A Three-Gene Assay for Monitoring Immune Quiescence in Kidney Transplantation

CLINICAL RESEARCH www.jasn.org

† ‡ Silke Roedder,* Li Li, Michael N. Alonso, Szu-Chuan Hsieh,* Minh Thien Vu,* Hong Dai,* | | | | Tara K. Sigdel,* Ian Bostock,§ Camila Macedo, Diana Metes, Adrianna Zeevi, Ron Shapiro, ‡ ‡ ‡ Oscar Salvatierra, John Scandling, Joseﬁna Alberu,§ Edgar Engleman, and Minnie M. Sarwal*

*Department of Surgery, Division of Transplant Surgery, University of California San Francisco, San Francisco, California; †Department of Biostatistics, Mount Sinai School of Medicine, New York, New York; ‡Department of Pathology, Stanford University, Palo Alto, California; §Department of Surgery, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; and |Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania

ABSTRACT Organ transplant recipients face life-long immunosuppression and consequently are at high risk of comorbidities. Occasionally, kidney transplant recipients develop a state of targeted immune quiescence (operational tolerance) against an HLA-mismatched graft, allowing them to withdraw all immunosuppression and retain stable graft function while resuming immune responses to third-party antigens. Methods to better understand and monitor this state of alloimmune quiescence by transcriptional profiling may reveal a gene signature that identifies patients for whom immunosuppression could be titrated to reduce patient and graft morbidities. Therefore, we investigated 571 unique peripheral blood samples from 348 HLA-mismatched renal transplant recipients and 101 nontransplant controls in a four-stage study including microarray, quantitative PCR, and flow cytometry analyses. We report a refined and highly validated (area under the curve, 0.95; 95% confidence interval, 0.92 to 0.97) peripheral blood three-gene assay (KLF6, BNC2, CYP1B1) to detect the state of operational tolerance by quantitative PCR. The frequency of predicted alloimmune quiescence in stable renal transplant patients receiving long-term immunosuppression (n=150) was 7.3% by the three-gene assay. Targeted cell sorting of peripheral blood from operationally tolerant patients showed a significant shift in the ratio of circulating monocyte-derived dendritic cells with significantly different expression of the genes constituting the three-gene assay. Our results suggest that incorporation of patient screening by specific cellular and gene expression assays may support the safety of drug minimization trials and protocols.

J Am Soc Nephrol 26: 2042–2053, 2015. doi: 10.1681/ASN.2013111239

Our current limited ability to assess varying im- maintenance immunosuppression.7,8 These pa- mune adaptive states to the allograft in different tients are conventionally called operationally recipients results in the use of standard protocol- driven maintenance doses of immunosuppression in all patients. As a result, patients experience drug- Received November 27, 2013. Accepted September 23, 2014. speciﬁc toxicities, mainly cardiovascular morbidity, S.R. and L.L. contributed equally to this work. 1–4 infections, diabetes, cancer, and nephrotoxicity. Published online ahead of print. Publication date available at Many patients, however, reveal stable graft function www.jasn.org. off immunosuppression without developing signif- Correspondence: Dr. Minnie M. Sarwal, Division of Multi Organ icant detrimental immune reactions or immune Transplantation, Department of Surgery, University of California deﬁcits.5,6 This suggests that operational transplant San Francisco, G893D, 513 Parnassus, San Francisco, CA 94107. tolerance is likely a transient state of alloimmune Email: [email protected] quiescence that can develop under the umbrella of Copyright © 2015 by the American Society of Nephrology

2042 ISSN : 1046-6673/2608-2042 JAmSocNephrol26: 2042–2053, 2015 www.jasn.org CLINICAL RESEARCH tolerant (TOL) and provide a unique repertoire for study and biology by identifying potentially protolerogenic cell subsets development of monitoring methods that help to differentiate in blood. transplant recipients receiving immunosuppression with dif- fering immune thresholds and thus help identify patients who may safely minimize their immunosuppression. Transcrip- RESULTS tional studies in peripheral blood by our group and others have identified gene signatures for TOL after kidney7–9 and We investigated 571 unique peripheral blood samples collected liver10,11 transplantation. But these studies are limited by in- from 348 renal transplant recipients and 101 nontransplant sufficient cross-validations in independent cohorts, and, im- controls, in four stages, by microarray, quantitative PCR (qPCR), portantly, the frequency of a TOL signature is poorly defined and FACS (Figure 1). Patient demographic characteristics for new in stable transplant recipients receiving immunosuppression. microarray analysis (stage 1A) and qPCR validation, training, Therefore, the goals for the present study were to provide a andprediction(stages2and3)arelistinTables1and2;patient highly cross-validated TOL gene signature in blood as a po- demographic characteristics for TOL cell-specific analyses (stage tential measure of immune quiescence to eventually guide safe 4, C and D) can be found in Table 3. Additional patient gene reduction of immunosuppression; to evaluate the frequency of expression data used in this study were downloaded from the this signature in patients receiving different immunosuppres- public domain7,8,12 and used for the microarray cross-validations sive regimens; and to further elucidate the underlying TOL (stage 1, B and C) and for TOL biology analysis (stage 4, A and B).

Figure 1. Study design. Four-stage study design: New microarray discovery (n=31) (A) and cross-platform microarray validations (B) (I [n=29] and II [n=58]) (stage 1) in peripheral blood to reﬁne the present gene signature for TOL7 to a 21-gene signature; qPCR validation in 59 independent peripheral blood samples (stage 2); qPCR modeling and prediction in 220 peripheral blood samples for developing and training a three-gene assay in 70 samples (stage 3A) and for prediction of the prevalence of TOL under the umbrella of immunosuppression in 150 samples (stage 3B); and TOL biology analysis (stage 4) to identify TOL-speciﬁc cell types with enrichment of the 21 TOL genes by FACS and gene expression analysis. A total of 571 human blood and tissue samples across transplant centers in the United States and Mexico were investigated.

