3099).

[0118] While comparing Tutivia with other commercially available tests for predicting allogeneic transplant rejection is challenging (due to assay type, trial design, BANFF endpoint, and prevalence), Tutivia's sensitivity of 0.75 and 0.78 is the highest across all tests listed in Table 5 for predicting rejection for any reason. Notably, the only other commercially available gene expression test listed in Table 5 is TruGraf (Friedewald et al., Am J Transplant. 2019; 19(1):98-109), which is contraindicated in the first 90 days. TruGraf was designed and validated to exclude the need for biopsies in quiescent patients, which is quite different from Tutivia. Furthermore, the current version of TruGraf has 120 genes in its algorithm, none of which overlap with Tutivia. This is not surprising, given that TruGraf was developed using microarray technology on surveillance-only biopsies from quiescent kidneys with stable renal function as an exclusion test. In comparison, Tutivia uses RNA sequencing and has been developed as an 'all-comers' test regardless of clinical status. Gene discovery in biomarker development is highly influenced by design, training cohorts, and the clinical definition of rejection. For example, in TruGraf, tubulitis scores of t2 or t3 with i0 are classified as marginal (Park et al., Clin J Am Soc Nephrol. 2021; 16(10):1539-1551). This differs from the BANFF criteria, while Tutivia is consistent with BANFF 2019. Meanwhile, in subclinical acute rejection (i.e., surveillance biopsy), Tutivia has a PPV of 0.25 (95% CI: 0.09-0.53), a sensitivity of 0.15 (0.05, 0.36), and an NPV of 0.82 (95% CI: 0.73-0.89), with a specificity of 0.90 (0.81, 0.94). Additional efforts are underway to improve the rejection prediction of Tutivia in the context of surveillance biopsy; however, in its current form, the assay performs very well in ruling out rejection in a subclinical context.Similar challenges have also been reported for other tests (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 in 2020; Lee et al., Transplant. Published online in 2023; and Veríssimo Veronese et al., Clin Transplant. 2005; 19(4):518-521).

[0119] Table 5: Comparison of Tutivia with other commercially available tests for predicting allogeneic transplant rejection in kidney transplant recipients Finally, renal biopsies were evaluated for PVAN in those with SV40 staining; BK virus may be difficult to distinguish from rejection using current biomarker tests. Biopsies from 6 patients (4%) were determined to be PVAN positive. SV40+ staining was highly correlated with low-risk Tutivia outcomes compared to the group with negative SV40 (which included both rejected and non-rejected patients) (C = 0.78).

[0120] 4. Discussion In the aforementioned multicenter, international prospective study, the prognostic performance of Tutivia in predicting the risk of acute rejection was validated by its correlation with histopathology of renal biopsies that were monitored or clinically indicated as defined by the BANFF 2019 guidelines (see Nankivell et al., N Engl J Med. 2010; 363(15):1451-1462; Veríssimo Veronese et al., Clin Transplant. 2005; 19(4):518-521).

[0121] The results identified a high-risk Tutivia score for 83% of early, indicated (clinically causative) biopsies diagnosed as rejection as characterized by BANFF 2019, highlighting its exceptional discriminatory power in predicting early clinical acute rejection with a PPV of 75% and an NPV of 63%. For surveillance biopsies, Tutivia performed exceptionally well for ruling out rejection, with an NPV of 82% and a specificity of 90%, compared to a suboptimal performance for identifying acute rejection with a PPV of 25% and a sensitivity of 15%, as shown in Table 3. Furthermore, the combined acute rejection prediction with an NPV of 79% and a PPV of 60% regardless of biopsy type truly supports the broader clinical use of the Tutivia assay. This is particularly important because the aforementioned characteristics were validated in a prospective, relevant real-world evidence study involving all applicants, which provided clinically valid information at both ends of the rejection spectrum.

