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A Peripheral Blood Gene Expression Signature to Diagnose Subclinical Acute Rejection

Weijia Zhang,1 Zhengzi Yi,1 Karen L. Keung,2 Huimin Shang,3 Chengguo Wei,1 Paolo Cravedi,1 Zeguo Sun,1 Caixia Xi,1 Christopher Woytovich,1 Samira Farouk,1 Weiqing Huang,1 Khadija Banu,1 Lorenzo Gallon,4 Ciara N. Magee,5 Nader Najafian,5 Milagros Samaniego,6 Arjang Djamali ,7 Stephen I. Alexander,2 Ivy A. Rosales,8 Rex Neal Smith,8 Jenny Xiang,3 Evelyne Lerut,9 Dirk Kuypers,10,11 Maarten Naesens ,10,11 Philip J. O’Connell,2 Robert Colvin,8 Madhav C. Menon,1 and Barbara Murphy1

Due to the number of contributing authors, the affiliations are listed at the end of this article.

ABSTRACT Background In kidney transplant recipients, surveillance biopsies can reveal, despite stable graft function, histologic features of acute rejection and borderline changes that are associated with undesirable graft outcomes. Noninvasive biomarkers of subclinical acute rejection are needed to avoid the risks and costs associated with repeated biopsies. Methods We examined subclinical histologic and functional changes in kidney transplant recipients from the prospective Genomics of Chronic Allograft Rejection (GoCAR) study who underwent surveillance biopsies over 2 years, identifying those with subclinical or borderline acute cellular rejection (ACR) at 3 months (ACR-3) post-transplant. We performed RNA sequencing on whole blood collected from 88 indi- viduals at the time of 3-month surveillance biopsy to identify transcripts associated with ACR-3, developed a novel sequencing-based targeted expression assay, and validated this gene signature in an independent cohort. Results Study participants with ACR-3 had significantly higher risk than those without ACR-3 of subse- quent clinical acute rejection at 12 and 24 months, faster decline in graft function, and decreased graft survival in adjusted Cox analysis. We identified a 17-gene signature in peripheral blood that accurately diagnosed ACR-3, and validated it using microarray expression profiles of blood samples from 65 trans- plant recipients in the GoCAR cohort and three public microarray datasets. In an independent cohort of 110 transplant recipients, tests of the targeted expression assay on the basis of the 17-gene set showed that it identified individuals at higher risk of ongoing acute rejection and future graft loss. Conclusions Our targeted expression assay enabled noninvasive diagnosis of subclinical acute rejection and inflammation in the graft and may represent a useful tool to risk-stratify kidney transplant recipients.

JASN 30: 1481–1494, 2019. doi: https://doi.org/10.1681/ASN.2018111098 CLINICAL RESEARCH Kidney transplantation is the therapy of choice for ESRD. Although short-term allograft outcomes Received November 8, 2018. Accepted May 1, 2019. including clinical acute rejection episodes (i.e., occurring in the presence of graft dysfunction) Published online ahead of print. Publication date available at www.jasn.org. have declined over past decades, proportionate im- provement in long-term allograft survival remains Correspondence: Dr. Barbara Murphy, Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount 1–3 unrealized. The rates of clinically detected epi- Sinai, One Gustave L Levy Place, Box 1243, New York, NY 10029. sodes of acute rejection within the first year in the Email: [email protected] modern tacrolimus/mycophenolate era are ,10% Copyright © 2019 by the American Society of Nephrology

JASN 30: 1481–1494, 2019 ISSN : 1046-6673/3008-1481 1481 CLINICAL RESEARCH www.jasn.org among adult kidney recipients in the United States.2 The effect Significance Statement of these episodes on long-term graft survival is variable, de- pending on the severity of episode, time from transplantation, Biomarkers for noninvasive diagnosis of subclinical acute rejection and effective treatment of these episodes with return of creat- are needed to enable risk-stratification and tailoring of immuno- inine to prerejection baseline.4 suppression for kidney transplant recipients. Using RNA sequencing fl analyses of whole blood collected from a cohort of transplant re- Graft in ammation, however, may also occur initially in the cipients at the time of surveillance biopsy, the authors identified a absence of graft function decline. The wide prevalence of sub- transcriptional signature on the basis of a set of 17 genes that ac- clinical rejection, i.e., lymphocytic tubulitis and interstitial in- curately detects ongoing subclinical rejection. After extensive val- flammation in early surveillance biopsy specimens, and its idation, they developed a sequencing-based targeted expression effect on long-term allograft histology, function, and survival assay on the basis of this gene set that was able to identify subclinical rejection at 3 months post-transplant and increased risk of graft loss 5–10 has been appreciated only recently. The effect of milder in an independent cohort of 110 patients. This assay represents a subclinical inflammation, i.e., suspicious or borderline le- potentially useful tool to monitor kidney transplant recipients and sions, on allograft outcomes is even less characterized.11 optimize immunosuppressive therapy, although larger studies are In current clinical practice, the diagnosis of either clinical or needed to validate the assay’s clinical utility. subclinical allograft rejection requires a biopsy, a procedure burdened by clinical risks and costs. To obviate these issues, readings .1000 were taken as positive. The surveillance biopsy fi prior studies have tested noninvasive pro ling of urinary pro- specimens in the patients in the Belgian cohort were taken at the 12,13 14–16 teins and blood transcriptomic signatures, but results same time points (3, 6, 12, and 24 months) as for the GoCAR have been inconsistent so far. An assay that could be used in cohort. Highly sensitized patients requiring desensitization were clinical practice for the purpose of accurately diagnosing sub- excluded from the GoCAR study and not present in the Belgium clinical rejection offers the potential to identify and treat un- cohort. We used United Network for Organ Sharing and The derlying subclinical rejection without the need for a biopsy. Australia and New Zealand Dialysis and Transplant Registry da- Herein, we examined the incidence of subclinical rejection tabases to determine long-term outcomes for the GoCAR and borderline lesions over time in the Genomics of Chronic enrollees. Allograft Rejection study (GoCAR), a prospective, multicenter center study in which kidney transplant recipients underwent Genomic Experiments and Data Analysis serial surveillance biopsies. We determined the effect of sub- The details regarding genomic experiments (RNA sequencing, clinical graft inflammation on allograft function and survival, microarray, and targeted RNA expression [TREx] assay) are and developed a clinically applicable assay that detects sub- provided in the Supplemental Material and the data analysis clinical acute rejection by measuring the transcriptome in pe- workflow is depicted in Supplemental Figure 1. Briefly, mRNA ripheral blood. sequencing (Illumina HiSeq4000 sequencer) was performed on 88 samples obtained at 3 months post-transplant in the GoCAR cohort as discovery set for identification of gene sig- METHODS natures associated with ACR-3. After read quality control, mapping, and normalization steps on the raw sequencing Patients reads, the expression data were compared between ACR-3 The study included participants of the GoCAR study and kidney and non-ACR at 3 months (NACR-3), with both induction transplant recipients prospectively followed-up at the University therapy and deceased donor as confounders, to identify dif- Hospitals Leuven, Leuven, Belgium. The GoCAR study is a pro- ferentially expressed genes (DEGs) with a P value ,0.05 using spective,multicenterstudy(UnitedStatesandAustralia)aimedat unpaired LIMMA test,20 a linear model to assess differential investigating the genetics and genomics associated with the de- gene expression in the context of multiple variables. The DEGs velopment of allograft rejection or injury in kidney transplant were then subjected to enrichment analysis for canonical recipients. Patients underwent surveillance biopsies pretrans- pathway, gene ontology, and immune cell types to identify plant (before implantation), and at 3, 6, 12, and 24 months after classes or groups of genes that were associated with ACR. transplant. Patients were followed up for at least 5 years or until Next, a more focused gene set associated with ACR-3 was death. The details of patient enrollment criteria and study design identified from the DEGs using a randomization approach have been previously described.17,18 All biopsy specimens were described previously.18. An optimal gene set with the highest reported for Banff component scores by a central core laboratory AUC (area under the receiver operating characteristic curve) at Massachusetts General Hospital. The diagnosis of acute cel- score for diagnosis of ACR-3 was then determined by fitting a lular rejection (ACR) at 3 months (ACR-3) was made by apply- penalized logistic regression model on the expression data of ing Banff 2013 Classification19 on all clinically indicated and the focus gene set following a 5000-iterations methodology18 surveillance biopsy samples and included borderline subclinical (Supplemental Material). The AUC for the final gene set was rejection. Donor-specific anti-HLA antibodies (DSAs) were crossvalidated using a leave-one-out crossvalidation method measured before transplant and clinically indicated after trans- to avoid potential over-fitting. The gene set was then validated plant by Luminex (ThermoFisher). Mean fluorescence intensity for the diagnosis of ACR-3 using the microarray data from the

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GoCAR study (n=65; 26 overlapping with the RNAseq co- were lost before 12- or 24-month biopsies within each group. hort, Supplemental Material) and three public blood micro- Univariate comparisons of continuous variables were done array datasets for clinical acute rejection (GSE14346,21 using unpaired t test (Mann–Whitney test for corresponding GSE15296,22 and GSE5008419). GSE14346 is an expression nonparametric analysis). Normality of sample distributions was dataset of 75 patients with 38 clinical acute rejections.21 confirmed using Kolmogorov–Smirnov and Shapiro–Wilk tests. GSE15296 is an expression dataset for 75 patients receiving Kaplan–Meier survival curves were calculated with graft loss kidney transplant with 51 clinical acute rejections including 12 (all-cause and death-censored) as outcome. Cox proportional borderline cases.22 GSE5008 is an expression dataset of 42 hazard models were used for multivariable survival associa- patients receiving kidney transplant including 28 cases with tions, including donor and recipient demographics on the ba- DSAs and clinical acute rejection. All three datasets were gen- sis of a priori–determined clinical importance for outcome of erated on the Affymetrix platform.19 interest (i.e., graft survival). Only patients with 3-month bi- The novel sequencing-based targeted expression (TREx) opsy samples were included in the Kaplan–Meier or Cox re- analysis technology (Illumina) was used to develop a molecular gression survival analyses; hence, all cases had survived past 3 assay for the 17-gene set and 12 house-keeping genes with the months. Time was determined from the day of transplant to potential for application in the diagnosis of acute rejection in the graft loss. All statistical analyses for demographic and clinical practice (experimental details in the Supplemental clinical data were completed using SPSS Statistics V23 (IBM Material). The assay first evaluated sensitivity and reproduc- Analytics) and GraphPad Prism V6 (La Jolla, CA). Statistical ibility with universal reference RNA, brain reference RNA, and significance was considered with two-tailed P,0.05. RNA extracted from peripheral blood samples from GoCAR. Of the original 127 GoCAR samples used in the RNAseq and microarray cohorts, only 113 had RNA that was of sufficient RESULTS quality to be used for the TREx training set. A separate 64 samples from GoCAR patients combined with 46 patient sam- Study Cohorts ples from the Belgian cohort, with similar demographic and One hundred and ninety-one patients from the GoCAR study clinical characteristics in both cohorts (n=110, Supplemental that had peripheral blood RNA concurrent with 3-month kid- Table 1), composed the TREx independent testing set. The ney biopsy were included in this study (Figure 1).18 From this penalized logistic regression model was built on the expression group, 127 patients were randomly assigned to be used for values of 17 genes from the training dataset and tertile cutoffs identification of a peripheral blood gene signature associated on the basis of the probability scores were defined to stratify with subclinical acute rejection using RNA sequencing (n=88) patient risk of ACR into three groups: high, intermediate, and or microarray (n=65). Twenty-six patients underwent both low. The model from the training set was then applied to the RNAseq and microarray analyses, allowing correlation of independent testing set to compute the probability risk score gene expression between the two technologies (Supplemental and positive predictive value (PPV) and negative predictive Figure 2, Supplemental Material). Hence, 127 patients were value (NPV) on the basis of the tertile cutoffs in the second used as the training set for the development of a sequence- set of patients. based targeted expression assay (TREx) which was then vali- The later clinical end points of acute rejection after 3 dated on an independent cohort of 110 patients (64 GoCAR months, fibrosis (CADI score), and the risk of graft loss patients and 46 patients from the biobank of the University were evaluated in the high-, intermediate-, and low-risk Hospitals Leuven, Belgium) (Figure 1, Supplemental Table 1). groups, given the association between ACR-3 and these end The demographic and clinical characteristics of the sub- points in GoCAR. clinical ACR-3 and NACR-3 groups show similar graft function The RNA sequencing and microarray datawere deposited in at 3-month surveillance biopsy (Table 1). The only significant the National Center for Biotechnology Information Gene Ex- differences between ACR-3 and NACR-3 were donor age pression Omnibus database (GSE120398). (P=0.01), induction therapy (P=0.01), and m3 creatinine (P=0.04) (Table 1); however, these factors combined were un- Statistical Analyses able to accurately diagnose acute rejection (AUC=0.720 and Descriptive statistics (means and SD) were used to summarize crossvalidated AUC [cAUC]=0.672, Supplemental Figure 3A). the baseline characteristics of the ACR-3 and NACR-3 cohorts, Subclinical ACR is associated with later allograft fibrosis, and were compared using the chi-squared test and Fisher’s function decline, and loss. To study the natural history of allo- exact test. The diagnosis with selected significant demo- grafts with subclinical cellular rejection or borderline changes, graphic or clinical factors was estimated with logistic regres- we examined subclinical longitudinal histologic and functional sion and the AUC was calculated. Centrally reported Banff/ changes in the ACR-3 and NACR-3 GoCAR cohorts with 3-, CADI component scores were used for histologic compari- 12-, and 24-month surveillance biopsies. We excluded two pa- sons. For composite scores utilized in analysis (i.e.,CADI, tients with BK nephropathy on 3-month biopsy from outcome Ci+Ct), we imputed CADI scores=8 and Ci+Ct=6 (highest analysis. The ACR-3 group had significantly higher CADIscores scores in biopsy specimens at 12 months) for allografts that at 3-, 12-, and 24-month surveillance biopsies (CADI-3, CADI-12,

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GoCAR Cohort (N=191)

Gene set Transcriptomic Analysis Discovery (N=127) (N=127)

