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• W2856664011 abstract "Noninvasive biomarkers are needed to monitor stable patients after kidney transplant (KT), because subclinical acute rejection (subAR), currently detectable only with surveillance biopsies, can lead to chronic rejection and graft loss. We conducted a multicenter study to develop a blood-based molecular biomarker for subAR using peripheral blood paired with surveillance biopsies and strict clinical phenotyping algorithms for discovery and validation. At a predefined threshold, 72% to 75% of KT recipients achieved a negative biomarker test correlating with the absence of subAR (negative predictive value: 78%-88%), while a positive test was obtained in 25% to 28% correlating with the presence of subAR (positive predictive value: 47%-61%). The clinical phenotype and biomarker independently and statistically correlated with a composite clinical endpoint (renal function, biopsy-proved acute rejection, ≥grade 2 interstitial fibrosis, and tubular atrophy), as well as with de novo donor-specific antibodies. We also found that <50% showed histologic improvement of subAR on follow-up biopsies despite treatment and that the biomarker could predict this outcome. Our data suggest that a blood-based biomarker that reduces the need for the indiscriminate use of invasive surveillance biopsies and that correlates with transplant outcomes could be used to monitor KT recipients with stable renal function, including after treatment for subAR, potentially improving KT outcomes. Noninvasive biomarkers are needed to monitor stable patients after kidney transplant (KT), because subclinical acute rejection (subAR), currently detectable only with surveillance biopsies, can lead to chronic rejection and graft loss. We conducted a multicenter study to develop a blood-based molecular biomarker for subAR using peripheral blood paired with surveillance biopsies and strict clinical phenotyping algorithms for discovery and validation. At a predefined threshold, 72% to 75% of KT recipients achieved a negative biomarker test correlating with the absence of subAR (negative predictive value: 78%-88%), while a positive test was obtained in 25% to 28% correlating with the presence of subAR (positive predictive value: 47%-61%). The clinical phenotype and biomarker independently and statistically correlated with a composite clinical endpoint (renal function, biopsy-proved acute rejection, ≥grade 2 interstitial fibrosis, and tubular atrophy), as well as with de novo donor-specific antibodies. We also found that <50% showed histologic improvement of subAR on follow-up biopsies despite treatment and that the biomarker could predict this outcome. Our data suggest that a blood-based biomarker that reduces the need for the indiscriminate use of invasive surveillance biopsies and that correlates with transplant outcomes could be used to monitor KT recipients with stable renal function, including after treatment for subAR, potentially improving KT outcomes. Kidney transplant (KT) remains the treatment of choice for most patients with end-stage kidney disease (ESRD),1Tonelli M Wiebe N Knoll G et al.Systematic review: kidney transplantation compared with dialysis in clinically relevant outcomes.Am J Transplant. 2011; 11: 2093-2109Crossref PubMed Scopus (736) Google Scholar,2US Renal Data System2016 USRDS annual data report: Epidemiology of kidney disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD2016Google Scholar but long-term outcomes remain suboptimal.3Hart A Smith JM Skeans MA et al.Kidney.Am J Transplant. 2016; 16: 11-46Crossref PubMed Google Scholar,4Meier-Kriesche HU Schold JD Srinivas TR Kaplan B Lack of improvement in renal allograft survival despite a marked decrease in acute rejection rates over the most recent era.Am J Transplant. 2004; 4: 378-383Crossref PubMed Scopus (974) Google Scholar After KT, clinically unsuspected subclinical acute rejection (subAR) occurs in 20% to 25% of patients in the first 12 to 24 months and is associated with de novo donor-specific antibody (dnDSA) formation, interstitial fibrosis/tubular atrophy (IFTA), chronic rejection, and graft loss.5Nankivell BJ Chapman JR The significance of subclinical rejection and the value of protocol biopsies.Am J Transplant. 2006; 6: 2006-2012Crossref PubMed Scopus (137) Google Scholar, 6Kee TY Chapman JR O’Connell PJ et al.Treatment of subclinical rejection diagnosed by protocol biopsy of kidney transplants.Transplantation. 2006; 82: 36-42Crossref PubMed Scopus (87) Google Scholar, 7Heilman RL Devarapalli Y Chakkera HA et al.Impact of subclinical inflammation on the development of interstitial fibrosis and tubular atrophy in kidney transplant recipients.Am J Transplant. 2010; 10: 563-570Crossref PubMed Scopus (0) Google Scholar, 8Loupy A Vernerey D Tinel C et al.Subclinical rejection phenotypes at 1 year post-transplant and outcome of kidney allografts.J Am Soc Nephrol. 