[0122] The GoCAR study (see Zhang et al., J Am Soc Nephrol. 2019; 30(8):1481-1494) provided initial evidence of the feasibility of using peripheral blood transcripts to successfully identify individuals at high risk of acute rejection and future graft failure 3 months post-transplantation. While serial surveillance biopsies can provide crucial information to characterize the current immune response and guide clinical care decisions, they are time-consuming, expensive, invasive, and often carry an increased risk of secondary complications. Therefore, non-invasive clinical bioassays that provide assessment of the graft without the need for biopsy are highly advantageous (see Menon et al., J Am Soc Nephrol. 2017; 28(3):735-747; Eikmans et al., FrontMed. 2019; 6(JAN):358; and Naesens et al., J Am Soc Nephrol. 2018; 29(1):24-34).

[0123] These data provide evidence that Tutivia is a useful assay for identifying and potentially monitoring both low-risk and high-risk kidney transplant recipients across a variety of clinical situations. The current study design is prospective and includes all applicant adult kidney transplant recipients from multiple sites worldwide, ensuring that the results are not biased by patient selection criteria or lack of diversity. Furthermore, researchers and central pathologists in all studies do not have access to all study results to remove any bias in evaluating kidney biopsies in all patients. The study is also unique in that most patients underwent planned surveillance biopsies independent of suspected rejection, rather than enrolling only patients with clinical indications (for any reason) for a biopsy after transplantation.

[0124] A recent review paper details the importance of non-invasive 'liquid biopsy' methods for predicting and monitoring transplant rejection, particularly for kidney transplant patients (Benincasa et al., Hum Immunol. 2023; 84(2):89-97). In the paper, the authors introduce the field of 'transplantomics', emphasizing the necessity of 'network' machine learning methods for deciphering and providing clarity when introducing 'omics' into clinical rejection. Tutivia has followed machine learning strategies for gene identification and broad applicability to validate generalizable features equivalent to gene expression profiles for predicting early AR. Diverse gene groups represent specific cell-based protein processing and receptor biology mechanisms directly consistent with classical immune regulation supporting the previously mentioned complex 'networks'.

[0125] Furthermore, a recently published post-hoc evaluation of a fully independent Tutivia assay from a prospective randomized treatment trial in 21 patients to predict subacute or clinical rejection (see Tawhari et al., Front Immunol. 2022; 13). The study reported an NPV of 0.92 (95% CI: 0.63–98.60) and a PPV of 0.70 (95% CI: 0.45–0.87) with an AUC of 0.83, further supporting the generalizability of the method and the promising performance of the Tutivia assay as a tool for predicting rejection likelihood.

[0126] Serum creatinine has been the most commonly used test for assessing renal function to date and remains the gold standard in clinical practice as a predictor of acute kidney injury (Aldea et al., Front Pediatr. 2022; 10:841). In the current study, Tutivia showed a significant improvement over serum creatinine measurements in identifying acute kidney rejection when used as a biomarker. Furthermore, Tutivia is effective in ruling out rejection if monitoring and clinically required for biopsy, and studies are underway to determine the molecular drivers behind diagnosed tissue-based (acute) rejection and their relationship (if any) to long-term graft survival. Overall, Tutivia provides a more accurate prediction of acute rejection, representing an improvement over current standard clinical care alone. Another important finding was the performance of Tutivia in blood samples collected after biopsies from eight patients, seven of whom were etiologically linked and all had received different types and durations of treatment (except for one patient with a subclinical biopsy). All were identified as high-risk by Tutivia genetic stigmatization. Therefore, the limited time window from biopsy to blood collection, combined with the reported therapeutic variability, supports the stability and robustness of the Tutivia genetic signature in an acute context.

[0127] Perhaps even more promising is the correlation between patients with BK nephropathy and lower Tutivia scores, which could allow for the differentiation between acute transplant rejection and virus-related processes. While clinically interesting and relevant to the field, these early observations require further confirmation from additional patients with BK nephropathy due to the lack of biomarkers identifying BK-related inflammation.