Discovery Cohort Validation Cohort (RNAseq N=88) (Microarray N=65 *)

Bad QC for TREx (N=14)

TREx Assay Development GoCAR BELGIAN (N=223) Independent Independent Cohort (N=64) Cohort (N=46)

TREx TREx Training Set Testing Set (N=113) (N=110)

*26 patients overlapped with RNAseq cohort

Figure 1. GoCAR (n5191) and Belgian (n546) cohorts were used in this study. Of 191 patients, 129 were randomly selected for transcriptomic analysis using RNA sequencing (n=88, discovery set) and microarray (n=65, validation) for identification of a peripheral blood gene signature to diagnose subclinical acute rejection. Of note, 26 patients were overlapped between the RNAseq and the microarray cohorts for correlation analysis of gene expression between the two technologies. The sequencing-based targeted ex- pression (TREx) assay was developed on the gene set identified from transcriptomic analysis. In TREx assay, 113 of 127 patients from the transcriptomic analysis cohort were used for the training set to build the penalized logistic regression statistical model which was validated on an independent testing cohort of 110 patients (64 GoCAR patients and 46 patients from the Belgian cohort). and CADI-24) compared with NACR-3 (Figure 2, A and B). When ACR-3 diagnosis did not correlate with increased CADI at 12 or analyzed separately, patients with borderline ACR (BACR-3) 24 months. This suggests that clinically meaningful subclini- alone also had increased CADI scores compared with NACR-3 cal ACR was missed by local reporting (Supplemental Table 3). at 3-, 12-, and 24-month surveillancebiopsies(Figure2, AandB). The incidence of de novo DSAs during 24-month follow-up Interestingly, patients with increased i or i+t scores at 3-month was similar in ACR-3 and NACR-3 (Table 1). Eleven patients biopsies were also more likely to have higher i and t scores on developed acute antibody-mediated rejection (ABMR), all of biopsy at 12 and 24 months, suggesting a persistent inflammatory which were in the first 3 months, eight of them in the ACR-3 phenotype in these patients (Table 2). Consistent with this, the group, and three in NACR-3 (ACR-3 versus NACR-3, OR, presence of ACR-3 was associated with a significantly higher risk 8.29; 95% confidence interval 1.89 to 50.55; Fisher’s of acute rejection at 12 (odds ratio [OR], 7.09; 95% confidence P,0.01). Only one of these cases of ABMR was seen on the interval, 2.82 to 17.81; P,0.01) and 24 months (OR, 3.96; 95% 3-month surveillance biopsy specimen (i.e., subclinical confidence interval, 1.20 to 13.02; P=0.02) compared with ABMR), whereas other cases occurred before the 3-month bi- NACR-3 (Figure 2, C and D). In biopsy specimens with ACR-3, opsy. Microvascular inflammation scores (g+ptc) and C4d chronic injury was already increased at 3 months compared with staining were increased in ACR-3 biopsy specimens (Table NACR-3 (Figure 2A). However, in multivariable regression anal- 2). When the 11 ABMR cases were excluded, microvascular ysis, ACR-3 was associated with significantly higher CADI scores inflammation scores were still higher in ACR-3 versus NACR- at 12 and 24 months even after adjustment for 3-month CADI 3 in the remaining 178 patients. Furthermore, the ACR-3 and Ci+Ct scores (Supplemental Table 2, Table 2). group, excluding ABMR cases, had higher CADI-12 and As part of GoCAR, all biopsy specimens were read by a CADI-24 scores and an increased risk of ACR-12 and ACR- central core with three experienced transplant pathologists. 24 when compared with NACR-3 (Supplemental Table 4). Forty-seven percent of the biopsy specimens in the GoCAR These data suggest that ACR-3 had increased histologic decline cohort were also read by the local pathologist, enabling com- independent of preceding ABMR episodes. Because four of 37 parison of scoring. Forty-eight percent of the BACR-3 cases ACR-3 biopsy specimens were C4d positive, meeting probable identifiedbythecorelaboratorywereclassified as NACR ABMR diagnosis despite absent DSA, and because g/ptc scores locally. In contrast to the core ACR-3 diagnosis, the local in the absence of DSA could result from TCMR lesions,23 we

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Table 1. Demographic characteristics of ACR-3 and NACR-3 Table 1. Continued group patients in GoCAR cohort Characteristics ACR-3 (n=46)b NACR-3 (n=145) P Value Characteristics ACR-3 (n=46)b NACR-3 (n=145) P Value CNI .0.99 Recipient age 46.52613.22 49.42613.66 0.2 N 1 (2.17) 4 (2.76) Recipient sex 0.07 Y 45 (97.83) 141 (97.24) Female 21 (45.65) 44 (30.34) 6 Male 25 (54.5) 101 (69.66) Data are mean SD or number (%). P value, comparison of ACR-3 with NACR- 3 (unpaired t test or nonparametric test), chi-squared or Fisher’s exact test. Recipient race 0.09 DM, diabetes mellitus; HTN, hypertension; PKD, polycystic kidney disease; White 30 (65.22) 90 (62.07) NDSA, non donor specific anti-HLA-antibody; DSA, donor specificantibody; Black 7 (15.22) 26 (17.93) CNI, calcineurin inhibitors. a Hispanic 1 (2.17) 13 (8.97) Only deceased-donor allografts included in analysis. bACR-3 includes 35 patients with BACR and 11 patients with $1a cellular Asian 7 (15.22) 7 (4.83) rejection. Patients with BK nephropathy at 3-mo biopsy were included in Other 1 (2.17) 9 (6.21) NACR-3. Kidney disease 0.12 DM 11 (23.91) 54 (37.24) GN 10 (21.74) 34 (23.45) examined ACR-3 biopsy specimens with/without C4d. C4d- HTN 6 (13.04) 25 (17.24) negative ACR-3 biopsy specimens had significantly higher PKD 7 (15.22) 10 (6.9) g+ptc score than NACR-3 (Supplemental Figure 3B). These bi- Other 12 (26.09) 22 (15.17) opsy specimens likely represent microvascular inflammation as- Previous renal transplant 0.48 sociated with TCMR. These cases also had higher CADI-12 and N 41 (89.13) 122 (84.14) CADI-24 scores and increased risk of ACR-12 and ACR-24 when Y 5 (10.87) 23 (15.86) compared with NACR-3 (Supplemental Table 5). These data Donor age 46.67617.08 39.46615.75 0.01 Donor sex 0.87 suggest that ACR-3 alone is associated with adverse outcomes Female 22 (47.83) 72 (49.66) independent of antibody-mediated injury. Male 24 (52.17) 73 (50.34) We compared the changes of eGFR from 3 (or 6) months Donor race 0.76 to 12 (or 24) months between ACR-3 and NACR-3 groups White 38 (82.61) 117 (80.69) (mean D eGFRs). ACR-3 recipients had greater declines in Black 4 (8.7) 9 (6.21) mean eGFR by 12 and 24 months compared with NACR-3 Hispanic 2 (4.35) 11 (7.59) (Supplemental Figure 3C). Adjusted Cox models showed Asian 2 (4.35) 4 (2.76) that, over a median (interquartile range) follow-up of 1713 Other 0 (0) 4 (2.76) (165–2793) days, ACR-3 was associated with an increased haz- Donor status 0.73 ard of death-censored and all-cause allograft loss versus Living 20 (43.48) 58 (40) NACR-3 (Figure 2E, Supplemental Table 6 [death-censored Deceased 26 (56.52) 87 (60) 3-mo serum creatininea 1.7661.28 1.3460.37 0.04 graft loss], Supplemental Figure 3D [all-cause graft loss]). Cold ischemia time (h)a 9.4567.93 9.86680.76 Delayed graft function 0.67 Peripheral Blood Transcriptomic Signatures Are N 36 (78.26) 118 (81.38) Associated with Subclinical Rejection Y 10 (21.74) 27 (18.62) We next evaluated transcriptomic signatures associated with ACR- Anti-HLA antibodies 3inperipheralbloodtakenfrom88patients(22ACR-3and66 Class-I 0.74 NACR-3)at the time of thebiopsy.Datawereanalyzed according to NDSA 19 (82.61) 22 (75.86) the outline depicted in Supplemental Figure 1. Comparison of DSA 4 (17.39) 7 (24.14) gene expression by LIMMA test20 on normalized data adjusted for Class-II 1 confounders associated with ACR in the GoCAR cohort (induc- NDSA 12 (85.71) 19 (86.36) tion therapy and deceased donor) identified 1115 DEGs (609 up- DSA 2 (14.29) 3 (13.64) P, De novo DSA 0.28 and 506 downregulated) associated with ACR-3 ( 0.05; Figure N 7 (77.78) 23 (92) 3A). Gene ontology enrichment analysis revealed that upregulated Y 2 (22.22) 2 (8) genes were involved in transcriptional regulation and cell cycle, Induction therapy ,0.01 whereas downregulated genes were involved in transport and cy- Lymphocyte-depleting 26 (56.52) 44 (30.34) toskeleton organization processes (Figure 3B). Canonical and in- Nondepleting 10 (21.74) 55 (37.93) genuity pathway analysis showed that these dysregulated genes None 10 (21.74) 46 (31.72) were involved in multiple pathways, including those related to 3-mo maintenance immunosuppression extracellular matrix, cell cycle, TGF-b signaling, B cell receptor Steroid 0.3 signaling, integrin signaling, Jak/Stat signaling, and leukocyte ex- N 1 (2.17) 11 (7.59) travasation signaling (Supplemental Figure 4A). The immune re- Y 45 (97.83) 134 (92.41) sponse genes were enriched in the most downregulated genes

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AB 6 4 ** *** ** ***** * ACR-3 3 4 BACR-3 NACR-3 2 Ci+Ct 2 CADI-scores 1

0 0 3-months 12-months 24-months 3-months 12-months 24-months

C D ACR-12 ACR-24 150 NACR-12 150 NACR-24 *** * 100 100

50 50 Percentage (n) Percentage (n)

0 0 NACR-3 ACR-3 NACR-3 ACR-3

E 1.0 NACR-3, N=145

0.9

ACR-3, N=46 0.8 Cum Survival 0.7

p < 0.01 0.6

.0 500.0 1000.0 1500.0 2000.0 2500.0 Follow-up duration (days)

Figure 2. ACR-3/BACR-3 is associated with adverse outcomes compared with NACR-3. Line graphs compare CADI (A and B) Ci+Ct scores between ACR-3 (bold red line), BACR-3 (BACR at 3 months, dotted red line), and NACR-3 (no-ACR at 3 months, green line) on serial 3-, 12-, and 24-month surveillance biopsy specimens (line through median, whiskers=EM). Bar graphs compare ACR prevalence on (C) 12- and (D) 24-month biopsy specimens in ACR-3 and NACR-3 groups. These increases in ACR and CADI at 12 or 24 months are subclinical observations. (E) Kaplan–Meier curves compare adjusted death-censored survival of ACR-3 (green) and NACR-3 (blue) groups in the GoCAR cohort (see Supplemental Table 1B). *P,0.05; ***P,0.001. ranked by fold-change (Supplemental Figure 4B). These findings pattern was replicated in publicly available blood expression are in keeping with the known immunologic processes involved in datasets of patients with acute rejection (Figure 3D). acute rejection. Immune cell type enrichment analysis using immune cell A Peripheral Blood Gene Signature Accurately profiles in the ImmGene database24 showed that, in addition to Diagnoses Subclinical Cellular Rejection macrophage and natural killer cells, pro-B or pre-T cell genes Of 1115 DEGs for subclinical acute rejection, we identified an were significantly associated with ACR-3 (Figure 3C), and this optimal gene set for the diagnosis of ACR-3 using a combination

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b of logistic regression and permutation-based approaches, as pre- 18 n 0.01 0.01 0.01 0.01 0.01 viously described. The discovery set ( =88) was randomly di- Value , , , , ,

P vided into two equal groups and LIMMA analysis was performed with 100 iterations to identify 240 transcripts (significant on $2 iterations) (see Methods section; Supplemental Table 7). A 17- =16) 0.9 0.6 0.01 1.1 0.8 0.38 0.3 0.39 0.7 0.5 0.72 1.1 0.01 2.1 2.6 n 6 6 6 6 6 6 6 6 6 6 gene set was then determined as an optimal set for the diagnosis of ACR-3 (AUC=0.980) with a leave-one-out crossvalidation (cAUC=0.833) (Figure 4A, Table 3). Diagnosis of ACR-3 using the 17-gene set was internally

24-mo validated on our microarray cohort (n=65; AUC/cAUC=1.000/ =56) ACR-3 ( n 0.7 0.81 0.7 0.94 0.3 0.63 0.4 0.27 0.7 0.07 0.6 0.94 0.7 0.20 0.7 1.56 1.9 2.76 2.7 4.33 0.802, respectively; Figure 4B, Supplemental Table 8). In ad- 6 6 6 6 6 6 6 6 6 6 dition, it accurately diagnosed ACR in three public microarray 6 (13.3) 0 (0.0) 0.09 3 (6.6%)4 (8.8%) 0 (0.0) 0 (0.0) 0.34 0.29 datasets that used blood taken at the time of clinically indi- NACR-3 ( cated biopsies at varying times post-transplant (Figure 4, C–E; b e e e GSE14346: AUC/cAUC=0.959/0.818; AUC=1.000 GSE15296: AUC/cAUC=0.980/0.832; GSE50084: AUC/cAUC=1.000/ 0.01 0.01 0.01 0.41 0.01 0.27 0.01 0.50 0.01 1.45 0.01 2.26 Value , , , , , , ,