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Table 1. Demographic information for TOL patients and SI patients, and varying Stage 1: Cross-Platform Microarray kidney function used for novel discovery (stage 1), qPCR validation (stage 2), and Discovery and Cross-Validation TOL modeling (stage 3) (n=121 unique patients) Stage 1A: 21-Gene Signature for Variable TOL (n=43)a SI (n=78)a Operational Tolerance Recipients In the new microarray discovery set of 31 Male patients (%) 68.4% 74.0% peripheral blood samples, 141 unique genes Mean age6SD (yr) 28620 15613 (153 Agilent probes) were significantly dif- Race (%) ferentially expressed in TOL (statistical anal- White 78 56 ysis of microarrays [SAM],13 false discovery Hispanic 0 11 rate [FDR], 5%) (Supplemental Table 1). Asian 22 0 Among these, a minimal set of 21 unique African American 0 22 genes (34 Agilent probes) (Table 4) correctly Other 0 11 classified TOL patients (n=16) from patients Post-transplant time (mo) with chronic allograft injury (CAN) (n=10) Mean 216.8 47.6 and from healthy nontransplant individuals Median6SD 195.76139.2 23.5671.7 Minimum/maximum. 11.4/460 0.36/300 (HC) (n=5) (prediction analysis of micro- 14 Induction therapy NA Daclizumab/antithymocyte globulin arrays [PAM] ) (Figure 2A) and provided Maintenance therapy – CNI+steroids/MMF, with or without AZA excellent segregation of samples by unsuper- Serum creatinine (mg/dl) 0.9560.2 2.9262.9 vised hierarchical clustering (Figure 2B). Donors LRD donor source (%) 0.32 0.67 Stage 1B: Discrimination of TOL Patients 6 6 6 Mean HLA mismatch (x/6) SD 0.75 1.5 2.92 2.9 in Two Public Microarray Datasets Male donors (%) 0.5 0.42 Homologues of the 21 genes from the 6 6 6 Mean age SD (yr) 39.8 16.6 42.86 10.84 Agilent arrays were evaluated for their NA, not applicable; CNI, calcineurin inhibitor (cyclosporine, tacrolimus); MMF, mycophenolate mofetil; ability to reclassify independent TOL blood AZA, azathioprine; LRD, living-related donor; x/6, number of HLA mismatches out of a total of 6. aUnique patients used in novel microarray discovery; qPCR validation and modeling. samples analyzed on two different microarray platforms from a 34 blood sample set Table 2. Patient demographic information for the SI patient of TOL, CAN, and stable immunosuppression (SI) patients on group (n=150) used for independent prediction (stage 3B) the cDNA Lymphochip,7 and from a separate 58 blood sample (n=150 unique patients) set of TOL, SI, and HC patients on the Affymetrix HG U133 8 Variable Data plus 2.0 gene chip (GSE22229 ). Given the 4-fold smaller rep- Recipients resentation of genes on the Lymphochip versus the Agilent Male patients (%) 63.3 platform, re-annotation to the most recent National Center fi Mean age6SD (yr) 33.3619.2 for Biotechnology Information gene identi ers and mapping Post-transplant time (mo) across different platforms using Array Information Library Mean 25.5 Universal Navigator (http://ailun.stanford.edu)15 found five Median6SD 29.0611.4 overlapping genes on the cDNA Lymphochip (IGJ, Minimum/maximum 0.3/63.2 TNFRSF17, SPC25, KLF6, and UHRF1), which provided sam- Induction therapy (%) ple classification similar to that reported in the published Daclizumab 25.3 study7 (Supplemental Figure 1A). These genes were also sig- Alemtuzumab 21.3 nificantly differentially expressed in TOL (FDR, 5%) (Table 4). Basiliximab 53.3 In the Newell dataset,8 all 21 genes were present and Maintenance therapy(%) CNI 60.7 provided a high rate of accurate sample segregation by phe- Cyclosporine 14 notype (Supplemental Figure 1B) with correct class assign- Tacrolimus 19.86 ment of 15 of the 19 TOL samples and of 34 of the 41 SI and Belatacept 34.7 HC samples. Ten of the 21 genes in the Newell dataset were Steroid-Free 39.3 also significantly differentially expressed in TOL (SAM, FDR Serum creatinine (mg/dl) 1.2760.34 5%) (Table 4). GFR (ml/min. per 1.73 m2) 60.95622.8 Donor fi LRD donor source (%) 59.3 Stage 2: qPCR Validation of TOL-Speci c Genes HLA mismatch 2.9361.74 Twenty-one TOL Genes Discriminate an Independent Set of Male donor (%) 48.0 31 TOL Patients by qPCR Donor age (yr) 37.7613.1 Standard qPCR (SABiosciences Superarray) was done in 59 Patients in the SI group were receiving maintenance immunosuppression and independent peripheral blood samples (31 TOL, 28 SI) (Table 1) had stable clinical graft function. for the 21 genes (plus 18S). The qPCR data allowed clear

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Table 3. Patient demographic information for the TOL and SI patient groups Stage 3: qPCR Modeling of a used for FACS analyses of TOL cells (n=11 unique patients) Three-Gene TOL Assay Variable TOL (n=5)a SI (n=6)b Stage 3A: Selection of KLF6, BNC2, and Recipients CYP1B1 for a Minimal Three-Gene Assay Male patients (%) 40.00 100.00 for TOL fl Mean age6SD (yr) 2467.87 1166.86 High-throughput micro uidic qPCR (Bio- Post-transplant time (mo) mark HD, Fluidigm, CA) for the 21 genes in Mean 221.8 54.4 a second independent sample set (n=70, Median6SD 219.1656.08 16.7664.06 17 TOL, 53 SI) (Table 1) resulted in a quality Minimum/maximum 157.7/291.3 11.9/159.2 control filtering of five genes and seven sam- Induction therapy NA Daclizumab/antithymocyte globulin ples. The best-performing and minimal Maintenance therapy(%) – CNI+steroids/MMF, with or without AZA gene-set to detect TOL was a set of three 6 6 Serum creatinine (mg/dl) 0.95 0.2 1.4 0.73 genes (KLF6, BNC2, and CYP1B1), which Donors correctly classified the TOL samples by pe- LRD donor source (%) 80 66.70 6 nalized logistic regression with 84.6% sensi- HLA mismatch NA 0.75 1.5 fi Male donor (%) 75 50 tivity, 90.2% speci city, and an area under fi Donor age (yr) 27.568.06 NA the curve of 0.95 (95% con dence interval, NA, not applicable. 0.97 to 0.92) (Figure 4A). Penalized logistic aOperational tolerant. regression provided accurate estimates for b Stable immunosuppression. the regression coefficients and a numeric probability estimate for each patient (rather fi Table 4. Signi cant changes between CAN and SI versus TOL for 21 genes in than a simple categorical class estimate), cal- 417 peripheral blood samples across 5 independent platforms. culated as a percentage predicted probability TOL versus q Value (%) P Values Gene ID of TOL. The cutoff for the predicted proba- CAN Agilent Lymphochip Affymetrix SAB Fluidigm bility for a sample to be classified as TOL (u) BNC2 54796 0.0000 Not expressed 0.0000 0.0002 0.0000 was u=0.25, which had the best sensitivity CYP1B1 1545 0.0000 Not expressed 0.0000 0.0001 0.0000 and specificity by maximizing the correct KLF6 1316 1.4100 3.5000 3.2468 0.0000 0.0096 rate. These three genes also separated TOL IGFL2 147920 4.9800 Not expressed 0.0000 0.0059 0.0000 from SI by unsupervised clustering (Figure CCL4 6351 0.0000 Not expressed 0.0000 0.0000 0.04 4B) and had significantly different (two- SHCBP1 79801 0.0000 Not expressed NS 0.0001 0.03 sided t test) expression levels (P,0.05) in SPC25 57405 0.0000 0.8500 NS 0.014 0.07 TOL (Figure 4A and Table 4). UHRF1 29128 1.4100 0.0000 NS 0.0099 0.0024 NXF3 56000 3.7000 Not expressed 5.9524 0.0000 Not expressed IGHA2 3494 0.0000 Not expressed Not expressed 0.02 0.47 Stage 3B: Assessing the Frequency of TOL TNFRSF17 608 0.0000 0.0000 0.0000 0.0009 0.22 Prediction in 150 Stable Patients Receiving IGJ 3512 0.0000 0.8500 0.0000 0.17 0.71 Standard Immunosuppression IGHG1a 3500 0.0000 Not expressed NS 0.068 0.27 In a purely observational analysis, we applied IGHG4a 3503 0.0000 Not expressed NS 0.068 0.27 the three-gene assay (KLF6, BNC2, and IGH@ 3492 0.0000 Not expressed 0.7666 0.47 Not expressed CYP1B1) to peripheral blood samples from FAM110C 642273 0.0000 Not expressed 0.0000 0.0000 0.67 150 stable renal transplant recipients (mean VN1R2 317701 1.4100 Not expressed NS 0.02 0.36 serum creatinine6SD, 1.2760.34 mg/dl; CLVS1 157807 1.4100 Not expressed NS 0.05 Not expressed mean GFR, 60.95623.81ml/min per 1.73 m2; GDEP 118425 3.7000 Not expressed NS 0.0005 Not expressed no detectable donor-specific antibody [DSA]) C1QC 714 3.7000 Not expressed NS 0.0000 0.64 PRAMEF3 401940 4.9800 Not expressed NS 0.02 Not expressed receiving long-term maintenance immuno- TFDP3 51270 3.7000 Not expressed NS 0.30 0.66 suppression with a minimal 3-year clinical Significance of the 21 genes between TOL and CAN were calculated by SAM13 for microarray data follow-up (Table 2). Immunosuppression (Agilent, Lymphochip, Affymetrix) and between TOL and SI by two-sided t test for qPCR data (SAB, for induction and maintenance in these pa- Fluidigm). Any q value #5% and P value #0.05 were considered to represent a statistically significant tients differed: anti-CD52 (alemtuzumab) difference. NS, nonsignificant by q#5%. aIGHG1/IGHG4 represented by same qPCR primer/probe set. plus calcineurin inhibitor (CNI) (n=32); anti-CD25 (daclizumab) plus CNI (n=38); segregation of the TOL and SI phenotypes by unsupervised anti-CD25 (basiliximab) plus CNI (n=21); and anti-CD25 principal component analysis (PCA, 70.3%) (Figure 3A) and (basiliximab) plus belatacept (n=59). CNI consisted of tacro- by hierarchical clustering (Figure 3B); qPCR also validated the limus (n=73) or cyclosporine (n=21); belatacept recipients differential expression of 17 of the 21 genes in TOL (two-sided were a subset of patients enrolled in BENEFIT (Belatacept t test, P,0.05) (Table 4 and Figure 3B). Evaluation of Nephroprotection and Efficacy as First-line