2015; 26: 1721-1731Crossref PubMed Scopus (210) Google Scholar, 9Mehta R Bhusal S Randhawa P et al.Short-term adverse effects of early subclinical allograft inflammation in kidney transplant recipients with a rapid steroid withdrawal protocol.Am J Transplant. 2018; 18: 1710-1717Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar, 10Parajuli S Reville PK Ellis TM Djamali A Mandelbrot DA Utility of protocol kidney biopsies for de novo donor-specific antibodies.Am J Transplant. 2017; 17: 3210-3218Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar, 11El-Zoghby ZM Stegall MD Lager DJ et al.Identifying specific causes of kidney allograft loss.Am J Transplant. 2009; 9: 527-535Crossref PubMed Scopus (612) Google Scholar, 12Gourishankar S Leduc R Connett J et al.Pathological and clinical characterization of the ’troubled transplant’: data from the DeKAF study.Am J Transplant. 2010; 10: 324-330Crossref PubMed Scopus (0) Google Scholar, 13El Ters M Grande JP Keddis MT et al.Kidney allograft survival after acute rejection: the value of follow-up biopsies.Am J Transplant. 2013; 13: 2334-2341Crossref PubMed Scopus (0) Google Scholar Serum creatinine and immunosuppression levels, used almost exclusively to monitor KT recipients, are both insensitive and nonspecific.14Bouamar R Shuker N Hesselink DA et al.Tacrolimus predose concentrations do not predict the risk of acute rejection after renal transplantation: a pooled analysis from three randomized-controlled clinical trials (dagger).Am J Transplant. 2013; 13: 1253-1261Crossref PubMed Scopus (0) Google Scholar Surveillance biopsies can be used to monitor patients with stable renal function, but biopsies are invasive and associated with sampling error and there is a lack of consensus around both histologic interpretation (especially for “borderline changes”) and the effectiveness of treatment.5Nankivell BJ Chapman JR The significance of subclinical rejection and the value of protocol biopsies.Am J Transplant. 2006; 6: 2006-2012Crossref PubMed Scopus (137) Google Scholar,15Seron D Moreso F Protocol biopsies in renal transplantation: prognostic value of structural monitoring.Kidney Int. 2007; 72: 690-697Abstract Full Text Full Text PDF PubMed Scopus (72) Google Scholar, 16Morgan TA Chandran S Burger IM Zhang CA Goldstein RB Complications of ultrasound-guided renal transplant biopsies.Am J Transplant. 2016; 16: 1298-1305Crossref PubMed Scopus (59) Google Scholar, 17Mehta R Sood P Hariharan S Subclinical rejection in renal transplantation: reappraised.Transplantation. 2016; 100: 1610-1618Crossref PubMed Google Scholar, 18Becker JU Chang A Nickeleit V Randhawa P Roufosse C Banff borderline changes suspicious for acute T cell-mediated rejection: where do we stand?.Am J Transplant. 2016; 16: 2654-2660Abstract Full Text Full Text PDF PubMed Scopus (41) Google Scholar Moreover, the vast majority (75%-80%) of surveillance biopsy specimens show normal histology (ie, the absence of subAR) and, therefore, expose patients to unnecessary biopsy risks. As a result, the current standard of care in monitoring patients after KT ranges from not using surveillance biopsies at all, to using them selectively in “high–immunologic risk” patients, to routine use in all patients.19Mehta R Cherikh W Sood P Hariharan S Kidney allograft surveillance biopsy practices across US transplant centers: a UNOS survey.Clin Transplant. 2017; 31: e12945Crossref Scopus (45) Google Scholar There is, therefore, a clear need to better detect the presence or absence of subAR, and genomic biomarkers in the blood or urine may provide useful noninvasive monitoring of KT recipients.17Mehta R Sood P Hariharan S Subclinical rejection in renal transplantation: reappraised.Transplantation. 2016; 100: 1610-1618Crossref PubMed Google Scholar,20Lo DJ Kaplan B Kirk AD Biomarkers for kidney transplant rejection.Nat Rev Nephrol. 2014; 10: 215-225Crossref Scopus (93) Google Scholar,21Menon MC Murphy B Heeger PS Moving biomarkers toward clinical implementation in kidney transplantation.J Am Soc Nephrol. 2017; 28: 735-747Crossref PubMed Scopus (41) Google Scholar The Clinical Trials in Organ Transplantation 08 (CTOT-08; NCT01289717) study was designed to develop molecular biomarkers for a number of clinical phenotypes in KT recipients. The focus of the current study was to develop and evaluate the performance and clinical validity of a novel gene expression profile biomarker that correlates with the presence or absence of subAR in the peripheral blood in patients with stable renal function after KT. We enrolled 307 subjects after KT into a multicenter 24-month observational study (CTOT-08) between March 2011 and May 2014. KT recipients underwent surveillance biopsies at 2 to 6, 12, and 24 months after KT as well as for-cause biopsies for acute renal dysfunction (Figure S1). Participating sites that routinely perform surveillance biopsies were geographically selected to provide racial and ethnic diversity. Study inclusion criteria were: male or female KT recipients (negative pregnancy test within 6 weeks of enrollment), age ≥18 years, ability