P 0.979). These data show that the differential expression of our 17-gene set is identifiable in peripheral blood during sub- clinical or clinical ACR. =30) 0.8 0.03 0.41 0.7 0.04 0.57 0.5 0.30 0.09 0.9 0.03 0.14 0.8 0.41 0.23 0.5 0.8 1.1 1.9 2.8 brosis; Ct, tubular atrophy; Cv, vascular intimal sclerosis; mm, mesangial n fi 6 6 6 6 6 6 6 6 6 6 Development of TREx Assay for Diagnosis of ACR 0.20 Using sequencing-based TREx analysis technology, we developed a molecular assay to measure expression of the 17 12-mo genes on whole blood RNA that demonstrated high sensitiv- =90) ACR-3 ( SD. Ci, interstitial n 0.8 0.77 0.5 0.7 0.90 0.6 0.58 0.5 0.23 0.9 0.73 0.5 0.47 1.2 0.93 1.5 2.17 2.1 3.65 6 ity and reproducibility when evaluated with human universal 6 6 6 6 6 6 6 6 6 6 reference, brain reference RNA, and RNA from our clinical 5 (6.4%)4 (5.2%) 8 (32.0%) 2 (8.0%) 0.63 7 (10.0%) 8 (32.0%) samples (Supplemental Material, Supplemental Figures 1, 5, NACR-3 ( and 6, A–E). a e e e Next, we performed the TREx assay on 113 of the original 0.01 0.41 0.01 0.01 0.01 0.01 0.28 0.01 0.12 0.01 0.43 0.01 0.16 0.01 1.89 127 samples from the transcriptomic analysis to build the sta- Value , , , , , , , , , P tistic diagnostic model on the basis of the expression values

c of the 17 genes (TREx training set; Figure 1, Supplemental Figure 1). Fourteen samples were excluded because of poor =10) 1.0 0.7 0.01 0.67 0.7 0.15 0.27 1.1 0.0 0.47 0.13 0.7 1.7 0.5 1.7 0.03 1.16 2.4 n 6 6 6 6 6 6 6 6 6 6 QC. Expression of the 17 genes by TREx validated our original findings using RNAseq and microarray, clearly differentiat- ing between ACR-3 and NACR-3 (Figure 5A), with an AUC of 0.830 (Figure 5B). Tertile probability cutoffs (0.146 and 0.463) were defined to stratify the patients into three groups =36) ACR-3 ( n (high, intermediate, and low risk) with NPV=0.98 and 0.8 0.60 0.6 0.60 0.3 0.30 0.7 1.80 0.8 0.00 0.7 0.30 0.9 3.20 0.4 1.66 1.3 1.22 2.0 4.20 6 6 6 6 6 6 6 6 6 6 PPV=0.79 (Figure 5C). 3-mo ammation; t, tubulitis; CADI, Chronic Allograft Damage Index; ptc, peritubular capillaritis. 5 (14.2%) 2 (20.0%) Using the same analytic model derived on the training fl set, the 17-gene TREx assay was validated in the totally inde- pendent cohort of 110 subjects (64 independent GoCAR s test. ’ subjects+46 subjects from the Belgian cohort, Figure 1, Sup-

=143) Borderline ( plemental Figure 1; clinical epidemiologic data in Supplemen- 0.5 0.66 0.5 0.72 0.7 0.07 0.1 1.35 0.5 0.24 0.4 0.37 0.3 1.52 0.3 0.25 0.8 1.36 1.5 2.53 n 6 6 6 6 6 6 6 6 6 6 tal Material and Supplemental Table 1). The 17-gene TREx 2 (1.8%) 0.22 0.40 0.36 0.01 0.10 0.07 0.03 0.03 0.61 1.36 assay accurately diagnosed ACR-3 on the testing set with NPV=0.89 and PPV=0.73 using the tertile cutoffs (Figure 5D). NACR-3 ( value. P suspicious at 3-mo biopsy. 2 5 (4.5%) 8 (22.8%) 4 (40.0%) fi fi . $ test TREx Risk Pro le Strati es Risk for Late ACR and U Graft Loss CADI subscores in serial biopsy specimens Because ACR-3 correlated with graft loss in the GoCAR cohort, Wallis ANOVA and post-test Dunn 2 3 (2.7%) 5 (14.2%) 4 (40.0%) d – Whitney 0 or g+ptc 0 we determined whether the 17-gene TREx assay predicted sub- $ – . . sequent ACR and graft loss using all GoCAR patients included Banff Subscore Mann C4d by immunohistochemistry method. Chi-squared test comparing ACR-3 and NACR-3 groups. Kruskal ACR-3, Banff ACR n Table 2. At 12-mo and 24-mo biopsies, 83.3% and 81.2% of ACR-3 groupa were from BACR-3 group, respectively. Datab are mean c d e ci score matrix increase; g, glomerulosclerosis; i, interstitial in g+ptc C4d ct score cv score t score mm score g score I+t score i score Ci+Ct score CADI score C4d in this study ( =177). Patients in the high/intermediate group

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A

1.0 ETAA1

PHTF2 AVIL ZMAT1

ETNK1 ZBTB1 USP32P1 FHDC1 ANKRD12 ZNF493 KIAA1033NFYB FAR1 STAG2 ZNF292 FLJ31306 CAPZA2 ASTN2 SENP7 0.5 LYSMD3 THAP5 CLK1 LINC00672 CCDC82

BBS10 CLK4 DNAJB14 SP3 PSMC6 PCMTD1

0.0

THBS1 SIL1 PARVB ITGA2B Log2Rat SPARC PLTP BCKDHA FAM127A GP9 TSC22D1 SND1 SPOCD1 BRE –0.5 CD14 S100A10 TUBB1 CLU CTSD PRF1 PTGDS F13A1 MLC1 GZMH LGR6 GPR56 Log2Rat CMKLR1 S1PR5 CLIC3 0.5 SPON2 –1.0 0.0

FGFBP2 –0.5 –1.0

23 –log10(pvalue.AR) B regulation of transcription vesicle–mediated transport transcription protein localization RNA splicing intracellular transport 10.0 10.0 cell cycle cytoskeleton organization 7.5 7.5 RNA splicing response to DNA damage stimulus 5.0 5.0 cellular response to stress 2.5 regulation of lymphocyte differentiation 2.5 protein amino acid lipidation 0.0 cell death 0.0 DNA repair Ras protein signal transduction meiosis I positive regulation of leukocyte activation

0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 –log(p) –log(p) C D

-1.0 1.0 0.0 3.0

Macrophages GSE14346 GSE15296 GSE50084 Microarray RNAseq

Natural Killer pro-B cell preT cell Nature Killer cells Pro-B cells Macrophages Pre-T cells CD8 T cells Stem Cells CD4 T cells

B Cells Stromal cells Dendritic Stem Cells Monocytes NK T Cellsgd T Cells Pro-B Cells Cells NeutrophilsPre-T Cells CD4 T CellsCD8 T Cells Monocytes Macrophages Stromal Cells Nature KillerCD4CD8 Cell T Cells

Figure 3. Whole blood transcriptomic signatures of the patients at 3 months post-transplant are associated with subclinical acute rejection (ACR-3). (A) The volcano plot of DEGs between the recipients who developed or did not develop ACR-3. The x axis depicts the log2 ratio of gene expression and the y axis depicts the 2log10 of LIMMA P test. The top up- or downregulated genes are labeled with boxes. (B) The bar chart of significant gene ontology function groups by enrichment analysis on DEGs. The bars represent 2log10 P value of enrichment significance of gene pathways by Fisher exact test; the lengths of red and green bars represent the percentages of up- and downregulated genes, respectively. (C) Immune cell enrichment analysis of DEGs associated with ACR-3. The heatmap

1488 JASN JASN 30: 1481–1494, 2019 www.jasn.org CLINICAL RESEARCH had higher risk of subsequent subclinical ACR at 12 months sequencing of peripheral blood to identify a 17-gene set that (31.9% versus 13.6%; P=0.04) and 24 months (53.8% versus strongly correlated with the presence of ACR-3. Using TREx 32.1%; P=0.08) or of clinical ACR (Banff 1A or greater) at any technology with MiSEQ sequencer, a targeted sequencing time post-transplant (20.6% versus 6.1%; P,0.01) than the platform approved for clinical application, we developed low-risk group (Supplemental Table 9). This was also signif- and validated the 17-gene assay, demonstrating that recipients icant when the intermediate group was considered alone, with stratified into high, intermediate, and low risk for ACR-3. subsequent ACR 1A or higher and ACR/BACR occurring more The current approach to immunosuppression in kidney frequently than the low-risk group (14 of 85 versus four of 70; transplantation is protocol-driven, with adjustments dic- OR, 3.15; P=0.04, and 39 of 85 versus 20 of 70; OR, 2.12; tated by changes in serum creatinine—a poorly sensitive P,0.01, respectively). ABMR episodes, nine of which oc- marker for graft damage. Surveillance biopsies could help curred before the 3-month biopsy, and de novo DSAs were define the intensity of the alloimmune response and guide not significantly different between risk groups (Supplemental immunosuppression accordingly, but they are costly, time- Table 9). consuming, and burdened by a risk of potential complica- The clinical outcomes of GoCAR and the Belgian cohort tions. Our assay enables the identification of underlying in- used for TREx assay are summarized in Supplemental Table 10. flammation, while serum creatinine is still within the normal Multivariable Cox models performed on the combined range, allowing surveillance of the graft status without the GoCAR/Belgian cohorts (n=223) demonstrated that patients need for a biopsy and before there is functional evidence in the high- or intermediate-risk groups had lower death- of injury. Interestingly, it identified ACR in four patients censored graft survival compared with the low-risk group with DGF in whom creatinine is not a functional marker (Figure 5E, Supplemental Table 11). Collectively, these data of rejection. The 17-gene set has the potential to be useful indicate that our TREx assay of the 17-gene set not only ac- to diagnose subclinical rejection at other time points, iden- curately differentiates those at high versus low risk of ACR-3, tifying clinical ACR in the publicly available data sets in but also gives their ongoing risk for rejection and graft loss. which biopsy specimens were taken at multiple times post- Despite the fact that 74.2% of the intermediate group were transplantation. Therefore, these data provide the back- NACR-3, we observed that the group overall had a significantly ground for future studies testing the hypothesis that serial higher risk of graft loss compared with the low-risk group measurements of our assay may guide maintenance immu- (Supplemental Table 11). Stratified analysis comparing the nosuppression management more accurately than our cur- intermediate-risk NACR-3 (I-NACR-3) with the low-risk rent approach using creatinine. NACR-3 (L-NACR-3) demonstrated that the I-NACR-3 and Interestingly, even in the absence of ACR-3 in the interme- the L-NACR-3 had similar Banff subscores at 3 months, with diate group, the TREx-based risk stratification also associates the exception of the mm-scores (Supplemental Table 12). with detection of inflammatory infiltrates in subsequent sur- However, I-NACR-3 developed significantly higher CADI veillance biopsy specimens, and graft function decline and loss. and Ci+Ct scores by 24 months (P=0.01 and ,0.01, respec- This may be explained by the fact that renal biopsy specimens tively; Supplemental Table 12) and had greater risk of graft loss represent 3-mm sections of the total core and may not than L-NACR-3 in adjusted Cox regression (Figure 5F, Sup- capture a focal phenomenon like allograft rejection.25 Besides plemental Table 13). Of note, graft survival in I-NACR-3 did limitations in overall representation, the clinical utility of graft not differ significantly from I-ACR-3 (data not shown). These biopsy is limited by the subjectivity of biopsy reporting. These data demonstrate that biopsy specimens with NACR classified findings emphasize the potential clinical utility of the 17-gene as intermediate are not simply misclassified, but rather have set to avoid pitfalls in the reporting of renal biopsy specimens. outcomes that are truly intermediate between the high- and Although baseline anti-HLA antibodies, including DSAs low-risk groups. and non-DSA, all of which were at low level, and de novo DSAs did not significantly associate with graft loss in our co- hort, ABMR episodes and microvascular inflammation scores DISCUSSION were all more common in ACR-3 versus NACR-3, implying the coexistence of endothelial injury with cellular inflamma- In this study, we used the extensively phenotyped GoCAR co- tion. Of the ten episodes of ABMR, nine were clinical ABMR hort to accurately identify ACR-3 and demonstrate its associ- and occurred before the 3-month biopsy. ation with progressive allograft damage and functional decline, The DEGs associated with ACR-3 were predominantly resulting in a higher risk for graft loss. We then used RNA from pathways not related to the immune response or

shows expression of DEGs that were significantly enriched for immune cell types in the ImmGene dataset. (D) The heatmap of enrichment P value (2log10 P) of immune cell–specific signatures in DEGs between ACR and non-ACR in GoCAR RNAseq, microarray, and three public datasets (GSE14346, GSE15296, and GSE50084). AR, acute rejection; gd, gama delta; NK, natural killer.

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AB 1.0 1.0

0.8 0.8

0.6 0.6

0.4 0.4 True Positive Rate True Positive Rate 0.2 0.2 original AUC = 0.980 original AUC = 1.000 cross validated AUC = 0.833 cross validated AUC = 0.802 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 False Positive Rate False Positive Rate

CDGSE14346 GSE15296 1.0 1.0

0.8 0.8

0.6 0.6

0.4 0.4 True Positive Rate True Positive Rate 0.2 0.2 original AUC = 0.959 original AUC = 0.980 cross validated AUC = 0.818 cross validated AUC = 0.832 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 False Positive Rate False Positive Rate

E GSE50084 1.0

0.8

0.6

0.4 True Positive Rate 0.2 original AUC = 1.000 cross validated AUC = 0.979 0.0 0.0 0.2 0.4 0.6 0.8 1.0 False Positive Rate

Figure 4. The 17-gene set for diagnosis of ACR-3 was identified from GoCAR and validated in internal and external datasets. (A) The receiver operating characteristic (ROC) curve for diagnosis of ACR-3 with 17-gene set in GoCAR RNAseq discovery set (n=88; AUC=0.980, shown by black curve; leave-one-out cAUC=0.833, shown by blue curve). (B) The ROC curve for diagnosis of ACR-3 with 17-gene set in GoCAR microarray validation set (n=65, AUC/cAUC=1.000/0.802). (C) The ROC curve for diagnosis of ACR with 17-gene set in a public dataset (GSE14346: AUC/cAUC=0.959/0.818). (D) The ROC curve for diagnosis of ACR with 17-gene set in a public dataset (GSE15962: AUC/cAUC=0.988/0.832). (E) The ROC curve for diagnosis of ACR with 17-gene set in a public dataset set (GSE50084: AUC/cAUC=1.000/0.979).