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Figure 2. Refining the TOL signature to 21 unique genes for correct prediction (A) and segregation of TOL, CAN, and HC samples (B): 21 genes were identified from a set of 141 significantly differentially expressed genes and were correctly classified in 16 TOL, 10 CAN, and 5 HC samples by nearest shrunken centroid (PAM FDR, 5%) from our new Agilent microarray discovery set (A) and were correctly segregated the same patients by phenotype by unsupervised hierarchical clustering (B). Part A shows predicted probabilities for TOL by PAM using 21 genes. The threshold for TOL prediction was set at predicted probability score .50%.

Immunosuppression Trial).16 To reduce false-positive rates for and CNI maintenance (n=2 of 59; 3.4%) (Supplemental Figure predicting whether a patient has a TOL phenotype, the three- 2B). According to the small numbers of patients in each drug gene assay specificity was increased to 98.4% to maximize assay treatment subgroup, none of the differences in frequencies safety by increasing the threshold u for TOL prediction from 0.25 were statistically significant by two-sided chi-squared test to 0.6. As a result, 11 patients (7.33%) were predicted as being (threshold of significance P#0.05). Additional clinical param- TOL (Supplemental Figure 2, A and B). Of these, 5 patients were eters, such as time since transplantation, donor age, donor receiving belatacept (8.5%) compared with 2 patients receiving source, HLA mismatch, and cause of ESRD, did not affect the CNI (3.4%) after the same induction (CD25; n=118) (Supple- frequency of predicted TOL phenotype. Three patients with a mental Figure 2A). Receiving CNI for maintenance (n=91), 4 pa- predicted TOL phenotype had repeat blood samples within tients with alemtuzumab induction were predicted as TOL (n=4 1 year of the original sample, and the predicted probabilities of 32; 12.5%) compared with 2 patients with anti-CD25 induction for the TOL phenotype by the three-gene assay were consistently

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Figure 3. Validation of 21 TOL genes in 59 independent patients (28 SI, 31 TOL) from multiple centers in the United States by qPCR. This assay validated significance of identified TOL genes in 31 TOL and 28 SI patients. Unsupervised principal component analysis showed 70.3% segregation between SI and TOL using 18 of 21 genes with sufficient expression levels (A) and correctly clustered samples by phenotype (26 of 28 SI patients and 26 of 31 TOL patients clustered correctly) by unsupervised hierarchical clustering (B); significant differential expression between TOL and SI (B) (P values were calculated by two-sided t test). elevated in all 3 patients. On clinical follow-up of all 11 patients with peripheral gene expression profiling of operationally toler- with predicted TOL phenotypes, none experienced a rejection ant liver transplant recipients10; KLF6 belongs to the Krueppel- episode or had detectable DSAs within 12 months after sam- like family of transcription factors, which participate in diverse pling; despite a transient drift in serum creatinine in 2 of the aspects of leukocyte growth, development, differentiation, and 11 patients, all continued to have stable graft function at 3-year activation of cells of myeloid lineages.17 Finally, CYP1B1 be- follow-up (average serum creatinine, 1.31 mg/dl; mean GFR, longed to the cytochrome P450 family of monooxygenases 59.89 ml/min per 1.73 m2). that catalyzes reactions involved in drug metabolism and synthesis of cholesterol, steroids, and lipids.18,19 Stage 4: Biologic Analysis of TOL Genes Stage 4A: Peripheral Blood Cell Subset Analysis Stage 4C: FACS Quantitative Analysis of Selected Peripheral To identify the peripheral blood cell types that were specifically Blood Cell Subsets contributing to the differential expression of the 21 TOL gene set, On the basis of the significant TOL gene enrichment in dendritic, we first analyzed their expression profiles in 158 microarrays myeloid, B, and NK cells, FACS analysis was conducted on these from 79 normal human cells and tissues (BioGPS, GSE1133). selected cell populations in 5 TOL patients, 6 SI patients, and Hypergeometric enrichment analysis of the 21 TOL genes in this 5 HCs (Table 3). Although the total numbers of T cells were sig- dataset identified a signature suggestive of maximal enrichment nificantly lower in TOL patients than in HCs (P,0.001) (Figure in dendritic cells (n=7 of 21 genes; P=0.013), with additional 5A), the difference between T cells in TOL and SI patients did not gene enrichment in B lymphocytes (B cells; n=7 of 21 genes; reach significance (Figure 5A). When T cells were sorted for CD4 2 P=0.047) and NK cells (n=6 of 21 genes; P=0.042) (Table 5). staining, CD4 /CD3+ T-cells were significantly lower in TOL Gene expression .3-fold higher in a given cell/tissue compared than in both HCs (P,0.01) and SI patients (P=0.05); this dif- with the median expression in all samples was considered sig- ference was not seen for CD4+ T-cells (Figure 5A). As suggested nificant enrichment. KLF6 and CYP1B1 were also highly en- by the hypergeometric gene enrichment data for the 21 TOL- 2 riched in dendritic cells and myeloid cells. specific genes, CD14hi CD16 CD11C- monocytes were significantly enriched in the TOL patients compared with the SI patients (P=0.03) and HCs (P=0.0005). In addition, CD11C+, Stage 4B: Inferred Biologic Function of the TOL Genes 2 Downstream analyses of the 21 TOL genes for their biologic CD304 , CD14low dendritic cells were significantly increased in function by Ingenuity Pathway Analysis (Ingenuity, Redwood TOL patients compared with both SI patients (P=0.05) and HCs City, CA) revealed that 13 of the 21 genes were involved in an (P=0.028) (Figure 5B, Supplemental Table 2). In examining NK 2 2 apoptosis network with a central signaling role for TNF, IL6, and cells, CD14 , CD16 , and CD56bright NK cells were increased IL4 (Supplemental Figure 3A), and canonical pathway analyses in TOL patients compared with HCs (P=0.0084); there were no identified the complement system (P=0.03) and the B cell acti- significant changes in NK cells between TOL and SI patients, vating factor signaling pathway (P=0.04) associated with these although NK cells trended toward lower numbers (P=0.13) in genes (Supplemental Figure 3B). BNC2, which encodes for a TOL compared with SI. Interestingly, the number of B cells that DNA-/metal-binding protein, has previously been associated stained positive for CD19 did not show consistent elevation in

