WO2017136709A2

WO2017136709A2 - Molecular assays for regulating immunosuppression, averting immune-mediated rejection and increasing graft survival

Info

Publication number: WO2017136709A2
Application number: PCT/US2017/016482
Authority: WO
Inventors: Daniel R. Salomon; Sunil M. Kurian; Brian D. MODENA
Original assignee: The Scripps Research Insitute
Priority date: 2016-02-03
Filing date: 2017-02-03
Publication date: 2017-08-10
Also published as: WO2017136709A3

Abstract

Disclosed herein are methods of detecting, predicting or monitoring immune-mediated rejection, immunosuppression, or risk of graft loss in a transplant recipient undergoing an immunosuppression regimen. These methods may be useful even In patients with kidney damage not thought to result from ongoing immune activation. Also provided are molecular profiles that more sensitively detect ongoing immune-mediated injury and are a better indicator of later graft loss than standard microscopic biopsy examination.

Description

MOLECULAR ASSAYS FOR REGULATING IMMUNOSUPPRESSION, AVERTING IMMUNE-MEDIATED REJECTION AND INCREASING GRAFF SURVIVAL

CROSS-REFERENCE TO RELATED APPLICATIONS

[δθ®1] This application claims the benefit of U.S. Provisional Patent Application No.

62/290,657. filed February 3, 2016, which is mcorporaled by reference in its entirety.

STATEMENT AS TO FEDERALLY SPONSORED RESEARCH

[0002] This invention was made with government support under U1 AI063603 (DRS_f SMK, TG, TSM, SRH) arid CTSA L2 TR001 ! 12 (BM) awarded by National Institutes of Health (NIH). The government has certain rights in the invention.

BACKGROUND

[00031 Kidney transplant recipients routinely take immunosuppressant dings in order to reduce the risk of acute or chronic rejection of the allograft. The health of the transplanted organ is usually closely monitored using blood tests for kidney function (such as creatinine levels) and kidney biopsies, which are then evaluated histologically for pathological evidence of rejection. Medical decisions regarding the course of treatment thus often hinge on histological characterization of biopsies, which include findings of pathological changes associated with acute rejection, chronic rejection or interstitial fibrosis and/or tubular atrophy (IFTA). Acute rejection and chronic rejection are immune-mediated events associated with abnormal kidney function and ma lead to eventual graft loss, IFTA describes a common histological abnormality seen in kidney transplant biopsies in which norma! cortical structures are replaced by interstitial fibrosis as a result of tissue injury and subsequent scarring, IFTA, when accompanied by histological evidence of inflammation, may correlate with decreased graft survival IFTA is evident histologically in 25% or more of 1 -year surveillance biopsies despite concomitant stable renal function. Our new results indicate that at the molecular level, IFTA can be the histological manifestation of chronic immune-mediated rejection,

[0004] The 10-year graft survival rates for kidney transplant recipients have not improved in the last decade despite the widespread use of effective immunosuppressive drugs. There is thus a dire need in the field for improved management of immunosuppression regimens, for tests that provide early detection of graft rejection even in the absence of definitive histological findings and tests that are objective and non-biased in contrast to the current practice of histology-based pathology that is largely subjective and can vary significantly with only about 70% inter- observer concordance from different pathologists in the same transplant center and/or different between transplant centers. SUMMARY

[S005] In some aspects, a method of administering immunosuppressant drugs is provided herein comprising: (a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen and wherein the nucleic acids comprise expression products from a panel of genes; (b) diagnosing immune-mediated rejection or inadequate immunosuppression sn the kidney transplant recipient based on levels of the expression products from the panel of genes, wherei the panel of genes is specifically selected to detect immune-mediated rejection or inadequate immunosuppression in a kidney transplant subject irrespective of histological findings; and (e) adjusting the immunosuppression drug regimen administered to the kidney transplant recipient based on the levels of the expression products from the panel of genes specifically selected to detect immune-mediated rejection or inadequate immunosuppression in a kidney transplant subject irrespective of histological findings.

[00061 ^¹ some aspects, provided herein is a method of managing an immunosuppression regimen in a kidney transplant recipient comprising: (a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen and wherein the nucleic acids comprise gene expression products from a panel of genes; (b) diagnosing immune-mediated rejection or inadequate immunosuppression in the kidney transplant rec ipient using levels of the expression products from the panel of genes , wherein the panel of genes comprises: (i) twenty or more genes listed in Table 37, (Π) twenty or more genes iisted in Table 38, (iii) twenty or more genes listed in Table 39, (iv) twenty or more genes listed in Table 40, (v) twenty or more genes iisted in Table 41, (vi) twenty or more genes listed in Table 42, (vii) twenty or more genes Iisted in Table 45, (viii) twenty or more genes listed in Table 47, (ix) twenty or more genes listed in Table 18; (x) twenty or more genes listed in Table 23, or (xi) twenty or more genes listed in Table 48; and (c) adjusting the Immunosuppression drug regimen administered to the kidney transplant recipient based on the levels of the expression products from the panel of genes comprising (i) twenty or more genes iisted in Table 37, (ii) twenty or more genes listed in Table 3S, (iii) twenty or more genes Iisted in Table 39, (iv) twenty or more genes listed in Table 40, (v) twenty or more genes Iisted in Table 41, (vi) twenty or more genes listed in Table 42, (vii) twenty or more genes listed in Table 45, (viii) twenty or more genes listed in Table 47, (ix) twenty or more genes listed in Table 18; (x) twenty or more genes listed in Table 23, or (xi) twenty or more genes listed in Table 48. [0007] In some embodiments, the diagnosing of any of the foregoing methods comprises diagnosing immune-mediated rejection without distinguishing between acute and chronic rejection,

[0ΟΘ8] In some aspects, provided herein is a method of administering immunosuppressant drugs to a kidney transplant recipient comprising: (a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen and the nucleic acids comprise expression products from a panel of genes that are commonly regulated in acute and chronic rejection; (b) detecting presence or absence of immune-mediated rejection in the kidney transplant recipient without

distinguishing between acute and chronic rejection, wherein the detecting is based on levels of the expression products from the panel of genes commonly regulated in acute and chronic rejection; and (c) adjusting the immunosuppression drug regimen of the kidney transplant recipient based on the presence or absence of immune-mediated allograft rejection in the kidney transplant recipient

[§§09] In some aspects, provided herein is a method of administering immunosuppressant drugs comprising: (a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen; (b)diagnosing immune-mediated rejection or inadequate immunosuppression in the kidney transplant recipient using expression levels of a nucleic acids wherein the panel of genes is selected from a single set of up to 250 markers and wherein the single set of up to 250 markers specifically detects immune-mediated rejection or inadequate immunosuppression in subjects with acute rejection and subjects with chronic rejection; and (c) adjusting the

immunosuppression drug regimen in the kidney transplant recipient based on the diagnosing of immune -mediated rejection or inadequate immunosuppression in the kidney transplant recipient. [ΘΜ®] in some embodiments of any of the preceding methods, the adjusting the

immunosuppression drug regimen comprises increasing a dose of a drug within the

immunosuppressive drug regimen. In some embodiments of any of the preceding methods, the adjusting the immunosuppression drug regimen comprises decreasing a dose of a drug within the immunosuppressive drug regimen, in some embodiments of any of the preceding methods, the panel of genes specifically detects immune-mediate allograft rejection in the kidney transplant recipient regardless of measurable renal function of the kidney transplant recipient, In some embodiments of any of the preceding methods histological examination of a biopsy from the kidney transplant recipient indicates or would indicate that the kidney transplant recipient does not have immune-mediated allograft rejection, in some embodiments of any of the preceding methods, the panel of genes specifically detects immune-mediated rejection in the biological sample obtained from the kidney transplant recipient. In some embodiments of any of the foregoing methods, the detecting is completely based on levels of the expression products from the panel of genes commonly regulated in acute and chronic rejection. In some cases, the detecting is partially based on levels of the expression products from the panel of genes commonly regulated in acute and chronic rejection.

[0011 } In some embodiments of any of the preceding methods, the method is capable of detecting immune-mediated rejection when the kidney transplant recipient has no detectable impairment of renal function, wherein the panel of genes is specifically selected to detect immune-mediated rejection in a kidney transplant subject with interstitial fibrosis or tubular atrophy, independently of whether the interstitial fibrosis or tubular atrophy is accompanied by evidence of inflammation. In some embodiments of any of the preceding methods, the kidney transplant subject has interstitial fibrosis without inflammation or tubular atrophy without inflammation, In some embodiments of any of the preceding methods, the kidney transplant subject has interstitial fibrosis with inflammation or tubular atrophy with inflammation, Sn some embodiments of any of the preceding methods, the method is capable of detecting immune- mediated rejection when the kidney transplant recipient has no detectable impairment of renal function.

[0012] In some embodiments of any of the preceding methods, the immunosuppression drug regimen comprises a drug selected from the group consisting of: calcineurin inhibitors, corticosteroids, cyclosporines, antimetabolites, and mTO inhibitors. In some embodiments of any of the preceding methods, the immunosuppression drug regimen comprises a drug selected from the group consisting of: Tacrolimus, Prograf, Astagraf XL, Hecoria, Envarsus XR_S Neoral, Sandimmune, Gengraf, Prednisone, Deltasone, Prednisolone. Orapred, P diapred, Millipred, Methylprednisolone, Medrol, and Solu-Medrol, Mycophenolate mofetil. CellCept, Myfortic, Azathioprine, Imuran, and Azasan, Sirolimus, Rapamune, Everolimus, Zortress, Belatacept, Nulojix, Basiliximab, Simuleci, Antithymocyte globulin rabbit, ATG rabbit. Thyreoglobulin, and Alemtuzumab.

[0013] In some embodiments of any of the preceding methods, the biological sample is a blood sample, in some embodiments of any of the preceding methods, the biological sample is a kidney biopsy sample. In some embodiments of any of the preceding methods the biological sample is a urine sample. In some eases, the biological sample comprises on or more of the following: T cells, peripheral blood mononuclear cells, peripheral blood lymphocytes, B cells, or monocytes, in some eases, the biological sample comprises whole blood. [0014] ϊπ some embodiments of any of the preceding methods, the panel of gerses comprises genes listed in Table 18, 23, 45, or 47. In some embodiments of any of the preceding methods, the panel of genes comprises at least 5, at least 10, at least 20, at least S0_S at least 100, or at least 200 genes listed in Table 1 8, 23, 45, or 47. In some embodiments of any of the preceding methods, the pane! of gerses comprises genes listed in Table 37, 38, 39, 40, 41, 45, or 47. In some embodiments of any of the preceding methods, the panel of genes comprises least 5, at least 10, at least 20, at least 50, at least 100, or at least 200 genes listed in Table 37_f 38, 39, 40, 41 , 45, or 47.

[0015] Irs some embodiments of any of the preceding methods, the expression levels are RNA expression levels. In some embodiments of any of the preceding methods, the RNA expression levels are mRNA expression levels. In some cases, the expression levels are detected by analyzing DNA derived from RNA,

[0016] In some embodiments of any of the preceding methods, the diagnosing comprises using a micro-array assay, DNA sequencing assay or RNA sequencing assay. In some embodiments of any of the preceding methods, the diagnosing comprises using hybridizing probes to gene expression products of the panel of genes. In some cases, the probes specifically bind to the gene expression products. In some cases, the probes comprise nucleic acids, DNA, or RNA,

[0H17J In some embodiments of any of the preceding methods, the methods further comprise comparing the expression levels of the panel of genes with expression levels of reference markers that specifically detect immune-mediated rejection in kidney transplant recipients irrespective of histological findings. In some embodiments of any of the preceding methods, the method further comprises comparing the expression levels of the panel of genes with expression levels of reference markers that specifically detect immune-mediated rejection or inadequate immunosuppression in kidney transplant recipients with interstitial fibrosis without inflammation or with tubular atrophy without inflammation, In some embodiments of any of the preceding methods, the method further comprises comparing the expression levels of the panel of genes with expression levels of reference markers that specifically detect immune-mediated rejection or inadequate immunosuppression in kidney transplant recipients with interstitial fibrosis with inflammation or with tubular atrophy with inflammation. In gome embodiments of any of the preceding methods, the method further comprises repeating steps (a)~(c),

[0018] In some embodiments of any of the preceding methods, the expression levels of the panel of genes indicate that the kidney transplant recipient has a greater than 70% chance of graft survival In some embodiments of any of the preceding methods, the expression levels of the panel of genes indicate that the kidney transplant recipient has a greater than 50%, greater than 75%, greater than 80%, greater than 85%, greater than 90%, or greater than 95% chance of graft survival. In some embodiments of any of the preceding methods the expression levels of the panel of genes indicate that the kidney transplant recipient has a less than 50% chance of graft survival In some embodiments of any of the preceding methods the expression levels of the panel of genes indicate that the kidney transplant recipient has a less than 80%, less than 70%, less than 60%, less than 50%, less than 40%, less than 30%, less than 20%, less than 10%, or less than 5% chance of graft survival

[0019] In some embodiments of any of the preceding methods, the adjusting the

immunosuppression drag regimen is not based on a histological examination of a kidney biopsy of the kidney transplant recipient, in some embodiments of any of the preceding methods the kidney transplant recipient has acute rejection or subclinical acute rejection, In some

embodiments of any of the preceding methods the kidney transplant recipient has chronic rejection. In some embodiments of any of the preceding methods, the panel of genes specifically detects immune-mediated rejection in kidney transplant subjects with interstitial fibrosis and tubular atrophy without inflammation, In some embodiments of any of the preceding methods, the panel of genes specifically detects acute rejection. In some embodiments of any of the preceding methods, the method further comprise applying an algorithm to the expression levels of the panel of genes. In some cases, the algorithm is a trained algorithm. In some cases, the trained algorithm is trained with gene expression data from samples from at least three different cohorts. In some cases, the trained aigorithm comprises a linear classifier. In some cases, the linear classifier comprises linear discriminant analysis, Fisher's linear discriminant, Naive Bayes classifier, Logistic regression, Perceptron, Support vector machine (SVM), or a combination thereof. In some eases, the algorithm comprises a Diagonal Linear Discriminant Analysis

(DLDA) algorithm, a Nearest Centroid algorithm, a Random Forest algorithm or statistical bootstrapping, a Prediction Analysis of Microarrays (PAM) algorithm, or a combination thereof.

[0020] in some aspects, provided herein is a method of detecting, monitoring, or prognosing immune-mediated rejection or inadequate immunosuppression in a kidney transplant recipient comprising: (a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from the kidney transplant recipient on an immunosuppression drug regimen; (b) performing an assay on the nucleic acids to determine expression levels of a panel of genes, wherein the panel of genes is specifically selected to detect immune-mediated rejection or inadequate immunosuppression in a kidney transplant subject irrespective of histological findings; and (c) detecting, monitoring, or prognosing immune-mediated rejection or inadequate immunosuppression based on the expression levels of the panel of genes specifically selected to detect immune-mediated rejection or inadequate immunosuppression in a kidney transplant subject irrespective of histological findings in the subject

[0021] In some aspects, provided herein is a method of detecting, monitoring, or prognosing immune-mediated rejection or inadequate immunosuppression in a kidney transplant recipient comprising: (a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from the kidney transplant recipient on an immunosuppression drug regimen; (fa) performing an assay on the nucleic acids to obtain expression levels of a panel of genes, wherein the panel of gerses comprises (i) twenty or more genes listed in Table 37, (ii) twenty or more genes listed in ^"fable 38, (iii) twenty or more genes listed in Table 39, (iv) twenty or more genes listed in Table 40, (v) twenty or more genes listed in Table 41, (vi) twenty or more ge es listed in Table 42, (vii) twenty or more genes listed in Table 45, (viH) twenty or more genes listed in Table 47, (ix) twenty or more genes listed in Table 1 , fx) twenty or more genes listed in Table 23, or (xi) twenty or more genes listed in Table 48; and (b) detecting, monitoring, or prognosing tmmune-mediated rejection or inadequate immunosuppression based on the expression levels of the panel of genes comprising (i) twenty or snore genes listed in Table 37, (ii) twenty or more genes listed in Table 38, (Ui) twenty or more genes listed in Table 39, (iv) twenty or more genes listed in Table 40, (v) twenty or more genes listed in Table 41, (vi) twenty or more genes listed in Table 42, (vii) twenty or more genes listed in Table 45, (viii) twenty or more genes listed in Table 47, (ix) twenty or more genes listed in Table 18, (x) twenty or more genes listed in Table 23, or (xi) twenty or more genes listed in Table 48,

[0022] In some aspects,, provided herein is a method of detecting, monitoring, OF prognosing immune-mediated rejection or inadequate immunosuppression in a kidney transplant recipient comprising: (a)obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen; (b) performing an assay on the nucleic acids to determine expression levels of a panel of genes, wherein the panel of genes is selected from a single set of up to 250 markers and wherem the single set of up to 250 markers specifically detects immune-mediated rejection in subjects with subclinical acute rejection, clinical acute rejection, subclinical chronic rejection, or clinical chronic rejection; and (e) detecting, monitoring or prognosing immune-mediated rejection or inadequate immunosuppression based on the expression levels of the panel of genes,

[0023] In some aspects, provided herein is a method of administering immunosuppressant drugs comprising: (a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen and wherein the nucleic acids comprise expression products from a panel of genes, wherein the panel of genes comprises genes dysregtdated in both acute rejection and chronic rejection; (b) detecting immune-mediated rejection or inadequate immunosuppression based on levels of the expression products from the pane! of genes; and (c) adjusting the immunosuppression drug regimen administered to the kidney transplant recipient based on the detecting of imm ne- mediated rejection or inadequate immunosuppression.

[ΘΘ24] In some embodiments of any of the preceding methods, the genes dysregulated in both acute rejection and chronic rejection are upregulated in both acute rejection and chronic rejection, when compared to a stable or norma! transplant condition. Irs some embodiments of any of the preceding methods, the genes dysregulated in both acute rejection and chronic rejection are downregu!ated in both acute rejection and chronic rejection, when compared to a stable or norma! transplant condition. In some embodiments of any of the preceding methods, the genes dysregulated in both acute rejection and chronic rejection are at least 1.5-fold upregulated in both acute rejection and chronic rejection compared to a normal or stable transplant condition, In some embodiments of any of the preceding methods, the genes dysregulated in both acute rejection and chronic rejection are at bast 1 ,5-fold down-regulated in both acute rejection and chronic rejection compared to a normal or stable transplant condition.

[Q025] In some embodiments of any of the preceding methods, the panel of genes does not comprise imrnunoglobulm-encodlng transcripts or transcripts preferentially expressed in mature B~cells. In some embodiments of any of the preceding methods, the panel of genes comprises immunoglobulin-encoding transcripts or transcripts preferentially expressed in mature B-cells. In some embodiments of any of the preceding methods, the panel of genes comprises at least five genes from table 37, at least five genes from table 38, at least five genes from table 39, at least five genes from table 40, at least five genes from table 43, or at least five genes from table 42. in some embodiments of any of the preceding methods, the pane! of genes comprises genes implicated in T-eell-mediated immune responses or inflammation. In some embodiments of any of the preceding methods, the panel of genes comprises at least five genes from table 37, at least five genes from table 38, or at least five genes from table 39, In some embodiments of any of the preceding methods, the panel of genes comprises at least five genes involved in metabolism or tissue integrity. In some embodiments of any of the preceding methods, the panel of genes comprises at least five genes implicated in tissue integrity, amino acid turnover, glucose metabolism, fatty acid metabolism, energy production, cellular detoxification, or solute transport. In some embodiments of any of the preceding methods ,the panel of genes comprises at least five genes from table 40, at least five genes from table 41, or at least five genes from table 42. |θ©26) In some embodiments of any of the preceding methods, the expression products are RNA. In some embodiments of any of the preceding methods the expression products are cDNA or DNA. In some embodiments of any of the preceding methods, the expression products comprise mRNA extracted from the biological sample or nucleic acids derived from the mRNA extracted from the biological sample. , In some embodiments of any of the preceding methods, the expression products comprise cDNA or DNA derived from mRNA extracted from the biological sample.

[0027] In some embodiments of any of the foregoing methods, the acute rejection is clinical acute rejection. In some embodiments of any of the foregoing methods, the acute rejection is sub-clinical acute rejection. In some embodiments of any of the foregoing methods, the chronic rejection is clinical chronic rejection. In some cases, the chronic rejection is sub-clinical chronic rejection.

[00281 In some embodiments of any of the foregoing methods, the method further comprises reporting a result of the method to the kidney transplant recipient or to a caregiver of the transplant recipient. In some cases, the result is a diagnosis or detection of immune-mediated rejection or inadequate immunosuppression. In some eases, the result reported is that immune- mediated rejection, or inadequate immunosuppression is not detected.

[0029] In some aspects, also disclosed herein is a method of administering

immunosuppressant drugs comprising: (a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen; (b) performing an assay on the nucleic acids to determine expression levels of a panel of genes, wherein the panel of genes is specifically selected to detect immune-mediated rejection or inadequate immunosuppression in a kidney transplant patient irrespective of histological findings; and (c) adjusting the immunosuppression drug regimen administered to the kidney trans lant recipient based on the expression levels of the pane! of genes specifically selected to detect immune-mediated rejection or inadequate immunosuppression in a kidney transplant patient irrespective of histological findings.

[003Θ] Also disclosed herein is a method of managing an immunosuppression regimen in a kidney transplant recipient comprising: (a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen; (b) performing an assay on the nucleic acids to obtain levels of expression products of a panel of genes, wherein the panel of genes comprises: (i) twenty or more genes listed in Tables 37, (ii) twenty or more genes listed in Table 38, (iii) twenty or more genes listed in Table 39, (iv) twenty or more genes listed in Table 40, (v) twenty or more genes listed in Table 41 , (vi) twenty or more genes listed in Table 42, (vii) twenty or more genes listed in Table 45, (viii) twenty or more genes listed in Table 47, (ix) twenty or more genes listed in Table 18; or (x) twenty or more genes listed in Table 23 and (c) adjusting the

immunosuppression drug regimen based on the levels of the expression products of the panel of genes comprising (i) twenty or more genes listed in Tables 37, (ii) twenty or more genes listed in Table 38, (Hi) twenty or more genes listed in Table 39, (iv) twenty or more genes listed in Table 40, (v) twenty or more genes listed in Table 41, (vi) twenty or more genes listed in Table 42, (vii) twenty or more genes listed in Table 45, (viii) twenty or more genes listed in Table 47, (ix) twenty or more genes listed en Table 18; or (x) twenty or more genes listed in Table 23.

[©031] Farther disclosed herein is a method of administering immunosuppressant drugs comprising: (a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen; (b) performing an assay on the nucleic acids to determine levels of expression products of a panel of genes, wherein the panel of genes are selected from a single set of less than 250 markers and wherein the single set of less than 250 markers specifically detects immune-mediated rejection or inadequate immunosuppression in patients with subclinical acute rejection, clinical acute rejection, subclinical chronic rejection, and clinical chronic rejection; and (c) adjusting the immunosuppression drug regimen based on the levels of the expression products of the panel of genes.

[Θ032] In some embodiments, the adjusting the immunosuppression drug regimen comprises increasing the dose of the immunosuppressive drug regimen. In some embodiments, the adjusting the immunosuppression drug regimen comprises decreasing the dose of the immunosuppressive drug regimen. In some embodiments, the panel of genes specifically detects immune-mediated rejection in a kidney transplant recipient regardless of measurable renal function of the kidney transplant rec ipient. In some embodiments, the kidney transplant recipient has no detectable impairment of renal function. In some embodiments, histological examination of a biopsy from the kidney transplant recipient indicates that the kidney transplant recipient does not have immune-mediated rejection. In some embodiments, histological examination of a biopsy from the kidney transplant recipient indicates that the kidney transplant recipient does not have immune-mediated rejection and wherein the kidney transplant recipient has no detectable impairment of renal function. In some embodiments, the panel of genes specifically detects immune-mediated rejection irs the biological sample obtained from the kidney transplant recipient. I some embodiments, the panel of genes is specifically selected to detect immune-mediated rejection in a kidney transplant patient with interstitial fibrosis or tubular atrophy, independently of whether the interstitial fibrosis or tabular atrophy is accompanied by evidence of inflammation.

[0©33] In some embodiments, the method detects inadequate immunosuppression or immune-mediated rejection in the kidney transplant patient. In some embodiments, the kidney transplant patient has interstitial fibrosis without inflammation or tubular atrophy without inflammation. In some embodiments, the kidney transplant patient has interstitial fibrosis with inflammation or tubular atrophy with inflammation.

[0034] In some embodiments, the immunosuppression drug regimen comprises a drug selected from the group consisting of: calcineurin inhibitors, corticosteroids, e closporines, antimetabolites and rnTOR inhibitors. In some embodiments, the immunosuppression drug regimen comprises a drug selected from the group consisting of: Tacrolimus, Prograf, Astagraf XL, Hecoria, and Envarsus XR, In some embodiments, the immunosuppression drug regimen comprises a drug selected from the group consisting of: Neoral, Sandimmune, and Gengraf. In some embodiments, the immunosuppression drug regimen comprises a drug selected from the group consisting of: Prednisone, Deltasone, Prednisolone, Qrapred, Pediapred, Miliipred; Methy!prednisolone, Medrol, and Solu-Medrol. In some embodiments, the immunosuppression drug regimen comprises a drug selected from the group consisting of: Mycophenolate mofetit CeliCept, Myforlic, Azathioprine, Imuran, and Azasan, In some embodiments, the

immunosuppression drug regimen a drug selected from the group consisting of: SiroHmus, Rapamune, Everoiimus, Zortress, Belatacept, Nulojix, Basiliximab, Simuleet, Antithymocyte globulin rabbit, ATG rabbit, Thymoglobulin, and Alemtuzumab.

[Θ035] In some embodiments, the sample is a blood or urine sample. In some embodiments, the sample is a kidney biopsy sample. In some embodiments, the panel of genes comprises genes listed in Table 18, 23, 45 and/or 47, In some embodiments, the panel of genes comprises genes fisted in Table 37, 38, 39, 40, 41 , 45 and/or 47,

[0©36] In some embodiments, the expression levels are RNA expression levels. In some embodiments, the expression levels are niRNA expression levels. In some embodiments, the assay is a microarray assay, In some embodiments, the assay is a DNA sequencing assay OF RMA sequencing assay. In some embodiments, the assay comprises hybridizing probes to gene expression products of the panel of genes. In some embodiments, the assay comprises hybridizing probes to gene expression products of the panel of genes and wherein the probes are designed to specifically bind to the gene expression products. In some embodiments, the assay comprises hybridizing probes to gene expression products of the panel of genes and wherein the probes comprise nucleic acids, DNA, or RNA. [0037] In one aspect, the method further comprises comparing the expression levels of the panel of genes with expression levels of reference markers that specifically detect immune- mediated rejection in kidney transplant recipients Irrespective of histologic evidence of immune- mediated rejection or of inflammation. In another aspect, the method further comprises comparing the expression levels of the panel of genes with expression levels of reference markers that specifically detect immune-mediated rejection or inadequate immunosuppression in kidney transplant recipients with interstitial fibrosis without inflammation or with tubular atrophy without inflammation. Irs another aspect the method further comprises comparing the expression levels of the panel of genes with expression levels of reference markers that specifically detect immune-mediated rejection or inadequate Immunosuppression in kidney transplant recipients with interstitial fibrosis with inflammation or with tubular atrophy with inflammation.

{QQ3S] In some embodiments, the method comprises repeating steps (a)-(c) at a second time point to obtain a second set of expression levels of the panel of genes, in some embodiments, the expression levels of the panel of genes Indicate that the kidney transplant recipient has a greater than 70% chance of graft survival In some embodiments, the expression levels of the panel of genes indicate that the kidney transplant recipient has a less than 50% chance of graft survival. |©039j In some embodiments, the adjusting the immunosuppression drug regimen is not based on a histological examination of a kidney biopsy of the kidney transplant recipient. In some embodiments, the kidney transplant recipient has interstitial fibrosis and tubular atrophy, In some embodiments, the kidney transplant recipient has interstitial fibrosis and tubular atrophy with inflammation, in some embodiments, the kidney transplant recipient has interstitial fibrosis and tubular atrophy without inflammation. In some embodiments, the kidney transplant recipient has acute rejection, in some embodiments, the kidney transplant recipient has subclinical acute rejection, in some embodiments, the kidney transplant recipient has chronic rejection. In some embodiments, the panel of genes specifically detects immune-mediated rejection In kidney transplant recipients with Interstitial fibrosis and tubular atrophy without inflammation. In some embodiments, the panel of genes specifically detects acute rejection,

[05140] In another aspect, the assay further comprises applying an algorithm to the expression levels of the panel of genes. In some embodiments, the algorithm is a trained algorithm. In some embodiments, the trained algorithm is trained with gene expression data from biological samples from at least three different cohorts. In some embodiments, the trained algorithm comprises a linear classifier. In some embodiments, the linear classifier comprises one or more linear discriminant analysis, Fisher's linear discriminant, NaYve Bayes classifier, Logistic regression, Perception, Support vector machine (SVM) or a combination thereof, 1st some embodiments, the algorithm comprises a Diagonal Linear Discriminant Analysis (DLDA) algorithm, a Nearest Centroid algorithm, a Random Forest algorithm or statistical bootstrapping, or a Prediction Analysis of Microarrays (PAM) algorithm, or combination thereof,

10041] Further disclosed herein is a method of detecting, monitoring, or prognosing immune- mediated rejection or inadequate immunosuppression in a kidney transplant recipient comprising: (a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen; (b) performing an assay on the nucleic acids to determine expression levels of a pane! of genes, wherein the panel of genes is specifically selected to detect immune-mediated rejection or inadequate immunosuppression in a kidney transplant patient irrespective of histological findings; and (e) detecting, monitoring or prognosing iramune-mediated rejection or inadequate immunosuppression based on the levels of the expression products of the pane! of genes specifically selected to detect immune-mediated rejection or inadequate immunosuppression in a kidney transplant patient irrespective of histological findings.

j^'0042] Further disclosed herein is a method of detecting, monitoring, or prognosing immune- mediated rejection or inadequate immunosuppression in a kidney transplant recipient comprising; (a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen; (b) performing an assay on the nucleic acids to obtain levels of expression products of a panel of genes, wherein the panel of genes comprises (i) twenty or more genes listed in Tables 37, (ii) twenty or more genes listed in Table 38, (Hi) twenty or more genes Hsted in Table 39, (iv) twenty or more genes listed in Table 40_s (v) twenty or more genes listed in Table 41 , (vi) twenty or more genes listed in Table 42, (vii) twenty or more genes listed in Table 45, (viii) twenty or more genes listed in Table 47, (ix) twenty or more genes listed in Table 18; or (x) twenty or more genes listed in Table 23; and (c) detecting, monitoring or prognosing immune-mediated rejection or inadequate immunosuppression based on the levels of the expression products of the set of genes comprising (i) twenty or more genes listed in Tables 37, (ii) twenty or more genes listed in Table 38, (iii) twenty or more genes listed in Table 39, (iv) twenty or more genes listed in Table 40, (v) twenty or more genes listed in Table 41, (vi) twenty or more genes listed in Table 42, (vii) twenty or more genes Hsted in Table 45 (viii) twenty or more genes listed in Table 47, (ix) twenty or more genes listed in Table 18; or (x) twenty or more genes fisted in Table 23.

[§043] Further disclosed herein is a method of detecting, monitoring, or prognosing immune- mediated rejection or inadequate immunosuppression in a kidney transplant recipient comprising: (a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen; (b) performing an assay on the nucleic acids to determine levels of expression products of a panel of genes, wherein the panel of genes is selected from a single set of less than 250 markers and wherein the single set of less than 250 markers specifically detects immune-mediated rejection in patients with subclinical acute rejection, clinical acute rejection, subclinical chronic rejection, and clinical chronic rejection, and (c) detecting, monitoring or prognosing immune- mediated rejection or inadequate immunosuppression based on the levels of expression products of the set of genes from the pane! of genes,

[0044] in some embodiments, panel of genes specifically detects immune-me iated rejection in kidney transplant recipients with interstitial fibrosis without inflammation or with tubular atrophy without inflammation. In some embodiments, panel of genes specifically detects immune-mediated rejection in kidney transplant recipients with interstitial fibrosis with inflammation or with tubular atrophy with inflammation, in some embodiments, pane] of genes specifically detects immune-mediated rejection in kidney transplant recipients with acute rejection or subclinical acute rejection. In some embodiments, the single set of less than 250 markers is less than 150 markers,

[0045] In some embodiments, the panel of genes specifically detects immune-mediated rejection in a kidney transplant recipient regardless of measurable renal function of the kidney transplant recipient, In some embodiments, the kidney transplant recipient has no detectable impairment of renal function, in some embodiments, histological examination of a biopsy from the kidney transplant: recipient indicates that the kidney transplant recipient does not have immune-mediated rejection. In some embodiments, histological examination of a biopsy from the kidney transplant recipient indicates that the kidney transplant recipient does not have immune-mediated rejection and/or the kidney transplant recipient has no detectable impairment of renal function. In some embodiments, the panel of genes specifically detects immune- mediated rejection in the biological sample obtained from the kidney transplant recipient. In some embodiments, the panel of genes is specifically selected to detect immune-mediated rejection in a kidney transplant patient with interstitial fibrosis or tubular atrophy, independently of whether the interstitial fibrosis or tubular atrophy is accompanied by evidence of

inflammation, in some embodiments, the method detects inadequate immunosuppression or immune-mediated rejection in the kidney transplant patient. In some embodiments, the kidney transplant patient has interstitial fibrosis without inflammation or tubular atrophy without inflammation, In some embodiments, the kidney transplant patient has interstitial fibrosis with inflammation or tubular atrophy with inflammation,

[0046] In some embodiments, the sample is a blood or urine sample. In some embodiments, the sample is a kidney biopsy sample, In some embodiments, the panel of genes comprises genes listed in Table 18, 23, 45 and/or 47. In some embodiments, the panel of genes comprises genes listed in Table 37_s 38, 39, 40, 41, 45 and/or 47. In some embodiments, the expression products are RNA, In some embodiments, the expression products are m A,

[0Θ47] In some embodiments, the assay is a mieroarray assay. In some embodiments, the assay is a DNA sequencing assay or RNA sequencing assay, In some embodiments, the assay comprises hybridizing probes to gene expression products of the panel of genes. In some embodiments, the assay comprises hybridizing probes to gene expression products of the panel of genes and wherein the probes are designed to specifically bind to the gene expression products. In some embodiments, the assay comprises hybridizing probes to gene expression products of the panel of genes and wherein the probes comprise nucleic acids, DNA, or RNA, [004S] In another aspect, the method further comprises comparing the expression levels of the panel of genes with expression levels of reference markers that specifically detect immune- mediated rejection in kidney transplant recipients irrespective of histologic evidence of inflammation, In another aspect, the method further comprises comparing the expression levels of the panel of genes with expression levels of reference markers that specifically detect immune-mediated rejection or inadequate immunosuppression in kidney transplant recipients with interstitial fibrosis without inflammation or with tubular atrophy without inflammation. In another aspect, the method further comprises comparing the expression levels of the panel of genes with expression levels of reference markers that specifically detect immune-mediated rejection or inadequate immunosuppression in kidney transplant recipients with interstitial fibrosis with inflammation or with tubular atrophy with inflammation,

[0049] In some embodiments, the method comprises repeating steps (a)-(c) at a second time point to obtain a second set of expression levels of the panel of genes. In some embodiments, the expression levels of the panel of genes indicate that the kidney transplant recipient has a greater than 70% chance of graft survival in some embodiments, the expression levels of the panel of genes indicate that the kidney transplant recipient has a less than 50% chance of graft survival, in some embodiments, the kidney transplant recipient has interstitial fibrosis and tubular atrophy. In some embodiments, the kidney transplant recipient has interstitial fibrosis and tubular atrophy with inflammation, In some embodiments, the kidney transplant recipient has inierstitial fibrosis and tubular atrophy without inflammation, In some embodiments, the kidney transplant recipient

-I S- has acute rejection. In some embodiments, the kidney transplant recipient has subclinical acute rejection. In some embodiments, the kidney transplant recipient has chronic rejection.

[0050] In some embodiments, the panel of genes specifically detects irnniune-mediated rejection in kidney transplant recipients with interstitial fibrosis and tubular atrophy without inflammation. In some embodiments, the panel of genes specifically detects acute rejection.

[005!] In another aspect, the assay further comprises applying an algorithm to the expression levels of the panel of genes. In some embodiments, the algorithm Is a trained algorithm. In some embodiments, the trained algorithm is trained with gene expression data from biological samples from at least three different cohorts. In some embodiments, the trained algorithm comprises a linear classifier, In some embodiments, the linear classifier comprises one or more linear discriminant analysis, Fisher's linear discriminant, Naive Bayes classifier, Logistic regression, Perception, Support vector machine (SVM) or a combination thereof. In some embodiments, the algorithm comprises a Diagonal Linear Discriminant Analysis (DLDA) algorithm, a Nearest Centroid algorithm, a Random Forest algorithm or statistical bootstrapping, or a Prediction Analysis of Mieroarrays (PAM) algorithm, or combination thereof.

INCX>RFORATION BY REFERENCE

[8052] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference In their entireties to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be Incorporated by reference.

BRIEF DESCRIPTION OF THE BRA WINGS

[ Θ53] The novel features of the invention are set forth with particularity in the appended claims, A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative

embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

[0054] Figure I shows an exemplary method of treatment using the techniques herein where a sample 101 is provided by a kidney transplant patient on an immunosuppression regimen, the sample 101 is optionally pre-processed 105, gene expression levels of a panel are measured 110, and the immunosuppression regimen is adjusted according to standard medical practice IIS. The process may then iterate and start with a new sample 103 at a later time point.

[0055] Figssre 2A shows the graft survival according to histological phenotype. IFTA samples were classified into 3 subphenoiypes according to the degree of inflammation; IFTA plus AR, iFTA with inflammation and IFTA without Inflammation. Biopsies with only AR and normally functioning transplants (TX) were used for survival comparisons. The figure shows graft survival according to these phenotypes in days post-transplant The insert table shows the number of subjects at key time points by phenotypes,

[0056] Figure 2B shows differentially expressed genes shared between IFTA and AR, (a) is a Venn diagram showing differentially expressed genes (DEGs) shared between IFTA without inilarnrnation and AR, (b) plots the differential fold changes in gene expression (DEGs) comparing IFTA without inflammation vs. AR, A linear regression line and R2 statistic demonstrates a highly concordant direction of gene expression between phenotypes; c) and d) repeat and validate the analysis using an independent, external dataset. Note I : Differentially expressed genes and fold changes are calculated in relation to normal transplants (TX) defined by stable function and light histology, Note 2: The subphenotypes of IFTA with and without Inflammation were not available for the external data set.

[0057] Figsires 3A, 3B, and SC illustrate the process of generation for the Gene Co- expression Networks (GCNs) described herein, Gene co-expression networks (GCNs) were, discovered in an unbiased manner using the co-expression of differentially expressed genes for biopsies with AR, IFTA without AR (i.e. without inflammation) and IFTA with AR (e,g._s with inflammation), A number of GCN correlation thresholds (ranging from R2 values of 0.6 to 0.9) were tested to examine both loose and tight networks of co-expressed genes. With an. increase in the correlation coefficient threshold, a larg GCN network split into 3 smaller and tighter clusters with common biological functions for each 3C. Genes with the most connections (i.e. edges) to other genes in a network are given for each GCN,

[ΘΘ58] FIgssre 3A shows the three biologically distinct GCNs for acute rejection (top) and IFTA without acute rejection (bottom) alongside genes of interest in each GCN and key node genes.

[0059| Fsgsire 3B shows the three biologically distinct GCNs for IFTA with acute rejection alongside genes of interest in each GCN and key node genes.

[0060] Figsre 3C shows Verm diagrams illustrating the overlapping genes between the three different histopathologieal conditions (acute rejection, IFTA without acute rejection, and IFTA with acute rejection) for each biologically distinct GCN,

[0Θ61] Figure 4 shows the biological functions of AR-GCN1 and AR-GCN2 genes, The Figure illustrates the biological functions of 107 (56%) of the AR-GCN2 (Immune response/ Inflammation) genes and all 31 of the AR-GCN1 (B cell/ Immunoglobulin production) genes. The genes art the illustration with dashed red border are present in the GCNs. It is important to note that these genes are essentially the same in IFTA-GCN2.

[0062] Figure 5 shows using the geometric means for each gene co-expression network (GCN) to rank the impact by phenoiype. (a) Geometric means of A -GCN2 transcripts (immune response/inflammation) correlated with the degree of histological inflammation;

clinical AR > IFTA with AR > IFI^'A with inflammation (IPTA- i) > IFTA without inflammation > transplants with stable function and norma! histology (TX). Note that the geometric mean of AR-GCN2 in IFI^'A without inflammation was still significantly higher than TX (p^:::0,003). (b) In contrast, the geometric means of AR-GCN3 transcripts (metabolism/tissue integrity) were inversely related to inflammation; TX > IFTA without inflammation > AR > IFTA with inflammation > IFTA plus AR. (e,d) Same analyses using the IFTA-GCNs,

[0063] Figure shows the correlations between biopsy histology, Banff IFTA grades and the geometric means of the 3 IFTA-GCNs, The geometric means (y-axis) are plotted as a function of three IFTA phenotypesi IFTA with AR, all IFTA biopsies and IFTA without inflammation (IFTA without i) on the z-axis. In parallel, the geometric means are plotted as a function of Banff IFTA severity grades (x-axis),

[0064] Figures 7 A, 7B_S and 7C show the graft survival of subjects with IFTA without inflammation according to expression of our 3 gene co-expression networks (GCNs)

[0065] Figure 7A shows the gene clustering based on high vs low expression (a) and survival analysis (b) for the IFTA-GCN1 network. High vs. Low expression of GCN1 did not demonstrate a difference in graft survival (p~0.47).

[0§66] Figure 7B shows the gene clustering based on high vs low expression (e) and survival analysis (d) for the IFTA-GCN2 network.Here, graft survival of subjects with IFTA without inflammation con-elates with relative expression of GCN2 (p=0.02).