[0128] in conclusion This study provides clinical validation for Tutivia as an accurate predictor of noninvasive (early) acute rejection, exceeding current treatment standards. The implementation of the blood-based transcriptomics characterization provides clinicians with a noninvasive baseline and a future continuous method for monitoring the health of kidney transplant recipients.

[0129] Several embodiments of the invention have been described. However, it will be understood that various modifications can be made without departing from the spirit and scope of the invention. Therefore, other embodiments are also within the scope of the following claims.

[0130] It is important to understand that all values ​​are approximate and provided for descriptive purposes. Patents, patent applications, publications, product specifications, and regulations are referenced throughout this application, and their disclosures are incorporated herein by reference in their entirety for all purposes.

Claims

1. A method for identifying the risk of a kidney allograft recipient experiencing allograft rejection comprising the steps of: (a) isolating RNA from a biological sample from the kidney allograft recipient; (b) determining the expression levels of a preselected set of genes in the recipient's sample; wherein the preselected set of genes comprises the genes OSM, TSC22D1, ST8SIA1, RTN1, IFITM3, ANXA5, CAPS2, and SOCS3; (c) normalizing the expression levels of the preselected set of genes; (d) using an algorithm derived empirically, calculating a risk score from the normalized expression levels of the preselected set of genes; and (e) determining whether the recipient's risk score belongs to a high risk category or a low risk category of allograft rejection.

2. The method of claim 1, wherein the algorithm in the calculating step is a logistic regression model that utilizes the following formula: t = β0 + β1x1 + β2x2 +... + βnXn to determine the probability of allograft rejection, where t is the risk score, β0 is the y-intercept feature of the logistic regression algorithm, β1 is the coefficient of a gene, and x1 is the expression of the gene.

3. The method of claim 1 or 2, wherein the risk score varies between 0-100, and wherein a risk score of 51-100 indicates a high risk of experiencing allograft rejection.

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

5. The method of any one of claims 1-4, wherein the preselected set of genes comprises at least 9 of the genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

6. The method of any one of claims 1-4, wherein the preselected set of genes comprises at least 10 of the genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

7. The method of any one of claims 1-4, wherein the preselected set of genes comprises at least 11 of the genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. ​ ​ 8. The method of any one of claims 1-4, wherein the preselected gene set comprises at least 12 of genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

9. The method of any one of claims 1-4, wherein the preselected gene set comprises at least 13 of genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

10. The method of any one of claims 1-4, wherein the preselected gene set comprises at least 14 of genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

11. The method of any one of claims 1-4, wherein the preselected gene set comprises at least 15 of genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

12. The method of any one of claims 1-4, wherein the preselected gene set comprises at least 16 of genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

13. The method of any one of claims 1-4, wherein the preselected gene set comprises genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

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

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

16. The method of claim 15, wherein the algorithm in the calculating step is a logistic regression model that utilizes the following equation: to determine the probability of allograft rejection. where t is the risk score, β0is the y-intercept characteristic of the logistic regression algorithm, β1is the coefficient of the gene, and x1is the expression of the gene, 17. The method of claim 15 or 16, wherein the risk score varies between 0-100, and wherein a risk score of 51-100 indicates a high risk of experiencing allograft rejection.

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

19. The method of any one of claims 15-17, wherein the preselected gene set comprises at least 9 of the genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

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

21. The method of any one of claims 15-17, wherein the preselected gene set comprises at least 11 of the genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24. ​ 22. The method of any one of claims 15-17, wherein the preselected gene set comprises at least 12 of genes NCAPD2, OSM, KIF3B, STK24, TSC22D1, ST8SIA1, RTN1, PARN, IFITM3, DLG5, ANXA5, CAPS2, SOCS3, HNRNPAB, VDAC1, HLA-DPA1, and DHCR24.

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

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

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

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

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

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

29. The method of any one of claims 15-28, wherein the treatment to prevent allograft rejection comprises one or immunosuppressive therapy.

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