Biomarkers and uses therefor

Abstract

This disclosure relates generally to biomarkers of allograft dysfunction. More particularly, the present disclosure relates to biomarkers and their use in methods, compositions, apparatuses, devices and kits for determining an indicator that is useful for assessing a likelihood that allograft dysfunction is present or absent in a subject.

Description

BIOMARKERS AND USES THEREFOR1FIELD

[0001] This application claims priority to Australian Provisional Application No. 2024902548 entitled "Biomarkers and uses therefor" filed 15 August 2024, the contents of which are incorporated herein by reference in their entirety.

[0002] This disclosure relates generally to biomarkers of allograft dysfunction. More particularly, the present disclosure relates to biomarkers and their use in methods, compositions, apparatuses, devices and kits for determining an indicator that is useful for assessing a likelihood that allograft dysfunction is present or absent in a subject.BACKGROUND

[0003] Organ transplantation is a crucial therapeutic option for individuals with endstage organ failure, providing a mortality benefit and improved quality of life. Long-term graft survival varies among organs (currently, 82% for kidney transplants, 80% for liver, 59% for lung and 72.5% for heart), but longevity is universally limited by allograft dysfunction, a term that encompasses a broad range of pathologies. Allograft dysfunction can be driven by ischemia reperfusion injury manifesting as delayed graft function (DGF) (or primary non-function), activation of the adaptive immune response, which initiates rejection and tissue destruction, or maladaptive repair responding to injury cues that replaces functioning parenchyma with extracellular matrix and culminates in fibrosis. Molecular hallmarks of allograft dysfunction have already been established from organ-specific human studies, particularly kidney transplantation, which is the most frequently performed transplant surgery worldwide.

[0004] Numerous technological advances have supported rapid evolution of in silico research, revolutionizing understanding of allograft pathology at a molecular level, with the promise to transform our approach to healthcare. The complex data encapsulated by high- resolution multi-omics approaches provide a global assessment of tissue microenvironments, capable of dismantling the interaction between host and recipient, and the ensuing alloimmune response. Precise definitions of cell type and functional state facilitates analysis of more subtle allograft (patho)physiology compared to the limited interpretation arising from clinical and histological parameters. Despite a plethora of genomic knowledge and identification of potential biomarkers, however, there is limited consensus among organs and restrained incorporation of these data into routine clinical practice to supersede current (non-molecular) diagnostic standards for monitoring allograft function and modifying treatment. This has unacceptable implications for transplant recipients in which their survival and / or that of the graft has not advanced substantially in the past two decades.

[0005] A critical challenge in the field lies in the assumption that transplanted organs exhibit inherent molecular heterogeneity in response to cellular injury, rejection and repair. Studies previously demonstrated that biomarkers predictive of dysfunction in one transplant organ cohort fail to show concordance when applied to other allografts (Cao et al., Transplantation 2021; 105: 1225-1237; Lim et al., Korean J. Intern. Med. 2022; 37:520-533). Analytical accuracy is further complicated by the use of different technologies to generate transcriptomic signatures (Wang et al. NPJ Digit. Med. 2022; 5:85). To partly address these obstacles, an expansive, manually generated meta-analysis from pre-clinical and human transplant studies was performedto create the Banff Human Organ Transplant (BHOT), a gene array that reflects global allograft dysfunction (Mengel et al., Am. J. Transpl. 2020; 20:2305-2317). However, the current lack of a definitive quantitative capacity to compare molecular associations across transplant datasets significantly hampers the ability to acquire a comprehensive understanding of clinical pathologies across all transplanted organs.

[0006] Accordingly, there is an unmet need for biomarkers that are predictive of allograft dysfunction across different organ types for improving patient outcomes.SUMMARY

[0007] The present disclosure arises from the unexpected finding that certain host response biomarkers from blood, including RNA transcripts, have strong discrimination performance across different organ transplant types for specifically differentiating between transplant recipients with allograft dysfunction and those with allograft tolerance. In accordance with the present disclosure, it is proposed that these biomarkers are representative of pathophysiological transcriptomic signatures that are agnostic of organ type, and thus define "panorgan allograft status" biomarkers that may be used in discriminating between allograft dysfunction and allograft tolerance. Based on these findings, methods, compositions, devices and kits are disclosed, which take advantage of these "allograft status" biomarkers to determine a likelihood that allograft dysfunction is present or absent in a subject who has undergone an organ transplant.

[0008] Accordingly, in one aspect, disclosed herein are methods for determining an indicator used in assessing a likelihood that allograft dysfunction is present or absent in a subject who has undergone an organ transplant. These methods general comprise, consist or consist essentially of:(1) determining a biomarker value for each of a plurality of polynucleotide biomarkers (e.g., 2, 3, 4, 5, or more polynucleotide biomarkers) of a biomarker panel in a blood sample obtained from the subject, wherein a respective biomarker value is indicative of a level of a corresponding polynucleotide biomarker in the blood sample, wherein the biomarker panel comprises, consists or consists essentially of a CASP1 polynucleotide, and one or both of an APP polynucleotide and an AQP3 polynucleotide; and(2) determining the indicator using the biomarker values.

[0009] In accordance with the present disclosure, the APP and AQP3 polynucleotides represent polynucleotide biomarkers that have been determined to significantly improve the predictive performance of the CASP1 polynucleotide to differentiate between organ transplant recipient subjects with allograft dysfunction and organ transplant recipient subjects with allograft tolerance ( / .e., without allograft dysfunction).

[0010] The biomarker panel may further comprise at least one ancillary predictive performance-improving polynucleotide biomarker (e.g., 1, 2, 3, or more polynucleotide biomarkers) selected from TABLE 1 infra, with the proviso that when the biomarker panel is a biomarker panel of three polynucleotide biomarkers consisting of the CASP1 polynucleotide, the APP polynucleotide and the AQP3 polynucleotide, the at least one ancillary predictive performanceimproving polynucleotide biomarker is excluded from the biomarker panel.

[0011] In any of the aspects or embodiments disclosed herein, individual biomarker values may denote a measured amount or concentration of a corresponding biomarkerpolynucleotide in the sample, or a logarithmic representation of a measured amount or concentration of a corresponding biomarker polynucleotide in the sample.

[0012] In any of the aspects or embodiments disclosed herein, the organ may be a solid organ selected from heart, lung, kidney, liver, pancreas, skin, uterus, bone, cartilage, small or large bowel, bladder, brain, breast, blood vessels, esophagus, fallopian tube, gallbladder, ovaries, pancreas, prostate, placenta, spinal cord, limb including upper and lower, spleen, stomach, testes, thymus, thyroid, trachea, ureter, urethra, and uterus. In specific embodiments, the solid organ is selected from kidney, heart, and liver.

[0013] In any of the aspects or embodiments disclosed herein, the indicatordetermining methods of the present disclosure may further comprise applying a function to one or more biomarker values to yield at least one functionalized biomarker value and determining the indicator using the at least one functionalized biomarker value. The function may include at least one of: (a) multiplying biomarker values; (b) dividing biomarker values; (c) adding biomarker values; (d) subtracting biomarker values; (e) a weighted sum of biomarker values; (f) a log sum of biomarker values; (g) a geometric mean of biomarker values; and (h) a sigmoidal function of biomarker values. In specific embodiments, the indicator-determining methods may comprise determining for n pairs of said polynucleotide biomarkers a plurality of functionalized biomarker values, wherein a respective functionalized biomarker value is indicative of a ratio of concentrations of a pair of said polynucleotide biomarkers, wherein each functionalized biomarker value of said plurality of functionalized biomarker values is indicative of a ratio of concentrations of a different pair of said polynucleotide biomarkers, and wherein n represents the sum of all possible, non- redundant pairs of biomarker polynucleotides of the biomarker panel.

[0014] In any of the aspects or embodiments disclosed herein, the indicatordetermining methods of the present disclosure may further comprise combining biomarker values (e.g., non-functionalized and / or functionalized biomarker values) to provide a composite score and determining the indicator using the composite score. The biomarker values may be combined by adding, multiplying, subtracting, and / or dividing biomarker values.

[0015] In any of the aspects or embodiments disclosed herein, the indicatordetermining methods may further comprise combining the composite score with an additional feature value to produce a clinically adjusted composite score, wherein the additional feature value is for at least one additional feature type e.g., at least one clinical parameter) to characterize the likelihood that the subject has an increased likelihood of the presence of allograft dysfunction or an increased likelihood of the absence of allograft dysfunction.

[0016] In any of the aspects or embodiments disclosed herein, the indicatordetermining methods may further comprise analyzing the biomarker value(s), composite score or clinically adjusted composite score, with reference to one or more corresponding reference biomarker value ranges or threshold values, or composite score ranges or threshold values, or clinically adjusted composite score ranges or threshold values, to determine the indicator. The reference biomarker value ranges or threshold values, or composite score ranges or threshold values, or clinically adjusted composite score ranges or threshold values may be representative of one or more subjects with allograft dysfunction, or one or more subjects with allograft tolerance, or one or more subjects in whom allograft dysfunction is absent.

[0017] In any of the aspects or embodiments disclosed herein, the indicator may indicate an increased likelihood of a presence of allograft dysfunction if the biomarker values, composite score or clinically adjusted composite score is indicative of a level of the biomarker polynucleotides in the sample that correlates with an increased likelihood of a presence of allograft dysfunction relative to a predetermined reference biomarker value ranges or cut-off values, composite score range or cut-off value, or clinically adjusted composite score range or cut-off value, and wherein the indicator indicates a likelihood of the absence of allograft dysfunction if the biomarker values, composite score or clinically adjusted composite score is indicative of a level of the biomarker polynucleotides in the sample that correlates with an increased likelihood of an absence of allograft dysfunction relative to a predetermined reference biomarker value ranges or cut-off values, composite score range or cut-off value, or clinically adjusted composite score range or cut-off value.

[0018] In any of the aspects or embodiments disclosed herein, the blood sample may be a peripheral blood sample or fraction thereof (e.g. a peripheral blood mononuclear blood sample). In exemplary embodiments, the blood sample comprises leukocytes.

[0019] In any of the aspects or embodiments disclosed herein, the allograft dysfunction may be selected from an immune response (e.g., an innate immune response and / or an adaptive immune response) to the organ transplant), early allograft dysfunction, primary graft nonfunction, chronic allograft dysfunction, mixed rejection, subclinical rejection, borderline rejection, inflammation of the organ, and atrophy of the organ, rejection based on location (e.g., tubulitis, peritubular capillaritis, etc.), cellular infiltration, fibrosis and tubular atrophy, or any combination thereof. In representative examples, the allograft dysfunction is associated with any one or more of ischemia reperfusion injury, mitochondrial damage, antibody-mediated rejection of the allograft, T cell-mediated rejection of the allograft, and fibrosis of the allograft.

[0020] In any of the aspects or embodiments disclosed herein, the subject has at least one clinical sign of allograft dysfunction (e.g., at least one clinical sign of allograft rejection).

[0021] Disclosed herein in another aspect are compositions which are suitably useful for determining an indicator used in assessing a likelihood that allograft dysfunction is present or absent in a subject who has undergone an organ transplant. These compositions generally comprise, consist or consist essentially of a mixture of a DNA polymerase, a blood leukocyte cDNA sample obtained from a subject who has undergone an organ transplant, wherein the blood leukocyte cDNA sample comprises a plurality of cDNA biomarkers (e.g., 2, 3, 4, 5, or more cDNAs) of a biomarker panel, wherein the biomarker panel comprises, consists or consists essentially of a CASP1 cDNA, and one or both of an APP cDNA and an AQP3 cDNA, and wherein the composition further comprises for individual cDNA biomarkers of the biomarker panel at least one oligonucleotide primer or probe that hybridizes to the cDNA biomarker.

[0022] In some embodiments, the biomarker panel further comprises at least one ancillary predictive performance-improving cDNA biomarker (e.g., 1, 2, 3, or more cDNA biomarkers) selected from TABLE 1 infra, wherein the composition further comprises for the at least one ancillary predictive performance-improving cDNA biomarker at least one oligonucleotide primer or probe that hybridizes to the cDNA biomarker.

[0023] The blood leukocyte cDNA sample may comprise, consist or consist essentially of a whole cDNA preparation obtained from a blood sample (e.g., a peripheral blood sample orfraction thereof such as but not limited to a peripheral blood mononuclear cell (PBMC) sample) of the subject.

[0024] In any of the aspects and embodiments disclosed herein, the composition comprises for a respective cDNA biomarker two oligonucleotide primers that hybridize to opposite complementary strands of the cDNA biomarker. In some of the same and other embodiments, the composition comprises for a respective cDNA two pairs of oligonucleotide primers, wherein the oligonucleotide primers of a respective pair hybridize to opposite complementary strands of the cDNA, and wherein the oligonucleotide primers of one pair are nested ("nested oligonucleotide primers") relative the oligonucleotide primers of the other pair. In some of the same and other embodiments, the composition comprises for a respective cDNA biomarker an oligonucleotide probe that hybridizes to the cDNA biomarker or a polynucleotide corresponding thereto e.g., a polynucleotide product resulting nucleic acid amplification of the cDNA biomarker). The oligonucleotide probe may comprise a heterologous reporter molecule, and in illustrative examples of this type, the reporter molecule comprises a fluorescent label. In exemplary embodiments, the oligonucleotide probe is a real-time polymerase chain reaction probe.

[0025] In any of the aspects and embodiments disclosed herein, the composition may comprise for each of at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 of the cDNA biomarkers at least one oligonucleotide primer and / or probe that hybridizes to the cDNA biomarker. In some of the same and other embodiments, the composition may comprise for each of up to 2, 3, 4, 5, 6, 7, 8, 9, or 10 of the cDNA biomarkers at least one oligonucleotide primer and / or probe that hybridizes to the cDNA biomarker.

[0026] In any of the aspects and embodiments disclosed herein, individual cDNA biomarkers and their corresponding oligonucleotide primers and / or probes are present in separate reaction vessels. For example, two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, or 10) cDNA biomarkers and their corresponding oligonucleotide primers and / or probes may be present in the same reaction vessel.

[0027] In any of the aspects and embodiments disclosed herein, the DNA polymerase may be a thermostable DNA polymerase.

[0028] In still another aspect, devices are disclosed for nucleic acid amplification of blood leukocyte cDNA, wherein the device comprises a plurality of reaction vessels, individual reaction vessels comprising the composition of any aspect or embodiment disclosed herein.

[0029] In some embodiments, a device of the present disclosure consisting of 2 to 10, 2 to 9, 2 to 8, 2 to 7, 2 to 6, 2 to 5, 2 to 4 reaction vessels (and all integer reaction vessels in between). In some of the same and other embodiments, the device consists of 2, 3, 4, 5, 6, 7, 8, 9 or 10 reaction vessels.

[0030] In any of these embodiments, one or more reaction vessels may be used for single-plex amplification of cDNA. In some of the same and other embodiments, one or more reaction vessels are used for multiplex amplification of cDNA. In illustrative examples of this type, the multiplex amplification is 2-plex, 3-plex, 4-plex or 5-plex.

[0031] Further disclosed herein are methods for treating a subject who has undergone an organ transplant. These methods generally comprise, consist or consist essentially of:(1) performing the indicator-determining method as broadly described above and elsewhere herein to determine whether the subject has an increased likelihood of a presence or absence of allograft dysfunction;(2) exposing the subject to an allograft dysfunction treatment regimen, wherein the exposure to the treatment regimen is based at least in part on the indicator-determining method indicating that subject has an increased likelihood of the presence of allograft dysfunction; or(3) not exposing the subject to an allograft dysfunction treatment regimen, wherein the non-exposure to the treatment regimen is based at least in part on the indicatordetermining method indicating that subject has an increased likelihood of the absence of allograft dysfunction.

[0032] In some embodiments, the treatment methods may further comprise: taking a blood sample from the subject and determining an indicator indicative of a likelihood of a presence or absence of allograft dysfunction using the indicator-determining method. In some of the same and other embodiments, the treatment methods may further comprise: sending the blood to a laboratory at which the indicator is determined according to the indicator-determining method. Non-limiting examples of these embodiments may further comprise: receiving the indicator from the laboratory.

[0033] In any of the embodiments disclosed herein, the treatment regimen may comprise administration of at least one immunosuppressive agent, representative examples of which may be selected from calcineurin inhibitors, antiproliferative agents, mTOR inhibitors, steroids, lymphocyte e.g., B cell and / or T cell) depleting antibodies, non-depleting antibodies, and fusion proteins comprising an Fc fragment of a human IgGl immunoglobulin linked to the extracellular domain of CTLA-4. In some embodiments, the subject may be already receiving an immunosuppressive agent and the treatment comprises administering an increased concentration of the immunosuppressive agent. In some of the same and other embodiments, the treatment regimen may comprise retransplantation of the transplanted organ.

[0034] Disclosed herein in still another aspect are kits for determining an indicator used in assessing a likelihood that allograft dysfunction is present or absent in a subject who has undergone an organ transplant. These kits generally comprise for each of a plurality of polynucleotide biomarkers (e.g., 2, 3, 4, 5, or more polynucleotide biomarkers) of a biomarker panel at least one oligonucleotide primer and / or at least one oligonucleotide probe that hybridizes to the polynucleotide biomarker, wherein the biomarker panel comprises, consists or consists essentially of a CASP1 polynucleotide, and one or both of an APP polynucleotide and an AQP3 polynucleotide. Respective polynucleotide biomarkers of the biomarker panel may be RNA (e.g., mRNA) or cDNA biomarkers.

[0035] In some embodiments, the biomarker panel may further comprise at least one ancillary predictive performance-improving polynucleotide biomarker (e.g., 1, 2, 3, or more polynucleotide biomarkers) selected from TABLE 1 infra, and wherein the kit further comprises for a respective predictive performance-improving polynucleotide biomarker at least one oligonucleotide primer and / or at least one oligonucleotide probe that hybridizes to the predictive performance-improving polynucleotide biomarker. The kits may further comprise any one or more of the following: (1) a DNA polymerase (e.g., a thermostable DNA polymerase), (2) for eachpolynucleotide biomarker a pair of forward and reverse oligonucleotide primers that permit nucleic acid amplification of at least a portion of the polynucleotide biomarker to produce an amplicon, (3) for each polynucleotide biomarker two pairs of forward and reverse oligonucleotide primers, wherein the oligonucleotide primers of one pair are nested ("nested oligonucleotide primers") relative to the oligonucleotide primers of the other pair, wherein a respective pair of oligonucleotide primers permits nucleic acid amplification of at least a portion of the polynucleotide biomarker to produce an amplicon, (4) for each polynucleotide biomarker an oligonucleotide probe that comprises a heterologous label and hybridizes to the polynucleotide biomarker or an amplicon of the polynucleotide biomarker, (5) one or more reagents for preparing mRNA from a cell or cell population from a blood sample of the subject, (6) one or more reagents for preparing cDNA from the mRNA, (7) one or more reagents for amplifying cDNA, (8) deoxynucleotides, (9) buffer(s), (10) positive and negative controls, (11) reaction vessel(s), and (12) instructions for performing the indicator-determining method as broadly described above and elsewhere herein. Suitably, components of the kit when used to determine the indicator are combined to form a mixture.

[0036] Yet another aspect of the present disclosure provides methods of monitoring treatment efficacy or disease status in a subject diagnosed with an allograft dysfunction. These methods generally comprise, consist or consist essentially of:1) providing a blood sample obtained from the subject;2) performing the indicator-determining method as broadly described above and elsewhere herein on the blood sample obtained from the subject before treatment, or at intervals between treatments, or at time intervals in the absence of treatment; and3) determining that the treatment is effective, or that the disease status is improved, based at least in part on the indicator-determining method indicating that subject has an increased likelihood of the absence of allograft dysfunction; or4) determining that the treatment is not effective, or that the disease status is unchanged or worsened, based at least in part on the indicator-determining method indicating that subject has an increased likelihood of the presence of allograft dysfunction.

[0037] In some embodiments, the methods further comprise: a) administering a treatment to the subject; and b) performing the indicator-determining method after administration of the treatment, wherein the treatment is determined to be effective based at least in part on the indicator-determining method indicating that subject has an increased likelihood of the absence of allograft dysfunction, or wherein the treatment is determined to be not effective based at least in part on the indicator-determining method indicating that subject has an increased likelihood of the presence of allograft dysfunction.

[0038] In some of the same and other embodiments, the methods further comprise: i. obtaining a first blood sample from the subject before administration of the treatment to the subject; ii. performing the indicator-determining method before the administration to determine a first disease status of the subject; ill. administering a treatment to the subject;iv. obtaining a second blood sample from the subject after administration of the treatment to the subject; v. performing the indicator-determining method after the administration to determine a second disease status of the subject, and vi. determining that the treatment is effective if the second disease status is improved relative to the first disease status, or vii. determining that the treatment is not effective if the second disease status is unchanged or worsened relative to the first disease status.BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is schematic representation illustrating how the top 20 genes, which consistently changed across all datasets, were first identified.

[0040] Figure 2 is schematic representation illustrating how genes that were important to building a model in peripheral blood samples were identified.

[0041] Figure 3 is a graphical representation showing a scatter plot of AUC metrics. Set A refers to 4-gene combinations of interest, whereas set B refers to the 16 genes remaining from the initial 20 gene pool. Models were trained on 22 datasets with the 23rd dataset being held out for model evaluation.

[0042] Figure 4 is a graphical representation showing a bar plot of genes that met identified thresholds in Figure 2. Bars are colored by the percentage (per) of chosen combinations they appeared in.

[0043] Figure 5 is a graphical representation showing the predictive performance of representative three-biomarker combinations for differentiating between subjects with allograft rejection and subjects with allograft tolerance as a function of organ transplant type (Heart, Kidney, Liver).

[0044] Figure 6 is a graphical representation showing the predictive performance of representative four-biomarker combinations for differentiating between subjects with allograft rejection and subjects with allograft tolerance as a function of organ transplant type (Heart, Kidney, Liver).

[0045] Figure 7 is a graphical representation showing the predictive performance of representative five-biomarker combinations for differentiating between subjects with allograft rejection and subjects with allograft tolerance as a function of organ transplant type (Heart, Kidney, Liver).

[0046] Figure 8 is a graphical representation showing a lambda path plot for different biomarker pairs of the three-biomarker combinations shown in Figure 5.

[0047] Figure 9 is a graphical representation showing a lambda path plot for different biomarker pairs of the four-biomarker combinations shown in Figure 6.

[0048] Some figures and text contain color representations or entities. Color illustrations are available from the Applicant upon request or from an appropriate Patent Office. A fee may be imposed if obtained from a Patent Office.DETAILED DESCRIPTION1. Definitions

[0049] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present disclosure belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present disclosure, preferred methods and materials are described. For the purposes of the present disclosure, the following terms are defined below.

[0050] The articles "a" and "an" are used herein to refer to one or to more than one (i.e. to at least one) of the grammatical object of the article. By way of example, "an element" means one element or more than one element.

[0051] The terms "aiding diagnosis" and "aiding in distinguishing between" different patient populations, are used interchangeably herein to refer to methods that assist in making a clinical determination regarding the presence, or nature, of a particular type of symptom or condition of a disease or disorder (e.g., allograft dysfunction). For example, a method of aiding diagnosis of a disease or condition as disclosed for example herein can comprise measuring certain biomarkers (e.g., the RNA biomarkers disclosed herein) in a biological sample (e.g., blood sample) of an individual.

[0052] The term "allograft status", as used herein, refers to the condition of a transplanted organ, tissue, or cells (an allograft) within a recipient's body, which may include allograft dysfunction and allograft tolerance. Monitoring allograft status may be used to assess allograft function, detect potential complications like rejection or infection, and guide treatment management (e.g., immunosuppression) to optimize transplant outcomes.

[0053] The "amount" or "level" of a biomarker is a detectable level or amount in a sample. These can be measured by methods known to one skilled in the art and also disclosed herein. These terms encompass a quantitative amount or level (e.g., weight or moles), a semi- quantitative amount or level, a relative amount or level (e.g., weight % or mole % within class), a concentration, and the like. Thus, these terms encompass absolute or relative amounts or levels or concentrations of a biomarker in a sample. The expression level or amount of biomarker assessed can be used to determine the response to treatment.

[0054] "Amplification," as used herein generally refers to the process of producing multiple copies of a desired sequence. "Multiple copies" mean at least two copies. A "copy" does not necessarily mean perfect sequence complementarity or identity to the template sequence. For example, copies can include nucleotide analogs such as deoxyinosine, intentional sequence alterations (such as sequence alterations introduced through a primer comprising a sequence that is hybridizable, but not complementary, to the template), and / or sequence errors that occur during amplification.