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Table 3. Seventeen-gene set for 3-month ACR diagnosis

Symbol RefSeq Name P Value log2 Ratio ZMAT1 NM_001011657 Zinc finger, matrin-type 1 0.01 0.744899 ETAA1 NM_019002 Ewing tumor-associated antigen 1 0.04 0.703351 ZNF493 NM_001076678 Zinc finger protein 493 0.002 0.663139 CCDC82 NM_024725 Coiled-coil domain containing 82 0.02 0.622008 NFYB NM_006166 Nuclear transcription factor Y, b 0.03 0.589093 SENP7 NM_001077203 SUMO1/sentrin specific peptidase 7 ,0.001 0.581319 CLK1 NM_001162407 CDC-like kinase 1 0.01 0.567156 SENP6 NM_001100409 SUMO1/sentrin specific peptidase 6 0.01 0.510019 C1GALT1C1 NM_001011551 C1GALT1-specific chaperone 1 0.01 0.292181 SPCS3 NM_021928 Signal peptidase complex subunit 3 homolog (S. cerevisiae) 0.03 0.221611 MAP1A NM_002373 Microtubule-associated protein 1A 0.01 20.27124 EFTUD2 NM_001142605 Elongation factor Tu GTP binding domain containing 2 0.001 20.33174 AP1M1 NM_001130524 Adaptor-related protein complex 1, mu 1 subunit ,0.001 20.33596 ANXA5 NM_001154 Annexin A5 ,0.001 20.36446 TSC22D1 NM_001243797 TSC22 domain family, member 1 0.01 20.44781 F13A1 NM_000129 Coagulation factor XIII, A1 polypeptide 0.02 20.5544 TUBB1 NM_030773 Tubulin, b 1 class VI 0.03 20.55441 lymphocyte activation, but rather cell repair, metabolism, and biopsy specimens we have been able to define a unique signa- stress response pathways (Figure 3B), in contrast to previously ture that effectively identifies even borderline subclinical re- reported expression of immune response–related genes in the jection, and predicts the risk for ongoing immune injury and graft biopsy specimens.18 This has previously been described graft loss. Second, validation on public cohorts demonstrated by others26 and could potentially reflect migration of immune the ability of the gene set to identify clinical ACR at multiple cells from the periphery to the allograft. However, immune time points. Third, we have technically validated our assay on cell analysis revealed enrichment for pro-B or pre-T cells or multiple platforms and developed an accurate and reproduc- stem cells in the GoCAR cohort and public validation datasets ible clinically applicable assay (TREx) using MiSEQ with clinical acute rejection, suggesting active B and T cell sequencer, a sequencing platform which is approved by the proliferation, consistent with an active immune response in Food and Drug Administration for clinical application. This the setting of ACR. is highly reproducible and provides absolute transcript Other studies have examined peripheral blood biomarkers measures. This contrasts significantly with other studies in to diagnose ACR.14,21,22,27,28 Initial data using donor-derived which the methods used will be hard to implement on a larger cell-free DNA demonstrated that levels correlated with clin- scale, such as, microarray15 or PCR.21 Lastly, our assay per- ically severe acute T cell–mediated rejection ($Banff Ib) and forms with a high degree of accuracy even in validation co- ABMR; however, leakage of donor-derived cell-free DNA horts with high NPVs and PPVs. This contrasts with a more may lack specificity for rejection and instead reflect graft in- recent paper using gene expression for diagnosis of ACR, in jury in general.29 Several studies have examined peripheral which a microarray assay with 57 genes reported by Friedwald gene expression for the diagnosis of ACR; however, there and colleagues,15 representing a technical challenge for clin- are several pertinent differences compared with the data pre- ical implementation, performed poorly, with PPVs in 51% sented here. First, we used an unbiased approach through and 47% in two validation cohorts, and NPVs in the range RNAseq profiling for gene selection, with both clinical and of 70%.15 Our 17-gene set did not overlap with their 57 technical validation on multiple platforms. Other groups have genes,15 which could be due to the following reasons: (1) taken a reductive approach, narrowing down the genes for Assay variation: we used RNA sequencing for biomarker dis- consideration on the basis of those that are related to immune covery, whereas microarray was applied in their study. RNA- cell expression, and have focused on clinical acute rejec- seq allows absolute quantification of transcripts. Microarray tion.14,21 Prior data have shown that immune response genes allows relative quantification and is restricted to predefined areelevatedintheallograftduringrejectionepisodes,but probe sets. (2) We only investigated 3-month blood profiles, share minimal overlap with simultaneous gene signatures in whereas they analyzed the expression profiles at various times periphery26; thus, by only focusing on immune response from 4 to 24 months; overall gene expression profiles in blood genes, the genes of highest expression and reflecting the great- were reported to change upon immune suppression admin- est changes in expression are excluded from the analysis. This istration post-transplant30. will also account for the lack of overlap between our gene set In summary, our study highlights the negative effect of andthegenesetinthestudybyRoedderet al. 14 By including untreated early subclinical inflammation on subsequent his- all genes and building the diagnostic model with protocol tologic and functional decline in kidney allografts. We

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A C -1.0 1.0 ACR ZNF493 CIGALT1C1 0.8 Graft Loss SENP7 CLK1 ACR and Graft Loss SENP6 CCDC82 0.6 ZMAT1 PPV = 0.79 NFYB ETAA1 SPCS3 0.4 AP1M1

ANXA5 Probabilities F13A1 MAP1A 0.2 EFTUD2 TSC22D1 NPV = 0.98 TUBB1 0.0 ACR-3 NACR-3 0 20406080100 Samples

B D 1.0 0.8 ACR 0.8 Graft Loss 0.6 ACR and Graft Loss PPV = 0.73 0.6 0.4 0.4

Probabilities 0.2 0.2 True Positive Rate AUC = 0.830 NPV = 0.89 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0 20406080100 False Positive Rate Samples

E F 1.00 1.00 Low Risk, N=89 Low Risk, N=67 0.95 0.95 High/ 0.90 Intermediate Intermediate Risk, N=134 0.90 Risk, N=63 0.85 0.85

Cum Survival 0.80 Cum Survival

0.80 0.75 p = 0.04 p = 0.03 0.70 0.75

.0 .0 500.0 500.0 1000.0 1500.0 2000.0 2500.0 1000.0 1500.0 2000.0 2500.0 Follow-up (days) Follow-up (days)

Figure 5. ACR-3 diagnosis with 17-gene set was validated by TREx assay. (A) The heatmap of expression of 17-gene set in TREx training set (n=113); ACR-3 or NACR-3 cases ordered by risk scores are on the left or right of the vertical yellow line, respectively. The up- or downregulated genes in ACR-3 are above or below the horizontal yellow line, respectively. (B) The ROC curve for diagnosis of ACR-3 with 17-gene set in the training set (n=113, AUC=0.830). (C) The dot plot of the probability risk scores for the patients in the training set (n=113, PPV=0.79, NPV=0.98 at tertile cutoffs). (D) The dot plot of the probability risk scores for the patients in the testing set (n=110, PPV=0.73, NPV=0.89 at tertile cutoffs defined from the training set). (E) Kaplan–Meier curve of graft loss for the kidney transplant recipients stratified into two groups (high/intermediate and low probability risks) in TREx (n=223). (F) The Kaplan–Meier curve of graft loss with the kidney transplant recipients without ACR (NACR-3) stratified by intermediate or low probability risks in TREx cohort. Cum, cumulative. found a peripheral blood 17-gene set utilizing a novel TREx with the ultimate goal of controlling subclinical intragraft assay that accurately diagnoses subclinical rejection, includ- inflammation and prolonging graft survival. However, these ing borderline lesions, and stratifies renal recipients accord- findings need further, larger studies to validate the clinical ing to those at high risk for histologic decline and allograft utility of this assay before it can be used as a substitute for loss. Our assay offers the potential to be used as an immune- renaltransplant biopsies for the determination of clinical and monitoring tool to guide the use of immunosuppression, subclinical rejection.

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ACKNOWLEDGMENTS Supplemental Methods. Supplemental Results. We thank the Genomics Resources Core Facility at Weill Cornell Supplemental Figure 1. Correlation between RNA sequencing and Medical Center for sequencing and TREx experiments and Scientific microarray data. Computing at the Icahn School of Medicine at Mount Sinai for Supplemental Figure 2. Association of demographic and patho- providing computational resources. logic characteristics with clinical outcomes. This project is a substudy of the GoCAR study supported by Supplemental Figure 3. Genomic data analysis workflow. National Institutes of Health grant 5U01AI070107-03. Supplemental Figure 4. Pathway and gene ontology enrichment Dr. Zhang, project leader, study design, analysis and interpretation analysis for DEGs associated with ACR-3. of genomic data, and drafting of manuscript. Miss. Yi, genomic data Supplemental Figure 5. The procedure of development of TREx analysis (major contribution) and interpretation. Dr. Keung, analysis assay for 17-gene set. and interpretation of clinical data. Dr. Shang, TREx experiments. Supplemental Figure 6. Development of TREx assay for 17-gene Dr. Wei, sample preparation and quality control. Dr. Cravedi, analysis set. and interpretation of clinical data and critical revision of draft. Supplemental tables. Mr. Sun, genomic data analysis. Miss. Xi, clinical data management/ Supplemental Table 1. Comparison of demographic statistics be- query. Mr. Woytovich, sample preparation. Dr. Farouk, analysis and tween GoCAR and Belgian dataset. interpretation of clinical data. Dr. Huang and Dr. Banu, data in- Supplemental Table 2. ACR-3 predicts CADI-12 and -24 in- terpretation and manuscript revision. Dr. Gallon, patient enrollment dependent of simultaneous chronic damage indices. and follow-up. Dr. Magee and Dr. Najafian, pathology reporting. Supplemental Table 3. Comparison of local and central biopsy Dr. Samaniego, patient enrollment and follow-up. Dr. Djamali, pa- reports at 3-month biopsy. tient enrollment and follow-up. Dr. Alexander, study design discus- Supplemental Table 4. Comparison of clinical characteristics be- sion. Dr. Rosales, pathology reporting. Dr. Smith, pathology re- tween ACR-3 and NACR-3 without AMBR. porting. Dr. Xiang, TREx and sequencing experiments. Dr. Lerut, Supplemental Table 5. Comparison of clinical outcomes post 3 sample management and preparation of Belgian cohort. Dr. Kuypers, months between C4d-negative ACR-3 and NACR-3 groups. clinical data management of Belgian cohort. Dr. Naesens, principle Supplemental Table 6. ACR-3 independently predicts long-term investigator of Belgian cohort study, and critical review of manu- allograft survival. script. Dr. O’Connell, interpretation of clinical data, discussion of Supplemental Table 7. The list of 240 focus genes set. study design, and critical review of manuscript. Dr. Colvin, pathology Supplemental Table 8. Demographiccharacteristics of RNAseq and reporting. Dr. Menon, analysis and interpretation of clinical data, and microarray cohorts in GoCAR cohort. critical review of drafting of manuscript. Dr. Murphy, principle in- Supplemental Table 9. Frequency of anytime rejection episodes in vestigator, study conception and design, and drafting of manuscript. TREx risk groups. Supplemental Table 10. Summary of clinical events of TREx co- horts post kidney transplant. DISCLOSURES Supplemental Table 11. TREx risk group status affects allograft survival. Dr. Zhang reports personal fees from RenalytixAI, outside the submitted Supplemental Table 12. Comparison of Banff scores between in- work; in addition, Dr. Zhang has a patent ‘Method for identifying kidney termediate- and low-risk NACR-3 groups. ’ ‘ allograft recipients at risk for chronic injury pending, a patent Methods for Supplemental Table 13. High-/intermediate-risk NACR-3 affects diagnosing risk of renal allograft fibrosis and rejection (miRNA)’ pending, a allograft survival. patent ‘Method for diagnosing subclinical acute rejection by RNA sequencing analysis of a predictive gene set’ pending, and a patent ‘Pretransplant predic- tion of post-transplant acute rejection’ pending. Dr. Kuypers reports grants and personal fees from Astellas, outside the submitted work. Dr. Murphy REFERENCES reports personal fees from RenalytixAI, outside the submitted work; in addi- tion, Dr. Murphy has a patent ‘Method for identifying kidney allograft recip- ients at risk for chronic injury’ pending, a patent ‘Methods for diagnosing risk 1. Hart A, Smith JM, Skeans MA, Gustafson SK, Stewart DE, Cherikh WS, of renal allograft fibrosis and rejection (miRNA)’ pending, a patent ‘Method et al.: Kidney. Am J Transplant 16[Suppl 2]: 11–46, 2016 for diagnosing subclinical acute rejection by RNA sequencing analysis of a 2. 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AFFILIATIONS

1Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York; 2Department of Medicine, Westmead Clinical School, The University of Sydney, Sydney, New South Wales, Australia; 3Department of Microbiology and Immunology, Cornell Medical Center, New York, New York; 4Department of Medicine-Nephrology, Northwestern University Feinberg School of Medicine, Chicago, Illinois; 5Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts; 6Division of Nephrology, Department of Medicine, Henry Ford Hospital, Detroit, Michigan; 7Division of Nephrology, Department of Medicine, University of Wisconsin, Madison, Wisconsin; 8Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Departments of 9Pathology and 11Nephrology and Renal Transplantation, University Hospitals Leuven, Leuven, Belgium; and 10Department of Microbiology and Immunology, Katholieke Universiteit Leuven, Leuven, Belgium

1494 JASN JASN 30: 1481–1494, 2019 1 Supplementary Materials 2 Table of Contents...... 1 3 Supplementary methods ...... 2 4 Supplementary results...... 6 5 Supplementary figures...... 8 6 Figure S1: Correlation between RNA sequencing and microarray data...... 8 7 Figure S2: Association of demographic and pathological characteristics with clinical 8 outcomes …………………………………………………...... 9 9 Figure S3: Genomic data analysis workflow….….………...... 10 10 Figure S4: Pathway and Gene Ontology enrichment analysis for DEGs associated 11 with ACR-3………………………………………………………..…………….11 12 Figure S5: The procedure of development of TREx assay for 17-gene set………...... 12 13 Figure S6: Development of TREx assay for 17-gene set…...... 13 14 Supplementary tables...... 14 15 Table S1: Comparison of demographic statistics between GoCAR and Belgian 16 dataset...... 14 17 Table S2: ACR-3 predicts CADI-12 and 24 independent of simultaneous chronic damage 18 indices...………………………………………………………………….……...16 19 Table S3: Comparison of local and central biopsy reports at 3-month biopsy……..……….17 20 Table S4: Comparison of clinical characteristics between ACR-3 and NACR-3 without 21 AMBR ….……………………………………………………………………….18 22 Table S5: Comparison of clinical outcomes post 3 month between C4d negative ACR-3 and 23 NACR-3 groups……………………………………….…………………………19 24 Table S6: ACR-3 independently predicts long-term allograft survival……...……………..20 25 Table S7: The list of 240 focus gene set……………………………………...…………….21 26 Table S8: Demographic characteristics of RNAseq and Microarray cohorts in GoCAR 27 Cohort……………………………………………………………………………30 28 Table S9: Frequency of anytime Rejection episodes in TREx risk groups………………...31 29 Table S10: Summary of clinical events of TREx cohorts post kidney transplant…………...32 30 Table S11: TREX-risk group status impact allograft survival………………………………33 31 Table S12: Comparison of Banff scores between Intermediate- and Low-risk NACR-3 32 Groups………………………………………………………………………………..34 33 Table S13: High/Intermediate risk NACR-3 impacts allograft survival……………………..35 34 References...... 36 35

36 37 Supplementary Methods:

38 RNA sequencing

39 Total RNA was extracted from blood samples using Trizol and the RNA quality was

40 assessed by the Bioanalyzer 2100 (Agilent Technologies). The libraries were generated by

41 following manufactory protocol and were sequenced on Illumina HisSeq2000 sequencer: Briefly,

42 mRNA was firstly extracted from 2ug of total RNA using oligo-dT magnetic beads and

43 fragmented at high temperature. A cDNA library was then prepared from the fragmented mRNA

44 by reverse transcription, second strand synthesis and ligation of specific adapters. Next generation

45 sequencing was performed on Illumina Hiseq 4000 (Illumina Inc.) with single-ended 51 read

46 cycles. Image analysis and bases calling was conducted in real-time by the Illumina analysis

47 pipeline.