[0067] Figure 7C shows the gene clustering based on high vs low expression (e) and survival analysis (f) for the IFTA-GCN3 network. Here, relative expression of GCN3

(metabolism/tissue integrity) also correlates with graft survival (p™0.G3),

[0068] Figure 8 shows the graft survival of subjects with IFTA without inflammation correlates with the expression of 224 differentially expressed 'high risk' genes, (a) shows IFTA without inflammation samples clustered (top) into high vs. low risk clusters based on expression of 224 differentially expressed transcripts, (b) The high vs, low risk sample clusters correlate with graft survival (bottom, p=0,GGl),

[0069] Figsre 9 shows validating the correlation between high risk gene expression and graft survival using an independent external data set, IFTA biopsies from an external dataset (GEO

-I S- accession number; GSE21374)(18) were clustered into high and low risk subgroups based on expression of the same 224 transcripts that correlated with graft loss (tap), Again, two subject clusters were identified with marked difference in survival curves (bottom, p-0.002). Note that the subphenotypes of IFTA with and without inflammation were not available for this external data set.

[0070] Figa e 10 shows the Venn diagram demonstrating the overlap of the 224

differentially expressed genes associated with graft loss to the genes comprising the three IFTA- GCNs,

[0071] Figsre 11 shows the technical validation of project using Biocondiictor R package LIMMA, The project was completely and independently created within R framework. We chose not to filter the data, only evaluate shared differentially expressed genes between cAR and iFTA without inflammation samples. In this non-filtered data, there is again demonstrated a very strong overlap with approximately 73% of the IFTA DEGs shared with AR,

[0072] Figu e 12 shows an example of co-expressed genes. In the diagram, each sample is represented as column and each gene is a row. As shown, the highlighted genes rise and fall together across samples, These genes are called 'co-expressed.' Gene co-expression is of biological interest since it suggests a relationship among co-expressed genes. E.g. co-expressed genes may be controlled by the same transcriptional regulatory program, related to the same molecular function, members of the same molecular pathway, or part of a larger common biological process.

fSG73j Figure 13 shows the extraction of the correlogrsm to an adjacency matrix. As shown in this figure produced by author Mohammed Oloomi, if the two genes (Gi and Gj) pass the similarity criterion (e.g. r2 > 0.9), the content of the correlogram matrix with the index (ij) is replaced with a +1. Similarly, if there is a strong negative correlation, (e.g. r2 < -0.9), the content of the correlogram matrix with the index (ij) is replaced with a -1 ,

[Θ074] Figure 14 shows the representation of gene co-expression networks. A gene co- expression network (GCN) is an undirected graph where each node corresponds to a gene. Each gene is linked to other genes by an edge if and only if there is a statistically significant co- expression relationship between the genes. All genes in a GCN needed to be co-expressed with at least one other gene to he included in the network. The r2 threshold was set at 0,6 and a large GCN was constructed using a one pass over the database. The t2 threshold was increased (-0,9) to identify smaller, tighter clusters of genes as shown by the box in the figure.

[0075] Figure 15 shows the survival curves according to phenotype and adjusted for potential confounders. [0076] Fi ure 16 shows the survival curves according to phenotype without adjustment for confounders.

[0077] Fi ure 17 shows the survival plot in IFTA without inflammation samples,

[0078] Figure 18 shows the geometric means of GC s without inflammation IFTA

'borderline" samples,

[©079] Figure 19 shows an exemplary computer system for use with the methods described herein,

|0OS | F!gere 20 shows an exemplar}' computer network for use with the methods described herein,

DETAILED DESCRIPTION OF THE INVENTION

[0081] Is rod cttoss

[1)082] This disclosure provides molecular assays and compositions for distinguishing between adequate and inadequate immunosuppression in kidney transplant patients in a manner that is generally independent of traditional histological classifications obtained by kidney transplant biopsies. The assays and compositions provided herein include assays and compositions for managing immunosuppression regimens in patients who have received transplants (particularly kidney transplants). The assays and compositions herein are useful for evaluating immunosuppression efficacy in patients with acute rejection and chronic rejection, and are especially useful for evaluating immunosuppressive efficacy irrespective of histological evidence of interstitial fibrosis arsd/or tubular atrophy (IFTA) with inflammation. They also may be used to avert immune-mediated rejection, reduce the number of unnecessary biopsies and to prolong graft survival.

O083] In general the methods provided herein involve detecting or diagnosing inadequate immune suppression or immune-mediate rejection based on gene expression (e.g., niRNA) in a biological sample. Of note, often the methods provided herein involve detecting or diagnosing such conditions without distinguishing between acute rejection and chronic rejection, In some cases, expression levels of one or more genes (or a gene panel) are used in the methods provided herein. The genes may be co-expressed (or co-regulated) in both acute rejection (e.g., clinical acute rejection) and chronic rejection (e.g., clinical chronic rejection) and may show similar expression patterns in each context. The genes may also be related by function. For example, the genes may be involved in metabolism or tissue integrity, or some aspect of an immune response, [0Θ84] Figure 1 provides a general overview of a method provided herein. The method may involve providing or obtaining a sample from a transplant recipient (e.g., kidney transplant recipient) who is on an immunosuppression regimen 101 prescribed by the transplant recipient's caregiver. The sample (e.g., blood sample) may be processed In some way, such as by extraction of RNA or mR A from the sample 105. Expression levels of the extracted RNA may be determined by an assay for detecting RNA expression such as a sequencing assay, gene array, amplification assay or other assay 110, The caregiver may detect or diagnose an Immune- mediated rejection in the transplant recipient or inadequate immune suppression based on the expression levels of a panel of genes 110. Often, such detection or diagnosis is performed in the methods herein without distinguishing between acute and chronic rejection, For example, the panel of genes may contain (all or in part) a set of genes commonly expressed in both acute rejection and chronic rejection. Depending on the result of the expression level of the panel of genes, the caregiver may decide to modify the immunosuppression regimen of the transplant recipient 115, In some cases, the regimen may be increased, decreased, or stopped. In some cases, the regimen is changed to a different regimen, such as a different drug or treatment.

[0085] The over-arching result is thai all the current "boxes" or "phenotypes" created by histological analysis of biopsies and agreed upon by the Field as "diagnostic" (e.g., acute rejection and chronic rejection) are likely actually all immune-mediated rejection at the molecular level as evidenced by finding highly shared immune pathways and mechanisms defining an arc of immune-mediated rejection rather than a series of separate histologieally- defined phenotypes with little connection to each other. The methods can be used for biopsy signatures, as well as blood signatures, for Immune-mediated rejection including subclinical and clinical acute rejection and subclinical and clinical chronic rejection signatures. The driving premise of this application is that immune-mediated rejection detected by our molecular ignatures in either blood or biopsies represent a failure of Immunosuppression (e.g., inadequate immunosuppression) for each individual patient at the time point when an immune-mediated rejection is detected moleculariy. In this context, clinicians may he alerted to a state of inadequate imm nosuppression with a molecular signal of immune-mediated rejection present regardless of the kidney function of the patient. Note, that if the patient has normal, stable kidney function with a molecular rejection signal it is called "subclinical" rejection and if the patient has abnormal and unstable kidney function with a molecular rejection signal it is called "c!inieaF rejection. The earlier inadequate immunosuppression is detected, hopefully In the subclinical state, the earlier clinicians can increase or change immunosuppression to be more effective. They can then confirm the efficacy of any drug dosing or regimen change by re-profiling the blood of the patient for resolution of the immune-mediated rejection signal and adjust immunosuppression further if indicated by continued evidence of molecular rejection, The methods provided herein may use early diagnosis of immune-mediated rejection by serial blood profiling to avoid the extensive and scarring kidney tissue injury present by the time patients present with abnormal kidney function and clinical rejection. They may also provide an objective molecular diagnosis of im une-mediated rejection whenever a biopsy is performed, either "for cause" or as clinical standard of care called "surveillance" or "protocol" biopsies, independent of the currently adopted histological diagnoses (i.e. phenotypes or "boxes") and predictive of the risk of graft loss.

The assays and other methods provided herein often involve use of panels of biamarkers (e.g., performed on blood or biopsies) that identify immune-mediated rejection in kidney transplant patients, including patients with iFTA without or with histological evidence of inflammation and any other patient positioned on the arc of immune-mediated rejection disease including subclinical acute rejection, acute clinical rejection, subclinical or clinical chronic rejection characterized by histological evidence of IFTA and/or tubular atrophy without or with inflammation (the latter cars be characterized by infiltration of the transplant tissue with inflammatory cells including any combination of T cells, B cells, macrophages, plasma cells, eosinophils and N cells). Subclinical acute and subclinical chronic rejection can be characterized by molecular or histological evidence of immune-mediated rejection in the presence of stable kidney transplant function measured by serial serum creatinine levels and/or estimated Glomerular Filtration Rates (eGFRs). Most of these patients also have normal or near normal range measures of microalbuminuria, another early but non-specific marker of kidney dysfunction. Additional methods disclosed herein include methods for detecting or forecasting immune-mediated rejection in kidney transplant patients and methods of determining risk of graft loss in kidney transplant patients, generally independent of traditional histological characterizations of kidney biopsies (e.g., acute rejection, chronic rejection with IFTA with inflammation, and chronic rejection with IFTA without inflammation). These tests can be done on both biopsies and blood samples (e.g., whole blood samples) collected from the transplant patients, This disclosure is useful for managing immunosuppression and detecting immune- mediated rejection in patients with histologically-identified IFTA without inflammation - a class of patients generally not treated by current post-transplant protocols based on the incorrect assumption that this class of patients has no increased risk of graft loss or treatable underlying immune-mediated rejection, As shown here, the class of transplant patients can have the same increased risk of transplant graft loss as patients with hi sto logically-defined FTA with inflammation and this is associated with molecular signatures for immune-mediated rejection. However, it is equally useful for an kidney transplant recipient including patients with chronic rejection and IFTA with inflammation or with subclinical or clinical acute rejection, as it provides powerful detection approaches that do not depend on hisiology and therefore m y obviate the need for histological assays altogether,

[0088] Subjects

\ M9] The methods and compositions are useful for a wide variety of subjects, particularly a wide variety of subjects who are kidney transplant recipients, In most eases, the subject is a kidney transplant recipient who is being monitored for evidence of post-transplant rejection, graft dysfunction, or failure of immunosuppression,

[$090] In some cases, the subject has IFTA, identified by histological examination of a kidney biopsy. IFTA describes a common histological abnormality seen in kidney transplant biopsies in which normal cortical, tubular and interstitial structures are replaced by interstitial fibrosis and tubular atrophy. IFTA is thought to result from cumulative injury to the allograft, IFTA, when accompanied by histological evidence of inflammation, has been reported by multiple groups to con-elate with decreased graft survival. The subject may have IFTA with histological evidence of inflammation or without histological evidence of inflammation. Thus, the subject may be a patient who has had a kidney biopsy that is evaluated by histology, the only current method to identify the presence of IFTA and attempt a subjective quantification of its extent.

[0091] The subject may have IFTA graded by severity graded according to the Banff 2005 diagnostic criteria by histological examination of a kidney biopsy. This refers to pervasiveness of damage, Banff IFTA grades are 1 ("Mild fibrosis and tubular atrophy" <25% of cortical area), 2 ("moderate fibrosis and tubular atrophy", 26-50% of cortical area), and 3 ("severe fibrosis and tubular atrophy or loss", >50% of cortical area),

[Θ092] In some cases, the subject is a patient who has not had a kidney biopsy evaluated by histology, Such patient may have— or be at risk of having - IFTA or even acute rejection; but without a biopsy the IFTA or acute rejection has not been detected or confirmed, However, were such subject to undergo such histological examination, It would reveal IFTA with or without inflammation,

[ Θ93] The transplant recipient may be in some stage of rejection of the allograft. For example, the subject may have one or more conditions such as a condition along the following are of disease: subclinical acute rejection, clinical acute rejection, subclinical chronic rejection and clinical chronic rejection. Subclinical acute rejection (subA ) is currently characterized as normal and/or stable creatinine and/or eGFR measures of renal function but acute rejection by- histology. Clinical acute rejection (cAR) is characterized by rising creatinine levels, abnormal renal function (e.g., decreasing eGFRs) and acute rejection by histology. Subclinical chronic rejection (subCR.) involves normal or only modestly increased creatinine levels with mild renal dysfunction but early stage (Banff grade 1} IFTA by histology. Clinical chronic rejection (cCR) is generally characterized by rising creatinine levels, abnormal renal function and IFTA (Banff grade 2-3) by histology. As used herein, the term "acute rejection" may encompass subclinical and/or clinical acute rejection, unless otherwise indicated by context. As used herein, the term "chronic rejection" may encompass subclinical and/or clinical chronic rejection, unless otherwise indicated by context.

[0094] In some cases, the subject is a kidney transplant recipient with a normally functioning allograft without evidence of rejection. In some cases, the subject is a kidney transplant recipient at risk for developing immune-mediated rejection, or suspected of having immune-mediated rejection. For example, the subject may be suspected of having immune mediated-rejection because of abnormal renal function, such as a rising creatinine value, or because a histological observation of IFTA (early or advanced) with or without inflammation,

[§095] The subject is generally a transplant recipient who is on an immunosuppressive regimen, which may include one or more immunosuppressive drugs such as the drugs described herein. The subject may be monitored for the adequacy of such immunosuppressive regimen, for example, by serial blood gene expression profiling and/or identification of molecular signals for a quiescent immune state (e.g., adequate or effective levels of immunosuppression arid/or molecular evidence of immune-mediated rejection characterized as subclinical or clinical rejection depending on renal function status. In some cases, the i munosuppressive regimen is adequate to control or prevent immune-mediated rejection. In some cases, the

immunosuppressive regimen is inadequate to control or prevent immune-mediated rejection, In some eases, the immunosuppressive regimen may be inadequate due to a decision by a subject's caregiver (e.g. physician) to reduce dosing of immunosuppressive drugs or for some other reason such as patient non-adherence to prescribed medication dosing and/or a concomitant viral or bacterial infection or an environmental toxin or other immune-activating event. These situations can result in inadequate immunosuppression and/or a high risk for immune-mediated rejection at any given time point after a kidney transplant.

[0096] The subject is preferably a human subject or patient and can be of any gender and any age. In some cases, the subject is an infant, child, young adult, middle-aged adult or senior citizen and can fit in any age bracket (e.g., 5 years and younger, between 5 and 20 years, between 20 and 40 years, between 40 and 60 years, older than 60 years). In some cases, the methods and compositions are used for non-human subjects such as laboratory animals (including non-human primates, monkeys, apes, pigs, cows, sheep, rats, mice, etc.), The subject may also be a farm animal or other type of domestic animal

|0097J The transplant recipient may show signs of a transplant dysfunction or rejection as indicated by an elevated serum creatinine level and/or a decreased eGFR. in some instances, a transplant subject with a particular transplant condition (e.g., subAR, cAR, subCR, cCR, IFTA, etc.) may have ars increase of a serum creatinine level over t me of at least 0.1 mg/dL, 0,2 mg/dL, 0.3 mg/dL, 0.4 mg/dL, 0.5 mg/dL, 0,6 mg dL, 0.7 mg/dL 0.8 rng/dL, 0,9 mg/dL, 1.0 mg/dL, 1 , 1 mg/dL, 1.2 mg/dL, 1.3 mg'dL, 1.4 rng/dL, 1.5 mg/dL_} 1 ,6 mg/dL, 1.7 mg dL, 1.8 mg dL, 1.9 mg dL, 2.0 mg/dL, 2, 1 mg/dL, 2.2 mg/dL, 2,3 mg/dL, 2,4 rng dL, 2,5 mg dL, 2.6 mg/dL, 2.7 mg dL, 2.8 mg/dL, 2,9 mg dL, 3.0 mg dL, 3.1 mg/dL, 3.2 mg/dL, 3.3 mg dL, 3.4 mg/dL, 3,5 mg/dL, 3.6 mg/dL, 3.7 mg/dL, 3,8 mg dL, 3,9 mg/dL, or 4.0 mg dL. In some instances, a transplant subject with a certain transplant condition (e.g., subAR, cAR, subCR, cCR, IFTA, etc.) may have an increase of a serum creatinine level of at least 10%, 2G%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% from baseline, in some instances, a transplant subject with a certain transplant condition (e.g., subAR, cAR, subCR, cCR, IFTA, etc.) may have an increase of a serum creatinine Isvel of at least 1-fold, 2-fold, 3-fold, 4-foid, 5~fold, 6-fold, 7- fold, 8-fold, 9-fold, or 10-fold from baseline, in some eases, the increase in serum creatinine (e.g., any increase in the concentration of serum creatinine described herein) may occur oyer about .25 days, 0.5 days, 0,75 days, 1 day. 1.25 days, 1 ,5 days, 1.75 days, 2,0 days, 3.0 days, 4,0 days. 5,0 days, 6.0 days, 7.0 days, 8.0 days, 9.0 days, 10,0 days, 15 days, 30 days, 1 month, 2 months, 3 months, 4 months, 5 months, or 6 months, or more. In some cases, the serum creatinine may be stable over time. In some instances, a transplant subject with a particular transpiant condition (e.g., subAR, cAR, subCR, c-CR, IFTA, etc) may have a decrease of a eGFR of at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% from baseline. In some cases, the decrease in eGFR may occur over .25 days, 0.5 days, 0,75 days, 1 day, 1.25 days, 1.5 days, 1.75 days, 2,0 days, 3.0 days, 4.0 days, 5.0 days, 6,0 days, 7,0 days, 8,0 days, 9,0 days, 10,0 days, 15 days, 30 days, 1 month, 2 months, 3 months, 4 months, 5 months, or 6 months, or more. In some cases, the eGFR may be stable over time, in some Instances, diagnosing, predicting, or monitoring the status or outcome of a transplant or condition comprises determining transplant recipient-specific baselines and/or thresholds.

[®Θ98] As described herein, the disclosure is especially useful for kidney transplant recipients. However, in some cases, the methods, compositions and markers provided herein may also be useful for detecting immune-mediated rejection for other types of transplant recipients such as lung, heart or liver transplant recipients. f0099] The donor organ, tissue, or cells may be derived from a subject who has certain similarities or compatibilities with the recipient subject. For example, the donor organ, tissue, or celts may be derived from a donor subject who is age-matched, ethnicity-matched, gender- matched, blood-type compatible, or HLA-type compatible with the recipient subject. In some circumstances, the donor organ, tissue, or cells may be derived from a donor subject that has one or more mismatches in age, ethnicity, gender, blood-type, or HLA markers with the transplant recipient due to organ availability, The organ may be derived from a living or deceased donor. (0 1100] Sam les

[510101] The biological sample obtained from a transplant recipient in the methods provided herein may be any type of biological sample. Generally, the sample is a biopsy - particularly a kidney biopsy (renal biopsy) or kidney allograft biopsy ~~ in which a portion of the subject's transplanted kidney (or allograft) is removed for later analysis. Some examples of a biopsy include surgical biopsy, needle biopsy, incisional biopsy, excisional biopsy, punch biopsy, shave biopsy, or skin biopsy, in some cases, the method of needle aspiration may be fine needle aspiration, core needle biopsy, vacuum assisted biopsy, or large core biopsy, in some instances, the sample is not obtained by biopsy, In some cases, the sample may be formalin fixed and/or embedded in paraffin.

[00102] The sample need not be a kidney biopsy sample. For example, the sample may be a blood sample (e.g., whole blood, peripheral blood, peripheral blood mononuclear ceils, peripheral blood lymphocytes), serum sample, plasma sample, urine sample, or sputum sample. In some cases, the sample is not a blood sample,

[00103] Multiple samples (e.g., at least 2. at least 3, at least 4, at least 5, at least 7, at least 10, at least 15, at least 20 samples) may be obtained from a subject. In some cases, the samples are obtained over time, e.g., over days, weeks, months, or years (e.g. serial profiling), In such cases, the samples may be used to monitor the adequacy of immunosuppressive therapy over time or to monitor the course of immune-mediated rejection and/or response to therapy, often beginning at early onset of subclinical immune-mediated rejection and ending at clinical acute or chronic rej ction and/or the loss of the kidney graft. In some cases, the samples are takers at the same time, e.g., multiple biopsies of different sites of the kidney. In some cases, different types of samples (e.g., biopsy and blood samples) are taken at the same time or at different times, For example, a subject may have a kidney biopsy and a blood biopsy at or near the same time.

[ΘΘ1Θ4] In general, a biological sample may Include tissues, cells, nucleic acids, genes, gene fragments, expression products, polypeptides, exosomes, gene expression products, gene expression product fragments, or other biological material from a subject to be tested. The cells may be obtained from processing a tissue sample such as by enzymatic treatment. Nucleic acids within the biotogica] sample may include DNA, RNA, mJ¾NA_s miRNA, siRNA or other form of nucleic acid. Its some cases, the biological sample may comprise cDNA or cRNA produced directly or indirectly from native nucleic acids (e.g., mRNA). The molecules within a biological sample may be altered or modified by any method known in the art. For example, cRNA may be biotinylated.

[Θ0Ι05] The methods, kits, and systems disclosed herein may comprise specifically detecting, profiling, or quantitatimg molecules (e.g., nucleic acids, DNA, RNA, mRNA, cDNA, cRNA, miRNA, siRNA, polypeptides, etc.) that are within a biological sample. In some instances, genomic expression products, including RNA (e.g., mRNA), or polypeptides, may be isolated or extracted from the biological sample, in some cases, nucleic acids, DNA, RNA, polypeptides may be isolated from a ceil-free source. In some cases, nucleic acids, DNA, RNA, polypeptides may be isolated from cells derived from the transplant recipient.

[ΘΘ1Θ6] Esps-essioss Level Asmtysis

[00 J 07] The methods disclosed herein may comprise detecting gene expression, often by RNA expression profiling or other method in the art. Measuring gene expression levels may comprise reverse transcribing RNA (e.g., mRNA) within a sample in order to produce cDNA and, sometimes, using the cD as a template to produce cRNA. The cDNA or cRNA may be measured or detected using any of the methods described herein or known in the art.

00108! The expression level data may be determined or detected by any method known in the art, including microarray, SAGE, sequencing, blotting, electrophoresis, PGR amplification (e.g. RT-PCR, quantitative PGR, digital PGR, droplet digital PGR), and non-PC methods for gene detection and nest generation RNA or cDNA sequencing. One preferable approach is where the expression ieyei is determined or detected by microarrays. Exemplary microarrays include but are not limited to the Affymetrix human genome microarrays, Hlumina arrays, Agilent arrays, For example, the microarray may be an Affymetrix HG U 133 Plus PM peg array, Microarrays may comprise probes described herein attached to a substrate such as a slide. In some cases, arrays (e.g., Hlumina arrays) may use different probes attached to different particles or beads. In such arrays, the identity of which probe is attached to which particle or beads is usually determinable from an encoding system. The probes used in any nucleic acid microarray described herein can be oligonucleotides. In some cases, the probes may comprise several match probes with perfect complementarity to a given target mRNA, optionally together with mismatch probes differing from the mateh probes. See, e.g., (Lockhari, et al._f Nature Biotechnology 14: 1675-1680 (1996); and Lipschutz, et aL Nature Genetics Supplement 21 : 20-24, 1999), Such arrays may also include various control probes, such as a probe complementary to a

housekeeping gene likely to be expressed in most samples or spike-ίη controls based on non- human sequences (or non-species-specific sequences if testing in animais or other organisms) or artificially designed, non-human sequences (or non-species-specific sequences if testing in animals or other organisms) and reverse transcribed RNAs. Regardless of the specifics of array design, an array generally contains one or more probes either perfectly complementary to a particular target mRNA or sufficiently complementary to the target mRNA to distinguish it from other mRNAs in the sample, The presence of such a target mRNA can be determined from the hybridization signal of such probes, optionally by comparison with mismatch or other control probes included in the array. Typically, the target bears a fluorescent label, in which case hybridization intensity can be determined by, for example, a scanning eonfocal microscope in photon counting mode. Appropriate scanning devices are described by e.g.„ U.S. Pat. No.

5,578,832, and U.S. Pat. No. 5,631 ,734, The intensity of labeling of probes hybridizing to a particular mRNA or its amplification product may provide a raw measure of expression level. [©0109] In certain preferred embodiments, the expression level of the gene products (e.g., RNA) is determined by sequencing, such as b RNA sequencing (e.g., of cRNA or mRNA) or by DNA sequencing (e.g., of cDNA generated from reverse-transcribing RNA (e.g., mRNA) from a sample), Sequencing may be perfonned by any available method or technique. Sequencing methods may include: high-throughput sequencing, pyrosequencing, classic Sangar sequencing methods, sequencing-b -Ilgatlon, sequencing by synthesis, sequencing-by-hybrid izaiion, RNA- Seq (I!lumina), Digital Gene Expression (Helicos), next generation sequencing, single molecule sequencing by synthesis (SMSS) (Helicos), Ion Torrent Sequencing Machine (Life

Techno iogies Thermo-Fisher), massively-parallel sequencing, clonal single molecule Array (Solexa), shotgun sequencing, Maxam-Giibert sequencing, primer walking, and any other sequencing methods known in the art.

[1)0110] In some instances, the gene products may be polypeptides. In such instances, the methods may comprise measuring polypeptide gene products, Methods of measuring or detecting polypeptides may be accomplished using any method or technique known in the art. Examples of such methods include proteorrucs, expression proteomics, mass spectrometry, 2D PAGE, 3D PAGE, electrophoresis, proteomie chips, proteomic microarrays, Lummex-based assays, and/or Edman degradation reactions.

[00111] The data pertaining to the sample may be compared to data pertaining to one or more control samples, which may be samples from the same patient at different times. In some cases, the one or more control samples may comprise one or more samples from healthy subjects. unhealthy subjects, or a combination thereof. The healthy subjects may be subjects who are immunosuppressed, but with normal transplant function.

[00112] Biomarker refers to a measurable indicator of some biological state or condition. In some instances, a biomarker can be a substance found in a subject a quantity or level of the substance, or some other indicator. For example, a biomarker may be the amount of RNA, mRMA, tRNA, miRNA, mitochondrial RNA, sIRNA, polypeptides, proteins, DNA, cDNA and/or other gene expression products in a sample. Gene expression products are generally protein or RNA. The RNA useful in the methods herein is preferably rrsRNA or eRNA. In some instances, RNA may be an expression product of non-protein coding genes such as nbosoma! RNA (rRNA), transfer RNA (tRNA), micro RNA (miRNA), or small nuclear RNA (snRNA) genes. In certain examples, a biomarker or gene expression product may be artificially produced, such as DNA complementary or corresponding to RNA expression products in a sample or cRNA,

[00! 13] The assays, methods, compositions and systems as described here also relate to the use of biomarker panels and/or gene expression products (e.g., in blood or biopsy samples), particularly for the purpose of detecting immune-mediated rejection in the absence of histological classification by a kidney transplant biopsy. The methods can be used for purposes of identification, diagnosis, classification, prognosis, treatment or to otherwise characterize immune-mediated rejection, immunosuppression adequacy, or other condition associated with a transplant. Sets of biomarkers and/or gene expression products useful for classifying biological samples are provided, as we!! as methods of obtaining such sets of biomarkers. Often, the pattern of levels of gene expression biomarkers in a panel (also known as a signature) is determined and then used to evaluate the signature of the same panel of biomarkers in a sample, such as by a measure of similarity between the sample signature and the reference signature,

[00114] The biomarker (gene) panels are generally specifically selected to detect one or more conditions of the transplant recipient, in some instances, biomarker panels or gene expression products are selected to distinguish between adequate and inadequate immunosuppression and'or between presence and absence of immune-mediated rejection, in some eases they are selected in order to detect risk of graft loss. In some instances, they are used to distinguish high (>70%) risk of graft loss, medium (50%-70% risk of graft loss), and low (<50%) risk of graft loss, in some cases, they are selected to detect immune-mediated rejection, including AR, CR, cAR, subAR, subCR and/or cCR. In some particular cases, they are selected to detect immune-mediated rejection in the absence of histological classifications obtained from kidney transplant biopsies. In some cases, they can detect immune-mediated rejection even in subjects with IFTA without inflammation.

[00Π5] In some cases, a single panel is selected to detect more than one condition such as eAR, subAR, cCR, and/or subCR. In some cases, a single panel may detect immune-mediated rejection in a patient, independent of whether the patient has sAR, subAR, cCR, or subCR, Such a pan-i mime-mediaied rejection panel may be especially useful in the absence of histological classi fication of kidney biopsy or in the absence of certain clinical data such as kidney function data,

[00116] The expression level may be normalized. In some instances, normalization may comprise quantiie normalization. Normalization may comprise frozen robust multichip average (fRMA) normalization. Determining the expression level may comprise normalization by frozen robust multichip average (fRMA).

[00117] Often, the methods provided herein entail analyzing gene expression profiles from a biological sample in view of gene expression profiles associated with a certain condition such as AR, CR, subAR, or immune-mediated rejection in the absence of histological markers of IFTA with or without inflammation. The profiles may comprise expression of panels of genes,, such as genes provided in tables provided herein, The panels of genes may be genes associated with a particular biological phenomena or biological pathway, In some cases, the panel of genes may comprise genes associated with immune and/or inflammatory responses (e.g., T-eell or B-ce!l mediated responses) and molecular pathways, such as one or more genes in Table 37. In some cases, the panel of genes may comprise one or more genes or gene identifiers in any table herein, such as Table 45 or Table 47 (e.g., two or more, three or more, four or more, five or more, ten or more, 20 or more₅ 50 or more). In some cases_s the panel of genes may comprise one or more genes or gene identifiers in Table 37, 38, 39, 40, 41 , 45 or 47. In some cases, the genes may be one or more genes (e.g., 1 , 2, 3, 4, 5, 7, 10) associated with metabolic/tissue integrity molecular pathways, such as Table 4. For example, the genes may be one or more genes (e.g., 1, 2, 3, 4, 5, 7, 10) that encode enzymes important In amino acid turnover, glucose and fatty acid metabolism, energy production, and/or cellular detoxification. In some cases, the panel of genes may include one or more genes (e.g., 1 , 2, 3, 4, 5, 7. 10) genes that encode membrane transporters of various solutes, organic anions and/or drugs,

[00118] The sets of genes or panels of genes provided herein may comprise one or more genes from an of Tables 1 -47, The one or more genes may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 1 10, 120, 130, 140, 150, 160, 170, 180, 190, 200, 300, 400, 500 or more genes found in Tables 1-47. In some cases, the one or more genes raay comprise less than 20_s 25, 30, 40, 50, 60, 70, 80, 90, 1 0, ! 10, 120, 130, 140, 150, 160, 170, 180₅ 190, 200, 300, 400, 500 genes found in any of Tables 1-47,

[00119] Classificaiioa System

IO 120J Disclosed herein is the use of a classification system thai comprises one or more classifiers to classify a sample from a subject. In some instances, the classifier is a 2-, 3~₅ 4~, 5~, 6-, 7-, 8~, 9~, 10-way, or 15-way classifier or higher. In some preferred embodiments, the classifier Is a two-way classifier; in some cases, the classifier is a three-way classifier. In some embodiments, the classifier is a four-way classifier, The classifiers may be used to assign a sample to one or more classes.

[00121] Αΐ least one of the classes may be less than 50% risk of graft loss, between 50% and 70% risk of graft loss, or greater than 70% risk of graft ioss. Two of the classes may be less than 50% risk of graft loss, between 50% and 70% risk of graft loss, or greater than 70% risk of graft loss. All three of the classes may be less than 5G% risk of graft loss, between 50% and 70% risk of graft loss, or greater than 70% risk of graft loss, In some eases, at least one class is immune- mediated rejection. In some cases, one class is immune-mediated rejection and one class is stable or normal transplant function,

[00122] At least one of the classes may be IFTA without inflammation, IFF A with inflammation or acute rejection, and any combination thereof, At least two of the classes may be IFTA without inflammation, IFTA with inflammation or acute rejection, and any combination thereof. All three of the classes may be IFTA without inflammation, IFTA with inflammation and acute rejection, and any combination thereof. At least one of the classes may be adequate Immunosuppression or inadequate immunosuppression. One class may be adequate

immunosuppression and a second class may be inadequate immunosuppression.

100123} Algorithms

[00124] The methods, kits, and systems disclosed herein may comprise one or more algorithms or uses thereof. The one or more algorithms may be used to classify one or more samples from one or more subjects. The one or more algorithms may be applied to data from one OF more samples, The data may comprise gene expression data. The data may comprise sequencing data. The data may comprise array hybridization data.

[08125] The methods disclosed herein may comprise assigning a classification to one or more samples from one or more subjects. Assigning the classification to the sample may comprise applying an algorithm to the expression level. In some cases, the gene expression levels are inputted to a trained algorithm for classifying the sample into a risk category or a drug response category. [O012S] The algorithm may provide a record of its output including a classification of a sample and/or a confidence level In some instances, the output of the algorithm can be the possibility of the subject of having ongoing immune-mediated rejection.

(00127] The algorithm may be a trained algorithm. The algorithm may comprise a linear classifier. The linear classifier may comprise one or more linear discriminant analysis. Fisher's linear discriminant, Naive Bayes classifier, Logistic regression, Perception, Support vector machine, or a combination thereof._T¾e linear classifier may be a Support vector machine (SVM) algorithm,

[00128] The algorithm may comprise one or more linear discriminant analysis (LDA), Basic perceptron, Elastic Net, logistic regression, (Kernel) Support Vector Machines (SVM), Diagonal Linear Discriminant Analysis (DLDA), Goiub Classifier, Parzen-based, (kernel) Fisher

Discriminant Classifier, k-nearest neighbor, Iterative RELIEF, Classification Tree, Maximum Likelihood Classifier, Random Forest, Nearest Centroid, Prediction Analysis of Microarrays (PAM), k-medians clustering, Fuzzy C-Means Clustering, Gaussian mixture models, or a combination thereof. The algorithm may comprise a Diagonal Linear Discriminant Analysis (DLDA) algorithm. The algorithm may comprise a Nearest Centroid algorithm. The algorithm may comprise a Random Forest algorithm. The algorithm may comprise a Prediction Analysis of Microarrays (PAM) algorithm,

[00129] The methods disclosed herein may comprise use of one or more classifier equations. Classifying the sample may comprise a classifier equation. The classifier equation may be

wnerem;

[00131] k is a number of possible classes;

[00132] may be the discriminant score for class

[©0133] represents the expression level of gene ^l;

[00134] % represents a vector of expression levels for all p genes to be used for classification drawn from the sample to be classified;

[00 OS] ^k may be a shrunken centroid calculated from a training data and a shrinkage factor;

[00136] ^xik may be a component of ^x'k corresponding to gene ^¾;

[00137] &i is a pooled within-class standard deviation for gen ¹ in the training data; OS] ^so is a specified positive constant; and

[001391 represents a prior probability of a sample belonging to class &.

[08140] Assigning the classification may comprise calculating a class probability ..Calculating the class probability ^ ' may be calculated by Equation 2:

0142] Assigning the classification may comprise a classification rule. The classification rale Ht r* )

^ >^l may be expressed by Equation 3:

C(x* ) ^{■■■■■} arg max

100143) k€{ l ,K ^>

[00144] Algorithm Application

[00145] Algorithms may be applied for the classification of samples using a suitable software suite for analysis of genome-scale gene expression analysis. One such application is the Partek Genomics Suite v.6.6,

[001 6] Nearest CestroM

[00147] The samples may be classified using a nearest centroid algorithm. The Nearest Centroid classification method is based on [Tibshirani, R,, Hastie, T._s Naraslmham, B,, and Chu, G (2003): Class Prediction by Nearest Shrunken Centroids, with Applications to D A Microarrays. Statist, Sci. Vol. 18 (1):1G4~117] and [Ton, J.T., and Gonzalez, R.C. (1974): Pattern Recognition Principals, Addison- Wesley, Reading, Massachusetts]. The centroid classifications are done by assigning equal prior probabilities.

[00148] Support Vector Machines

[00149] The samples may be classified using a Support Vector Machines (SVM) algorithm.

Support Vector Machines (SYMs) attempt to find a set of hyperplanes (one for each pair of classes) that best classify the data. It does this by maximizing the distance of the hyperplanes to the closest data points on both sides. Partek uses die one-against-one method as described in "A comparison of methods for multi-class support vector machines" (CM. Hsu and C J. Lin, IEEE Transactions on Neural Networks, 13(2002), 415-425).

[ΘΘ15Θ] To run model selection with SVM cost with shrinking is used. Cost of 1 to 1000 with Step 100 was chosen to run several models. The radial basis kernel (gamma) is used. The kernel parameters are 1 /number of column s. [00151] Di¾ge>Ml Lissear ffimiissinant Analysis

[00152] The Discriminant Analysis method can do predictions based on the class variable.

The linear with equal prior probability method is chosen,

j W 153] Linear Discriminant Analysis is performed in Partek using these steps:

[00154] Calculation of a common (pooled) covariance matrix and within-group means

[©01 SS] Calculation of the set of linear discriminant functions from the common covariance and the within-group means

[00156] Classification using me linear discriminant unctions

[00157] The common covariance matrix is a pooled estimate of the within-group covariance matrices:

[00158] ∑SWi

[00159]

[00160] Tni - Ci

[00161] Thus, for linear discriminant analysis, the linear discriminant function for class i is defined s&: d (x) ^~ - I { x ~ m )t S ~\ ( x - m) + In P(w ).

[00162] Methods of sisassaglag iBt uaosuppressioB/deteeimg or monitoring imH.ui.e- me raied FejectioM/determistag risk of graft loss

[00163J The methods and compositions provided herein can be used to manage or adjust immunosuppression regimens even in the absence of histological classification of a kidney transplant biopsy, and sometimes in the absence of clinical functional information. The methods and compositions may be used to detect, diagnose, predict or monitor immune-mediated rejection in a transplant recipient— specially in the absence of histological classification of a kidney transplant biopsy. Methods of predicting risk of graft loss and other methods are also provided. The methods may include methods of detecting, diagnosing, monitoring, or predicting inadequate immunosuppression, often without distinguishing between acute and chronic rejection. In some cases, the methods include detecting, diagnosing, monitoring or predicting immune-mediated rejection, often without distinguishing between acute and chronic rejection. In some cases, the detecting, diagnosing, monitoring or predicting may invol ve detection of the presence of absence of a condition, e.g., inadequate immunosuppression, immune-mediated rejection,

[00164] In some cases, methods of managing a drug therapy (e.g., immunosuppressive therapy) are provided. Drug management (e.g., immunosuppressive management) may entail continuing with a particular therapy (e.g., immunosuppressive therapy), modifying a particular therapy, altering the dosage of a particular therapy, stopping or terminating a particular therapy, altering the frequency of a therapy, introduce a new therapy, introducing a new therapy to be used in combination with a current therapy, or any combination of the above.

[00165] With respect to immunosuppression therapy, the 2009 Kidney Disease: Improving Global Outcomes (KDIGO) guidelines outline an exemplary immunosuppression regimen for a kidney transplant recipient. Prior to transplant, a patient receives an "induction" combination of immunosuppressants, ideally comprising a biologic agent such as an IL-2 receptor antagonist (e.g. faasiliximab or da izumah) or a lymphocyte-depleting agent (e.g. antithymocyte globuli , antilymphocyte globulin and monomurab-CD3). The use of a lymphocyte-depieting agent may be recommended for patients considered at high risk of immune-mediated rejection. Calcineurin inhibitors (CNls, e.g. tacrolimus) may be additionally used in the "induction" phase, After transplant, a patient may betreated with an initial maintenance immunosuppression regimen which ideally comprises a calcineurin inhibitor (e.g. tacrolimus) or an mTO inhibitor (e.g. sirolimus) and an antiproliferative agent (e.g. myeophcnolate raofetii). The initial maintenance regimen may optionally additionally comprise a corticosteroid. Within 2-4 months after transplantation with no acute rejection, the immunosuppression regimen may be adjusted to a long-term maintenance phase, where the lowest planned doses of immunosuppressants are used, calcineurin inhibitor therapy is continued (if originally used), and corticosteroid therapy is continued (if used beyond the first week of transplant).

[00166] An additional immunosuppressant regimen to note is a "breakout" regimen used for treatment of any acute rejection episodes that occur after organ transplant. This may be a permanent adjustment to the maintenance regimen or temporary drug therapy used to minimize damage during the acute rejection episode. The adjustment may comprise temporary or long- term addition of a corticosteroid, temporary use of lymphocyte-depleting agents, and long-term addition of antiproliferative agents (e.g. mycopheno!ate mofetil or azathioprine, for patients not already receiving it), and any combination thereof. Treatment may also comprise plasma exchange, intravenous immunoglobulin, and anti-CD-20 antibody therapy, and any combination thereof.

[00167] The methods and systems used in this disclosure may guide the decision points in these treatment regimens (e.g. addition of agents to the immunosuppression regimen due to increased evaluation of risk). For example, they may allow the evaluation of a patient with low time-of-transpiant risk factors (e.g. high HLA matching between recipient and donor organ) as having high-risk of graft rejection, justifying the adjustment of an immunosuppression regimen as described for treatment of acute rejection in the absence of clinical signs of host-vs-graft immune activation. 0ΦΙ68] An assay provided herein may delect inadequate immunosuppression (or the presence of immune-mediated rejection) and, based on that finding, a caregiver (e.g., physician) ma change an existing Immunosuppressant regimen administered to the patient, A change in such existing immunosuppressant regimen in such case may include administering an additional or d ifferent drug, increasing the dosage of a drug within Che existing immunosuppressant regimen, or increasing the frequency of a drug within the existing immunosuppressant regimen, In some cases, the caregiver may take some other action such as transplanting a new organ, removing the failed graft, and/or returning the patient to dialysis due to graft (e.g. kidney) transplant failure. Conversely, if the value or other designation of aggregate expression levels of a patient indicates the patient does not have or is at reduced risk of transplant rejection, a caregiver may continue an existing immunosuppressive regimen, or even decrease the doss or frequency of a drug administered to a patient. In this case, a caregiver could do serial blood (or biopsy) molecular profiling to insure that any immunosuppressive drug decrease or change in regimen is not resulting later in a molecular signal/signature for immune-mediated rejection (e.g. inadequate immunosuppression),

[00169] Also provided herein are methods for predicting, detecting, or monitoring graft survival or loss in a kidney transplant recipient. Such assays may also lead to treatment decisions, particularly in the management of immunosuppressive regimens. For example, detection of increased markers of graft loss, may provoke a physician or other health care worker to increase or adjust immunosuppressive therapy, as described herein or to take another action, such as treatment of acute rejection with pulse intensification of prednisone or use of anil- lymphocyte globulin. Similarly, detection of decreased markers of immune-mediated rejection and/or increased risk of graft loss may result in continuation or reduction in a patient's immunosuppressive regimen,

[©0170] In some embodiments, the methods provided herein can predict a condition (e.g. graft survival or loss) prior to actual onset of the conditions. In some instances; the methods provided herein can predict the condition (e.g. graft survival or loss) in a transplant recipient at least 1 day, 5 days, 30 days, 30 days, 50 days or 100 days prior to onset. In other instances, the methods provided herein can predict the condition (e.g. graft survival or loss) in a transplant recipient at least 1, 2, 3, 4, 5, 6, 7, 8, 9_S 10, 1 1 , 12, S3, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or 31 days prior to onset. In other instances, the methods provided herein can the condition (e.g. graft survival or loss) in a transplant recipient at least 1 , 2, 3, 4, 5, 6, 7, 8_» 9, 10, 1 1 , or 12 months prior to onset, In some instances, the methods provided herein can predict acute rejection, chronic rejection, subclinical acute rejection, subclinical chronic rejection, IFTA with inflammation, IFTA with inflammation or other disorders in a transplant recipient at least 1 day, 5 days, 10 days, 30 days, 50 days or 100 days prior to onset. In other instances, the methods provided herein can predict acute rejection, chronic rejection, subclinical acute rejection, subclinical chronic rejection, IFTA with inflammation, IFTA with inflammation or other disorders in a transplant recipient at least 1 , 2, 3, 4, 5, 6, 7, S, 9, 10, 1 1, 12, 13, 14, 15, 16, 17, 18, 1 , 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or 31 days prior to onset, Irs other instances, the methods provided herein can predict acute rejection, chronic rejection, subclinical acute rejection, subclinical chronic rejection, IFTA with inflammation, IFTA with inflammation or other disorders in a transplant recipient at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, or 12 months prior to onset.