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Figure 4. Development of a peripheral blood three-gene TOL assay. Independent qPCR for the 21 genes was performed in 65 peripheral blood samples. A three-gene model (KLF6, BNC2, and CYP1B1) predicted TOL with an area under the curve (AUC) of 0.95 (95% confidence interval [95% CI], 0.97 to 0.92), with 84.6% sensitivity and 90.2% specificity; the threshold for TOL prediction was set at u=25% (A); the same genes segregated TOL and SI samples by unsupervised principal component analysis (B) and were significantly increased in TOL samples (P,0.05, two-sided t test with Welch correction) (C). Shown are individual gene expression fold changes with mean and SEM calculated against a universal RNA using the ddCt method.43 ROC, receiver-operating characteristic curve.

TOL patients, as suggested in other publications7,8; however, showed signiﬁcant differential expression in CD11c+ cells iso- because of limited sample volumes we could not perform sub- lated from the TOL patients (BNC2, P=0.04; CYP1B1, P=0.01; staining for immature and transitional B cells. KLF6, P=0.05) compared with non-TOL patients.

Stage 4D: Expression of the BNC2, KLF6,andCYP1B1 TOL Genes in CD11c+ Dendritic Cells in TOL Patients DISCUSSION Toinvestigate whether the three-gene TOL signature identiﬁed in whole blood from TOL patients originated from the CD11c+ Transcriptional signals in peripheral blood and tissue have cells enriched in TOL, we isolated CD11c+ cells from 5 TOL been found to track with the state of clinical operational and 13 non-TOL patients and analyzed the expression of the transplant tolerance7–9,11,20,21 but lack cross-validation and TOL genes (BNC2, KLF6, and CYP1B1). All three genes additional analyses in stable transplant patients receiving

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Table 5. Hypergeometric enrichment analysis of 21 genes in actual increased presence in blood of TOL patients by down- peripheral blood cells from healthy samples stream FACS analysis with additional differential expression of Common Mapped the three genes in these cells. Related Targets Upregulated Hypergeometric Most patients receiving long-term maintenance immuno- Cell with ‡3-Fold of Probes P Value suppressionwithstablegraftfunctionwillrequirecontinuationof Median Intensity in TOL (n) their long-term maintenance immunosuppression, as the fre- Dendritic 3922 7 0.01 quencies for the presence of a TOL phenotype predicted by gene cells signatures in these patients are low: approximately 7% of patients B cells 5028 7 0.05 in this United States and Mexican multicenter study defined by + CD56 NK 3802 6 0.04 our three-gene assay; 8% of patients reported in our previous cells 7 + multicenter study from the United States, Canada, and Europe ; CD8 T 3278 3 0.26 and 3.5% of patients in a single-center European cohort.30 Given cells + these small patient numbers it is not possible to definitively assess CD4 T 3157 3 0.25 cells whether a protolerogenic cell composition and a protolerogenic fi Monocytes 3241 3 0.26 milieu can be achieved by speci c immunosuppression options. CD34+ 3898 3 0.27 However, the trend toward more patients receiving a belatacept/ cells CNI-free regimen seen in the present study is thought provoking CD33+ 11,218 4 0.21 and may correlate the excellent long-term graft function in this cells cohort. The association of anti-HLA DSAs and persistence of the Frequency and hypergeometric P values for common targets between predicted TOL probability is unclear. Low levels of DSA have fi BioGPS from HU133A platform (GSE1133) and identi ed 18 upregulated been observed in TOL-predicted patients in some studies,7,31 but targets specific to tolerance from Agilent platform and P values of enrichment across related blood cell types are shown. DSA were not detected in any of the TOL-predicted patients in this study. different immunosuppression regimens. The present study In conclusion, this study provides a highly validated, highlights a peripheral three-gene assay that detected operational peripheral blood, three-gene assay to detect a TOL patient TOL in HLA-mismatched, clinically stable renal transplant re- phenotype and infers mechanisms into this state of operational cipients off immunosuppression with high sensitivity and spec- allospecific tolerance. The three-gene assay offers a potential ificity, highly confirmed by independent cross-validations. When means to monitor for donor-specific hyporesponsiveness and applied to HLA-mismatched stable renal allograft recipients re- graft accommodation in all immunosuppressed transplant ceiving long-term maintenance immunosuppression, this assay recipients, segregating patients who may be on a larger burden also discerned a clinical homeostatic state of low alloimmune risk of long-term immunosuppression than is “immunologically” in 7.3% of patients receiving immunosuppression. Additionally, necessary for customized immunosuppression management. TOL patients showed significant enrichment of myeloid-derived cells in peripheral blood with significantly differential expression of BNC2, KLF6, and CYP1B1 constituting the three-gene assay CONCISE METHODS of TOL. Different TOL-specific gene panels have been reported in All study methods are described briefly below and fully detailed in the different studies of both liver and kidney transplant toler- Supplemental Methods. ance.7,8,21,22 Interestingly, BNC2 and CYP1B1 in our three-gene assay have been independently linked to TOL in liver and kidney Design transplantation by us and others,7,8,10 and CYP1B1 has addition- The study was performed in four distinct stages (Figure 1). Stage 1 ally been reported as drug target to influence antitumor immune consisted of new microarray TOL gene signature discovery (A) and responses.23,24 Different studies have also reported different cell cross-platform validation (B) (Figure 1) in 118 samples from 101 subtypes to play a role in operational tolerance, particularly unique renal transplant recipients (44 TOL, 21 CAN, 36 SI) and B cells and NK cells,8,21 with emerging evidence for a role of 17 HCs. New Agilent 4344-k whole-genome arrays were performed the antigen presenting dendritic cells.25,26 An immunosuppres- in 31 samples for identification of the 21 gene set (GEO: GSE45218) sive, protolerogenic role for the myeloid-derived dendritic cell (A); cross-microarray platform validations I and II (B) were performed subpopulation27,28 and other cells of myeloid lineage7,8 has been using publicly available microarray data from 87 samples (validation I, recently shown, and in the ONE-Study different monocyte- n=29, cDNA Lymphochip; validation II, n=58, Affymetrix HG U133 derived regulatory cell populations are being tested for their Plus2.0, GSE22229). Next, qPCR validation (stage 2) for the 21 TOL cell therapy potential29 in renal transplant recipients. While we genes was performed in 59 independent samples by standard qPCR. also noted enrichment of the 21 TOL genes in B cells and NK Stage 3 comprised qPCR TOL gene-assay modeling and prediction in cells by microarray analyses, in addition to the highest enrich- 220 independent samples by microfluidic high-throughput qPCR. We ment of the genes seen in dendritic cells, only cells of myeloid trained a minimal TOL gene panel in 70 samples (A) (17 TOL, 53 SI) origin (myeloid-derived dendritic cells, monocytes) showed and we applied the final three-gene model to 150 unique peripheral

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blood samples from patients receiving long-term immunosuppression to test for the frequency of a potentially tolerant phenotype as deﬁned by the three-gene model (B). For TOL biology analysis (stage 4), we investigated whether the TOL gene signature corresponded to spe- ciﬁc blood cell types. Initially, enrichment of the 21 TOL genes was investigated in whole- genome expression data from different blood lymphocytes (GSE1133) in normal patients (A). Predicted TOL gene-enriched cell subsets were then pursued by FACS in blood from TOL patients, HCs, and SI patients (C) and by gene expression analysis in the most informative cell type from the FACS analysis (D). Patient demographic characteristics can be found in Tables 1–3.