[0055] As used herein, the term "amplicon" refers to a nucleic acid that is the product of amplification. Thus an amplicon may be homologous to a reference sequence, a target sequence, or any sequence of nucleic acid that has been subjected to amplification. Generally, within a reaction sample, the concentration of amplicon sequence will be significantly greater than the concentration of original (template) nucleic acid sequence.

[0056] As used herein, "and / or" refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (or).

[0057] The term "biomarker" as used herein refers to an indicator agent, substance, compound or molecule, e.g., a predictive, diagnostic, and / or prognostic agent, substance, compound or molecule, which can be detected in a sample. The presence absence or level of the biomarker may serve as an indicator of a particular subtype of a disease or disorder e.g., allograft dysfunction), characterized by certain, molecular, pathological, histological, and / or clinical features, and / or may serve as an indicator of a particular cell type or state and / or or response to therapy. Biomarkers include, but are not limited to, polynucleotides (e.g., DNA, and / or RNA), polynucleotide copy number alterations (e.g., DNA copy numbers), polypeptides, polypeptide and polynucleotide modifications (e.g., posttranslational modifications), carbohydrates, and / or glycolipid-based molecular markers. A biomarker may be present in a sample obtained from a subject before the onset of a physiological or pathophysiological state (e.g., allograft dysfunction), including a symptom, thereof (e.g., increased creatinine level in kidney allograft dysfunction, increased troponin T level in heart allograft dysfunction, increased ALT level in liver allograft dysfunction, etc.). Thus, the presence of the biomarker in a sample obtained from the subject can be indicative of an increased risk that the subject will develop the physiological or pathophysiological state or symptom thereof. Alternatively, or in addition, the biomarker may be normally expressed in an individual, but its expression may change (i.e., it is increased (upregulated; over-expressed) or decreased (downregulated; under-expressed) before the onset of a physiological or pathophysiological state, including a symptom thereof. Thus, a change in the level of the biomarker may be indicative of an increased risk that the subject will develop the physiological or pathophysiological state or symptom thereof. Alternatively, or in addition, a change in the level of a biomarker may reflect a change in a particular physiological or pathophysiological state, or symptom thereof, in a subject, thereby allowing the nature (e.g., severity) of the physiological or pathophysiological state, or symptom thereof, to be tracked over a period of time. This approach may be useful in, for example, monitoring a treatment regimen for the purpose of assessing its effectiveness (or otherwise) in a subject. As herein described, reference to the level of a biomarker includes the concentration of a biomarker, or the level of expression of a biomarker, or the activity of the biomarker.

[0058] The term "biomarker value" refers to a value measured or functionalized for at least one corresponding biomarker of a subject and which is typically indicative of an abundance or concentration of a biomarker in a sample obtained from the subject. Thus, the biomarker values could be measured biomarker values, which are values of biomarkers measured for the subject. These values may be quantitative or qualitative. For example, a measured biomarker value may refer to the presence or absence of a biomarker or may refer to a level of a biomarker, in a sample. The measured biomarker values can be values relating to raw or normalized biomarker levels (e.g., a raw, non-normalized biomarker level, or a normalized biomarker levels that is determined relative to an internal or external control biomarker level) and to mathematically transformed biomarker levels (e.g., a logarithmic representation of a biomarker level such as amplification amount, cycle time, etc.). Alternatively, the biomarker values can be functionalized biomarker values, which are values that have been functionalized from one or more measured biomarker values, for example by applying a function to the one or more measured biomarker values.Biomarker values can be of any appropriate form depending on the manner in which the values are determined. For example, the biomarker values could be determined using high-throughput technologies such as mass spectrometry, sequencing platforms, array and hybridization platforms, immunoassays, flow cytometry, or any combination of such technologies and in representative examples, the biomarker values relate to a level of activity or abundance of an expression product or other measurable molecule, quantified using a nucleic acid assay such as real-time polymerase chain reaction (RT-PCR), sequencing or the like. In the context of nucleic acid amplification assays such as PCR-based assays, the biomarker values can be in the form of amplification amounts, or cycle times, which are a logarithmic representation of the levels of the biomarker within a sample and which thus correspond to mathematical transformations of raw or normalized biomarker levels, as will be appreciated by persons skilled in the art. Thus, in situations in which mathematically transformed biomarker values are used as measured biomarker values, the expression "functionalized biomarker value" in the context, for example, of a ratio of levels of a pair of biomarkers in a sample obtained from a subject does not necessarily mean that the functionalized biomarker value is one that results from a division of one measured biomarker value by another measured biomarker value. Instead, the measured biomarker values can be combined using any suitable function, whereby the resulting functionalized biomarker value is one that corresponds to or reflects a ratio of non-normalized (e.g., raw) or normalized biomarker levels.

[0059] The terms "biomarker signature", "signature", "biomarker expression signature", or "expression signature" are used interchangeably herein and refer to one or a combination of biomarkers the expression of which provides an indicator, e.g., a predictive, diagnostic, and / or prognostic indicator. The biomarker signature may serve as an indicator of a particular subtype of a disease or disorder e.g., allograft dysfunction, etc.) or symptom thereof (e.g., increased creatinine level in kidney allograft dysfunction, increased troponin T level in heart allograft dysfunction, increased ALT level in liver allograft dysfunction, etc.) characterized by certain molecular, pathological, histological, and / or clinical features. In some embodiments, the biomarker signature is a "gene signature". The term "gene signature" is used interchangeably with "gene expression signature" and refers to one or a combination of polynucleotides whose expression is an indicator, e.g., predictive, diagnostic, and / or prognostic. In some embodiments, the biomarker signature is a "protein signature." The term "protein signature" is used interchangeably with "protein expression signature" and refers to one or a combination of polypeptides whose expression is an indicator, e.g., predictive, diagnostic, and / or prognostic. A biomarker signature may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or more biomarkers. In some embodiments, a biomarker signature comprises hundreds, or even thousands, of biomarkers or indications thereof. A biomarker signature can further comprise one or more controls or internal standards. In certain embodiments, a biomarker signature comprises at least one biomarker, or indication thereof, that serves as an internal standard. In other embodiments, a biomarker signature comprises an indication of one or more types of biomarkers. The term "indication" as used herein in this context merely refers to a situation where the biomarker signature contains symbols, data, abbreviations or other similar indicia for a biomarker, rather than the biomarker molecular entity itself. The term "biomarker signature" is also used herein to refer to a biomarker value or combination of at least two biomarker values, wherein individual biomarker values correspond to values of biomarkers that can be measured or functionalized from one or more subjects, which combination is characteristicof a discrete condition, stage of condition, subtype of condition or a prognosis for a discrete condition, stage of condition, subtype of condition. The term "signature biomarkers" is used to refer to a subset of the biomarkers that have been identified for use in a biomarker signature that can be used in performing a clinical assessment, such as to determine a likelihood of the presence or absence of specific conditions, different stages or severity of conditions, subtypes of different conditions or different prognoses, or to rule in or rule out specific conditions, different stages or severity of conditions, subtypes of different conditions or different prognoses. The number of signature biomarkers will vary, but is typically of the order of 16 or less (e.g., 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1).

[0060] The term "blood sample" as used herein includes any blood sample that may be extracted, untreated, treated, diluted or concentrated from a subject. Such blood samples may include, without limitation, whole blood, serum, red blood cells, white blood cells, plasma, cell lysates, and cellular secretion products. Advantageous samples may include ones comprising any one or more biomarkers as taught herein in detectable quantities. Suitably, the sample is readily obtainable by minimally invasive methods, allowing the removal or isolation of the sample from the subject. In some embodiments, the sample may contain peripheral blood, or a fraction or extract thereof. The sample may comprise blood cells such as mature, immature or developing leukocytes, including lymphocytes, polymorphonuclear leukocytes, neutrophils, monocytes, reticulocytes, basophils, coelomocytes, hemocytes, eosinophils, megakaryocytes, macrophages, dendritic cells natural killer cells, or fraction of such cells (e.g., a nucleic acid or protein fraction). In specific embodiments, the blood sample comprises peripheral blood mononuclear cells (PBMCs).

[0061] As used herein, the phrase "a classifier" refers to a machine learning model or algorithm that is capable of distinguishing between two or more states (e.g., allograft dysfunction versus stable allograft function; tolerance versus stable graft function requiring immunosuppression; graft versus patient survival, etc.).

[0062] The term "clinical parameter", as used herein, refers to any clinical measure of a disease state (e.g., allograft dysfunction) of a patient; for example, age, sex, ethnicity, BMI, patient global health assessment, erythrocyte sedimentation rate (ESR), alarmin (e.g., heat shock proteins, interleukin la (IL-la), IL-33, HMGB1, etc.) level, CRP level, co-morbidities, physical activity parameters, histological analysis, imaging analysis (e.g., x-ray, fluoroscopes, MRI, nuclear magnetic resonance (NMR) analysis, CT, computed axial tomography (CAT) scanners, positron emission tomography (PET) scanners, and ultrasonography (ultrasound) scanners)), donor age at death, date of donor death, donor sex, cold ischemia time, warm ischemia time, HLA matching, PRA, positive or negative transplant cross-match, DSA, MFI, graft loss, date of graft loss, graft loss cause, date of last follow-up, additional medication, response and adverse effect of medications, allergies, RNA profile, single cell RNA sequencing, metabolomics, microbiome, genomics, epigenomics, and microRNA.

[0063] As used herein, the term "clinical sign", or simply "sign", refers to objective evidence of the presence of disease or condition (e.g., allograft dysfunction) in a subject. Symptoms and / or signs associated with a particular disease or condition and the evaluation of such signs are routine and known in the art. Non-limiting examples of signs of allograft dysfunction include: fatigue, fever, flu-like symptoms (e.g., chills, nausea, vomiting, diarrhea, body aches, headache), cough, anuria or decreased urine output, hepatosplenomegaly, shortness of breath,dizziness, fainting, generalized edema, high blood pressure, weight gain, rash, bloody diarrhea, and pain or tenderness at the site of transplantation.

[0064] The terms "complementary" and "complementarity" refer to polynucleotides ( / .e., a sequence of nucleotides) related by the base-pairing rules. For example, the sequence "A- G-T," is complementary to the sequence "T-C-A." Complementarity may be "partial," in which only some of the nucleic acids' bases are matched according to the base pairing rules. Or, there may be "complete" or "total" complementarity between the nucleic acids. The degree of complementarity between nucleic acid strands has significant effects on the efficiency and strength of hybridization between nucleic acid strands.

[0065] Throughout this specification, unless the context requires otherwise, the words "comprise," "comprises" and "comprising" will be understood to imply the inclusion of a stated step or element or group of steps or elements but not the exclusion of any other step or element or group of steps or elements. Thus, use of the term "comprising" and the like indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present. By "consisting of" is meant including, and limited to, whatever follows the phrase "consisting of". Thus, the phrase "consisting of" indicates that the listed elements are required or mandatory, and that no other elements may be present. By "consisting essentially of" is meant including any elements listed after the phrase, and limited to other elements that do not interfere with or contribute to the activity or action specified in the disclosure for the listed elements. Thus, the phrase "consisting essentially of" indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present depending upon whether or not they affect the activity or action of the listed elements.

[0066] As used herein, the term "composite score" refers to an aggregation of the obtained values for biomarkers measured in a sample from a subject optionally in combination with one or more patient clinical parameters. In some embodiments, the obtained biomarker values are normalized to provide a composite score for each subject tested. When used in the context of a risk categorization table and correlated to a stratified population grouping or cohort population grouping based on a range of composite scores in a risk categorization table, the "biomarker composite score" is used, at least in part, for example, by a machine learning system to determine the "risk score" for each subject tested wherein the numerical value (e.g., a multiplier, a percentage, etc.) indicating increased likelihood of having an allograft dysfunction disclosed herein for the stratified grouping becomes the "risk score".

[0067] As used herein, the term "correlates" or "correlates with" and like terms, refers to a statistical association between two or more things, such as events, characteristics, outcomes, numbers, data sets, etc., which may be referred to as "variables". It will be understood that the things may be of different types. Often the variables are expressed as numbers e.g., measurements, values, likelihood, risk), wherein a positive correlation means that as one variable increases, the other also increases, and a negative correlation (also called anti-correlation) means that as one variable increases, the other variable decreases. In various embodiments, correlating a biomarker signature with the presence or absence of a condition e.g., allograft dysfunction) comprises determining the presence, absence, level or amount of at least one biomarker in a subject that has that condition; or in persons known to be free of that condition. In specific embodiments, a profile of biomarker levels, absences or presences is correlated to a global probability or a particular outcome, using receiver operating characteristic (ROC) curves.

[0068] The terms "cut-off value" and "threshold value" are used interchangeably herein to refer to a level (or concentration) which may be an absolute level or a relative level, which is indicative of whether a subject has a particular disease or condition e.g., allograft dysfunction), or is at risk of having a particular disease or condition e.g., allograft dysfunction). Depending on the biomarker or combination of biomarkers, a subject is regarded as having an increased likelihood of having the disease or condition or being at risk of having the disease or condition if either the level of the biomarker(s) detected and determined, respectively, is lower than the cut-off value, or the level of the biomarker(s) detected and determined, respectively, is higher than the cut-off value.

[0069] As used herein, the terms "detectably distinct" and "detectably different" are used interchangeably herein to refer to a signal that is distinguishable or separable by a physical property either by observation or by instrumentation. For example, a fluorophore is readily distinguishable either by spectral characteristics or by fluorescence intensity, lifetime, polarization or photo-bleaching rate from another fluorophore in a sample, as well as from additional materials that are optionally present. In certain embodiments, the terms "detectably distinct" and "detectably different" refer to a set of labels (such as dyes, suitably organic dyes) that can be detected and distinguished simultaneously.

[0070] The term "disease status" as used herein is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art and is not to be limited to a special or customized meaning. The term specifically may refer, without limitation, to health condition and / or medical condition and / or disease stage. For example, the disease status may be healthy or ill and / or presence or absence of disease. For example, the disease status may be a value relating to a scale indicative of disease stage.

[0071] As used herein, the terms "diagnosis", "diagnosing" and the like are used interchangeably herein to encompass determining a likelihood that a subject will develop a condition, or the existence or nature of a condition in a subject. These terms also encompass determining a severity of disease or episode of disease, as well as in the context of rational therapy, in which the diagnosis guides therapy, including initial selection of therapy, modification of therapy (e.g., adjustment of dose or dosage regimen), and the like. By "likelihood" is meant a measure of whether a subject with particular measured or derived biomarker values actually has a condition (or not) based on a given mathematical model. An increased likelihood for example may be relative or absolute and may be expressed qualitatively or quantitatively. For instance, an increased likelihood may be determined simply by determining the subject's measured or derived biomarker values for at least two biomarkers and placing the subject in an "increased likelihood" category, based upon previous population studies. The term "likelihood" is also used interchangeably herein with the term "probability". The term "risk" relates to the possibility or probability of a particular event occurring at some point in the future. "Risk stratification" refers to an arraying of known clinical risk factors to allow physicians to classify patients into a low, moderate, high or highest risk of developing a particular disease or condition.

[0072] As used herein, a "diagnostic amount" of a biomarker refers to an amount of a biomarker in a subject's sample that is consistent with a diagnosis of increased likelihood of allograft dysfunction. A diagnostic amount can be either an absolute amount (e.g., pg / mL) or a relative amount (e.g., relative intensity of signals).

[0073] The term "differentially expressed" refers to differences in the quantity and / or the frequency of a biomarker present in a sample obtained from patients having, for example,allograft dysfunction as compared to subjects with allograft tolerance, or without allograft dysfunction. For example, a biomarker can be a polynucleotide or polypeptide which is present at an elevated level or at a decreased level in samples of patients with allograft dysfunction compared to samples of subjects with allograft tolerance, or without allograft dysfunction. Alternatively, a biomarker can be a polynucleotide or polypeptide which is detected at a higher frequency or at a lower frequency in samples of patients with allograft dysfunction compared to samples of subjects with allograft tolerance, or without allograft dysfunction. A biomarker can be differentially present in terms of quantity, frequency or both.

[0074] The term "expression product", as used herein, refers to any product produced during the process of gene expression including polypeptide products and polynucleotide products.

[0075] "Fluorophore" as used herein to refer to a moiety that absorbs light energy at a defined excitation wavelength and emits light energy at a different defined wavelength. Examples of fluorescence labels include, but are not limited to: Alexa Fluor dyes (Alexa Fluor 350, Alexa Fluor 488, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 633, Alexa Fluor 660 and Alexa Fluor 680), AMCA, AMCA-S, BODIPY dyes (BODIPY FL, BODIPY R6G, BODIPY TMR, BODIPY TR, BODIPY 530 / 550, BODIPY 558 / 568, BODIPY 564 / 570, BODIPY 576 / 589, BODIPY 581 / 591, BODIPY 630 / 650, BODIPY 650 / 665), Carboxyrhodamine 6G, carboxy-X-rhodamine (ROX), Cascade Blue, Cascade Yellow, Cyanine dyes (Cy3, Cy5, Cy3.5, Cy5.5), Dansyl, Dapoxyl, Dialkylaminocoumarin, 4',5'-Dichloro-2',7'-dimethoxy-fluorescein, DM-NERF, Eosin, Erythrosin, Fluorescein, FAM, Hydroxycoumarin, IRDyes (IRD40, IRD 700, IRD 800), JOE, Lissamine rhodamine B, Marina Blue, Methoxycoumarin, Naphthofluorescein, Oregon Green 488, Oregon Green 500, Oregon Green 514, Pacific Blue, PyMPO, Pyrene, Rhodamine 6G, Rhodamine Green, Rhodamine Red, Rhodol Green, 2',4',5',7'-Tetra-bromosulfone-fluorescein, Tetramethyl-rhodamine (TMR), Carboxytetramethylrhodamine (TAMRA), Texas Red and Texas Red-X.

[0076] The term "gene", as used herein, refers to a stretch of nucleic acid that codes for a polypeptide or for an RNA chain that has a function. While it is the exon region of a gene that is transcribed to form mRNA, the term "gene" also includes regulatory regions such as promoters and enhancers that govern expression of the exon region.

[0077] As used herein, the term "higher" with reference to a biomarker measurement refers to a statistically significant and measurable difference in the level of a biomarker compared to the level of another biomarker or to a control level where the biomarker measurement is greater than the level of the other biomarker or the control level. The difference is suitably at least about 10%, or at least about 20%, or of at least about 30%, or of at least about 40%, or at least about 50%.

[0078] As used herein the terms "homology", "homologous" and the like refer to the level of similarity between two or more nucleic acid sequences in terms of percent of sequence identity. Generally, homologous sequences or sequences with homology refer to nucleic acid sequences that exhibit at least 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% sequence identity to one another. Alternatively, or in addition, homologs, homologous sequences or sequences with homology refer to nucleic acid sequences that hybridize under high stringency conditions to one another. High stringency conditions include and encompass from at least about 31% v / v to at least about 50% v / v formamide and from at least about 0.01 M to at least about 0.15 M salt for hybridization at 42 °C, and at least about 0.01 M to at least about 0.15 M salt for washing at42 °C. High stringency conditions also may include 1% BSA, 1 mM EDTA, 0.5 M NaHPC (pH 7.2), 7% SDS for hybridization at 65 °C, and (i) 0.2 x SSC, 0.1% SDS; or (ii) 0.5% BSA, ImM EDTA, 40 mM NaHPC (pH 7.2), 1% SDS for washing at a temperature in excess of 65 °C.

[0079] As used herein, the term "increase" or "increased' with reference to a biomarker level refers to a statistically significant and measurable increase in the biomarker level compared to the level of another biomarker or to a control level. The increase is suitably an increase of at least about 10%, or an increase of at least about 20%, or an increase of at least about 30%, or an increase of at least about 40%, or an increase of at least about 50%.

[0080] The term "indicator", as used herein with reference to the indicator-determining methods of the present disclosure, refers to a result or representation of a result, including any information, number (e.g., biomarker value, functionalized biomarker value, composite score, and / or clinically adjusted composite score), ratio, signal, sign, mark, or note by which a skilled artisan can estimate and / or determine a likelihood or risk of whether or not a subject is suffering from a given disease or condition. In the case of the present disclosure, the "indicator" may optionally be used together with other clinical characteristics, to arrive at a diagnosis (that is, the occurrence or nonoccurrence) of allograft dysfunction or allograft tolerance or a prognosis for allograft dysfunction or allograft tolerance in a subject. That such an indicator is "determined" is not meant to imply that the indicator is 100% accurate. The skilled clinician may use the indicator together with other clinical indicia, including clinical parameters and / or clinical signs disclosed for example herein, to arrive at a diagnosis.

[0081] The term "label" is used herein in a broad sense to refer to an agent, substance, compound or molecule that is capable of providing a detectable signal, either directly or through interaction with one or more additional members of a signal producing system and that has been artificially added, linked or attached via chemical manipulation to a molecule. Labels can be visual, optical, photonic, electronic, acoustic, optoacoustic, by mass, electro-chemical, electro-optical, spectrometry, enzymatic, or otherwise chemically, biochemically hydrodynamically, electrically or physically detectable. Labels can be, for example tailed reporter, marker or adapter molecules. In specific embodiments, a molecule such as a nucleic acid molecule is labeled with a detectable molecule selected form the group consisting of radioisotopes, fluorescent compounds, bioluminescent compounds, chemiluminescent compounds, metal chelators or enzymes. Examples of labels include, but are not limited to, the following radioisotopes (e.g.,3H,14C,35S,125I,131I), fluorescent labels (e.g., FITC, rhodamine, lanthanide phosphors), luminescent labels such as luminol; enzymatic labels (e.g., horseradish peroxidase, beta-galactosidase, luciferase, alkaline phosphatase, acetylcholinesterase), biotinyl groups (which can be detected by marked avidin, e.g., streptavidin containing a fluorescent marker or enzymatic activity that can be detected by optical or calorimetric methods), predetermined polypeptide epitopes recognized by a secondary reporter (e.g., leucine zipper pair sequences, binding sites for secondary antibodies, metal binding domains, epitope tags).

[0082] As used herein, the term "lower" with reference to a biomarker measurement refers to a statistically significant and measurable difference in the level of a biomarker compared to the level of another biomarker or to a control level where the biomarker measurement is less than the level of the other biomarker or the control level. The difference is suitably at least about 10%, or at least about 20%, or of at least about 30%, or of at least about 40%, or at least about 50%.

[0083] As used herein, the term "nested" is used to describe a positional relationship between the annealing site of a primer of a primer pair and the annealing site of another primer of another primer pair. For example, a second primer may be nested by 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 or more nucleotides relative to a first primer, meaning that it binds to a site on the template strand that is frame-shifted by 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 or more nucleotides.

[0084] As used herein, the term "nested primers" or "nested oligonucleotide primers" refers to primers that anneal to a target sequence in an area that is inside the annealing boundaries of another pair of primers, which are typically a primer pair that is used to start a nucleic acid amplification ("also known as "starting primers"). Because the nested primers anneal to the target sequence inside the annealing boundaries of the starting primers, the predominant amplified product of the starting primers is necessarily a longer sequence, than that defined by the annealing boundaries of the nested primers. The amplified product of the nested primers is an amplified segment of the target sequence that cannot, therefore, anneal with the starting primers. Advantages to the use of nested primers include the large degree of specificity, as well as the fact that a smaller sample portion may be used and yet obtain specific and efficient amplification.

[0085] As used herein, the term "normalization" and its derivatives, when used in conjunction with measurement of biomarkers across samples and time, refer to mathematical methods, including but not limited to multiple of the median (MoM), standard deviation normalization, sigmoidal normalization, etc., where the intention is that these normalized values allow the comparison of corresponding normalized values from different datasets in a way that eliminates or minimizes differences and gross influences.

[0086] The term "nucleic acid" or "polynucleotide" as used herein includes RNA, messenger RNA (mRNA), micro RNA (miRNA), copy RNA (cRNA), copy DNA (cDNA), mitochondrial DNA (mtDNA), or DNA. The term typically refers to a polymeric form of nucleotides of at least 10 bases in length, either ribonucleotides or deoxynucleotides or a modified form of either type of nucleotide. The term includes single and double stranded forms of DNA or RNA.

[0087] By "obtained" is meant to come into possession. Samples so obtained include, for example, nucleic acid extracts or polypeptide extracts isolated or derived from a particular source. For instance, the extract may be isolated directly from a biological fluid or tissue of a subject.

[0088] The terms "organ transplant" or "organ transplantation" generally refer to the transfer of an organ (e.g., a solid organ), including tissues and / or cells of an organ from a donor individual into a recipient individual. A donor and recipient may or may not be from the same species. Thus, for example, a human recipient may receive a solid organ from a non-human animal in some embodiments. An "allograft" further indicates a transfer of tissues, cells, or a solid organ between different individuals of the same species. In contrast, if the donor and recipient are the same individual, the graft is referred to as an "autograft."