[00171] The methods and compositions provided herein can he used for detecting, or monitoring a condition of a transplant recipient including any form of immune-mediated rejection. Exemplar)' conditions that can be detected or diagnosed with the present methods can include organ transplant rejection, acute clinical rejection, chronic clinical rejection, subclinical acute rejection, subclinical chronic rejection, and IFTA with or without inflammation that can be histological equivalents of molecular signatures indicating immune-mediated chronic rejection. They may also be used to detect or monitor immune-mediated rejection independent of a histological classification of a kidney biopsy. They may also detect immune-mediated rejection in unexpected scenarios, for example in the setting of IFTA without a finding of inflammation. They may be further used to identify patients for adjunct immunosuppressant treatment in an unexpected background, for example in the setting where IFTA was diagnosed in the patient and the IFTA was previously not thought to result from immune-mediated injury.

100172] The diagnosis or detection of condition of a transplant recipient may be used to avert or prevent immune-mediated rejection and increase long-term graft survival rates. They may also limit the number of invasive diagnostic interventions that are administered to the patient. For example, the methods provided herein may limit or eliminate or justify the need for a transplant recipient (e.g., kidney transplant recipient) to receive a biopsy (e.g., kidney biopsies) or to receive multiple biopsies. In some instances, the methods provided herein may also help interpreting a biopsy result, especially when the biopsy result is inconclusive.

[00173] In some cases, the methods provided herein can be used alone or in combination wiih other standard diagnosis methods currently isscd to detect or diagnose a condition of a transplant recipient such as but not limited to results of biopsy analysis for kidney allograft rejection, results of histopathology of the biopsy sample, serum creatinine level, creatinine clearance, ultrasound, radiological imaging results for the kidney, urinalysis results, elevated levels of inflammatory molecules such as neopterin, and lymphokines, elevated plasma interlsukin (IL)~I in asathioprine-treated patients, elevated IL-2 in eyelosporine-treated patients, elevated IL-6 in serum and urine, intrarenal expression of cytotoxic molecules (granzyme B and perforin) and immunoregulatory cytokines (IL-2, -4, -10, interferon gamma and transforming growth factor- b ).

[9© 174] The monitoring of a condition of a transplant recipient may be conducted using a number of different approaches. Often, the monitoring can be conducted by serial testing, such as serial non-invasive tests, serial minimally-invasive tests (e.g., blood draws), serial invasive tests (biopsies), or some combination thereof. In some instances, the transplant recipient is monitored as needed using the methods described herein. Alternatively the transplant recipient may be monitored hourly, daily, weekly, monthly, yearly or at any specified intervals, for example,, based on the individual patient's condition as a^■function of time and/or decisions by caregivers (e.g. physicians) to assess any patient or patient group for a molecular signal of immune-mediated rejection, in some instances, the transplant recipient is monitored at least once every 1, 2, 3, 4, 5, 6_S 7, 8, 9, 10, 1 1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22_} 23 or 24 hours. In some instances the transplant recipient is monitored at least once ever)' 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or 31 days. In some instances, the transplant recipient is monitored at least once every !, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, or 12 months. In some instances, the transplant recipient is monitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 years or longer, for the lifetime of the patient or the graft, in some instances, gene expression levels in the patients can be measured, for example, within one month, three months, six months, one year, two years, five years or ten years after a transplant. In some methods, gene expression levels are determined at regular intervals, e.g., every 3 months, 6 months or every year post-transplant, either indefinitely, or until evidence of a condition is observed, in which case the frequency of monitoring is sometimes increased. In some methods, baseline values of expression levels are determined in a subject before a transplant in

combination with determining expression levels at one or more time points thereafter,

[00175] Many different drugs may be used in the methods herein including

Immunosuppressive drugs such as calcineurin inhibitors (e.g., cyclosporins, tacrolimus), mTO inhibitors (e.g., siro!imus and everolimus), anti-proliferaiives (e.g., azathioprine, mycophenolic acid), corticosteroids (e.g., prednisolone and hydrocortisone) and antibodies (e.g., basiliximab, daclizumab, Orthoc!one, anti-thymocyte globulin and anti-lymphocyte globulin), other drugs known in the art or descri bed herein. Examples of therapeutic regimen can include administering compounds or agents that are e.g., compounds or agents having immunosuppressive properties (e.g., a calcmeurin inhibitor, cyclosporins A or FK 506); a mTOR inhibitor (e.g., rapamycin, 40- 0-(2~hydroxyethyI)~rapamycin, CCI779, ABT57B, AF23373, bioiimus~7 or biolimus-9); an ascomycin having imnmno-suppressive properties (e.g., ABT-28 L ASM981 , etc.);

corticosteroids; cyclophosphamide; azathioprene; methotrexate; leflunomide; mizoribine;

myeophenolic acid or salt; mycophenolate mofetil; I S-deoxyspergualine or an

immunosuppressive homologue, analogue or derivative thereof; a PKC inhibitor (e.g., as disclosed in WO 02/38561 or WO 03/82859); a JAK3 kinase Inhibitor (e.g., N-benz l-3,4- d i hydroxy- benz l idene -cy anoacetaraide a-cyano-(3 ,4~d ihydroxy)-]N~benzy lcirmamamide (Tyrphostin AG 490), prodigiosan 25-C(PNU 156804), [4-(4'-hydroxyphenyl)-aniino-6,7- dimethoxyqulnaz.o!ine] (WHI-Pl 31), [4-(3'-bromo-4'-hydFoxylphenyl)-amino-6_s7- dimethoxyquinazoline] (WHI~P154), ^^'.S'-dibromo^'-h drox lphen O-amino-ej- diraethoxyquinazoline] WHI-P97, KRX-211, 3-{(3R,4RH-methyl-3-[raethy1-(7H-pyrrolo[2,3- d3pyrimidin-4-yl)-amino3-pi- peridin-l-ylJ -S-oxo-propioniirile, in free form or in a

pharmaceutical ly acceptable salt form, e.g., mono-citrate (also called CP-690.S50), or a compound as disclosed in WO 04/052359 or WO 05/066156); a SIP receptor agonist or modulator (e.g., FTY720 optionally phosphorytated or an analog thereof, e.g., 2-amino-2-[4-(3- berizyloxyphenyIthio)~2-ch!orophenyl3ethyl~l,3-pK)panediol optionally phosphorylated or l-{4- [ l-(4-cyclohexyl-3 rifluoromethyl-benzyloxyimino)-et yl]-2-ethyS-be- nzyl}-azetidine-3- carboxylic acid or its pharmaceutically acceptable salts); immunosuppressive monoclonal antibodies (e.g., monoclonal antibodies to leukocyte receptors, e.g., MHC, CD2, CD3, CD4, CD7, CD8, CD25, CD28, CD40, CD45, CD52, CD58, CD80, CD86 or their ligands); other immunomodulatory compounds (e.g., a recombinant binding molecule having at least a portion of the extracellular domain of CTLA4 or a mutant thereof, e.g., an at least extracellular portion of CTLA4 or a mutant thereof joined to a non-CTLA4 protein sequence, e.g., CTLA4ig (for ex, designated ATCC 68629) or a mutant thereof, e.g., LEA29Y); adhesion molecule inhibitors (e.g., LFA-1 antagonists, iCAM-l or -3 antagonists, VCAM-4 antagonists or VLA-4 antagonists). These compounds or agents may also be used alone or in combination. Immunosuppressive protocols can differ in different clinical settings. In some instances, in AR, the first-line treatment is pulse methylprednisolone, 500 to 1000 mg, given intravenously daily for 3 to 5 days. In some instances, if this treatment fails, than OKT3 or polyclonal anti-T cell antibodies will be considered. In other instances, if the transplant recipient is still experiencing AR, anti- thymocyte globulin (ATG) may be used,

[1)0176] In some eases, the methods provided herein can be applied in an experimental setting, e.g., clinical trial. In some cases, a clinical trial can be performed on a drug in similar fashion to the monitoring of ail individual patient described above, except thai drug is administered in parallel to a population of transplant patients, usually in comparison with a control population administered a placebo,

[00177] Sensitivity, Specificity, md Aeessraey

[00178] The methods, kits, and systems disclosed herein may be characterized by having a specificity of at least about 50%, The specificity may be at least about 50%, 53%_s 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%_s 77%, 78%, 79%, 80%, 81 %, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%,

[00179] in another aspect, the methods, kits, and systems disclosed herein may be characterized by having a sensitivity of at least about 50%, The sensitivity may be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%,

[00180] In another aspect, the methods, kits and systems disclosed herein may be characterized by having an accuracy of at least about 50%. The accuracy may be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%,

[00181] in some cases, the methods, kits and systems disclosed herein may be characterized by having a specificity of at !east about 50% and/or a sensitivity of at least about 50%. The specificity may be at least about 50% and/or the sensitivity may be at least about 70%, The specificity may be at least about 70% and/or the sensitivity may be at least about 70%. The specificity may be at least about 70% and/or the sensitivity may be at least about 50%. The specificity may be at least about 60% and/or the sensitivity may be at least about 70%. The specificity may be at least about 70% and/or the sensitivity may be at least about 60%, The specificity may be at least about 75% and/or the sensitivity may be at least about 75%,

[00182] In some cases, the methods, kits, and systems may be characterized by having a negative predictive value (NPV) greater than or equal to 90%. The NPV may be at least about 90%, 91%, 92%, 93%, 94%, 95%, 95.2%, 95.5%, 95.7%, 96%, 96.2%, 96,5%, 96,7%, 97%, 97.2%, 97.5%, 97.7%, 98%, 98.2%, 98,5%, 98.7%, 99%, 99.2%, 99,5%, 99.7%, or 100%.

[00183] In some cases, the methods, kits, and or systems disclosed herein may be characterized by having a positive predictive value (PPV) of at least about 30%. The PPV may be at least about 32%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 95.2%, 95.5%, 95.7%, 96%, 96.2%, 96.5%, 96,7%, 97%, 97.2%, 97.5%, 97.7%, 98%, 98.2%, 98.5%, 98.7%, 99%, 99.2%, 99.5%, 99.7%, or 100%.

[00184] In some cases, the methods, kits, and/or systems disclosed herein may be characterized by having a NPV may be at least about 90% and/or a PPV may be at least about 30%, The NPV may be at least about 90% and/or the PPV may be at least about 50%, The NPV may be at least about 90% and/or the PPV may be at least about 70%. The NPV may be at least about 95% and/or the PPV may be at least about 30%. The NPV may be at least about 95% and/or the PPV may be at least about 50%. The NPV may be at least about 95% and/or the PPV may be at feast about 70%.

f OlSSJ Computer program

[00186] The methods, kits, and systems disclosed herein may include at least one computer program, or use of the same, A computer program may include a sequence of instructions, executable in the digital processing device's CPU, written to perform a specified task, Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), data structures, and the like, that perform particular tasks or implement particular abstract data types, in light of the disclosure provided herein, those of skill in the art will recognize that a computer program may be written in various versions of various languages.

|00187] The functionality of the computer readable instructions may be combined or distributed as desired in various environments. The computer program will normally provide a sequence of instructions from one location or a plurality of locations. In various embodiments, a computer program includes, in part or in whole, one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plug-ins, extensions, add-ins, or add-ons, or combinations thereof.

[θθ!8$] Further disclosed herein are systems for classifying one or more samples and uses thereof. The system may comprise (a) a digital processing device comprising an operating system configured to perform executable instructions and a memory device; (b) a computer program including instructions executable by tlie digital processing device to classify a sample from a subject comprising; (i) a first software module configured to receive a gene expression profile of one or more genes from the sample from the subject; (ii) a second software module configured to analyze the gene expression profile from the subject; and (in) a third software module configured to classify the sample from the subject based on a classification system comprising three or more classes. Analyzing the gene expression profile from the subject may comprise applying an algorithm. Analyzing the gene expression profile may comprise normalizing the gene expression profile from the subject. Irs some instances, normalizing the gene expression profile does not comprise quantiie normalization.

[©©189] Figure 1 shows a computer system (also "system" herein) 1901 programmed or otherwise configured for implementing the methods of the disclosure, such as producing a selector set and/or for data analysis. The system 1901 includes a central processing unit (CPU, also "processor" and "computer processor" herein) 1905, which can be a single core or mult- core processor, or a plurality' of processors for parallel processing. The system 1901 also includes memory 1910 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 1915 (e.g., hard disk), communications interface 1920 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 1925, such as cache, other memory, data storage and/or electronic display adapters. The memory 1910, storage unit 1915, interface 192Θ and peripheral devices 1925 are in communication with the CPU 1905 through a communications bus (solid lines), such as a motherboard. The storage unit 1915 can be a data storage unit (or data repository) for storing data. The system 1901 is operatively coupled to a computer network ("network") 1930 with the aid of the communications interface 1920. The network 1930 can be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet. The network 1930 in some instances is a telecommunication and/or data network. The network 193© can include one or more computer servers, which can enable distributed computing, such as cloud computing. The network 1930 in some instances, with the aid of the system 1901, can implement a peer-to-peer network, which may enable devices coupled to the system 1901 to behave as a client or a server.

[00190] The system 1901 is in communication with a processing system 1935. The processing system 1935 can be configured to implement the methods disclosed herein. In some examples, the processing system 1935 is a nucleic acid sequencing system, such as, for example, a next generation sequencing system (e.g., IHumina sequencer, Ion Torrent sequencer, Pacific

Biosciences sequencer), The processing system 1935 can be in communication with the system 1901 through the network 1930, or by direct (e.g., wired, wireless) connection. The processing system 1935 can be configured for analysis, such as nucleic acid sequence analysis.

[00191] Methods as described herein can be implemented by way of machine (or computer processor) executable code (or software) stored on an electronic storage location of the system 19Θ1, such as, for example, on the memory 1910 or electronic storage unit 1915. During use, the code can be executed by the processor 19S5. In some examples, the code can be retrieved from the storage unit 1915 and stored on the memory 1910 for ready access by the processor 1905. In some situations, the electronic storage unit 1915 can be precluded, and machine-executable Instructions are stored on memory 191 ,

[00192] Com uter Network

{00193] The methods, systems, kits and compositions provided herein may also be capable of generating and transmitting results through a computer network. As shown in Figure 20, a sample 2015 is first collected from a subject (e.g. transplant recipient, 2010). The sample is assayed 2020 and gene expression products are generated, A computer system 2025 is used in analyzing the data and making classification of the sample. The result is capable of being transmitted to different types of end users via a computer network 2030. in some instances, the subject (e.g. patient) may be able to access the result by using a standalone software and/or a web-based application on a local computer capable of accessing the internet 2050. In some instances, the result can be accessed via a mobile application 2045 provided to a mobile digital processing device (e.g. mobile phone, tablet, etc.), In some instances, the result may be accessed by physicians or other medical caregivers and help them identify and track conditions of their patients 2035. in some instances, the result may be used for other purposes 2040 such as education and research.

[00194] Bsgital rocessing device

[00195] The methods, kits, and systems disclosed herein ma include a digital processing device, or use of the same. In further embodiments, the digital processing device inc ludes one or more hardware central processing units (CPU) that carry out the device's functions. In still further embodiments, the digital processing device further comprises an operating system configured to perform executable instructions. In some embodiments, the digital processing device is optionally connected a computer network. In further embodiments, the digital processing device is optionally connected to the Internet such that it accesses the World Wide Web, In still further embodiments, the digital processing device is optionally connected to a cloud computing infrastructure. In other embodiments, the digital processing device is optionally connected to an intranet. In other embodiments, the digital processing device is optionally connected to a data storage device.

[00196] in accordance with the description herein, suitable digital processing devices include, by way of non-limiting examples, server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, netpad computers, set-top computers, handheld computers, Internet appliances, mobile smartphones, tablet computers, personal digital assistants, video game consoles, and vehicles. Those of skill in the art will recognize that many smartphones are suitable for use in the system described herein. Those of skill in the art will a!so recognize that select televisions, video players, and digital music players with optional computer network connectivity are suitable for use in the system described herein. Suitable tablet computers include those with booklet, slate, and convertible configurations, known to those of skill in the art,

[00197] The digital processing device will normally include an operating system configured to perform executable instructions, The operating system is, for example, software, including programs and data, which manages the device's hardware and provides services for execution of applications. Those of skill in the art will recognize that suitable server operating systems include, by way of non-limiting examples, FreeBSD, OpenBSD, NetBSD^®, Linus, Apple^® Mac OS X Server^®, Oracle^® Solaris^®, Windows Server^®, and Novell* NetWare^®. Those of skill in the art will recognize that suitable personal computer operating systems include, by way of non- limiting examples, Microsoft^® Windows^®, Apple^® Mac OS X*. UNIX®, and UNIX-like operating systems such as GNU Lmux^®, in some embodiments, the operating system is provided by cloud computing. Those of skill in the art will also recognize that suitable mobile smart phone operating systems include, by way of non-limiting examples, Nokia^® Symbian® OS, Apple^® iOS^¾ ₅ Research in Motion^® BlackBcrr OS^® Google^® Android^®, Microsoft^® Windows Phone^® OS, Microsoft^® Windows Mobile® OS, Linux^®, and Palm^® WebOS*

[00198] The device generally includes a storage and/or memory device, The storage and/or memory device is one or more physical apparatuses used to store data or programs on a temporary or permanent basis. In some embodiments, the device is volatile memory and requires power to maintain stored information. In some embodiments, the device is non-volatile memory and retains stored information when the digital processing device is not powered, in further embodiments, the non-volatile memory comprises flash memory. In some embodiments, the non-volatile memory comprises dynamic random-access memory (DRAM). In some

embodiments, the non-volatile memory comprises ferroelectric random access memory (FRAM). In some embodiments, the non-volatile memor}' comprises phase-change random access memory (PRAM). In other embodiments, the device is a storage device including, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, magnetic disk drives, magnetic tapes drives, optical disk drives, and cloud computing based storage, in further embodiments, the storage and/or memory device is a combination of devices such as those disclosed herein.

[0Θ199] A display to send visual information to a user will normally be initialized, Examples of displays include a cathode ray tube (CRT, a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT-LCD, an organic light emitting diode (OLE.D) display, In various further embodiments, on OLED display is a passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display. In some embodiments, the display may be a plasma displa ., a video projector or a combination of devices such as those disclosed herein.

[&Θ2ΘΘ] The digital processing device would normally include an input device ΐο receive information from a user. The input device may be, tor example, a keyboard, a pointing device including, by way of non-limiting examples, a mouse, trackball track pad, joystick, game controller, or siylus;a touch screen, or a multi-touch screen, a microphone to capture voice or other sound input, a video camera to capture motion or visual input or a combination of devices such as those disclosed herein.

[0Θ201] Non-transitory com uter readable storage medsssm

[0Θ2Θ2] The methods, kits, and systems disclosed herein may include one or more non- transitory computer readable storage media encoded with a program including instructions executable by the operating system to perform and analyze the test described herein; preferably connected to networked digital processing device, The computer readable storage medium is a tangible component of a digital that is optionally removable from the digital processing device. The computer readable storage medium includes, by way of non-!imiting examples, CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, opticai disk drives, cloud computing systems and services, and the like. In some instances, the program and instructions are permanently, substantially permanently, semi-permanent!y, or non- transitori!y encoded on the media.

[00203] A non-transitory computer-readable storage media may be encoded with a computer program including instructions executable by a processor to create or use a classification system. The storage media may comprise (a) a database, in a computer memor _f of one or more clinical features of two or more control samples, wherein (i) the two or more control samples may be from two or more subjects; and (ii) the two or more control samples may be differentially classified based on a classification system comprising three or more classes; (b) a first software module configured to compare the one or more clinical features of the two or more control samples; and (c) a second software module configured to produce a classifier set based on the comparison of the one or more clinical features.

[00204] Web application

[00205] In some embodiments, a computer program includes a web application. In light of the disclosure provided herein, those of skill in the art will recognize that a web application, in various embodiments, utilizes one or more software frameworks and one or more database systems, in some embodiments, a web application is created upon a software iramework such as Microsoft® .NET or Ruby on Rails (RoR), in some embodiments, a web application utilizes one or mote database sysieras including, by way of non-limiting examples, relational, non-relational, object oriented, associative, and XML database systems. In further embodiments, suitable relational database systems include, by way of non-limiting examples, Microsoft^® SQL Server, mySQL™, and Oracle^®, Those of skill in the art will also recognize thai a web application, in various embodiments, is written in one or more versions of one or more languages. A web application may be written in one or more markup languages, presentation definition languages, client-side scripting languages, server-side coding languages, database query languages, or combinations thereof, In some embodiments, a web application is written to some extent in a markup language such as Hypertext Markup Language (HTML), Extensible Hypertext Markup Language (XHTML), or extensible Markup Language (XML). In some embodiments, a web application is written to some extent in a presentation definition language such as Cascading Style Sheets (CSS). In some embodiments, a web application is written to some extent in a client-side scripting language such as Asynchronous Javascript and XML (AJAX), Flash^® Actionscript, Javascript, or Silverlight^®. In some embodiments, a web application is written to some extent in a server-side coding language such as Active Server Pages (ASP), ColdFusion^®, Perl, Java™, JavaServer Pages (JSP), \ Sypertexi Preprocessor (PHP), Python™, Ruby, Tel, Smalltalk, WebDNA^®, or Groovy. In some embodiments, a web application is written to some extent in a database query language such as Structured Query Language (SQL), In some embodiments, a web application integrates enterprise server products such as IBM^® Lotus Domino^®. In some embodiments, a web application includes a media player element. In various further embodiments, a media player element utilizes one or more of many suitable multimedia technologies including, by way of non-limiting examples, Adobe® Flash^®, HTML 5, Apple^® QuickTime®, Microsoft^® Silverlight^® Java™, and Unity^®.

[00206] Mobile application

[00207] in some embodiments, a computer program includes a mobile application provided to a mobile digital processing device, In some embodiments, the mobile application is provided to a mobile digital processing device at the time it is manufactured. In other embodiments, the mobile application is provided to a mobile digital processing device via the computer network described herein,

[ΘΘ2@81 In view of the disclosure provided herein, a mobile application is created by techniques known to those of skill in the art using hardware, languages, and development environments known to the art. Those of skill in the art will recognize that mobile applications are written in several languages. Suitable programming languages Include, by way of non- limiting examples, C₅ C-H-. C#, Objective-C, Java™, Javascript, Pascal. Object Pascal, Python™, Ruby, VB.NET, WML, and XHTML/HTML with or without CSS_S or combinations thereof,

| Θ2Θ9] Suitable mobile application development environments are available from several sources. Commercially available development environments include, by way of non-limiting examples, AirplaySDK, alcheMo, Appcelerator® Celsius, Bedrock, Flash Lite, .NET Compact Framework, Rhomobi!e, and WorkLight Mobile Platform. Other development environments are available without cost including, by way of non-limiting examples, Lazarus, MobiFlex, MoSync, and Phonegap. Also, mobile device manufacturers distribute software developer kits including, by way of non-limiting examples, iP one and iPad (iOS) SDK, Android™ SDK, BlackBerry^® SDK, BREW SDK, P lm^® OS SDK, Symbian SDK, webOS SDK, and Windows* Mobile SDK. (90210] Those of skill in the ait will recognize that several commercial forums are available for distribution of mobile applications including, by way of non-limiting examples, Apple³⁵ App Store. Android™ Market, B!ackBerry^® App World, App Store for Palm devices, App Catalog for webOS, Windows^® Marketplace for Mobile, Ovi Store for Nokia^® devices, Samsung^® Apps, and Nintendo^® DSi Shop.

[©0211] Stsiiadalone application

{00212J in some embodiments, a computer program includes a standalone application, which is a program that is run as an independent computer process, not an add-on to an existing process, e.g., not a plug-in. Those of skill in the art will recognize that standalone applications are often compiled. A compiler is a computer program(s) that transforms source code written in a programming language into binary object code such as assembly language or machine code. Suitable compiled programming languages include, by way of non-limiting examples. C_s C++, Objective-C, COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB .NET, or combinations thereof. Compilation is often performed, at least in part, to create an executable program. In some embodiments, a computer program includes one or more executable complied applications.

[00213] Web browses- plssg-irs

[00214] n some embodiments, the computer program includes a web browser plug-in. In computing, a plug-in is one or more software components that add specific functionality to a larger software application. Makers of software applications support plug-ins to enable third- party developers to create abilities which extend an application, to support easily adding new features, and to reduce the size of an application. When supported, plug-ins enable customizing the functionality of a software application. For example, plug-ins are commonly used in web browsers to play video, generate interactivity, scan for viruses, and display particular file types, Those of skill in the art will be familiar with several web browser plug-ins including, Adobe* Flash^® Player, Microsoft^® Silverlight^®, and Apple^® QuickTime^®. In some embodiments, the toolbar comprises one or more web browser extensions, add-ins, or add-ons. In some embodiments, the toolbar comprises one or more explorer bars, tool bands, or desk bands, [00215] In view of the disclosure provided herein, those of skill in the art will recognize that several plug-in frameworks are available that enable development of plug-ins in various programming languages, including, by way of non-limiting examples, C++, Delphi, Java™, PHP, Python™, and YB .NET, or combinations thereof.

00216] Web browsers (also called Internet browsers) are software applications, designed for use with network-connected digital processing devices, for retrieving, presenting, and traversing information resources on the World Wide Web, Suitable web browsers include, by way of non- limiting examples, Microsoft^® Internet Explorer^®, Mozilla® Firefox^®, Google^® Chrome, Apple^® Safari^®, Opera Software® Opera^®, and KDE Konqueror. in some embodiments, the web browser is a mobile web browser. Mobile web browsers (also called mirerobrowsers, mini-browsers, and wireless browsers) are designed for use on mobile digital processing devices including, by way of non-limiting examples, handheld computers, tablet computers, netbook computers, subnotebook. computers, smartphones, music players, personal digital assistants (PDAs), and handheld video game systems. Suitable mobile web browsers include, by way of non-limiting examples, Google^® Android^® browser, RIM BlackBerry^® Browser, Apple^® Safari^®, Palm® Blazer, Palm^® WebOS^® Browser, Mozilla® Firefox^® for mobile, Microsoft^® Internet Explorer^® Mobile, Amazon^® Kindle^® Basic Web, Nokia^® Browser, Opera Software^® Opera^® Mobile, and Sony^® PSP™ browser.

[1)0218] The methods, kits, and systems disclosed herein may include software, server, and/or database modules, or use of the same. In v ie w of the disclosure provided herein, software modules are created by techniques known to those of skill in the art using machines, software, and languages known to the art. The software modules disclosed herein are implemented in a multitude of ways, in various embodiments, a software module comprises a file, a section of code, a programming object, a programming structure, or combinations thereof. In further various embodiments, a software module comprises a plurality of files, a plurality of sections of code, a plurality of programming objects, a plurality of programming structures, or combinations thereof. In various embodiments, the one or more software modules comprise, by way of non- limiting examples, a web application, a mobile application, and a standalone application. In some embodiments, software modules are in one computer program or application. In other embodiments, software modules are in more than one computer program or application, in some embodiments, software modules are hosted on one machine. In other embodiments, software modules are hosted on more than one machine. In further embodiments, software modules are hosted on eioud computing platforms. In some embodiments, software modules are hosted on one or more machines in one location. In other embodiments, software modules are hosted on one or more machines in more than one location,

[Θ0219] Databases

£00220] The methods, kits_., and systems disclosed herein may comprise one or more databases, or use of the same. In view of the disclosure provided herein, those of skill in the art will recognize that many databases are suitable for storage and retrieval of information pertaining to gene expression profiles, sequencing data, classifiers, classification systems, therapeutic regimens, or a combination thereof. In various embodiments, suitable databases include, by way of non-limiting examples, relational databases, non-relational databases, object oriented databases, object databases, entity-relationship model databases, associative databases, and XML databases, in some embodiments, a database is internet-based. In further embodiments, a database is web-based, in still further embodiments, a database is cloud computing-based. In other embodiments, a database is based on one or more local computer storage devices.

[W22I] Data transmission

[ΘΘ222] The methods, kits, and systems disclosed herein may be used to transmit one or more reports. The one or more rsports may comprise information pertaining to the classification and/or identification of one or more samples from one or more subjects. The one or more reports may comprise information pertaining to a status or outcome of a transplant in a subject. In some embodiments, the reports comprise information pertaining to the risk of graft loss in the subject. The one or more reports may comprise information pertaining to therapeutic regimens for use in treating transplant rejection in a subject in need thereof. In some embodiments, the one or more reports comprise information relating to whether an immunosuppressant regimen of the subject is sufficient or insufficient to prevent transplant rejection or host-versus-graft immune activation in the subject. The one or more reports may comprise information pertaining to therapeutic regimens for use in treating transplant dysfunction in a subject in need thereof The one or more reports may comprise information pertaining to therapeutic regimens for use in suppressing an immune response in a subject in need thereof. In some embodiments, the one or more reports comprise information suggesting the optimal dose of an immunosuppressant. In other embodiments, the one or more reports comprise information suggesting the optimal composition of a mu It i -component immunosuppression regimen of the subject. | 0223] The one or more reports may he transmitted to a subject or a medical representative of the subject. The medical representative of the subject may be a physician, physician's assistant, nurse, or other medical personnel The medical representative of the subject may be a family member of the subject. A family member of the subject may be a parent, guardian, child, sibling, aunt, uncle, cousin, or spouse. The medical representative of the subject may be a legal representative of the subject,

[M224] EXAMPLES

I00225J Ksample 1

[00226| Acute T-cell mediated rejection (TCMR), presenting as either clinical acute rejection (cAR) or subclinical acute rejection (subAR; histological AR without graft dysfunction only demonstrated by surveillance biopsies), is clearly linked to a higher risk of IFTA.(9-1 1) In a study of 797 recipients, early episodes of e.AR led to more fibrosis and inflammation in 1 and 2 year protocol biopsies than those without an occurrence of cAR, cAR episodes followed by abnormal histology also resulted in reduced graft survivaL(12) Likewise, subAR also increases the risks of developing chronic rejection with IFTA and graft loss and occ urs in as many as 20% of surveillance biopsies done Irs the first year post-transplant, (3, 13-17) Given these strong associations of cAR and subAR with the future development of chronic rejection with IFTA, we questioned whether IFTA biopsies contained unrecognized cellular rejection. In our model IFTA marks chronically uncontrolled rejection and its development may associate with a higher risk of graft failure.

|00227] We performed gene expression profiling on 234 kidney graft biopsies obtained for both surveillance and cause from over 1000 patients at 7 transplant centers with matching clinical and outcome data. 81 samples were given a diagnosis of IFTA, in which there was histological evidence of IFTA without a clear etiology (i.e. BK nephropathy or recurrent glomerulonephritis). These IFTA samples were then classified into subphenofypes based on the degree of inflammation identified on light histology, including IFTA with concomitant acute rejection (IFTA with AR by histology; 0=2 IFTA with inflammation (n=10) and IFTA without inflammation (n=42), Samples with biopsy-proven cAR (n^~54) and normally functioning transplants (TX; n=99) were included for comparison. Confirmatory outcome data were obtained by data query to the United Network for Organ Sharing (UNOS). The gene expression results were validated using a published data set derived from an independent, external cohort of late biopsies (GEO; GSE2I374),(18, 19)

[00220] By molecular biopsy profiling we found that differentia! gene expression In all IFTA phenotypes was strongly enriched for the same dysregulated gene profiles seen in cAR biopsies. All IFTA phenorypes (n=81) demonstrated as much as 81% commonality in differentially expressed genes with cAR, and a strong enrichment for cAR immune/inflammatory arsd meiabo He/tissue integrity molecular pathways. This finding was true even for IFTA samples without any histological evidence of inflammation (n= 0), a group currently thought to be low risk for graft loss. Thus, molecular profiling indicated that most IFTA samples have ongoing and often subclinical immune-mediated injury that is more sensitively detected with gene expression profiling than by light histology. Further, in JFTA samples without histological evidence of inflammation, we found thai the relative expression of AR-affiliated genes correlated with a higher risk of graft loss at 5 or more years. All these results support the conclusion that immune- mediated rejection can be the common molecular mechanism unifying all forms of current recognized "rejection" and thus, can represent the arc of a single disease of immune-mediated rejection, Thus, IFTA as a histological diagnosis reflecting chronic tissue injury and scaning can be in molecular terms immune-mediated chronic rejection,

[0Θ229] Abbreviations

[0023SJ ABMR: antibody-mediated rejection, ANOVA: analysis of variance, AR: clinical acute rejection, CCF: Cleveland Clinic Foundation, DEG: differentially expressed gene, dnDSA: de novo donor-specific antibody, DSA: donor-specific antibody, FC: fold change, FDR: false discoveiy rate, fRMA: frozen Robust Multichip Average, GCN; gene coexpression network, GEO; Gene Expression Omnibus, HLA: human leukocyte antigen, IFN-γ; inter feron-ga ma, IFTA; Interstitial fibrosis and tubular atrophy, IRB; institutional review board, MC; Mayo Clinic, Phoenix, mm: mismatch, N/A: not applicable, NIH: National Institutes of Health, PRA; panel reactive antibody, SE: standard error, SGH: Scripps Green Hospital, SRTR: Scientific Registry for Transplant Recipients SubAR: subclinical acute rejection, SVMC: Saint Vincent's Medical Center, Los Angeles, TCMR: T cell mediated rejection, TGCG: Transplant Genomic Collaborative Group, TX; Treatment group with excellent functioning kidney, UCHSC;

University of Colorado Health Sciences Center, DM; University of Michigan, UNOS: United Network for Organ Sharing, NU: Northwestern University.

METHODS

) Study P&pui if&n

[00231 234 kidney allograft biopsies were collected as part of an Ή-f nded Transplant Genomics Collaborative Group (TGCG) from 2005 to 201 1 by protocol or "for cause" from 210 patients from seven clinical centers. More than one biopsy from the same patient was included only if there was a change in pathology, The only exclusions were biopsies that did not conform to the study's inclusion/exclusion criteria (Supplement 1), such as a diagnosis of BK nephritis or recurrent GN (n=5). Each biopsy was reviewed locally well as by a blinded central pathologist (LG) with no clinical information provided. When there was a discrepancy between the two reports, the senior investigator (X)RS) reviewed the histology slides and reached a conclusion including discussion and agreement with the pathologists as necessary, The phenot pes were defined as follows: AR is biopsy-proven TCMR with a rising serum creatinine; IFF A with inflammation is Banff IFTA+i; 1FTA with AR are eases where local and centra! pathology reviews called both present and TX are controls based on surveillance biopsies done from I to 2 years. Institutional review boards approved all research protocols.

b) Analysis ofphenofypic data

| 0232] AM OVA and chi-squared tests were used to detect differences in continuous and categorical variables between phenotypes and p-values were adjusted with Bonferroni correction for multiple hypothesis testing. Less than 1% of the phenotypie features were missing. Survi val curve analysis was performed on death-censored data using J P software (SAS, Gary, NC) and Wiicoxon's ranked tests. Hazard ratios for clinical phenotypic characteristics were calculated using a Cox proportional hazards model adjusting for multiple clinical variables: age, sex, race/ethnicity, time post transplant, C4d₅ donor age, BMI and phenotypes (see Results and Supplement 2).

c) Biff erentiul gene expression and pathway mapping

|Θ0233] Micfoarray protocols are in Supplement 1 and array data is available online (MCBFs Gene Expression Omnibus database; http://www.ncbi.nlm.nih.gov/geo/; Accession number GSE76882). Differentially expressed genes (DEGs) between phenotypes were determined by two-sample t-tests with False Discovery Rates (FDRs) calculated using the method of Storey ei ai. (20) to account for multiple hypothesis testing. Immune pathway mapping and gene set enrichment for biological processes were performed using gene ontology (GO) and ingenuity Pathway Analysis (IPA). To avoid false positive enrichment, based on cell type, kidney gene expression (as found in our biopsy dataset) was used as the background gene set.

d) Gene Co-expresshit Network Analysis

[ΘΘ234] By having gene expression profiles (GEPs) for many samples, we can look for pairs of genes that demonstrate a similar expression pattern across samples. I.e. Two genes in which the transcript levels rise and fail together across the samples. These two genes are called 'co- expressed genes.' Gene co-expression is of biological Interest since it suggests a relationship among co-expressed genes. A gene co-expression network (GCN) is simply an undirected graph where each node corresponds to a gene, and each gene is linked to other genes by an edge if there exists a statistically significant co-expression. GCNs do not attempt to infer a causa! relationship between genes and the edges represent only a correlation in gene expression across samples,

J S235] GCNs can separate groups of ssmiiar-behsYing (and likely to be biologically-related) genes from a larger gene set, and do so without the introduction of user bias when groups of genes are identified based investigator interpretations of external data and immune paradigms. Thus, these groups of genes or CJCNS help identify related genes with a specific function within the framework of a larger biological process, e.g. co-expressed immunoglobulin genes within a large set of genes differentially expressed in acute rejection. In this study, we built GCNs from IFTA and AR differential iy expressed genes, and thus delineated the bioiogical processes that define these phenotypes. The mathematical model and Ml explanation for GCN construction is outlined in Supplement 3 , Section 4.

Results

) Patient Characteristics and Outcomes

[00236] A total of 234 biopsies (1 14 surveillance, 120 'for cause⁵} comprise this retrospective study (54 AR_; 42 IFTA without inflammation, 30 IFTA with inflammation, 29 IFTA with AR and 99 TX; Table 1). 21 of the participants had two biopsies analyzed, but the biopsies were taken at different time points and demonstrated a change in pathology. Only the phenotype at the time of most recent biopsy was used to calculate survival analysis. 33 (44%) of all IFTA samples were classified as mild (Banff Grade 1 : IFTA without inflammation⁵^ 5%; iFTA-hAR==41%; IFTA+i=50%). 28 (40%) of IFTA samples were classified as moderate (Grade 2; IFTA without inflammation= 0%; iFTA+AR=36%; IFTA+i=50%). The remaining 1 1 (16%) were classified as severe IFTA (Grade 3), There were no differences in IFTA grades by subgroups (p~0.67).

[00237] Table 3 , Demographics and outcomes of 210 participants grouped by histological phenotypes.

A I TA IFTA IFTA with TX Gnrap wit oat ltfe AR isfiammation Co are* iajflawamatioB

Time to death- 1450 ± 2747 ±

2935 ±346 1708 ±747 NA 0,015 censored graftoss 334 390

Time from biopsy to

665 ± 183 452*189 678*213 412 ±408 NA. 0.78 graft loss

Death 12(24%) 6(15%) 2(7%) 1 (10%) 3(4%) NA

1304± 1417 ± 1549 +

¾ne to death 18!3i3gS 1324.x 944 0.87

273 667 408

Qis5k¾io35¾r CCF 9(18%) 5(52,5%) 2(7%) 2(20%) 12(15%) N/A

SGH 10(20%) 7(17,5%) 2(7%,) 2(20%) 48 (58%) N/A

SVMC 20(40%) 14 (35%) 15 (54%) 4(40%) 2(2%) N/A

MC 1(2%) 5 (12.5%) 0 2(20%) 14(17%) MA.

UCHS

6(12%) 7(17.5%) 5(18%) 0 3(4%) N/A C

UM i(2%) 2(5%) 4(14%) 0 0 N/A

NU 3(6%) 0 0 0 3(4%) N/A

* Analysis method: ANOVA for quantitative data (Probability > F statistic), Pearson's ch squared test for dichotomous data, Significant intergroup comparisons found in Supplement 1. † Missing data: 9 (3%) diabetes, 5 (2%) donor sex, 6 (2%) donor race, 27 (10%) PRA studies, 11 (4%) deceased donor, IFTA grade 4 (5%)

Typed HLA antigens: HLA-A1, HLA-A2, HLA-B1, HLA-B2, HLA-DR1, HLA-DR2 § Induction therapy includes: Anti-thyrnocyte globulin (Thymoglobulin), M romonab~CD3 (O T3), Basiliximab (Sirau!ect), Daclizumab (Zenapax), Alemtuzumab (Campath)

Abbreviations: AR: acute rejection, CCF; Cleveland Clinic Foundation, HLA: human leukocyte antigen, IFTA: interstitial Fibrosis and tubular atrophy, MC: Mayo Clinic, Phoenix, mm:

mismatch, N/A: not applicable, PRA: panel reactive antibody, SE: standard error, SGH: Seripps Green Hospital, SVMC: Saint Vincent's Medical Center, Los Angeles, TX: Treatment group with excellent functioning kidney, UCHSC: University of Colorado Health Sciences Center, UM: University of Michigan, NU; Northwestern University.