Patients and Samples We collected a total of 326 peripheral blood samples from 282 unique renal allograft recipients and 10 nontransplant HCs. Renal allograft recipients belonged to three distinct clinical phenotypes: (1) TOL, defined as long-term stable graft function without any immunosuppressive drug for .2 years and no history of rejection off immunosuppression (n=69); (2) long-term SI, patients with varying graft function in the absence of acute rejection as defined by the Banff classification receiving double or triple maintenance immunosuppression and with or without history of rejection (n=97); within the SI group, we included 10 CAN samples from patients with chronic allograft histologic injury (biopsy specimens confirmed by Banff classification and Chronic Allograft Dam- age Index32,33); and (3) patients with stable graft function and absence of rejection who were receiving SI (n=150): one immunosuppressant (minimal immunosuppression; n=9) or conventional triple immunosuppression (n=141). There were differences Figure 5. Variations in peripheral blood cell subsets in operational tolerance. Quan- in immunosuppression induction: daclizumab, titative analyses of peripheral blood cells in TOL show decreased CD8 T cells (A) and n=3834; antithymocyte globulin, n=16635,36; increased cell populations of myeloid lineage (B). Dendritic cells, monocytes, NK cells, alemtuzumab, n=3237–39;andbasiliximab, fi and B cells were quanti ed in PBMCs from 5 TOL patients, 6 SI patients, and 5 HCs by n=80.16,40 Immunosuppression maintenance FACS. TOL patients showed significantly reduced numbers of T cells, which addi- also varied: steroid-free (n=54) or steroid-based tionally stained negative for CD4 compared with both HCs and SI patients (A). Dendritic 16,40 lo ( n=262); CNI free (belatacept, n=59 )orCNI cells that additionally stained low for CD14 (CD14 DC) and monocytes that stained high for CD14 (CD14hi) were significantly enriched in TOL compared with HC and SI based (tacrolimus or cyclosporine, n=257). The 2 samples (B, upper panel left and right; ratio to CD4 T cells). In contrast, NK cells that study adhered to the Declarations of Helsinki stained bright for CD56 (CD56Bright) were significantly enriched in TOL samples only and Istanbul and was approved by the institu- compared with HC samples and showed slightly decreased numbers in TOL com- tional review boards of California Pacific Medical pared with SI samples (lower panel left). Graphs in A show mean cell counts per million Center (San Francisco, CA), Stanford University live cells; graphs in B show mean cell counts per million live cells calculated as ratio to (Stanford, CA), the University of Pittsburgh 2 CD4 T cells. Significance was calculated by two-sided t test. *P#0.05; **P,0.01; (Pittsburgh, PA), and the Instituto Nacional , ***P 0.001. de Ciencias Medicas y Nutricion (Mexico City, Mexico), with written informed consent obtained

2050 Journal of the American Society of Nephrology J Am Soc Nephrol 26: 2042–2053, 2015 www.jasn.org CLINICAL RESEARCH from all participants. De-identified samples were also provided for this reaction volumes for the 21 genes according to the manufacturer’s study by Bristol-Myers Squibb from a subset of patients receiving instructions. The qPCR primers were custom designed from publicly belatacept and cyclosporine from the BENEFIT study40 as part of an available mRNA sequences for the 21 TOL genes using Primer 3.0 investigator-initiated grant. (http://frodo.wi.mit.edu) and synthesized by SABiosciences. For subsequent qPCR modeling and prediction studies, we chose the Sample Collection, RNA Extraction, and Cell Isolation Fluidigm microfluidic high-throughput qPCR platform (Fluidigm Inc., Peripheral blood samples were collected in PAXgene Blood RNA Tubes South San Francisco, CA) for sensitive and simultaneous analysis of (Qiagen, Hilden, Germany) and in EDTA tubes for gene expression 96 samples across TOL genes in duplicates with a 143103-fold reduction analysis and for isolation of PBMCs for FACS analysis in TOL and SI in template required per reaction cycle compared with our previous samples and in leukoreduction system chambers for isolation of PBMCs standard qPCR, due to the use of a target specificamplification step for FACS analysis in HC samples. Total RNA was extracted from whole (18 cycles) in this assay. Experiments were performed according to the blood using the PAXgene Blood RNA Kit or the RNeasy kit (both from manufacturer’s protocols and described in the Supplemental Methods. Qiagen), RNAconcentrations were measured (NanoDrop Technologies, Wilmington, DE), and RNA integrity was assessed (Agilent 2100 PCR Data Processing and Statistical-Analyses Bioanalyzer). Only samples with an RNA integrity number .7 were Raw Ct values were imported into Excel 2007 (Microsoft, Redmond, accepted for further processing. FACS analysis was done using the WA) for quality control and calculation of relative expression values FACSAria II flow cytometer. against 18S and universal human reference RNA (Stratagene; Agilent Technologies) using the delta Ct (ddCt) method as described.43 Genes , Microarray Preparation and Hybridization that were expressed in 80% of samples and samples with expression , Standard published protocols41 were used for hybridization of samples 90% of genes were excluded from further analyses, resulting in onto Agilent Whole Human Genome 4344-k 60-mer oligonucleotide 16 genes and 65 samples (14 TOL, 51 SI). DdCt values were analyzed arrays (G4112F, Agilent Technologies, Santa Clara, CA), using 150ng of in Partek Genomic Suite, version 6 (Partek Inc., St. Louis, MO) and total RNA as template/sample. The arrays were scanned on an Agilent GraphPad Prism for data visualization and unsupervised clustering. scanner and further processed using Agilent Feature Extraction Soft- Development of a mathematical model for detecting and predicting ware (Agilent Technologies). TOL and clinical confounder analyses were performed in the latest versions of R (R 2.14.2); Bioconductor packages were used for further fi Microarray Gene Expression Data Processing and normalization of data, feature selection, and classi er development Analysis (Supplemental Methods). F-statistic P values by Fisher exact test or chi-square test were applied for the nested models with and without Using a cutoff for absolute value of log2 red channel/green channel .0.5 fi for at least one array Agilent array data were processed and normalized one of the following factors to test whether any of these were signi - using LOWESS in Gene Spring GX7.3 (Agilent Technologies). SAM and cantly associated with the TOL prediction score: recipient age, catego- # PAM programs with two- and three-class comparison analyses and nes- rized as pediatric and adolescent (age 18 years) and into adults (age . ted loop cross-validation of PAM with a minimum error rate were used 18 years), time since transplantation, immunosuppression induction, to identify the minimal gene set differentiating TOL from CAN and HCs and immunosuppression maintenance protocol. with an FDR,5%. Agilent microarray raw data have been deposited in GEO(GSE45218).IngenuityPathwayAnalysiswasusedtoassessbio- Isolation of TOL Cell Types by Flow Cytometry for logic functions and examine canonical pathways for the genes significant Quantitative Analyses in TOL. BioGPS (http://biogps.org/downloads/, GSE1133) was used to FACS and quantitative analysis of cell subtypes were performed in assess cell-specific target genes based on their relative expression as ex- PBMCs from 6 SI patients, 5 TOL patients, and 5 HCs; PBMCs were amined in 79 different human cells and tissues. Genes .3 times higher thawed in 15-ml conical tubes containing 12 ml of IMDM medium expressed in a given cell type compared with the median expression in all (GIBCO,Invitrogen)supplementedwith10%humanABserum,100U/ml m other cell types were considered specificforaspecific cell type and tested penicillin, 100 g/ml streptomycin, 2 mM L-glutamine, sodium pyruvate, m for significant enrichment by hypergeometric enrichment analysis.42 nonessential amino acids, and 50 M 2-ME. PBMCs were washed two Probe sets on Agilent, Lymphochip, and Affymetrix Gene chip times in PBS containing 2 mM EDTA and 2% human AB serum before fl platforms were reannotated using Array Information Library Universal staining with uorescently labeled monoclonal antibodies against CD3, Navigator15 and mapped to Gene Entrez IDs before interrogation of the CD4, CD11c, CD19, CD56, HLA-DR (BD Biosciences, San Jose, CA), 21 genes on the public domain Affymetrix and on the Lymphochip CD14, CD16 (BioLegend, San Diego, CA), and CD304 (Miltenyi Biotech, cDNA array data, which were originally discovered from the Agilent Cologne, Germany) along with propidium iodide (Invitrogen). Samples fl arrays in this study. were analyzed and isolated on a BD FACSAria II ow cytometer. qPCR For TOL qPCR validation and minimal gene set selection, a custom- ACKNOWLEDGMENTS ized qPCR platform (RT2 QPCR System) was used with standard PCR technology (Superarray, SABiosciences, Qiagen) in which 10 ng total We are grateful for the help from physicians, clinical coordinators, RNA transcribed into cDNA was analyzed in duplicate, each in 25-ml research personnel, patients, and patient families. M.S. received