48 The raw RNAseq data was processed as follows: The clean reads with good quality were

49 firstly aligned to several human reference databases including hg19 human genome, exon,

50 splicing junction and contamination database including ribosome and mitochondria RNA

51 sequences using BWA 1 alignment algorithm. After filtering the reads mapped to contamination

52 database, the reads that are uniquely aligned to the exon and splicing-junction sites with a

53 maximal 2 mis-matches for each transcript were then counted as the expression level for

54 corresponding transcript and further subjected to quantile normalization cross samples after log2

55 transformation.

56 Microarray experiments

57 Microarray experiments using Affymetrix human Exon 1.0 ST geneChip were performed

58 on total RNA of blood samples following standard protocols provided by the manufacturer

59 (Affymetrix). Briefly, ENCORE amplification and labeling kit (NuGen, San Carlos, CA) was

60 applied to blood RNA samples starting with approximately 100 ng of total RNA to generate

61 biotin-labeled RNA fragments for hybridization to the chip. The chips were scanned by GeneChip

62 Scanner 7G ( Affymetrix Inc) 63 The raw intensity data of Exon geneChip experiments at gene level were extracted and

64 summarized with RMA algorithm 2 and data quality was assessed in Affymetrix Expression

65 Console (Affymetrix Inc). The Affymetrix control probesets or the probesets with low intensity

66 across all samples were excluded from downstream analysis.

67 Correlation of microarray and RNA sequencing data were investigated on 26 patients.

68 Top 10 percentile of genes with the most variable expression levels across the samples were

69 selected from RNA sequencing and microarray data for Pearson correlation analysis.

70 Design of sequencing-based targeted expression (TREx) assay

71 The TREx assay for diagnosis of acute cellular rejection using peripheral blood was

72 designed using the 17-gene set (Figure S5). Twelve house-keeping genes with following criteria

73 were included: 1) minimum variation gene expression across samples and 2) expression values at

74 the average level for all genes detected by RNA sequencing. Sixty-four PCR primers assays were

75 designed for the 17 genes, 12 housekeeping genes and the controls from Illumina Design Studio.

76 The assay kit was manufactured by Illumina Inc (Product No. 75629, Illumina Inc.). PCR was

77 performed on total RNA to amplify the groups of genes using the primer sets and to generate

78 sequencing libraries on the amplicons. After barcoding, the libraries were sequenced using

79 MiSEQ sequencer. The short sequencing data for each sample were analyzed using the

80 sequencing analysis pipeline after de-multiplexing mixed raw sequences.

81

82 Bioinformatics data analysis:

83 Identification of ACR-3 gene set using RNA sequencing

84 Data analysis workflow to identify a set of focus genes for the diagnosis of acute

85 rejection post-transplant and subsequently develop a TREx assay was depicted in Suppl. Figure

86 1.

87 Using RNA sequencing data of 88 patients we identified genes correlated with ACR-3,

88 based on unpaired differential LIMMA test 3 with p value < 0.05 by including clinical factors 89 (induction therapy and deceased donor) as confounders. Biological functional/pathways for the

90 DEGs were determined by enrichment analysis with fisher-exact test using the databases of Gene

91 Ontology (GO) and pathways (KEGG, Ingenuity, BIOCARTA, NABA, Panther, PID,

92 REACTOME, Wiki-pathway). The immune cell types correlated with ACR-3 were evaluated by

93 fisher-exact test of enrichment of immune cell specific genes amongst the DEGs. The immune

94 cell specific genes were identified from ImmGene databases as described previously 4.

95 We next chose a focused geneset that was specifically associated with ACR-3 from the

96 pre-selected ACR-3 genes using an approach of 100-times randomization analysis described

97 previously 4. Briefly, the whole cohort was randomly assigned to 2 groups of equal size (1:1 ratio)

98 and LIMMA testing was performed on each group to identify DEG associated with ACR-3, and

99 this process was repeated 100 times. Genes that occurred more than twice in the 100 iterations

100 with a LIMMA P<0.05 were considered as the focused geneset. An optimal gene set with the

101 highest AUC (area under the receiver operating characteristic (ROC) curve) for prediction of

102 ACR-3 was then determined after fitting penalized logistic regression model on expression data

103 of the focus geneset with 5000 time iterations4. The process started by randomly selecting 20

104 genes from the focus geneset. The expression values of the 20-gene group were fitted into the

105 penalized regression model in logistf R package for ACR-3 diagnosis. The penalized logistic

106 regression model used Firth’s bias reduction method to reduce the bias of maximum likelihood

107 estimates due to small sample size, which will resolve the issue of overfitting from standard

108 logistic regression method. The genes that were significantly associated with AC-3 were

109 identified from the regression model for the 20-gene group. These steps were repeated 5000 time

110 and statistically significant genes (P<0.05) were identified from each iteration. The occurrence of

111 significant genes from the 5000 iterations was computed. Finally, the top 40 genes ranked by the

112 occurrences were applied back to the penalized logistic regression model for ACR-3 diagnosis.

113 Statistically significant genes (P<0.05) in this model were considered as the final optimal geneset. 푒∑ 훽푋 114 The probability was calculated based on the formula 푃 = where 훽 are coefficients 1+푒∑ 훽푋

115 generated from the penalized logistic regression model using the final geneset and 푋 are

116 expression values ( normalized sequencing read count). The AUC was calculated to estimate the

117 overall accuracy for the diagnosis of ACR-3. The final gene set was cross-validated using a leave-

118 one-out cross-validation method to avoid over-fitting issue of self-training of the dataset. In leave

119 one crossvalidation for a dataset (n samples), one sample was left out and the model was built on

120 the rest of samples (n-1) and applied to the left-out one sample to generate the probability score.

121 This step was repeated n times until all samples were tested. AUC was calculated based on the

122 probability scores of all the samples. The gene set was validated for diagnosis of ACR-3 on

123 microarray data for 65 GOCAR patients and 3 public blood microarray datasets for clinical acute

124 rejection (GSE14346 5, GSE15296 6 and GSE50084 7)

125

126 127 Supplementary Result:

128 Correlation of RNA sequencing and microarray data

129 Due to different scale of gene expression measurement by RNA sequencing and

130 microarray technologies, we firstly subtracted the expression value of each gene by its median

131 value across 26 samples to generate relative expression values for RNAseq and microarray,

132 respectively. We then calculated the sample correlation between RNAseq and microarray based

133 on the relative expression values of top 10% of the genes with the most variable expression. The

134 average Pearson correlation was 0.83±0.06 (Figure S2). Our data indicated that RNA sequencing

135 and microarray had good correlation in detecting expression changes among the samples

136

137 Development of TREx assay

138 We developed a molecular assay of 17 gene set from blood RNA to diagnose acute

139 rejection using sequencing-base targeted RNA expression (TREx) analysis technology (Figure

140 S5). Prior to application of blood RNA from transplant patients, the TREx assay was tested with

141 universal human RNA (UHR) and brain RNA samples. The replicated experiments with test

142 samples showed high reproducibility (R=0.993 for UHR samples; Figure S6a), and the fold

143 change for brain RNA versus UHR samples showed concordance between standard RNA

144 sequencing and targeted RNA sequencing ( R=0.949; Figure S6b). The reproducibility of TREx

145 assay on blood RNA from transplant recipients was similar to universal reference samples

146 (R=0.998; in Figure S6c) and overall reproducibility by heatmap (median R =0.978

147 [0.840~0.998]; Figure S6d). The median correlation between standard RNA sequencing and

148 targeted RNAseq data of blood RNA from 87 transplant recipients is 0.87 [0.933-0.745] (Figure

149 S6f). These data on high quality reference samples and clinical blood RNA from kidney

150 transplant recipients indicated that the TREx assay we developed is a reliable assay for clinical

151 diagnosis.

152 153 Demographic characteristics of Belgian cohort

154 46 patients with 3-month allograft biopsies and simultaneous PAXgene RNA were

155 collected from University of Leuven hospital, Belgium. The clinical epidemiologic characteristics

156 of the Belgian cohort are compared with GoCAR cohort in Table S1. The Belgian cohort had

157 similar a ACR-3 rate and follow-up as GoCAR, but differed in donor/recipient demographics

158 given the Northern European ancestry of its population and immunosuppression protocols with no

159 lymphocyte depleting therapy and a statistically higher number of patients on a steroid

160 withdrawal maintenance regimen (Table S1).

161

162 Figure S1. Genomic data analysis workflow: The genomic data analysis workflow includes transcriptomic analysis for identification of ACR-3 diagnosis gene set and TREx assay validation. The transcriptomic analysis identified the transcriptomic signatures and pathways associated with ACR-3 in the RNAseq discovery set and further discovered a 17- gene set for ACR-3 diagnosis which was validated in GoCAR microarray and public dataset. TREx assay was developed for 17-gene set. 113 out of 127 patients in transcriptomic analysis cohort was used as training set to build a penalized logistic regression model which was validated on an independent ACR-3 testing cohort (N= 110 ).

m3-ACR Geneset Discovery Discovery set (m3, N=127)

RNAseq Set (N=88)

Expression Differential Analysis

Pathway/Function m3-ACR Analysis associated Genes Microarray Set (N=65) Logistic Regression (26 duplicated with RNAseq) Analysis Validation 17 gene set 3 Public data sets

TREx Assay Validation TREx Assay Panel (17 genes+12 Training set (N=113, GoCAR discovery set) Housekeepers) Building model

Technical Validation Logistic Model and (reference RNA) Tertile Cutoffs Model validation

Independent cohort (m3 ACR, N=110) Figure S2. The heatmap of Pearson correlation matrix of RNA sequencing and Microarray data on 26 patients.

P9

P8

P7

P6

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P4

P3

P26

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P24

P23 Correlation P22 (−0.8,0.3] P21 (0.3,0.6] P20 (0.6,0.8] P2 (0.8,1] P19

P18

P17 RNA sequencing RNA P16

P15

P14

P13

P12

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P1

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16 P17 P18 P19 P20 P21 P22 P23 P24 P25 P26

Microarray Figure 3S. Association of demographic and pathological characteristics with clinical outcomes: a) The Receiver operating charac- teris- tic (ROC) curve of association of donor age, induction types and 3m creatinine with ACR-3 ( AUC (area under the curve) = 0.720 (black curve) and cross-validated AUC=0.672 (blue curve)); c) The bar charts compares delta eGFR ( 12m-6m, 12m-3m, 24m-3m or 24m-6m) between ACR-3 and NACR-3 d) Kaplan Meier curves compare all cause survival of ACR-3 (green) and NACR-3 (blue) groups in the GoCAR cohort.

a) b) 1.0 0.8 e Rate 0.6 ositi v 0.4 ure P T

0.2 original AUC = 0.720 cross validated AUC = 0.672 0.0

0.0 0.2 0.4 0.6 0.8 1.0 False Positive Rate d)

c) d) e)

ns NACR-3, N=145 50 * *** **

R 0 F

G ACR-3, N=46 e a t l e

D -50

12m-to-3m 12m-to-6m 24m-to-3m 24m-to-6m -100 p < 0.01 -3 -3 -3 -3 -3 -3 -3 -3

ACR ACR ACR ACR NACR NACR NACR NACR

Follow-up duration (days) Figure S4: Pathway and Gene Ontology enrichment analysis on differentially expressed genes (DEGs): a) The barchart of enriched Canonical pathways for DEGs from multiple public pathway databases (upper panel) and Ingenuity (lower panel) . The bar represents –log10 p value of enrichment significance of gene pathways by Fisher-exact test; the lengths of red and green bars represent the percentage of up and down regulated genes, respectively; b) The barchart of enriched Gene Ontology functional terms for the top 100 upregulated (upper panel) or down-regulated (lower panel) genes.

a) b) Canonical Pathway Analysis

SPLICEOSOME(KEGG) PLATELET_ACTIVATION_SIGNALING(REACTOME) regulation of transcription 10 ECM_PATHWAY(BIOCARTA) transcription 8 TGF-BETA SIGNALING PATHWAY(WIKI) 6 REGULATION_OF_APOPTOSIS(REACTOME) change macromolecule catabolic process 4 Dn REGULATION_OF_ACTIN_CYTOSKELETON(KEGG) 2 Up EGF/EGFR_SIGNALING_PATHWAY(WIKI) positive regulation of biosynthetic process DNA_REPLICATION(REACTOME) 0.0 5.0 10.0 15.0 CELL_CYCLE_CHECKPOINTS(REACTOME) SIGNALING_BY_THE_B_CELL_RECEPTOR(REACTOME) MAPK_PATHWAY(BIOCARTA) CELL_CYCLE_MITOTIC(REACTOME) response to organic substance 0.0 2.5 5.0 7.5 wound healing Ingenuity Pathway Analysis lipid transport Integrin Signaling 8 ERK/MAPK Signaling response to wounding PTEN Signaling 6 cell death Caveolar-mediated Endocytosis Signaling 4

B Cell Receptor Signaling change vesicle−mediated transport 2 IGF-1 Signaling Dn Up VEGF Signaling cell adhesion Apoptosis Signaling response to oxidative stress Leukocyte Extravasation Signaling JAK/Stat Signaling immune response 0.0 2.5 5.0 7.5 0.0 5.0 10.0 15.0 -log10(P) −log(p)

Figure S5: The procedure of development of TREx assay for 17-gene set: 12 genes that had the least expression variation and similar expression range as for 17-gene set were selected as house-keeping genes. 64-assay kit was designed for 29 genes (17- gene set and 12 house-keeping genes) with at least 2 amplicons for each gene. The sequencing library were generated on 96-well plate and sequenced by MiSEQ sequencer. The sequence read count of 17-gene set was normalized using the house keeping genes as reference.