[0089] As used herein, the term "panel" refers to specific combination of biomarkers used to determine an indicator for assessing a likelihood that allograft dysfunction or allograft tolerance is present or absent in a subject. The term "panel" may also refer to an assay comprising a set of biomarkers used for such a determination. This term can also refer to a profile or index of expression patterns of one or more biomarkers described herein. The number of biomarkers usefulfor a biomarker panel is based on the sensitivity and specificity value for the particular combination of biomarker values.

[0090] As used herein, the term "positive response" means that the result of a treatment regimen includes some clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or a slowing of the progression of the condition. By contrast, the term "negative response" means that a treatment regimen provides no clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or increases the rate of progression of the condition.

[0091] The term "predictive performance" refers to the measurement of performance by using evaluation metrics based on the analysis of data, and includes within its scope the accuracy of a model in predicting the presence or absence or a condition e.g., allograft dysfunction, allograft tolerance, etc.) or forecasting future outcomes.

[0092] By "primer" is meant an oligonucleotide which, when paired with a strand of DNA, is capable of initiating the synthesis of a primer extension product in the presence of a suitable polymerizing agent. The primer is preferably single-stranded for maximum efficiency in amplification but can alternatively be double-stranded. A primer must be sufficiently long to prime the synthesis of extension products in the presence of the polymerization agent. The length of the primer depends on many factors, including application, temperature to be employed, template reaction conditions, other reagents, and source of primers. For example, depending on the complexity of the target sequence, the primer may be at least about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 50, 75, 100, 150, 200, 300, 400, 500, to one base shorter in length than the template sequence at the 3' end of the primer to allow extension of a nucleic acid chain, though the 5' end of the primer may extend in length beyond the 3' end of the template sequence. In certain embodiments, primers can be large polynucleotides, such as from about 35 nucleotides to several kilobases or more. Primers can be selected to be "substantially complementary" to the sequence on the template to which it is designed to hybridize and serve as a site for the initiation of synthesis. By "substantially complementary", it is meant that the primer is sufficiently complementary to hybridize with a target polynucleotide. Desirably, the primer contains no mismatches with the template to which it is designed to hybridize but this is not essential. For example, non-complementary nucleotide residues can be attached to the 5' end of the primer, with the remainder of the primer sequence being complementary to the template. Alternatively, non-complementary nucleotide residues or a stretch of non-complementary nucleotide residues can be interspersed into a primer, provided that the primer sequence has sufficient complementarity with the sequence of the template to hybridize therewith and thereby form a template for synthesis of the extension product of the primer.

[0093] As used herein, the term "probe" refers to a molecule that binds to a specific sequence or sub-sequence or other moiety of another molecule. Unless otherwise indicated, the term "probe" typically refers to a nucleic acid probe that binds to another nucleic acid, also referred to herein as a "target polynucleotide", through complementary base pairing. Probes can bind target polynucleotides lacking complete sequence complementarity with the probe, depending on the stringency of the hybridization conditions. Probes can be labeled directly or indirectly and include primers within their scope.

[0094] The term "prognosis" as used herein refers to a prediction of the probable course and outcome of a clinical condition or disease. A prognosis is usually made by evaluatingfactors or symptoms of a disease that are indicative of a favorable or unfavorable course or outcome of the disease. The skilled artisan will understand that the term "prognosis" refers to an increased probability that a certain course or outcome will occur; that is, that a course or outcome is more likely to occur in a subject exhibiting a given condition, when compared to those individuals not exhibiting the condition.

[0095] As used herein, the term "quencher" includes any moiety that in close proximity to a donor fluorophore, takes up emission energy generated by the donor fluorophore and either dissipates the energy as heat or emits light of a longer wavelength than the emission wavelength of the donor fluorophore. In the latter case, the quencher is considered to be an acceptor fluorophore. The quenching moiety can act via proximal ( / .e., collisional) quenching or by Forster or fluorescence resonance energy transfer ("FRET"). Quenching by FRET is generally used in TaqMan™ probes while proximal quenching is used in molecular beacon and Scorpion™ type probes. Suitable quenchers are selected based on the fluorescence spectrum of the particular fluorophore. Useful quenchers include, for example, the Black Hole™ quenchers BHQ-1, BHQ-2, and BHQ-3 (Biosearch Technologies, Inc.), and the ATTO-series of quenchers (ATTO 540Q, ATTO 580Q, and ATTO 612Q; Atto-Tec GmbH).

[0096] As used herein, a "reaction vessel" refers to any container, chamber, device, or assembly, in which a reaction can occur in accordance with the present disclosure. In some embodiments, a reaction vessel may be a microtube, for example, but not limited to, a 0.2 mL or a 0.5 mL reaction tube such as a MicroAmp™ Optical tube (Applied Biosystems™, Thermo Fisher Scientific) or a micro-centrifuge tube, or other containers of the sort in common practice in molecular biology laboratories. In some embodiments, a reaction vessel may be a well in a microtiter plate (e.g., 96-well plate, 384-well plate) such as a TaqMan™ Array plate (Applied Biosystems™; Thermo Fisher Scientific), a spot on a glass slide, a well in an Applied Biosystems™ TaqMan™ Array Card or Plate (Thermo Fisher Scientific) or a through-hole of an Applied Biosystems™ TaqMan™ OpenArray™ plate (Thermo Fisher Scientific). For example, a plurality of reaction vessels may reside on the same support. In some embodiments, lab-on-a-chip-like devices, available for example from Caliper, Fluidigm and Life Technologies Corp., including the Ion 316™ and Ion 318™ Chip, may serve as reaction vessels in the disclosed methods and devices. In some embodiments, various microfluidic approaches may be employed. It will be recognized that a variety of reaction vessels are available in the art and fall within the scope of the present disclosure.

[0097] As used herein, the term "reduce" or "reduced" with reference to a biomarker level refers to a statistically significant and measurable reduction in the biomarker level compared to the level of another biomarker or to a control level. The reduction is suitably a reduction of at least about 10%, or a reduction of at least about 20%, or a reduction of at least about 30%, or a reduction of at least about 40%, or a reduction of at least about 50%.

[0098] The terms "subject", "individual" and "patient" are used interchangeably herein to refer to a mammalian subject, suitably a primate subject such as a human subject. The subject suitably has at least one (e.g., 1, 2, 3, 4, 5 or more) clinical sign of allograft dysfunction.

[0099] As used herein, the term "treatment regimen" refers to prophylactic and / or therapeutic ( / .e., after onset of a specified condition) treatments, unless the context specifically indicates otherwise. The term "treatment regimen" encompasses natural substances andpharmaceutical agents ( / .e., "drugs") as well as any other treatment regimen including but not limited to dietary treatments, physical therapy or exercise regimens, surgical interventions, and combinations thereof.

[0100] It will be appreciated that the terms used herein and associated definitions are used for the purpose of explanation only and are not intended to be limiting.2. Allograft status biomarkers and their use for aiding the diagnosis of allograft dysfunction and allograft tolerance

[0101] Disclosed herein are methods, compositions, devices and kits for aiding in distinguishing subjects with allograft dysfunction from subjects with allograft tolerance. These methods, compositions, devices and kits are useful inter alia for the detection of allograft dysfunction including earlier detection of allograft dysfunction, thus allowing better treatment decisions for subjects with symptoms of allograft dysfunction.

[0102] The present inventors have determined that certain polynucleotide biomarkers are commonly, specifically and differentially expressed between blood samples obtained from subjects with allograft dysfunction and those with allograft tolerance. The results presented herein provide clear evidence that specific polynucleotide biomarkers can be used, optionally in combination with clinical parameters, to differentiate between allograft dysfunction and allograft tolerance with a remarkable degree of accuracy.

[0103] Based on these findings, the polynucleotide biomarkers disclosed herein are proposed to have utility in laboratory and point-of-care diagnostics that allow for rapid screening for allograft dysfunction or allograft tolerance, which may result in significant cost savings to the medical system, as subjects with allograft dysfunction can be categorized with increased accuracy and exposed to management procedures and therapeutic agents that are suitable for treating allograft dysfunction. These polynucleotide biomarkers are indicative of allograft status e.g., allograft dysfunction, allograft tolerance, etc.) or a proxy for allograft status, and are also referred to herein as "allograft status polynucleotide biomarkers".

[0104] Allograft status polynucleotides biomarkers that are useful in the practice of the methods, compositions, devices and kits include a CASP1 polynucleotide, and one or both of an APP polynucleotide and an AQP3 polynucleotide, which have been determined herein to significantly improve the predictive performance of the CASP1 polynucleotide in the biomarker panels of the disclosure to differentiate between organ transplant recipient subjects with allograft dysfunction and organ transplant recipient subjects without allograft dysfunction e.g., allograft tolerance).2.1 Biomarker panels

[0105] Representative biomarker panels, which may be used in the practice of the methods, compositions, devices and kits of the present disclosure, may further comprise at least one ancillary predictive performance-improving polynucleotide biomarker (e.g., 1, 2, 3, or more polynucleotide biomarkers) selected from TABLE 1.Table 1: Predictive performance-improving biomarkers for CASP1 plus APP and / or AQP3

[0106] Biomarker panels disclosed herein typically comprise at least 2 biomarkers and up to 30 biomarkers, including any number of biomarkers in between, such as 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 biomarkers. In certain embodiments, a biomarker panel comprises at least 2, or least 3, or at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 11, or at least 12, or at least 13, or at least 14, or at least 15 or at least 16 or more biomarkers. In some embodiments, a biomarker panel comprises up to 4, or up to 5, or up to 6, or up to 7, or up to 8, or up to 9, or up to 10, or up to 11, or up to 12, or up to 13, or up to 14, or up to 15, or up to 16 biomarkers.

[0107] In representative embodiments, the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the APP polynucleotide and an ancillary predictive performance-improving polynucleotide biomarker selected from TABLE 2.Table 2: Predictive performance-improving biomarkers for three-biomarker panels comprisingCASP1 and APP

[0108] In illustrative examples of this type, the biomarker panel is selected from the biomarker panels set forth in TABLE 3.Table 3: Three-biomarker panels comprising CASP1 and APP

[0109] In other representative embodiments, the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the AQP3 polynucleotide and a predictive performance-improving polynucleotide biomarker selected from TABLE 4. Table 4: Predictive performance-improving biomarkers for three-biomarker panels comprising CASP1 and AQP3

[0110] Non-limiting examples of biomarker panels of this type may be selected from the biomarker panels set forth in TABLE 5.Table 5: Three-biomarker panels comprising CASP1 and AQP3

[0111] Suitably, in instances when the biomarker panel is a biomarker panel of three polynucleotide biomarkers consisting of the CASP1 polynucleotide, the APP polynucleotide and the AQP3 polynucleotide, the at least one ancillary predictive performance-improving polynucleotide biomarker is excluded from the biomarker panel.

[0112] In still other representative embodiments, the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the APP polynucleotide and two predictive performance-improving polynucleotide biomarkers selected TABLE 6.Table 6: Predictive performance-improving biomarkers for four-biomarker panels comprising CASP1 and APP

[0113] Exemplary biomarker panels of this type may be selected from the biomarker panels set forth in TABLE 7.Table 7: Four-biomarker panels comprising CASP1 and APP

[0114] In other embodiments, the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the AQP3 polynucleotide and two predictive performanceimproving polynucleotide biomarkers selected from TABLE 8. Table 8: Predictive performance-improving biomarkers for four-biomarker panels comprising CASP1 and AQP3

[0115] Representative biomarker panels of this type may be selected from the biomarker panels set forth in TABLE 9.Table 9: Four-biomarker panels comprising CASP1 and AQP3

[0116] In further embodiments, the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the APP polynucleotide, the AQP3 polynucleotide and a predictive performance-improving polynucleotide biomarker selected from TABLE 10. Table 10: Predictive performance-improving biomarkers for four-biomarker panels comprising CASP1, APP and AQP3

[0117] Illustrative biomarker panels of this type are set forth in TABLE 11.Table 11: Four-biomarker panels comprising CASP1 APP and AQP3

[0118] Still other embodiments of the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the APP polynucleotide and three predictive performanceimproving polynucleotide biomarkers selected from TABLE 12. Table 12: Predictive performance-improving biomarkers for five-biomarker panels comprising CASP1 and APPIn non-limiting examples of this type, the biomarker panel is selected from the biomarker panels set forth in TABLE 13.Table 13: Five-biomarker panels comprising CASP1 and APP

[0119] In further representative embodiments, the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the AQP3 polynucleotide and three predictive performance-improving polynucleotide biomarkers selected from TABLE 14.Table 14: Predictive performance-improving biomarkers for five-biomarker panels comprising CASP1 and AQP3

[0120] Exemplary biomarker panels of this type may be selected from the biomarker panels set forth in TABLE 15.Table 15: Five-biomarker panels comprising CASP1 and AQP3

[0121] In still other embodiments, the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the APP polynucleotide, the AQP3 polynucleotide and two predictive performance-improving polynucleotide biomarkers selected TABLE 16. Table 16: Predictive performance-improving biomarkers for five-biomarker panels comprising CASP1, APP and AQP3

[0122] Representative biomarker panels of this type may be selected from the biomarker panels set forth in TABLE 17.Table 17: Five-biomarker panels comprising CASP1 APP and AQP3

[0123] In any of the aspects or embodiments disclosed herein, an increased likelihood of allograft dysfunction in the subject may be indicated when:• the CASP1 polynucleotide is present in the blood sample at a higher level than in a reference blood sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• the CASP1 polynucleotide is present in the blood sample at about the same level as in a reference blood sample obtained from a subject with allograft dysfunction;• the APP polynucleotide is present in the blood sample at a lower level than in a reference blood sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• the APP1 polynucleotide is present in the blood sample at about the same level as in a reference blood sample obtained from a subject with allograft dysfunction;• the AQP3 polynucleotide is present in the blood sample at a lower level than in a reference blood sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• the AQP3 polynucleotide is present in the blood sample at about the same level as in a reference blood sample obtained from a subject with allograft dysfunction.

[0124] In any of the aspects or embodiments disclosed herein in which an increased likelihood of allograft dysfunction is absent in the subject:• the CASP1 polynucleotide is present in the blood sample at a lower level than in a reference blood sample obtained from a subject with allograft dysfunction;• the CASP1 polynucleotide is present in the blood sample at about the same level as in a reference blood sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• the APP polynucleotide is present in the blood sample at a higher level than in a reference blood sample obtained from a subject with allograft dysfunction;• the APP polynucleotide is present in the blood sample at about the same level as in a reference blood sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• the AQP3 polynucleotide is present in the blood sample at a higher level than in a reference blood sample obtained from a subject with allograft dysfunction;• the AQP3 polynucleotide is present in the blood sample at about the same level as in a reference blood sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction.

[0125] In any of the aspects or embodiments disclosed herein, the at least one ancillary predictive performance-improving polynucleotide biomarker may be present in the blood sample at a higher level than in a reference blood sample obtained from a healthy subject or from a subject without allograft dysfunction, wherein the at least one ancillary predictive performance-improving polynucleotide biomarker is selected from TABLE 18.Table 18: Biomarkers upregulated in allograft dysfunction

[0126] In any of the aspects or embodiments disclosed herein, the at least one ancillary predictive performance-improving polynucleotide biomarker may be present in the blood sample at a lower level than in a reference blood sample obtained from a healthy subject or from a subject without allograft dysfunction, wherein the at least one ancillary predictive performance-improving polynucleotide biomarker is selected from TABLE 19.Table 19: Biomarkers downregulated in allograft dysfunction

[0127] Allograft status polynucleotide biomarkers that are also useful in the practice of the methods, compositions, devices and kits disclosed herein include biomarker panels that comprise a CASP1 polynucleotide and at least one predictive performance-improving polynucleotide biomarker (e.g., 1, 2, 3, or more polynucleotide biomarkers) selected from TABLE 1. Unlike the biomarker panels discussed above, these panels do not rely on the use of an APP polynucleotide or an AQP3 polynucleotide, and thus the present disclosure further extends to methods, compositions, devices and kits in which the APP polynucleotide or an AQP3 polynucleotide of the aforementioned aspects and embodiments are replaced with at least one predictive performance-improving polynucleotide biomarker (e.g., 1, 2, 3, or more polynucleotide biomarkers) selected from TABLE 1.

[0128] Representative alternate three-biomarker panels of this type may be selected from the biomarker panels set forth in TABLE 20.Table 20: Three-biomarker panels comprising CASP1 and 2 predictive performance biomarkersPRPF19, TIMP1], [CASP1, PRPF6, TNFAIP3], [CASP1, RHOH, TIMP1], [CASP1, SREBF2, TIMP1]

[0129] Representative alternate four-biomarker panels may be selected from the biomarker panels set forth in TABLE 21.Table 21 : Four-biomarker panels comprising CASP1 and 3 predictive performance biomarkers

[0130] Representative alternate five-biomarker panels may be selected from the biomarker panels set forth in TABLE 22.Table 22: Five-biomarker panels comprising CASP1 and 4 predictive performance biomarkers

[0131] Biomarker values that are indicative of the levels of biomarkers in a blood sample may be obtained by any suitable means known in the art. Exemplary blood samples include whole blood or any fraction thereof, including blood cells, red blood cells, white blood cells or leucocytes, platelets, serum and plasma. The blood sample may be extracted, untreated, treated, diluted or concentrated from a subject. Blood samples can be obtained from a subject by any means known in the art including but not limited to venipuncture and phlebotomy.

[0132] Measurement of the expression level of a biomarker in the blood sample can be direct or indirect. For example, the abundance levels of RNAs or proteins can be directly quantitated. Alternatively, the amount of a biomarker can be determined indirectly by measuring abundance levels of cDNAs, amplified or messenger RNAs or DNAs, or by measuring quantities or activities of RNAs, proteins, or other molecules e.g., metabolites) that are indicative of the expression level of the biomarker. The methods for measuring biomarkers in a sample have many applications. For example, one or more biomarkers can be measured to aid in the diagnosis of allograft dysfunction or allograft tolerance, to determine the appropriate treatment for a subject, to monitor responses in a subject to treatment, or to identify therapeutic compounds that modulate expression of the biomarkers in vivo or in vitro.2.2 Polynucleotide assays

[0133] In some embodiments, the expression levels of allograft status polynucleotide biomarkers are determined by measuring polynucleotide biomarker levels. For example, the levels of transcripts of specific biomarker genes can be determined from the amount of mRNA, or polynucleotides derived therefrom, present in a blood sample. Polynucleotides can be detected and quantitated by a variety of methods including, but not limited to, microarray analysis, polymerase chain reaction (PCR), reverse transcriptase polymerase chain reaction (RT-PCR), Northern blot, and serial analysis of gene expression (SAGE).

[0134] In illustrative polynucleotide assays, nucleic acid is isolated from cells contained in the biological sample according to standard methodologies (Sambrook, et al., "MOLECULAR CLONING. A LABORATORY MANUAL", Cold Spring Harbor Press, 1989; and Ausubel et al., "CURRENT PROTOCOLS IN MOLECULAR BIOLOGY", John Wiley & Sons Inc., 1994-1998). The nucleic acid is typically fractionated (e.g., poly A+ RNA) or whole cell RNA. Where RNA is used as the subject of detection, it may be desired to convert the RNA to a complementary DNA. In some embodiments, the nucleic acid is amplified by a template-dependent nucleic acid amplification technique. Numerous template dependent processes are available to amplify the allograft statusbiomarker sequences present in a given template sample. An exemplary nucleic acid amplification technique is PCR, which is described in detail in U.S. Pat. Nos. 4,683,195, 4,683,202 and 4,800,159, Ausubel et al. (supra), and in Innis et al., ("PCR Protocols", Academic Press, Inc., San Diego Calif., 1990). Briefly, in PCR, two primer sequences are prepared that are complementary to regions on opposite complementary strands of the biomarker sequence. An excess of deoxynucleotide triphosphates is added to a reaction mixture along with a DNA polymerase, e.g., Taq polymerase. If a cognate allograft status biomarker sequence is present in a sample, the primers will bind to the biomarker and the polymerase will cause the primers to be extended along the biomarker sequence by adding on nucleotides. By raising and lowering the temperature of the reaction mixture, the extended primers will dissociate from the biomarker to form reaction products, excess primers will bind to the biomarker and to the reaction products and the process is repeated. A reverse transcriptase PCR amplification procedure may be performed in order to quantify the amount of mRNA amplified. Methods of reverse transcribing RNA into cDNA are well known and described in Sambrook et al., 1989, supra. Alternative methods for reverse transcription utilize thermostable, RNA-dependent DNA polymerases. These methods are described in WO 90 / 07641. Polymerase chain reaction methodologies are well known in the art. In specific embodiments in which whole cell RNA is used, cDNA synthesis using whole cell RNA as a sample produces whole cell cDNA.

[0135] In certain advantageous embodiments, the template-dependent amplification involves quantification of transcripts in real-time. For example, RNA or DNA may be quantified using the Real-Time PCR (RT-PCR) technique (Higuchi, 1992, et al., Biotechnology 10: 413-417). By determining the concentration of the amplified products of the target DNA in PCR reactions that have completed the same number of cycles and are in their linear ranges, it is possible to determine the relative concentrations of the specific target sequence in the original DNA mixture. If the DNA mixtures are cDNAs synthesized from RNAs isolated from different tissues or cells, the relative abundance of the specific mRNA from which the target sequence was derived can be determined for the respective tissues or cells. This direct proportionality between the concentration of the PCR products and the relative mRNA abundance is only true in the linear range of the PCR reaction. The final concentration of the target DNA in the plateau portion of the curve is determined by the availability of reagents in the reaction mix and is independent of the original concentration of target DNA. In some embodiments, multiplexed, tandem PCR (MT-PCR) is employed, which uses a two-step process for gene expression profiling from small quantities of RNA or DNA, as described for example in US Pat. Appl. Pub. No. 20070190540. In the first step, RNA is converted into cDNA and amplified using multiplexed gene specific primers. In the second step each individual gene is quantitated by RT-PCR. Real-time PCR is typically performed using any PCR instrumentation available in the art. Typically, instrumentation used in real-time PCR data collection and analysis comprises a thermal cycler, optics for fluorescence excitation and emission collection, and optionally a computer and data acquisition and analysis software.

[0136] In some embodiments of RT-PCR assays, a TaqMan™ probe is used for quantitating nucleic acid. Such assays may use energy transfer ("ET"), such as fluorescence resonance energy transfer ("FRET"), to detect and quantitate the synthesized PCR product. Typically, the TaqMan™ probe comprises a fluorescent label e.g., a fluorescent dye) coupled to one end (e.g., the 5'-end) and a quencher molecule is coupled to the other end (e.g., the 3'-end), such that the fluorescent label and the quencher are in close proximity, allowing the quencher tosuppress the fluorescence signal of the dye via FRET. When a polymerase replicates the chimeric amplicon template to which the fluorescent labeled probe is bound, the 5'-nuclease of the polymerase cleaves the probe, decoupling the fluorescent label and the quencher so that label signal (such as fluorescence) is detected. Signal (such as fluorescence) increases with each PCR cycle proportionally to the amount of probe that is cleaved.

[0137] TaqMan™ probes typically comprise a region of contiguous nucleotides having a sequence that is identically present in or complementary to a region of an allograft status biomarker polynucleotide such that the probe is specifically hybridizable to the resulting PCR amplicon. In some embodiments, the probe comprises a region of at least 6 contiguous nucleotides having a sequence that is fully complementary to or identically present in a region of a target allograft status biomarker polynucleotide, such as comprising a region of at least 8 contiguous nucleotides, at least 10 contiguous nucleotides, at least 12 contiguous nucleotides, at least 14 contiguous nucleotides, or at least 16 contiguous nucleotides having a sequence that is complementary to or identically present in a region of a target allograft status biomarker polynucleotide to be detected and / or quantitated.

[0138] In addition to the TaqMan™ assays, other real-time PCR chemistries useful for detecting PCR products in the methods presented herein include, but are not limited to, Molecular Beacons, Scorpion probes and intercalating dyes, such as SYBR Green, EvaGreen, thiazole orange, YO-PRO, TO-PRO, etc. For example, Molecular Beacons, like TaqMan™ probes, use FRET to detect and quantitate a PCR product via a probe having a fluorescent label (e.g., a fluorescent dye) and a quencher attached at the ends of the probe. Unlike TaqMan™ probes, however, Molecular Beacons remain intact during the PCR cycles. Molecular Beacon probes form a stem-loop structure when free in solution, thereby allowing the fluorescent label and quencher to be in close enough proximity to cause fluorescence quenching. When the Molecular Beacon hybridizes to a target, the stem-loop structure is abolished so that the fluorescent label and the quencher become separated in space and the fluorescent label fluoresces. Molecular Beacons are available, e.g., from Gene Link™ (see, www.genelink.com).