J Am Soc Nephrol 26: 2042–2053, 2015 Three-Gene Assay for Immune Quiescence 2051 CLINICAL RESEARCH www.jasn.org

funding for this work from Grant R01-AI 61739-01 from the National customizable portal for querying and organizing gene annotation re- Institute of Allergy and Infectious Diseases/National Institute of Health. sources. Genome Biol 10: R130, 2009 fi Part of this study was presented as an abstract during the World 13. Tusher VG, Tibshirani R, Chu G: Signi cance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 98: Transplant Congress 2014. 5116–5121, 2001 14. Tibshirani R, Hastie T, Narasimhan B, Chu G: Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad DISCLOSURES Sci U S A 99: 6567–6572, 2002 15.ChenR,LiL,ButteAJ:AILUN:Reannotatinggeneexpressiondata None. automatically. Nat Methods 4: 879, 2007 16. 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J Am Soc Nephrol 26: 2042–2053, 2015 Three-Gene Assay for Immune Quiescence 2053 Supplemental Information:

Supplement Figure 1a: Segregation of 35 independent TOL, CAN and STA by the 21 gene-set

5/21 genes were present on the cDNA lymphochip and were used to reclassify samples from the Brouard Study 1. Sample S9 from a stable patient (STA) was also classified as TOL in the

Brouard Study 1.

Supplement Figure 1b: Segregation of 58 independent TOL, CAN and STA by the 21 gene-set

21 genes were present on the Affymetrix HG U133 plus 2.0 gene chip and used to reclassify samples from the publicly available study (2, GSE2229) in 19 TOL, 21 patients on standard immunosuppression (SI) and 12 HC.

Supplement Figure 2: Frequency of predicted TOL Phenotype in stable Patients on standard

Immunosuppression

Blood samples (n=150) from patients on immunosuppression with stable graft function were analyzed by the 3-gene assay: using a threshold for TOL-phenotype prediction (Theta=Θ) of

Θ=60% predicted TOL probability, a total of 11 patients (7.33%) were predicted as TOL. 2A: the frequency of predicted TOL phenotype was higher in patients receiving Belatacept maintenance-IS compared to patients receiving CNI maintenance-IS (8.5% vs. 3.4.%; p=0.44). 2B: while on the same CNI maintenance regimen, the frequency of predicted TOL phenotypes was higher in patients after Campath Induction compared to patients after anti-CD25 Induction

(Daclizumab, Basiliximab) (12.5% vs. 3.4%; p=0.09); 2-sided Chi-Square test was used to evaluate significant associations (p<0.05 was considered significant).

Supplement Figure 3a: Apoptosis Network associated with 13/21 TOL genes

Supplement Figure 3b: Significant association of the B-cell activating factor signaling pathway with identified kidney TOL genes

Supplement Table 1: Differentially expressed genes by microarray

The top 100 unique genes (102 Agilent Probe-IDs) of the 141 genes (153 Agilent Probe-IDs), differentially regulated between TOL and CAN by microarray (SAM, FDR 5%).

Agilent Probe-ID Gene Symbol FC (TOL vs. CAN) SAM q-value [%] A_32_P200144 IGH@ 2.88 0.00 A_23_P158817 IGH@ 2.19 0.00 A_24_P24371 IGHG4 2.91 0.00 A_23_P209625 CYP1B1 1.65 0.00 A_32_P47643 FAM110C 2.30 0.00 A_23_P37736 TNFRSF17 3.05 0.00 A_23_P207564 CCL4 1.71 0.00 A_32_P12232 BNC2 1.80 0.00 A_23_P51085 SPC25 1.74 0.00 A_32_P96719 SHCBP1 1.83 0.00 A_23_P167168 IGJ 2.99 0.00 A_23_P50096 TYMS 1.50 0.00 A_23_P78342 LMAN1 1.68 0.00 A_23_P201551 VAV3 1.53 0.00 A_32_P506600 RAN 1.39 0.00 A_23_P63017 LGALS8 1.50 0.00 A_23_P401 CENPF 1.39 0.00 A_23_P120237 STARD7 1.33 0.00 A_23_P8582 FAM126A 1.38 0.00 A_23_P129695 VASN 1.55 0.00 A_23_P350719 PRSS30P 2.18 0.00 A_24_P933448 CLVS1 1.63 1.86 A_24_P932981 KLF6 1.95 1.86 A_32_P133916 BNC2 2.12 1.86 A_23_P402279 VN1R2 1.63 1.86 A_23_P108404 CENTG2 1.69 1.86 A_24_P548866 HIGD1A 1.52 1.86 A_23_P116123 CHEK1 1.57 1.86 A_24_P38895 H2AFX 1.21 1.86 A_32_P175539 RCN2 1.43 1.86 A_23_P107421 TK1 1.39 1.86 A_32_P163858 SCD 1.37 1.86 A_23_P135239 TLE1 1.78 1.86 A_23_P25653 INTS6 1.51 1.86 A_23_P501547 ADCY6 1.38 1.86 A_23_P104876 SPA17 1.62 1.86 A_23_P306964 SEPT11 1.39 1.86