17 diagnosis 12 house-keeping genes genes

29 gene panel

Illumina Design Studio

64 assay primers

Assay Kit

RNA 96-well Plate extraction

Library generation Data Analysis and sequencing

Diagnosis Figure S6: Development of TREx assay for 17-gene set: a) The scatterplot of log2 count of duplicated experiments using RNA sample of the Universal Human Reference (UHR) (R=0.994); b)The scatterplot of log2 ratio of the count data of brain vs UHR RNA between duplicated experiments ( R=0.949); c) The scatterplot of log2 count of duplicated experiments using RNA sample of the blood of a kidney transplant (R=0.998); d) The heatmap of expression correlation of duplicated experiments for a group of transplant patients. e) The barchart of correlation of 17 gene expression from TREx assay and RNA sequencing (mean correlation coefficient=0.864, [0.745-0.933], IQR=0.049).

c) a) b)

6 14

12 13

4 R = 0.994 R = 0.998 R = 0.949 10 12 2 8 11 0 6 PXG44_1

log2(UHR R2) 10

4

log2(BR/UHR MiSeq) −2

9 2 −4 8

0 −6 7 0 2 4 6 8 10 12 −6 −4 −2 0 2 4 6 7 8 9 10 11 12 13 14 log2(UHR R1) log2(BR/UHR RNAseq) PXG44 d) e)

PXG9_1 PXG8_1 PXG7_1 PXG6_1 PXG5_1 0.9 PXG47_1 PXG46_1 PXG45_1 PXG44_1 0.8 PXG43_1 PXG42_1 PXG41_1 PXG40_1 0.7 PXG4_1 PXG39_1 PXG38_1 cor PXG37_1 0.6 PXG36_1 PXG35_1 PXG34_1 Correlation 0.90 PXG33_1 0.5 PXG32_1 (0,0.65] PXG30_1 (0.65,0.85] 0.85 PXG3_1 PXG29_1 (0.85,0.95] 0.4 PXG28_1 0.80 PXG27_1 (0.95,1] PXG26_1 PXG25_1 0.3 0.75 PXG24_1 Correlation Coefficient PXG23_1 PXG22_1 PXG21_1 0.2 PXG20_1 PXG2_1 PXG19_1 PXG18_1 0.1 PXG17_1 PXG16_1 PXG15_1 PXG14_1 PXG13_1 0.0 PXG12_1 PXG11_1 PXG10_1 PXG1_1 P9246 P5889 P6649 P5947 P4291 P9171 P3600 P8608 P6219P6606 P6490 P5647 P7720 P7710 P6637 P18494P28104P15761P31410P24557 P36977P17695P30609P28655P32851P29987P28899P29221P19210 P15197 P36787P16220P27306 P37843P29719P32937P19769P40423P28214P26166P30106P30765 P21504P16573P29077P17767P24369 P12886P28317P32502P21037P21359P20325P26747 P35132P14222 P38480P19338P44290 P12598P29757P36313 P30009P36682 P34886P28900P28992P42356P14190P36577P18673P36312P27222P12758P15531P28942P16756P34943P24467P14912 P27224P26608P20212

PXG1 PXG2 PXG3 PXG4 PXG5PXG6PXG7PXG8PXG9 PXG10PXG11PXG12PXG13PXG14PXG15PXG16PXG17PXG18PXG19 PXG20PXG21PXG22PXG23PXG24PXG25PXG26PXG27PXG28PXG29 PXG30PXG32PXG33PXG34PXG35PXG36PXG37PXG38PXG39 PXG40PXG41PXG42PXG43PXG44PXG45PXG46PXG47 Table S1: Comparison of demographic statistics between GoCAR and Belgian dataset

Characteristics GOCAR Belgian pvalue ACR/NACR 1 N 145(75.92) 35(76.09) Y 46(24.08) 11(23.91) Follow up (days) 1637.12±597.78 1659.09±482.31 0.79 Recipient age 48.73±13.58 51.85±14.92 0.2 Recipient gender 0.39 Female 65(34.03) 19(41.3) Male 126(65.97) 27(58.7) Recipient race <0.01 White / Caucasian 120(62.83) 46(100) Black or African American 33(17.28) 0(0) Hispanic 14(7.33) 0(0) Asian 14(7.33) 0(0) Other 10(5.24) 0(0) Kidney Disease <0.01 DM 65(34.03) 4(8.69) GN 44(23.04) 9(19.57) HTN 31(16.23) 2(4.35) PKD 17(8.90) 8(17.39) OTHER 34(17.80) 23(50.00) Donor age 41.2±16.33 48±14.78 <0.01 Donor gender 0.74 Female 94(49.21) 21(45.65) Male 97(50.79) 25(54.35) Donor race 0.04 White / Caucasian 155(81.15) 46(100) Black or African American 13(6.81) 0(0) Hispanic 13(6.81) 0(0) Asian 6(3.14) 0(0) Other 4(2.09) 0(0) Donor status <0.01 Living 78(40.84) 4(8.7) Deceased 113(59.16) 42(91.3) m3 creatinine 1.44±0.72 1.71±0.54 <0.01 Delayed graft function 0.32 N 154(80.63) 34(73.91) Y 37(19.37) 12(26.09) Anti_HLA_Ab_Class_I 0.71 N 139(72.77) 35(76.09) Y 52(27.23) 11(23.91) Anti_HLA_Ab_Class_II 0.31 N 155(81.15) 34(73.91) Y 36(18.85) 12(26.09) DSA 1 N 27(67.5) 10(71.43) Y 13(32.5) 4(28.57) Induction Type <0.01 LND 70(36.65) 21(45.65) LymDep 65(34.03) 0(0) None 56(29.32) 25(54.35) Steroid <0.01 N 12(6.28) 10(21.74)

Y 179(93.72) 36(78.26) CNI 0.03 N 5(2.62) 5(10.87) Y 186(97.38) 41(89.13) Legend:

# ACR –Acute rejection at 3months; CNI- Calcineurin inhibitors; DSA- Donor-specific antibody; LND - non lymphocyte depletion; LymDep – lymphocyte depletion

P-value – comparison of ACR-3 with NACR-3(unpaired T test or non-parametric test), Chi-square or Fisher’s exact test

*Only deceased-donor allografts included in analysis

Table S2: ACR-3 predicts CADI-12 and 24 independent of simultaneous chronic damage indices.

Covariates: Outcome: Outcome: CADI-12 CADI-24 Coefficient P-Value Coefficient P-Value

ACR-3 1.16 0.01 1.71 0.02 Ci+Ct 3m -0.16 0.58 -0.17 0.72 CADI 3m 0.53 <0.01 0.35 0.24

Ci+Ct= Ci score + Ct score at 3 months, CADI- Chronic allograft dysfunction index score; ACR-3: Borderline or greater cellular rejection at 3 month biopsy

Table S3: Comparison of local and central biopsy reports at 3-month biopsy 3-month Central reports NACR BACR ACR Total . Local diagnosis NACR 48 13 3 64 BACR 2 8 1 11 ACR 5 6 4 15 Total 55 27 8 90

Table S4: Comparison of clinical characteristics between ACR-3 and NACR-3 without AMBR

ACR-3 NACR-3 P-value N=38 N=140 mean±SD mean±SD 3 month g-score 0.32± 0.66 0.07±0.40 <0.01 3-month ptc-score 0.21± 0.58 0.01± 0.09 <0.01 3-month g+ptc score 0.54± 0.93 0.08± 0.41 <0.0001 3-month C4d Y/N 4/34 2/138 0.01 Outcomes CADI-12 3.36± 2.69 1.85± 2.14 <0.01 CADI-24 4.00± 2.44 2.24± 2.73 <0.01 ACR-12 Y/N 14/11 13/74 <0.0001 ACR-24 Y/N 10/3 17/36 <0.01

Table S5: Comparison of clinical outcomes post 3 month between C4d negative ACR-3 and NACR-3 groups

C4d negative ACR-3 NACR-3 P value N=33 N=140 mean±SD mean±SD CADI-12 3.48± 2.66 1.85± 2.14 <0.01 CADI-24 4.46± 2.25 2.24± 2.73 <0.01 ACR-12 Y/N 14/10 13/74 <0.0001 ACR-24 Y/N 9/2 17/36 <0.01

Table S6: ACR-3 independently predicts long-term allograft survival

Covariates (Reference Outcome: Outcome: parameter) Death censored allograft loss All-cause allograft loss HR P-value HR P-value

ACR-3 4.113 <0.01 2.718 <0.01 Donor status (LD) 2.921 0.12 1.250 0.59 Donor race (W) NA 0.26 NA 0.07 Donor gender (F) 0.706 0.46 0.678 0.27 Donor age 1.019 0.26 1.010 0.41 Recipient race (W) NA 0.28 NA 0.38 Recipient gender (F) 0.826 0.68 0.616 0.14 Recipient age 0.946 0.08 0.994 0.69 Recipient ESRD diagnosis (DM) NA 0.59 NA 0.53 Induction type (none) 1.982 0.26 1.746 0.19 Anti HLA antibodies (none) 1.617 0.36 1.507 0.25

LD- Live-donor; W- White/Caucasian, F- Female, DM- Diabetic kidney disease, ACR-3: Borderline or greater cellular rejection at 3 month biopsy Race categories– White/Caucasian, African American, Hispanic, Other; HR-Hazard ratio