[0139] In some embodiments, Scorpion probes can be used as both sequence-specific primers and for PCR product detection and quantitation. Like Molecular Beacons, Scorpion probes form a stem-loop structure when not hybridized to a target nucleic acid. However, unlike Molecular Beacons, a Scorpion probe achieves both sequence-specific priming and PCR product detection. A fluorescent label (e.g., a fluorescent dye molecule) is attached to the 5'-end of the Scorpion probe, and a quencher is attached to the 3'-end. The 3' portion of the probe is complementary to the extension product of the PCR primer, and this complementary portion is linked to the 5'-end of the probe by a non-amplifiable moiety. After the Scorpion primer is extended, the target-specific sequence of the probe binds to its complement within the extended amplicon, thus opening up the stem-loop structure and allowing the fluorescent label on the 5'-end to fluoresce and generate a signal. Scorpion probes are available from, e.g., Premier Biosoft International (see www.premierbiosoft.com / tech_notes / Scorpion.html).

[0140] In some embodiments, labels that can be used on the FRET probes include colorimetric and fluorescent dyes such as Alexa Fluor dyes, BODIPY dyes, such as BODIPY FL; Cascade Blue; Cascade Yellow; coumarin and its derivatives, such as 7-amino-4-methylcoumarin, aminocoumarin and hydroxycoumarin; cyanine dyes, such as Cy3 and Cy5; eosins and erythrosins; fluorescein and its derivatives, such as fluorescein isothiocyanate; macrocyclic chelates oflanthanide ions, such as Quantum Dye™ ; Marina Blue; Oregon Green; rhodamine dyes, such as rhodamine red, tetramethylrhodamine and rhodamine 6G; Texas Red; fluorescent energy transfer dyes, such as thiazole orange-ethidium heterodimer; and, TOTAB.

[0141] Specific examples of dyes include, but are not limited to, those identified above and the following: Alexa Fluor 350, Alexa Fluor 405, Alexa Fluor 430, Alexa Fluor 488, Alexa Fluor 500. Alexa Fluor 514, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 555, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 610, Alexa Fluor 633, Alexa Fluor 647, Alexa Fluor 660, Alexa Fluor 680, Alexa Fluor 700, and, Alexa Fluor 750; amine-reactive BODIPY dyes, such as BODIPY 493 / 503, BODIPY 530 / 550, BODIPY 558 / 568, BODIPY 564 / 570, BODIPY 576 / 589, BODIPY 581 / 591, BODIPY 630 / 650, BODIPY 650 / 655, BODIPY FL, BODIPY R6G, BODIPY TMR, and, BODIPY-TR; Cy3, Cy5, 6- FAM, Fluorescein Isothiocyanate, HEX, 6-JOE, Oregon Green 488, Oregon Green 500, Oregon Green 514, Pacific Blue, REG, Rhodamine Green, Rhodamine Red, Renographin, ROX, SYPRO, TAMRA, 2',4',5',7'-Tetrabromosulfonefluorescein, and TET.

[0142] Examples of dye / quencher pairs ( / .e., donor / acceptor pairs) include, but are not limited to, fluorescein / tetramethylrhodamine; lAEDANS / fluorescein; EDANS / dabcyl; fluorescein / fluorescein; BODIPY FL / BODIPY FL; fluorescein / QSY 7 or QSY 9 dyes. When the donor and acceptor are the same, FRET may be detected, in some embodiments, by fluorescence depolarization. Certain specific examples of dye / quencher pairs ( / .e., donor / acceptor pairs) include, but are not limited to, Alexa Fluor 350 / Alexa Fluor488; Alexa Fluor 488 / Alexa Fluor 546; Alexa Fluor 488 / Alexa Fluor 555; Alexa Fluor 488 / Alexa Fluor 568; Alexa Fluor 488 / Alexa Fluor 594; Alexa Fluor 488 / Alexa Fluor 647; Alexa Fluor 546 / Alexa Fluor 568; Alexa Fluor 546 / Alexa Fluor 594; Alexa Fluor 546 / Alexa Fluor 647; Alexa Fluor 555 / Alexa Fluor 594; Alexa Fluor 555 / Alexa Fluor 647; Alexa Fluor 568 / Alexa Fluor 647; Alexa Fluor 594 / Alexa Fluor 647; Alexa Fluor 350 / QSY35; Alexa Fluor 350 / dabcyl; Alexa Fluor 488 / QSY 35; Alexa Fluor 488 / dabcyl; Alexa Fluor 488 / QSY 7 or QSY 9; Alexa Fluor 555 / QSY 7 or QSY9; Alexa Fluor 568 / QSY 7 or QSY 9; Alexa Fluor 568 / QSY 21; Alexa Fluor 594 / QSY 21; and Alexa Fluor 647 / QSY 21. In some embodiments, the same quencher may be used for multiple dyes, for example, a broad spectrum quencher, such as an Iowa Black™ quencher (Integrated DNA Technologies, Coralville, Iowa) or a Black Hole Quencher™ (BHQ™ ; Sigma-Aldrich, St. Louis, Mo.).

[0143] In some embodiments, for example, in a multiplex reaction in which two or more moieties (such as amplicons) are detected simultaneously, each probe comprises a detectably different dye such that the dyes may be distinguished when detected simultaneously in the same reaction. One skilled in the art can select a set of detectably different dyes for use in a multiplex reaction. In some embodiments, multiple target allograft status biomarker polynucleotides are detected and / or quantitated in a single multiplex reaction. In some embodiments, each probe that is targeted to a different allograft status biomarker polynucleotide is spectrally distinguishable when released from the probe. Thus, each target allograft status biomarker polynucleotide is detected by a unique fluorescence signal.

[0144] Specific examples of fluorescently labeled ribonucleotides useful in the preparation of real-time PCR probes for use in some embodiments of the methods described herein are available from Molecular Probes (Invitrogen), and these include, Alexa Fluor 488-5-UTP, Fluorescein-12-UTP, BODIPY FL-14-UTP, BODIPY TMR-14-UTP, Tetramethylrhodamine-6-UTP, Alexa Fluor 546-14-UTP, Texas Red-5-UTP, and BODIPY TR-14-UTP. Other fluorescent ribonucleotides are available from Amersham Biosciences (GE Healthcare), such as Cy3-UTP and Cy5-UTP.

[0145] Examples of fluorescently labeled deoxyribonucleotides useful in the preparation of real-time PCR probes for use in the methods described herein include Dinitrophenyl (DNP)-l'- dUTP, Cascade Blue-7-dUTP, Alexa Fluor 488-5-dUTP, Fluorescein-12-dUTP, Oregon Green 488-5- dUTP, BODIPY FL-14-dUTP, Rhodamine Green-5-dUTP, Alexa Fluor 532-5-dUTP, BODIPY TMR-14- dUTP, Tetramethylrhodamine-6-dUTP, Alexa Fluor 546-14-dUTP, Alexa Fluor 568-5-dUTP, Texas Red-12-dUTP, Texas Red-5-dUTP, BODIPY TR-14-dUTP, Alexa Fluor 594-5-dUTP, BODIPY 630 / 650- 14-dUTP, BODIPY 650 / 665-14-dUTP; Alexa Fluor 488-7-OBEA-dCTP, Alexa Fluor 546-16-OBEA- dCTP, Alexa Fluor 594-7-OBEA-dCTP, Alexa Fluor 647-12-OBEA-dCTP. Fluorescently labeled nucleotides are commercially available and can be purchased from, e.g., Invitrogen.

[0146] In some embodiments, SAGE analysis is used to determine RNA abundances in a cell sample (see, e.g., Velculescu et al., 1995, Science 270:484-7; Carulli, et al., 1998, Journal of Cellular Biochemistry Supplements 30 / 31 :286-96). SAGE analysis does not require a special device for detection, and is one of the preferable analytical methods for simultaneously detecting the expression of a large number of transcription products. First, poly A+RNA is extracted from cells. Next, the RNA is converted into cDNA using a biotinylated oligo (dT) primer, and treated with a four-base recognizing restriction enzyme (Anchoring Enzyme: AE) resulting in AE-treated fragments containing a biotin group at their 3' terminus. Next, the AE-treated fragments are incubated with streptavidin for binding. The bound cDNA is divided into two fractions, and each fraction is then linked to a different double-stranded oligonucleotide adapter (linker) A or B. These linkers are composed of: (1) a protruding single strand portion having a sequence complementary to the sequence of the protruding portion formed by the action of the anchoring enzyme, (2) a 5' nucleotide recognizing sequence of the IIS-type restriction enzyme (cleaves at a predetermined location no more than 20 bp away from the recognition site) serving as a tagging enzyme (TE), and (3) an additional sequence of sufficient length for constructing a PCR-specific primer. The linker- linked cDNA is cleaved using the tagging enzyme, and only the linker-linked cDNA sequence portion remains, which is present in the form of a short-strand sequence tag. Next, pools of shortstrand sequence tags from the two different types of linkers are linked to each other, followed by PCR amplification using primers specific to linkers A and B. As a result, the amplification product is obtained as a mixture comprising myriad sequences of two adjacent sequence tags (ditags) bound to linkers A and B. The amplification product is treated with the anchoring enzyme, and the free ditag portions are linked into strands in a standard linkage reaction. The amplification product is then cloned. Determination of the clone's nucleotide sequence can be used to obtain a read-out of consecutive ditags of constant length. The presence of mRNA corresponding to each tag can then be identified from the nucleotide sequence of the clone and information on the sequence tags.

[0147] In certain embodiments, target nucleic acids are quantified using blotting techniques, which are well known to those of skill in the art. Southern blotting involves the use of DNA as a target, whereas Northern blotting involves the use of RNA as a target. Each provides different types of information, although cDNA blotting is analogous, in many aspects, to blotting or RNA species. Briefly, a probe is used to target a DNA or RNA species that has been immobilized on a suitable matrix, often a filter of nitrocellulose. The different species should be spatially separated to facilitate analysis. This often is accomplished by gel electrophoresis of nucleic acid species followed by "blotting" on to the filter. Subsequently, the blotted target is incubated with a probe (usually labeled) under conditions that promote denaturation and rehybridization. Because the probe is designed to base pair with the target, the probe will bind a portion of the target sequenceunder renaturing conditions. Unbound probe is then removed, and detection is accomplished as described above. Following detection / quantification, one may compare the results seen in a given subject with a control reaction or a statistically significant reference group or population of control subjects as defined herein. In this way, it is possible to correlate the amount of allograft status biomarker nucleic acid detected with the progression or severity of the disease.

[0148] Also contemplated are microarray based technologies such as those described by Hacia et al. (1996, Nature Genetics 14: 441-447) and Shoemaker et al. (1996, Nature Genetics 14: 450-456). Briefly, these techniques involve quantitative methods for analyzing large numbers of genes rapidly and accurately. By tagging genes with oligonucleotides or using fixed nucleic acid probe arrays, one can employ microarray technology to segregate target molecules as high-density or low density arrays and screen these molecules on the basis of hybridization. See also Pease et al. (1994, Proc. Natl. Acad. Sci. U.S.A. 91: 5022-5026); Fodor et al. (1991, Science 251: 767- 773). Briefly, nucleic acid probes to allograft status biomarker polynucleotides are made and attached to microarrays to be used in the detection methods disclosed herein. The nucleic acid probes attached to the microarray are designed to be substantially complementary to specific expressed allograft status biomarker nucleic acids, i.e., the target sequence (either the target sequence of the sample or to other probe sequences, for example in sandwich assays), such that hybridization of the target sequence and the probes of the present disclosure occur. This complementarity need not be perfect; there may be any number of base pair mismatches, which will interfere with hybridization between the target sequence and the nucleic acid probes. However, if the number of mismatches is so great that no hybridization can occur under even the least stringent of hybridization conditions, the sequence is not a complementary target sequence. In certain embodiments, more than one probe per sequence is used, with either overlapping probes or probes to different sections of the target being used. That is, two, three, four or more probes, with three being desirable, are used to build in a redundancy for a particular target. The probes can be overlapping (i.e. have some sequence in common), or separate.

[0149] In an illustrative microarray analysis, oligonucleotide probes on the microarray are exposed to or contacted with a nucleic acid sample suspected of containing one or more allograft status biomarker polynucleotides under conditions favoring specific hybridization. Sample extracts of DNA or RNA, either single or double-stranded, may be prepared from fluid suspensions of biological materials, or by grinding biological materials, or following a cell lysis step which includes, but is not limited to, lysis effected by treatment with SDS (or other detergents), osmotic shock, guanidinium isothiocyanate and lysozyme. Suitable DNA, which may be used in the method of the present disclosure, includes cDNA. Such DNA may be prepared by any one of a number of commonly used protocols as for example described in Ausubel, et al., 1994, supra, and Sambrook, et al., 1989, supra.

[0150] Suitable RNA, which may be used in the detection methods disclosed herein, includes messenger RNA, complementary RNA transcribed from DNA (cRNA) or genomic or subgenomic RNA. Such RNA may be prepared using standard protocols as for example described in the relevant sections of Ausubel, et al. 1994, supra and Sambrook, et al. 1989, supra).

[0151] cDNA may be fragmented, for example, by sonication or by treatment with restriction endonucleases. Suitably, cDNA is fragmented such that resultant DNA fragments are of a length greater than the length of the immobilized oligonucleotide probe(s) but small enough to allow rapid access thereto under suitable hybridization conditions. Alternatively, fragments of cDNAmay be selected and amplified using a suitable nucleotide amplification technique, as described for example above, involving appropriate random or specific primers.

[0152] Usually the target allograft status biomarker polynucleotides are detectably labeled so that their hybridization to individual probes can be determined. The target polynucleotides are typically detectably labeled with a reporter molecule illustrative examples of which include chromogens, catalysts, enzymes, fluorochromes, chemiluminescent molecules, bioluminescent molecules, lanthanide ions (e.g., Eu34), a radioisotope and a direct visual label. In the case of a direct visual label, use may be made of a colloidal metallic or non-metallic particle, a dye particle, an enzyme or a substrate, an organic polymer, a latex particle, a liposome, or other vesicle containing a signal producing substance and the like. Illustrative labels of this type include large colloids, for example, metal colloids such as those from gold, selenium, silver, tin and titanium oxide. In some embodiments in which an enzyme is used as a direct visual label, biotinylated bases are incorporated into a target polynucleotide.

[0153] The hybrid-forming step can be performed under suitable conditions for hybridizing oligonucleotide probes to test nucleic acid including DNA or RNA. In this regard, reference may be made, for example, to NUCLEIC ACID HYBRIDIZATION, A PRACTICAL APPROACH (Homes and Higgins, eds.) (IRL press, Washington D.C., 1985). In general, whether hybridization takes place is influenced by the length of the oligonucleotide probe and the polynucleotide sequence under test, the pH, the temperature, the concentration of mono- and divalent cations, the proportion of G and C nucleotides in the hybrid-forming region, the viscosity of the medium and the possible presence of denaturants. Such variables also influence the time required for hybridization. The preferred conditions will therefore depend upon the particular application. Such empirical conditions, however, can be routinely determined without undue experimentation.

[0154] After the hybrid-forming step, the probes are washed to remove any unbound nucleic acid with a hybridization buffer. This washing step leaves only bound target polynucleotides. The probes are then examined to identify which probes have hybridized to a target polynucleotide.

[0155] The hybridization reactions are then detected to determine which of the probes has hybridized to a corresponding target sequence. Depending on the nature of the reporter molecule associated with a target polynucleotide, a signal may be instrumentally detected by irradiating a fluorescent label with light and detecting fluorescence in a fluorimeter; by providing for an enzyme system to produce a dye which could be detected using a spectrophotometer; or detection of a dye particle or a colored colloidal metallic or non-metallic particle using a reflectometer; in the case of using a radioactive label or chemiluminescent molecule employing a radiation counter or autoradiography. Accordingly, a detection means may be adapted to detect or scan light associated with the label which light may include fluorescent, luminescent, focused beam or laser light. In such a case, a charge couple device (CCD) or a photocell can be used to scan for emission of light from a probe:target polynucleotide hybrid from each location in the microarray and record the data directly in a digital computer. In some cases, electronic detection of the signal may not be necessary. For example, with enzymatically generated color spots associated with nucleic acid array format, visual examination of the array will allow interpretation of the pattern on the array. In the case of a nucleic acid array, the detection means is suitably interfaced with pattern recognition software to convert the pattern of signals from the array into a plain language genetic profile. In certain embodiments, oligonucleotide probes specific for different allograft statusbiomarker polynucleotides are in the form of a nucleic acid array and detection of a signal generated from a reporter molecule on the array is performed using a 'microarray reader'. A detection system that can be used by a microarray reader is described for example by Pirrung et al. (U.S. Patent No. 5,143,854). The microarray reader will typically also incorporate some signal processing to determine whether the signal at a particular array position or feature is a true positive or maybe a spurious signal. Exemplary microarray readers are described for example by Fodor et al. (U.S. Patent No., 5,925,525). Alternatively, when the array is made using a mixture of individually addressable kinds of labeled microbeads, the reaction may be detected using flow cytometry.

[0156] In certain embodiments, the allograft status biomarker is a target RNA e.g., mRNA) or a DNA copy of the target RNA whose level or abundance is measured using at least one nucleic acid probe that hybridizes under at least high stringency conditions to the target RNA or to the DNA copy, wherein the nucleic acid probe comprises at least 15 e.g., 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or more) contiguous nucleotides of allograft status biomarker polynucleotide. In some embodiments, the measured level or abundance of the target RNA or its DNA copy is normalized to the level or abundance of a reference RNA or a DNA copy of the reference RNA. Suitably, the nucleic acid probe is immobilized on a solid or semi-solid support. In illustrative examples of this type, the nucleic acid probe forms part of a spatial array of nucleic acid probes. In some embodiments, the level of nucleic acid probe that is bound to the target RNA or to the DNA copy is measured by hybridization (e.g., using a nucleic acid array). In other embodiments, the level of nucleic acid probe that is bound to the target RNA or to the DNA copy is measured by nucleic acid amplification (e.g., using a polymerase chain reaction (PCR)). In still other embodiments, the level of nucleic acid probe that is bound to the target RNA or to the DNA copy is measured by nuclease protection assay.

[0157] Sequencing technologies including DNA sequencing and RNA sequencing, such as Sanger sequencing, pyrosequencing, sequencing by ligation, massively parallel sequencing, also called "Next-generation sequencing" (NGS), whole transcriptome shotgun sequence (WTSS) ( also referred to as"RNAseq"), nanopore sequencing, nanostring sequencing and other high-throughput sequencing approaches with or without sequence amplification of the target can also be used to detect or quantify the presence of allograft status biomarker polynucleotides in a sample. Sequence-based methods can provide further information regarding alternative splicing and sequence variation in previously identified genes. Sequencing technologies include a number of steps that are grouped broadly as template preparation, sequencing, detection and data analysis. Current methods for template preparation involve randomly breaking genomic DNA into smaller sizes from which each fragment is immobilized to a support. The immobilization of spatially separated fragment allows thousands to billions of sequencing reaction to be performed simultaneously. A sequencing step may use any of a variety of methods that are commonly known in the art. One specific example of a sequencing step uses the addition of nucleotides to the complementary strand to provide the DNA sequence. The detection steps range from measuring bioluminescent signal of a synthesized fragment to four-color imaging of single molecule. In some embodiments in which NGS is used to detect or quantify the presence of allograft status nucleic acid biomarker in a sample, the methods are suitably selected from semiconductor sequencing (Ion Torrent; Personal Genome Machine); Helicos True Single Molecule Sequencing (tSMS) (Harris et al. 2008, Science 320:106-109); 454 sequencing (Roche) (Margulies et al. 2005, Nature, 437, 376-380); SOLID technology (Applied Biosystems); SOLEXA sequencing (Illumina); single molecule, real-time (SMRT™) technology of Pacific Biosciences; nanopore sequencing (Son! and Meller, 2007. Clin Chem 53: 1996-2001); DNA nanoball sequencing; sequencing using technology from Dover Systems (Polonator), and technologies that do not require amplification or otherwise transform native DNA prior to sequencing e.g., Pacific Biosciences and Helicos), such as nanopore-based strategies e.g., Oxford Nanopore, Genia Technologies, and Nabsys).2.3 Compositions

[0158] In non-limiting embodiments of the polynucleotide assays, compositions are prepared for use in the indicator-determining methods disclosed herein. These compositions may comprise a mixture of a DNA polymerase (e.g., a thermostable DNA polymerase), blood leukocyte cDNA from a subject who suitably has at least one clinical sign of allograft dysfunction, wherein the blood leukocyte cDNA comprises a plurality of allograft status cDNA biomarkers (e.g., 2, 3, 4, 5, or more cDNAs) of a biomarker panel, wherein the biomarker panel comprises, consists or consists essentially of a CASP1 cDNA, and one or both of an APP cDNA and an AQP3 cDNA, and wherein the composition further comprises for individual cDNA biomarkers of the biomarker panel at least one oligonucleotide primer or probe that hybridizes to the cDNA biomarker. In some embodiments, the biomarker panel further comprises at least one ancillary predictive performance-improving cDNA biomarker (e.g., 1, 2, 3, or more cDNA biomarkers) selected from TABLE 1, wherein the composition further comprises for the at least one ancillary predictive performance-improving cDNA biomarker at least one oligonucleotide primer or probe that hybridizes to the cDNA biomarker.

[0159] In some of the same or other embodiments, the compositions comprise for respective cDNA two oligonucleotide primers that hybridize to opposite complementary strands of the cDNA. In representative embodiments, the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the APP polynucleotide and an ancillary predictive performance-improving polynucleotide biomarker selected from TABLE 2. In illustrative examples of this type, the biomarker panel is selected from the biomarker panels set forth in TABLE 3. In other representative embodiments, the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the AQP3 polynucleotide and a predictive performanceimproving polynucleotide biomarker selected from TABLE 4. Non-limiting examples of biomarker panels of this type may be selected from the biomarker panels set forth in TABLE 5. In still other representative embodiments, the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the APP polynucleotide and two predictive performance-improving polynucleotide biomarkers selected TABLE 6. Exemplary biomarker panels of this type may be selected from the biomarker panels set forth in TABLE 7. In other embodiments, the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the AQP3 polynucleotide and two predictive performance-improving polynucleotide biomarkers selected from TABLE 8. Representative biomarker panels of this type may be selected from the biomarker panels set forth in TABLE 9. In further embodiments, the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the APP polynucleotide, the AQP3 polynucleotide and a predictive performance-improving polynucleotide biomarker selected from TABLE 10. Illustrative biomarker panels of this type are set forth in TABLE 11. Still other embodiments of the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the APP polynucleotide and three predictive performance-improving polynucleotide biomarkers selectedfrom TABLE 12. In non-limiting examples of this type, the biomarker panel is selected from the biomarker panels set forth in TABLE 13. In further representative embodiments, the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the AQP3 polynucleotide and three predictive performance-improving polynucleotide biomarkers selected from TABLE 14. Exemplary biomarker panels of this type may be selected from the biomarker panels set forth in TABLE 15. In still other embodiments, the biomarker panel may comprise, consist or consist essentially of the CASP1 polynucleotide, the APP polynucleotide, the AQP3 polynucleotide and two predictive performance-improving polynucleotide biomarkers selected TABLE 16. Representative biomarker panels of this type may be selected from the biomarker panels set forth in TABLE 17.

[0160] In some of the same or other embodiments, the compositions comprise for a respective cDNA an oligonucleotide probe that hybridizes to the cDNA or a polynucleotide corresponding thereto e.g., a polynucleotide product resulting nucleic acid amplification of the cDNA). The oligonucleotide probe may comprise a heterologous label e.g., a fluorescent label). In embodiments in which the oligonucleotide probe comprises a heterologous label, the labeled oligonucleotide probe may comprise a fluorophore. In representative examples of this type, the labeled oligonucleotide probe further comprises a quencher. In certain embodiments, different labeled oligonucleotide probes are included in the composition for hybridizing to different cDNAs, wherein individual oligonucleotide probes comprise detectably distinct labels e.g. different fluorophores), or at least a subset of oligonucleotide probes comprises the same label e.g. same fluorophore). In some embodiments, the compositions comprise for each of at least 2, 4, 5, 6, 7, or 8 of the cDNAs at least one oligonucleotide primer and / or probe that hybridizes to the cDNA. In other embodiments, the compositions comprise for each of up to 2, 4, 5, 6, 7, or 8 of the cDNAs at least one oligonucleotide primer and / or probe that hybridizes to the cDNA. Individual cDNAs and their corresponding oligonucleotide primer(s) and / or probe(s) may be present in separate reaction vessels or in the same reaction vessel.