to provide informed consent, and recipient of a first or subsequent KT from either a deceased or a living donor. Subject with combined or “en bloc” kidney grafts and subjects with HIV or hepatitis C virus infection were excluded. We contemporaneously enrolled KT recipients into the Northwestern University (NU) transplant program’s biorepository study (NCT01531257), with eligibility criteria identical to those of CTOT-08. Patients undergo surveillance biopsies at NU with a frequency similar to that of CTOT-08. Patients who did not participate in CTOT-08 were enrolled into the NU biorepository study. Clinical care followed a standard practice at each participating center. However, CTOT-08 subjects diagnosed with subAR on a surveillance biopsy, who were managed based on each site’s interpretation of the histopathology results, also underwent an intensive monitoring protocol consisting of blood sample collection every 2 weeks for laboratory and biomarker determination and a repeat biopsy at week 8. The intense monitoring (IM) was limited to 1 subAR episode per subject due to the need for a repeat biopsy. All biopsy specimens were processed locally for routine histology, Simian virus 40, and C4d staining and were centrally read and interpreted by a pathologist using Banff 2007 criteria who was blinded to the clinical course.22Solez K Colvin RB Racusen LC Haas M Sis B Mengel M et al.Banff 07 classification of renal allograft pathology: updates and future directions.Am J Transplant. 2008; 8: 753-760Crossref PubMed Scopus (1609) Google Scholar Clinical phenotypes (CPs) were assigned by the Data Coordinating Center (DCC at Rho Federal Systems) for both the discovery and validation cohorts for each paired sample by using the following predefined algorithm:1SubAR: histology on a surveillance biopsy consistent with acute rejection (≥Banff borderline cellular rejection and/or antibody-mediated rejection) AND stable renal function, defined as serum creatinine <2.3 mg/dL and <20% increase in creatinine compared with a minimum of 2 or 3 prior values over a mean period and range of 132 and 75-187 days, respectively.2Transplant excellent (TX [ie, no subAR]): normal histology on surveillance biopsy (no evidence of rejection: Banff i = 0 and t = 0, g = 0, ptc = 0; ci = 0 or 1 and ct = 0 or 1) AND stable renal function as defined in item 1. While a previous study has shown that KT recipients with a serum creatinine level >1.5 mg/dL in the first 12 months have worse outcomes compared with those with levels of <1.5 mg/dL,23Hariharan S McBride MA Cherikh WS Tolleris CB Bresnahan BA Johnson CP Post-transplant renal function in the first year predicts long-term kidney transplant survival.Kidney Int. 2002; 62: 311-318Abstract Full Text Full Text PDF PubMed Scopus (610) Google Scholar we chose an upper limit of 2.3 mg/dL for 3 reasons: (1) we included a second criterion to ensure stability (<20% change in serum creatinine compared with the minimum of the previous 2 or 3 samples), (2) the follow-up in our study was 24 vs 12 months, and (3) there were very few samples in the group with higher creatinine levels, but all met our histology criteria (Banff ci and ct score = 0 or 1, respectively, and no other findings), eliminating other ongoing causes of renal injury. For these reasons, we did not perform sensitivity analyses for this small group but thought that these patients should be included because they reflect “real-life” patients and because eliminating them from our analyses might have introduced selection bias. Thus, peripheral blood samples were collected at the time of each surveillance biopsy from CTOT-08 subjects. At the time of CTOT-08 analysis, we searched the biorepository for, and identified all peripheral blood samples paired with, surveillance biopsies available for validation studies in this independent cohort. All blood samples were drawn directly into PAXgene (BD BioSciences, San Jose, CA) tubes and processed as previously described24Kurian SM Williams AN Gelbart T Campbell D Mondala TS Head SR et al.Molecular classifiers for acute kidney transplant rejection in peripheral blood by whole genome gene expression profiling.Am J Transplant. 2014; 14: 1164-1172Crossref PubMed Scopus (79) Google Scholar in batches by using Affymetrix HT HG-U133+PM Array Plates on the Gene Titan MC instrument (Thermo Fisher Scientific, Waltham, MA) (GEO Accession No. GSE107509). Background correction based on the discovery data set were saved and applied to all discovery and validation samples by using frozen robust multiarray analysis.25McCall MN Bolstad BM Irizarry RA Frozen robust multiarray analysis (fRMA).Biostatistics. 2010; 11: 242-253Crossref PubMed Scopus (0) Google Scholar Figure S2 illustrates the workflow used for the discovery of the gene expression profile (GEP) and for assessment of biologic relevance.26Johnson WE Li C Rabinovic A Adjusting batch effects in microarray expression data using empirical Bayes methods.Biostatistics. 