Table S7: The list of 240 focus gene set Symbol Refseq Name P value Log2Ratio ZMAT1 NM_001282400 zinc finger, matrin-type 1 0.010685 0.744899 USP32P1 NR_003190 ubiquitin specific peptidase 32 pseudogene 1 0.003553 0.710375 ETAA1 NM_019002 Ewing tumor-associated antigen 1 0.040757 0.703351 ANKRD12 NM_001083625 ankyrin repeat domain 12 0.00716 0.66995 ZNF493 NM_001076678 zinc finger protein 493 0.001647 0.663139 ZNF292 NM_015021 zinc finger protein 292 0.004037 0.642252 CCDC82 NM_024725 coiled-coil domain containing 82 0.019098 0.622008 LINC00672 NR_038847 long intergenic non-protein coding RNA 672 0.002386 0.618656 FLJ31306 NR_029434 uncharacterized LOC379025 0.001288 0.591691 NFYB NM_006166 nuclear transcription factor Y, beta 0.029003 0.589093 ASTN2 NM_001184734 astrotactin 2 0.001204 0.587956 SENP7 NM_001077203 SUMO1/sentrin specific peptidase 7 0.000291 0.581319 CLK1 NR_027855 CDC-like kinase 1 0.007945 0.567156 SP3 NM_003111 Sp3 transcription factor 0.004195 0.538865 OSBPL8 NM_001003712 oxysterol binding protein-like 8 0.005226 0.526053 UGDH-AS1 NR_047679 UGDH antisense RNA 1 0.00088 0.516391 TMF1 NM_007114 TATA element modulatory factor 1 0.006042 0.514646 KCNQ1OT1 NR_002728 KCNQ1 opposite strand/antisense transcript 1 (non-protein coding) 0.000984 0.513155 SENP6 NM_001100409 SUMO1/sentrin specific peptidase 6 0.005788 0.510019 NAA38 NM_032356 N(alpha)-acetyltransferase 38, NatC auxiliary subunit 0.005145 0.503528 MAB21L3 NM_152367 mab-21-like 3 (C. elegans) 0.000991 0.478708 MALAT1 NR_002819 metastasis associated lung adenocarcinoma transcript 1 (non-protein coding) 0.00558 0.474576 TBC1D15 NM_001146213 TBC1 domain family, member 15 0.001248 0.474139 PGM5P2 NR_002836 phosphoglucomutase 5 pseudogene 2 0.0017 0.470392 DCP2 NR_038352 decapping mRNA 2 0.007665 0.467892 ANKDD1A NM_182703 ankyrin repeat and death domain containing 1A 0.005791 0.460633 CCDC144B NR_036647 coiled-coil domain containing 144B (pseudogene) 0.002709 0.459372 PRPF39 NM_017922 pre-mRNA processing factor 39 0.001465 0.458515 ZMYM2 NM_001190964 zinc finger, MYM-type 2 0.006753 0.439666 ZNF772 NM_001144068 zinc finger protein 772 0.003221 0.43705 ZNF681 NM_138286 zinc finger protein 681 0.001113 0.432181 LOC286437 NR_039980 uncharacterized LOC286437 0.000345 0.41494 ZNF626 NM_145297 zinc finger protein 626 0.002308 0.412885 NUFIP2 NM_020772 nuclear fragile X mental retardation protein interacting protein 2 0.002554 0.410953 SLK NM_014720 STE20-like kinase 0.007308 0.400349 LOC100131257 NR_034022 zinc finger protein 655 pseudogene 0.000535 0.391003 TP53INP1 NM_033285 tumor protein p53 inducible nuclear protein 1 0.002797 0.39032 LOC646719 NR_046262 uncharacterized LOC646719 0.004209 0.380808 PARP8 NM_001178056 poly (ADP-ribose) polymerase family, member 8 0.002312 0.371843 TIGD7 NM_033208 tigger transposable element derived 7 0.003467 0.371245 SYCP2 NM_014258 synaptonemal complex protein 2 0.005715 0.370693 ZFX NM_001178086 zinc finger protein, X-linked 0.004024 0.367898 LOC643406 NR_029405 uncharacterized LOC643406 0.000704 0.358162 MARCH7 NM_001282805 membrane-associated ring finger (C3HC4) 7, E3 ubiquitin protein ligase 0.003041 0.357718 TTBK2 NM_173500 tau tubulin kinase 2 0.005404 0.35577 LINC00547 NR_040244 long intergenic non-protein coding RNA 547 0.000879 0.353288 RBM33 NM_053043 RNA binding motif protein 33 0.001573 0.352226 TMEM212 NM_001164436 transmembrane protein 212 0.001346 0.348308 ARHGEF26- NR_037901 ARHGEF26 antisense RNA 1 0.000501 0.348275 AS1 N4BP2L2 NM_001278432 NEDD4 binding protein 2-like 2 0.005388 0.347375 CENPK NM_001267038 centromere protein K 0.004712 0.345004 TVP23C NM_001135036 trans-golgi network vesicle protein 23 homolog C (S. cerevisiae) 0.002806 0.342506 ZNF43 NM_001256649 zinc finger protein 43 0.006685 0.33965 SCRN3 NM_024583 secernin 3 0.000647 0.339225 METTL21D #N/A #N/A 0.006524 0.336662 SNRNP48 NM_152551 small nuclear ribonucleoprotein 48kDa (U11/U12) 0.001466 0.336015 SRFBP1 NM_152546 serum response factor binding protein 1 0.000816 0.334015 ORC4 NM_001190881 origin recognition complex, subunit 4 0.00064 0.331708 FAM73A NM_001270384 family with sequence similarity 73, member A 0.00098 0.323062 CARD8 NR_033680 caspase recruitment domain family, member 8 0.002618 0.320462 CEP135 NM_025009 centrosomal protein 135kDa 0.008398 0.320318 ZNF148 NM_021964 zinc finger protein 148 0.001813 0.319371 LOC642236 NR_033907 FSHD region gene 1 pseudogene 0.002214 0.318163 DPY19L4 NM_181787 dpy-19-like 4 (C. elegans) 0.004208 0.312664 LOC646214 NR_027053 p21 protein (Cdc42/Rac)-activated kinase 2 pseudogene 0.00581 0.31057 STXBP3 NM_007269 syntaxin binding protein 3 0.001745 0.305446 NFE2L3 NM_004289 nuclear factor, erythroid 2-like 3 0.002168 0.305192 SEPSECS NM_016955 Sep (O-phosphoserine) tRNA:Sec (selenocysteine) tRNA synthase 0.004177 0.29773 LOC100507032 #N/A #N/A 0.005423 0.295099 LOC100130557 #N/A #N/A 0.00601 0.294332 SLU7 NM_006425 SLU7 splicing factor homolog (S. cerevisiae) 0.01357 0.293447 SNTG2 NM_018968 syntrophin, gamma 2 0.000824 0.29246 C1GALT1C1 NM_001011551 C1GALT1-specific chaperone 1 0.005362 0.292181 LYRM7 NM_181705 LYR motif containing 7 0.002832 0.289061 PIK3C2A NM_002645 phosphatidylinositol-4-phosphate 3-kinase, catalytic subunit type 2 alpha 0.003215 0.288895 ZMYM5 NM_001039649 zinc finger, MYM-type 5 0.000711 0.283367 ZFAND6 NM_001242912 zinc finger, AN1-type domain 6 0.003972 0.279163 NAA30 NM_001011713 N(alpha)-acetyltransferase 30, NatC catalytic subunit 0.002624 0.278991 ANKRD20A9P NR_027995 ankyrin repeat domain 20 family, member A9, pseudogene 0.000544 0.278946 KIAA1456 NM_020844 KIAA1456 0.001203 0.277708 ZNF471 NM_020813 zinc finger protein 471 0.001736 0.275492 SCAI NM_001144877 suppressor of cancer cell invasion 3.38E-05 0.274807 CDKN2B-AS1 NR_047536 CDKN2B antisense RNA 1 0.003036 0.274342 SHISA9 NM_001145204 shisa family member 9 0.001969 0.268846 THAP6 NM_144721 THAP domain containing 6 0.000966 0.262071 CUL5 NM_003478 cullin 5 0.004477 0.260938 AMY2B NM_020978 amylase, alpha 2B (pancreatic) 0.003767 0.255726 ACADSB NM_001609 acyl-CoA dehydrogenase, short/branched chain 0.002158 0.255625 ZNF737 NM_001159293 zinc finger protein 737 0.002296 0.253601 MGC27345 NR_046216 uncharacterized protein MGC27345 0.003909 0.251378 L2HGDH NM_024884 L-2-hydroxyglutarate dehydrogenase 0.000377 0.251247 TAT NM_000353 tyrosine aminotransferase 0.001832 0.250305 SAR1B NM_001033503 secretion associated, Ras related GTPase 1B 0.001127 0.24866 ZNF793 NM_001013659 zinc finger protein 793 0.007068 0.24427 KRBOX4 NM_001129900 KRAB box domain containing 4 0.004665 0.244081 CCNL1 NM_020307 cyclin L1 0.007558 0.240872 ZFP14 NM_020917 ZFP14 zinc finger protein 0.002957 0.238291 MCTS1 NM_014060 malignant T cell amplified sequence 1 0.001188 0.236175 AKAP5 NM_004857 A kinase (PRKA) anchor protein 5 0.001768 0.231229 CCDC41 #N/A #N/A 0.000633 0.227085 FLJ31662 NR_033966 uncharacterized LOC440594 0.00485 0.226492 SPCS3 NM_021928 signal peptidase complex subunit 3 homolog (S. cerevisiae) 0.025128 0.221611 ACOT13 NM_001160094 acyl-CoA thioesterase 13 5.94E-06 0.220781 RSRC2 NR_036435 arginine/serine-rich coiled-coil 2 0.003263 0.219897 TBCC NM_003192 tubulin folding cofactor C 0.000139 0.219769 FLJ43663 #N/A #N/A 0.003319 0.218677 TMEM167B NM_020141 transmembrane protein 167B 0.005043 0.216933 ZNF818P NR_073396 zinc finger protein 818, pseudogene 0.004324 0.205814 LOC284581 NR_046097 uncharacterized LOC284581 0.005106 0.19901 MCFD2 NM_001171508 multiple coagulation factor deficiency 2 0.002665 0.196994 CCT6P1 NR_003110 chaperonin containing TCP1, subunit 6 (zeta) pseudogene 1 0.001993 0.192564 PGM2L1 NM_173582 phosphoglucomutase 2-like 1 0.001637 0.192297 MFSD8 NM_152778 major facilitator superfamily domain containing 8 0.001699 0.191118 FAM184B NM_015688 family with sequence similarity 184, member B 0.000526 0.190255 OMA1 NM_145243 OMA1 zinc metallopeptidase 0.001243 0.189318 FLJ10038 NR_026891 uncharacterized protein FLJ10038 0.002391 0.181249 ATP6V0A2 NM_012463 ATPase, H+ transporting, lysosomal V0 subunit a2 0.001804 0.179872 HEXIM1 NM_006460 hexamethylene bis-acetamide inducible 1 0.000698 0.175234 RCN2 NM_001271837 reticulocalbin 2, EF-hand calcium binding domain 0.000375 0.166166 LOC100289230 NR_036530 uncharacterized LOC100289230 0.001915 0.1631 AP1S3 NR_110905 adaptor-related protein complex 1, sigma 3 subunit 0.002637 0.162669 C6orf170 #N/A #N/A 0.002535 0.159546 MTMR9 NM_015458 myotubularin related protein 9 0.003152 0.142602 ABCC2 NM_000392 ATP-binding cassette, sub-family C (CFTR/MRP), member 2 0.002316 0.140493 TACO1 NM_016360 translational activator of mitochondrially encoded cytochrome c oxidase I 0.010064 0.138106 PLK1S1 #N/A #N/A 0.00516 0.136264 NGLY1 NM_001145295 N-glycanase 1 0.001745 0.134795 TPM3 NR_103461 tropomyosin 3 0.003304 -0.06326 P4HB NM_000918 prolyl 4-hydroxylase, beta polypeptide 0.002652 -0.07978 ACACA NM_198837 acetyl-CoA carboxylase alpha 0.003379 -0.1034 GTF2F1 NM_002096 general transcription factor IIF, polypeptide 1, 74kDa 0.004371 -0.12437 DOCK2 NM_004946 dedicator of cytokinesis 2 0.002408 -0.12792 ILK NM_001014794 integrin-linked kinase 0.003594 -0.12892 ECD NM_007265 ecdysoneless homolog (Drosophila) 0.000851 -0.13423 STK24 NM_001032296 serine/threonine kinase 24 0.001692 -0.13662 ARCN1 NM_001142281 archain 1 0.003408 -0.13942 VAC14 NM_018052 Vac14 homolog (S. cerevisiae) 0.005718 -0.14201 PSMC4 NM_006503 proteasome (prosome, macropain) 26S subunit, ATPase, 4 0.004188 -0.14776 WDR1 NM_005112 WD repeat domain 1 0.001574 -0.15196 EXOC4 NM_001037126 exocyst complex component 4 0.002071 -0.15438 HNRNPUL2 NM_001079559 heterogeneous nuclear ribonucleoprotein U-like 2 0.005773 -0.15438 RHOA NM_001664 ras homolog family member A 0.000474 -0.15707 PKN1 NM_002741 protein kinase N1 0.002776 -0.15887 ARL2-SNX15 NR_037650 ARL2-SNX15 readthrough (NMD candidate) 0.00745 -0.16377 UBAP2L NM_001127320 ubiquitin associated protein 2-like 0.005124 -0.1644 PCCA NM_000282 propionyl CoA carboxylase, alpha polypeptide 0.008922 -0.16576 PSMD1 NM_001191037 proteasome (prosome, macropain) 26S subunit, non-ATPase, 1 0.004619 -0.17145 PTPN18 NM_001142370 protein tyrosine phosphatase, non-receptor type 18 (brain-derived) 0.003853 -0.17162 TLN1 NM_006289 talin 1 0.002461 -0.17227 CSNK2A1 NM_177559 casein kinase 2, alpha 1 polypeptide 0.000724 -0.17287 GNB1 NM_002074 guanine nucleotide binding protein (G protein), beta polypeptide 1 0.002856 -0.17308 XPNPEP1 NR_030724 X-prolyl aminopeptidase (aminopeptidase P) 1, soluble 0.004141 -0.17539 SH3KBP1 NM_001184960 SH3-domain kinase binding protein 1 0.000984 -0.17634 ZNF79 NM_001286698 zinc finger protein 79 0.002766 -0.17698 SCAF4 NM_001145444 SR-related CTD-associated factor 4 0.001875 -0.17832 WDR60 NM_018051 WD repeat domain 60 1.99E-05 -0.18329 ZC3H18 NM_144604 zinc finger CCCH-type containing 18 0.005911 -0.18472 PSMD2 NM_002808 proteasome (prosome, macropain) 26S subunit, non-ATPase, 2 0.000867 -0.18492 TMEM214 NM_017727 transmembrane protein 214 0.00151 -0.18805 PPP1CA NM_206873 protein phosphatase 1, catalytic subunit, alpha isozyme 0.00462 -0.18872 UBA1 NM_153280 ubiquitin-like modifier activating enzyme 1 0.004154 -0.1896 YWHAH NM_003405 tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, 0.003149 -0.19575 eta SEC23B NM_001172745 Sec23 homolog B (S. cerevisiae) 0.002745 -0.19816 MSN NM_002444 moesin 0.004451 -0.19833 DAK NM_015533 dihydroxyacetone kinase 2 homolog (S. cerevisiae) 0.002058 -0.19962 ACO2 NM_001098 aconitase 2, mitochondrial 0.000528 -0.20439 AIFM1 NM_004208 apoptosis-inducing factor, mitochondrion-associated, 1 0.002388 -0.20691 CSNK2A3 NM_001256686 casein kinase 2, alpha 3 polypeptide 0.007062 -0.2073 HNRNPUL2- NR_037946 HNRNPUL2-BSCL2 readthrough 0.001509 -0.21401 BSCL2 NCKAP1L NM_005337 NCK-associated protein 1-like 0.001668 -0.21588 AACS NM_023928 acetoacetyl-CoA synthetase 0.004333 -0.21711 POTEE NM_001083538 POTE ankyrin domain family, member E 0.006779 -0.21804 CHMP4B NM_176812 charged multivesicular body protein 4B 0.00394 -0.22168 MCM5 NM_006739 minichromosome maintenance complex component 5 0.007251 -0.2221 SMARCAL1 NM_014140 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, 0.001475 -0.22253 subfamily a-like 1 NCOR2 NM_001206654 nuclear receptor corepressor 2 0.003793 -0.2232 SMG9 NM_019108 SMG9 nonsense mediated mRNA decay factor 0.002132 -0.22511 SNX15 NM_147777 sorting nexin 15 0.01338 -0.22723 DPP3 NM_130443 dipeptidyl-peptidase 3 0.004145 -0.22835 KDELR1 NM_006801 KDEL (Lys-Asp-Glu-Leu) endoplasmic reticulum protein retention receptor 0.001309 -0.2297 1 AP1B1 NM_145730 adaptor-related protein complex 1, beta 1 subunit 0.003784 -0.23089 CDIP1 NM_013399 cell death-inducing p53 target 1 0.003277 -0.23208 VIM NM_003380 vimentin 0.005627 -0.23276 PLOD3 NM_001084 procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3 0.000379 -0.23603 ALDOA NM_001243177 aldolase A, fructose-bisphosphate 0.001223 -0.23811 CAPZB NM_001206541 capping protein (actin filament) muscle Z-line, beta 0.001198 -0.24334 IPO13 NM_014652 importin 13 0.00071 -0.24421 SCAMP2 NM_005697 secretory carrier membrane protein 2 0.004873 -0.24567 DDX19B NM_001014451 DEAD (Asp-Glu-Ala-Asp) box polypeptide 19B 0.002377 -0.2474 IDH3G NM_174869 isocitrate dehydrogenase 3 (NAD+) gamma 0.0022 -0.24835 INTS9 NM_001172562 integrator complex subunit 9 0.001924 -0.24949 SH3BGRL3 NM_031286 SH3 domain binding glutamate-rich protein like 3 0.000913 -0.25377 LSP1 NM_001242932 lymphocyte-specific protein 1 0.003391 -0.25752 KLHL26 NM_018316 kelch-like family member 26 0.005511 -0.25938 EXT2 NM_001178083 exostosin glycosyltransferase 2 0.002941 -0.26011 ACTG1 NR_037688 actin, gamma 1 0.002775 -0.26136 VCL NM_003373 vinculin 0.0048 -0.26335 LOC442459 #N/A #N/A 0.003032 -0.26406 CAPN1 NM_005186 calpain 1, (mu/I) large subunit 0.001312 -0.26462 SF3A1 NM_005877 splicing factor 3a, subunit 1, 120kDa 0.00313 -0.26544 PIGT NR_047693 phosphatidylinositol glycan anchor biosynthesis, class T 0.000903 -0.27039 MAP2K5 NM_002757 mitogen-activated protein kinase kinase 5 0.004707 -0.27111 UQCRC1 NM_003365 ubiquinol-cytochrome c reductase core protein I 0.003185 -0.27113 MAP1A NM_002373 microtubule-associated protein 1A 0.008216 -0.27124 APP NM_001204303 amyloid beta (A4) precursor protein 0.000348 -0.27241 HDLBP NM_203346 high density lipoprotein binding protein 0.001542 -0.27329 C16orf62 NM_020314 chromosome 16 open reading frame 62 0.000326 -0.27372 ARHGDIA NM_001185078 Rho GDP dissociation inhibitor (GDI) alpha 0.000604 -0.27471 PPP2R4 NM_178000 protein phosphatase 2A activator, regulatory subunit 4 0.005519 -0.2825 CFL1 NM_005507 cofilin 1 (non-muscle) 0.001075 -0.29047 HEATR2 NM_017802 HEAT repeat containing 2 0.003104 -0.2946 RNF40 NM_001207034 ring finger protein 40, E3 ubiquitin protein ligase 0.001848 -0.30261 OGDH NM_001165036 oxoglutarate (alpha-ketoglutarate) dehydrogenase (lipoamide) 0.000322 -0.30669 UCP2 NM_003355 uncoupling protein 2 (mitochondrial, proton carrier) 0.000854 -0.30851 CHST14 NM_130468 carbohydrate (N-acetylgalactosamine 4-0) sulfotransferase 14 0.000634 -0.31805 PLBD2 NM_173542 phospholipase B domain containing 2 0.002506 -0.32099 TPP1 NM_000391 tripeptidyl peptidase I 0.000684 -0.32453 EHD3 NM_014600 EH-domain containing 3 0.00783 -0.32757 VRK3 NM_001025778 vaccinia related kinase 3 0.005303 -0.33009 EFTUD2 NM_001142605 elongation factor Tu GTP binding domain containing 2 0.001449 -0.33174 AP1M1 NM_032493 adaptor-related protein complex 1, mu 1 subunit 0.000161 -0.33596 MAP4 NM_030885 microtubule-associated protein 4 0.002285 -0.33685 CTNNBL1 NM_030877 catenin, beta like 1 0.000682 -0.33777 NUP93 NM_001242796 nucleoporin 93kDa 0.00098 -0.34037 SSBP3 NM_018070 single stranded DNA binding protein 3 0.002873 -0.34183 CTSA NM_001167594 cathepsin A 0.009862 -0.34479 RNH1 NM_203385 ribonuclease/angiogenin inhibitor 1 0.006126 -0.35105 BCAS3 NM_017679 breast carcinoma amplified sequence 3 0.006131 -0.35844 TRAPPC9 NM_031466 trafficking protein particle complex 9 0.001364 -0.35975 ANXA5 NM_001154 annexin A5 0.000328 -0.36446 BCKDHA NM_001164783 branched chain keto acid dehydrogenase E1, alpha polypeptide 0.004641 -0.36764 SND1 NM_014390 staphylococcal nuclease and tudor domain containing 1 0.000666 -0.37393 FAM127A NM_001078171 family with sequence similarity 127, member A 0.002681 -0.38623 BRE NM_199191 brain and reproductive organ-expressed (TNFRSF1A modulator) 0.001421 -0.40204 CLU NM_001831 clusterin 0.004967 -0.42146 CAPNS1 NM_001003962 calpain, small subunit 1 6.94E-05 -0.4336 CTSD NM_001909 cathepsin D 0.004336 -0.43371 TSC22D1 NM_001243797 TSC22 domain family, member 1 0.007989 -0.44781 F13A1 NM_000129 coagulation factor XIII, A1 polypeptide 0.02124 -0.5544 TUBB1 NM_030773 tubulin, beta 1 class VI 0.032961 -0.55441