[0161] In embodiments in which there is an increased likelihood of the presence of allograft dysfunction in the subject:• the CASP1 cDNA is typically present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• the CASP1 cDNA is typically present in the blood leukocyte cDNA sample at about the same level as in a reference blood sample obtained from a subject with allograft dysfunction;• the APP cDNA is typically present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• the APP cDNA is typically present in the blood leukocyte cDNA sample at about the same level as in a reference blood sample obtained from a subject with allograft dysfunction;• the AQP3 cDNA is typically present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject orfrom a subject who has undergone an organ transplant without experiencing allograft dysfunction;• the AQP3 cDNA is typically present in the blood leukocyte cDNA sample at about the same level as in a reference blood sample obtained from a subject with allograft dysfunction.

[0162] In embodiments in which there is an increased likelihood of the absence of allograft dysfunction in the subject:• the CASP1 cDNA is typically present in the blood leukocyte cDNA sample at a lower level than in a reference blood sample obtained from a subject with allograft dysfunction;• the CASP1 cDNA is typically present in the blood leukocyte cDNA sample at about the same level as in a reference blood sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• the APP cDNA is typically present in the blood leukocyte cDNA sample at a higher level than in a reference blood sample obtained from a subject with allograft dysfunction;• the APP cDNA is typically present in the blood leukocyte cDNA sample at about the same level as in a reference blood sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• the AQP3 cDNA is typically present in the blood leukocyte cDNA sample at a higher level than in a reference blood sample obtained from a subject with allograft dysfunction;• the AQP3 cDNA is typically present in the blood leukocyte cDNA sample at about the same level as in a reference blood sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction.

[0163] Blood samples e.g., blood leukocyte samples) may comprise polynucleotide biomarkers that are expressed in patients that have undergone an organ transplant, and that define a common biomarker profile or signature that is characteristic of, and shared between, such subjects regardless of the allograft status. Such "organ transplant" polynucleotide biomarkers include but are not limited to TANK, DNAJA1, MAN1A1, HMOX2, SDHB, DSTN, and DDX24 polynucleotides.

[0164] In representative embodiments of this type, the blood leukocyte cDNA is characteristic of a subject having undergone an organ transplant, wherein allograft dysfunction is present in the subject, or wherein allograft dysfunction is absent in the subject, wherein:• TANK cDNA is typically present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a healthy subject;• DNAJA1 cDNA is typically present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a healthy subject;• MAN1A1 cDNA is typically present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a healthy subject;• HMOX2 cDNA is typically present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject;• SDHB cDNA is typically present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject;• DSTN cDNA is typically present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject;• DDX24 cDNA is typically present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject.

[0165] In some of the same and other embodiments, the blood leukocyte cDNA is characteristic of a subject having undergone an organ transplant, wherein allograft dysfunction is present in the subject, or wherein allograft dysfunction is absent in the subject, wherein:• TANK cDNA is typically present at a lower level than HMOX2 cDNA in the blood leukocyte cDNA sample;• TANK cDNA is typically present at a lower level than SDHB cDNA in the blood leukocyte cDNA sample;• TANK cDNA is typically present at a higher level than DSTN cDNA in the blood leukocyte cDNA sample;• TANK cDNA is typically present at a lower level than DDX24 cDNA in the blood leukocyte cDNA sample;• DNAJA1 cDNA is typically present at a higher level than HMOX2 cDNA in the blood leukocyte cDNA sample;• DNAJA1 cDNA is typically present at a higher level than SDHB cDNA in the blood leukocyte cDNA sample;• DNAJA1 cDNA is typically present at a higher level than DSTN cDNA in the blood leukocyte cDNA sample;• DNAJA1 cDNA is typically present at a higher level than DDX24 cDNA in the blood leukocyte cDNA sample;• MAN1A1 cDNA is typically present at a higher level than HMOX2 cDNA in the blood leukocyte cDNA sample;• MAN1A1 cDNA is typically present at a higher level than SDHB cDNA in the blood leukocyte cDNA sample;• MAN1A1 cDNA is typically present at a lower level than DSTN cDNA in the blood leukocyte cDNA sample;• MAN1A1 cDNA is typically present at a lower level than DDX24 cDNA in the blood leukocyte cDNA sample.

[0166] In some embodiments, the blood leukocyte cDNA is characteristic of a subject with allograft dysfunction, wherein:• TANK cDNA is typically present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a healthy subject, or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• DNAJA1 cDNA is typically present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a healthy subject, or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• MAN1A1 cDNA is typically present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a healthy subject, or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• HM0X2 cDNA is typically present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject, or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• SDHB cDNA is typically present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject, or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• DSTN cDNA is typically present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject, or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• DDX24 cDNA is typically present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject, or from a subject who has undergone an organ transplant without experiencing allograft dysfunction.

[0167] In some embodiments, the blood leukocyte cDNA is characteristic of a subject without allograft dysfunction, wherein:• TANK cDNA is typically present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a subject with allograft dysfunction;• DNAJA1 cDNA is typically present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a subject with allograft dysfunction;• MAN1A1 cDNA is typically present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a subject with allograft dysfunction;• HMOX2 cDNA is typically present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a subject with allograft dysfunction;• SDHB cDNA is typically present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a subject with allograft dysfunction;• DSTN cDNA is typically present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a subject with allograft dysfunction;• DDX24 cDNA is typically present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a subject with allograft dysfunction.2.4 Analysis of biomarker data

[0168] Biomarker data may be analyzed by a variety of methods to identify biomarkers and determine the statistical significance of differences in observed levels of biomarkers between test and reference expression profiles in order to evaluate whether a patient has allograft dysfunction or allograft tolerance. For any particular allograft status biomarker, a distribution of allograft status biomarker levels for subjects with allograft dysfunction or allograft tolerance willlikely overlap. Under such conditions, a test does not absolutely distinguish a first condition (e.g., allograft dysfunction) and a second condition e.g., allograft tolerance) with 100% accuracy, and the area of overlap indicates where the test cannot distinguish the first condition and the second condition. A threshold is selected, above which (or below which, depending on how an allograft status biomarker changes with a specified condition or prognosis) the test is considered to be "positive" and below which the test is considered to be "negative." The area under the ROC curve (AUC) provides the C-statistic, which is a measure of the probability that the perceived measurement will allow correct identification of a condition (see, e.g., Hanley et al., Radiology 143: 29-36 (1982)).

[0169] Alternatively, or in addition, thresholds may be established by obtaining an earlier biomarker result from the same patient, to which later results may be compared. In these embodiments, the individual in effect acts as their own "control group." In biomarkers that increase with condition severity or prognostic risk, an increase over time in the same patient can indicate a worsening of the condition or a failure of a treatment regimen, while a decrease over time can indicate remission of the condition or success of a treatment regimen.

[0170] In some embodiments, a positive likelihood ratio, negative likelihood ratio, odds ratio, and / or AUC or receiver operating characteristic (ROC) values are used as a measure of a method's ability to predict risk or to diagnose a disease or condition (e.g., allograft dysfunction or allograft tolerance). As used herein, the term "likelihood ratio" is the probability that a given test result would be observed in a subject with a condition of interest divided by the probability that that same result would be observed in a patient without the condition of interest. Thus, a positive likelihood ratio is the probability of a positive result observed in subjects with the specified condition (e.g., allograft dysfunction or allograft tolerance) divided by the probability of a positive results in subjects without the specified condition. A negative likelihood ratio is the probability of a negative result in subjects without the specified condition divided by the probability of a negative result in subjects with specified condition. The term "odds ratio," as used herein, refers to the ratio of the odds of an event occurring in one group (e.g., allograft dysfunction) to the odds of it occurring in another group (e.g., allograft tolerance), or to a data-based estimate of that ratio. The term "area under the curve" or "AUC" refers to the area under the curve of a receiver operating characteristic (ROC) curve, both of which are well known in the art. AUC measures are useful for comparing the accuracy of a classifier across the complete data range. Classifiers with a greater AUC have a greater capacity to classify unknowns correctly between two groups of interest (e.g., allograft dysfunction and allograft tolerance). ROC curves are useful for plotting the performance of a particular feature (e.g., any of the allograft status biomarkers disclosed herein and / or any item of additional biomedical information) in distinguishing or discriminating between two populations (e.g., allograft dysfunction and allograft tolerance). Typically, the feature data across the entire population (e.g., subjects with allograft dysfunction and subjects with allograft tolerance) are sorted in ascending order based on the value of a single feature. Then, for each value for that feature, the true positive and false positive rates for the data are calculated. The sensitivity is determined by counting the number of cases above the value for that feature and then dividing by the total number of cases. The specificity is determined by counting the number of controls below the value for that feature and then dividing by the total number of controls. Although this definition refers to scenarios in which a feature is elevated in one patient group compared to another patient group, this definition also applies to scenarios in which a feature is lower in one patient groupcompared to the other patient group (in such a scenario, samples below the value for that feature would be counted). ROC curves can be generated for a single feature as well as for other single outputs, for example, a combination of two or more features (e.g., a combination of two or more biomarker values) can be mathematically combined e.g., added, subtracted, multiplied, etc.) to produce a single value, and this single value can be plotted in a ROC curve. Additionally, any combination of multiple features (e.g., a combination of multiple biomarker values), in which the combination derives a single output value, can be plotted in a ROC curve. These combinations of features may comprise a test. The ROC curve is the plot of the sensitivity of a test against the specificity of the test, where sensitivity is traditionally presented on the vertical axis and specificity is traditionally presented on the horizontal axis. Thus, "AUC ROC values" are equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. An AUC ROC value may be thought of as equivalent to the Mann-Whitney U test, which tests for the median difference between scores obtained in the two groups considered if the groups are of continuous data, or to the Wilcoxon test of ranks.

[0171] In some embodiments, a panel of allograft status biomarkers comprising (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) is selected to discriminate between subjects with a first condition (e.g., allograft dysfunction) and subjects with a second condition (e.g., allograft tolerance) with at least about 50%, 55% 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% accuracy or having a C-statistic of at least about 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95.

[0172] In the case of a positive likelihood ratio, a value of 1 indicates that a positive result is equally likely among subjects in both the "first condition" and "second condition" groups; a value greater than 1 indicates that a positive result is more likely in the first condition group; and a value less than 1 indicates that a positive result is more likely in the second condition group. In this context, "first condition" group is meant to refer to a group having one characteristic (e.g., the presence of allograft dysfunction, or the presence of allograft tolerance) and "second condition" group lacking the same characteristic. In the case of a negative likelihood ratio, a value of 1 indicates that a negative result is equally likely among subjects in both the "first condition" and "second condition" groups; a value greater than 1 indicates that a negative result is more likely in the "first condition" group; and a value less than 1 indicates that a negative result is more likely in the "second condition" group. In the case of an odds ratio, a value of 1 indicates that a positive result is equally likely among subjects in both the "first condition" and "second condition" groups; a value greater than 1 indicates that a positive result is more likely in the "first condition" group; and a value less than 1 indicates that a positive result is more likely in the "second condition" group. In the case of an AUC ROC value, this is computed by numerical integration of the ROC curve. The range of this value can be 0.5 to 1.0. A value of 0.5 indicates that a classifier (e.g., an allograft dysfunction biomarker profile) is no better than a 50% chance to classify unknowns correctly between two groups of interest (e.g., allograft dysfunction and allograft tolerance), while 1.0 indicates the relatively best diagnostic accuracy. In certain embodiments, individual allograft status biomarker panels are selected to exhibit a positive or negative likelihood ratio of at least about 1.5 or more or about 0.67 or less, at least about 2 or more or about 0.5 or less, at least about 5 or more or about 0.2 or less, at least about 10 or more or about 0.1 or less, or at least about 20 or more or about 0.05 or less.

[0173] In certain embodiments, individual allograft status biomarker panels are selected to exhibit an odds ratio of at least about 2 or more or about 0.5 or less, at least about 3 ormore or about 0.33 or less, at least about 4 or more or about 0.25 or less, at least about 5 or more or about 0.2 or less, or at least about 10 or more or about 0.1 or less.

[0174] In certain embodiments, individual allograft status biomarker panels are selected to exhibit an AUC ROC value of greater than 0.5, preferably at least 0.6, more preferably 0.7, still more preferably at least 0.8, even more preferably at least 0.9, and most preferably at least 0.95.

[0175] In some cases, multiple thresholds may be determined in so-called "tertile," "quartile," or "quintile" analyses. In these methods, the "diseased" and "control groups" (or "high risk" and "low risk") groups are considered together as a single population, and are divided into 3, 4, or 5 (or more) "bins" having equal numbers of individuals. The boundary between two of these "bins" may be considered "thresholds." A risk (of a particular diagnosis or prognosis for example) can be assigned based on which "bin" a test subject falls into.

[0176] In other embodiments, particular thresholds for the allograft status biomarkers measured are not relied upon to determine if the biomarker levels obtained from a subject are correlated to a particular diagnosis or prognosis. For example, a temporal change in the biomarkers can be used to rule in or out one or more particular diagnoses and / or prognoses. Alternatively, allograft status biomarkers may be correlated to a condition, disease, prognosis, etc., by the presence or absence of one or more allograft status biomarkers in a particular assay format. In the case of allograft status biomarker panels, the detection methods disclosed herein may utilize an evaluation of the entire population or subset of allograft status biomarkers disclosed herein to provide a single result value (e.g., a "panel response" value expressed either as a numeric score or as a percentage risk). In such embodiments, an increase, decrease, or other change (e.g., slope over time) in a certain subset of allograft status biomarkers may be sufficient to indicate a particular condition or future outcome in one patient, while an increase, decrease, or other change in a different subset of allograft status biomarkers may be sufficient to indicate the same or a different condition or outcome in another patient.

[0177] In certain embodiments, a panel of allograft status biomarkers is selected to assist in distinguishing a pair of groups ( / .e., assist in assessing whether a subject has an increased likelihood of being in one group or the other group of the pair) selected from "allograft dysfunction" and "allograft tolerance" or "high risk" and "low risk" with at least about 70%, 80%, 85%, 90% or 95% sensitivity, suitably in combination with at least about 70% 80%, 85%, 90% or 95% specificity. In some embodiments, both the sensitivity and specificity are at least about 75%, 80%, 85%, 90% or 95%.

[0178] The phrases "assessing the likelihood" and "determining the likelihood," as used herein, refer to methods by which the skilled artisan can predict the presence or absence of a condition (e.g., allograft dysfunction or allograft tolerance) in a patient. The skilled artisan will understand that this phrase includes within its scope an increased probability that a condition is present or absent in a patient; that is, that a condition is more likely to be present or absent in a subject. For example, the probability that an individual identified as having a specified condition actually has the condition may be expressed as a "positive predictive value" or"PPV." Positive predictive value can be calculated as the number of true positives divided by the sum of the true positives and false positives. PPV is determined by the characteristics of the predictive methods disclosed herein as well as the prevalence of the condition in the population analyzed. The statistical algorithms can be selected such that the positive predictive value in a population havinga condition prevalence is in the range of 70% to 99% and can be, for example, at least 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0179] In other examples, the probability that an individual identified as not having a specified condition actually does not have that condition may be expressed as a "negative predictive value" or "NPV." Negative predictive value can be calculated as the number of true negatives divided by the sum of the true negatives and false negatives. Negative predictive value is determined by the characteristics of the diagnostic or prognostic method, system, or code as well as the prevalence of the disease in the population analyzed. The statistical methods and models can be selected such that the negative predictive value in a population having a condition prevalence is in the range of about 70% to about 99% and can be, for example, at least about 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0180] In some embodiments, a subject is determined as having a significant likelihood of having or not having a specified condition (e.g., allograft dysfunction or allograft tolerance). By "significant likelihood" is meant that the subject has a reasonable probability (0.6, 0.7, 0.8, 0.9 or more) of having, or not having, a specified condition.

[0181] The allograft status biomarker analysis disclosed herein permits the generation of high-density data sets that can be evaluated using informatics approaches. High data density informatics analytical methods are known and software is available to those in the art, e.g., cluster analysis (Pirouette, Informetrix), class prediction (SIMCA-P, Umetrics), principal components analysis of a computationally modeled dataset (SIMCA-P, Umetrics), 2D cluster analysis (GeneLinker Platinum, Improved Outcomes Software), and metabolic pathway analysis (biotech.icmb.utexas.edu). The choice of software packages offers specific tools for questions of interest (Kennedy et al., Solving Data Mining Problems Through Pattern Recognition. Indianapolis: Prentice Hall PTR, 1997; Golub et al., (2999) Science 286:531-7; Eriksson et al., Multi and Megavariate Analysis Principles and Applications: Umetrics, Umea, 2001). In general, any suitable mathematic analyses can be used to evaluate a plurality e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.) of allograft status biomarkers disclosed herein with respect to a condition selected from allograft dysfunction and allograft tolerance. For example, methods such as multivariate analysis of variance, multivariate regression, and / or multiple regression can be used to determine relationships between dependent variables (e.g., clinical measures) and independent variables (e.g., levels of allograft status biomarkers). Clustering, including both hierarchical and non- hierarchical methods, as well as non-metric Dimensional Scaling can be used to determine associations or relationships among variables and among changes in those variables.

[0182] In addition, principal component analysis is a common way of reducing the dimension of studies, and can be used to interpret the variance-covariance structure of a data set. Principal components may be used in such applications as multiple regression and cluster analysis. Factor analysis is used to describe the covariance by constructing "hidden" variables from the observed variables. Factor analysis may be considered an extension of principal component analysis, where principal component analysis is used as parameter estimation along with the maximum likelihood method. Furthermore, simple hypothesis such as equality of two vectors of means can be tested using Hotelling's T squared statistic.

[0183] In some embodiments, the data sets corresponding to allograft status biomarker panels are used to create a diagnostic or predictive rule or model based on the application of a statistical and machine learning algorithm. Such an algorithm uses relationships between a allograft status biomarker panel and a condition selected from allograft dysfunction and allograft tolerance observed in control subjects or typically cohorts of control subjects (sometimes referred to as training data), which provides combined control or reference allograft status biomarker panels for comparison with allograft status biomarker panels of a subject. The data are used to infer relationships that are then used to predict the status of a subject, including the presence or absence of one of the conditions referred to above.

[0184] Practitioners skilled in the art of data analysis recognize that many different forms of inferring relationships in the training data may be used without materially changing the detection methods disclosed herein. The data presented in the Tables and Examples herein has been used to generate illustrative minimal combinations of allograft status biomarkers (models) that differentiate between allograft dysfunction and allograft tolerance using feature selection based on AUC maximization in combination with analytical model classification, including for example classification using one or more of: an additive model; a linear model; a support vector machine; a neural network model; a random forest model; a regression model; a genetic algorithm; an annealing algorithm; a weighted sum; a nearest neighbor model; and a probabilistic model. The biomarker tables disclosed herein provide illustrative lists of allograft status biomarkers ranked according to their p value. Illustrative models comprising at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8 allograft status biomarkers were able to develop a classifier or generative algorithm for discriminating between two control groups as defined above with significantly improved positive predictive values compared to conventional methodologies. This algorithm can be advantageously applied to determine presence or probability of allograft dysfunction or allograft tolerance in a patient, and thus diagnose the patient as having or as likely to have allograft dysfunction or allograft tolerance.

[0185] In some embodiments, evaluation of allograft status biomarkers includes determining the levels of individual allograft status biomarkers, which correlate with the presence or absence of a condition, as defined above. In certain embodiments, the techniques used for detection of allograft status biomarkers may include internal or external standards to permit quantitative or semi-quantitative determination of those biomarkers, to thereby enable a valid comparison of the level of the allograft status biomarkers in a biological sample with the corresponding allograft status biomarkers in a reference sample or samples. Such standards can be determined by the skilled practitioner using standard protocols. In specific examples, absolute values for the level or functional activity of individual expression products are determined.

[0186] In semi-quantitative methods, a threshold or cut-off value is suitably determined, and is optionally a predetermined value. In particular embodiments, the threshold value is predetermined in the sense that it is fixed, for example, based on previous experience with the assay and / or a population of affected and / or unaffected subjects. Alternatively, the predetermined value can also indicate that the method of arriving at the threshold is predetermined or fixed even if the particular value varies among assays or may even be determined for every assay run.

[0187] In some embodiments, the level of a allograft status biomarker is normalized against a housekeeping biomarker. The term "housekeeping biomarker" refers to a biomarker orgroup of biomarkers (e.g., polynucleotides and / or polypeptides), which are typically found at a constant level in the cell type(s) being analyzed and across the conditions being assessed. In some embodiments, the housekeeping biomarker is a "housekeeping gene." A "housekeeping gene" refers herein to a gene or group of genes which encode proteins whose activities are essential for the maintenance of cell function and which are typically found at a constant level in the cell type(s) being analyzed and across the conditions being assessed.

[0188] There is no intended limitation on the methodology used to normalize the values of the measured biomarkers provided that the same methodology is used for testing a human subject sample as was used to generate a risk categorization table or threshold value. Many methods for data normalization exist and are familiar to those skilled in the art. These include methods such as background subtraction, scaling, MoM analysis, linear transformation, least squares fitting, etc. The goal of normalization is to equate the varying measurement scales for the separate biomarkers such that the resulting values may be combined according to a weighting scale as determined and designed by the user or by the machine learning system and are not influenced by the absolute or relative values of the biomarker found within nature.

[0189] In certain embodiments, the biomarkers are measured and those resulting values normalized and then summed to obtain a composite score. In certain aspects, normalizing the measured biomarker values comprises determining the multiple of median (MoM) score. In other aspects, the present method further comprises weighting the normalized values before summing to obtain a composite score. In illustrative examples of this type, the median value of each biomarker is used to normalize all measurements of that specific biomarker, for example, as provided in Kutteh et al. (Obstet. Gynecol. 1994;84:811-815) and Palomaki et al. (Clin. Chem. Lab. Med 2001;39: 1137-1145). Thus, any measured biomarker level is divided by the median value of a disclosed condition group (e.g., a group selected from allograft dysfunction and allograft tolerance), resulting in a MoM value. The MoM values can be combined (namely, summed or added) for each biomarker in the panel resulting in a panel MoM value or aggregate MoM score for each sample.

[0190] If desired, a machine learning system may be utilized to determine weighting of the normalized values as well as how to aggregate the values (e.g., determine which polynucleotide biomarkers are most predictive, and assign a greater weight to these markers).

[0191] In specific embodiments, a composite score, which equates to a risk probability score disclosed herein, for determining an indicator used in assessing a likelihood of a subject having allograft dysfunction or allograft tolerance, is determined using an algorithm that constructs ratios from individual genes within a data matrix. In illustrative examples of this type, the algorithm employs the following process, in which reference to genes equates to a reference to polynucleotide biomarkers disclosed herein:1. Input: The algorithm accepts a data matrix where each column represents a gene, and each row represents a sample. The matrix suitably has column names corresponding to the gene names.2. Sorting Genes: The genes (columns) are sorted alphabetically to ensure a consistent and reproducible order in ratio construction.3. Ratio Calculation: For each gene, the algorithm calculates the ratio of its expression level to the expression levels of all subsequent genes in the sorted list. This generates a series of ratios for each gene with every other gene that comes after it in the order.4. Naming Ratios: Each ratio is named by concatenating the names of the two genes involved, separated by a double dashThis naming convention helps in identifying the gene pairs used to compute each ratio.5. Combining Results: The algorithm combines all the calculated ratios into a single matrix, where each column represents a specific gene ratio, and each row corresponds to a sample.

[0192] For example, in embodiments in which the biomarker panel consists of 3 genes, A, B and C, as an input matrix, an illustrative algorithm may proceed as follows:1. Sorting: The genes are sorted alphabetically: A, B, C.2. Ratio Calculation: The algorithm calculates the following ratios: o A / B, A / C o B / C3. Naming Ratios: Each ratio is named by concatenating the gene names, for example, with a double dash: o A-B, A-C o B-C4. Combined Matrix: The resulting matrix will have columns named: A--B, A--C, B--C. Each column contains the ratios of gene expressions for all samples.

[0193] Alternatively, in embodiments in which the biomarker panel consists of 4 genes, A, B, C and D, as an input matrix, an illustrative algorithm may proceed as follows:1. Sorting: The genes are sorted alphabetically: A, B, C, D.2. Ratio Calculation: The algorithm calculates the following ratios: o A / B, A / C, A / D o B / C, B / D o C / D3. Naming Ratios: Each ratio is named by concatenating the gene names, for example, with a double dash: o A-B, A-C, A-D o B-C, B-D o C-D4. Combined Matrix: The resulting matrix will have columns named: A--B, A--C, A--D, B--C, B--D, C--D. Each column contains the ratios of gene expressions for all samples.