[0023§] Median foilow-up time was 1,613 days post-transplant (-4.4 years). Only 1 patient was lost to follow-up. There were no differences in age, sex, % African American, % diabetics, number of HLA mismatches or % deceased donors across phenotypes. There were a total of 24 deaths, but no significant differences in mortality in the 'non-TX" groups according to survival analyses,

[00239] Med an time to biopsy was 420 days (374 and 1,200 days for surveillance and 'tor cause', respectively). The times to biopsy were significantly greater for AR (800 ± 164), IFTA without inflammation (1796 ± 178), IFTA with inflammation (1008 ± 356) and IFTA with AR (2121 ± 213) when compared to the TX phenotype (603 ± 127 days) (pO.0001). In over half of the subjects with AR, onset was >12 months post-transplant,

[ΘΘ240] After censoring death, 43/210 (20%) had graft loss with a median time of 1,885 days (-5,2 years; 43 to 9,302 days). Graft survival was significantly lower in subjects with AR, IFTA with AR, IFTA with inflammation and IFTA without inflammation in comparison to TX (Figare 2A). Despite differences in graft loss risk, times from biopsy to graft loss did not significantly differ by phenotype: iFTA with inflammation (432 days), iFTA without inflammation (452 days), AR (665 days) and IFTA with AR (678 days) (p=0.78).

[Q€241] A Cox proportional hazards model was also used to examine the effect of various clinical variables on survival times. We created a model including following variables: tirrse from transplant to biopsy, phenotype, age, sex, black race, diabetes. C4d status, and donor age (Supplement 2, Section 1), Of these variables, only days from transplant to biopsy (p<0.0001), phenotype (p<0.G00! ) and donor recipient age (p-0,04) were foiind to be statistically significant. We then adjusted the above survival curves for age and time of biopsy post transplant using a Stratified Cox model, in the adjusted model, both AR and IFTA phenotypes showed the same results of equally poor long-term graft survival rates (Supplement 2, Section 2).

|0S242] A majority (n=84; 71 %) of the 'for cause' biopsies and a minority (n=21 ; 19%) of the protocol biopsies had C4d staining performed. There was no difference in death-censored graft survival between those with positive vs. negative C4d staining (p=G.3). The calculated Cox hazard ratios for C4d positivity vs. negativity were not statistically significant (CI: 0.58 to 4.2) (Supplement 2, Section 1). The majority of the samples with future graft loss were C4d negative (74%). We do not have donor-specific antibody (DSA) data. These biopsies were collected prior to the current practices of measuring serial DSAs,

b) Gene expression eomp &n between AM nd IFTA samples

[ΘΘ243] Four gone expression profiles were created by independently comparing each histological phenotype (AR, IFTA with AR, IFTA with inflammation and IFTA without inflammation) to the controls (TX). A threshold calculated false discovery rate (FDR) of <0.05 and fold-change (FC) of >1.2 was used (full gene lists; Supplement 3), The majority (72 to 81%) of differentially expressed genes (DEGs) in biopsies with IFTA and histological evidence of inflammation were common ΐο AR DEGs (Table 2), Surprisingly, OEGs for IFTA without inflammation were also highly shared with AR (80%; Figure 2B, panel a) and differentially expressed in a concordant pattern (Figure 2B, panel b). Moreover, 25 of the top 50 IFTA without inflammation DEGs (ranked by absolute fold change) were shared with the top 50 for AR, A literature review of the top IFTA without inflammation DEGs showed that these have been associated with AR in prior studies (Table 3). (1 8, 23-47) Finally, there was strong enrichment for AR immune/inflammator and metabolic molecular pathways using Ingenuit ' gene set enrichment tools (Table 4),

[00244] Table 2. Shared differentially expressed transcripts between IFTA subphenotypes (IFTA plus AR, IFTA with inflammation and IFTA without inflammation) and clinical acute rejection (cAR),*

All sam les IFTA it&o&t IFTA with IFTA plus with IFTA iisflammatieii infkmiimtk* AR (a=78) (n-40) a (n=10) (a=*28)

Number (%) shared with

4 466 cAR differentially 3,817 (81%) 2,610 (80%) 1 ,040 (69%)

(72%) expressed transcript list

* In comparison of AR samples to patients with normal, well-functioning transplants (control; TX), there were 5,345 differentially expressed transcripts (FDR*<0,05; FC*>1.2). This table shows the large number and percent of gene transcripts shared between cAR and each IFTA subphenotype.

Abbreviations: AR; acute rejection; IFTA: interstitial fibrosis and tubular atrophy; FC: fold- change; FDR: false discover}' rate

[80245] Table 3, IFTA without inflammation differe tially expressed genes (DEGs) (threshold FDR<0.05) ranked by absolute fold change are shared with AR.*

IFTA j IFTA 1 FiisaeiiOT j External 1 AM Sigisifieassee Geise FC Site at^re AR EG

diisgs Raak

IGLC 2,6- Ig light chain Tissue (23, 24) j Indicates

(9)* 7,1 production j Immunoglobulin

producing allograft infiltrating B cells

LTF 6.1 Innate antiTissue (18, 24- 1 Literature review

microbial, 28) connects with kidney inflammatiori- injury and #1 AR gene, relaied which links AR. and

IFTA to injury

J IGJ 5.9 Ig linker Tissue (24) 12 ndicates

protein Immunoglobulin

producing allograft infiltrating B ceils j

-4. ! Main protein Tissue (29) 8 Previously showed to j in blood, be decreased in AR carrier protein (29) and kidney injury, for steroids, (39) likely reflects fatly acids and metabolic disturbance, j i hormones,

SE PI 4.0 Protease Tissue (18, 27, 3 Literature review j NA3 inhibitor, 28, 30, 3 1) associates with kidney cleaves PMN injury and cell cathepsin G turnover. As the #3 AR and MC gene, connects AR and chymase IFTA to injury ]

CXCL6 3.5 interacts with j Tissue (18) 24g Indicates likely

CXCR1 and 2, neutrophil allograft chernosttraetan chemotaxis

t for PM s

1 recruitment

enzymes.

with ALB gene.

acute rejection (AR). The third coiumrs provides the biological function, and often demonstrates a gene with a ro!e in immune response and inflammation. The fourth column provides literatures references where the genes are linked to AR in prior studies, The fifth column gives the ranking in the AR ranked gene list and thus demonstrates that these genes are also some of the most highly ranked AR genes. [08246] Table 4, Results of pathway and gene enrichment tool analysis for cAR and iFTA without inflammation differentially expressed transcripts.*

cAJR I FT A wiikmt to&mm^te

IL4 L80E-54 IL10 i 4S4E- 39

1L1B E77E- 8 !LIB 3.09E- 37

IF -alpha I 27B-45 CD40LG J i>2B- 34

L60.E-43 TGFB1 EHE- 30

STATS /.44E-43 1.53E- 30

11,6 L54B-4I IL.2 ;.)s;

29

STAT] ES9E-44 !L6 EiOE-

28 lafcibited Upstream Issllfeited IJ rew RegAtw

Itegnlator Assslysss ¹ Analyss^†

MAP l L31E28 !LIO A 8.89E- 20

HJRN 2J3E-27 PTGER4 7.25E- 8

^ L i ORA 4/79B-2? EL1RN ES2E- 16

FPARA 5.83E-21 CD3 8.64E-- 15

PTGER4 3.75B-2 SOCS1 i.OOE- 54

TRIM24 2.84E-I9 FRDMi L66E-

.13

NKX2-3 54 E--19 Nrlh 2.44:

13

PMDM! 2.40E-IS KX2-3 2,24B~

11 * Mapping of AR and IFTA without inflammation differentially expressed genes (DEGs) to canonical functional biological pathways was performed using Ingenuity Pathway Analysis (IPA). Enrichment of these DEGs for immune and biological pathways was performed by using genes significantly expressed in the kidney as the background. Pathways or genes highlighted In gray are shared between AR and !FTA without inflammation. These data underline the high level of shared immune/inflammatory-based pathways according to unbiased pathway enrichment tools.

Benjamini-Hochberg correction applied to p-vaiues account for multiple test comparisons, Abbreviations: IFNr interferon, IL: interfeukin, TNF; tumor necrosis factor

[©0247] These findings were then validated using a puhlically available gene expression dataset that consisted of 105 ^sfor cause' late biopsies taken between 1 and 31 years post- transplant (GEO; GSE21374).(18) Using this external dataset and our thresholds for FDR and FC, we found 2,523 transcripts (1868 genes) were differentially expressed in subjects with IFTA (Supplement 4). Sub-phenotypes of IFTA with or without inflammation and IFTA with AR were not specifically described. Nonetheless, DEGs in the external dataset were highly shared with our AR and IFTA biopsy profiles (77%; Figure 2B, panel c) and differentially expressed irs the same concordant patterns (Figure 2B, panel d).

c) Development of 'rejection ^! gem co-expression networks (GCNs)

[SS24S] Gene co-expression networks (GCNs) were created using the DEGs from; 1) AR biopsies, 2) IFTA with AR and, 3) IFTA without AR samples. Our intent was to identify groups of genes indicative of discrete acute rejection mechanisms, and then determine and compare the expression of these gene groups in IFTA samples. Using a relatively low co-expression threshold (0,6), a large network of 1,825 AR genes was formed (Supplement 5). ^sHub^s transcripts, or 'highly connected' genes with the most connections to other genes in a network, were also determined. Increasing the stringency of the co-expression threshold In order to identify smaller, tighter clusters of co-expressed genes resulted in three major dense networks of AR GCNs (Fig&re 3A, 3B, 3C; Supplement 5). The same procedure applied to the IFTA samples identified the same 3 networks as found with the AR samples, reflecting their highly shared molecular mechanisms and this was confirmed in the external dataset (Supplement 4).

|Θ0249] The first network named AR-GCNI , consisted of only 27 up-regulated transcripts, of which 25 were immunoglobulin (93%), The two remaining genes, TNFRSF17 and FCRL5, are B cell receptor associated transcripts critical for B ceil activation. As expected, our biopsies with pathology-defined T cell mediated rejection (TCMR) contain B cells. (48) The second network (AR-GCN2), consisted of 190 genes, all up-regulated in AR. 186 of these genes (93%) had known biological functions identifiahly related to T cell immune responses and inflammation (Supplement 6). Figure 4 illustrates the function and connection of the AR-GCNI and AR- GC 2 genes. The illustration includes 107 (56%) of the AR-GCN2 genes, The gene set defining AR-GCN2 was also independently validated using the external GEO data,

Μ25Θ] A -GCN3 consisted of 186 genes that mapped functionally to ce!lular

metabolism/tissue integrity (Supplement 6). 89 (48%) of these genes were found to code enzymes important in amino acid turnover, glucose and fatty acid metabolism, and energy production. 25 (13%) coded for proteins involved in cellular detoxification, and 33 (18%) were membrane transporters of various important solutes, organic anions and drugs. Importantly, all the AR-GCN3 genes are down-regulated.

d) Shared expression of the three key GCNs discovered in AM psii ts m the IFTA samples

[00251] The geometric means of the AR and IFTA GCN genes were next determined for a!i the IFTA phenotypes. Among the IFTA phenotypes, the geometric mean of GCN2 transcripts (immune response) was highest in samples with IFTA and concomitant histological AR (Figure 5; p = 0,0001 when compared TX). The changes were second highest in IFTA with

inflammation samples, and lowest in IFTA without inflammation samples. Of note, the expression in IFTA without inflammation was still significantly higher than TX (p=0,003)_} which demonstrates the key point of the increased sensitivity of gene expression profiling to detect an ongoing immune response and inflammation. The geometric means of the

metabolism/tissue integrity-related AR-GCN3 genes showed the same hierarchy in the inverse direction compared to TX. controls from the lowest in IFTA plus AR, higher in IFTA with inflammation and highest in IFTA without inflammation (Figure 5). Thus, metabolic and tissue integrity gene dysregulation tracks with degrees of inflammation.

[00252] Next, we examined the geometric means according to IFTA grades: Banff I (mild), 2 (moderate) and 3 (severe). The geometric means of GCN ! and GCN2 increase in relation to both the degree of inflammation and the severity of IFTA (Figure 6), Likewise, the geometric- mean of GCN3 decreases with both the degree of in flammation and the extent of IFTA.

IFTA-GCNs eorrei te with graft loss in biopsies w h IFTA m no mfl nmuitlon

f 00253] First, we clustered IFTA samples without inflammation into sample clusters based on the relative gene expression of the 3 !FTA-GCN transcript iists (note; IFTA-GCNs are highly matched to the AR-GCNs). The heat maps in Figure 7 show that the samples clearly separate based on the expression of each GCN. Second, we separated the samples into 2 clusters for each GCN: IFTA GCN-high and IFTA GCN-!ow, We then compared graft survival for each sample cluster (Figure 7). Our results show significantly increased rates of graft loss rates in patients with IFTA without inflammation based on FTA GCN2 {p™0,02) and GCN3 (p-Q.Q3). No correlation to graft loss is seen with GCN1 (p^~0,47). Thus, gene expression profiling detects correlations with graft loss risk for individual patients that are not detected by histology.

e) A set of 224 differentially expressed genes distinguish two groups of IFTA without mffammai n biopsies with higher vs, lower risk of grafi loss

[Θ 254] In the subset of IFTA patients without inflammation (11=40), we determined differential gene expression between patients with and without graft loss (n-14 vs, 26; 35% vs. 65%). This analysis revealed 224 differentially expressed transcripts (FDR<0.05) (Supplement 7). 125 (57%) of these genes were common to the top-ranked AR DEGs. Many of these DEGs have been identified in previous studies of acute and chronic transplant injury associated with graft loss (e.g. LTF, SERPINA3. CXCL6, MMP7, AFM, ISG20 and CXCLi ; see Table 3). Figure 2b₅d shows these genes are also among the most ⁶ up-regulated' genes in AR.

[Q025S] To determine whether these 224 DEGs could delineate IFTA without inflammation patients into groups at high vs. low risk of graft loss, we clustered all 40 samples based on the expression of these 224 transcripts. Using complete linkage, hierarchical clustering, two groups were identified with the expected differences in survi al curves (FIgssre 8). Enrichment of these 224 genes for well-known immune rejection and inflammation pathways, and the ability of these genes to cluster our study population into subgroups at high and low risk graft loss, provides both biological and technical plausibility to their discovery. Next, we validated these results in an independent, externa! cohort of 'late' biopsies with IFTA (n=105) (GEO#; GSE21374). The expression of these 224 genes was also able to separate this external cohort into high and low risk phenotypes (Figure 9),

[00256] Given that GCNs 2 and 3 correlate with graft survival, we examined the overlap of the GCN-defining genes and the 224 graft loss set (F!gsre 10). The results reveal 188 non- overlapping genes that refine the GCN classifiers for graft loss (Supplement 7), Pathway enrichment analysis (GO) demonstrated the highest correlations with immune responses (p=3.2xl0-9), cytokine-mediated signaling (p=2.8x10-6), interferon gamma signaling

(p= 1.7x30-5} and antigen presentation via MHC class I (p=2.2 l0-5) There was no overlap with GCN I (B cell genes),

[00257] Finally, the majority (n— 84; 71%) of the 'for caus ' biopsies and a minority (n=21 ; 19%) of the protocol biopsies had C4d staining performed. 756 genes were differentially expressed between C4d~stained positive vs, negative (Supplement 3). 17 of these 756 genes were shared with the 224 graft loss genes, including 2 HLA molecules (HLA-F,-G), 3 proteasorne subun is (PSMB8, 9, 10) and TAP!— genes that are ah in GCN2 (T cell-mediated immune response) and consistent with activated interferon signaling and antigen presentation. Indeed, pathway enrichment analysis using gene ontology of the 17 overlapping genes showed the highest correlations with type 1 intsrferon signaling (p^::: 1.98x10- 1 1) and antigen processing and presentation (p^~8,8xl 0-7). None were linked mechanistically to .8 cell networks.

Discussion

[0025S] In this multicenter, retrospective analysis, we used gene expression profiles and multiple biomformatics tools to show that all the biopsies with IFTA (n~81) demonstrate strong molecular evidence of immune rejection, injury and decreased metabolism/tissue integrity. This finding was true for biopsies of IFTA without histological inflammation (n=40). in all cases, IFTA was defined by biopsy histology without identifiable causes present (i.e. B nephritis or recurrent disease), We used a bioinformatic method called Gene Co-expression Network analysis (GCN) to identify the underlying biological networks without introducing any user selection bias. A key point is that the molecular GCNs identified in IFTA were essentially the s m as found for biopsies with AR. The relative expression of differentially expressed genes comprising the GCNs correlated with graft loss and the severity of IFTA based on Banff grades. These findings indicate that IFTA biopsies, in which there is no other explanation for pathogenesis, demonstrate evidence of ongoing, cellular immune-mediated injury that is more sensitively detected with gene expression than by light histology.

[00259] There were several salient findings in the clinical data. Firstj patients with a histological diagnosis of AR or IFTA at any time post-transplant demonstrate decreased graft survival compared to those with normal biopsies (TX). Second, our cohorts show 51% of AR and 99% of "IFTA with AR" samples were diagnosed > I year post-transplant. This finding confirms SRTR and DeKAF data and growing evidence that AR episodes often occur late post- transplant in both adult (10, 49, 50) and pediatric populations (51).

[00260] In this study we describe a network of objectively identified, tightly co-expressed genes with clear biological function related to T cell driven immunity and inflammation (GCN2; Figure 4), The geometric means of these genes correlated with histologicaily-identified inflammation and Banff IFTA grades: AR > IFTA with AR > IFTA with inflammation > IFTA without inflammation (Figures 5,6), indicating the increased expression of cellular immune response genes, A relevant study listed 28 genes that could most successfully predict AR vs. non-AR status that included biopsies with both antibody-mediated rejection (ABMR) and TCMR, (6) Of these 28 genes, 26 (93%) were found in the top 150 differentially expressed genes in IFTA without inflammation. 19 (68%) were found in the GCN2s for both AR and IFTA. Several of these genes, including CXCL9. CXCL1 1 , GZMA and CCL5, were the most differentially expressed genes in IFTA without inflammation (Table 3). Our results are also consistent with a recent study of 33 kidney biopsies with "IFTA and inflammation"

demonstrating an increase in the expression of genes associated with both B and cytotoxic T cells (51), Although we cannot say that the expression of these genes cause IFTA, our study demonstrates that graft loss rates and IFTA grades are associated with higher relative expression of these genes and this is equally trite for the subset of patients with IFTA without inflammation. Our hypothesis is that AR and IFTA phenotypes arc different stages along the arc of the same ailoimmurie process,

[Θ026Ι] Since GCN2 was identified objectively based on gene co-expression, the comprising genes, particularly those with a high number of connections to other genes, may provide new mechanistic and biological understanding of acute and chronic rejection (Figures 2, 4). For examples, dedicator of cytokinesis 2 (DOC 2) is the most connected AR-GCN2 hub gene (Figure 3) and ranked 15 and 10, respectively, in the IFTA-GCN2 hub genes in our data and the external datasei (Supplement 5). DOCK2 is critical to lymphocyte homing and the formation of immunological synapses. Deficiency of DOCK2 attenuates AR in mouse cardiac allografts. (52) Another AK-GCN2 hub gene, the inter!eukin 10 receptor alpha (IL10RA), codes for a receptor to the potent anti-inflammatory cytokine, IL-10. it is also identified in the IFTA-GCN2s for our data and the external dataset. IL-10 expression has been associated with acute rejection (25, 29, 53) and the over-expression of IL-10 improved renal function and survival in rat rejection models, (35) IL-10 expression parallels T^'hl cytokine expression suggesting a protective mechanism limiting the immune response. (54)

[00262] in contrast, we demonstrated an inverse relationship between the metabolism/tissue integrity network (GCN3) to histological inflammation and IFTA grades, results consistent with previously published data. (39, 55) Similar to GCN2, we revealed that many IFTA samples without histological inflammation had higher rates of graft loss correlating with decreased GCN3 gene expression, The bioiogicai function of GCN3 genes may explain the response to immune- mediated tissue injury. For example, PEPD and XPNPEP2 code for enzymes important to regulating collagen metabolism. Decreased expression of these genes may contribute to fibrosis. MME encodes for neutral endopeptidase, a protein that inactivates several peptide hormones including angiotensin II and glucagon, Deficiency in MME leads to fetal membranous glomerulopathy. (36) The key point is that therapeutic targeting of the metaboSic/functiona! impacts of rejection on tissue integrity may ultimately turn out to be another effective strategy to preserve graft function and survival. [ΘΘ263] Our mode! is that perpetual T cell-drivers Imm ne activation and inflammation due to ineffective immunosuppression leads to eel! breakdown, release of alloantigens and the creation of an inflammatory milieu that promotes T cell-mediated B ceil activation including production of donor specific antibodies. For example, B-eell activating factor (TNFSFI3B) was found in the GCN2 while its receptor (TNFRSP17) clustered tightly among the GCN1 genes. The AT-Hook Transcription Factor (AK A) was found in GCN2, and has been shown to up-regulate transcription of the reeeptor-ligand pair CD40 and CD4GL, an essential interaction for B cell activation and antibody isotype switching, (64, 65) Another GCN2 gene, SLAMF8 plays a role in B lineage development and modulation of B cell activation through B cell receptor signaling. (66) Finally, the GCN2 gene, RANTES (CCL5). is involved in activation of both T and B cells and immunoglobulin switching in B cells. (67)

[00264] Consistent with our model, molecular profiling demonstrates that the relative expression of genes related to immunoglobulin production (GCN1) did not independently correlate with graft loss or worse outcomes for either AR or IFTA phenotypes. However, our model recognizes the close connections between humoral and T cell immunity. Although antibody-mediated rejection (ABMR) has been associated with FTA and increased risk of graft loss (57), the majority of patients with de novo DSA (dnDSA) followed for 5 years or more do not lose their grafts, (58, 59) Other studies demonstrate that: 1) the development of dnDSA correlates with medication non-adherence and AR episodes, 2) dnDSA correlate with transplant glomerulopathy but not IFTA, and 3) biopsies with ABMR frequently show concomitant histological evidence of TCMR, (60-63) Our gene expression and functional mapping is consistent with this literature by showing a high correlation between C4d staining and T cell immune networks,

[00265] The major (Imitation in this retrospective, longitudinal study is that the majority of patients had a single biopsy. These biopsies only provide a cross-sectional view of pathology on a large population of transplant patients with known outcomes. This is not a prospective study that follows patients from the time of transplantation, obtains multiple biopsies and gene profiles and monitors patient events and other variables over time. Thus, although our IFTA samples demonstrated strong evidence for cellular rejection and inflammation at the time of biopsy, there may have been preceding non-immunologica! insults that also played a role in the development of IFTA prior to the biopsy. Likewise, we do not have any data on medication nonadherent. However, our model is that chronic rejection leads to tissue injury and IFTA. The corollary is that chronic rejection is the result of inadequate immunosuppression. Thus, whether inadequate immunosuppression was the decision of a physician to reduce dosing or due to patient medication nonadherence is not relevant to our conclusions. Another limitation is that the overal percentage of African Americans in this study was less than the percentage that receive kidney transplants (10% vs. 34%) (22), Finally, this study cannot account for the possibility of ABMR co-existing with TCMR in some biopsies. At the time this study was designed, dnDSA were not routinely measured except when pathologists found positive C4d staining. Moreover, the Banff criteria at that time did not include the current metrics for defining ABMR on biopsies, 00266] This study demonstrates that IFTA biopsies without alternative explanations for pathogenesis (i.e. BK or recurrent disease) reveal differential gene expression evidence of ongoing cellular immune-mediated injury, Specifically, gene co-expression networks (GCNs) and the mapping of genes to functional pathways demonstrate significant molecular overlap to profiles of AR biopsies, supporting our model that IFTA is a manifestation of chronic rejection. The connection between AR and IFTA profiles is true even for biopsies of IFTA without inflammation, Expression of GCN2 (immune response) and GCN3 (metabolism/tissue integrity) genes correlate with increased risk of graft loss. Further, a set of 224 genes differentially expressed with graft loss refines the functional pathways found by GCN analysis. The clinical relevance is that a future prospective trial may demonstrate that informing immunosuppressive and monitoring protocols for individual patients based on serial gene expression profiling of biopsies improves long-term clinical outcomes.

Supplement I : Expanded Methods

Section, I; Inclusion/Exclusion Criteria

[002671 Genera! Inclusion Criteria:

[00268] I) Adult kidney transplant (age > 18 years): first or multiple transplants, high or low risk, cadaver or living donor organ recipients; 2) Any cause of end-stage renal disease except as described in Exclusions; 3) Consent to allow gene expression and proteomie studies to be done on samples; and 4) Meeting clinical and biopsy criteria specified below for Groups 1-3,

[00269] General Exclusion Criteria;

[00278] 1) Combined organ recipients: kidney/pancreas, kidney/islet, heart kidney and liver/kidney; 2} A recipient of two kidneys simultaneously unless the organs are both adult and considered normal organs (rationale is to avoid inclusion of pediatric en bloc or dual adult transplants with borderline organs); 3) Any technical situation or medical problem such as a known bleeding disorder in which protocol biopsies would not be acceptable for safety reasons in the best judgment of the clinical investigators; 4) Patients with active immune-related disorders such as rheumatoid arthritis, SLE, scleroderma and multiple sclerosis; 5) Patients with acute viral or bacterial infections at the time of biopsy; 6) Patients with chronic active hepatitis or HIV; 7) CAN, thai at the time of identification are in the best judgment of the clinicians too far along in the process or progressing to rapidly to make it likely that they will still have a functioning transplant a year later and 8) Patients enrolled in another research study that in the best judgment of the clinical center investigator involves such a radical departure from standard therapy that the patient would not be representative of the groups under study in the Program Project.

[05)271] *A special note regarding why noncompliance is not an exclusion criterion is important to emphasize. Noncompliance is not a primary issue in determining gene expression and proteomscs profiles associated with molecular pathways of transplant immunity and tissue injury/repair.

[0Θ272] Acute Rejection (AR) Specific Inclusion Criteria:

[00273] Clinical presentation with acute kidney transplant dysfunction at any time post transplant

[00274] Rise in creatinine of > 25% increase from the baseline creatinine confirmed by at least two measurements. Biopsy-proven AR with tubulomtcrstitial cellular rejection with or without acute vascular rejection

[00275] Acute Rejection (AR) Specific Exclusion Criteria:

[08276] Evidence of concomitant acute infection: CMV; BK nephritis; Bacterial

pyelonephritis; Other

[00277] Evidence of anatomical obstruction or vascular compromise

[0027§] If the best judgment of the clinical team prior to the biopsy is that the acute decrease in kidney function is due to dehydration, drug effect (i.e. ACE inhibitor) or caleineurin inhibitor excess

[00279] If the biopsy is read as drug hypersensitivity (i,e, si fa-mediated interstitial nephritis) [©§280] Evidence of hemolytic uremic syndrome

[0Θ281] WeU-functioning Transpiant/No Rejection (ΓΧ) Specific Inclusion Criteria:

[Θ0282] Patient between 12 and 24 months post transplant

[00283] Stable renal function defined as at least three creatinine levels over a three month period that do not change more than 20% and without any pattern of a gradual increasing creatinine.

[00284] No history of rejection or acute transplant dysfunction by clinical criteria or previous biopsy

[00285] Serum creatinines <1.5 mg dl for women, <I ,6 mg/dl for men

[00286] They must also have a calculated or measured creatinine clearance >45 ml/minute [00287] They must have well controlled blood pressure defined according to the JNC 7 guidelines of <140/90 (http:// ¾¾^nhlbi.nih.gov/g idelincs h>^,periensioa

[Θ0288] Wett-fimctioning Transplant/No Rejection (FX) Specific Exclusion Criteria;

|00289] Patient less than one month after steroid withdrawal

00290] Patients with diabetes (Type I or ΪΪ, poorly controlled)

[00291] Evidence of concomitant acute infection: CMV; B nephritis; Bacterial

pyelonephritis

Section 2: Bonfermni C&rreeti&n for Clinical Variables

[00292] The Bonferroni correction factor in our study was based on the number of hypotheses tested, In our study, there were a total of five phenotypes (IFTA without i_f IFTA with L IFTA plus AR, cAR and TX). According to standard practice, we chose a desired level of significance and an a = 0.05 (likelihood of incorrectly rejecting a null hypothesis), In the case of this study, the 5 phenotypes were compared to each other in a pairwise fashion (i.e. 10 total hypotheses tested). Therefore, the Bonferroni correction factor would test each individual hypothesis at a 0.05/10 ^~ 0.005 p value. Traits that did not meet this level of significance were determined to be not statistically significant. This analysis pertains to all the clinical variables in Table 1.

Section 3; Microarray Experiment Protocols and Dat Processing

ΘΘ293] RNA was extracted from biopsy samples using the RNEasy kit (Qiagen), biotinylated cRNA prepared using Ambion MessageArop Biotin II and hybridized to Affyrnetrix HG U133 Plus PM peg arrays (http //aflymetrlx.corn/index,aff ).

[00294] Data Processing

[00295] Probe intensity data were log2-transformed and normalized using Bioconductor R package Frozen Robust Multichip Average (fRMA) and Barcode (1 -4). In fRMA, probe-specific effects and variances are precompiled and frozen using large public repositories of gene expression. fRMA is a stable normalization process that is less susceptible to the effects batch effects, especially when samples are processed in smaller batches. There was no batch effect correction applied to any of the data. Low-variance probes (< 20% variance) were filtered and low value probes (>90% with signal less than log2 value of 4) according to industry standards. Median values were taken for probesets with the same non-redundant RefSeq ID number.

Finally, probes without a RefSeq ID were filtered. The filtering process was all performed using programming code built in Matlab software. The ftitered gene list consisted of 17,564 transcripts prior to class comparisons and farther data analysis. [00296] Technical Validation

[00297] The project was also completely and independently analyzed using R Bioeonductor package UMMA. fRMA was used again for normalization and again there was no batch correction used or needed. There was no filtering applied to the data. Using the completely unfiltered data, we verified again a very strong overlap between IFTA without inflammation differentially expressed genes (DEGs) and cAR DEGs (see Flgisre 11 below),

Section 4: Gen Co-expression Network (GCN) Analysis Details

[00298] By having gene expression profdes (GEPs) for numerous samples, we can look for pairs of genes that show a similar expression pattern across samples. I.e. Two genes in which the transcript levels rise and fail together across the samples (Figure 12), These two genes are called 'co-expressed genes.⁵ Gen co-expression is of biological interest since it suggests a relationship among co-expressed genes. E.g. co-expressed genes may be controlled by the same

transcriptional regulatory program, related to the same molecular function, members of the same molecular pathway, or part of a larger common biological process.

[00299] Definition of Gem Co-expression Networks

[00300] A gene co-expression network (GCN) is an undirected graph where each node corresponds to a gene, Each gene is linked to other genes by an edge if and only if there is a statistically significant co-expression relationship between the genes. GCNs do not attempt to infer a causal relationship between genes and the edges represent only a correlation. Thus, a GCN differs from a gene regulatory network (GRN). in a GRN, a directed edge connects two genes. The directed edge infers a causal relationship and may represent any number of processes, such as cellular signal transduction, metabolic pathways, gene regulatory networks and protein- protein interaction (PPI) networks (3) (6), A number of network models have been proposed to inter these interrelationships among genes, such as Bayesian (7) and Boolean networks (8).

[00301] GCNs can separate groups of similar-behaving and related genes from a larger gene set. E.g. a constructed GCN may consist of tightly co-expressed immunoglobulin genes within a larger set of genes differentially expressed in acute rejection. This process avoids bias that occurs when investigators interpret genes sets based personal background knowledge and accepted immune paradigms. E.g. a researcher inquiring about the presence of B cells in a set of kidney biopsy samples may acquire all B cell-related genes from a public database or literature search. While this sort of data query is necessary at times, it is highly vulnerable to user subjectively mid bias. [00302] Approach to Creating GCNs

[00303] In this study, we used eorrelograni matrix approach for computing similarly between two target genes based on the degree of fluctuation or co-expression between them across samples (6), This technique uses a one pass over the database to construct the co-expression network of genes. Particularly, let G - (Gi, G¾ ... , <¾? be a set of N genes and T = {Tj, ... T_M} be the set of M samples in our cohort. The gene expression database D is represented by an N x M matrix D_NxM where each entry dy represents the normalized, log2 transformed gene expression. A similarity score (co-expression measure) was calculated between each pair of rows in the expression matrix. A Pearson correlation coefficient (r²) was used for a similarity score in this study (9). Two genes (Gi, Gj) were said to be strongly correlated if the absolute value if the absolute valise of Pearson eorreiation(Gi, Gj) excedded a user defined ihreshold. In this study, the correlation threshold ranged from correlated (r^G.6) to very highly correlated (r²>0.9). The threshold was varied to elicit both loose and tight networks of eo-expressed genes in an unbiased manner.

[00384] As mentioned, we used a correlogram matrix approach for computing similarity between two target genes based on the similarity between them. Specifically, we read a row In the database and check whether the a consecutive gene (Gj and G_j) satisfy the similarity criterion (i.e. > threshold). This step is repeated for genes Gi and G_j÷i,G_j+2,...G_K, This iterative process is repeated for all pairs of gene for each row (6) (Figure 13). The extraction of the correlogram to adjacency matrix is then very simple. If the two genes (Gj and G_j) pass the similarity criterion (e.g. r2 > 0,9), the content of the correlogram matrix with the index (ij is replaced with a +1. Similarly, if there is a strong negative correlation, (e.g, r2 < -0,9), the content of the correlogram matrix with the index (i j) is replaced with a -1. Based on ail strongly positively and negatively connected pairs, the adjacency matrix (A) is computed as follows:

( -!^■ 1 if Gi md Gj sre strongfy positively correlated

[00305] A ( ) ··^"— | -i if Gi tmd Gj are strongly negatively correlated

i Q otherwise

[00306] Where 0 indicates the lack of a significant relationship between the two genes (Gj and Gj) (6).

[00307] Gene co-expression networks connecting various genes were constructed using the adj cency matrix. Particularly, a co-expression network is a undirected graph (7), such that T = {G ', E}_s where G ' are a subset of genes that are highly connected. If two genes (Gi, Gj) e G ¹ are connected by an arc Eij e E, then Gi and Gj are strongly connected to each other (6), The proof of this method is detailed by Lemma 1-3 in the paper by Roy et. Al. (6)., Thus, genes in a GCN needed to be co-expressed with at least one other gene to be included in the network, [ΘΘ3Θ§| Co-expression measure a id ihreshold

[00309] The expression for genes (Gi, Gj) are represented as two vectors of length M, where M is the number of samples in the cohort. Thus, the calculation of the co-expression between Gi and Gj is the same as calculating the similarity measure for two vectors of numbers, There are number of measures typically utilised, including Euclidean distance, Spearman's rank and Pearson's correlation coefficient, A Pearson's correlation coefficient, which takes a value between -1 and 1 _s measures the tendency for two genes to rise and fail across samples. Pearson's correlation coefficient near 1 represents strong direct correlation between the two genes, whereas a value of ^»1 represents very strong inverse correlation, Of note, a Pearson correlation measure assumes a normal distribution between two genes. This is assumption is acceptable since microarray data is typically normalized as a preprocessing step.

[0O31C ] In this study, Pearson's correlation (r*) measure was the correlation measure chosen. The f² threshold was set at 0.6 and a large GCN was identified. The r² threshold was also increased to identify smaller, tighter clusters of genes (Figure 14). The final range was determined by where the data split from one large GCN into 3 smaller and tighter clusters, A literature search and gene enrichment tools were utilized to determine the biological function of each GCN,

[00311] Gems with (he most edges

[00312] Gene transcripts with the most 'edges,' i.e. highest number of connections to other genes in a network were mentioned as potential important genes. However, it should be noted that a GCN is undirected graph, GCNs cannot be used to imply causality and we do not attempt to infer directional relationship between genes. Gene regulator}' networks (GRNs) are used to model biological processes in which genes interact and are connected by causality. In the GRN model, disease states occur as result of perturbation of the interactive network. An objective to the construction of GRNs is the establishment of robust networks with the identification of key nodes or hub genes that could be targets for therapeutic intervention. The genes with the most edges identified in this study are not be confused with hub genes since we can only make note of correlations in gene expression with other genes. Thus, we purposely avoided the use of terminology such as 'hubs' and 'key nodes' to avoid this confusion.

00313] References for supplement 1

1 , McCail MN₅ Boistad BM. Irizarry RA. Frozen robust multiarray analysis (f MA), Biostatistics. 2010:1 1(2):242-53.

2. McCail MN, Jaffee HA, Irizarry RA. fRMA ST; frozen robust multiarray analysis for Affymetrix Exon and Gene ST arrays. Bioinformatics. 2012;28(23):3153-4. 3. McCali MN, Murakami PN, Lukk M_s Hnber W, Irizarr RA. Assessing affyrnetrix GeneChip microarray quality. BMC Biomformatics. 201 1 ;12: I 37,

4. McCall MN, Uppal K, Jaffee HA, Zilllox MJ, Irizarry RA. The Gene Expression Barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes. Nucleic Acids Res. 201 l ;39(Dafabase sssue):DI01 1-5.

5. arlebach G, Shamir . Modelling and analysis of gene regulatory networks, Nat Rev Mol Cell Biol. 2008;9(10):770-80.

6. Roy S, Bhaltacharyya D , alita IK. Reconstruction of gene co-expression network from microarray data using local expression patterns. BMC Bioinformatics, 2014; 15 Suppl 7:S10.

7. Friedman N, Link! M, Nachman I, Pe'er D, Using Bayesian networks to analyze expression data. Journal of computational biology : a journal of computational molecular cell biology. 2Q0G;7(3~4):6Qi~20.

8. Davidich MI, Bornhoidt S. Boolean network model predicts cell cycle sequence of fission yeast. PloS one. 2008;3(2):el672.

9. Song L, Langfdder F, Horvath S. Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinformatics. 2012;13:328,

Su lement 2: Association of Clinical or Histological Factors wills Graft Loss

[Θ0314] Section 1 : Cox proportional hazard models for clinical variables

[00315] Cox proportional hazards model and hazard ratios for phenotype variables in relation to graft loss.

[D0316] Proportional Hazards Fit; Censored By: graft_faiSure_zero

[©0317] Table 5: Effect Summary

Table 6: Effect Likelihood Ratio Tests

Sourc Nparm !>F L-R CMSqnare Prob>ChiSq

Fhttatype 4 4 24,3781724 <JO01*

C4d state 2 2 334823561 0.1875

P ys Bet een lYssspk ! ! 19.418561$ <.MOI* sad Bio sy

Age 1 1, 410460387 0J428*

Sex 1 ! 0.42279626 0.5155

Black } ! 2.19548238 0,1384

Do r ags i 1 1.1076! 686 0.2926

Diabetes J I 0.09536411 0,7575

[00319] Table 7: Risk Ratios (Per unit change in regressor)

Term Risk Ratio Lower 95% Upper 95% Reci rocal

B¾ys Between rans tef 0.998912 0,998326 0.999429 tmmm and Biopsy

Age #.968727 0.938866 0.99896? 1.0322831

Donor age 1.013608 0.988349 1 ,03961 1 0.9865747

|00320j Table 8: Range Risk Ratios (Per change in regressor over erstire range)

Te m Risk Ratio L er 95% U per 9S% ed roesd

Ba s Between Tnmpl t 0.007577 0,600544 6.076971 131.98473 &md Biopsy

Age 0.14397 0.021321 0,938929 6.9458869

Donor age 2.718823 0.420123 17.71875 0.3678062

[96321] Table 9: Risk Ratios for Phenolype

Leve!2 Risk Ratio Pr b>C¾isq Lower 95% Upper 95%

IFTA AR 1.1604214 0.7320 0.4867793 2.70S2912 iPTA AR AR. 0.9797448 0,967! 0.3593312 2.S632438

IFTA+AR IFTA 0,8443008 0.7273 0.3159291 2, 1825202 1 Lewi! /LeveB Risk Ratio Prob>C!asq Lower 95% Upper 9S%

IFTA+i AR 0.8505658 0.8317 0.1302857 3.1098797

IFTA+i IFT 0.7329801 0.69 ? 0.1082442 3.0067129

IFTA+i IFTA-i-AR 0.8681504 0.8642 0.1233662 3.8406775

TX AR 6,SL¾~!3 TMM11* 0 0.124678

TX IFTA 5.6I2®~I3 <.0091* 0 A 109144

TX IFTA+AR 6M7e^»l3 <T)0S 0 0.1403068

TX FTA+i 7.6S 3 0,0939* 0 0,2286364

AR IFTA 0.86I7S59 0.7320 0.369646 2,0543189

AR IFTA+AR 1 ,020674 0.9671 0.3901307 2,7829479

IFTA IFTA+AR 1.18441 19 0,7273 0.4581859 3, 1652674

AR IFTA+i 1.I 7568S 0.8317 0,3125118 7.6754411

IFTA !I-TA t; 1 642935 0.6917 0.3325891 9.2383727

IFTA-i-A IFTA÷! 1.1518741 0.8642 0.2603707 8 J 059498 ^"

AR TX L536e+12 <J§»1* 8,1⁾206588

IFTA TX l,782e+12 ^' «.8001* 9.0159583

IFTA+AR TX LS§4®+12 <.0tM.lL- 7.12724

I^'FTA+i TX $ om* 4.373757

Θ322] Table 10: Risk Ratios for C4d status

Levell /L wl2 Risk Ratio Prs>fe>C¾k<i Lower 95% Upper 95%

Pos Neg 2,3937176 0.0921 0.8586046 6.2669239

Neg Pos 0.4177602 0,0928 0.1595679 1 ,1646805

0323] Table 11 : Risk Ratios for Ssx

Level! /Leve Risk Rati Lower 95% Upper 95%

F 0.793101 0.5155 0.3974886 1.6130842

F M 1.260872 0.5155 0,6199304 2.5157957

1)324] Table 12: Risk Ratios for Black

Level! /LeveI2 Risk o Lower 95% ] Upper 95%

1 0 1 ,9814286 0.1384 0.7888724 | 4,5287287

0 1 0,5046864 0.1384 0.2208125 I 1.2676322 [013325] Table 13 : Risk Ratios for Diabetes

l e el! Risk Ratio Prets C¾feq Lower §5% Upper 95%,

1 0 0.7904513 0,7575 0.1201993 3.0185415

0 1 1265100! 0, 7575 0.3312858 S.3195 B7

[00326] Section 2: Sunival curves adjusted for age, sex and time post-transplant of biopsy using a Stratified Cox mode!.