A_23_P211659 CERK 1.45 1.86 A_23_P26107 RFXDC2 1.64 1.86 A_23_P28213 PPIL3 1.68 1.86 A_23_P127186 TACC2 1.49 1.86 A_23_P208880 UHRF1 1.85 2.62 A_23_P117602 GZMB 1.59 2.62 A_23_P103631 EBNA1BP2 1.29 2.62 A_23_P87769 C12orf48 1.60 2.62 A_24_P237586 ANKRD37 1.63 2.62 A_23_P334608 GUSB 1.25 2.90 A_24_P186994 C1orf151 1.27 2.90 A_23_P121533 SPON2 1.96 2.90 A_23_P117683 C15orf63 1.18 2.90 A_23_P164814 C19orf57 1.40 2.90 A_32_P110872 A2LD1 1.62 2.90 A_23_P309361 C1orf59 1.38 2.90 A_23_P402819 CSN1S2A 1.77 2.90 A_32_P117322 LOC100506130 1.64 2.90 A_32_P141135 TRAF3IP3 1.74 3.29 A_24_P690285 CNOT6L 1.26 3.29 A_23_P102000 CXCR4 1.39 3.29 A_32_P101031 LYPD1 1.45 3.29 A_24_P680947 KIF18B 1.57 3.67 A_32_P25273 HSPD1 1.32 3.67 A_23_P309701 PTPN2 1.55 3.67 A_23_P6362 DERL3 1.45 3.67 A_24_P943613 TBC1D1 1.30 4.76 A_23_P124022 MED10 1.26 4.76 A_23_P126716 ATPIF1 1.34 4.76 A_23_P10518 TFDP3 0.53 5.30 A_23_P171336 NXF3 0.57 5.30 A_23_P125977 C1QC 0.47 5.30 A_23_P250747 GDEP 0.60 5.30 A_23_P144151 DCUN1D1 0.66 5.30 A_24_P153820 HYDIN 0.58 5.30 A_24_P550924 BTBD19 0.42 5.30 A_32_P192430 CKS1B 1.30 5.30 A_23_P761 PSMB4 1.24 5.30 A_23_P303087 PTN 1.48 5.30 A_24_P310894 CAPZA1 1.41 5.30 A_32_P8402 SYNCRIP 1.27 5.30 A_23_P157405 CHCHD2 1.30 5.30 A_23_P253484 AADAT 1.62 5.30 A_23_P16630 OR7A5 0.62 5.30

A_24_P564462 C12orf5 1.48 5.30 A_24_P251866 ADAMTS2 0.76 5.30 A_23_P99579 C14orf142 1.62 5.30 A_23_P130113 ASGR2 1.41 5.30 A_24_P240166 PHLDB2 1.91 5.30 A_24_P23527 THAP5 1.45 5.30 A_23_P366098 NUP210L 1.57 5.30 A_32_P12552 UG0898H09 0.42 5.30 A_24_P934008 LOC100131826 0.59 5.30 A_23_P158096 COL27A1 1.29 5.30 A_23_P422724 PPIC 1.36 5.30 A_24_P234856 CAMLG 1.59 5.30 A_23_P164536 PIK3C3 1.26 5.30 A_23_P18392 PRKCI 1.50 5.30 A_23_P203120 CADM1 1.33 5.30 A_32_P117354 LIMCH1 1.56 5.30 A_23_P129075 WDR76 1.59 5.30 A_23_P200222 LRP8 1.34 5.30 A_32_P76156 RWDD4A 1.73 5.30 A_23_P153571 IGFL2 0.33 5.92 A_24_P204690 PRAMEF3 0.38 5.92

Supplement Table 2: p-values and Fold changes (Fc) for quantitative TOL cell type analysis in

Blood

Cell Type TOL vs. SI TOL vs. HC SI vs. HC

p-value Fc p-value Fc p-value Fc

CD14hi Mono 0.0322 2.31 0.0005 3.76 0.3121 1.63

CD14lo DC 0.0501 3.79 0.028 4.2 0.8304 1.11

CD56Bright NK* 0.1292 0.58 0.0084 2.26 0.0036 3.87

CD14- DC 0.1294 3.37 0.0571 5.44 0.1871 1.62

CD16+ Mono 0.1294 3.37 0.0571 5.44 0.1871 1.62

CD19+ B cells* 0.3592 3.28 0.2086 8.28 0.0467 2.53

Supplement Table 3: TaqMan Assay IDs for primers and probes used in Fluidigm QPCR

Gene Symbol Assay_ID Amplicon Length [bp] 18S Hs03003631_g1 69 BNC2 Hs00417700_m1 79 C1QC Hs00757779_m1 99 CCL4/CCL4L1/CCL4L2 Hs00605740_g1 151 CLVS1 (RLBP1L1) Hs00542966_m1 CYP1B1 Hs00164383_m1 118 FAM110C Hs00297933_s1 102 GDEP Hs00328566_m1 104 IGFL2 Hs03645208_g1 117 IGHA2 AIHSNQS IGHG1 / IGHG4 /IGHG3 /IGHM /IGHV4-31 Hs00378230_g1 137 IGJ Hs00950678_g1 85 KLF6 Hs00810569_m1 57 NXF3 Hs00222815_m1 76 SHCBP1 Hs00226915_m1 87 SPC25 Hs00221100_m1 88 TFDP3 Hs00539413_s1 77 TNFRSF17 HS03045080_m1 151 UHRF1 Hs01086727_m1 63 VN1R2 Hs00545195_s1 1230

Supplement Experimental Procedure

Study Design

The study was performed in 4 Stages as outlined in Figure 6 and described as follows:

Stage 1 – Microarray TOL gene-signature Discovery:

Microarray Discovery and Cross-Platform Verification of Significant Genes for Operational

Tolerance: Gene discovery was initially conducted on 31 unique peripheral blood samples on the Agilent array (16 TOL, 10 CAN and 5 HC samples) (GSE45218). The microarray discovery approach was redone in this study in an approach similar to our previous publications 1, 3 where healthy controls were included in the analysis to control for absence of immunosuppression usage in the TOL patients. The present study was improved by the following: 1) larger number of TOL and CAN patients (26 overlapping from our previous publication); 2) all CAN patients on maintenance immunosuppression (unlike our previous publication that also had CAN patients with failed grafts, no immunosuppression and on dialysis) and 3) scanning a 4X larger set of genes (44k in this study vs. 11k in our previous publication). These improvements enabled the refinement of the TOL biomarkers to 21 genes for subsequent application in stable transplant patients on immunosuppression and evaluation of their TOL phenotype. Stringent bioinformatic microarray analyses allowed for the selection of a highly statistically significant set of 21 genes that defined all TOL patients apart from HC, SI and CAN patients. This TOL gene- set was verified for its ability to discriminate a different sub-group of TOL patients in other published and publicly available microarray studies. We accessed, downloaded, re-normalized and analyzed peripheral blood microarray data from 75 additional renal transplant patients and from 12 HC including a total of 28 TOL patients, from 2 separate studies published by Newell et

10 al. 2 using Affymetrix arrays and by Brouard, Mansfield et al. 1 using cDNA Lymphochip arrays.

The combined cross-platform (Agilent, Affymetrix and cDNA) analyses allowed for the utilization of 118 unique microarrays.

Stage 2 – TOL Q-PCR Validation and Minimal Gene-Set Selection: QPCR validation analysis was next conducted on the subset of 21 genes discovered and verified in Stage 1 by microarrays on

59 independent peripheral blood samples (31 TOL; 28 SI) by standard Q-PCR using a customized

384 well platform (SAB biosciences: Superarray).