[0194] In some embodiments in which the biomarker panel consists of 5 genes, A, B, C, D and E, as an input matrix, an illustrative algorithm may proceed as follows:1. Sorting: The genes are sorted alphabetically: A, B, C, D, E.2. Ratio Calculation: The algorithm calculates the following ratios:o A / B, A / C, A / D, A / E o B / C, B / D, B / E o C / D, C / E o D / E3. Naming Ratios: Each ratio is named by concatenating the gene names, for example, with a double dash: o A-B, A-C, A-D, A-E o B-C, B-D, B-E o C-D, C-E o D-E4. Combined Matrix: The resulting matrix will have columns named: A--B, A--C, A--D, A--E, B--C, B--D, B--E, C--D, C--E, D--E. Each column contains the ratios of gene expressions for all samples.

[0195] In non-limiting embodiments, composite scores may be calculated as a weighted sum of the ratios, where the weights are suitably coefficients derived from a linear regression model, as for example outlined in Example 7.

[0196] In some embodiments, the composite score is combined with an additional feature value to produce a clinically adjusted composite score, wherein the additional feature value is for at least one additional feature type to characterize the likelihood that the subject has an increased likelihood of the presence of allograft dysfunction or an increased likelihood of the absence of allograft dysfunction. The at least one additional feature type may be at least one additional clinical parameter.

[0197] For example, when the transplant is a kidney transplant, exemplary clinical parameters that may be combined with the composite include, but are not limited to age, sex, ethnicity, body mass index (BMI), alarmin (e.g., heat shock proteins, interleukin la (IL-la), IL-33, high mobility group box 1 (HMGB1), etc.) level, beta-trace protein (BTP) level, cystatin C level, kidney injury molecule 1 (KIMI) level, tissue inhibitor of metalloproteinases 2 (TIMP2) level, insulin like growth factor binding protein 7 (IGFBP7) level, blood urea nitrogen (BUN) level, neutrophil gelatinase-associated lipocalin (NGAL) level, proenkephalin (PENK) level, creatinine level, creatinine clearance, serum creatinine (SCr) level, urea level, histological analysis, imaging analysis (e.g., ultrasound, magnetic resonance imaging (MRI) analysis, computer tomography (CT) analysis), end-stage renal disease (ESRD) classification, ESRD cause, donor age at death, date of donor death, donor sex, cold ischemia time, warm ischemia time, human leukocyte antigen (HLA) matching, panel reactive antibody (PRA), positive or negative transplant cross-match, donor specific antibody (DSA), mean fluorescence index (MFI), graft loss, date of graft loss, graft loss cause, date of last follow-up, pediatric risk of mortality III [PRISM -III] score, pediatric index of mortality 2 [PIM-II] score, Apache Score, additional medication, response and adverse effect of medications, allergies, RNA profile, single cell RNA sequencing, metabolomics, microbiome, genomics, epigenomics, and microRNA.

[0198] Alternatively, when the transplant is a heart transplant, the at least one clinical parameter may be selected from age, sex, ethnicity, BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), alarmin (e.g., heat shock proteins, IL-la, IL-33, HMGB1, etc.) level, B-type natriuretic peptide levels, diet parameters, cholesterol parameters (e.g., total cholesterol level,low-density lipoprotein (LDL) cholesterol level, high-density lipoprotein (HDL) cholesterol level, non-HDL cholesterol level), C-reactive protein (CRP) level, triglyceride level, troponin T level, comorbidities, physical activity parameters, family history of cardiovascular disease and / or cardiometabolic disease, stress parameters, alcohol consumption parameters, smoking and tobacco usage, imaging studies, electrocardiogram (ECG) studies, additional medication, response and adverse effect of medications, allergies, donor age at death, date of donor death, donor sex, cold ischemia time, warm ischemia time, HLA matching, PRA, positive or negative transplant crossmatch, DSA, MFI, graft loss, date of graft loss, graft loss cause, date of last follow-up, RNA profile, single cell RNA sequencing, metabolomics, microbiome, genomics, epigenomics, and microRNA.

[0199] In other examples, when the transplant is a liver transplant, the at least one clinical parameter is selected from age, sex, ethnicity, BMI, platelet count, alarmin e.g., heat shock proteins, IL-la, IL-33, HMGB1, etc.) level, autotaxin level, plasma C4 level, total bilirubin level, serum alanine aminotransferase (ALT) level, serum gamma-glutamyltransferase (GGT), serum aspartate transaminase (AST) level, patatin like phospholipase domain containing 3 (PNPLA3) genotype, additional medication, response and adverse effect of medications, allergies, donor age at death, date of donor death, donor sex, cold ischemia time, warm ischemia time, HLA matching, PRA, positive or negative transplant cross-match, DSA, MFI, graft loss, date of graft loss, graft loss cause, date of last follow-up, RNA profile, single cell RNA sequencing, metabolomics, microbiome, genomics, epigenomics, and microRNA.

[0200] In certain embodiments, the detection methods utilize a risk categorization table to generate a risk score for a patient based on a composite score or clinically adjusted composite score by comparing the composite score or clinically adjusted composite score with a reference set derived from a cohort of patients with allograft dysfunction and / or from a cohort of patients with allograft tolerance. The detection methods may further comprise quantifying the increased risk for the presence of allograft dysfunction or for the presence of allograft tolerance for the subject as a risk score, wherein the composite score or clinically adjusted composite score is matched to a risk category of a grouping of stratified subject populations wherein each risk category comprises a multiplier (or percentage) indicating an increased likelihood of having allograft dysfunction or allograft tolerance correlated to a range of composite scores or clinically adjusted composite scores. This quantification is based on the pre-determined grouping of a stratified cohort of subjects. In some embodiments, the grouping of a stratified population of subjects, or stratification of a disease cohort, is in the form of a risk categorization table. The selection of the disease cohort, the cohort of subjects that share allograft dysfunction or allograft tolerance risk factors, are well understood by those skilled in the art of organ transplantation research. However, the skilled person would also recognize that the resulting stratification, may be more multidimensional and take into account further environmental, occupational, genetic, or biological factors (e.g., epidemiological factors).

[0201] After quantifying the increased risk for presence of allograft dysfunction or presence of allograft tolerance in the form of a risk score, this score may be provided in a form amenable to understanding by a physician. In certain embodiments, the risk score is provided in a report. In certain aspects, the report may comprise one or more of the following: patient information, a risk categorization table, a risk score relative to a cohort population, one or more biomarker test scores, a biomarker composite score, a biomarker clinically adjusted compositescore, a master composite score, identification of the risk category for the patient, an explanation of the risk categorization table, and the resulting test score, a list of biomarkers tested, a description of the disease cohort, environmental and / or occupational factors, cohort size, biomarker velocity, genetic mutations, family history, margin of error, and so on.3. Kits

[0202] All the essential reagents required for detecting and quantifying the allograft status biomarkers disclosed herein may be assembled together in a kit. In some embodiments, the kit comprises a reagent that permits quantification of a panel of allograft status biomarkers disclosed herein. In the context of the present disclosure, "kit" is understood to mean a product containing the different reagents necessary for carrying out the methods of the disclosure packed so as to allow their transport and storage. Additionally, the kits of the present disclosure can contain instructions for the simultaneous, sequential or separate use of the different components contained in the kit. The instructions can be in the form of printed material or in the form of an electronic support capable of storing instructions such that they can be read by a subject, such as electronic storage media (magnetic disks, tapes and the like), optical media (CD-ROM, DVD) and the like. Alternatively or in addition, the media can contain internet addresses that provide the instructions. The kits may contain software for interpreting assay data to determine the likelihood of the presence or absence of allograft dysfunction or allograft tolerance, and / or for ruling out allograft dysfunction. In some embodiments, the kits may provide a means to access a machine learning system provided, for example, as a software as a service (SaaS) deployment.

[0203] Reagents that allow quantification of an allograft status biomarker include compounds or materials, or sets of compounds or materials, which allow quantification of the allograft status biomarker. In specific embodiments, the compounds, materials or sets of compounds or materials permit determining the expression level of a gene (e.g., allograft status biomarker gene) include without limitation the extraction of RNA material, the determination of the level of a corresponding RNA, etc., primers for the synthesis of a corresponding cDNA, primers for amplification of DNA, and / or probes capable of specifically hybridizing with the RNAs (or the corresponding cDNAs) encoded by the genes, TaqMan™ probes, etc.

[0204] Kit reagents can be in liquid form or can be lyophilized. Suitable containers for the reagents include, for example, bottles, vials, syringes, and test tubes. Containers can be formed from a variety of materials, including glass or plastic. The kit can also comprise a package insert containing written instructions for methods of diagnosing an increased likelihood of the presence or absence of allograft dysfunction and allograft tolerance.

[0205] The kits may also optionally include appropriate reagents for detection of labels, positive and negative controls, washing solutions, blotting membranes, microtiter plates, dilution buffers and the like. For example, a nucleic acid-based detection kit may include (I) an allograft status biomarker polynucleotide (which may be used as a positive control), (II) a primer or probe that specifically hybridizes to an allograft status biomarker polynucleotide. Also included may be enzymes suitable for amplifying nucleic acids including various polymerases (reverse transcriptase, Taq polymerase, Sequenase™, DNA ligase etc. depending on the nucleic acid amplification technique employed), deoxynucleotides and buffers to provide the necessary reaction mixture for amplification. Such kits also generally will comprise, in suitable means, distinct containers for each individual reagent and enzyme as well as for each primer or probe. The kit can also feature variousdevices {e.g., one or more) and reagents {e.g., one or more) for performing one of the assays described herein; and / or printed instructions for using the kit to quantify the expression of an allograft status biomarker gene and / or carry out an indicator-determining method, as broadly described above and elsewhere herein.

[0206] The reagents described herein, which may be optionally associated with detectable labels, can be presented in the format of a microfluidics card, a reaction vessel, a microarray or a kit adapted for use with the assays described in the examples or below, e.g., RT- PCR or Q PCR techniques described herein.

[0207] The reagents also have utility in compositions for detecting and quantifying the biomarkers of the present disclosure. For example, a reverse transcriptase may be used to reverse transcribe RNA transcripts, including mRNA, in a nucleic acid sample, to produce reverse transcribed transcripts, including reverse transcribed mRNA (also referred to as "cDNA"). In specific embodiments, the reverse transcribed mRNA is whole cell reverse transcribed mRNA (also referred to herein as "whole cell cDNA"). The nucleic acid sample is suitably derived from a sample disclosed herein.

[0208] The reagents are suitably used to quantify the reverse transcribed transcripts ( / .e., cDNA). For example, oligonucleotide primers that hybridize to the cDNA can be used to amplify at least a portion of the cDNA via a suitable nucleic acid amplification technique, e.g., RT- PCR or qPCR techniques described herein. Alternatively, oligonucleotide probes may be used to hybridize to the cDNA for the quantification, using a nucleic acid hybridization analysis technique {e.g., microarray analysis), as described for example above. Thus, in some embodiments, a respective oligonucleotide primer or probe is hybridized to a complementary nucleic acid sequence of a cDNA in the compositions of the present disclosure. The compositions typically comprise labeled reagents for detecting and / or quantifying one or more cDNAs. Representative reagents of this type include labeled oligonucleotide primers or probes {e.g., TaqMan™ probe) that hybridize to RNA transcripts or reverse transcribed RNA, labeled RNA, labeled cDNA as well as labeled oligonucleotide linkers or tags {e.g., a labeled RNA or DNA linker or tag) for labeling {e.g., end labeling such as 3' end labeling) RNA or reverse transcribed RNA. The primers, probes, RNA or cDNA (whether labeled or non-labeled) may be immobilized or free in solution. Representative reagents of this type include labeled oligonucleotide primers or probes that hybridize to cDNA as well as labeled cDNA. The label can be any reporter molecule as known in the art, illustrative examples of which are described above and elsewhere herein.

[0209] The kits disclosed herein also encompasses non-reverse transcribed RNA embodiments in which cDNA is not made and the RNA transcripts are directly the subject of the analysis. Thus, in other embodiments, reagents are suitably used to quantify RNA transcripts directly. For example, oligonucleotide probes can be used to hybridize to transcripts for quantification of allograft status biomarkers of the present disclosure, using a nucleic acid hybridization analysis technique {e.g., microarray analysis), as described for example above. Thus, in some embodiments, a respective oligonucleotide probe is hybridized to a complementary nucleic acid sequence of an allograft status biomarker transcript in the disclosed compositions. In illustrative examples of this type, the compositions may comprise labeled reagents that hybridize to transcripts for detecting and / or quantifying the transcripts. Representative reagents of this type include labeled oligonucleotide probes that hybridize to transcripts as well as labeled transcripts. The primers or probes may be immobilized or free in solution.

[0210] The present kits have a number of applications. For example, the kits can be used to determine if a subject has allograft dysfunction or allograft tolerance arising from an organ transplantation. In another example, the kits can be used to determine if a patient should be treated for allograft dysfunction, for example, with immunosuppressive agents or by retransplantation of the transplanted organ. In another example, kits can be used to monitor the effectiveness of treatment of a patient allograft dysfunction. In a further example, the kits can be used to identify compounds that modulate expression of one or more of the allograft status biomarkers in in vitro or in vivo animal models to determine the effects of treatment.4. Treatment embodiments

[0211] Also disclosed herein are methods for treating or managing the development or progression of allograft dysfunction in a subject who has undergone organ transplantation and / or at least one clinical sign of allograft dysfunction. A subject positively identified as having allograft dysfunction may be exposed to an immunosuppressive agent, illustrative examples of which include: calcineurin inhibitors (e.g., tacrolimus, cyclosporine, voclosporin, pimecrolimus), antiproliferative agents (e.g., mycophenolate mofetil, mycophenolate sodium, azathioprine), mTOR inhibitors (e.g., sirolimus, everolimus), steroids (e.g., corticosteroids, prednisone), lymphocyte (e.g., B cell and / or T cell) depleting antibodies (e.g., antithymocyte globulin, alemtuzumab, rituximab, basiliximab), non-depleting antibodies (e.g., daclizumab), fusion proteins comprising an Fc fragment of a human IgGl immunoglobulin linked to the extracellular domain of CTLA-4 (e.g., belatacept, abatacept) and the like.

[0212] In other embodiments, the treatment regimen may comprise retransplantation of the transplanted organ.5. Monitoring embodiments

[0213] The indicator-determining methods of the present disclosure can also be used to monitor disease status or the efficacy of treatment in a subject diagnosed with an allograft dysfunction. Early detection of allograft dysfunction or rejection may allow for timely intervention, potentially preventing irreversible damage and improving long-term transplant outcomes. Personalized treatment management (e.g., immunosuppression), guided by monitoring, may also reduce the risk of rejection while minimizing the side effects of the treatment. The monitoring methods of the present disclosure may comprise providing a blood sample obtained from the subject, performing an indicator-determining method as broadly described above and elsewhere herein on the blood sample obtained from the subject before treatment, or at intervals between treatments, or at time intervals in the absence of treatment, and determining that the treatment is effective, or that the disease status is improving, based at least in part on the indicatordetermining method indicating that subject has an increased likelihood of the absence of allograft dysfunction, or determining that the treatment is not effective, or that the disease status is unchanged or worsening, based at least in part on the indicator-determining method indicating that subject has an increased likelihood of the presence of allograft dysfunction. In instances in which the treatment is determined to be effective, the subject is determined to have a positive response to the therapy. Alternatively, in instances in which the treatment is determined to be ineffective, the subject is determined to have a negative response to the therapy. In some embodiments, the indicator-determining method is used to monitor the subject over a period of time for a change in disease status, wherein an improved disease status over the period of time is indicative oftreatment efficacy, and wherein an unchanged or worsened disease status is indicative of a lack of treatment efficacy.