[00327] in this model, the survival of an "average" subject is estimated while adjusting for possible con founders. Irs the Cox hazards model (Section 1 above), age and time post-transplant of biopsy were found to influence the survival . After adj usting for age, sex and time post- transplant, there was still no difference in survival between AR and IFTA phenotypes. Sec the two figures below for comparison. Figure IS depicts survival with adjustment, while FIgare 16 depicts survival without adjustment.

[00328] Section 3: Graft loss in IFTA without inflammation samples called 'borderline "* [00329] * Note: These samples were cai!ed 'borderline* on the initial pathologist report. These samples were then read by a second, central pathologist and thought to NOT have significant inflammation, Table 14 and the corresponding Figsre 17 depict survival analysis of this group.

)] Table 14: Summary

umbe N¾mfe@r Mess Sid felled cesiored Error

No 7 20 3144.57 Biased 240.982

Yes 7 6 4009,8 508.527

Combined 14 26 3913.25 Biased 351.441

] Table 15: Quantiles

Lower Upper 25% 75%

Tiaae 95% 9S% Wmlnrm

No 3718 2516 2913

Yes 4255 261 5 5756 2673 5133

Combined 3718 2673 5756 2673 5756

D332] Table 16: Tests Betvw ;en Groups

Test ChiSqaare »F Fro¾>CWS¾ j

Log-Rank 0.0556 1 0.8136 j

Wilcoxon 0, 1902 1 0.6628 j |0Θ333] Section 4: Geometric Means of GCNs Without Inflammation IFTA 'borderline ' samples,

|00334] As shown below, the geometric means of GCNl (B cells) and GCN2 (Immune response) was higher in these samples called 'borderline' compared to the remaining IFTA without inflammation samples; this is depicted graphically in Figisre 18. Likewise, the geometric mean of GCN3 was slightly decreased in these 'borderline' samples. We have Included these results below for the Editor's reference,

[80335] Table 17: Geometric Means of IFTA GCNs According to being called 'borderline' in initial pathologist report.

Bontarlfoe t Bord r!is©

GCN ! 154.8 130.8 0.6S

GCNl 126.6 98.1 0.19

GC 3 592.0 655.2 0.42

Sispplemeai 3 Differentially Expressed G^sses According to Phessoiype

|S@336| Table 18: The top 100 differentially expressed genes in IFTA (all)

RNASE6 P2 Y13 IDH1 HLA-E

CP CSF2 B EVI2A A2LD1

HLA-DRB 1 /// HLA-A ODF3B HLA-DQBl ///

HLA-DRB LAPTM5 CPEB3 LOC 100294318

SOX9 NCF4 LOC 100130100 RAB40B

CD69 DTX3L III C1QC

SLC34A3 HCST LOC.100291464 HLA-DMB

RGS19 IGK@ /// IG C FSMB9

CSF!R /// IL10RA

CARD 16 LOC 100291464 PARPI2

C1QB NELL! SIRT4

GPR98 ClorBS j PL15RA

[00337] Table 1 : The top 100 differential fy expressed genes in IFTA without inflammaiion - DEGs Identified Matlab/Our data processing

Gene Symbol GABBR1 /// UBD HLA--DRB4 11,32

IGH@///IGHG1 PLP2 CCL5 PYCARD

/// IGHM /// COROIA HLA-DRB1 TME 73

IGHV4-3I /// SLA A2 ARL4C

LOC100290146 FILA-C SLC2A12 UPP1

SYT11 TPAF1 LOC5414 1 /// RAC2

1FITM3 KJAA1 49 NCRNA0 152 BIRC3

HCLS1 SEC14L1 PXYD5 PDLI 1

ISG20 MRPS12 IGLL1 /// IGLL3 HLA-DQBl

1FITM2 EDN1 / LOC91316 CAF1

IG @ /// IGKC /// SLC02A1 ITM2C LDLRAD3

1G V3-20 III HLA-DQB! /// VCAM1 SH2B3

LOC 100291682 LOC 100294318 NFKBIZ CD53

TMSB10 ClRi, FAM26F DOC 2

SOX9 LOCI 00303728 LAFT 5 SERPINGI

1FFFMI CD2 ARHGAP30 CHRNG

SIOOAI! CP£B3 IER3 RGSI9

I-iLA-DPBl GLRX5 MAFF 1RF9

DTX L HLA-DRB1 /// HCF5 EOF

[ Θ338] Table 20; The top 100 differenti ally expressed genes in IFIV without inflammation -

Techeical Validation using LIMMA R pac <age

ARHGDIB LOC9I3I6 EGR! !G VlD-13

GAD I CCL5 LDLRAP3 TMC8

ARHGAP30 IL7R KLF4 HLA-DPB1

H83ST1 CLU 1PIT 1 ) U^'NB

WFDC2 R.RA.S ClO rf!O NFKBIZ

KLE6 TNFAIP3 I.TPR3

00339] Table 21 ; The lop i 00 differentially expressed genes in IFTA with inflammation

Gen© Symbol PMS2 // RBMS1 SUGTIL!

TRPC7 PMS2L1 /// SCIJBEI LOG 100287166

LGSN PMS2L14 /// ATP6V1F PPBP

I'IBGl ///HBG2 PMS?i .? /// MBP 1JSP4

AKAP12 PMS2L5 LOC497256 NELL1

UNC93A GRIA3 ^•nci LR-RTM1

OPCML ACOT6 TF CF1.AR

ΊΊΜΡ3 FSMBS LATS2 C12ori35

BPL31 // DLGAP2 C!SorfiSI NCRNA00113

TBC1D8 SLC30A8 SH3BPS KLRC3

THSD7B KLF6 OLIG1 RABGAP1L

ZNF6S8 CB 4 HLA-C LOC 100288911 DR65 GAS2 STIM2 FfLA-E

LOC255512 ZC3HAV1 APOBEC3G POM121L8P///

FAB PC I LOC2850 3 LCE!B POM12IL9P

LOC440981 /// MYL 4 COX5B C!orf74

PLSCR2 GOLGA8A /// ZCCHC6 SHX9

ERLEC1 GOLGA8B PPAPDC!A GFODl

A AP13 CBL NDUFB 11 PR CH

TAf.,2 GCGR OBSC GABRA2

KBTBD12 ATPSI TP53TG1 ACSLS

BRD2 JOSD! RPR ZCCHC2

KCNK10 LOC727930 ΖΒΪΒ38 CI ori57

EL F I ΊΊΜΜ8Β R.NASEL NCRNA00168

LOCI 00289811 PN A2 HLA-G ID! 2

///LOC441259/// COMMD! MYL6B CCDC!SO

00340] Table 22: The top 100 differential! y expressed genes in IFT A plus AR

Gene Symbol RXRA LOCI 00303728 ! R.AP2B

MHO A2LD1 FQXRED2 SLC25A30

NECAB2 FLJ3S424 ^"i^"TR CXCL2

PDOM1 BA1AP2 SLC22A1 DOCK2

TMSB IO GPR9S L2HGDH KCNK1G

S I Al i A.LAD LETM1 C 1 QB

TRIM50 LOCI 0013178.1 PDK2 SPHK2 1

FOLD3 CCL5 FITM2 ELF3

MY07A SNTA1 TOL1JP EVI2A

ACSBG2 RNF152 CYP3A4 A 2 I CNN2 LGSN CASP4 NEKB!Z

GLTPD2 NFS1 PKLR Λ€Ο Π 1 1

CRYBB3 ΪΑΡΙ C! 8orf56 ISG20 1

MIR21 PRODH GLR.X5 NIJDT16L ! 1

SLC2A5 LOC541471 /// HAG! GBP2 I iom NCRNA00152 DARC IFITM1 1

Hi Λ-Α HS3ST! SLC2A4RG SLC2AI 2 I CNE3 CE Pi BIRC3 OYPA I

ALPL TMEM207 C6orfl23 CARD! 6 I

GATSL3 /// C8orf55 PFKFS2 JUN I

TB CI D ID A DAK GZMA PDXP /// SH3BP! |

SLC34A3 SLC5A1 1 GPT2 CD52 1

ATP2C2 ACOT6 IF1T 2 CCDC 109B I

NELL! FAM83D EVI2B NFATC2 1

CYP3A7 AGXT2U CD3D ARPC2 I

DIP2C HLA-DPB 1 A NA

8034IJ Tabie 23: The top 100 differentially exr. pressed genes in cAB - - DEGs identified -latiab/Our data processing-

Gene Symbol 1 \ LOC I 0O 1781 | j ALB 1 AFM 1 APOH SLC12A3 SFXN2

FAMI51A ACP5 KHK

KLK1 PROZ PC

SLC7AI3 OSTalpha AGXT

LPA /// PEG UGT1AS/// FOLH1B LOC10013069!

CTXN3 UGT1A9 AMDHD1 SLC23A3

M O NECAB2 SLC5A11 LDHO

DH12 MAPI UPBl ANGPTL3

GPR98 DHDH CRYAA RHCG

SLC6A1 DPEPI SLC39A5 DMGDH

HPD SOST SLC10A2 C2oif40

PLG C9orfi¾ TRIM50 NPR3

TMEM174 DIOi FTCD CALML3

SLC22A8 XPNPEP2 FCK2 A.8CC2

CYP4F2 FIRG SLC34AI CYP4F3

DAG TMEM207 MME CTSL2

SERPINA6 LOC14S83? ENPP6 PM20D1

PRODH2 SB9 FM04 APOM

CYP4F2 Iff CYP3A7 GGT6 PIPOX

CYP4F3 PNPL-A3 C ALB 1 SLC4A1

CYP2B6 / FAB PI DNMT3L RALYL

CYP2B7P1 SLC22A6 TM4SF5 AZGP!

M!OX ABP! CDHR5 A2LDI

G6PC LOC727944 KNG1 LFFR!

AGXT2L! SLC23A1 DDC GLYAT

Table 24: The top 100 differentially expressed genes in cAR - -Validation using LIMMA R package

Symbol ί.,. .4..·9 LOG! 00291464 IGHV4-33 //

IGH@ // IGEiAl SERPINA3 CXCLII LGC1G029Q146

///IGHA2/// CXCL!! TRBCi

LOC100126583 GBPS CXCL13 LOCI 00293440

IGHM IGK@ /// IG C IGH@//7iGBG1 IL23A ///

LTF // /// IGHM /// TRBV19 FAM26F LY2 CDS2 LCK

MS4A1 IGLVt-44 TRBCI SLAMF7

FAM26F IGK@ /// IGKC FOU2AF1 RAC2

LOG 100130100 /// IG V3-20 /// NNMT CD84

/// LOCI 00291682 CD2 IGK@ /// IGKC

LOCI 002 1464 !GLV!-44 IGSFo ///IGKV3D-1S

CD3D GZMA COROIA !GLB

IGL@ CCI.,5 IGJ IGLL1 //IGLL3

ISG20 ALOXS IGK@ /// IGKC ///LOC91316

IGK@ /// IGKC L!LRBl IGK@ /// IGKC IL7R

FCGR!B IG @ /// IGKC RGS! CIQI3

IGL@ /// LOC652493 /// IGLV2-23 /// LCP2

IG4CV1DG3 LOC652694 LOG 100293440 PLEK

CYATi /// IGLV2-23 LOC 100287723 BCL2A1

IGLV1-44 IGL@ FFP C MIR155HG

CXCLIO ISG20 EVI2B

!DOI CCLS — CSF2.RB

PLACE GZMK CB163 EV12A

C08A SLAMFS IGK@ /// IGKC CCR2

CD52 IGK@ /// IGKC GF 17I NNMT

CD48 /// LOC652493 IG @ /// IGKC CSTA

CCI.,5 AD MDECI TLR8 l^'GKV^'4-1

CCL4 1GK@ Λ7 !GKC FFPRC CD 163

PCGRLA /// /// IG V3-20 /// LYZ SLPI

FCGR!C LOG 100291682 LOC652493

[©0343] Table 25: The top 100 differentially expressed genes in C4d Associated - Genes associated with C4d positivity using LIMMA R package

Sy bol Iff /// IOC652493 /// IGK@ /// IGKC

CXCL11 LOC 100291464 LOC652694 /// LOC652493

IGK{¾ /// IGKC CXCL1S CCL4 IGLV2--23 /// CXCL9 LOC100293440 IGLJ3

LOC100291464 IGLV3G9 IGK@ /// IGKC IG V1D-I3

LOC100130100 IGK@ /// IGKC IGLJ3 LOC652493

-SB-

Table 26: Genes Associated with C4d positivltv and Graft Loss

Genes HLA-G RR 2

ACSL5 ISG2G S AMI-ID I

Α.ΟΙΊ LAYN STAT!

ASS! OAS2 SYT1 !

CAS 4 PRC! TA.Fi

CE PW PSMB!O UGT2B28

GBP2 PS MBS

HLA-F PS B9 supplement 4 Exten VaS idado ii Balsa

00345] Table 27: ^' Phe top 100 g enes from the \ 'alidation data^■■■■ externa! IFTA

Symbol CCLS TRBCS LCK

CXCLG FCGR3A /// R.GS1 CCL4

CXCL11 FCGR3B CCLS MS4A1

CXC.L9 GZMA €.02 EVI2B

CXCLI3 CD 8 A PTPRC SAMMD1

CXCL!O LYZ CD69 CDI63

ADAMDEC1 SLA F7 !TGAL PTPRC

FAM26P TRBC1 ΓΓ IL7R

GBP! CD52 PCGRIB APOBEC3G

GBP1 GZM HLA-DQAl If/ HLA-DPA!

IDO; C!QB HLA-DQA2 /// TRAF3IF3

FA.M 6F TLR8 LOG 100507718 CTSS

GABBR1 /// USD MIRI55 / /// C!QC

PTFRC MIR155HG LOCI 00509457 GSP2

CCL5 IILA-DQAi CSP2R.B LYZ

GBP! GBP5 TAGAP PC BRIG

LCP2 IKZFi PLACE HCLS1

G.BP2 C!6orf54 T FSF B SA S I

G8PS SLAMF8 STAT1 CM QA PSMB9 GIMAP2 GPR17I TRAC ///TRAJ17

CD163 ALOX5 MS4A4A /// T11AV20

LY96 CXCR4 IGHG1 /// IGHG2 CD52

TNFSF13B CSTA ///IGHM/// CLEC7A

ADCY7 TRBC1 1GHV4-31 BCL2A1

MS4A6A HLA-DQB 1 HI XCL1 /// XCL2 LOC642838

NLRC5 LOCI 00293977 MPEG I RAC2

!GHM MIR3929 // CTSS

IRF! NLRC3 HLA-DQB1 ///

EVI2A CYTIP LOCI00293977

W3 6] Table 28: The top 100 genes from the validation daia - interna! cAR

LOC 100131781 CD52 1GLV2-23 //^" CD69

LTF 1DO! LOCI 00293440 CD3G

CXCL9 LOCI 0 130100 PTPRC FOB

SE P1NA3 III EV12B MS4A6A

Gene Symbol LOC100291464 LOC652493 VS1G4

IGK@ / 1GKC FCGRI A // SLPI C1QC

III FCGR1C CSF2RB C lorn 02

LOCI 00291464 CDS A TNFSF13B LAFFM5

CXCL11 ISG20 BCL2A1 XCLl

CXCL13 LYZ CTSS M DA

CD3D IG @ /// !GKC IL-10RA HCLS1

CXCL10 /// IG V3-20 /// IL7R CCL19

PLAC8 LOCI 00291682 LY96 LCK

!GJ CD2 SELL RARRES1

FCGRIB GBP5 CSTA S A HD 1

CCL5 ADAMDEC1 XCL1 /// XCL2 GABBR1 Hi UBD lGH@7/K3PlGl G2M FPRI GBP1

/// IGHM /// COROIA EY12A RNASE6

IGHV4-31 /// LOCI 00287723 CD53 GBP2

LOC100290146 IGSF6 AJM2 ARHGAP9

CCL4 C1QB MIR155HG REG! A

GZMA GPR171 FCER1G IRFI TYROBP SLAMF8 /// IGHA2 /// CXCL2

GZMB CASP1 IGHG1 /// IGHG2 FC I

VCAN DOCK2 /// IGHG3 /// LST1

CYT!P ARHGAP30 IGH /// IGHV4- IGLC! /VIGLLS

CYBB RASSF2 31 /// ///IGLV3-16///

FA 26F CARD 16 LOG 100126583 1GLV3-25

1- sT C3 ///

KLR 1 MIR2I LOCI 00290036

C16or£54 IGH@ /// IC3HA1 HLA-DQB 1 0347] Table 29: The top 100 genes from the validation data -internal IFTA (all)

IGH@ ί/ί !Gl-IGI LOG 1002 16S2 MLRC5 ! TR!MSO

/// IGHM /// HLA-C DARC RNASE6

IGHV4-31 /// TAP! EVI2B CP

LOG 100290146 C!RL ACSL5 HLA-DRB1 ///

ISG20 IFITM2 STAIil HLA-DRB4

SYT11 SERP1NG1 MRO SOX 7

IFITM1 NFATC2 SLA CD69

CCL5 MAFF ATPAFl SLC34A3

T SB10 SLC2A12 G.BP2 RGS1

COROIA TMEM173 PLP2 CSF1R

HCLS1 FXYD5 GZMA CARD! 6

BIRC3 LOCI 00303728 PDLPvij C!QB

DOCK2 FAM26.P CD53 GPR98

FKBIZ IL7R NECAB2 P2RY13

CD2 CAPI EGF CSP2RB

A NA C1 orf54 ΚΪΑΑ1 49 Η1.Α-Λ

HLA-DPB 1 LOC541471 /// GPR17I IAFTM5

ARHGAP30 NCRNA00152 Pi S3 ST i NCF4

ΪΨΠΜ3 PYCARD HLA-DRB1 DTX3L

GLRX5 ARL4C MYOIF !ICST

RAC2 RASSF5 SASH3 1GK@ /// IGKC

!GK@ /// IGKC IGLL1 /// IGLL3 T FALF3 III

/// IG V3-20 /// ///LOC 1316 CI off 162 LOG 100291464 NELL! LOCI 00130100 S1R!^'4 RAB40B

CiorBi III IL!SRA C1QC

IDH! LOC100291464 HLA-E HLA-D B

EVI2A PS B9 A2LD1

ODF3B IL!ORA HLA-DQB I ///

CPEB3 FAR? 12 LOG 100294318 348] Table 30: The validation data - AR comparison ^te ss ! Data Si»iy .O fs

Gene PC Gene FC

CXCL13 3.9 LTF 11.3

CXC!., 3.9 CXCL9 10.4

CXCL10 3,2 SERPINA3 3.2

ADAMDEC! 3.1 CXCL11 8.6

CXCL11 2.9 IGLC (6)* 5.2-8.0

GABBRl /// IJBD 2.9 IGHC (1) 7.3

CCLS 2.6 CXCL13 7.7

GBP1 26 ALB -6.7

CD8A 2.5 CD3D 6.6

LYZ 2.5 CXCLIO 6.6

CIQB 2,5 PLAC8 6,5

GZMA 2.5 !GJ 6,4

CTSS 2.4 FCGR B 6,4

CD 163 2.4 CCLS 6.3

CD52 2.4 CCL4 6.3

FCGR1B 2,4 GZMA 6,1

C1QC 2.4 CDS 2 6.!

FCGR1A_;B_?C 2,3 !DOl 6,1

!DOI 2,3 CDSA 6

HLA-DQAL2 2.3 FCGR1A.C 6

GZMK 2,3 1SG20 ,5,9

CIQA 2,3 LYZ 5.8

PSMB 2.2 CD2 5.7 Exterul BsRs SAKA BsP<

RARRES3 2.2 GBP5 5.7

CD3D 22 ADA DECl 5,6

CD2 2.2 APR1 -5.6

FCER!G 2,2 GZ K 5.5

TAP! CORO!A 5,5

TYROBP 2.2 IGSF6 5.3 S4A6A 2.1 APOH A A

GB PS 2,1 C1QB 5.3

STAT1 2,1 GPR171 5.2

CCL58 2 J FPPRC 5.2

FLAGS 2,1 EVI2B 5

IGSF6 2.1 CSF2RB 4.9

TNFSF1 B 2 J SLP1 4.8

PTPEC 2.1 IR133HG 4,8

LAPT 5 2.1 TNFSF13S 4,7

PCGR3A, 3B 2,1 BCL2A1 4.7

SELF 2 J CTSS 4,7

G8F2 2,1 ILIORA 4.7

GSTA 2 RLK! -4,7

FAM26F 2 IL7R 4,5

COROIA SLC7 13 -4.6

IRFI LY96 4.6

HLA-DQB^'i 2 SELL 4.6

LY96 1,9 CSTA 4.5 ecu L9 XCLF XCL2 4,5

VSIG4 1,9 FF 1 4.5

RRM2 L9 EVI2A 4.4

Supplement 5 Genes of AR IFTA $ IFTA plm AR GCN§

[00349] Table 31 : The top 00 genes from the large AR GCN (GCN Large, inclusive; CO0.60) Nm er Namfeer

Prebeset Geae Pr fee&et Geae

of edges of edggs

3I845JPMjat ELF4 681 207697...PM.._.x_...at LILRB2 630

2074I...PM ^ & ADCY7 676 205639._.PM.ai AOAH 629

21 947 _PM_at CLEC4A 669 21 1 1 PM s at BIN2 629

203922.J¾i..s..at CYSB 667 203047_PM_at STK10 629

207IO4_PM_x_at LiLRBl 666 20807 lJPMjs_at LAIR) 628

205159 PM.. at CSF2RB 665 203760_„PM_s_at SLA 627

223303_..PM,_..at FERMT3 662 2Q5147JPM_x_at NCF 627

20S269_..PM_..at LCP2 660 221293_...PM_...s_..at DEP6 627

224927_.PM.ai JAA1949 659 204882 PM &i ARHGAP25 626

206011 PM at CASP1 6S7 228869_PMjat SNX20 626

22350!_.. _PM_at TNFSF13B 656 203508 PMjit TNFRSF1B 624

218S02_PM_at CCDCI09 656 20S6S5.PM.JU CD86 624

204336_...PM_...s_..at RGS19 653 203217_PM_s_at ST3GAL5 623

207571_PM_x_ai CIorBS 651 2i88Q5JPM_at GIMAP5 623

213160 PM at DOC 2 648 21 243_PM_at GIMAP4 621

21^Q538JPM__.s.at BIRC3 642 204S02_..PM at SAMHD! 619

220005 PM at P2RY13 642 207957_.PM...s._.al PR CB 618

209S14 PM_s_at RAB27A 640 2 i 1768 PM st LAT2 618

2G3416_PM._.at CDS 639 225353 JPM__s__at C1QC 617

21938SJ>M_at SLAMF8 639 204118. PM.. at CD48 617

20SOI8_PM_s_„at HCK 638 233359.. PM ¾t LRRC33 617

219574_...FM,..at 1-Mar 637 203470_...PM_.s_..ai PLEK 616

206332. P _s _...at 1ΡΠ6 636 3080Ι2_..ΡΜ _..ηΐ SP110 616

202643 JPMjBjrt TNFAIP3 635 224451 PM x at ARHGAP9 616

2I8130„PM_at C17orfS2 635 208436...PM.s.ai 1RF7 616

226219.. PM_..ai AH HGAP30 634 22i666_..PM_.A_.¾ PYCARD 615

204959_...PM..at MNDA 634 209949_...PM_..at NCF2 614

203561_PM_at FCGR2A 632 210176 PM at TLR1 613

20£923_.P _.sr ST8SIA4 631 218322..PMj ¾ two 613

203932 FM_...at HLA-DMB 631 218223JPM_s _...at PLEKH01 613

206991 J>M..s.at CCR5 630 21 666_..PM...at MS4A6A 612 Nunber KOsber

Protest Gene Pro es©! Geae

of edges srf edges

207419_. PM...S . at RAC2 612 204122_...PM_...ai TYROBP 603

2062l9...PM...s_..ai VAVI 612 204912__.PM_._at ILIORA 603

204014 PM_.. at DUSP4 612 206420_...PM_..ai IGSP6 603

235306JPM_..s1 GIMAP8 61 ! 213733_...PM_...ai MYOIF 603

223S83.._..PM_..at TNFAIP8L2 61 1 202910_.. PM_s_ at CD97 603

205474JPM_at CRLF3 610 i553043_„PM_a_at CD300LF 603

21408 J»M_x_ai LOCI 00289 610 226474„PM_at NLRCS 602

727 /// 220146_PM_at TLR7 602

NCF1 /// 202625_PM_at LYN 60!

NCF1 B /// 2042?9J¾l _.ai PS MB 9 60!

NCF!C 203104_...PM_...ai CSF1.R 601

229437_...PM_...ai MIR155HG 608 213475_..PM_...s_...at !TGAL 600

21451 1_...PM...X . si FCG 1 B 607 220832_...PM_...ai TLR8 600

203185JPM_at RASSF2 607 21969 l _...FM_...ai SAMD 600

203320_...PM_...ai SH2B3 60? 2G4489JPMji_at CD44 599

206120_PM_at CD33 607 214467_...PM_...a£ GPR65 599

202901 JPM_x_at CTSS 606 206707_..P _x at FAM65B 599

228094_..PM...S† AMIGA 1 606 2I3 ! 36_...PM._.ai PTPN2 598

2065 I3_PM_st AIM2 605 204923 PA a ι SASH3 598

STAMBPTT 604 223562 .PM at PARVG 597

207691 JPM_x_at ENTPD1 604 205859_...FM_..at LY86 596

Ι0Θ35Θ] Table 32: The top 100 genes from the large IFTA plus AR GCN (GCN Large, inclusive; CC=0.60)

Number amber

Probeset Geae Probeset Geae

of edges of edges

203335_PM_at PHYH 899 205222_PM_at EHH .DH 874

206325_...PM . SERPINA6 891 226649_PM_at PA K1 869

226991_FM_ at NFATC2 883 220?53JP _s_at CRYL1 857

234974 PM...at GALM 882 204289_.. PM...ai ALDH6A! 857

219527_„PM_at OSC2 875 219298_„PM_ai ECHDC3 856

205750...P _...at BPHL 875 203S60_...PM._..at GGH 846 Number Nraber

Probesei G«ae Protest Gese

of ed es of ed es

205269__PMj.t LCP2 845 20603Q_..PM_._at ASPA 784

3!845_PM_at ELF4 836 207567JPM_at SLC13A2 784

23G830_PM_at 0 STB ETA 8.32 206522_PM_at MGAM 784

2056?3_.PM_„s_„at ASB9 83! 210538JPM__s__at BIRC3 782

219076 PM...S. t PXMP2 831 203559J¾4_s_at ABP1 782

2306i9_PM__s_at HYALI 829 2Q4997_PM_al GPD1 780

22837S_PM_at IGSPii 824 219543. PM_^at PBLD 780

226382JPM_.._..at CAM 1D 820 205489JPM_at C YM 779

/// 203178JPM_at GAT 77?

LOC283070 218805._. PM_at GIMAP5 777

1552755_. PMjit C9orf¾6 818 2188Q2JPM_ai CCDC109B 775

200^5 PM at ACAA! 8 7 205942 PM s t ACSM3 775

209605 PM at TST 816 218025_PM_s_at PECI 773

205751_.. PM_.._. si SH3GL2 814 218021_PM_„at DHRS4 /// 773

20930___P „s_jt CASP4 813 DH S4L2

219191__PM_sjat BJN2 811 22l565__PM_s_at CALHM2 773

207184JP at SLC6A13 8Π 206226_...PM_....at HR.G 772

203185_Ρ _βί ASSP2 810 2i3160_PM_at DOCK2 769

206155_PM_at ABCC2 807 232422_..PM_...al A2LD1 767

2G4i58_...PM_...s_...at TC!RGI 804 2(MK78JPM at DAO 766

20J035_„PM_s_ai HADH 799 207076_. PM_. ¾l ASS! 765

20522 U⁵M_.._.ai HGD 799 223658_.PM_.at KCNK6 764

204347_...PM_...at AK3L! 793 204044_PM_at QPRT 764

209696_PM_at FBP! 792 219902_„PM_„at BHMT2 764

22445 iJPM__x_at ' ARHGAP9 790 203790.._.PM_...s._.at HRS 12 76!

207407_. PM_...x_...»t CYP4A11 790 215966 J>M__xjst GK3P 760

22492 _..PM_.jd IAA1 49 787 204882_PM__at A HGAP25 760

226474_PM_ai NLRC5 787 205978_PM_at L 760

231790_.. PM_.. at DMGDH 717 20754 JfMjurt ADH6 758

201562. PM_..s...8t SORD 786 204608_PM_ai A Si, 757

2009O3_...PM_....s_...a AHCY 785 20239 iJPMjat BASP1 756 Nsmber Stabe

Frobesei Profeeset Geae

of ed es of edges

205i?5_PM_s_at KRK 755 220952_.PM...S at PLEKHAS 736

231376. PM_at UPP2 752 2252Q3__PM_at PPP1R16A 729

227865_PM_at C9orf!03 751 205364_...PM_...at ACO.X2 727

2045]0_PMjtt CDC7 749 210343 P .S... at SLC22A6 726

2Q5355JPM_at ACADSB 748 227695„PM„st GLYATLl 726

228274_ΡΜ_...¾ί SDSL 746 206I19JPM_at BHMT 725

2l7985J>M_s_at BAZ1A 74S 204388_..PM...S...S† MAO A 723

207387 _P _s_at G 74 209978_PM_„s_„at LPA /// PLG 722

2084S0_.PM_...s_..at ABCC6 /// 744 226743_.. PM at SLP 11 7'' 1

LOG 100292 206514 ΡΜ. . at CYP4P2 /// 721 715 CYP4F3

20707 l_P j5_at ACQ! 744 205757 PM at ENTPD5 721

20422 Ϊ...ΡΜ..^χ.δί GL!PRi 742 2295G2_PM_at CHDH 721

2104S2_...FM x at CYP4F2 7 1 223732_...PM_...at SLC23A1 721

202108_. PM_.. at PEPD 740 2l iS = _. Ρ _.αΐ DOCK 10 721

209980^Μ_5_8ί SHMT1 737 218322 _PM_s_at ACSL5 720

0Θ351 ] Table 33 : The top 100 genes from t jrge IFTA GCN (GCN Large, inclusive; DC-0.60)

Nsiiiber Number

Protest Gene Probeset Geae

of edges of edges

201720_..PM_...s_...at LAPTM5 415 203416_..,PM_.,.ai CD53 367

2Q2957_P _at HCLS1 407 203508 PM_at T FRSF1B 36/

2t8802.FM_...ai CCDCI09B 404 204336_..PM_...s_...at RGS3 363

204912 P ¾t 1L10RA 394 201666.. PM. at TiMPl 362

202206 PM a; AR1.-4C 388 203760_PM_sjit SLA 358

223501_. _PM_at TNFSF13B 387 224927JPM_at KIAA1 49 358

205269_P _at LCP2 377 8025._. PM.. a? ML L 356

221666_...PM_.._,s..ai PYCARD 375 21 S74_PM_at !-Mar 356

206 2. Ρ ;η IP! 16 374 20757 l_PM_„x_ai ClorfSe 353

203I85_PM_at RASSF2 370 217762_...PM_..A_.ai RAB31 353

204502 P ?u SAMHD1 368 22621 J>Mj*t ARHGAP30 352 Number Nemiwr

Probeset Prefceset

®f edges of edges

204882_...FM_...at A HGAP25 351 202910_„PM_„s_„at CD9? 321

225602__PMjit 350 202590_„PM_„8_„at PD 2. 320

2!3160JPM_ DOC 2 349 2Q9368_PM_at EPHX2 320

20535S_..PM. ¾t ACADSB 339 2056?5..PM_...at MTTP 319

201601 JPMjijrt !FITMI 337 207544 PM s at ADF16 319

218322JPM_s_at ACSL5 336 229S26JPM_et AQP11 318

207 19JP A « AC2 335 2!9655_PM_at C7orf!0 318

2l776lJP at AD!l 334 24140!__PM_at C4orfl2 318

205786_P _s_at ITGAM 333 21 ! 742 PM s at EVI2B 318

20i 6!JPM_ai MTHFD2 333 202748_.PM_..al GBP2 318

205147_PM_x_at NCP4 333 20S364._.P _...ai ACOX2 317

205306_...PM_..x_..at KMC) 331 202S30_...PM_...s_..at SLC37A4 316

209!9!JMjs TUBB6 331 206840_PM_at AFM 315

22441! PM at PLA2G12B 330 205750_„PM_„at BPHL 315

208296 JPMjs._at TNFA1P8 330 203790 J?M_s_at HRSP12 315

206584_.. PMjat LY96 330 231070_..PM_...a IYD 315

201506 PM_.. at TGFB1 330 202948JPM _...at PL!RI 315

231790 PM at DMGDH 329 204774 M EVI2A 314

228532_PM_at Clorfl62 329 21 696_PM_s_at PRODH2 313

203932_.FM_.at HLA-D B 32g 20497 JPM_ai DDX!O 313

227865_PM_at C9orfl03 327 217733J*M_s_at T SB10 313

2269 1_PM_ at FATC2 32? 20S159J^JM_„at CSF2RB 313

203434JPM_s^j* MME 326 214389_...PM_...at SLC5A12 312

2O9083_P _ai CORO!A 324 205673 J»M_s_at ASB9 310

22!840__PM_at PTPRE 324 2!2701_...PM_...at TL.N2 309

213293_...PM_...s_...at TRIM22 323 2221 0_..PM_...s_...ai GPR89A /// 309

226430_P _at RELL1 323 GPR89B ///

201035„P _s_at HADH 322 GPR89C

21 527 PM at MOSC2 321 204565_^PM_„at ACOT13 309

20 I55_..PM ju ABCC2 321 231623_.. PM_. at TMEM174 309

204057_...PM_...al IRF8 321 218223JPM_s_at PLE HOl 309 Number osislmr

Profeeset Gene ProtaKt

of sdg®s f edges

2G8480_PM_s _...at ABCC6 /// 308 203682._.PM_...s_..at IVD 306

LOG 100292 223732. P at SLC23A1 306 715 23l632JPM_ai ClOorfoO 306

229916 PM_.. at ENPP6 308 224733__PM_at CM37 3 306

204289JPM_at ALDH6A1 308 212067_.. P _„s_at C1R 306

228375 P _.._.at IGSFII 307 2l 789JPM__at NPR3 305

229S34_PM_at ACOT4 307 15S2755_.PM _...at C905-PS6 305

219873_.. PM_... at COLEC!l 307 23 773_. P _..&† NOX4 305

208885_PM_^at LCP1 30/ 231021 J'MjU SLC6A19 304

225415_.. _PM_at DTX3L 307 227S60_...PM_...at SFXN2 304 m] Table 34: The genes from the AR GCN1 (GCN l - B cell/immunog!obulin Genes; CC=Q,90) and the geometric means.

Probst Number

of «dg¾s

21l644_P _x__at IG @ /// IGKC /// 1GKV3-2G /// LOCI 00291682 7

2172S1 PM _x_at IGH@ /// !GHAl /// IGHA2 /// IGHGl /// IGHG2 /// 7

IGHG3 /// IGHM /// IGHV4-31 /// LOC I GO 126583 ///

LOCI 00290036

!GH@ /// IGHAl /// IGHA2 /// IGH.D /// IGHGl // 7 1GHG3 /// IGHG4 /// IGHM /// IGHV3-23 /// LOC100126583 /// LOC 100290146 /// LOC652S28

IGHD /// LOC100290059 /// LOCI 00292999 7

20664! _...PM_...ai TNFRSP1? 6

211634J⁵M x at IGH@ /// IGHAl /// IGHD /// IGHGl /// IGHG3 /// 6

IGHG4 /// IGHM /// IGHV3-23 /// IGHV4-31 ///

LOC 100290146 /// LOC 100290528

224404 PM_...s_...at FCRL5 5

21655?_.. PMjt_at IGHA 1 // IGHD // IGHGl /// IGHG3 /// IGHM /// 5

IGHV4-3 /// LOG 100290320 /// LOCI 00291190

2I6S42 PM_...x.._.at IGHAl /// IGHGl /// IGHM /// LOC 100290293 5

2164!2_...FM_..x_...at LOC 100290557 4

206478_...PM_...at IAA0125 4

2!5Ι21_.._.ΡΜ_...χ...8ΐ CYAT1 ///IGLV1-44 4

215214_...PM_...ai IGLC! /// 1G.LL5 /// IGLV3-16 /// =OLY3-25 3

2n430_...PM_...s_..ai IGH@ // IGHGl // IGHM /// IGHV4-31 /// 3

LOCI 00290146

211633JPMjc_,_at IGH@ // IGHAl /// IGHA2 /// IGHGl /// IGHG3 /// 3

IGHM /// IGHY4-31

212592_. PM_. at IGJ 2

2!S949_...PM_...x_...ai IGHM /// LOC652494 Ί

20590 !_...PM_...at PNOC 1

213502_P _x_at LOC 1316 1 lS56I83_...PM_..ai FU40330 1

IFTA IFTAJ i TA_AR AR TX IFTA IF^'TAJ IFTA_„A» AR TX

Gsosnetrie m&a.m 105.9942 143.5605 276.7236 197. 1 01 ,30.94948

Sids of georoe&ss 1 12.6823 S 59.6326 274,2033 336.8681 46.32292

IFTA 0 0,496009 0-003165 0.067379 0.00013

IFTAJ 0/196009 0 0,074054 0.438889 0,052962

IFTA A 0.00 165 0.074054 0 0.249522 4,54B 0S

AR 0,067379 0,438889 0,249522 0 0,000677

^'FX 0.00013 0.052962 4.5 :· - 05 0.000677 0

IFTA without inflammation s m les

Banff 1 Banff 2 Banff 3

Geometric means 108.4002 77,51817 206,6657

Stds of gao?neans 1 ,83923 9,204427 66,2010?

Banff 1 0 0.291329 0,401043

Banff 2 0.291329 0 0,284822

Banff 3 0,401043 0.284822 0

IFTA with AR samples

Banff 1 Banff 2 Bam! 3

Geometric mea s 235.9339 295.495 371 ,6461

Stds of gsomeans 53,07497 41.76315 34.5727

Banff 1 0 0,627019 0,205086

Banff 2 0.627019 0 0,420619

BanfT 3 0.205086 0.420619 0

All IFI A samples

Banff 1 Banff 2 Banff 3

Geometric ears s 146,5702 163,278 298,3214

Stds of goo-means 34,52263 32.43683 55,5084

Banff 1 0 0,725424 0,034801

Banff 2 0.725424 0 0.054399

Banff 3 0,034801 0.054399 0

100353] Tabic 35: The genes from the AR+iFTA GCNl (GCNl ~ B cell genes; COO.903) md the geometric means. Freheset smfeer of edges

216984JPMjtjat IGLV2-23 /// LOCI 00293440 12

216401_.. PM _...JE _...at LOC6S2493 11

216576_PM_x_at IGK@ /// IG C /// LOC652493 Λ7 LOC652694 10

2!7378_..FM_..x_...at LOC 10013 100 // LOG 1002 1464 10

21S176J¾ .x_.ai !GK@ /// IGKC /// LOC 100291464 9

2I!64 J¾i %..¾ IG @ /// IGKC /// IGKV3--20 // LOC 1 0291682 9

217480 PM x at LOCI 00287723 9

215946_. PM_.x. t IGLL! /// IGLL3 / LOC9I316 9

221286__PM_s_at MGC29506 ?

