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

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A Three-Gene Assay for Monitoring Immune Quiescence in Kidney Transplantation CLINICAL RESEARCH www.jasn.org A Three-Gene Assay for Monitoring Immune Quiescence in Kidney Transplantation † ‡ 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 con- stituting 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. J Am Soc Nephrol 26: 2042–2053, 2015 Three-Gene Assay for Immune Quiescence 2043 CLINICAL RESEARCH www.jasn.org 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 micro- array 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
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