[0214] In some embodiments, the methods comprise administering a treatment to the subject, and performing the indicator-determining method after administration of the treatment, to determine whether disease status of the subject is improved, unchanged or worsened. In illustrative examples of this type, the methods further comprise: obtaining a first blood sample from the subject before administration of the treatment to the subject, performing the indicatordetermining method before the administration to determine a first disease status of the subject, administering a treatment to the subject, obtaining a second blood sample from the subject after administration of the treatment to the subject, performing the indicator-determining method after the administration to determine a second disease status of the subject, and determining that the treatment is effective if the second disease status is improved relative to the first disease status, or determining that the treatment is not effective if the second disease status is unchanged or worsened relative to the first disease status.PARTICULAR EMBODIMENTS OF THE DISCLOSURE1. A method for determining an indicator used in assessing a likelihood that allograft dysfunction is present or absent in a subject who has undergone an organ transplant, the method comprising, consisting or consisting essentially of:(1) determining a biomarker value for each of a plurality of polynucleotide biomarkers e.g., 2, 3, 4, 5, or more polynucleotide biomarkers) of a biomarker panel in a blood sample obtained from the subject, wherein a respective biomarker value is indicative of a level of a corresponding polynucleotide biomarker in the blood sample, wherein the biomarker panel comprises, consists or consists essentially of a CASP1 polynucleotide, and one or both of an APP polynucleotide and an AQP3 polynucleotide; and(2) determining the indicator using the biomarker values.2. The method of embodiment 1, wherein the biomarker panel further comprises at least one ancillary predictive performance-improving polynucleotide biomarker (e.g., 1, 2, 3, or more polynucleotide biomarkers) selected from TABLE 1, with the proviso that when the biomarker panel is a biomarker panel of three polynucleotide biomarkers consisting of the CASP1 polynucleotide, the APP polynucleotide and the AQP3 polynucleotide, the at least one ancillary predictive performance-improving polynucleotide biomarker is excluded from the biomarker panel.3. The method of embodiment 1 or embodiment 2, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 polynucleotide, the APP polynucleotide and a predictive performance-improving polynucleotide biomarker selected from TABLE 2.4. The method of embodiment 3, wherein the biomarker panel is selected from the biomarker panels set forth in TABLE 3.5. The method of embodiment 1 or embodiment 2, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 polynucleotide, the AQP3 polynucleotide and a predictive performance-improving polynucleotide biomarker selected from TABLE 4.6. The method of embodiment 5, wherein the biomarker panel is selected from the biomarker panels set forth in TABLE 5.7. The method of embodiment 1 or embodiment 2, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 polynucleotide, the APP polynucleotide and two predictive performance-improving polynucleotide biomarkers selected TABLE 6.8. The method of embodiment 7, wherein the biomarker panel is selected from the biomarker panels set forth in TABLE 7.9. The method of embodiment 1 or embodiment 2, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 polynucleotide, the AQP3 polynucleotide and two predictive performance-improving polynucleotide biomarkers selected from TABLE 8.10. The method of embodiment 9, wherein the biomarker panel is selected from the biomarker panels set forth in TABLE 9.11. The method of embodiment 1 or embodiment 2, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 polynucleotide, the APP polynucleotide, the AQP3 polynucleotide and a predictive performance-improving polynucleotide biomarker selected from TABLE 10.12. The method of embodiment 11, wherein the biomarker panel is selected from the biomarker panels set forth in TABLE 11.13. The method of embodiment 1 or embodiment 2, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 polynucleotide, the APP polynucleotide and three predictive performance-improving polynucleotide biomarkers selected from TABLE 12.14. The method of embodiment 13, wherein the biomarker panel is selected from the biomarker panels set forth in TABLE 13.15. The method of embodiment 1 or embodiment 2, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 polynucleotide, the AQP3 polynucleotide and three predictive performance-improving polynucleotide biomarkers selected from TABLE 14.16. The method of embodiment 15, wherein the biomarker panel is selected from the biomarker panels set forth in TABLE 15.17. The method of embodiment 1 or embodiment 2, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 polynucleotide, the APP polynucleotide, the AQP3 polynucleotide and two predictive performance-improving polynucleotide biomarkers selected TABLE 16.18. The method of embodiment 17, wherein the biomarker panel is selected from the biomarker panels set forth in TABLE 17.19. The method of any one of embodiments 1 to 18, wherein an increased likelihood of the presence of allograft dysfunction in the subject is indicated when the CASP1 polynucleotide is present in the blood sample at a higher level than in a reference blood sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction.20. The method of any one of embodiments 1 to 19, wherein an increased likelihood of the presence of allograft dysfunction in the subject is indicated when the APP polynucleotide is present in the blood sample at a lower level than in a reference blood sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction.21. The method of any one of embodiments 1 to 20, wherein an increased likelihood of the presence of allograft dysfunction in the subject is indicated when the AQP3 polynucleotide ispresent in the blood sample at a lower level than in a reference blood sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction.22. The method of any one of embodiments 1 to 18, wherein an increased likelihood of the absence of allograft dysfunction in the subject is indicated when the CASP1 polynucleotide is present in the blood sample at a lower level than in a reference blood sample obtained from a subject with allograft dysfunction.23. The method of any one of embodiments 1 to 19, wherein an increased likelihood of the absence of allograft dysfunction in the subject is indicated when the APP polynucleotide is present in the blood sample at a higher level than in a reference blood sample obtained from a subject with allograft dysfunction.24. The method of any one of embodiments 1 to 20, wherein an increased likelihood of the absence of allograft dysfunction in the subject is indicated when the AQP3 polynucleotide is present in the blood sample at a higher level than in a reference blood sample obtained from a subject with allograft dysfunction.25. The method of any one of embodiments 2 to 21, wherein an increased likelihood of the presence of allograft dysfunction in the subject is indicated when at least one ancillary predictive performance-improving polynucleotide biomarker is present in the blood sample at a higher level than in a reference blood sample obtained from a healthy subject or from a subject without allograft dysfunction, wherein the at least one ancillary predictive performance-improving polynucleotide biomarker is selected from TABLE 18.26. The method of any one of embodiments 2 to 21, wherein an increased likelihood of the presence of allograft dysfunction in the subject is indicated when at least one ancillary predictive performance-improving polynucleotide biomarker is present in the blood sample at a lower level than in a reference blood sample obtained from a healthy subject or from a subject without allograft dysfunction, wherein the at least one ancillary predictive performance-improving polynucleotide biomarker is selected from TABLE 19.27. The method of any one of embodiments 2 to 20 and 22 to 24, wherein an increased likelihood of the absence of allograft dysfunction in the subject is indicated when at least one ancillary predictive performance-improving polynucleotide biomarker is present in the blood sample at a lower level than in a reference blood sample obtained from a subject with allograft dysfunction, wherein the at least one ancillary predictive performance-improving polynucleotide biomarker is selected from TABLE 18.28. The method of any one of embodiments 2 to 20 and 22 to 24, wherein an increased likelihood of the absence of allograft dysfunction in the subject is indicated when at least one ancillary predictive performance-improving polynucleotide biomarker is present in the blood sample at a higher level than in a reference blood sample obtained from a subject with allograft dysfunction, wherein the at least one ancillary predictive performance-improving polynucleotide biomarker is selected from TABLE 19.29. The method of any one of embodiments 1 to 28, wherein individual biomarker values are representative of a measured amount or concentration of a corresponding biomarker polynucleotide in the sample.30. The method of any one of embodiments 1 to 28, wherein individual biomarker values are a logarithmic representation of a measured amount or concentration of a corresponding biomarker polynucleotide in the sample.31. The method of any one of embodiments 1 to 30, wherein the organ is a solid organ selected from heart, lung, kidney, liver, pancreas, skin, uterus, bone, cartilage, small or large bowel, bladder, brain, breast, blood vessels, esophagus, fallopian tube, gallbladder, ovaries, pancreas, prostate, placenta, spinal cord, limb including upper and lower, spleen, stomach, testes, thymus, thyroid, trachea, ureter, urethra, and uterus.32. The method of any one of embodiments 1 to 31, further comprising applying a function to one or more biomarker values to yield at least one functionalized biomarker value and determining the indicator using the at least one functionalized biomarker value.33. The method of embodiment 32, wherein the function includes at least one of: (a) multiplying biomarker values; (b) dividing biomarker values; (c) adding biomarker values; (d) subtracting biomarker values; (e) a weighted sum of biomarker values; (f) a log sum of biomarker values; (g) a geometric mean of biomarker values; and (h) a sigmoidal function of biomarker values.34. The method of embodiment 32 or embodiment 33, comprising determining for n pairs of said polynucleotide biomarkers a plurality of functionalized biomarker values, wherein a respective functionalized biomarker value is indicative of a ratio of concentrations of a pair of said polynucleotide biomarkers, wherein each functionalized biomarker value of said plurality of functionalized biomarker values is indicative of a ratio of concentrations of a different pair of said polynucleotide biomarkers, and wherein n represents the sum of all possible, non-redundant pairs of biomarker polynucleotides of the biomarker panel.35. The method of any one of embodiments 1 to 34, further comprising combining biomarker values to provide a composite score and determining the indicator using the composite score.36. The method of embodiment 35, wherein the biomarker values are combined by adding, multiplying, subtracting, and / or dividing biomarker values.37. The method of embodiment 35 or embodiment 36, further comprising combining the composite score with an additional feature value to produce a clinically adjusted composite score, wherein the additional feature value is for at least one additional feature type to characterize the likelihood that the subject has an increased likelihood of the presence of allograft dysfunction or an increased likelihood of the absence of allograft dysfunction.38. The method of embodiment 37, wherein the at least one additional feature type comprises at least one clinical parameter of the subject.39. The method of embodiment 38, wherein the transplant is a kidney transplant and the at least one clinical parameter is selected from age, sex, ethnicity, body mass index (BMI), alarmin (e.g., heat shock proteins, interleukin la (IL-la), IL-33, high mobility group box 1 (HMGB1), etc.) level, beta-trace protein (BTP) level, cystatin C level, kidney injury molecule 1 (KIMI) level, tissue inhibitor of metalloproteinases 2 (TIMP2) level, insulin like growth factor binding protein 7 (IGFBP7) level, blood urea nitrogen (BUN) level, neutrophil gelatinase-associated lipocalin (NGAL) level, proenkephalin (PENK) level, creatinine level, creatinine clearance, serum creatinine (SCr) level, urea level, histological analysis, imaging analysis (e.g., ultrasound, magnetic resonance imaging (MRI) analysis, computer tomography (CT) analysis), end-stage renal disease (ESRD)classification, ESRD cause, donor age at death, date of donor death, donor sex, cold ischemia time, warm ischemia time, human leukocyte antigen (HLA) matching, panel reactive antibody (PRA), positive or negative transplant cross-match, donor specific antibody (DSA), mean fluorescence index (MFI), graft loss, date of graft loss, graft loss cause, date of last follow-up, pediatric risk of mortality III [PRISM-III] score, pediatric index of mortality 2 [PIM-II] score, Apache Score, additional medication, response and adverse effect of medications, allergies, RNA profile, single cell RNA sequencing, metabolomics, microbiome, genomics, epigenomics, and microRNA.40. The method of embodiment 38, wherein the transplant is a heart transplant and the at least one clinical parameter is selected from age, sex, ethnicity, BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), alarmin e.g., heat shock proteins, IL-la, IL-33, HMGB1, etc.) level, B-type natriuretic peptide levels, diet parameters, cholesterol parameters e.g., total cholesterol level, low-density lipoprotein (LDL) cholesterol level, high-density lipoprotein (HDL) cholesterol level, non-HDL cholesterol level), C-reactive protein (CRP) level, triglyceride level, troponin T level, co-morbidities, physical activity parameters, family history of cardiovascular disease and / or cardiometabolic disease, stress parameters, alcohol consumption parameters, smoking and tobacco usage, imaging studies, electrocardiogram (ECG) studies, additional medication, response and adverse effect of medications, allergies, RNA profile, single cell RNA sequencing, metabolomics, microbiome, genomics, epigenomics, and microRNA.41. The method of embodiment 38, wherein the transplant is a liver transplant and the at least one clinical parameter is selected from age, sex, ethnicity, BMI, platelet count, alarmin (e.g., heat shock proteins, IL-la, IL-33, HMGB1, etc.) level, autotaxin level, plasma C4 level, total bilirubin level, serum alanine aminotransferase (ALT) level, serum gamma-glutamyltransferase (GGT), serum aspartate transaminase (AST) level, patatin like phospholipase domain containing 3 (PNPLA3) genotype, additional medication, response and adverse effect of medications, allergies, RNA profile, single cell RNA sequencing, metabolomics, microbiome, genomics, epigenomics, and microRNA.42. The method of any one of embodiments 1 to 41, comprising analyzing the biomarker value(s), composite score or clinically adjusted composite score, with reference to one or more corresponding reference biomarker value ranges or threshold values, or composite score ranges or threshold values, or clinically adjusted composite score ranges or threshold values, to determine the indicator.43. The method of any one of embodiments 1 to 42, wherein the indicator indicates an increased likelihood of a presence of allograft dysfunction if the biomarker values, composite score or clinically adjusted composite score is indicative of a level of the biomarker polynucleotides in the sample that correlates with an increased likelihood of a presence of allograft dysfunction relative to a predetermined reference biomarker value ranges or cut-off values, composite score range or cutoff value, or clinically adjusted composite score range or cut-off value, and wherein the indicator indicates a likelihood of the absence of allograft dysfunction if the biomarker values, composite score or clinically adjusted composite score is indicative of a level of the biomarker polynucleotides in the sample that correlates with an increased likelihood of an absence of allograft dysfunction relative to a predetermined reference biomarker value ranges or cut-off values, composite score range or cut-off value, or clinically adjusted composite score range or cut-off value.44. The method of any one of embodiments 1 to 43, wherein the blood sample is a peripheral blood sample or fraction thereof e.g. a peripheral blood mononuclear blood sample).45. The method of any one of embodiments 1 to 44, wherein the blood sample comprises leukocytes.46. The method of any one of embodiments 1 to 45, wherein the allograft dysfunction is early allograft dysfunction or primary graft nonfunction.47. The method of any one of embodiments 1 to 46, wherein the allograft dysfunction is a chronic allograft dysfunction.48. The method of any one of embodiments 1 to 47, wherein the allograft dysfunction is associated with ischemia reperfusion injury.49. The method of any one of embodiments 1 to 48, wherein the allograft dysfunction is associated with mitochondrial damage.50. The method of any one of embodiments 1 to 49, wherein the allograft dysfunction is associated with antibody-mediated rejection of the allograft.51. The method of any one of embodiments 1 to 50, wherein the allograft dysfunction is associated with T cell-mediated rejection of the allograft.52. The method of any one of embodiments 1 to 51, wherein the allograft dysfunction is associated with fibrosis of the allograft.53. A composition comprising a mixture of a DNA polymerase, a blood leukocyte cDNA sample obtained from a subject who has undergone an organ transplant, wherein the blood leukocyte cDNA sample comprises a plurality of cDNA biomarkers (e.g., 2, 3, 4, 5, or more cDNAs) of a biomarker panel, wherein the biomarker panel comprises, consists or consists essentially of a CASP1 cDNA, and one or both of an APP cDNA and an AQP3 cDNA, and wherein the composition further comprises for individual cDNA biomarkers of the biomarker panel at least one oligonucleotide primer or probe that hybridizes to the cDNA biomarker.54. The composition of embodiment 53, wherein the biomarker panel further comprises at least one ancillary predictive performance-improving cDNA biomarker (e.g., 1, 2, 3, or more cDNA biomarkers) selected from TABLE 1, wherein the composition further comprises for the at least one ancillary predictive performance-improving cDNA biomarker at least one oligonucleotide primer or probe that hybridizes to the cDNA biomarker.55. The composition of embodiment 53 or embodiment 54, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 cDNA, the APP cDNA and a predictive performance-improving cDNA biomarker selected from TABLE 2.56. The composition of embodiment 55, wherein the biomarker panel is selected from TABLE 3.57. The composition of embodiment 53 or embodiment 54, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 cDNA, the AQP3 cDNA and a predictive performance-improving cDNA biomarker selected from TABLE 4.58. The method of embodiment 57, wherein the biomarker panel is selected from TABLE 5.59. The composition of embodiment 53 or embodiment 54, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 cDNA, the APP cDNA and two predictive performance-improving cDNA biomarkers selected TABLE 6.60. The composition of embodiment 59, wherein the biomarker panel is selected from TABLE 7.61. The composition of embodiment 53 or embodiment 54, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 cDNA, the AQP3 cDNA and two predictive performance-improving cDNA biomarkers selected from TABLE 8.62. The composition of embodiment 61, wherein the biomarker panel is selected from TABLE 9.63. The composition of embodiment 53 or embodiment 54, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 cDNA, the APP cDNA, the AQP3 cDNA and a predictive performance-improving cDNA biomarker selected from TABLE 10.64. The composition of embodiment 63, wherein the biomarker panel is selected from TABLE 11.65. The composition of embodiment 53 or embodiment 54, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 cDNA, the APP cDNA and three predictive performance-improving cDNA biomarkers selected from TABLE 12.66. The composition of embodiment 65, wherein the biomarker panel is selected from TABLE 13.67. The composition of embodiment 53 or embodiment 54, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 cDNA, the AQP3 cDNA and three predictive performance-improving cDNA biomarkers selected from TABLE 14.68. The composition of embodiment 67, wherein the biomarker panel is selected from TABLE 15.69. The composition of embodiment 53 or embodiment 54, wherein the biomarker panel comprises, consists or consists essentially of the CASP1 cDNA, the APP cDNA, the AQP3 cDNA and two predictive performance-improving cDNA biomarkers selected TABLE 16.70. The composition of embodiment 69, wherein the biomarker panel is selected from TABLE 17.71. The composition of any one of embodiments 53 to 70, wherein the subject has an increased likelihood of the presence of allograft dysfunction, and in the blood leukocyte cDNA sample:• the CASP1 cDNA is present at a higher level than in a reference blood leukocyte cDNA sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• the APP cDNA is present at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• the AQP3 cDNA is present at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject or from a subject who has undergone an organ transplant without experiencing allograft dysfunction.72. The composition of any one of embodiments 53 to 70, wherein the subject has an increased likelihood of the absence of allograft dysfunction, and in the blood leukocyte cDNA sample:• the CASP1 cDNA is present at a lower level than in a reference blood sample obtained from a subject with allograft dysfunction;• the APP cDNA is present at a higher level than in a reference blood sample obtained from a subject with allograft dysfunction;• the AQP3 cDNA is present at a higher level than in a reference blood sample obtained from a subject with allograft dysfunction.73. The composition of any one of embodiments 53 to 72, wherein the blood leukocyte cDNA is characteristic of a subject having undergone an organ transplant, and wherein allograft dysfunction is present or absent in the subject, wherein:• TANK cDNA is present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a healthy subject;• DNAJA1 cDNA is present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a healthy subject;• MAN1A1 cDNA is present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a healthy subject;• HMOX2 cDNA is present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject;• SDHB cDNA is present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject;• DSTN cDNA is present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject;• DDX24 cDNA is present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject.74. The composition of any one of embodiments 53 to 72, wherein the blood leukocyte cDNA is characteristic of a subject with allograft dysfunction, wherein:• TANK cDNA is present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a healthy subject, or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• DNAJA1 cDNA is present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a healthy subject, or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• MAN1A1 cDNA is present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a healthy subject, or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• HMOX2 cDNA is present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject, or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• SDHB cDNA is present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject, or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• DSTN cDNA is present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject, or from a subject who has undergone an organ transplant without experiencing allograft dysfunction;• DDX24 cDNA is present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a healthy subject, or from a subject who has undergone an organ transplant without experiencing allograft dysfunction.75. The composition of any one of embodiments 53 to 72, wherein the blood leukocyte cDNA is characteristic of a subject without allograft dysfunction, wherein:• TANK cDNA is present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a subject with allograft dysfunction;• DNAJA1 cDNA is present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a subject with allograft dysfunction;• MAN1A1 cDNA is present in the blood leukocyte cDNA sample at a lower level than in a reference blood leukocyte cDNA sample obtained from a subject with allograft dysfunction;• HMOX2 cDNA is present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a subject with allograft dysfunction;• SDHB cDNA is present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a subject with allograft dysfunction;• DSTN cDNA is present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a subject with allograft dysfunction;• DDX24 cDNA is present in the blood leukocyte cDNA sample at a higher level than in a reference blood leukocyte cDNA sample obtained from a subject with allograft dysfunction.76. The composition of any one of embodiments 48 to 75, wherein the blood leukocyte cDNA sample comprises, consists or consists essentially of a whole cDNA preparation obtained from a blood sample of the subject.77. The composition of embodiment 76, wherein the blood sample is a peripheral blood sample or fraction thereof e.g. a peripheral blood mononuclear blood sample).78. The composition of any one of embodiments 48 to 77, wherein the composition comprises for a respective cDNA biomarker two oligonucleotide primers that hybridize to opposite complementary strands of the cDNA biomarker.79. The composition of any one of embodiments 48 to 77, wherein the composition comprises for a respective cDNA two pairs of oligonucleotide primers, wherein the oligonucleotide primers of a respective pair hybridize to opposite complementary strands of the cDNA, and wherein the oligonucleotide primers of one pair are nested ("nested oligonucleotide primers") relative the oligonucleotide primers of the other pair.80. The composition of any one of embodiments 48 to 79, wherein the composition comprises for a respective cDNA biomarker an oligonucleotide probe that hybridizes to the cDNA biomarker or a polynucleotide corresponding thereto e.g., a polynucleotide product resulting nucleic acid amplification of the cDNA biomarker).81. The composition of embodiment 80, wherein the oligonucleotide probe comprises a heterologous reporter molecule.82. The composition of embodiment 81, wherein the reporter molecule comprises a fluorescent label.83. The composition of any one of embodiments 80 to 82, wherein the oligonucleotide probe is a real-time polymerase chain reaction probe.84. The composition of any one of embodiments 48 to 83, wherein the composition comprises for each of at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 of the cDNA biomarkers at least one oligonucleotide primer and / or probe that hybridizes to the cDNA biomarker.85. The composition of any one of embodiments 48 to 84, wherein the composition comprises for each of up to 2, 3, 4, 5, 6, 7, 8, 9, or 10 of the cDNA biomarkers at least one oligonucleotide primer and / or probe that hybridizes to the cDNA biomarker.86. The composition of embodiment 84 or embodiment 85, wherein individual cDNA biomarkers and their corresponding oligonucleotide primers and / or probes are present in separate reaction vessels.87. The composition of embodiment 86, wherein two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, or 10) cDNA biomarkers and their corresponding oligonucleotide primers and / or probes are present in the same reaction vessel.88. The composition of any one of embodiments 48 to 87, wherein the DNA polymerase is a thermostable DNA polymerase.89. The composition of any one of embodiments 48 to 88, wherein the organ transplant is a kidney, heart or liver transplant.90. A device for nucleic acid amplification of blood leukocyte cDNA, the device comprising a plurality of reaction vessels, individual reaction vessels comprising the composition of any one of embodiments 48 to 89.91. The device of embodiment 90, consisting of 2 to 10, 2 to 9, 2 to 8, 2 to 7, 2 to 6, 2 to 5, 2 to 4 reaction vessels (and all integer reaction vessels in between).92. The device of embodiment 90 or embodiment 91, consisting of 2, 3, 4, 5, 6, 7, 8, 9 or 10 reaction vessels.93. The device of any one of embodiments 90 to 92, wherein one or more reaction vessels are used for single-plex amplification of cDNA.94. The device of any one of embodiments 90 to 93, wherein one or more reaction vessels are used for multiplex amplification of cDNA.95. The device of embodiment 94, wherein the multiplex amplification is 2-plex, 3-plex, 4- plex or 5-plex.96. A method for treating a subject who has undergone an organ transplant, the method comprising:(1) performing the indicator-determining method of any one of embodiments 1 to 46 to determine whether the subject has an increased likelihood of a presence or absence of allograft dysfunction;(2) exposing the subject to an allograft dysfunction treatment regimen, wherein the exposure to the treatment regimen is based at least in part on the indicator-determining method indicating that subject has an increased likelihood of the presence of allograft dysfunction; or(3) not exposing the subject to an allograft dysfunction treatment regimen, wherein the non-exposure to the treatment regimen is based at least in part on the indicator-determiningmethod indicating that subject has an increased likelihood of the absence of allograft dysfunction.97. The method of embodiment 96, further comprising: taking a blood sample from the subject and determining an indicator indicative of a likelihood of a presence or absence of allograft dysfunction using the indicator-determining method.98. The method of embodiment 97, further comprising: sending the blood to a laboratory at which the indicator is determined according to the indicator-determining method.99. The method of embodiment 98, further comprising: receiving the indicator from the laboratory.100. The method of any one of embodiments 96 to 99, wherein the treatment regimen comprises administration of at least one immunosuppressive agent.101. The method of embodiment 100, wherein the subject is already receiving an immunosuppressive agent and the treatment comprises administering an increased concentration of the immunosuppressive agent.102. The method of embodiment 100 or embodiment 101, wherein the immunosuppressive agent is selected from calcineurin inhibitors, antiproliferative agents, mTOR inhibitors, steroids, lymphocyte e.g., B cell and / or T cell) depleting antibodies, non-depleting antibodies, and fusion proteins comprising an Fc fragment of a human IgGl immunoglobulin linked to the extracellular domain of CTLA-4.103. The method of any one of embodiments 96 to 99, wherein the treatment regimen comprises retransplantation of the transplanted organ.104. A kit for determining an indicator used in assessing a likelihood that allograft dysfunction is present or absent in a subject who has undergone an organ transplant, the kit comprising for each of a plurality of polynucleotide biomarkers (e.g., 2, 3, 4, 5, or more polynucleotide biomarkers) of a biomarker panel at least one oligonucleotide primer and / or at least one oligonucleotide probe that hybridizes to the polynucleotide biomarker, wherein the biomarker panel comprises, consists or consists essentially of a CASP1 polynucleotide, and one or both of an APP polynucleotide and an AQP3 polynucleotide.105. The kit of embodiment 104, wherein the biomarker panel further comprises at least one ancillary predictive performance-improving polynucleotide biomarker (e.g., 1, 2, 3, or more polynucleotide biomarkers) selected from TABLE 1, and wherein the kit further comprises for a respective predictive performance-improving polynucleotide biomarker at least one oligonucleotide primer and / or at least one oligonucleotide probe that hybridizes to the predictive performanceimproving polynucleotide biomarker.106. The kit of embodiment 104 or embodiment 105, further comprising a DNA polymerase.107. The kit of embodiment 106, wherein the DNA polymerase is a thermostable DNA polymerase.108. The kit of any one of embodiments 104 to 107, further comprising: for each polynucleotide biomarker a pair of forward and reverse oligonucleotide primers that permit nucleic acid amplification of at least a portion of the polynucleotide biomarker to produce an amplicon.109. The kit of any one of embodiments 104 to 107, further comprising: for each polynucleotide biomarker two pairs of forward and reverse oligonucleotide primers, wherein the oligonucleotide primers of one pair are nested ("nested oligonucleotide primers") relative to the oligonucleotide primers of the other pair, wherein a respective pair of oligonucleotide primerspermits nucleic acid amplification of at least a portion of the polynucleotide biomarker to produce an amplicon.110. The kit of any one of embodiments 104 to 109, further comprising: for each polynucleotide biomarker an oligonucleotide probe that comprises a heterologous label and hybridizes to the polynucleotide biomarker or an amplicon of the polynucleotide biomarker.111. The kit of any one of embodiments 104 to 110, wherein components of the kit when used to determine the indicator are combined to form a mixture.112. The kit of any one of embodiments 104 to 111, wherein respective polynucleotide biomarkers of the biomarker panel are RNA (e.g., mRNA) or cDNA biomarkers.113. The kit of any one of embodiments 104 to 112, further comprising: one or more reagents for preparing mRNA from a cell or cell population from a blood sample of the subject.114. The kit of any one of embodiments 104 to 113, further comprising: one or more reagents for preparing cDNA from the mRNA.115. The kit of any one of embodiments 104 to 114, further comprising: one or more reagents for amplifying cDNA.116. The kit of any one of embodiments 104 to 115, further comprising one or more of deoxynucleotides, buffer(s), positive and negative controls, and reaction vessel(s).117. The kit of any one of embodiments 104 to 116, further comprising instructions for performing the indicator-determining method of any one of embodiments 1 to 47.118. A method of monitoring treatment efficacy or disease status in a subject diagnosed with an allograft dysfunction, the method comprising, consisting or consisting essentially of:1) providing a blood sample obtained from the subject;2) performing the indicator-determining method as broadly described above and elsewhere herein on the blood sample obtained from the subject before treatment, or at intervals between treatments, or at time intervals in the absence of treatment; and3) determining that the treatment is effective, or that the disease status is improving, based at least in part on the indicator-determining method indicating that subject has an increased likelihood of the absence of allograft dysfunction; or4) determining that the treatment is not effective, or that the disease status is unchanged or worsening, based at least in part on the indicator-determining method indicating that subject has an increased likelihood of the presence of allograft dysfunction.119. The method of embodiment 118, further comprising: a) administering a treatment to the subject; and b) performing the indicator-determining method after administration of the treatment, wherein the treatment is determined to be effective based at least in part on the indicator-determining method indicating that subject has an increased likelihood of the absence of allograft dysfunction, or wherein the treatment is determined to be not effective based at least in part on the indicator-determining method indicating that subject has an increased likelihood of the presence of allograft dysfunction.120. The method of embodiment 118 or embodiment 119, further comprising:i. obtaining a first blood sample from the subject before administration of the treatment to the subject; ii. performing the indicator-determining method before the administration to determine a first disease status of the subject; ill. administering a treatment to the subject; iv. obtaining a second blood sample from the subject after administration of the treatment to the subject; v. performing the indicator-determining method after the administration to determine a second disease status of the subject, and vi. determining that the treatment is effective if the second disease status is improved relative to the first disease status, or vii. determining that the treatment is not effective if the second disease status is unchanged or worsened relative to the first disease status.

[0215] In order that the present disclosure may be readily understood and put into practical effect, particular preferred embodiments will now be described by way of the following non-limiting examples.EXAMPLESEXAMPLE 1DISCOVERY OF BIOMARKERS FOR ACCURATELY PREDICTING ALLOGRAFT REJECTIONDataset Curation

[0216] A total of 23 publicly available gene expression datasets were obtained from National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) repository. Each dataset sampled transcriptomics differences between patients with different phenotypes of allograft rejection from liver (2), kidney (18) and heart (3) datasets. In total, there were 2422 samples analyzed. To accurately classify instances of allograft rejection across all transplanted organs, rejection phenotypes across organs were amalgamated. This addressed the challenges posed by the lack of uniform definitions of organs and the evolution of histopathological guidelines over time. Specifically, T-cell-mediated rejection (TCMR), antibody-mediated rejection (ABMR) and mixed phenotypes were consolidated under one comprehensive definition of rejection.Initial Critical Gene Selection.

[0217] To identify which gene is critical in building models across organs, the top 20 genes consistently changing across all datasets were first identified. In brief, differentially expressed genes were individually isolated from each dataset using a moderated t-test. A normal transformation was applied to the test statistics, obtained via t-test, for each dataset, converting test statistics to normal z-scores. Stouffer's method was used to combine the z-scores across all datasets for each gene. Z-scores were then converted to p-values using a normal distribution (Figure 1). The 20 genes with the lowest resulting p-values were kept for analysis.

[0218] To identify which genes were critical to building a model in peripheral blood samples, a simulation was created whereby 4 genes of the 20 available were chosen at random and designated as set A, with the remaining 16 genes being designated as set B. A logisticregression model was separately fitted to set A and set B on 22 of the 23 datasets, with the 23rdbeing held out for evaluation. The predictive performance of set A and set B was assessed using the left-out dataset, with AUC being used as the evaluation metric (due to its invariance to sample size). This procedure was repeated until all 4845 possible 4 gene combinations had been evaluated. This analysis is summarized schematically in Figure 2.

[0219] Finally, the AUC results from the models built using set A (4 genes) and set B (16 genes) were compared. With the objective to find the 4 gene combinations that maximize the performance of set A, while minimizing the performance of set B. A threshold of set A AUC > 0.68 and set B AUC < 0.65 was used to identify the 4 gene combinations of interest (Figure 3).

[0220] Of the genes that met the sought-after threshold, CASP1 was clearly the most frequently included in the four gene combinations (appearing in 84% of combinations) (Figure 4). CASP1 (Caspase-1) was thus determined to be the most important gene for the construction of models from peripheral blood datasets.Australian Chronic Allograft Dysfunction (AUSCAD) Study

[0221] Study overview. The AUSCAD is a single-center, prospectively recruited observational cohort study at Westmead Hospital in Australia. Consent was obtained before transplantation with procedures approved by the Western Sydney Local Health District Human Research Ethics Committee (HREC / 12 / WMEAD / 190). Demographic and clinical data, as well as blood and kidney biopsies, were collected at implantation and at 3 months after transplantation. No statistical methods were used to pre-determine sample sizes, but our sample sizes are similar to those reported in previous publications available in our PROMAD atlas.Sample collection and histopathological evaluation.