2ii641_PM_x_at IGH@ /// IGBA! /// IGHA2 HI IGHD /// iGHGl /// 7

IGHG3 /// IGHM /// IGHV4-31 /// LOC 100290320

///LOC 100291190

2172Si_P _x _...at 1GH@ /// IGHA1 /// IGHA2 /// IGHGl /// IGHG2 7

/// IGHG3 /// IGHM /// TGHV4-31 ///

LOC100126S83 ///LOC 100290036

2Ii637JPM_x__at IGH@ /// 1GH I /// IGHA2 ill IGHD /// IGHGl /// ^" 7

IGHG3 /// IGHG4 /// IGHM /// IGHV3-23 /// LOC100126583 /// LOC 100290146 /// LOC652128

2H050JPM_x__at IGHA1 US IGHD /// IGHGl /// IGHG3 /// IGHM /// 7

IGHV1-69 /// IGHV3-23 /// IGHV4-31 ///

LOC 100126583 /// LOC 100290375

216557J*M_x_at IGHA1 /// IGHD /// IGHGl /// IGHG3 /// IGHM /// 7

IGHY4-3! /// LOC 1002 0320 ///LOCI 00291190

2!I634_.PM_...x_..at IGH@ /// 1GHA1 /// IGHD /// IGHGl /// IGHG3 Hi 7

IGHG4 /// IGHM /// IGHV3-23 /// IGHV4-31 ///

LOC100290146 /// LOC100290528

2 i ¾;4 PM a IGLCl /// IGLLS /// IGLV3-16 /// IGLV3-25 6

21 16JPM_x__at IGH@ /// IGHAI /// IGHA2 /// IGHGl /// IGHG3 6

/// IGHM /// IGHV3-23 /// 1GHV4-31 ///

LOC 100290375

2H430_PM_„s_„ai 1GH@ /// IGHGl /// IGHM /// IGHV4-3 ! /// 5

LOC 100290146 Nn nfe r of

edges

2066 1JPM_ at 12 ^'F SF!7 5

214973J>M x at IGHD /// LOG 100290059 /// LOG 100292999 5

215121_.. PMjs at CYAT! /// 1GLV1-44 3

212592 P _. a? 1GJ 3

213502_..PM_..x_..at LOC913 I6 2

205267_...FM_...ai POI.12AF1

216412J>M x at LOCI 00290557 1

224342 ? LOC966I 0 1

224404 . PM s at FCRL5 1

21 1633JPM_xjst 1GH@ /// IGHAI /A IGHA2 A/ IGHGl A/ IGHG3 1

/A IGHM /// H4HV4-A 1

2l6542J»M ij_t IGHA 1 Λ7 1GHG 1 A/ IGHM A/ LOG 100290293

IFTA IFTA J IFTA All All TX

Geometric means 126,4965 165.2388 315, 103 2.18E-H02 34,43450500 ^"

Stds of georoeans 137.7338 112.1506 310.9085 3.56B-M>2 56.33832600

IFTA 0 0.540295 0,004125 0.089001 0,000123765 iPTA_.j 0.540295 0 0.077026 0.49 891 0.049642079

1FTA_ AR 0.00412S 0.077026 0 0.201472 4.13E--05

All 0,089001 0.491891 0.201472 0 0,000423503

TX 0,000124 0.049642 ^" 4.13E-05 0,000424 0

IFTA without inflammation samples

Banff 1 Banff 2 BanfT3

Geometric me ns 130,0212 92,20178 244.2892

Stds of geomeans 23.28685 11.40552 79.41401

Banff 1 0 0.295933 0.414505

Banff 2 0,295933 0 0.292167

Banff 3 0.414505 0.29216/ 0

IF A with AR ssxnp!es

Banff 1 Banff2 Banff 3 Geometric means 265,7646 331.6039 438,1468

Stds of geomesns 60,00163 46.2188 4L21783

Banff ! 0 0,631633 0.161379

Banff 2 0.631633 0 0,322036

Ba¾ff3 0.161379 0.322036 0

AH IFTA samples

Banff 1 Banff2 Banff 3

Geometric me ns 169,7446 186.8695 351.9879

Stds of gsomeaas 39.50922 36.1782? 66.13868

Banff 1 0 0.75021.5 0.032555

Banff 2 0,7502 IS 0 0.047103

Banff 3 0.032555 0.047103 0

[00354] Table 36: The genes from the AR+IFTA GCNl (GCNl - B cellimmunoglobulin

Genes; CC^:::0,85) and the geometric means,

Probed Gt Naraber

of edges

2i6401_„PM_„x_„at LOC652493 20

2i7281J>M_x_»t 1GH@ /// IGHAl /// IGHA2 /// 1GHG1 /// IGHG2 /// 19

IGHG3 /// IGHM /// IGHV4-31 /// LOC100126583 ///

LOCI 00290036

21 576_P _x_at ;« @ /// IGKC // LOC652493 // LOC652694 13

217480„.PM...x.« ^' LOCI 00217723 1

217378 PM x si LOC 100130100 /// LOG 100291464 17

2l1641J»M_x_at !GH@ // IGHAl /// IGHA2 /// IGHD // 1GHG1 // 17

IGHG3 /// IGHM /// IGHV4-3 ! //7 LOCI 00290320 ///

LOC 100291190

216557 PM.A.ai IGHA i /// IGHD /// IGHG 1 /// IGBG3 /// IGHM /// 17

IGHV4-3I Hi LOC100290320 // LOC 1002 1190

211644_PM_„x_„at 1G @ /// IGKC /// IGKV3-20 /// LOCI 002 1682 16

221286_„PM_s_at MGC29506 16

214973JPM_x_at IGHD /// LOC 100290059 /// LOC 100292999 16 Probeset N mber of edges

21i650JPM_x_at IGHAI /// IGHD /// IGHGl /// IGHG3 // IGHM /// 16

IGHV1-69 /// IGHV3-23 /// IGHV4-31 ///

LOC100126583 /// LOCI 00290375

216984JPMjx_at IGLY2- 1 /// LOCI 00293440 15

216542JP _xjit IGHAI / IGHGl /// IGHM /// LOG 100290293 15

1GK@ // IGKC Hi LOG 1002 1464 13

215946. PM_..x _...at IGLLl /// IGLL3 /// LOGO 1316 Π

2II65/..PM. x. ¾t 1GH@ /// IGHAI /// IGHA2 // IGHD / IGHGl /// 11

IGHG3 /// IGHG4 /// IGHM ///^' IGHV3-23 ///

LQC1Q0126583 /// LOG 100290146 /// LOC652128

21491 _„ΡΜ. _x_jat IGH@ /// IGHAI /// IGHA267 IGHGl /// 1GHG3 /// 11

IGHM /// 1GHV3-23 A/ IGHV4-31 /// LOCI 00290375

211633J>M_x_at 1GH@ /// IGHA ! // IGHA2 /// IGHGl /// IGHG3 / 10

IGHM//7IGHV4G1

213502_...PM_.„x_...at LOC913.16 7

211430. PM..S. si IGH@ /// IGHGl Λ7 IGHM ,'// 1GHV4- G Hi 6

LOCI 00290146

206641 JPMjst TNFRSF1? 6

20634 ΡΜ_..Λ_.-Α !GH@ /// IGHAI /// IGHD 67 IGHGl Hi 1GHG3 Hi 4

IGHG4 /// IGHM /// IGHV3-23 /// IGHV4-31 ///

LOG 100290146 // LOCI 00290528

2164I2_...PM_..x_..at LOCI 00290557 3

215121JP _x_at CYAT1 /// IGLVG44 3

224342..PM.. _..ai LOC966I0 2

212S92_„P jA IGJ 2

2G5267_PM_at POIJ2AF1 I

IFTA IFTAJ IFTA_ AR AR TX

IFTA j 0519186 0 0..109003 0.54923 0,052766

IFTA _. AR 0-004952 0, 109003 0 ¹ 0.227056 4.32E-05

AR 0,09242 ! 0.54923 " 0.227056 <f 0.000417

TX 0.0001 17 0.052766 4.32E-05 0,000417 0

IFTA without infiammaiion samples

Basff l Banff 2 Banff 3

Geometric means 141.667 101.5884 259.9166

Stds of georneans 25,57193 12,8958 84,7351

Banff ! 0 0,31528 0,427742

Banff 2 0.31 S28 0 0.302085

Banff 3 0.427742 0,302085 0

IFTA with AR samples

Banff ! Banff 2 Banff 3

Geometric me ns 282.6646 346.6014 466.3286

Stds of geotneans 63.50335 49.45075 43.38553

Banff 1 0 0,661287 0, 158285

Banff 2 0.661287 0 0.297386

Banff 3 0.158285 0,297386 0

All IFTA samples

Banff 1 Banff 2 Banff 3

Geometric oans 182,506! 201.7296 374,5899

Stds of geomeans 42,08593 39.10256 70,34674

Banff 1 0 0.738956 0.03396

Banff 2 0.738956 0 0,050665

Banff 3 0,03396 0.050665 0

[TO355J Table 37: The genes from the AR GCN2 (GCN2 - Immime/ lasnmatory resjjonse; CC=0.903) and the geometric means.

Probesef Gene umber Pre eset Gen© umbe of edges of edges

213160 P . at DOC 2 36 226219_. PM_. at ARHGAP30 30

203416__PM_at CD53 31 205831_„PM_ at CD2 26 Probesei Geae Number Probeset G«ae umber f edges <$f ed es

219I91JPM_js_st BI 2 25 206991 _PM_s_at CCR5 10

204118_PM _..at CD48 24 204912._. PM_at IL10RA 10

205269JPM_at LCP2 23 223322 PM. at RASSFS 10

22445 l_PM_x_at ARHGAP9 23 20529 l__P _at IL2RB 10

207957 PM s at PRKCB 22 1559584 JPM_a_at C16orf54 10

223501 JPM_et TN SP B 2! 2!9S28_„P _s.at BCL1IB 10

204882_PM_at ARHGAP25 20 213S39J¾i_.ai CD3D 10

203922_...PM_...s_.&i CYBB 20 2i0279_„PM_mat GPR18 10

204923_...PM_...at SASH3 20 204959 PM..at M A 9

20489O..PM,..x.3« I.C 20 207571..PM....X. jo. ClorOS 9

203932_..PM_.ai HLA-D3V1B 1 208071_...PM_...s_..at LAI l 9

2Π742_.PM_...s_...at EVI2B 19 210031_..PM:.._,S1 CD247 9

218805_PM_at GIMAP3 17 210029_PM_al IDOi 9

202307_...PM_..s_..a5 TAP! 17 21347S...PM...S.. t 17 GAP 8

204279 PM.« PSMB 16 229625_„PM_„ai GBP3 8

209083_..P _..al CO 01A 16 212873..PM_...al H.MHA 1 8

207419_...PM_...s_..ai IIAC2 16 220005_.. P _...at P2RYB 8

20S639_PM_..at AO AH 14 2Q9827J» js_at IU6 8

2i9385JPM_ai SLAMF8 14 207823 PM ¾ as ASP! 8

202531_PM_at IRF1 14 206513...P _..ai AIM2 7

2080I8JPMjs_at PICK. 14 204336J⁵M._.s_..at RGS19 7

DEP6 13 203470_„P _„s_„ai PLE 7

20601 i_„PM..at CASPI 12 220474_PiVf_at NLRC5 7

225353JPM__s_at C1QC 12 20t649JPM « UBE2L6 7

209606 JPM_at CYT!P 12 203508 FM..at TNPRSP1B 7

223303 PM _...at FERMT3 12 2Q7697_PM_x_at L1LRB2 7

203760_„PM_„s_at SLA 11 207104_. PM_x__at ULR81 7

219574_...PM ai MARCH 1 11 204I22LPM_...at TVROBP 7

205159_PM_at CSF2 B 11 202269_..PM_. X &t GBP1 7

20765 l_..PM_..at GPR17! 11 200628.. PM_s_at WARS 7

205758_PM_„at CD8A 11 223640_...F _...at HCST 7

Profeesei Number Prebeset Gsm J Number o edges of ed es

204670_.. PM..x_..at HLA-DRB1 3 219812 PM st PVRIG 2

/// HLA- 207238_..PM s_.. at PTPRC 2

D B4 220330_.. PM_...s_..ai SAMSN1 2

211656JPM_x_ at HL A-DQB 1 206925_...PMj¾i ST8SIA4 2

/// 228094_FM_ai AMICAI 2

LOCI 00294 209846 PM_s_at BTN3A2 2 318 207485_...PM_..x at BTN3A1 2

! 552701 J>M_a_ at CARD 16 200904_...PM_...at HLA-E "j

21743δ_ ΡΜ__χ_. at HLA-A /// 208§12_...PM_..x_. ai HLA-C 2

HLA--F 77/ 210176 FM. ai TLRl ¹ HLA-J 205890_...PM_...s_...at GABBR 1 ///

¹

210514J*M_ xjit BLA-G 3 USD

204971_.. PM_. at CSTA 3 225701 JPMjit AK A ¹

20872 J*M_xjit HLA-B 3 21 014 PM_.. st FLAG 8 ¹

204057 _PM_at IRF8 2 20994 _..PM_...at NCF2 ¹

205147 JPM... at NCF4 2 20612Q..PM,..at CD33 ¹

210538_P _s_at BIRC3 2 201422 PM_...at IFI30 ¹

228362__PM_s_at FA 26F 2 2Q5859JPM__at LY86 ¹

203 t§S_...PM_...at RASSF2 2 I 209879_PM_at SELPLG ¹

2049SI _..PM_...at RHOH ^■

238439_PM_at AN RD22 ¹

204197JPM_ B_at RUNX3 2 1 202643._..PM: s at TNFAIP3 ¹

206219 FM_...s_..ai VAV! 209723. PM.. at SERPL B9 ¹

20S685J>Mjrt CD86 2 J 207375J^sM_..s_...at IL15RA ¹

204774._PM. at EVI2A 2 1 213415_ PM__at CLIC2 ^S

205474_PM_at CRLF3 2 1 203104_P _at CSP1R ¹

206914_. PM_.. at CRT AM '? !

217147JPM_s_ »t TRAT1 ¹

20274S_...PM_...ai GBP2 2 1 206296_...PM_...x_..at ΜΑΡ4ΚΊ ¹

219716_PM_at APOL6 2 1 213975_PM_s_at LYZ ¹

206682JPMjtf CLECIOA 2 1 205285JPM_s_at FYS ¹

228869_...PM_. ai SNX20 2 I 210152_PM_ai LILRB4 ¹

207 22..PM j:. ai ATP2A3 2 I 21 777 PM at G1MAP6 ⁵ Vmb et Gt umber Probes®! Gene

of edges of edges i 20496!_.. PM...S .._.at NCF! /// I 213958_PM_at CD6 1

NCFIB/// 220485 PM s at SIRPG 1 CF!C 2;7992 PM s at EFRD2 !

231276_...PM_...ai HOMES ¹ 212671JP _sjit HLA-DQAI 1

20888 _..P at LCP ! /// PiLA-

218322J»M_s_a* ACSLS ¹ DQA2

204821_.. PM_. at ΒΊΉ3Α3 ¹ 1552703. PM_s_„ at CARD 16 ///

223834J¾l.at CD274 CASP1

235175_PM_at GBP4 ³ 220!46J¾4.al TLR7 ;

2257S3_P _at CSD! ¹ 208894__PM_at Hi, -DP A 1

22862? Pfvt s a- RASAL3 ¹

IFTA IFTAJ IFTAJtR AR TX

Ge metrc means 85.61774 122,5719 137.5012 174.8767 48.494076

Std§ of geonieans 38,99418 64.81561 91.03025 129,4847 21,178022 ^'

IFTA 0 0.11260! 0,006533 9.91E-06 3,84B-0?

IFTA 1 0,ii26Gl 0 0.579813 0.06441 0,00559627

4

IFTA_..AR 0,006533 ^' 0.579813 0 0,130054 1.37E-0S

AR 9. 1E-06 0,06441 0.130054 0 29VH-0V

T 3.84E-07 0.005596 1 7E-05 2.49E-09 0

IFTA without inflammation samples

Banff I Banff 2 Banff 3

Geometric mea s 78.41973 88.77898 112.5618

Stds of geomeans 6.59402! 6.307235 10.6810!

Banff 1 0 0.407308 0,121172

Banff 2 0,407308 0 0.24759!

Banff 3 0,121172 0.247591 0 FTA with AR samples

1 Banff 1 Banff 2 Banff 3 Geomerc me ns 120 J 054 165.0992 148.0561

Stds of geomear^ 10.31097 28.2347! 8.729176

Banff I 0 0,441378 0.20844

Banff 2 0.44] 378 0 0.761158

Banff 3 0.20144 0,76! 158 ^' 0

All IFTA sam les

Banff 1 Banff 2 Banff 3

Geometric means 96.7724 118,1873 132,2808

Bids of geomeans 8.686045 17.23113 10,9437!

Banff 1 0 0.273606 0.019S59

Banff 2 0.273606 0 0.494564

Banff 3 0.019559 0,494564 0

[00356] Table 38 The genes fi-om the AR+IFTA GCN2 (GCN2 - Immuneflnflammatoiy response; CC=0.903) and the geometric- means.

Protest Gene umber Probeset Gene Number of edges of edges

2(M912JPM_at IL10RA 29 205291..PM. at IL2RB 10

224451..PM % st ARHGAP9 27 226991_„PM_ai ^'NFATC2 10

2048S2_.PM_.at ARHGAP25 23 213475_...PM_..s_...at ITGA.L 10

203416_.. PM_at CD53 19 2!9528J^sM_.s_..si BCL!!B 10

205831_PM_at CD2 18 21!742_..PM_...s.._.ai EVI2B 9

203760„PMjs_at SLA 16 2 3539..PK4 ;n CD3D 8

2262! 9_.. PM .at ARHGAP30 16 209795.PM. U CD69 8

20172QJ>M_s_at LAPTM5 15 226382 _PM_at CAMKID 8

2090S3_..PM_at COR01A 15 W

203470 J>Mj_at PLBK 15 LOC283070

22492?_...PM._.at ΚΪΑΑΙ 49 14 213!60J»Mjat DOCK2 8

218802_PM_at CCDC109B 13 20795? P ..s_..si PRKCB 8

204890 J»M_s_at LC 12 2Q4774JPM _..at EV 2A 7

203185_.PM at RASSP2 12 2!8805_...PM_...st GIMAP5 7

21919; P s ai BIN 2 11 223640_..PMjrt BCST 7

209723_. PM._. at SERPINB9 Π 203508_..PM_.at TNFRSF!B 7

202957. _PM_at jHCLSl 10 2021S6..PM_..A_..at CELP2 7 Profeesef G*ae l amber Pr bes®! Gem Namber

of edges of edges

206?07...PM.. x_ at FAM65B 7 202988_ PM__.s__.al RGSi 4

209606_...PM_...st CYTIP 6 206584 . PM.. at LY96 4

2O6804_...PMj5i CD3G 6 213733_.. PM_... t MYOI F 4

20S269_...BM_..at LCP2 6 224733_..PM at CMTM3 4

2^Q4221JPMjt__at GLIPR1 6 204220_PM_at GMFG 4

2G4057_PM_at IRF8 6 204971 JPM_at CSTA 4

22350 = PKl n TNFSF13B 6 2i0279__PM_at GPR18 4

226789_PM_ at EMB 6 205474_.. P _at CRLF3 4

2i0538_PM_s_ai BIRC3 6 2.5151_..PM_. at DOCK 10 4

1559584_. PM_...a_...at C!6oriS4 6 21 1990JPM_at HLA-DPA1 4

203922 _. FM_...s_...ai CY.B.B 6 205859JPM_at LY86 3

209827. PM s st IL..16 6 205419 PM.. at GPRS 83 3

20601 IJPMjit CASP1 6 204336_...P _...s_...at RGSI 9 3

206296_._PM_x_s MAP4K1 6 214467_...PM_...at GPR6S 3

207419_PM_„s_„at RAC2 5 204661 JPM_at CD52 3

226818_PM_at MPEG! 5 2061 18_ P at STAT4 3

20 i 2_...PM_...ni TYROBP 5 232Q24_PM_ at GIMAP2 3

22§362_PM__.,s_at FAM26P S 2i 0629_PM__.x__.ai LSTI 3

203932_...PM_...at HLA-DMB 5 20253 l_PM__at IRF! 3

204923 __.PM_ _.at SASH 3 5 20290 l_....PM_...x_...at CTSS 3

20495 lJPM_at RF10H S 236295_P _s_at NLRC3 3

221666_. PM_.. s_...at PYCARD 5 206761JPM_at CD96 3

2Q2748_ PM_at GBP2 5 205758__PM_at CD8A 3

206332_...PM s at IFI 16 5 204279_PM_al PSMB9 3

219505JPMjrt CECR1 5 20482 l_...PM_...at BTN3A3 3

228094 JPMjit AMIGA 1 5 2 i 9777 PM &t G MAP6 3

210031. PM. at CD247 5 228258JPM_at TBC!DIOC 3

20S488_PM_at GZMA 5 228532 PM at C!orfl62 2

204197_.. PM_._s_...ai EUNX3 .5 2Q4232_PM_at FCER1G 2

206666__.PM_._at G2MK 4 202953...PM..;n C1QB 2

208018_..PM_...s_..at HC 4 223303 PM at FER T3 2

-I 13- Profeesef 1 €eae Number Probeset Gene N«!»ber of edges of edges

21 812_PM_at PVR!G j 2 213S66J>M. at R ASE6 i

217838J¾4_s_at EVL j 2 2I4084JPM_x_at LOG 100289 1

2041I8_P _at CD4S j 2 727//7 CF1

206513_PM_at AIM2 I ² //7NCF1B

20582 l_„P _„at LR ! ///NCF!C

2264?4·.ΡΜ...8ί NLRC5 j 2 j 2Q5159JPM.at CSF2KB

1552701J>M_ajit CAP.D16 2 208885_. PMjat LCP1 ¹

20567 lJPM_s_at HLA-DOB 2 ] 21 0!4_...PM_..at PLAC8 ¹

20615O_PM_at CD27 2 211339JPIvi_s_at ΪΤΚ ¹

204820 P s at BTN3A2 /// 2 J 204794_. PM. at DUSP2 ¹

BTN3A3 205786_. PM...S. at FTGAM ¹

202625 PM. at LYN 2 j 204959_PM_at MNDA ¹

238Q25_PM_at MLKL 2 j 202643JPM_sjat T FAIP3 ¹

219033_^ PM t PAR? 8 2 j 225353 J»M_„s_at C1QC ¹

204563_PM_..at SELL 2 j 207375_PM_s_at IL15RA ¹

1405. PM. j_„at CCL5 "> i i 31845_...PM_..at BLP4

206366_PM_x_at XCL1 2 ] 203320 PM SH2B3

204103_...PM_.. at CCI,4 2 1 202910J* _s_at CD97 ¹

203964JPM_at NMI -> ! 20510!_.,.PM_..ai CIFPA ¹

2l861!_...FM_..at IER5 2 j 221 87_....PM...S._. APOL3 ¹

213293.PM..s.ai TRIM22 2 I 228869JPM_at SNX20 ¹

20333?_PM_s_ai HLA-DPBl 2 j 1553043_. PM_...a_. at CD300LF ¹

!558971_..PM...at THEMIS Ί ! 209949_..PM_...at NCF2 ¹

228677. PM_.s_. at RASAL3 I 220 i8JPM_at C2!orf96 ¹

2I1656JPM_x_at HLA-DQB 1 2 j 205642_PM_„ai CEP! 10 ¹

Hi 228752 !>Μ..¾ΐ EFCAB4B ¹

LOCI 00294 207777_. PM_.A_.ai SF140 ¹ 318 j 2i04? PM .¾ a- BCL!IA ¹

205639_..P _...at AO AH j \ \ 1 52703 at CARD! 6///

^J

20807 l_PM_s_at LAi l j 1 ] CASPl

218084_,..PM_...x.ai F.XYD5 j I I 2!9385.._.PM_...ai SLAMF8 1

-1 1 Fro sei Geae Number Pr test Geee Number of d es of edges j 207823 JPM_s_at AiFI 1 1559263_...PM_...s_.at PPIL4 /// 1

1229625.. _PM_at GBP5 1 ZC3H12D

j209846_...PM_...s _.at BTN3A2 1 202307 PM..S at TAP! ¹

|204352_PMjit TRAPS i 2G2659_PMj$t PSMBIO ¹

|227210_PM_at SPMB4^'2 1 218322_PM_s_at ACSL5

|2Π209_..ΡΜ_.χ SH2D1A I 226603 JPM_at SA D9L ¹ i l2I4567_PM_„s_at XCL1 /// 1 235306_.PM_...at G1MAP8 ⁵

XCL2 2i2873_PM_at P!MHAI ¹ I

205114_„PM_s_at ecu // 1 2!7147_...PM_.A_.at TRAT! ¹ 1

CCL3L1 /// 204670_PM_x_at Fil-A- Bl

CCL3L3 /// HLA-

238063_FM_at TMEM154 1 DRB4

209881 M. S at LA I /// 1 217478J¾fl_sjit HLA-DMA 1 ■

SPNS! 2Q948QJPM_...at HL-A-DQB 1

IFTA IFTAJ WT A_AR AM T.X

Geomeirie me&us 82,99476 119,4273 143.9954 164.4409 45 J 1409

Sids ofgso means 40.62669 63.6638 117.4587 121.6929 21.36179

IPTA 0 0,112442 0.011224 1.9214-45 5.77E-07

1ΡΪΑ J 0.112442 0 0.414494 0.097349 0.004957

IFTA_..AR 0.011224 0. 14494 0 0.458278 0,000101

AR I.92E-05 0.097349 0.458278 0 2.22E-09

^'FX 5.77E-07 0.004957 0.000101 2.22E-09 0

IPTA without inflammation samples

Banff 1 Banff 2 Banff 3

Geometric means 76.33953 86.09082 107.2144

Stds of georaeans 7.074741 6.531144 8.894891

Banff 1 0 0.4596S8 0.10S392

Banff 2 0.459658 0 0.233453

Banff 3 0.105392 0.233453 0 ^" IFTA wih AR sam les

Banff 1 Banff 2 Banff 3

Geometric me ns 119.8801 190.3242 145.2843

Stds of geomeans 10.92746 3834149 4385744

Banff ! 0 0370172 0367546

Banff 2 0370172 0 0.553992

Banff 3 0.267546 0353992 0

All IFTA samples

Baa r 1 Bsaff2 Banff 3

Geometrc means OS 037 ; 123,6056 128.3644

Stds of georasans 9.072969 2232168 10.54795

Banff] 0 0.246953 0,025564

Banff 2 03469S3 0 0.849371

Banff 3 0.025564 0349371 0

[00357] Table 39: The genes from the IFTA GCN2 (GCN2 Immune/inflammatory response; CO0.S7) and the geometric means.

Probeset Gem Frofeesei Gene Number of ed es of edges

203416JPM_at CD53 28 206332_. PM_..s_.st IF! 16 10

204 i2_..F _.ai 11,1 OR A 21 204122_. _PM_at TYROBP 10

2262I _.PM__.at ARHGAP30 19 21 666_.PM_.at MS4A6A 10

201720J _„sj.t L APT MS 18 204336_FM___s_.__at RGS 1 9

204774 JWjat EVI2A 18 2!9I91_...PM_.A_.at B1142 9

211742 P _{.. .} i EVI2B 14 2(M925_..P _..a: SASH3 9

209083 J»M_al COROIA 13 204232_PM_at FCER1G 9

207419 PM.s..al RAC2 13 2O4890_...PM_...s_...ai LC 9

204!1¾ pfv! &t CD4S 13 202957 J»M_ai HCLS1 8

205269JPM_at LCP2 12 203S08_PM_„at T FRSF1B 8

223501_.. PM at THFSF13B 12 207238_...PM s _.at PTPRC 8

204882 JPM_at ARHGAP25 II 2256G2JPM__at GL1PR2 8

22445 l_PM_x_„ai ARJ-IGAP9 11 22364G_PM_at HCST 7

20466 l_P jat CD52 11 20½84JPM.at LY96 7

2]3]60JPM_at DOC 2 10 203185_PM_„at RASSF2 7

Geae Neatiber Gem Namber

Of £dg&3 of edges

225353..PM ._.s at C1QC 2 229686_PM_at P2RY8 ¹

2030S3.Ph.iat THBS2 2 204158._. PM..s_...ai CIRGI

217966_PM_s_at RAM 129 A 208438J?M„s_at FC3R ¹

2G979SJ*M_at CD69 2 204436_PM_at PLEKB02 ¹

206337_..PM_...at CC .7 2 223303..PM._..at FERMT3 ¹

204959JPM__at MNDA 2! 9 14_ PM_ t F3LAC8 ¹

205821..PM.. at LRK1 2 2047S7_PiVf_at VSIG4 ¹

22?628_...PM_...at Gpxe 2 224733_...PM_..at CMTM3 ¹

201590_PM_x_ai A.NXA2 2 208018._.PM_...s._.at PICK ¹

210I39_.PM_...<;_.._.ai PMP22 2061!8_.PM_...at STAT4 ⁵

203065_...PM s .. at CAV1 236295JPM„s_at NLRC ¹

212873_.. PM. as HMHA1 ⁵ 219812_„?M_ai PVRIG ¹

203047 PM_. at STKio ¹ 217147_PM_s_at TRATI ¹

228532JFM__at C!oril62 ¹ 1552701.. PM_.a_. at CARD 16 ¹

22699 l_FM_ai NFATC2 ⁵ 213975J?M_s_at I..YZ ¹

207957_PM_s_at PRKCB ¹ 208690_„PM_„s__.at PDL1M1 ¹

20422Q_...PM_..at GMFG ¹ 226382. PM. at CAM 1D ³

205291_PM_at IL2R.B ¹ HI

21861 l_..FM_...st 1ER5 ¹ LOC283070

238025_..PM _..at ML L ¹ 220485_„PM_s_„al SIRPG ¹

20!743_...FM.al CD! 4 ¹ 22166 J^sM_...s_...st PYCARD ¹

2243S7_...PM _s _...at MS4A4A ¹ 201666_.. PM_.. at TIMP1 ¹

204174_.J¾iat AL0X5AP ^! 201012_„PM_„at ANXA1 ¹

204438_.. PM_. at MRC1 /// 201601._..PM_...x_...at !F!TM! ¹

MRC!Li 225799_..PM_. at LOC5 1471 ¹

223620__PM_at GPR34 ¹ Hi

20S419_„PM at GPR183 NCRNAOOl

2]0845_...PM_....s_..ai PLAUR 52

¹

! i IFTA ! IFTA J ! IFTA AM T 1

IFTA without fl&mroat oB samples

Banff 1 Banff 2 Banff 3

Geometric means 95.5968 113, 1527 137.26

Stds of geomcaiis 9.221 19 8.853218 ! 0.53772

B«rOT 1 0 0.31 187 0.07S62S

Banff 2 0,317187 0 0.261224

Banff 3 0.075625 0.261224 0

IFTA with AR samples

Banff 1 Banff 2 Banff 3

Geometric means 150.6856 209, 17 1 170.8608

Stds of geo oeans 13.51412 33.68485 10 23213

Banff 1 0 0.406914 0.297361

Banff 2 0.406914 0 0.662038

Banff 3 0.297361 0,662038 0

AO IFTA samples

Banff I Banff 2 Banff 3

Geometric means 121 ,029 152,2357 160.9271

Stds of geomeans 1 1.88798 21.60116 12,24757

Banff 1 0 0.212412 0.027471

B&rsff 2. 0.212412 0 0.728429

Banff 3 0.027471 0.728429 0 [θ¾358| Table 40: The genes from, the AR GCN3 (GCN3 - Metabolism; CC=0. 1 } and the geometric means.

Number Prol©s©t Geae Number

of edges of ed es

231790_. PM_. at DMGDH 12 2047G4JPM_s_at ALDOB 6

214069_. PM. at ACSM2A /// 12 223784 PM_at TMEM2? 6

ACSM2B /// 230022 J⁵M at CLEC18A 5 LOG 1002 1 HI

873 CLEC18B

209309_...PM_.jst AZGP1 Π ///

205682_...PM..X. ai APOM II CLEC18C

20i562_..PM..A. ^; SORB 1! 206527_...PM_...at ABAT 5 j

205380 J>M_at ΡΌ2 Ι 11 205666_. PM_. st FMOI 5 j

202740_PM_at ACY1 10 203434_PM_s_at MME 5 !

223820 PM si BP5 10 224411_...PM_... at PLA2G12B 5 j

2U682JP ji_at UGT2B28 9 i 208596_...PM_...s_..¾ UGT1A1 /// 5. j

209605 i%!._..at TST 8 1 UGT1A10

209977.PM_.at PLG 8 1 /// IJGT1A3

1552755_...PM _...at C9or½6 8 // UGT1A4

206024_PM_a† HPD 8 ///UGH AS

223?82_PM_s_at T! AG 8 f/f UGT3A6

207429_..FM_..at SLC22A2 8 /// UGT1A7

225272 _PM_at SAT2 7 1 /// UGT1A8

22i552_...FM_..at ABHD6 7 1 ///UGT1A9

209696_„.ΡΜ._.¾ FBP1 7 j 20522 l_PM_at HGD 5 ]

20392 J»M_at GSTAt 7 j 209980_...PM_...s_...a:i SHMT1 5 j

203722_...PM._..at ALD.H4A 1 7 2G6964JPM_at NAT8B 4 ;

219792_. _PM_ at AGMAT 7 21 298JP _at EC.HDC3 4 j

23048 IJPMjrt ACY3 7 2203iO_„PM_at TUBALS 4 j

204289_PM_at ALDH6A1 7 230602__.PM_._at ACMSD 4 j

207076_..,PM...s..at ASS1 7 j 231632...PM..at C19orfS9 4 j

206U9JPM_at BKMT 7 j 243669 _PM__.s_aL FRAP! 4 j

220534 PM a^; SLC22A7 6 21 973 PM a- DCXR 4 j

204347 PM...at A 3LI 6 20531 l_PM_at DDC 4 j

2I6696_...PM_..s._..at PRODH2 6 219543_...FM_...at PBLD 4 j

207298 PM at SLC17A3 4 i Probeset Gsm Nmb r j Profeeset Gea© Number

of ed es of ed es

21061 JfMje_at HYAL1 4 201425._..PM_...at ALDH2 ,ώ

202830_...PM_...s_..at SLC37A4 4 20404! PM at MAOB 2

219655_PM_at C7orfi0 3 21 G76J^>M_s_at PXMP2 2

202847..PM at PC 2 3 209368_PM_at EPHX2 2

2I8862JPM at ASB13 3 202025 JP_Vi_ _at ACAAl ¹

2i9113_„PM_x_„at HSD17B14 3 23620S_P _at ABCC6P1 ¹

20S364_...PM_..at ACOX2 3 219222. PM _at RB S ¹

202!39_..FM...at A 7A2 3 204608 PM..at ASL ¹

209978 J¾i s at LPA /// PLG 3 2336Q4_PM_at FLJ22703 ¹

234974_...PM_..a GAL 3 23iQ7QJPM_at IYD ¹

206030_..PM_...at ASPA 3 221879 P ..at CALML4 ¹

22!298_PM_„s_„at SLC22A8 3 20356G_PM_at GGH ¹

203381_PM _s _..at APOE 3 205074_. PM_...at SLC22A5 .!

201349._. PM_„ at SLC9A3R! 3 205355JP _at ACADSB ¹

208480 JP _s_at ABCC6/// 3 223482 PM at TMEMI20A ¹

LOG 100292 231021 PM at SLC6A19 ¹ 715 22 1 0_..PM_.at SLC22A11

220376 PM at LRRC1 3 2i 773_PM_at NOX4

205768 J^>M.jsjat SLC27A2 3 204934. J%r.. ...:« HPN ¹

2127G!_PM_at TLN2 2 210H JPM_at KCNJ15 1

2!9S73._..PM;._.ai COLEC11 f 239860_PM_„at LOG 100130

205892 PM..s_..at FAB PI 2 232

217874JM at SIJCLG1 229596_„PM_„ai AMDHD1 ¹

227560_PMjat SFXN2 2 229520 PM ¾t AQP11

20S67S_...PM_...at ΜΤΓΡ 2 20707 I_„PM__s_at AGO I ¹

239093_PM_at DHDPSL 2 2I8546_..PM ¾ ClorfllS ¹

202772JPMjrt HMGCL 2 236225_..PM_...at GGT6 ¹

21 962._PM_.ai ACE2 2 227753 JPM_„at ΤΜΕΜΪ39 ¹

1554668 JPMjLat^' FAM151A 2 223699_„PM_ai CNDPi ¹

209667_PM_at CES2 2 204167_„PM_at BID ¹

231496_PM_at FGAMR 2

-1 IFTA WTAJ 1FTA_A AR TX

Geometric- 677.2562 519,7935 415.2672 495,6224 91 1 ,8599 means

Stds of 232,8723 412,4268 315.3429 262.55 3 258,6733 geomeans

IFTA 0 0.27043 0.000389 0.000542 9.32EA17

IFTA J 0,27043 0 0,477839 0.861575 0.014999

IFTA AR 0,000389 0.477839 0 0,247084 1.82E-09

AR 0,000542 0.861573 0.247084 0 9,97E- 16

IX 932B-07 0,014999 1.82E-09 9.97E--16 0

IFTA without raf!arem&tion samples

Banff 1 Banff 2 Banff 3

Geometric means 766.9414 577,3346 583.6746

Stds of geomeatis 36,95227 35.36783 67,20855

Banff 1 0 0.009573 0.169593

Banff 2 0.009573 0 0.957013

Banff 3 0, 169593 0.957013 0

IFTA with AR. sam les

Banff 1 B jiif 2 Banff 3

Geometric means 433.8149 459,2535 337.4054

Stds of geomeans 48,55479 81 ,9396 9.649173

Banff 1 0 0.886888 0,276783

Banff 2 0.886888 0 0.453983

Banff 3 0,276783 0.453983 0

All IFTA samples

Banff 1 Banff 2 Banff 3

Geometric mesas 615.6403 542,8052 446.8583

Stds of geomeans 5 1 66213 56.30521 60, 10235

Banff ! 0 0.344188 0.04471 1

Banff 2 0.344188 0 0.2S597 I

Banff 3 0,04471 1 0.255971 0 [00359] Table 41 : The genes from Ihe AR+IFTA GCN3 (GCN3 - Metabolism; CO0.923) and the geometric means.

-1 Frobeset Geae amber Protest Number of ed es &f ed es

205306_...PM..x..ai KMC) 3 226649_..PM at PANK1 2

2G5675_P jit MTTP 3 2Gi035J> js...at HADE! 2

205942_PM_s_at ACSM3 3 205355JP _at ACADSPJ 2

205342 _PM_s_al SULT1C2 3 2349?4_...PM_...st GALM ¹

20571 PM_„s_at PAH 3 238518J _..x_..at GLYCTK ¹

2IS025_...PM_...s_..at FECI 3 209577_PM_at PCYT2 ¹

20966?_...PM_..ai CES2 2 208034 J»Mjs_at P 02 ¹

21 452_..PM,_.x_. at CYP4F2 2 232271.._,PM_..at H F4G ¹

203145 PM at SPAG5 2 2Q6952_...PM_...at G6PC ¹

209645JPM_s_at ALDHIBI 2 205757 JPMjit ENTPD5 ¹

230022 JP ^'jit CLEC18A /// 2 22756GJPM_at SFXN2 ¹

CLEC18B // 22O604.._.PM...x_„.at FTCD ¹ CLEC18C 2]9739_PM_afc RNF186

200903...FM_.s_...st AHCY "S 232378._.FM._.ai SLC5A9 ¹

223482 PM at TMEM120A Ί 239594J¾ .at LOCI 45837 ¹

220740_P _s_at SLC1 A6 2 206522..PM..al MGAM ¹

24i 14JPM_sjtt ACSM2A /// 2 2G6535__PM_at SLC2A2 ^¾

ACSM2B 228274JPM_at SDSL ¹

219054 PM.. at C5orf23 2 23? 30..PMju LOC389332 ¹

223268 J»M_at Ctiorf54 2 206094._. PM...X. at UGT1A6 ¹

244723_PM_at LOCI 001294 2 229432.P ._.si NAGS ¹

88 219789_FM_at NPR3 ¹

231790JPM_at DMCIDH 2 221305._. PM...S. at UGT1A8 ///

207095 J*M_at SLC10A2 2 IJGT1A9

221879 PM jit CALML4 2 207542_.. PM s at AQPi

20?;3!_...HM_..x_. GGTI 2. 2i06!9_..PM_...s_..ai HYAL1 ¹

231068_._PM.jtt SLC47A2 2 203559_..PM_..s_...at ABP! ³

203126_. PM_..at LMPA2 2 239860_...PM_...ai LOC1001302

¹

2G1348_PM_ai GPX3 2. 32

239093_...PM_...at DHDPSL 2 224411_....PM_.. at PLA2G12B ¹

2G3963_PM_at CA12 2 236205_..PM at ABCC6P1

-1 Pro eaei Nsmfeer Probesel Geae Nemtser of edges of edges

2n416J*M_x _at GGTLfJ! 1 201695_.. PM _.s_... at PNP 1

2249I 8_...PM„ _...st MGSTi 1 206226_PMjit HRG !

205673_...PM _s_at AS89 I

IFTA IFTA I IFIA. AE AR TX

Geometric means 541.0168 422,572 ! 334.1991 3933162 7283744

Sid of geo meas¾s 189,4673 333,98 256.23 207.9826 21 1.1669

IFTA 0 030 151 0,000545 0,000483 1 ,426306

HG^"A J 030415 0 0.45929! 0,79704 0,01 8027 iFTA_..AR 0,000545 0,459291 0 0.287712 3.19E319

AR 0.0004S3 0.79704 0387712 0 6.53EG 6

TX 1.42E-06 0.018027 3J 9E-09 6.53E-16 0

IFTA wsthout inflammation samples

Banff i Banff 2 Banff 3

Geometric means 614.2982 457.8575 470.607

Stds of gcomeafis 30,37391 28.04886 54.10916

Banff 1 0 0,008326 0379409

Banff 2 0.008326 0 0,89278

Banff 3 0, 179409 0 89278 0

IFTA with AR samples

Banff ! Banff 2 Banff 3

Geometri c meaa s 350.0065 3713268 267.4036

Stds of gcomeans 39.18393 66.99905 6,955809

Banff I 0 0,883762 0.24865

Banff 2 0.883762 0 0.43S327

Banff 3 0.24865 0.435327 0

All IFTA samples

Banff I Banff 2 Banff 3

Geometric means 495.0049 433.8036 357.7162

Stds of geomeaos 41 ,68285 45.62495 48.95457

[00360] Table 42: The genes from the iFTA GCN3 (GCN3 ·- Metabolism; CC=0.91) and the geometric means.