Table S8: Demographic characteristics of RNAseq and Microarray cohorts in GoCAR cohort.

Characteristics Microarray Cohort RNAseq Cohort pvalue (n=65) (n=88) Age 49.51±13.87 48.33±12.41 0.5878 Gender 0.5011 F 22(33.85) 35(39.77) M 43(66.15) 53(60.23) Race 0.0087 Black or African American 13(20) 11(12.5) Others 22(33.85) 15(17.05) White / Caucasian 30(46.15) 62(70.45) CMV_Status_R 0.1058 No 9(13.85) 22(25) Yes 56(86.15) 66(75) CMV_Status_D 0.2539 No 32(49.23) 35(39.77) Yes 33(50.77) 53(60.23) Days_to_first_Dialysis 1516.25±1576.52 1213.44±1625.49 0.2484 Anti_HLA_Ab_Class_I 0.0434 No 53(81.54) 58(65.91) Yes 12(18.46) 30(34.09) Anti_HLA_Ab_Class_II 0.1622 No 59(90.77) 72(81.82) Yes 6(9.23) 16(18.18) Anti_HLA_Ab_Class 0.0434 No 53(81.54) 58(65.91) Yes 12(18.46) 30(34.09) Induction_Type 0.1582 LND 20(30.77) 40(45.45) LymDep 21(32.31) 25(28.41) None 24(36.92) 23(26.14) KD 0.9838 DM 22(33.85) 26(29.55) GN 15(23.08) 23(26.14) HTN 9(13.85) 14(15.91) PKD 6(9.23) 9(10.23) REFLUX DISEASE 4(6.15) 6(6.82) OTHER 9(13.85) 10(11.36) Donor_Age 39.63±17.87 40.74±16.28 0.6945 Donor_Gender 0.3276 F 25(38.46) 41(46.59) M 40(61.54) 47(53.41) Donor_Race 0.1928 Black or African American 8(12.31) 5(5.68) Others 11(16.92) 10(11.36) White / Caucasian 46(70.77) 73(82.95) Deceased_Donor 0.4962 No 21(32.31) 34(38.64) Yes 44(67.69) 54(61.36) CIT_min 671.17±503.15 562.55±457.96 0.1728 DGF 0.2918 NO 51(78.46) 75(85.23) Yes 14(21.54) 13(14.77) Baseline DSA 1 No 58(93.55) 77(92.77) Yes 4(6.45) 6(7.23)

Table S9: Frequency of anytime Rejection episodes in TREx risk groups: Group 12-month SCR* 24-month SCR** Any time ACR Anytime ABMR1 DDSA2 C4d>03 g+ptc ≥2 (ACR/BACR) (ACR/BACR) (1A or greater) ACR/BACR Y (3-months) (3-months) Low risk (70) 6 (13.61%) 9 (32.14%) 4 (6.06%) 20 (28.57%) 4 (5.72%) 3(13.61%) 3 (5.89%) 1 (1.96%) Intermediate risk (85) 13 (23.61%) 14 (46.67%) 14 (16.47%) 39 (45.88%) 5 (5.88% 7(24.14%) 3 (4.28%) 7 (9.33%) High Risk (22) 9 (64.28%) 7 (77.78%) 8 (36.3%) 20 (90.90%) 1 (4.54%) 1 (9.09%) 2 (10.0%) 3 (15.0%) *A total of 44, 55 & 14 surveillance biopsies were performed in Low, Intermediate and High risk groups at 12-months, respectively. ** A total of 28, 30 & 9 surveillance biopsies were performed in Low, Intermediate and High risk groups at 24-months, respectively. 1 ABMR- Acute Antibody-mediated rejection < 24 months. 10/11 of ABMR cases had TREx assay performed. 2 DDSA- Denovo DSA; Only 22, 29 and 11 patients in low-, intermediate, and high-risk groups had serum reported for DDSA within 24-months 3C4d- by immunohistochemistry method. Table S10: Summary of clinical events of TREx cohorts post kidney transplant

By training/testing set Training Testing (N=113) (N=110) ACR/BACR/NACR 7/17/89 6/23/81 Death Censored Graft 9 13 Loss (DCGS) All Cause Graft Loss 23 23 (ACGS)

By study cohort GOCAR Belgian (N=177) (N=46) ACR/BACR/NACR 10/32/135 3/8/35 Death Censored Graft 20 2 Loss (DCGS) All Cause Graft Loss 39 7 (ACGS)

Table S11: TREX-risk group status impact allograft survival (GoCAR+Belgian cohorts, n=223)1 Death Censored graft Survival Covariates HR P-Value HR P-Value TREx Risk group status TREx Risk group status 0.12 High/Intermediate 3.740 0.04 High 3.758 0.11 (Ref – Low risk) Intermediate 3.723 0.04 (Ref – Low risk) Induction therapy 2.178 0.24 Induction therapy 2.172 0.24 (Ref – None) (Ref – None) Donor Age 1.010 0.56 Donor Age 1.009 0.56 Recipient Age 0.980 0.34 Recipient Age 0.981 0.35 Donor Status 0.422 0.18 Donor Status 0.420 0.18 (Ref-Live) (Ref-Live) Anti-HLA antibody 1.638 0.34 Anti-HLA antibody 1.626 0.36 (Ref-none) (Ref-none) Donor Race (ref: Caucasian) 0.90 Donor Race (ref: Caucasian) 0.91 African American 0.747 0.74 African American 0.738 0.74 Hispanic 1.364 0.70 Hispanic 1.344 0.73 Other 1.904 0.64 Other 1.928 0.89 Recipient Race (ref: Caucasian) 0.08 Recipient Race (ref: Caucasian) 0.08 African American 4.410 0.01 African American 4.414 0.01 Hispanic 0.820 0.87 Hispanic 0.826 0.87 Other 1.093 0.93 Other 1.091 0.93

#Parsimonious covariate models TREx Risk group status TREx Risk group status 0.05 High/Intermediate 4.149 0.02 High 5.300 0.03 (Ref – Low risk) Intermediate 4.122 0.02 (Ref – Low risk) Induction therapy 3.258 0.05 Recipient Race (ref: Caucasian) 0.02 (Ref – None) African American 4.003 <0.01 Hispanic 0.852 0.88 Other 1.255 0.77

*HR = Hazard ratio 1There were 22 death-censored graft loss events in this group #Parsimonious models were generated using backward stepwise conditional predictor selection. Final models are displayed here. Table S12: Comparison of Banff scores between Intermediate- and Low-risk NACR-3 groups Low risk NACR-3 Intermediate NACR-3 P-value 3-month Banff Scores Mean±SD Mean±SD (n=67) (n=63) i-score 0.02±0.123 0.00±0.000 0.34 t-score 0.02±0.123 0.00±0.000 0.34 ti-score 0.06±0.240 0.05±0.218 0.78 Ci-score 0.24±0.498 0.18±0.466 0.47 Ct-score 0.36±0.485 0.42±0.529 0.54 Cv-score 0.35±0.803 0.39±0.671 0.78 Cg-score 0.02±0.124 0.03±0.181 0.52 g-score 0.03±0.248 0.13±0.536 0.16 mm-score 0.02±0.124 0.22±0.715 0.03 Ci+Ct-score 0.59±0.871 0.59±0.835 0.95 CADI-score 1.23±1.497 1.45±1.565 0.41

12-month Banff scores (n=42) (n=42) mm-score 0.00±0.000 0.28±0.793 0.03 Ci+Ct-score 0.90±1.225 1.33±1.603 0.17 CADI-score 1.48±1.742 2.29±2.361 0.07

24-month Banff scores (n=26) (n=29) mm-score 0.12±0.588 0.24±0.723 0.50 Ci+Ct-score 0.65±1.017 2.10±2.350 <0.01 CADI-score 1.15±1.592 2.89±3.075 0.01

Table S13: High/Intermediate risk NACR-3 impacts allograft survival Death Censored graft Survival Covariates HR P-Value HR P-Value TREx NACR-3 Risk group TREx NACR-3 Risk group I-H NACR-3 6.305 0.02 Intermediate NACR-3 5.265 0.03 (Ref – L-NACR-3) (Ref – L-NACR-3) Induction therapy 4.654 0.16 Induction therapy 3.907 0.20 (Ref – None) (Ref – None) Donor Age 0.990 0.59 Donor Age 0.994 0.76 Recipient Age 0.962 0.16 Recipient Age 0.979 0.46 Donor Status 1.882 0.36 Donor Status 1.700 0.45 (Ref-Live) (Ref-Live) Anti-HLA antibody 0.973 0.97 Anti-HLA antibody 1.128 0.87 (Ref-none) (Ref-none) I-H NACR-3=High/Intermediate TREX risk group NACR-3; L-NACR-3= Low risk TREx risk group NACR-3 HR= Hazard ratio. There were 12 death censored graft loss events in this cohort. Other models including Donor and Recipient race showed similar results. 163 Reference

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