[0222] Two biopsy cores were taken at each protocol or for-cause biopsy, with one used for histology and the other for bulk RNA-seq (described below). Biopsy cores reserved for histology underwent hematoxylin and eosin, periodic acid-Schiff, Masson's trichrome and C4d staining at the Institute of Clinical Pathology and Medical Research (Westmead Hospital) before evaluation by a single histopathologist, using the Banff 2019 schema69. RNA isolation and sequencing. Peripheral blood was collected into PAXgene™ Blood RNA tubes (QIAGEN), left at room temperature for 5 h and stored at -80 °C until RNA extraction. RNA was extracted by using a PAXgene Blood miRNA Kit™ (QIAGEN). All RNA samples were frozen and stored at -80 °C and then sent in bulk to the Australian Genome Research Facility. Sample quality control and library preparation were performed in-house, and the resultant libraries were sequenced using the NovaSeq 6000™ platform (Illumina) with 100-bp, paired-end read length.Downstream analysis and normalization.

[0223] Raw FASTQ files were first trimmed and aligned using the GRCh37-hgl9 reference genome. The resulting data were then organized into a gene counts matrix for each sample. The bulk RNA-seq data underwent initial filtering to remove reads too low for further analysis. This was followed by normalization using the trimmed mean of M values (TMM) method.Determining Other Important GenesTwo gene models

[0224] The present inventors next sought to determine which other features work best in combination with CASP1 in diagnosis of rejection from peripheral blood. To do so, they first calculated the ratio between CASP1 and all candidate genes (~20,000 available). Three different metrics were used to assess the performance of their two gene models.1. Mean AUC of gene ratio performance in our 23 datasets.2. AUC of gene ratio performance in AUSCAD.3. AUC of gene ratio performance, combined with creatinine slope, in AUSCAD.Three gene models

[0225] Next, the present inventors explored which two genes, combined with CASP1, formed the most useful 3-gene panel for the diagnosis of allograft rejection. To do so, the top 1000 genes were identified using a t-test across all 23 datasets. At random, two genes were selected from the 1000 possible candidate genes alongside CASP1, forming the gene panel to be tested. The pairwise ratios of the two genes of interest and CASP1 were then calculated before using a logistic regression model to evaluate the performance of a three gene assay on predicting allograft rejection. Once again, performance was assessed by:1. Mean AUC of gene ratio performance in our 23 datasets.2. AUC of gene ratio performance in AUSCAD.3. AUC of gene ratio performance, combined with creatinine, in AUSCAD.Four gene models

[0226] Next, the present inventors explored which three genes, combined with CASP1, formed the most useful 4-gene panel for the diagnosis of allograft rejection. To do so, the top 1000 genes were identified using a t-test across all 23 datasets. At random, three genes were selected from the 1000 possible candidate genes alongside CASP1, forming a gene panel to be tested. Pairwise ratios of the three genes of interest and CASP1 were then calculated before using a logistic regression model to evaluate the performance of the four-gene assay on predicting allograft rejection. Once again, performance was assessed by:

[0227] Mean AUC of gene ratio performance in our 23 datasets.

[0228] AUC of gene ratio performance in AUSCAD.

[0229] AUC of gene ratio performance, combined with creatinine, in AUSCAD.Five gene models

[0230] Finally, the present inventors explored which four genes, combined with CASP1, formed the best 5-gene panel for the diagnosis of allograft rejection. The process of selecting genes, building models and evaluating their performance was identical to previous 3- and 4-gene models.

[0231] The diagnostic performance of two-, three-, four- and five-gene models was evaluated for predicting allograft rejection. The models were assessed based on the Area Under the Curve (AUC) values, both as standalone gene-only models and in combination with clinical data inthe AUSCAD cohort. Models were also assessed for their robust performance on all 23 training datasets, to examine if the ratios did indeed work across different organs.EXAMPLE 2TWO-GENE MODELS

[0232] The mean AUC for the two-gene models ranged from 0.71 to 0.77, indicating moderate predictive power in an AUSCAD cohort of kidney transplant recipients. The gene-only model AUC scores varied between 0.71 and 0.77, while the gene-clinical model AUC scores were slightly higher, ranging from 0.75 to 0.79. The mean AUC values in PROMAD were between 0.66 and 0.67, across all organs. Among the two-gene combinations, CASP1,PCNA exhibited the highest performance with a gene-only model AUC of 0.77 and a gene-clinical model AUC of 0.79. It is worthwhile noting in this regard that several genes, in combination with CASP1, demonstrated performance that is on par with the CASP1,PCNA combination. The top 200 two-gene pairs are presented in TABLE 1.EXAMPLE 3THREE-GENE MODELS

[0233] The three-gene models exhibited an increase in predictive performance, with mean AUC values ranging from 0.78 to 0.81 in our AUSCAD cohort of kidney transplant recipients. The gene-only model AUC scores improved to between 0.77 and 0.81, and the gene + clinical data model AUC scores ranged from 0.80 to 0.84. The mean AUC values in PROMAD were once again between 0.64 and 0.66, across different organs. Among the three-gene combinations, AQP3,CASP1,ITGA2B showed the highest performance with a gene-only model AUC of 0.80 and a gene + clinical data model AUC of 0.84.

[0234] It is also worth noting that other genes such as APP contributed to gene-set pairs that had high performance, when combined with CASP1. This suggest that CASP1 needs another stabilizing gene to be useful when making predictions. Such stabilizing genes, of which APP and AQP3 were found to be the best performing, improve the predictive performance of CASP1 to differentiate between transplant recipients with allograft dysfunction and transplant recipients with allograft tolerance. The predictive performance of representative three-biomarker combinations, including ones based on CASP1 and one of APP and AQP3, for differentiating between subjects with allograft rejection and subjects with allograft tolerance, as a function of organ transplant type (heart, kidney, liver), is shown in Figure 5.

[0235] The top 200 [CASPl,APP]-containing three-gene combinations and the top 200 [CASPl,AQP]-containing three-gene combinations are presented in TABLES 23 and 24, respectively.Table 23: Three-biomarker panels comprising CASP1 and APP* Combined pairwise ratiosAUCGENE values based on gene expression data modelAUCCLIN values based on gene expression data + clinical data modelTable 24: Three-biomarker panels comprising CASP1 and AQP3* Combined pairwise ratiosAUCGENE values based on gene expression data modelAUCCLIN values based on gene expression data + clinical data modelEXAMPLE 4FOUR-GENE MODELS

[0236] The present inventors next chose to explore 4-gene models, with the hypothesis that their models may be able to use other genes as orthogonal information for prediction.

[0237] The four-gene models provided better good performance, with mean AUC values similar to the three-gene models, ranging from 0.78 to 0.80. The gene-only model AUC scores were between 0.78 and 0.81, while the gene-clinical model AUC scores showed further improvement, ranging from 0.83 to 0.85. The mean AUC across different organs within the PROMAD atlas ranged from 0.65 to 0.68. Among the four-gene combinations, APP,AQP3,CASP1,ITGB1 exhibited the highest performance with a gene-only model AUC of 0.78 and a gene-clinical model AUC of 0.85.

[0238] Interestingly, the four-gene models started to be dominated by the presence of APP, AQP3, CASP1 and another gene of interest. Indicating that APP / AQP3 and CASP1 play key roles in predicting rejection and are made useful by the inclusion of another informative gene. The predictive performance of representative four-biomarker combinations based on CASP1 and one of APP and AQP3, for differentiating between subjects with allograft rejection and subjects with allograft tolerance, as a function of organ transplant type (heart, kidney, liver), is shown in Figure 6.

[0239] The top 200 [CASPl,APP]-containing four-gene combinations, the top 200 [CASPl,AQP]-containing four-gene combinations, and the top 200 [CASPl,APP,AQP]-containing four-gene combinations are presented in TABLES 25, 26 and 27, respectively.Table 25: Four-biomarker panels comprising CASP1 and APP* Combined pairwise ratiosAUCGENE values based on gene expression data modelAUCCLIN values based on gene expression data + clinical data modelTable 26: Four-biomarker panels comprising CASP1 and AQP3* Combined pairwise ratiosAUCGENE values based on gene expression data modelAUCCLIN values based on gene expression data + clinical data modelTable 27: Four-biomarker panels comprising CASP1, APP and AQP3* Combined pairwise ratiosAUCGENE values based on gene expression data modelAUCCLIN values based on gene expression data + clinical data modelEXAMPLE 5FIVE-GENE MODELS

[0240] The five-gene models provided the best overall performance, with gene only AUC in the AUSCAD cohort up to 0.86. When combined with clinical data, the performance of these 5-gene ratios was 0.9. Demonstrating a very high degree of performance in kidney transplant rejection prediction. When considering the other organs, the five-gene combinations ranged between 0.65-0.7.

[0241] Here too, the five-gene models were made up of an assortment of genes, yet the key stabilizing ratio for this prediction was APP and CASP1 (Figure below). Just like the three- and four-gene models, this ratio was necessary for models to perform well.

[0242] The five-gene model demonstrated superior diagnostic performance for predicting allograft rejection, as evidenced by higher AUC scores compared to the two, three & four, gene models. The inclusion of clinical data further enhanced the model's predictive accuracy, highlighting its potential utility in clinical settings. The predictive performance of representative five-biomarker combinations based on CASP1 and one of APP and AQP3, for differentiating between subjects with allograft rejection and subjects with allograft tolerance, as a function of organ transplant type (heart, kidney, liver), is shown in Figure 7.

[0243] The top 200 [CASPl,APP]-containing five-gene combinations, the top 200 [CASPl,AQP]-containing five-gene combinations, and the top 200 [CASPl,APP,AQP]-containing five-gene combinations are presented in TABLES 28, 29 and 30, respectively.Table 28: Five-biomarker panels comprising CASP1 and APP* Combined pairwise ratiosAUCGENE values based on gene expression data modelAUCCLIN values based on gene expression data + clinical data modelTable 29: Five-biomarker panels comprising CASP1 and AQP3* Combined pairwise ratiosAUCGENE values based on gene expression data modelAUCCLIN values based on gene expression data + clinical data modelTable 30: Five-biomarker panels comprising CASP1, APP and AQP3* Combined pairwise ratiosAUCGENE values based on gene expression data modelAUCCLIN values based on gene expression data + clinical data modelEXAMPLE 6IDENTIFYING GENE RATIOS WITH ROBUST PREDICTIVE POWER

[0244] To explore which gene ratios were most informative, the present inventors applied Lasso regression models. Lasso (Least Absolute Shrinkage and Selection Operator) is a statistical technique that helps identify the most predictive variables by applying a penalty to the regression coefficients, shrinking some of them to zero and effectively performing variable selection.

[0245] In this analysis, it was decided to focus on the lambda path, which is a plot showing how the coefficients of the genes change as the penalty parameter (lambda) varies. These plots assist in determining which gene ratios are most influential in predicting allograft rejection. Each line in the lambda path plot represents a gene ratio, with the x-axis showing the log of the lambda values and the y-axis showing the corresponding beta values (regression coefficients). As lambda increases (moving left to right), the penalty becomes stronger, causing more coefficients to shrink to zero.

[0246] Lambda path plots for different biomarker pairs of the three-biomarker combinations shown in Figure 5 and for different biomarker pairs of the four-biomarker combinations shown in Figure 6 are presented in Figures 8 and 9, respectively. These lambda path plots illustrate the selection process of the gene ratios at different levels of lambda. From these plots, gene ratios can be identified, which retain significant coefficients even at higher penalty values, indicating their strong predictive power. Notably, the gene ratios involving CASP1 and APP or AQP3 demonstrated substantial predictive power for allograft rejection. By contrast, gene ratios involving CASP1 and other genes demonstrated lower predictive power, indicating that APP or AQP3 are significant genes that improve the predictive performance of CASP1.

[0247] Thus, by interpreting these plots, it can be seen that certain gene combinations consistently have non-zero coefficients across a range of lambda values, signifying their importance. For example, the combination of APP and CASP1 or AQP3 and CASP1 remain significant even as the penalty increases, highlighting its robustness in the predictive model.EXAMPLE 7LOGISTIC REGRESSION MODEL FOR PREDICTING ALLOGRAFT DYSFUNCTION AND TOLERANCE

[0248] A logistic regression model was developed to predict the log-odds of allograft dysfunction or allograft tolerance in transplant recipients. The model inputs included various biomarkers and clinical parameters, represented as ratios, which have been previously identified as significant indicators of allograft status.Step 1: Model Input and Coefficients

[0249] The logistic regression model utilized a set of input features (ratios) derived from the concentrations of specific biomarkers. These input features were multiplied by their respective coefficients, which were determined through model training using historical patient data. The coefficients represent the relative contribution of each input feature to the overall model prediction.

[0250] The linear predictor, z, is calculated as follows:Step 2: Interpretation of Log-Odds

[0251] The output, z, from the model represents the log-odds of allograft dysfunction. This value indicates the likelihood of allograft dysfunction in log-odds units, where positive values suggest an increased likelihood and negative values suggest a decreased likelihood.Step 3: Example Coefficients

[0252] By way of example, if three input features were used (ratios of biomarker concentrations A, B, and C), the equation for the log-odds may be set out as follows:Z = -1.5 + (2.3 x X,) + (1.8 + x X2) + (0.7 x X3) where the coefficients (-1.5, 2.3, 1.8, and 0.7) were derived from the logistic regression model training phase.Step 4: Transformation to Probability

[0253] To transform the log-odds , z, into a probability, the logistic function was applied as follows:1 P ~ l + e~zwhere P represents the probability of allograft dysfunction, providing a more intuitive measure of likelihood.EXAMPLE 8CLINICAL PARAMETERS AND CLINICALLY ADJUSTED COMPOSITE SCORE

[0254] This example details the incorporation of clinical parameters into the logistic regression model to adjust the log-odds score and provide a more clinically relevant prediction.Step 1: Selection of Clinical Parameters

[0255] For each organ allograft type, relevant clinical parameters were selected based on their known association with allograft outcomes. These parameters included donor age, recipient age, cold ischemia time, serum biochemistry, and presence of pre-existing conditions such as diabetes or hypertension.Step 2: Aggregation with Log-Odds Score

[0256] The initial log-odds score, calculated using the logistic regression model, was then adjusted by integrating these clinical parameters. The adjusted log-odds score:where:Z is the original log-odds score;Yj represents the coefficient for the jthclinical parameter; andCPj is the value of the jthclinical parameterStep 3: Adjusted Log Odds Calculation

[0257] By way of example, if the clinical parameters included donor age, recipient age, and cold ischemia time, the adjusted log-odds score may be calculated as follows: adj = Z + (0.2 x Donor Age) + (0.3 + x Recipient Age) + (0.7 x eGFR)

[0258] This adjustment refines the log-odds estimate by accounting for individual patient characteristics.Step 4: Transformation to Clinically Adjusted Probability

[0259] To obtain a probability from the adjusted log-odds score, the following logistic function was applied:1 p dj -1 + e~zadjwhere Padj provides a probability estimate of allograft dysfunction that considers both the biomarker data and clinical parameters, enhancing the clinical utility of the model.

[0260] The disclosure of every patent, patent application, and publication cited herein is hereby incorporated herein by reference in its entirety.

[0261] The citation of any reference herein should not be construed as an admission that such reference is available as "Prior Art" to the instant application.

[0262] Throughout the specification the aim has been to describe the preferred embodiments of the disclosure without limiting the disclosure to any one embodiment or specific collection of features. Those of skill in the art will therefore appreciate that, in light of the instant disclosure, various modifications and changes can be made in the particular embodiments exemplified without departing from the scope of the disclosure. All such modifications and changes are intended to be included within the scope of the appended claims.

Claims

WHAT IS CLAIMED IS:

1. A method for determining an indicator used in assessing a likelihood that allograft dysfunction is present or absent in a subject who has undergone an organ transplant, the method comprising, consisting or consisting essentially of:(1) determining a biomarker value for each of a plurality of polynucleotide biomarkers e.g., 2, 3, 4, 5, or more polynucleotide biomarkers) of a biomarker panel in a blood sample obtained from the subject, wherein a respective biomarker value is indicative of a level of a corresponding polynucleotide biomarker in the blood sample, wherein the biomarker panel comprises, consists or consists essentially of a CASP1 polynucleotide, and one or both of an APP polynucleotide and an AQP3 polynucleotide; and(2) determining the indicator using the biomarker values.

2. The method of claim 1, wherein individual biomarker values are representative of a measured amount or concentration of a corresponding biomarker polynucleotide, or a logarithmic representation of a measured amount or concentration of a corresponding biomarker polynucleotide in the sample.

3. The method of claim 1 or claim 2, wherein the organ transplant is a kidney, heart or liver.

4. The method of any one of claims 1 to 3, further comprising applying a function to one or more biomarker values to yield at least one functionalized biomarker value and determining the indicator using the at least one functionalized biomarker value.

5. The method of claim 4, wherein the function includes at least one of: (a) multiplying biomarker values; (b) dividing biomarker values; (c) adding biomarker values; (d) subtracting biomarker values; (e) a weighted sum of biomarker values; (f) a log sum of biomarker values; (g) a geometric mean of biomarker values; and (h) a sigmoidal function of biomarker values.

6. The method of claim 4 or claim 5, comprising determining for n pairs of said polynucleotide biomarkers a plurality of functionalized biomarker values, wherein a respective functionalized biomarker value is indicative of a ratio of concentrations of a pair of said polynucleotide biomarkers, wherein each functionalized biomarker value of said plurality of functionalized biomarker values is indicative of a ratio of concentrations of a different pair of said polynucleotide biomarkers, and wherein n represents the sum of all possible, non- redundant pairs of biomarker polynucleotides of the biomarker panel.

7. The method of any one of claims 1 to 6, further comprising combining biomarker values to provide a composite score and determining the indicator using the composite score.

8. The method of claim 7, wherein the biomarker values are combined by adding, multiplying, subtracting, and / or dividing biomarker values.

9. The method of claim 7 or claim 8, further comprising combining the composite score with an additional feature value to produce a clinically adjusted composite score, wherein the additional feature value is for at least one additional feature type to characterize the likelihood that the subject has an increased likelihood of the presence of allograft dysfunction or an increased likelihood of the absence of allograft dysfunction.

10. The method of claim 9, wherein the at least one additional feature type comprises at least one clinical parameter of the subject.

11. The method of claim 10, wherein the transplant is a kidney transplant and the at least one clinical parameter is selected from age, sex, ethnicity, body mass index (BMI), alarmin {e.g., heat shock proteins, interleukin la (IL-la), IL-33, high mobility group box 1 (HMGB1), etc.) level, beta-trace protein (BTP) level, cystatin C level, kidney injury molecule 1 (KIMI) level, tissue inhibitor of metalloproteinases 2 (TIMP2) level, insulin like growth factor binding protein 7 (IGFBP7) level, blood urea nitrogen (BUN) level, neutrophil gelatinase-associated lipocalin (NGAL) level, proenkephalin (PENK) level, creatinine level, creatinine clearance, serum creatinine (SCr) level, urea level, histological analysis, imaging analysis (e.g., ultrasound, magnetic resonance imaging (MRI) analysis, computer tomography (CT) analysis), end-stage renal disease (ESRD) classification, ESRD cause, donor age at death, date of donor death, donor sex, cold ischemia time, warm ischemia time, human leukocyte antigen (HLA) matching, panel reactive antibody (PRA), positive or negative transplant cross-match, donor specific antibody (DSA), mean fluorescence index (MFI), graft loss, date of graft loss, graft loss cause, date of last follow-up, pediatric risk of mortality III [PRISM-III] score, pediatric index of mortality 2 [PIM-II] score, Apache Score, additional medication, response and adverse effect of medications, allergies, RNA profile, single cell RNA sequencing, metabolomics, microbiome, genomics, epigenomics, and microRNA.

12. The method of claim 10, wherein the transplant is a heart transplant and the at least one clinical parameter is selected from age, sex, ethnicity, BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), alarmin (e.g., heat shock proteins, IL-la, IL-33, HMGB1, etc.) level, B-type natriuretic peptide levels, diet parameters, cholesterol parameters (e.g., total cholesterol level, low-density lipoprotein (LDL) cholesterol level, high- density lipoprotein (HDL) cholesterol level, non-HDL cholesterol level), C-reactive protein (CRP) level, triglyceride level, troponin T level, co-morbidities, physical activity parameters, family history of cardiovascular disease and / or cardiometabolic disease, stress parameters, alcohol consumption parameters, smoking and tobacco usage, imaging studies, electrocardiogram (ECG) studies, additional medication, response and adverse effect of medications, allergies, RNA profile, single cell RNA sequencing, metabolomics, microbiome, genomics, epigenomics, and microRNA.

13. The method of claim 10 wherein the transplant is a liver transplant and the at least one clinical parameter is selected from age, sex, ethnicity, BMI, platelet count, alarmin (e.g., heat shock proteins, IL-la, IL-33, HMGB1, etc.) level, autotaxin level, plasma C4 level, total bilirubin level, serum alanine aminotransferase (ALT) level, serum gammaglutamyltransferase (GGT), serum aspartate transaminase (AST) level, patatin like phospholipase domain containing 3 (PNPLA3) genotype, additional medication, response and adverse effect of medications, allergies, RNA profile, single cell RNA sequencing, metabolomics, microbiome, genomics, epigenomics, and microRNA.

14. The method of any one of claims 1 to 13, comprising analyzing the biomarker value(s), composite score or clinically adjusted composite score, with reference to one or more corresponding reference biomarker value ranges or threshold values, or compositescore ranges or threshold values, or clinically adjusted composite score ranges or threshold values, to determine the indicator.

15. The method of any one of claims 1 to 14, wherein the indicator indicates an increased likelihood of a presence of allograft dysfunction if the biomarker values, composite score or clinically adjusted composite score is indicative of a level of the biomarker polynucleotides in the sample that correlates with an increased likelihood of a presence of allograft dysfunction relative to a predetermined reference biomarker value ranges or cut-off values, composite score range or cut-off value, or clinically adjusted composite score range or cut-off value, and wherein the indicator indicates a likelihood of the absence of allograft dysfunction if the biomarker values, composite score or clinically adjusted composite score is indicative of a level of the biomarker polynucleotides in the sample that correlates with an increased likelihood of an absence of allograft dysfunction relative to a predetermined reference biomarker value ranges or cut-off values, composite score range or cut-off value, or clinically adjusted composite score range or cut-off value.

16. A composition comprising a mixture of a DNA polymerase, a blood leukocyte cDNA sample obtained from a subject who has undergone an organ transplant, wherein the blood leukocyte cDNA sample comprises a plurality of cDNA biomarkers (e.g., 2, 3, 4, 5, or more cDNAs) of a biomarker panel, wherein the biomarker panel comprises, consists or consists essentially of a CASP1 cDNA, and one or both of an APP cDNA and an AQP3 cDNA, and wherein the composition further comprises for individual cDNA biomarkers of the biomarker panel at least one oligonucleotide primer or probe that hybridizes to the cDNA biomarker.

17. A device for nucleic acid amplification of blood leukocyte cDNA, the device comprising a plurality of reaction vessels, individual reaction vessels comprising the composition of claim 16.

18. A method for treating a subject who has undergone an organ transplant, the method comprising:(1) performing the indicator-determining method of any one of claims 1 to 46 to determine whether the subject has an increased likelihood of a presence or absence of allograft dysfunction;(2) exposing the subject to an allograft dysfunction treatment regimen, wherein the exposure to the treatment regimen is based at least in part on the indicator-determining method indicating that subject has an increased likelihood of the presence of allograft dysfunction; or(3) not exposing the subject to an allograft dysfunction treatment regimen, wherein the non-exposure to the treatment regimen is based at least in part on the indicatordetermining method indicating that subject has an increased likelihood of the absence of allograft dysfunction.

19. A kit for determining an indicator used in assessing a likelihood that allograft dysfunction is present or absent in a subject who has undergone an organ transplant, the kit comprising for each of a plurality of polynucleotide biomarkers (e.g., 2, 3, 4, 5, or more polynucleotide biomarkers) of a biomarker panel at least one oligonucleotide primer and / or at least one oligonucleotide probe that hybridizes to the polynucleotide biomarker, whereinthe biomarker panel comprises, consists or consists essentially of a CASP1 polynucleotide, and one or both of an APP polynucleotide and an AQP3 polynucleotide.

20. A method of monitoring treatment efficacy or disease status in a subject diagnosed with an allograft dysfunction, the method comprising, consisting or consisting essentially of:1) providing a blood sample obtained from the subject;2) performing the indicator-determining method as broadly described above and elsewhere herein on the blood sample obtained from the subject before treatment, or at intervals between treatments, or at time intervals in the absence of treatment; and 3) determining that the treatment is effective, or that the disease status is improving, based at least in part on the indicator-determining method indicating that subject has an increased likelihood of the absence of allograft dysfunction; or4) determining that the treatment is not effective, or that the disease status is unchanged or worsening, based at least in part on the indicator-determining method indicating that subject has an increased likelihood of the presence of allograft dysfunction.

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