Frobesel Geae Nu ber Pro eset Ge»e Nember

f ©dg€S of edges

206065 J»M_s_a* DPY8 48 206963 JP jsjrt NATS Hi 23

22160S_....P _..S._.S† PIPOX 43 NAT8B

202I08_PM_at PEPD 38 223732.. PM_.&i SLC23A1 22

231623_P _at TMEM174 36 207076_. P at ASS1 22

220951.. PM j?_.j4 A1CF 36 204044JPM_at QP T 21

202847JPM_at PCK2 35 2I8844_.PM_..ai ACSF2 21

227695 PM at Gl.Y ILl 34 2Q7184_..PM_..at SLC6A13 2!

220753_.._.PM.._.s_....ai CR.YIJ 32 241 14JPM_s^j*t ACSM2A /// 20

220135 PM s_.st SLC7A9 31 ACSM2B

229229JPM_ai AGXT2 31 204289_PM_at ALDH6A1 19

21997^Q_PM_st GIPC2 30 22014SJPM_at ALDH8A1 19

21 792_PM_at AGMAT 29 227560_.._.PM_..ai SFXN2 18

206024_.PM.at HPD 28 22977?JPMj.t CXRN3 18

219655 J?M rt C7orf!0 27 209368_PM_at EPHX2 17

229916JPM_«l ENPP6 27 207S43_PMf_x_at CYB5A 17

2^Q5175J»M_sjat HK 26 205682 JPMjt_at APOM 16

219281.. PM.. at MSRA 26 205222_.P _...at EHHADH 16

23i?9G_PM_at DMGDH 26 230602JP _at ACMSD 16

206775 J>M_at CUBN 26 214389_...PM al SLC5AI2 15

2i9902_PM_at ΒΗΜΪ2 26 219298. PM_, at ECHDC3 15

2I 343J»M_s_ai SLC22A6 25 1552755 PM a C9orf66 14

227S^Q6JP _..ai SLCI6A9 25 206457..P .A..ai D!O! 1

206484 JPM_s_a* XPNPEP2 23 201135_PM„at ECHS1 14

209309_PM_at AZGP^'I 23 206527..PM at ABAT 14

206930__PMj>i GLYAT 23 20522 l_PM_at HGD 14

219954_.,PM_..A_„at GBA3 14 Gem ^"~Nomber Prefeeset €«»« dumber of edges of edges

207544JW_sjat ADH6 13 2065l5JPM_at CYP4P3 7

204867JPM__ai GCHFR 13 201349. jPMjtt SLC9A3R1 7

220554_„PM_..¾ SLC22A7 12 20270..PM at ACY1 7

22080 l_PM_s_at HA02 12 2057S0_PM_at BPHI, 7

231667JP * SLC39A5 11 219^Q76J?M_s__at PX.MP2 7

221298.._.PiVl._.s_...ffi SLC22A8 11 21851 l_P _s_at F PC) 7

206522_PM_at MGAM 11 204238_PM_s_at CfiorflOS 6

21 525_PM_at SLC47A1 11 205306 J»Mjt_at KMC) 6

209605 JPMjtt TST 1! 202772 PM si HMGCL 6

211682 PM x _...at UGT2B28 10 229534_. PM_at ACOT4 6

204934_PM_s_at HPN 10 209667_jPM_at CES2 6

205768_.. PM_s_at SLC27A2 10 205355...PM...at ACADSB 6

214033_P _at ABCC6 /// 10 219222J>M_at RBKS 6

ABCC6P2 205512_PM_s_at AiPM 6

2l91!3JPM xjrt HSD17B14 9 201G35J>M_s_at HADH 6

203335_PM_at PHYH 9 243669JPM_s_at PRAP1 5

22417 JPM_..s..at MIOX 9 205666JP at FMOl 5

206878_PM_at DAO 9 205983J> _at DPEP1 5

2;9739_. P j RMFI86 9 205311_...PM_. at DDC 5

20343 J»M_s_at ^' MME 9 2i3129J^»M_s_at GCSH /// 5

226767 _P _s_at FAHD! 9 LOCI 00329

2203?6..PM.at LRRC19 9 108

20379GJ*M_s_at HRSP12 9 212701JPM_at TLN2 4

229S96_PM__at AMDHDI 8 234974_PM_at GALM 4

231021_.. PMjR SI.C6A 19 8 225203 PM _...at PPP1R16A 4

2G4041_PM_at AOB 8 20502_PM_„s_„at HAGP! 4

223782_...PMj5_..at TiNAG 8 20756 PM at SLC13A2 4

219543_PM_at ?BLD 8 227055JPM_at ME1TL7B 4

205243..PM...at 8LC13A3 7 217874JPM_at SUCLG 4

2252/2..PM :H SAT2 7 20998 PM at SHMT1 4

20283O...P ..A.at SLC37A4 7 219962JPM_at ACE2 4

30- Namber of ed es

205052 PM_..at AUH 2

230 81_PM_„at ACY3

204844 PM at ENPEP 2

219176 PM si C2orf47 2

203924 JP ja GSTA1 ¹

203928_PM_„x_at MAPT ¹

205364.PM.._.ai ACOX2 ¹

212694_...PM s_...at PCCB

2I8952JPM__at PCSK!N ⁵

202178 PM.. at PR CZ ¹

23Q830JPM_at OS^'TBETA ¹

200961_PM_ t SEPHS2 ¹

203474 JP _at 1QGAP2 ¹

204294JPM_at AMI ¹

23851S.PM. .at GLYCTK ¹

236225._.PM_...at GGT6 ¹

2!0)!9_...PM_.. KCNJ15 ¹

20S942..PM...s...ar ACSM3 ¹

2088i3JPM_at GOTi ¹

22 H0..PM .3..: GPRS9A /// ⁵

GPR89B ///

GPR89C

202217..PM.._.at C21orB3 ¹

20928JP _at GHIT ¹

204S6S_..PM_...at ACOT13 ¹

2Q9833JPM_at CRADD ¹ I

218976_..PM_...at DNAJC12 ¹ !

1^'F!A IFfAJ 1FTA..A11 AM ΪΧ

Geometric means 637,1649 491,4)53 394.9956 473,4611 855.6262 IFTA IFTAJ IFTA .M AR TX

Stds of georaeans 214,0441 378.5614 285.1926 239.0971 235,774

!FTA 0 0.206688 0.000312 0,000649 6.070-07

IFTA j 0.266688 0 0,474756 0.887699 0.014122

11 A AR 0.000312 0.474756 0 0.212729 9,760- 10

AR 0.000649 0,887699 0,212729 0 S.72E- 16

TX 6.67E-07 0.014122 9.76E-1 Q 6.720. · ; 6 0

IFTA without inflammation sam l s

Banff 1 Banff 2 Banff 3

Geometric means 720.4S92 544.6156 549.2422

Sids of geomeans 33,80777 32.89827 59,36285

Banff 1 0 0.009199 0, 151452

Banff 2 0.009199 0 0.064745

Banff 3 0, 151452 0.964745 ^' 0^"

IFTA with AR s m s

Banff ! Banff 2 Banff 3

Geometric^' means 407.2553 441.3637 324.299

Stete ofgeoraearo 43,33275 75.02892 7.505331

Banff i 0 0,834214 0,29253

Banff 2 0.834214 0 0,432678

B nff 3 0,29253 0.432678 0

AO IFTA s les

Banff 1 Banff 2 Banff 3

Geometric me ns 579.28 513,601 424.2737

Stdb of geomeans 47,28804 51.57522 53.95699

Banff 1 0 0,351533 0.041675

Banff 2 0.351 533 0 0,243345

Banff 3 0.041675 0.24334S 0

Ssppkirsesl 6 Literature review of GCN2 aad GCN3

[00361] Table 43: Literature review of transcripts in gene co-expressed network AR-GCN2. Biological function derived by literature search using PubMed, The GeneCards Database of Human Genes. Note: ail the genes listed below for GCN2 are up regulated in patients with AR.

Used ia Gene j Snbgroup j LH Ref j smet!oft

Figure 4

X HLA-DRA j Antigen MHC Class II molecule j presentation

CD33 1 Adhesion / Putative adhesion molecule of

1 motility mvelornonocytic-dsrived cetis that mediates sialic-acid dependent binding to cells.

GABBRi 1 Neuronal Main inhibitory neurotransmitter in mammalian CNS x TLR1 Itntnunitv,, Toll-like receptor 1, innate

innate receptor, cooperates with TLR2 to mediate response to bacterial lipoproteins

X TLR7 immunit^y, Toll-like receptor 7, innate j

innate receptor, recognizes single- j

stranded RNA j

X HLA-A,- Antigen MHC Class I molecule | B,-Q-B,-J,- presentation

G

ST8SIA4 Neuronal Required for synthesis of I polysialic acid, a modulator of j the adhesive prapertkes of j neural cell adhesion molecule j (NCAM1).

SAMSN ! Adhesion / Negative regulation of B cell j motility activation, promotes RAC 1 - j

dependent ruffle formation and ] actin skeleton reorganization. 1

ATP2A3 j Other Transports calcium ions from J the cytosol into the j sarcoplasmic/endoplasmic j reticulum lumen. j Serves as a sorting protein tha cycles P-sclectin glycoprotein

!igand 1 (PSLG1) into endosomes with no impact on leukocytes reciuitment Probable role in regulatin _ ive and innate immune responses.

May affect movement of lipids in the cytoplasm

is to ροπο virus recep

(PVR), causing increased secretson of IL! O and decreased secretion of IL12B and suppresses T-cell activation by promoting the generation of mature iixum noregulatory

ic cells.

Phosphorylated by ZAP-70/Syk protein tyrosine kinase following activation of TCR signaling transduction pathway. Required for both TCR and FCGR3 signaling

I X LAT2 Immune j Involved in FCER1 (high signaling affinity immunoglobulin epsi!on receptor)~mediated signaling in mast cells, also involved in BCR and FCGR1 signaling in B and myeloid cells, respectively

I I TR1M21 Immune Mono biquilinates 1KBKB signaling (which negatively regulates NF~

J kappa B signaling)

NCKAPlf, Adhesion / Regulation of actio cytoskeieton

motility

J TBCIDIO Immune j Inhibits the Ras signaling j

C signaling pathway through its intrinsic Ras

GTPsse-activating protein

(GAP) activity. Negative feedback inhibitor of the calcineuf in signaling pathway, j Used Is j Geae j Subgroup j Lit Ref

Figare 4

1 GIMAP4 Lymphocyte May play a role in lymphocyte immunity apoptosis

x NCFL IB, Neutrophil Subunits of neutrophil NADPH j

!C oxidase ]

LYN B cell j Non-receptor tyrosine-protem | signaling i kinase that acts primarily as a j

negative regulator, required for j initiation of B cell response and j involved as an important ] mediator in several signaling | cascades j X HLA-F Antigen Non-classical heavy chain, j presentation located in ER and Golgi j

apparatus, exhibits few j polymorphisms. | X ZAP70 T cell This enzyme is phosphoryJated i signaling on tyrosine residues upon TCR j stimulation, and functions in the j initial step of TCR signaling j

X PSMB8 Antigen Fart of the proteasome j processing muSticatalytic proteinase j

complex j

WARS Other Aminoacyi-tRNA synthetases, j

catalyze the aminoacyiation of j tRNA by their cognate amino j acid. i

UBE2L6 Ubiquin&tion Catalyzes covalent attachment of i

ubiquitin for degradation 1 processes. 1 Jsed ia Gene 1 Subgr wp 1 Lit Ref \ F»action

I Figure 4

AIFI Immune Induced by interferon, negative signaling regulation of vascular smooth muscle ceils, aeiin binding protein that enhances ruffling and RAC activation, enhances actin-bundling activity of LCPi, plays a role in phagocytosis.

P2RY13 immune eel! G-protein coupled receptor that ;

signaling is a receptor for ADP and may | p!a a role in hematopoiesis and | immune system signaling. i

HM!-IAl Immunity, Minor histocompatibility antigen j other immunogenic peptide j X NKG7 CTL / NK Tissue Expressed activated NK cells j and C^'FLs (gamma-delta) j

X CST7 CTL / N Cysteine protease inhibitor j

found in human j fluids/secretions, may play a role j in immune regulation by j inhibition of target within the j hematopoetic system j

X EOMES 1 CTL / Tissue ^' ' Di fferentiation of CD8+ T eel is

X IL2RB CTL. / NK IL--2 receptor, important for T { eeli immunity, expressed by NK j and lymphocytes. Signals j through LCK pathway through j PLC-gamma as well Akt j pathway, j

!

I Used is Gose 1 Sub roup

I¾ssr® 4

X 1 LCK 1 f cell Associates with CD4 and CDS ! signaling co-receptors for TCR signaling,

1 interacts CD3D,

1 GPR18 I n.a. 1 Receptor for endogeneous lipid

! neurotransmitters, linked to microglial migration in the CNS through lipid metabolite anandamide

! ^x ΛΙΜ2 inflammasorne Interferon-inducible, part of inf!airimasome

I X CD27 T cell Co-stimulatory ofT FR family, signaling important in generation and maintenance of T cell immunity, regulates B cell activation, Found in memory T ceils and may represent a more 'resting' and plasticity population of memory cells. ⁷ Associated with

T regulator cells in chronic beryllium lung disease, ⁸. Links through TRAF2 and TRAF5 to activate NF-kB and c~Jun kinase signaling.

X RHOH T cell Critical regulator of thymocyte signaling development and TCR signaling by recruitment/activation of ZAP-70, LCK phosphorylates.

X CD69 T ceil Acquired during lymphocyte and signaling N activation, signal transmitting receptor 1 Used ia j Geae 1 Subgroup Lit Ref 1 Faction

1 Figure 4

1 x 1 CYTIP 1 Ceil Migration ] Binds eytohesuj-1 (CYTH!) which regulates adhesiveness of i the integrins for NK and T ceils

X SP.f ΐ . Ceil Migration Se lectin family, mediates adherence of lymphocytes to endothelial cells

! ^x PRKCB Cell Migration Protein kinase C (P C): B ceil activation, apoptosis, among other functions. Tec-kinases signal through leading to cell adhesion and migration.

1 X ARHGAP2 Cell Migration Negative regulation of Rho

5 GTPases, converting to inactive j

GDP-bound state j

X A HGAP9 Cell Migration Negative regulation of Rho j

GTPases, converting to inactive j

GDP-bound state, regulates 1 adhesion of hematopoesiic cells 1

X BIN 2 Ceil Migration Promotes cell motility and j

migration j

X LCP2 T cell A.k.a. SLP-76, a substrate of j signaling ZAP-70 and hence plays a role j in T cell receptor signaling as a j scaffold protein, Also found to i be important in mtegrin j activation, signaling and T cell j adhesion. ¹¹ LCK f phosphoryiates. j

X 1 D0C 2 ] Cell Migration Cytoskefeta! rearrangements j required for lymphocyte j migration j

signaling j Used ia j Gene Subgroup j Lit Kef 1 F¾»cti©8

j Figure 4

X ] CD2 T cell 1 Costimulator arsd cell adhesion signaling j molecule on N and T cells,

j LCK (above) associates directly j with cytoplasmic tail of CD2, leading to activation of LCK, IL2 increases activity.

X j CD48 j Ligaad for CD2 (above), signaling probably involved in T cell activation

1 x A NA Transcription factor that immunity specifically activates expression of CD40 receptor and its ligand

CD40L

X FERMT3 Adhesion / Involved in tegrin activation, motility role in cell adhesion and migration

X MY01 F Adhesion / May e involved in cell motility adhesion and motility

SASH3 Lymphocyte May be signaling adaptor immunity protein in lymphocytes.

X PARVG Adhesion / Regulation of cell adhesion and motility cytoskeieton organization

X A HGAF3 Adhesio / Negative regulation of ho j

0 motility GTPases, converting to inactive j

GDP-bound state |

X COR01 A Adhesion / Aetin filament binding. { motility component of the cytoskeieton j

of highly motile cells j

X ITGAL Adhesion / ; LFA-1 component involved in ) motility ί CTL-mediated killing, leukocyte ] adhesion, migration, among j others. j Used JB 1 Gene Subgroup 1 Lit Ref Fonctiott

Figure 4

? DEF6 Lymphocyte Role in activation of Rho

immunity GTPases, Highly expressed in B

and T ceils

1 X RUNX3 1 T cell Tissue ^! LC phosphorylates.

signaling

I ^? ST 10 1 T ceil Negatively regulates IL-2 regulation expression in T cells

IAA1949 n.a. n.a.

1 ^x ILIGRA T cell Immunosuppressive signal of regulation IL-10, inhibits synthesis of proinflammatory cytokines !

MPEG! Macrophage Macrophage-expressed gene ! j

protein j

Ai_A.. Ϊ 1 n.a, Membrane-hound adenylate j

cyclase, catalyzes cyclic AMP j production. j

RASSF2 n.a. KRAS-speeific- effector protein, j

May be tumor suppressor, j

GLIP I Macrophage Increased expression associated 1 with macrophage differentiation. 1

! ^x ITGAM Adhesion / Integrin, membrane protein j

mot lity and important in the adhesion of j Phagocytosis monocytes, macrophages, and j

granulocytes as well as j phagocytosis of complement j coated particles. j

X IF! 16 Inflarnmasorne Binds double-stranded .O A.

IFN~Y inducible, Likely induces j nuclear CASP 1 -activating j inflammasome j

1 1\ΛΑί.<ί.£, Immunity, E3 ubiquitin ligase, Interferon- j

other induced antiviral protein, j Used Gene Sub roup Lit Ref Wmmctim

Fig re 4

X NLRC5 Antigen NOD-iike receptor. Regulates presentation interferon activity. Drives MHCI gene expression in lymphocytes,

X BTN3A 1 Antigen Bulyrophilm (BTN), (MHC)- presentation assotiated genes that encode type I membrane proteins with 2 extracellular immunoglobulin (Ig) domains and an intracellular (P YSPRY) domains. New evidence shows that these molec des are important in presentation of phosphorylated antigens and activation of γδ T

cells. ^:5

X BT 3A2- Antigen Butyrophilin (BIN), (MHC)-

BTN3A3 presentation associated genes that encode type I membrane proteins with 2 extracellular immunoglobulin (Ig) domains and an intracellular (PRYSPRY) domains. New evidence shows that these molecules are Important in presentation of phosphorylated antigens and activation of γδ T ceils, ¹⁵

X HLA- Antigen MHC class II

DPB1 presentation

X HLA- Antigen MHC class II

DRB i presentation Used in Gene j Sabgr ap j Lit Ref 1 Foscfion

Figure 4

X HLA- j Antigen

D B ! - j presentation

H LA-

D .64

X ] HL-A- j Antigen MHC class 1! ]

DPA1 j presentation

X j HLA- 1 Antigen 1—— ¹ MHC class II |

DMA j presentation

X HLA- Antigen MHC class Π |

DQB 1 presentation

X HLA- Ants gen MHC class II |

DQB1 presentation

MARCH 3 Antigen Mediates ubiquitination of MHC | presentation Class II proteins, promotes their j subsequent endocytosis and ! sorting to lysosomes via | multivesicular bodies, i

X RAC2 Tissue ^' Ras supcriamily of small j

Migration guanosine triphosphate (GTP)- j metabolizing proteins, localizes j to plasma membrane, involved j in cellular processes such as j secretion, phagocytosis, j migration. Also involved in j generation of reactive oxidative j species (ROS), j

EVI2B Immunity, Eeoiropic viral integration site j

other 2B

X j CDS 3 T ceil Tissue Transmembrane 4 suuerfami!y. j signaling 12.53, 16,17 Contributes transduction of j

CD2-generaied signals in T cells j and N cells. j 4?- Used Is Gene Sabgroap Lit Ref Function

Figure 4

LAPTM5 Other Tissue ^¾ Lysosome transmembrane

receptor, lysosomes are organelles that contain acid hydrolase enzymes and break down waste material, debris, engulfed bacteria/viruses. x 1RF8 1^" ceil interferon regulatory factor

immunity (IRF) family transcription factor.

Regulates genes stimulated by type I IFNs and involved In CDS dendritic cell differentiation. Specifically, 1RF8 controls maturation of CD8a+ DCs and pDCs, which are responsible for induction of CDS T eel! responses to viruses by the unique ability to cross-present exogenous Ag through the MHC Class~I pathway. pDCs produce IFN-a/b, responsible to anti -viral response, with promotion of Thl differentiation.

X AMIGA 1 T cell Transmembrane adhesion and immunity signaling molecule, activates gamma-delta T cells,

X AO AH immunity, Removes tatty aeyle chains from innate bacterial lipopoiysaccharides.

C ! orf38 Macrophage May control, macrophage j

inflammatory response j

CSF2RB Immunity, Common subunit for GM-CSF, j other 1L-3, and iL-5 receptors |

X PLE 1 ra.a. n.a. j Used m _j Gesse Subgrou j Lit Ref Faction

Figure 4

1 HC EOS Non-receptor tyrosi ne protein kinase, multiple functions including connection of Fc receptor to activation of respiratory burst or generation of

ROS.

X CYBB EOS Superoxide generating enzyme whieh forms ROS

MNDA Macrophage Granulocyte/monocyte cell- specific response to interferon. x T FSF13 T cell B cell anti-apoptotic and growth e immunity (to factor 1

B cell)

X CASPl toflarnmasome T ssue Caspase- 1 : cleaves innate j cytokines IL~i and IL-18 to j mature form |

CTSS Antigen Lysosomal enzyme expressed by presentation antigen presenting cells (A PCs)

LYZ Phagocytosis Azurophilic neutrophil granules

X PTPEC Tissue ^'' CD45. Abundantly found on leukocytes, it has many debated functions, but is believed to have a positive effect on TCR signaling and T ceil migration by activating Lck j (dephosphorylates). i Used is i Geae 1 Subgr u i Lit Ref 1 IfcuctifM

Figure 4

I X 1 JTGB2 i IFN-y 1 Tissue ¹² 1 Integral receptor for KAMI -4, j Important for neutrophil adhesion and migration into 1 inflamed tissue. Defect in the j j gene causes leukocyte adhesion j deficiency type I (LADl ). IFN-γ | inducible.

Cell .migr t on_s Cross links and binds actin. i T cell RNA knockdown of LCP1 | signaling blocked migration of leukemia i ceils. ^{5 S} T cell activation role j through cosiimuiation through j TCR/CD3 and CD2 or CD28.

IDOI IFN-y Tryptophan oatabolism j

CXCL! ! IFN-y CXCR3 !igand, chemokine Thl | and NK ceils, ίΡΝ-γ inducible " ιΓΎ<^Χ 3 ft l N-γ CXCR3 iigand, chemokine Th 1 and NK cells, IFN-γ inducible X CXCL9 CXCR3 iigand, chemokine Thl and NK ceils, iF -γ inducible

GBP! IFN~y Anti-viral activity. iFN-γ i inducible. j

X !RFI IFN-y fa erferon~i¾gulai»ry factor |

GBP5 lafSammasorae Rheostat for NLRP3 |

, IFN-y inflammasome activation. IFN-y j inducible. j

GBP2 1 IFN-Y IFN-Y inducible. j

LIL B2 1 Antigen Binds MHC I on APCs and presentation inhibits immune response i

Antigen 1 Binds MHC I on APCs and j presentation j inhibits immune response j Used in Gese Subgroup Lit Ref FaaciioH

P!gwe 4

LILRB 1 Antigen Binds MHC I on APCs and presentation inhibits immune response

LAIR1 Immun , other Inhibitory receptor on NK, T and B cells

X TLR8 Immunity, Toll-like receptor 8, innate innate receptor for RNA viruses.

Modulates suppressive activity of regulator}' T cells

X PCGR! B Phagocytosis Receptor for Fc portion ofigG,

low affinity

X FCGR!A- Phagocytosis Receptor for Fc portion oflgG, FCGRi C high affinity (FCG 1 A)

X PSME2 Antigen Proteosome subunit, processing presentation, of class Ϊ MHC peptides.

IFN-y

X PS B 10 Antigen Processing for class I MHC presentation, peptides, induced by IFN-y IFN-y

FAM26F !FN-Y Tissue Voltage-gated ion channel

X TAP I IFN-Y MHC class I peptide processing.

IFN-y inducible

X PSMB9 IFN-y Tissue ^{: 5} Processing for class I MHC peptides, induced by IFN-y

X HCP5 n.a

ISG2 IFN-y Innate antiviral activity. IFN-y inducible.

X PLAC8 IFN-y Expressed In leukocytes, IFN-y inducible.

CD52 Immune, other A.k.a. CAMPATH-t antigen, present on surface of mature lymphocytes

unknown. [

[00362] Table 44: Literature review of transcript in gene co-expressed network AR-GC 3, Biological function was determined by search of PubMed or The GsrseCards Database of Human Genes, which ultimately determined this GCN to reflect rsormal cellular function,

Gene Category 1 Category 2 Faatctioji

symbol

SLC6A1 Membrane Transmembrane protein actively

transport transports amino acids across apical protein membrane in kidney

GALM Metabolic Enzyme that catalyzes the epimerization enzyme of hexose sugars such as glucose and galactose. Involved in galactose

metabolism

BPHL Detoxification Hydrolyiic enzyme thai may play role in enzyme detoxification process,

SF 2 Membrane Cation transmembrane transporter, likely transport transports iron

protein

H GCb Metabolic Mitochondrial Mitochondrial enzyme that plays key enzyme role in leucine degradation and ketone body Formation

SAT2 Metabolic Enzyme that maintains a key metabolic enzyme glutamine/glutamate balance likely

involved in neurotransmission

SUCLG1 Metabolic Energy Enzyme that catalyzes ligation of

enzyme production succinate and CoA to form succinyi-CoA

HSDI 7B 1 Detoxification Dehydrogenase involved in metabolism 4 enzyme of steroids and other substrates such as fatty acids, prostaglandins and

xenobiotics

AMDHD1. ma. n.a.

DAO Detoxification Peroxisomal Peroxisomal enzyme D-arnino acid

enzyme oxidase that may act as a detoxifying agent to remove D-arnino acids

-357- Gam Category 1 Category 2 F nciioii

j s m

I LPA-PLG Coagulation LPA: Serine proteinase that mhibits the

] enzymes j activity of tissue-type plasminogen j activator 1 , Elevated levels associated , with atherosclerosis. PLG: Plasmin ! dissolves the fibrin of blood clots and j acts as proteolytic factor, 1

ALDH A1 Metabolic Aldehyde dehydrogenase protein family j

enzyme with a role in valine and pyrimidine j catabol sm, !

QPRT Metaboli Key enzyme in caiabolism of quinoiinic 1 enzyme acid. j

SLC22A6 em rane Sodium-dependent transport involved in j

transport the renal elimination of endogeneous and j protein exogeneoits organic anions. j

SLC22A8 Membrane Plays important role in | transport excretion/detoxification of endogeneous j protein and exogenous organic anions, [

A.DH6 Metabolic Alcohol dehydrogenase, metabolizes 1 enzyme ethanol, retinol, hydroxysteroids and | lipid peroxidation products, 1 r .5..Λ.Ϊ Coagulation j Plasmin dissolves the fibrin of blood j enzymes clots and acts as proteolytic f ctor, j

HA02 Metabolic j 2-hydroxyacid oxidase, likely in i enzyme j peroxisome, catalyzes oxidation ofL- j alpha-hydroxy acids j

MME j Detoxification j Membrane metalio-endopeptidase in J ens me j proximal tubule of kidney, inactivates I hormones including glucagon, j enkephalins, substance P_s neurotensin, j oxytocin and bradykinin. j Gene 1 Category 1 { Category 2 I Function

symbol

AZGPl 1 Metabolic | j Stimulates lipid degradation in

enzyme j j adipocytes and causes the extensive fat

I losses associated with advanced cancers,

XP PEP2 Metabolic | Collagen j Hydrolase specific for collagen

enzyme j metabolism I degradation products, neuropeptides, vasoactive peptides and cytokines.

A R7A3 Metabolic j Aldo-keto reductase, involved in

enzyme j detoxification of aldehydes and ketones SLC6A13 Membrane j Sodium-dependent GABA and taurine transport j transporter, regulates GABA termination protein j through GABA uptake

ΜΪΟΧ Metabolic Aldo-keto reductase activity

enzyme

AGMAT Metabolic Hydrolase involved in urea cycle and enzyme metabolism of amino groups.

GSTAI Detoxification Glutathione S~transferase, key role in

enzyme detoxification of electrophilic j compounds, including carcinogens, j drugs, toxins, etc. j

ACY 1 Metabolic Zinc binding enzyme; likely functions in 1 enzyme eataboiisffl and salvage of acylated j amino acids. j

NAT8B Metabolic Highly similar to NATS, may be j

enzyme pseudogene. NATS is kidney and liver j protein, N-acetyliransferase with j homology to bacterial acetyltransferases j

ΠΪ ^'Ϊ i Metabolic

ϊ enzyme providing plasma T3 by deiodlnation by j

T4 in peripheral tissues such as liver and j kidney j

ACSF2 ! Metabolic Energy j Acyi-CoA synthase that catalyzes the j enzyme production j initial reaction in tatty acid metabolism, j Geae Category 1 Ca egory 2 FonctioB

s mbol

HPD Metabolic Enzyme with role in tyrosine cataboHsm, enz me

GIPC2 n.a. n.a.

A!CF mRNA Compo ent of apo!ipoprotein B mRNA editing editing enzyme

APOM Transport Apolipoprotem and member of Hpocaiin

protein protein family, likely involved in lipid transport

GLYATL1 Metabolic Aeyitransf rase which transfers an acyl enzyme group to the N-ter inus of glutamine

SLC7A9 Mem rane Amino acid transporter invol ved in transport sodium-independent transport of protein cysteine, function in cysteine resorption in kidney and deficit causes cystinuria,

GLYAT Detoxification G!ycsne-N-acyltransferase protein, enzyme important in detox fication of

endogenous and xenobiotic aeyl-CoA's

PIPOX Metabolic Oxidase enzymes that metabolizes enz me sareosme, L-pipecoHc acid and L-proime

CUBN .812 receptor Receptor for IF-Vifanu» B 12 complexes

SLC5A12 Membrane Sodium-coupled lactate transporter transport involved kidney resorption of lactate. protein

AG XT 2 Metabolic Aminotransferase that catalyzes enzym conversion of giyoxy!ate to glycine.

DPYS Metabolic Enzyme involved in py imidine enzyme eatabo!ism

FRAP! rs.a. May play important role in maintaining normal growth homeostasis in epithelial cells. 1 Gene 1 Category 1 1 Category 2 1 Function i symb l

1 TL 2^" Cytoske!etai Cytoskeletal protein that plays a

protein significant role in the assembly of actin

1 filame ts

MAPT Cytoskeletal Promotes microtubule assembly and

protein stability, may play a role in neuronal polarity

S ACAA ! Metabolic Acetyl-CoA acyltrsnsferase, enzyme enzyme production operative in beta-oxidation system of

peroxisomes j

1 FAH^'D! Metabolic Hydrolase enzyme j

enzyme

1 Ecesi Metabolic Energy Mitochondrial fatty acid beta-oxidation [

enzyme production pathway j

1 CRYL! Metabolic Glucose Enzyme in the urinate cycle, or j enzyme homeostasis alternative glucose metabolic pathway j

TST Detoxification Enzyme may play roe in cyanide f enzyme detoxification, formation of iron-sulfur proteins and modification of sulfur- j containing enzymes. i

HAGH Metabolic Thiolesterase enzyme catalyzes S-D- I enzyme lactoyi-glutathione to glutathione and D- |

lactate j

GLYCT Metabolic Enzyme that catalyzes phosphorylation 1 enzyme of R~giycerate in serine degradation and j fructose metabolism. j

PXMP2 Peroxisomal Peroxisomal membrane protein |

OSTBETA Membrane Membrane protein that transports estrone j

transport sulfate, taurocholate, digoxin and j protein prostaglandin E2. j 1 Geue 1 Category 1 j Category 2 1 FiSTC M S I symbol

1 AOil Metabolic 1 Enzyme in the aci-reduotone

enzyme dioxygenase family that catalyzes

KMTB from DHK-MTPene. Also down- regulates cell migration mediated by

MMP14.

1 ACADSB 1 Metabolic Acyl-CoA dehydrogenase family of j enzyme ! production enzymes involved in metabolism of fatty j acids and branched chain amino acids, j

SLC12A6 Membrane ~€i eotransporter integral membrane j transport protein activated by ceil swelling [ protein

CYP2B6- Detoxification P450 monooxygenase that caiaholizes j

CYP2B7P enz me reactions involved in drug metabolism |

I:

j ABCC2 Membrane ATP-binding cassette (ABC) j transport cotransporter that mediates hepatobiliar j protein excretion of numerous organic anions, j

1 SUSD2 Scavenger Scavenger receptor activity j receptor

1 CHDH Metabolic Mitochondrial Choline dehydrogenase that localizes to j enzyme the mitochondrion, j j ABHD6 Metabolic. Serine hydrolase enzyme that catalyzes | enzyme the hydrolysis of 2-arachidonylglycerol 1 1 PABP1 Transport Fatty acid binding protein involved in j protein fatty acid uptake, transport and j metabolism, |

SLC28A 1 Membrane Rapidly transports Caiicura ions during j transport excitation-contraction coupling. j protein

SLC22A? ' Membrane j Sodium-independent transport and j

transport excretion of organic anions. | protein j

Gene Category 1 Categ ry 2 Function

symbol

SLC9A3 Membrane Sod mm hydrogen exchan ger regulatory 1 transport cofactor involved in phosphate protein reabs rptson in the renal proximal tubules.

C YM Metabolic Crystallin family, enzyme that catalyzes enzyme the reduction of imine bonds in brain substrates, also binds thyroid hormone.

TBC 1 DI 3 n.a. GTPase-aclivatmg protein

COK S Cell adhesion Cadherin superfami!y, acts as Ca- protein dependent cell adhesion protein

SLC25A1 Membrane Mitochondria membrane protein 0 transport translocates small metabolites as protein substrates for Krebs cycle.

C6orf!08 Meisboiic Enzyme involved in pyrimidme base enzyme alteration

C^'T^'X J n.a. Cortexm 3, unknown fiinction

DMGDH Metabolic Mitochondrial Enzyme in the mitochondrial matrix enzyme involved in the catabolism of choline.

SORD Metabolic Sorbitol dehydrogenase part of the enzyme sorbitol pathway.

MGAM Metabolic A brush border membrane enzyme that enzyme plays a role in the digestion of starch.

AK3I, 1 Metabolic Mitochondrial Mitochondrial enzyme involved in enzyme nucleotide catabolism.

MTTP Transport Heterodimeric microsomal triglyceride protein transfer protein

SHMT1 Metabolic Enzyme that eataboHzes the conversion enzyme of serine to glycine

DDC Metabolic Enzyme involved in conversion of L- enzyme dopa to dopamine

PBLD n.a. n.a.

levels of iron inside ceils. esse Category 1 Category 2 Function

s mbol

UGT2B28 Detoxification Conjugation and subsequent elimination

enzyme of potentially toxic xenobiotics and endogeneous compounds,

GPX3 Detoxification Glutathione peroxidase family functions

en yme in the detoxification of hydrogen

peroxide,

ABAT Metabolic Aminotransferase responsible for

enz me catabolism of G ABA

DHT D1 Metabolic Mitochondrial Mitochondrial dehydrogenase involved

enzyme in the degradation of several amino acids.

BH T2 Metabolic Enzynie involved in homocysteine enzyme metabolism.

SLC I 6A9 Membrane Catalyzes rapid transport across the

transport plasma membrane of many protein monocarboxy iates .

NOX4 Detoxification Enzyme in non-phagooytie cells that

enzyme reduces ROS,

FMO! Detoxification NADPH-dependent flavoenzyrnes that

enzyme catalyzes the oxidation of drugs, pesticides and xenobiotics.

AKR7A2 Detoxification Enzyme in the aldo keto redtictase

enzyme superfamily involved in the

detoxification of the aldehydes and ketones,

DHDPSL " Metabolic Catalyzes the final step in the metabolic

erszyrne pathway of hydroxyproHne.

CLRN3 n,a. 1 ^'ran srn embrane prote in

FTCD Metabolic Foiate-dependent enzyme that displays

enzyme both transferase and deaminase activity, involved in histidine degradation pathway

1 G Category 1 ] Category 2 j Fraction 1 symbol

1 ACOT4 Metabolic j Energy Acyl-CoA thioesterases, group of

enzyme 1 production enzymes that catalyze the hydrolysis of

acy!-CoAs to the free f tty acid and coenzyme A.

1 ECHDC3 Metabolic ! Energy Enoyi Co A hydralase

enzyme production

j HYAL1 Detoxification Lysosomal hyaluronidase, intrace!lularly j

enzyme degrade hyaluronan, which are thought to be involved in eel! proliferation, j migration and differentiation j HGD Metabolic Enzyme in liver and kidney that brakes

enzyme down phenylalanine and tyrosine.

FNPO Metabolic Enzyme that catalyzes rate-limiting step enzyme in synthesis of vitamin B6

RB S Metabolic Enzyme involved in ribose metabolism.

enzyme

ASL Detoxification Enzyme that cstahoHzes a key step in the 1 enzyme liver detoxifying ammonia via the urea j

cycle. j j I.DHD Metabolic Belongs to the D-isomet specific 2- f

enzyme hydroxyacid dehydrogenase family i

1 CYP2B6 Detoxification Cytochrome P450 family, involved in j enzyme NADPH-dependent electron irasnsport I pathway, oxidizes compounds including j steroids, fatty acids and xenobioties. j

C1 1orf54 Metabolic Likely enzyme that exhibits ester j

enzyme hydrolase activity on the substrate p- j nitrophenyS acetate j

1 Gene 1 Category 1 I Category 2

1 symbo

1 E PEP Metabolic Glutarainyl aminopeptidase, appears to

enzyme have a role in the catabolic pathway of the renin-angiotensin system.

GGT6 Detoxification Gam a-glutamyltrarssf rase family, a enzyme membrane-bound extracellular enzyme that cleaves gamma-glutamvl peptide | bonds on glutathione and other peptides j and transfers the gamma-gSutamyl moiety to acceptors. Also key to

glutathione homeostasis,

SULTIC2 Detoxification Sulfotransferase enz mes catalyze the enzyme sulfate conjugation of many hormones, neurotransmitters, drugs and xenobiotic compounds.

AQP11 Membrane Aquaporin^ facilitates the transport of transport water and small neutral solutes across protein cell membranes.

P P Metabolic An enzyme that reversibly catalyzes the

enzyme phosphorolysis of purine nucleosides.

PNP deficiency linked to defective T-cell j and B-cell immunity. j

Ssspplesnest 7 Geses Associated wills Graft Loss im IFTA Samples

[ Θ363] Table 45; A set of 224 differentially expressed genes distinguish two groups of IFTA without inflammation biopsies with higher vs. lower risk of graft loss

Probe set Symbol FDR Probe set Symbol FDR

2Q9277_. PM_..at TFP12 0.01041328 206336JPM_at CXCL6 0.01 101042

205579_PM_ai HR.H I. 0.01076453 220139J?M_at DNMT3L 0.01103505

226 /3? at SLC25A42 0.01078972 ^" 1552510_.. PMjst SLC34A3 0,01 157097

21S005_PM_at NECAB2 0.01097354 ^' 202018_...PM_...s_...at LTF 0.01178284

205465 JPM..X at HS3ST1 0.0110063 2042!3_..PM_.. at PIGR 0.01182078

204619_„PM_S t VCAN 0.01 10064? 217767 PM_.. at C3 0.0 182755 Probe set Symbol FDR Probe set Symbol FDR

228080_PM_at LAYN 0.0 Π 96455 212012_.. PM_^ai PXDN 0.02052476

225520_PM_at MTHFD!L 0,0! 198816 2Q4502_,.PM...at SAMHOl 0.02054252

201666 PM_„ at I^'IMP! 0.0120421 212875_„PM_s_at C2CD2 0.02066867

204259_PM__at MMP? 0.0122005 205673_...PMj^ai ASB9 0.02084366

202659_PM_at PSMB!O 0.01256881 209546_...FM...s.ai APOL1 0.02110613

208429JPM__x__at HNF4A 0.0127924 201798_..PM_..s_...at MYOF 0,02284436

216699. PM..s. at LFCl 0,01309713 231146_PM_„al FAM24B 0,02285112

2i7755_PM_at H 1 0.01420742 218995._..P _...s_...at EDNl 0.02393105

218182_PM_8_at CI..D ! 0,0145208 203933_...PM._..at AB11FIP 0.02530861

222925 ..a 1X10C2 0.01484573 3

20271 PM s at TES 0.01604335 210020J¾l x_at CALML3 0.02540332

263 .JA1A.SI RARRES1 0,01642582 21«950J* _s_at FCGR1A 0.02544774

2015MJPM x_at KRT18 0.01646857 ^' //

2^Q8335JPM__5_at DARC 0.01647088^' FCGR!C

224530JPM__s_„al KCNIP4 0.01660483 2I 576_PM_at MAP7D3 0.02567634

201090_„PM.._.x_...at TUBA IB 0.01 75168^' 205892_..PM_...s_...at FABPl 0.02588357

201590J _x a* A XA2 0.01705116 203928_..PM_...x_.ai MAPT 0,02620808

202238_PM_„s_at NNMT 0.01733333 23I021_PM_at SLC6A1 0.02624387

22081 _PMjtf FRMD1 0.01751229 20 n8_PM_...s_...at TUBA 1 A 0.0262551

22?628_..PM_...at GPX8 0.01919384 2211 3_„PM_s_,at MLXIPL 0,02644151

2G8816_FM_x_at ANXA2P2 0.01929005 205843_..PM_...x_...at CHAT 0.02676932

224937 PM_...at PTGFRN 0.01945983 2012 1_.. PM_s_at TOP2A 0.0269554

201063_PM_at RCN1 0.01959306 229250_PM_ai TPCN2 0,02717893

209040_...PM_...s_.ai PSMB8 0.0196438 202748_„PM_at GBP2 0.02737269

227742 PM at CLJ.C6 0.01967814 2G1762_...PM_...s_..at PSME2 0,02839286

226936_PM_at CENPW 0.01979722 209197_„PM_at SYT11 0.0286816

2Q3252_PM_at CDK2AP2 0,01985649 20603G_PM_at ASPA 0.02881968

20235?_PM_sj*t CFB 0,01993667 201 49.PM.at UBE2E6 0.02893387

208189_. PM _s _...at MY07A 0.01994526 205820JPM_s_at APOC3 0.028951 7

2Q2206JPM_at ARL4C 0.02003068 224574JPM et C17orf49 0.02916867

218392_. PM_...x_...at SFXN! 0.02005911 21SS46_..PM_^.ai C lorn IS 0.02935301

202G25.._,PM..x_at ACAAl 0,0200796 212836_...PM_...ai POLD3 0.02939347 Probe set ymb l FDR Prot.se set Symbel FDR

208791_.._.PM_...at CLU 0.02955118 2067S4_„PM_s_at CYP2B6 / / 0,03508888

2Qi426„FM_s_st VIM; 0.0296053 CYP2B7P1

2i 8009_PM__.s__.at PRC 1 0.02963615 221868JPM_ a_ ΡΑΪΡ2Β 0.03527132

230475_.. PM_at C15orf59 0,0298547! 207076 J>M_s_at ASS1 0,0363832

200660 m at S100A11. 0.03058333 235257_PM_at ODF3B 0.03738274

204130_P _at HSD1 1 B2 0.03217922 2 r>203_...?M . .j*t IFITM3 0.03764268

2094O9_PM_at ORB 10 0.03224073 203234JPM_at UPP! 0.03782045

202376 J^>Mj„ SERPINA3 0.03273629 205978_...PM_...at KL 0.03794144

226875_._ P jst DOCK! ! 0,03282358 218322_„ΡΜ_ί_¾ίί ACSL5 0.03795268

202949_...PM_...s_...aC FHL2 0.03294338 20919iJPM_at TUBB6 0.03800738

225272 J^>M_at SAT2 0.03301097 20i l36._PM...at PLP2 0.03808292

204470_P _„at CXCL! 0.03311059 212531._. PM_^ai LCN2 0.03809429

, 225681_PM _at CTHRCl 0,03396068 219! 13_P _x_af HSD17B14 0.0381 1922

^: 2Ι0139 PM_s_at PMP22 0,03396852 207320_PM_.. _at STAU1 0,03819356

1553728_PM_at LRRC43 0.03402565 213293_PM_s_at TR1M22 0.03828175

206755_PM_at CYP2B6 0,03419593 223652_PM_ai AS3MT 0,03830178

21 722_„P _s _at GDPD3 0.0342 118 204304_..PM_...s_...at PROM1 0.03838748

202284_PM_s_at CDKN1A 0.03431007 21 1597_PM_s_„at HOPX 0.03856612

210Q85_PM_s__.at ANXA9 0.03443499 224839_PMjs__.at GPT2 0.0386277

206896JPMjnrt G G7 0,03443654 ^' 211682_..PM . at UGT2B28 0,03871536

20077QJPM. _s_at LAMC! 0.0344713 213874_PM_at SERPINA4 0.03929685

220S10...PM...at RHI3G 0.03455847 201506_PM_at TGFB! 0.03938996

226597_PM_at REEP6 0.03456675 234016J⁵Mj¾ LOC90499 0.03941842

217733_FM_s_at ^' TMSB 10 0.03464123 212311_FM_at SEL1L3 0.03946288

220357_P _s_at SG 2 0.03464389 204972_...PM_...at OAS2 0.03963592

222020_PM_s_at TM 0.03475996 AFFX- STAT! 0,03966685

225S70J>M_at SLC41A1 0.03477532 HUMISGF3A/

202307_„PM_sjK TAP! 0,03487489 M97935_5_at

2065 5_PM_a CYP4F3 0.03487732 200S98J¾t_.sjs HSP90B1 0.03981942

209154. PM. at TAXI BPS 0.03488305 206840_._ PM_„at AFM 0.03985783

204147_PM_s_„at TFDP1 0.03488905 205355_PM__at ACADS.B 0.03987848

23038 !_PM_at C!orfI 86 0.03991242 Probe set Symbol FDR Probe set Symbol FOR

2Q3438__PM_at STC2 0,03992955 202430J>M_„A..at Pi, SCR i 0.04454176

2I0514_PM_x_at HLA-G 0,03993972 206242_...PM_...at 1^"M4SF5 0,04465757

2!3202_...PM_.j¾t SETD1A 0.03994228 2!7738_P _at NAMPT 0.0453246

209773 JPM_s_at RRM2 0.03994276 2O8850JPM_sjit THY1 0.04543629

206595_P _at CST6 0.04013658 22233...PM_...at AP1B 0.04595822

209369_PM_ai ANXA3 0.04014858 209692_...PM_..a EYA2 0.04645733

214511...PM x_. s FCGR1B 0.04060588 208306_PM_x_at HLA- 0,04661852

206484_^PM___^at XPNPEP2 0.04084199 DRB!