Stage 3 – TOL Q-PCR Modeling and Prediction: At this stage of the study we utilized the

Fluidigm high throughput microfluidic Q-PCR platform (Fluidigm®, Biomark), the choice of this high-throughput platform, a change from Stage 2 to Stage 3, was based on the need to conserve the amount of RNA template utilized, to minimize technical time due to the large number of samples to be analyzed, to reduce the cost/reaction and to reduce interplate technical bias by reducing the number of plate runs. 70 independent samples (17 TOL, 53 SI) were used for re-training of a TOL gene expression model by Fluidigm QPCR on the 21 selected

TOL specific genes. A locked gene-model developed by logistic regression analysis provided excellent segregation of TOL samples (Figure 4b). Based on our earlier studies that have suggested that stable patients on immunosuppression can have high gene prediction scores, similar to the operationally tolerant patients 1, we used the TOL gene signature locked on the

Fluidigm platform for independent prediction of the TOL gene score in 150 unique peripheral blood samples from adult and pediatric renal transplant patients with stable graft function and on maintenance immunosuppression, transplanted at different transplant programs in the US 4 and Mexico. Frequencies of predicted TOL phenotype were evaluated in terms of

11 immunosuppression induction and maintenance usage. Stage 4 – Quantitative and Genomic

TOL-Cell Analysis: To ascertain whether the tolerance gene signature corresponded to specific blood cell types, the 21 TOL-genes from whole blood samples were interrogated for their enrichment against publicly available whole genome expression data from different blood lymphocytes. Array data was downloaded from GSE1133, normalized, and hypergeometric enrichment analysis was performed. 5. The predicted cell-subset enrichment was then pursued further evaluating phenotypic differences of these circulating cells in TOL patients. Predicted cell subsets were analyzed and isolated by fluorescence activated cell sorting (FACS) from leukoreduction system (LRS) samples from 5 healthy controls, and from PBMC isolated from 6 patients on standard immunosuppression (SI) and from 5 TOL patients.

Sample Collection and RNA Extraction

Peripheral blood samples were collected from all patients into PAXgene™ Blood RNA tubes

(PreAnalytiX, Qiagen). If samples were drawn off-site, tubes were shipped on ice via overnight mail or frozen at -80° C and batch-shipped on dry ice via overnight mail. Tubes were processed by one lab technician in a single laboratory at our site as per manufacturer’s instructions. Total-

RNA was extracted and processed using the PAXgene Blood RNA Kit (PreAnalytiX, Qiagen).

Total-RNA concentration was measured using NanoDrop® ND-1000 (NanoDrop Technologies,

Wilmington, DE) and the integrity of total-RNA was assessed using the RNA NanoChip with the

Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA), with an RIN >7 accepted as good quality RNA to be used for this study. Total-RNA was stored in -80° C until further use for microarray or Q-PCR.

Microarray Hybridization

For hybridization onto Agilent Whole Human Genome 4×44K 60mer oligonucleotide arrays

(G4112F, Agilent Technologies, Palo Alto, CA), 100 ng of total RNA was used in the Agilent LIRAK

PLUS, two-color Low RNA input Linear Amplification method, according to the manufacturer’s instructions. Briefly, first the total RNA was reverse transcribed into complimentary DNA (cDNA) using T7-promotor primer and MMLV reverse transcriptase. The cDNA was transcribed into complimentary RNA (cRNA), during which it was fluorescently labeled by incorporation of cyanine (Cy) 5-CTP (exposed samples) or Cy3-CTP (negative control samples). After purification, using the RNeasy mini kit (Qiagen), cRNA yield and Cy incorporation efficiency (specific activity) into the cRNA were determined using a NanoDrop Spectrophotometer (NanoDrop

Technologies). cRNAs showing a yield >825 ng and a specific activity of 8–20 pmol/µg were selected for further processing. Equal amounts of the exposed and negative control sample

(825 ng) were competitively hybridized onto Agilent Whole 4×44K Human oligonucleotide arrays in a hybridization oven at 65°C for 17 hours. Slides were washed according to the manufacturer’s instructions with washing buffers and finally dipped in Stabilization and Drying

Solution (Agilent Technologies) to protect them from environmental ozone. The arrays were scanned on an Agilent scanner and further processed using Agilent Feature Extraction Software.

Fluidigm Quantitative Real-time PCR (QPCR)

250ng of extracted total RNA were processed through steps of reverse transcription (RT, cDNA synthesis, Superscript II, Invitrogen, Carlsbad, CA), specific target amplification (STAmp) and sample dilution using gene specific Primers and Taqman Probes (subsequently annotated as

TaqMan assays) for 20 of the 21 TOL-genes (gene specific TaqMan assays were not available for

IGH-@) (Life Technologies, Foster City, CA; subsequently annotated as Life Tech.) (Supplement

Table 3). A total of 1.56ng of cDNA per sample in 2 µL generated using Superscript II (Invitrogen

Technologies Inc., Carlsbad, CA) from 250ng total RNA starting template along with an aliquot of the pooled Taqman assays (20 genes, except 18s) and Taqman PreAmp Master mix ((Life

Technologies, Applied Biosystems, Foster City, CA) to 5 µL final volume was amplified in a specific target amplification (Stamp) in the Eppendorf vapo.protect™ thermal cycler (Eppendorf,

Hauppauge, NY) for a total of 18 cycles then diluted 1:10 with sterile water (GIBCO, Invitrogen).

For subsequent microfluidic QPCR 2.25µL of the preamplified cDNA was mixed with TaqMan

Universal PCR Master Mix (Applied Biosystems) and Sample Loading Reagent (Fluidigm, San

Francisco, CA) and pipetted into the sample inlets of a Dynamic Array 96.96 chip (Fluidigm).

TaqMan gene expression assays (Applied Biosystems) for the 20 genes plus 18S as endogenous control gene were diluted with Assay Loading Reagent (1:2) (Fluidigm) and pipetted into the assay inlets of the same Dynamic Array 96.96 chip. After distributing assays and samples into the reaction wells of the chip in the NanoFlex controller (Fluidigm), the QPCR reactions were performed in the BioMark RT PCR system for a total of 40 cycles. Data was analyzed using the

BioMark RT-PCR Analysis Software Version 2.0 and raw Ct values were exported into Microsoft

Excel (Microsoft Office 2007, Microsoft Inc. USA) for calculation of delta Ct values using 18S as endogenous control gene.

QPCR Gene Expression Data Processing and Model Development

Due to missing values in >20% samples, 4 of the 20 genes were excluded from the Fluidigm analysis (CLVS1, NXF3, GDEP, PRAMEF3). Similarly, 5 samples were excluded from the analysis

(3 TOL, 2 SI) due to missing expression values in >10% of genes. Data was normalized by empirical Bayes method 6, and a penalized logistic regression model in a new set of 14 TOL and

51 SI was used to classify patient samples. The logistic equation is:

Where and are the expression of κ genes for observation .

The elastic-net fits this model by adding a mixed penalty term to the likelihood

| ∑ ∑ ∑| |

We used the regularization paths for generalized linear models via Coordinate Descent for the estimations 7, 8 . We fitted 100 Elastic Net logistic regression models to the 17 genes using bootstrapped samples (7 test, 58 training, sampled with replacement) to classify TOL vs. SI. For each bootstrap a nested cross-validation loop estimated the best value for λ according to the deviance. The parameter of the Elastic-Net was fixed at 0.95, the value recommended by. In order to rank the genes we counted the number of times each gene was selected by the Elastic-

Net over the 100 bootstraps. For each of the bootstrap samples, the Elastic-Net fits a subset of the 17 genes with non-zero coefficients. After running the 100 bootstrapped models, we selected the K genes with the greatest number of non-zero coefficients. In a second step, in order to have a unbiased estimation of the predictive performance (classification rate, sensitivity, specificity, PPV, NPV), we ran another set of 100 bootstrap Elastic-Net classifications with nested cross-validation for λ, this time using only the set of K genes selected in step 1. The resulting model was next applied to an independent expression set of 150 stable renal

15 transplant patients on different immunosuppression protocols. The incidence of the TOL phenotype was evaluated in these patients applying the 3-gene model to their QPCR expression profiles.

Supplement References

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