224053._.P ...8_...st SLC4A 0.04092343 240320 P _..¾ LOCI 0013 0.04663727

20499/.. PM_.. a^; GPDi 0.04100943 1781

204924J>Mjii TL 2 0.0414512 202108.. PM_.ai PEPD 0,04702818

2W872JPMjrt S100A10 0.04160359 23072QJPM_ai RNF182 0.04728965

22?5βΟ_...ΡΜ_„¾ SFX 2 0.041.6242! 204806._..P _...x_.st HLA-F 0.04731243

204698_.. PM_. at ISG20 0.04169533 1556199_PM_a_a RGS9BP 0,04748257

2079!4J¾l,x_..ai EVX1 0.04187717 t

201720_..PM_...s._..ai LAPTM5 0,04202368 216696_..i^>M_...s.._,at PRODH2 0.04751425

218638. P _„„s_„at SPON2 0,04296856 2!3386_...P _.at C9orfl25 0.04752935

20I193_PM_at IDH1 0,04347508 20243 l_„PM_Ajii MYC 0.04755431

2262$J> _...ai ADSSL1 0.043S9175 205175. PM_„s_al H 0,04758532

23097 l_PM_x_ai GLTPD2 0,04365709 23083G_PM_at OSTBETA 0.04759921

223877_.PM_.__at C1QT F7 0.04382733 2I2724_.PM_.at RND3 0.04764678

222905 J> _...s_...ai TME 143 0.04383959 20744§_...PM_..ai POFIJT2 0.0476579

213651_PM_at I PP5J 0.04385574 2Q3895JPM_at PLCS4 0.04774861

202862JP __at FAH 0.04398689 21S543_„PM._s_st PARP12 0,04776498

200021 JP _at CFL1 0.04406537 205222_PM _...at EFIHADF1 0.04778722

220135_PMj5jrt SLC7.A 0,0440904 2256G2JPM_at GLIPR2 0.04780739

21 9 1_PM_ai CNPY3 0.04412496 21 223_...PM_.ai CP 2 0.04785158

222i02_P _at GSTA3 0.04422299 214639_PM_s_at HOXA! 0,04788892

244565 PiV! ai HMX2 0.04429422 231077JPM_at C1orfl92 0.04796841

23I517...PM_at ZYGHA 0.04432106 2238Q5J¾l_..a OSBPL6 0.04797079

22i298._.PM_...s_..a? SLC22A8 0.04432726 200986_PM_ai SERPING1 0.04797242

203083_...PM _...at ΊΉ.Β82 0.04440035 202502_„PM_at ACADM 0,0480323 Probe set Symbol mm Probe set Symbol F1>R

2G176i_PM_at MTHFD2 0.04805429 2093!0_PM_s_at CASP4 0,04862681

243708_...PM_..al TMEM I 32 0.04807413 2J 8898_...PM_...a! FAM57A 0.04862965

E ! 556554_.. PMjtt TRIM50 0,0486363

20] 722_...P _..s.. at GALNT! 0.04808675 200989 P .. at HiFIA 0.04868589

217478_PM_s_„at HLA-DMA 0.04809752 204589_PM_at NUAK1 0,04870201

203889_ΡΜ_.¾ SCG5 0,04810933 2 ! 8997JPM__at POLR1 E 0.04871451

203476...PM..at TPBG 0.04810953 219891JPM_at PGPEP1 0.04871659

238353_. PM _. at RASL1 1A 0.04814666 230160_PM_x_at TMEM88B 0.04875777

223869_. PM _.. t SOST 0.04835464 2 i27Q1_PM_at TL 2 0,04878465

227I 8S_...FM_..ai C21or†63 0.04842494 229622_. PM_. at FAM 132.8 0,04925368

233047 PM _...at FRMD7 0.04844218 208296JF _x_at ^" TNFAIP8 0.04954001

227998_PM_at S100A16 0.04849026 204041_. PM_.. t MAOB 0,04963057

204279_PM_at PSMB9 0,04850539 23G912.. PM..at ASPDH 0,04970559

201422_.. PM_.. IFI30 0.04855457 212076_PM_at^" MIX 0.04978322

221666_PM_s_at PYCARD 0.04856477 231256_ PM_at LOC72794 0.04994597

217761_PM_at ADI 1 0.04859831 j 4

[00364] ^"fab Is 47: 188 Genes Non-overlapping with GCNs in Figure 10,

Probe set Symbol FDR Probe set Symbol FDR

202025_..PM_...x_...at ACAA1 0,0200796 230912 JPMjat ASPDH^' 0.04970559

202502_PM_at ACADM 0.0480323 230475_,..ΡΜ._..¾ί C15orf59 0.0298547

2 l 8322_PM_s_._at ACSL5 0,03795268 224574_...PM_..ai C1.7orf49 0.02916867

217761._..PM_.._. ADl i 0.04859831 218546_PM_at Clorfl i S 0.02935301

226325JPM_ai ADSSLI 0.04359175 23038 l_PM_at Cl orfl 86 0.03991242

206840 PM at AFM 0,03985783 231077_...PM _...at Cl orf1 2 0.04796841

209369_PM_at A XA3 0.04014858 223877_...PM_...at C 1QTNF7 0.04382753

210085JPM_8_ot ANXA9 0,03443499 227188_PM_„at C2!orf63 0.04842494

205820_...PM._..s_...at APOC3 0.02895167 212875_„PM_9_at C2CD2 0.02066867

209546_PM_s_at APOL1 0.021 10613 217767_PM_at C3 0.01 182755

202206JPM_ at ARL.4C 0,02003068 213386_PM_at C9orfl 25 0.04752935

223652_PM_at AS3MT 0.03830178 21 GG2Q_PMj£_at CALML 0.02540332

205673 JPM„sj_t ASB9 0.02084366 209310_PM_s_at CASP4 0,04862681

206030_...PM_...at ASPA 0.02881968 203252._..PM_...at CDK2AP2 0.01985649

-3

Probe set Sym ol Probe set Symbo FDR

20l422_PM_„at IFI30 0.04855457 204972_„PM_at OAS2 0.03963592

2I22Q3JPMji_at IFITM3 0.03764268 235257 j?M_at ODF3B 0.03738274

213651_. PM_. at JNPP5J 0,04385574 22380S PM_...at OSBPL6 0.04797079

204698 J» _...at ISG20 0.04169533 22!S68.FM_.at PA1P2B 0.03S27132

224530_...PM._..s_.ai CNIP4 0.01660483 218543_PMjs_at PARP12 0.04776498

2!6699JPM_aj* L 1 0.01309713 21989IJPMjtt PGPEP1 0.04871659

201596_„PM_x_„at KRTIS 0,01646857 204213. PM...ai PIGR 0.01182078

200770_PM_s_at LAMCl 0.0344713 203895_PM_at PLCB4 0.04774861

22e080_...PM « LAY 0.01196455 2G1136JPM_at PLP2 0.03808292

212531JPM...at LCN2 0.03809429 202430_„PM_sjat PLSCR1 0.04454176

2 0320JPM_et LOCI 0013 0.04663727 207448_„P _at POFUT2 0.0476579

1781 212836_PM_at POLD3 0,02939347

231256_. PM_at LOC72794 0.04994597 21l997.P ..al POLR1E 0.04871451

4 218009_„PM_„s_„at PRC! 0.02963615

234016JPM_«t LOC90499 0.03941842 204304 JPM_s ⁱ* FROMi 0.03838748

202018_..PM_„.s_...ai LTF 0.01178284 202659 PM...at PSMB10 0.01256881

212233.. PM_„at MAP IB 0.04595822 209040j?M_s_.at PSMB8 0,0196438

219576J>Mjrt MAP7D3 0.02567634 204279_PM_at PSMB9 0.04850539

212076_...PM._.ai MLL 0.04978322^' 201762_. PM..S...&† PSME2 0.02839286

221!63_PM_s_„at MLXIPL 0,02644151 224937_PM_at PTGP N 0.01945983

204259_PM_at MMP7 0.0122005 212012 _PM_at PXDN 0.02052476

225520_„PM_at MTHPD1 0.01198816 203933 JPMjrt RAB11FIP 0.02530861

L 3

201761JPMJ* MTHFD2 0.04805429 206392 PM..S.. t RARRES1 0.01642582

20243 lj? _..s_. at MYC 0.04755431 238353 j?M_at RASL11A " 0.04814666

208189JPMjsjtt MY07A 0.01994526 201063_.. P _..st RCN! 0.01959306

201798j?Mji_at MY OF 0.02284436 226597 PM.. ai REBP6 0.0345667S

217738_PM_„at AM^'PT 0,0453246 15561 9 PM s_..a RGS9BP 0.04748257

215005_...PM U KECA82 0.01097354 ί

202238 PKI ai NMT 0.01733333 220510...PM...at RH^'BG 0,03455847

222020JPM_sj-it NTM 0.03475996 212724_„PM_at R D3 0.04764678

204589_PM_at NUAK1 0.04870201 230?20_...PMj¾ R F182 0.04728965

-17 §-

[00366] Blood samples were collected from the same cohort of patients whose biopsies were analyzed in Example 1 (n=196), RN A was extracted from Paxgene tubes using the Paxgene Blood RNA system (PreAnaiytix) and GlobinClear (Ambion). Bioiinylated cRNA was prepared with Ambion MessageAmp Biotin H kit (Ambion) and hybridized to Affymetrix Human Genome II 133 Plus 2,0 GeneChips. Microarray data rsormalization and analysis was then performed as in example l_s supplement 1, section 3 above,

[0Θ367] Two gene expression profiles were created by independently comparing each histological phenotype (AR, IFTA) to the controls (TX). A threshold calculated false discovery rale (FDR) of <0,05 and fold-change (FC) of >S ,2 was used as in example 1.

[©0368] The majority (-66%) of differentially expressed genes (DEGs) in biopsies with IFTA were common to AR DEGs (Table 48). A minority (33%) of the DEGs did not agree with IFTA and AR, For all (100%) of these overiapping genes, the directionality of me changes in IFTA and AR were the same,

[W369] Table 48; ~175 differentially expressed genes (DEGs) (threshold FDRO.05) from blood samples shared between IFTA and AR

Gem Title P" Fold- p- Fold- AR I FT A Agree- val¾e(A Change value{CAN Chaage

YS. (AR ¥s„ vs. TX) (CAN vs.

XX) TX) TX)

solute carrier 4.60E-05 "1.22152 0.00053457 "1.18123 DOWN DOWN Yes family 27

(fatty acsd

transporter),

member 5

SH2 domain 4.62E-05 -1.74191 0,00174824 -1.51656 DOWN DOWN containing I B

chromosome 7 4,98E~05 -1.26031 2.22E-05 -1 .26977 DOWN DOWN Yes open reading

fram 68

zinc finger, 5.19E-05 -1.34818 ' L i iE-06 -1.43029 DOWN Yes DHHC-type

containing 1

CUGBP, Eiav- 5.28E-05 1.1556 4.28E-07 1.19772 UP UP Yes fike family

member 2

serpin 5,71 E-05 -1.48514 0.000143231 -1.44402 DOWN DOWN Yes peptidase

inhibitor, clade

F (alpha -2

antiplasmin,

pigment

epithelium

derived fa

t ioredoxm 5.97E-05 1 , 1 7234 2.07E-06 1 ,20547 UP UP Yes interacting

protein

FK5G6 binding 6.05E-05 1.81271 6.90E-06 1.93639 UP UP Yes protein 5 G$m Title P- P~ Fold- AR IFTA A ree¬

Chasge valne(CA Cfaaage ment

R vs, (AR vs. vs. TX) (CAN vs.

XX) TX) TX)

FK506 binding 6.27E-G5 1.47E-06 t 2.25845 UP UP Yes protein 5

Kruppel -Hke 6.93E-0S 1 .59913 1 .55E-05 1.65599 UP UP Yes factor 9

hi stone 6.96E-05 -1.17463 3J3E-05 - 1.1 81 DOWN DOWN Yes deacetylass 3

Ras association 7.53E-05 -1.40248 0.0001 1 1226 -1 .38414 DOWN DOWN Yes (RaiGDS/AF- 6) domain

family member

4

ras homolog 7.57E-05 -1.44994 2.53E-Q5 -1 .4781 DOWN DOWN Yes gene family,

member C

intercellular 7.91E-05 J.5475 0,000120457 -1.52012 DOWN DOWN Yes adhesion

molecule 4

(Larjdste ner- Wiener blood

group)

nuclear fragile 8.32E-05 L I 8772 2 4E-08 1 .27848 UP UP Yes X mental

retardation

protein

interacting

protein 2

growth factor 8.74E-05 2.29839 5.29E-05 2.33048 UP UP Yes receptor-bound

protein 10

peroxidase 1 831

Fold- AR JFTA A ree¬

Qsan e ment

R vs. vs. vs. TX) {CAN m

X)

malorryl

CoA:ACP

acy!transferase

(mitochondrial

)

ribosoraal

protein $26

pyridoxal

(pyridoxins;,

vitamin B6)

phosphatase ///

SH3~domain

binding protein

1

interferon

regulatory yotubularin j 0.000164

related protein

1 !

serine

palmitoyltransf

erase, long

chain base

subimit 2

malate

dehydrogenase

(Drosophi!a) 1 J Gens Title p- Fold- P" Fold- AR IFTA Agreevslae(A Osassge vaiue(CAN ment

Rys, (AM vs, vs. TX) (CAN vs.

XX) TX) TX)

golgi SNAP 0.000318 -1,24381 0.00454117 ■1.18343 DOWN DOWN Yes receptor 528

complex

member 2

m osin 0.000319 1,30825 0.000261128 1,30834 UP UP Yes regulatory light 532

chain

Interacting

protein

chromosome 2 0.000328 -1,24003 2.70E-05 -1.28315 DOWN DOWN Yes opm reading 161

frame 7

peptidase D 0.000338 4.31731 5.27E-05 -1.36049 DOWN DOWN Yes

88

chromosome 0.000339 -1.25811 Γθ.00223685 -1.21187 DOWN DOWN

15 open 556

reading frame

54

sjbiquinol- 0.000340 -1.21348 2.17E-0S -1.25557 DOWN DOWN Yes c tochrome e 75

reductase,

complex III

subimit X

N-giycanase 1 0.000344 i.31441 0.000103 ] 1.34052 UP UP Yes

716

zinc finger 0.000345 Λ752ΪΪ 0.0001221 2 -1.18676 DOWN DOWN Yes protein 207 329

mediator 0.000349 4.23567 0.000278775 -1.23624 DOWN DOWN Yes complex 248

subunii 20

1

G& Title P" Fold- P" Fold- AR JFTA reevslue(A Change vaiiK(CA Cfeasge ment

(A l vs. vs. TX) (CAN vs.

TX) TX) TX)

pyridoxamine 0.000387 - 1 ,23465 9J 4E-05 -1.25843 DOWN DOWN Yes

5 '-phosphate 823

oxidase

notch 4 0,000388 - 1.23442 1.85E-05 -1.28715 DOWN DOWN Yes

581

cytochrome bS 0.000403 -1 .30031 0.00264745 - 1 ,24469 DOW DOWN Yes type B (outer 512

mitochondrial

membrane)

tumor necrosis 0,000404 -1.32612 0,000 14709 "1.32002 DOWN DOWN Yes factor 9

Di George 0,000407 -1.14006 7.75E-05 DOWN DOWN Yes syndrome 021

critical region

gene 6-like

chromodomain 0.000407 1 . 16158 9.87E-06 1 ,20475 UP UP Yes helicase DMA 668

binding protein

2

myosin 0,000413 1 ,33722 1.Q8E-06 1.49396 UP UP Yes regulatory light 382

chairs

interacting

protein

AB! family, 0.000417 -1 ,306 0,00183642 -1.2604 DOWN DOWN Yes member 3 148

(subunit 9) j

protein Gene Title Fold- p- Fold- AM IFTA Agreev»f»&(A Change value(CAN C asge ment R vs, (AM vs. vs. TX) (CAM s,

TX) TX) TX)

SRY (sex 0.000477 4 ,4076 0.000941 182 4 .37507 DOWN DOWN Yes determining 651

region Y)-box

4

CD 0 0.000478 -1.48596 0,0001 17097 4.54035 DOWN DOWN Yes molecule, TNF 681

rec ptor

superfarnily

me ber 5

hyperrnethylate 0.000488 4 ,22142 0.0005158" 4 ,21686 DOWN DOWN Yes d in cancer 1 836

asialogivcoprot 0,000490 4 ,35229 δ.00058446Ϊ -1.34081 DOWN DOWN Yes eln receptor ! 647

musclebiind- 0.000496 1.28674 4.85E-07 1 ,44092 UP UP Yea like 399

(Drosophila)

chromosome 5 0.000498 1.24127 9.45E-06 1.31479 UP UP Yes open reading 673

frame 41

0,000507 4.38097 0.000421879 4.38095 DOWN DOWN Yes alpha- 855

reductase,

alpha

polypeptide 1

(3-oxo-S alpha- steroid delta 4- dehydroge Geae Title p- Fold- p- Fold- AR IFTA AgreeChange vAeiCAN Change ment

R vs. (AR vs. vs. TX) (CAN vs.

TX) TX) TX)

DPH1 0,000521 •1.25928 8.09E-05 -1.29603 DOWN DOWN Yes homolog (S, 711

cerevisiae) ///

ovarian tumor

suppressor

candidate 2

serine ihreonin 0,000525 -1.32105 2.07E-05 "1.40439 DOWN DOWN Yes e kinase 32C 544

nadix 0.000532 5.S5E-05 -1.20814 DOWN DOWN Yes

(nucleoside 32

diphosphate

linked moiety

X)-type motif

3

R o guanine 0,000534 -1.44251 4.36E-05 -1 ,53551 DOWN DOWN Yes nucleotide 828

exchaisge

factor (GEF)

10-like

Muscleblind- 0.00053 V 1.25158 7.02E-06 1 ,33676 UP UP Yes like 939

(Brosophila)

succinate 0.000546 -1 , 1966? S.24E-06 -1.26599 DOWN DOWN Yes dehydrogenase 40?

complex,

subunlt C,

integral

membrane

protein, !SkDa Gene Title p- Fold- i " Fold- AR IFTA Ag eevaiae(A Change value(CAN ment R vs. (A vs, vs. TX) (CAN vs.

TX) TX) TX)

apopiosas, 0.000548 -1,21292 3.85E-0S -1.25619 DOWN DOWN Yes caspase 972

activation

inhibitor

adaptor-related 0.000553 -1.2071 0.000716657 -1.19914 DOWN DOWN Γ Υ¾ ^~ protein 889

complex 4, mu

I subunit

phosphodiestef 0.000563 -1.27673 0.00417891 -1.2202 DOWN DOWN Yes ase 4 A, cAMP- 887

speciflc

peroxiredoxif? 0.000571 -1.43679 0,00230333 -1.3702 DOWN DOWN Yes

6 709

niyotub larin 0.000590 -1.5336 0.000380666 -1.54694 DOWN DOWN Yes related protein 28 i

11

Zinc finger 0.000603 -Ϊ.ΪΪ702 3.97E-0S -1.14057 DOWN DOWN Yes protein 70 618

chemokine (C- 0,000606 -1.6734 3.49E-07 -2.15595 DOWN DOWN Yes X--C motif) 69

ligand 5

gamma- 0.000620 1.10616 0.00273645 1.09059 UP UP Yes am inobutyric 386

acid (GABA)

A receptor, pi

taxin 7 0,000624 1.17188 1.22E-G6 1.25258 UP UP Yes

065

GDP-marmose 0.000637 -1.18247 0,000180341 -1.19926 DOWN DOWN Yes pyrophosphory 704

!ase B Gms Title P~ Fold- P~ Fold- AR 1FTA Agree- vatag(A Chan vsdite(CAN Chan e m t

R VS. (AR vs. vs. TX) (CAN vs.

'FX) TX) TX)

proteasome 0.000639 4.15275 0.0043335 -1,12357 IX⁾ W DOWN Yes (prosome, 34

macropain)

26S subunit,

non>ATPase_; 9

U2 small 0.000640 1.22965 0.000104673 1 ,26181 UP UP Yes nuclear RNA 305

auxiliary factor

1

RAS (RAD 0,000640 1,42304 0.00147872 1 ,3814 UP UP Yes and GEMVlike 882

OTP binding 2

basic 0.000649 -1,22551 0.000680901 -1.22095 DOWN Yes transcription m

factor 3

SH3 domain 0,000655 -1.27892 0.00560116 -1.21663 DOWN DOWN Yes containing ring 422

fin er 1

lysophosphatid 0.000656 -1.3824 0.00016196 -1.42547 DOWN DOWN Yes ic acid receptor 329

1

cytochrome e-1 0,000657 4.22996 0.000466569 4.2333 DOWN IX.⁾ WN Yes

423

SH2B adaptor 0.000659 -1.22016 0.000252943 4.23513 DOWN DOWN Yes protein 3 601 Gt Title p^» Fold- p- Fold- AR IFTA Agreeval«e(A Change vaiue(CA Chsnge ment R va. (AR vs. vs. TX) (CAN vs.

TX) TX) TX)

methylmalonic 0.000662 -1 , 13216 0,00189566 -1 , 1 1775 DOWN DOWN Yes aciduria 53?

(cobalamm

deficiency)

eblC type, with

homocystinuria

UDP-g!ucose 0.000673 1 ,40958 0,00286613 1.34398 UP UP Yes ceraroide 418

gliteosyiiransfe

rase

phosphatidyls ΊΪ000674 -1.281 0,00025404 -1.3021 DOWN DOWN Yes hanolamine N- 86

methyltransfer

ase

zinc finger 0.000679 -1. Ϊ 8793 0.00642491 -1.145 DOWN DOWN Yes protein 207 91 1

phosphoglycer 0,000682 -1.13989 LG1E-05 -1.18476 DOWN DOWN Yes ate mutase 1 856

(brain)

chromosome 0.000683 1 ,21322 0.0042501 1 1.1731 UP UP Yes 14 open 93 1

reading frame

1 1 8

COMM 0,000698 - 1.24632 4.46E-07 -1.38975 : DOWN DOWN Yes domain 375

containing 5 Gene Title p- Fold- Fold- AR IFTA Agreevalue(A Change va1ne(CA]N Change men R vs. (AR vs. s. TX) (CAN vs.

TX) TX) TX)

ligand 0,000699 1 .15364 4.97E-05 1.18493 UP UP Yes dependent 401

nuclear

receptor

compressor

CD 163 0,000708 1.56325 0.00196773 1.49394 UP UP Yes molecule 341

CD163 0.000710 1 .59814 0.00126674 1.55165 UP UP Yes molecule 938

R A binding 0.000714 1.15295 0.00267386 ΠΪ3209^" UP UP motif, single 247

s ra ded

interacting

protein }

SMA family 0.000717 1 .31451 0.00238258 1.27293 UP UP Yes member 4 83 1

sushi, nidogen 0,000721 -1.18063 0.000971661 -1.17293 DOWN DOWN Yes and EGF-like 02

domains 1

calmodulin- 0,000722 -i .25526 (100430064 -1 .20732 DOWN DOWN Yes

[ike 4 745

[00370] Example 3; Adjustment of patient mmmpp &m regimen based on detection of imniune-niedia ed rejection

[00371] Post-induction kidney transplant recipients with stable allograft function on a maintenance immunosuppressant regimen (e.g. calcineurin inhibitor or mTOR inhibitor plus mycophenolate mofetii) are surveiHed with peripheral blood draws on a defined schedule (e.g. i draw per 1-3 months) and protocol biopsies orj a defined schedule (e.g, at 3 or 6 months, followed by biopsy at 12 and 24 months). Gene expression analysis of biopsy and blood samples by microarray platform is performed as in examples 1 and 2. ΘΘ372] A classifier to detect immune mediated rejection is composed of differentially- expressed genes common to AR and IFTA (tables 37-42, 45, 47, 18, 23, or 48) using any of the methods previously described. The classifier is applied to the microarray gene expression data above to identify a patient sample as having immune-mediated rejection or not having immune- rnediated rejection.

[©0373] Patients in whom immune mediated-rejection or inadequate inimunosuppressian is detected by microarray profiling are treated with anti-rejection therapy. Anti-rejection therapy involves treatment with corticosteroids (e.g. intravenous solumedrol for 3 or more days) or ant lymphocyte antibodies (e.g. muronionab~CD3) at doses determined by a physician per standard medical practice.

[00374] Patients in whom immune mediated-rejection or inadequate immunosuppression is detected by microarray profiling receive additional blood draws and an additional biopsy on a schedule after detection of immune-mediated rejection or inadequate immunosuppression to enable further analysis of gene expression levels.

0037S| While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

WHAT IS CLAIMED IS:

1 , A method of administering immunosuppressant drugs comprising;

(a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen and wherein the nucleic acids comprise expression products from a panel of genes;

(b) diagnosing immune-rnediated rejection or inadequate immunosuppression in the kidney transplant recipient based on levels of the expression products from the panel of genes, wherein the panel of genes is specifically selected to detect immune-mediated rejection or inadequate immunosuppression in a kidney transplant subject irrespective of histological findings; and

(c) adjusting the immunosuppression drug regimen administered to the kidney transplant recipient based on the levels of the expression products from die panel of genes specifically selected to detect immune-mediated rejection or inadequate immunosuppression in a kidney transplant subject irrespective of histological findings,

2, A method of managing an immunosuppression regimen in a kidney transplant recipient comprising:

(a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen and wherein the nucleic acids comprise gene expression products from a panel of genes;

(b) diagnosixig immune-mediated rejection or inadequate imnumosuppression in the kidney transplant recipient using levels of the expression products from the panel of genes , wherein d e panel of genes comprises: (i) twenty or more genes listed In Table 37, (ii) twenty or more genes listed in Table 38, (iii) twenty or more genes listed in Table 39, (iv) twenty or more genes listed In Table 40, (v) twenty or more genes listed in Table 41, (vi) twenty or more genes listed in Table 42, (vil) twenty or more genes listed in Table 45, (viil) twenty or more genes listed in Table 47, (ix) twenty or more genes listed in Table 18; (x) twenty or more genes listed Irs Table 23, or (xi) twenty or more genes listed in Table 48; and (c) adjusting the immunosuppression drug regimen administered to the kidney transplant recipient based on the levels of the expression products from the panel of genes comprising (i) twenty or more genes listed in Table 37, (ii) twenty or more genes listed in Table 38, (in) twenty or more genes listed in Table 39, (iv) twenty or more genes listed in Table 40, (v) twenty or more genes listed in Table 4L (vi) twenty or more genes listed in Table 42, (vii) twenty or more genes listed in Table 45, (viii) twenty or more genes listed in Table 47, (ix) twenty or more genes listed in Table 18; (x) twenty or more genes listed in Table 23, or (xi) twenty or more genes listed in Table 48.

3. The method as to any one of the preceding claims, wherein the diagnosing of immune- mediated rejection is conducted without distinguishing between acute arid chronic rejection.

4. A method of administering immunosuppressant drugs to a kidney transplant recipient comprising:

(a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drag regimen and the nucleic acids comprise expression products from a panel of genes that are commonly regulated in acute and chronic rejection;

(b) detecting presence or absence of immune-mediated rejection in the kidney transplant recipient without distinguis ng between acute and chronic rejection, wherein the detecting is based on levels of the expression products from the panel of genes commonly regulated in acute and chronic rejection; and

(c) adjusting the immunosuppression drug regimen of the kidney transplant recipient based on the presence or absence of immune-mediated allograft rejection in the kidney transplant recipient.

5. A method of administering immunosuppressant drugs comprising:

(a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen;

(b) diagnosing immune-mediated rejection or inadequate immunosuppression in the kidney transplant recipient, using expression levels of a panel of genes wherein the panel of genes is selected from a single set of up to 250 markers and wherein the single set of up to 250 markers specifically detects immune-mediated rejection or inadequate immunosuppression in subjects with acute rejection and subjects with chronic rejection; and (c) adjusting the immunosuppression drug regimen in the kidney transplant recipient based on the diagnosing of immime-mediated rejection or inadequate immunosuppression in the kidney transplant recipient,

6. The method as to any one of the preceding claims, wherein the adjusting the

immunosuppression drug regimen comprises increasing a dose of a drug within the

immunosuppressive drug regimen.

7. The method as to any one of the preceding claims, wherein the adjusting t ie

immunosuppression drag regimen comprises decreasing a dose of a drug within the

immunosuppressive drug regimen,

8. The method as to any one of the precedi g claims, wherein the panel of genes specifically detects immune-mediate allograft rejection in the kidney transplant recipient regardless of measurable renal function of the kidney transplant recipient.

9. The method as to any one of the preceding claims, wherein histological examination of a biopsy from the kidney transplant recipient indicates thai the kidney transplant recipient does not have immune-mediated allograft rejection,

10. The method as to any one of the preceding claims, wherein the panel of genes speci fically detects immune-mediated rejection in the biological sample obtained from the kidney transplant recipient.

1 1. The method as to any one of the preceding claims, wherein the method is capable of detecting immune-mediated rejection when the kidney transplant recipient has no detectable impairment of renal function, wherein the panel of genes is specifically selected to detect immune- mediated rejection in a kidney transplant subject with interstitial fibrosis or tubular atrophy, independently of whether the interstitial fibrosis or tubular atrophy is accompanied by evidence of inf amrrsation.

12. The method as to any one of the preceding claims, wherein the kidney transplant subject has interstitial fibrosis without inflammation or tubular atrophy without inflammation,

13. The method as to any one of the preceding claims, wherein the kidney transplant subject has interstitial fibrosis with inflammation or tubular atrophy with inflammation.

14. The method as to any one of the preceding claims, wherein the method is capable of detecting immune-mediated rejection when the kidney transplant recipient has no detectable impairment of renal function.

15. The method as to any one of the preceding claims, wherein the immunosuppression drug regimen comprises a drag selected from the group consisting of: ealeinevjrm inhibitors, corticosteroids, cyclosporins, antimetabolites, and mTOR inhibitors,

16. The method as to any one of the preceding claims, wherein the immunosuppression drug regimen comprises a drug selected from the group consisting of: Tacrolimus, Prograf, Astagraf XL, Hecoria, Envarsus X , eoral, Sandimmune, Gengraf, Prednisone, Deltasone, Prednisolone, Orapred, Pediapred, Miilipred_s Methylprednisolone, Medrol, and Solu-Medrol, Myeophenoiate mofetil, CellCept, Myfortic₅ Azathioprine, Imuran, and Azasan, Sirolimus, Rapamune, Everolimus, Zortress, Belatacept, Nulojix, Basiliximab, Siniulect, Antithyniocyte globulin rabbit, ATG rabbit, Iliymoglobuli, and Alemtuzumab.

17. The method as to any one of the preceding claims, wherein the biological sample is a blood sample.

18. The method as to any one of the preceding claims, wherein the biological sample is a kidney biopsy sample.

1 . The method as to any one of the preceding claims, wherein the biological sample is a urine sample.

20. The method as to any one of the preceding claims, wherein the panel of genes comprises genes listed in Table 18, 23, 45, or 47.

21. The method as to any one of the preceding claims, wherein the panel of genes compri es genes listed in Table 37, 38, 39, 40, 41, 45, or 47.

22. The method as to any one of the preceding claims, wherein the expression levels are RNA expression levels,

23. The method of claim 22. wherein the RNA expression levels are mRNA expression levels,

24. The method as to any one of the preceding claims, wherein the diagnosing comprises using a micro-array assay, D A sequencing assay or RNA sequencing assay.

25. The method as to any one of the preceding claims, wherein the diagnosing comprises using hybridizing probes to gene expression, products of the panel of genes.

26. The method of claim 25, wherein the probes specifically bind to the gene expression products.

27. The method of claim 25, wherein the probes comprise nucleic acids, DNA, or R A.

28. The method as to any c-ηε of the preceding claims, further comprising comparing the expression levels of the panel of genes with expression levels of reference markers that specifically detect immune-mediated rejection in kidney transplant recipients irrespective of histological finding ,

29. The method as to any one of the preceding claims, further comprising comparing the expression levels of the panel of genes with expression levels of reference markers that specifically detect immune-mediated rejection or inadequate immunosuppression in kidney transplant recipients with interstitial fibrosis without inflammation or with tubular atrophy without inflammation.

30. The method as to any one of the preceding claims, further comprising comparing the expression levels of the panel of genes with expression levels of reference markers that specifically detect immune-mediated rejection or inadequate immunosuppression in kidney transplant recipients with interstitial fibrosis with inflammation or with tubular atrophy with inflammation,

31. The method as to any one of the preceding claims, further comprising repeating steps (a)-(c).

32. The method as to any one of the preceding claims, wherein the expression levels of the panel of genes indicate that the kidney transplant recipient has a greater than 70% chance of graft survival.

33. The method as to any one of the preceding claims, wherein the expression levels of the panel of genes indicate that the kidney transplant recipient has a less than 50% chance of graft survival.

34. The method as to any one of the preceding claims, wherein the adjusting the

immunosuppression drug regimen is not based on a histological examination of a kidney biopsy of the kidney transplant recipient.

35. The method as to any one of the preceding claims, wherein the kidney transplant recipient has acute rejection or subclinical acute rejection.

36. The method as to any one of the preceding claims, wherein the kidney transplant recipient has chronic rejection.

37. The method as to any one of the preceding claims, wherein the panel of genes specifically detects immune-mediated rejection in kidney transplant subjects with interstitial fibrosis and tubular atrophy without inflammation,

38. The method as to any one of the preceding claims, wherein the panel of genes specifically detects acute rejection.

39, The method as to any one of the preceding claims, further comprising applying an algorithm to the expression levels of the pane! of genes.

40, The method of claim 39, wherein the algorithm is a trained, algorithm.

41 , The method of claim 40, wherein the trained algorithm is trained with gene expression data from samples from at least three different cohorts,

42, The method of claim 40, wherein the trained algorithm comprises a linear classifier.

43, The method of claim 42, wherein the linear classifier comprises linear discriminant analysis. Fisher's linear discriminant, Naive Bayes classifier. Logistic regression, Perceptron, Support vector machine (SYM), or a combination thereof.

44, The method of claim 39, wherein the algorithm comprises a Diagonal Linear Discriminant Analysis (DLDA) algorithm, a Nearest Centroid algorithm, a Random Forest algorithm or statistical bootstrapping, a Prediction Analysis of Mieroarrays (PAM) algorithm, or a combination thereof,

45, A method of detecting, monitoring, or prognosing imm one-mediated rejection or inadequate immunosuppression in a kidney transplant recipient comprising:

(a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from the kidney transplant recipient on an immunosuppression drug regimen;

(b) performing an assay on the nucleic acids to determine expression levels of a panel of genes, wherein the panel of genes is specifically selected to detect irrm une-mediated rej ction or inadequate immunosuppression in a kidney transplant subject irrespective of histological findings; and

(c) detecting, monitoring, or prognosing immune-mediated rejection or inadequate

immunos ppression based on the expression level s of the panel of genes specifi cally selected to detect immirae-mediated rejection or inadequate immunosuppression in kidney transplant subject irrespective of histological findings in the subject.

46, A method of detecting, monitoring, or prognosing immune-mediated rejection or inadequate immunosuppression in a kidney transplant recipient comprising:

(a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from the kidney transplant recipient on an immunosuppression drug regimen; (b) performing an assay on t e nucleic acids to obtain expression levels of a panel of genes, wherein the panel of genes comprises (i) twenty or more genes listed in Table 37, (ii) twenty or more genes listed in Table 38, (iii) twenty or more genes listed in Table 39, (iv) twenty or more genes listed in Table 40, (v) twenty or more genes listed in Table 41, (vi) twenty or more genes listed in Table 42, (vii) twenty or more genes listed in Table 45, (viii) twenty or more genes listed in Table 47,

(ix) twenty or more genes listed in Table 18, (x) twenty or more genes listed in Table 23, or (xi) twenty or more genes listed in Table 48; and

immunosuppression based on the expression levels of the panel of genes comprising (i) twenty or more genes listed in Table 37, (ii) twenty or more genes listed in Table 38_? (iii) twenty or more genes listed in Table 39, (iv) twenty or more genes listed in Table 40, (v) twenty or more genes listed in Table 41, (vi) twenty or more genes listed in Table 42. (vii) twenty or more genes listed in Table 45, (viii) twenty or more genes listed in Table 47, (ix) twenty or more genes listed in Table 18,

(x) twenty or more genes listed in Table 23, or (xi) twenty or more genes listed in Table 48.

47, A method of detecting, monitoring, or prognosing immune-mediated rejection or inadequate immunosuppression in a kidney transplant recipient comprising:

(a) obtaining a biological sample comprising nucleic acids, wberein the biological sample is obtained from a kidney transplant recipient on an imrnunosisppression drug regimen;

(b) performing an assay on the nucleic acids to determine expression levels of a panel of genes, wherein the panel of genes is selected from a single set of up to 250 markers and wherein the single set of up to 250 markers specifically detects immune-mediated rejection in subjects wit!i subclinical acute rejection, clinical acute rejection, subclinical chronic rejection, or clinical chronic rejection; and

(c) detecting, monitoring or prognosing immune-mediated rejection or inadequate

immunosuppression based on the expression levels of the panel of genes,

48. A method of administering immunosuppressant drugs comprising:

(a) obtaining a biological sample comprising nucleic acids, wherein the biological sample is obtained from a kidney transplant recipient on an immunosuppression drug regimen and wherein the nucleic acids comprise expression products from a panel of genes, wherein the panel of genes comprises genes dysregulated in both acute rejection and chronic rejection;

(b) detecting immune-mediated rejection or inadequate immunosuppression based on levels of the expression products from the panel of genes; and

(c) adjusting the irmmraosuppression drug regimen administered to the kidney transplant recipient based on the detecting of immune-mediated rejection or inadequate immunosuppression,

49. The method of claim 48, wherein the genes dysregulated In both acute rejection and chronic rejection are upregulated in both acute rejection and chronic rejection, when compared to a stable or normal transplant condition.

50. The method of claim 48, wherein the genes dysregulated in both acute rejection and chronic rejection are down egulated in both acute rejection and chronic rejection, when compared to a stable or normal transplant condition,

51. The method of claim 48, wherein the genes dysregulated in both acute rejection and chronic rejection are at least 1,5 -fold upregulated in both acute rejection and chronic rejection compared to a norma! or stable transplant condition,

52. The method of claim 48, wherein the genes dysregulated in both acute rejection and chronic rejection are at least 1.5-fold down-regulated in both acute rejection and chronic rejection compared to a normal or stable transplant condition.

53. The method of any of the preceding claims, wherein the panel of genes does not comprise hranunoglobulin-encoding transcripts or transcripts preferentially expressed In mature B-cells.

54. The method as to any one of the preceding claims, wherein the panel of genes comprises immimoglobulin-eneoding transcripts or transcripts preferentially expressed in mature B-eeSls,

55. The method as to any one of the preceding claims, wherein the panel of genes comprises at least five genes from table 37, at least five genes from table 38, at least five genes from table 39, at least five genes from table 40_? at least five genes from table 41, or at least five genes from table 42.

56. The method as to any one of the preceding claims, wherein the panel of genes comprises ge es implicated in T-cell-mediated immune responses or inflammation.

57. The method as to any one of the preceding claims, wherein the panel of genes comprises at least five genes from table 37, at least five genes from table 38, or at least live genes from table 39,

58. The method as to any one of the preceding claims, wherein the panel of genes comprises at least five genes involved in metabolism or tissue integrity.

59. The method as to any one of the preceding claims, wherein the panel of genes comprises at least five genes implicated in tissue integrity, amino add turnover, glucose metabolism, fatty acid metabolisn j energy production, cellular detoxification, or solute transport,

60. The method as to any one of the preceding claims, wherein the panel of comprises at least five genes from table 40, at least five genes from table 41, or at least five genes from table 42.

61. The method as to any one of the preceding claims, wherein the expression products are RNA,

62. The method as to any one of the preceding claims, wherein the expression products are cDNA or DNA.

63. The method as to any one of the preceding claims, wherein the expression products comprise niRNA extracted from the biological sample or nucleic acids derived from the mRNA extracted from the biological sample,

64. The method as to any one of the preceding claims, wherein the expression products comprise cDNA or DNA derived from mRNA extracted from the biological sample.

65. The method as to any one of the preceding claims, the method further comprising reporting a result of the method to the kidney transplant recipient or to a caregiver of the transplant recipient.

66. The method as to any one of the preceding claims, wherein the detecting or diagnosing of immune-mediated rejection in the kidney transplant recipient is completely based on levels of expression products from a panel of genes commonly regulated in acute and chronic rejection.

67. The method as to any one of the preceding claims, wherein the detecting or diagnosing of immune-mediated rejection, in die kidney transplant recipient is partially based on levels of expression products from a panel of genes commonl regulated in acnte and chronic rejection.