2007; 8: 118-127Crossref PubMed Scopus (3983) Google Scholar, 27Leek JT Johnson WE Parker HS Jaffe AE Storey JD The SVA package for removing batch effects and other unwanted variation in high-throughput experiments.Bioinformatics. 2012; 28: 882-883Crossref PubMed Scopus (2186) Google Scholar, 28Ritchie ME Phipson B Wu D Hu Y Law CW Shi W et al.limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic acids research. 2015; 43: e47Crossref PubMed Scopus (14816) Google Scholar, 29Smyth GK limma: linear models for microarray data.in: Gentleman R Carey VJ Huber W Irizzary RA Dudoit S Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Statistics for Biology and Health, New York, NY2005: 397-420Crossref Google Scholar, 30Analysis IP. https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis. Published 2017. Accessed December 12, 2017.Google Scholar, 31Huang DW Sherman BT Tan Q Collins JR Alvord WG Roayaei J et al.The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists.GenomeBiol. 2007; 8: R183Google Scholar, 32Subramanian A Tamayo P Mootha VK Mukherjee S Ebert BL Gillette MA et al.Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc Natl Acad Sci U S A. 2005; 102: 15545-15550Crossref PubMed Scopus (25982) Google Scholar, 33Harrell Jr, FE Lee KL Mark DB Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.Stat Med. 1996; 15: 361-387Crossref PubMed Scopus (6973) Google Scholar Threshold selection was based on “out-of-bag” performance metrics of the discovery cohort. Based on dichotomous outcomes (either positive or negative predicted probabilities above or below the threshold), profiles were compared with the clinical phenotypes to determine the performance of the classifiers. The independent NU biorepository cohort was used to externally validate the locked model/threshold discovered on the CTOT-08 cohort. While sample-level predefined algorithms were used to define the CPs of either subAR or TX for each paired sample outlined here, CTOT-08 subjects underwent multiple surveillance biopsies during the 24-month study. Subjects demonstrated either subAR or TX phenotypes for any given sample but may have demonstrated instances of each phenotype in different samples over time. Thus, for the purpose of correlating biomarker results to CP, we classified samples used for biomarker development as either subAR or TX (sample level), whereas for the purpose of correlating both the CP and the biomarker classification to clinical outcomes, we stratified subjects into 3 phenotypic groups (subject level): subjects with surveillance biopsy specimens demonstrating subAR only (no TX), subjects with surveillance biopsy specimens demonstrating TX only (no subAR), and subjects with individual biopsy specimens demonstrating either subAR or TX. This third group, therefore, consisted of subjects who had experienced ≥1 instance of subAR and ≥1 instance of TX during the study period. Nonsurveillance biopsies (for cause) and surveillance biopsies with other findings (eg, recurrent disease, infections) were not included in this analysis. To assess whether subjects who experienced subAR or who had a positive biomarker test had worse transplant outcomes, we used a primary clinical composite endpoint (CCE) consisting of 3 separate validated endpoints, all previously used in other studies to measure transplant outcomes:124-Month biopsy (central read) showing evidence of chronic injury—IFTA (Banff grade ≥II IFTA [ci ≥2 or ct ≥2], OR2BPAR on any “for-cause biopsy” (central read), OR3Decrease in estimated glomerular filtration rate by >10 mL/min/1.73 m2 (Chronic Kidney Disease Epidemiology Collaboration) between 4 and 24 months posttransplant We also measured dnDSAs for both class I and II, known to associate with transplant outcome, as determined by each participating site per their practice; these were recorded as either positive or negative according to each site’s cutoff values. The study protocol required determinations at the time of the 12- and 24-month biopsies, but other values obtained at any time during the study were also used for our analyses. To assess the impact of both CP and GEP on transplant outcome in the first 12 months (clinical composite or individual endpoints), at 24 months, we used odds ratios (ORs) and the Fisher exact test. The 2-sample t test was used to assess the ability of GEP-predicted probabilities during IM to detect resolution of subAR based on the repeat biopsy. Analysis of covariance was used to adjust for differences in predicted probabilities at baseline. The CTOT-08 and NU biorepository studies were both subject to IRB approval, and informed consent was obtained from all patients. Oversight by the DCC included development of the study protocol, classification of CPs at the sample and patient levels, review of clinical site visits, monitoring of clinical data for integrity, review of clinical site visits, independent validation of all analyses related to clinical profile and GEP, associations between the CPs and endpoints, and manuscript preparation. Table 1 shows the donor and recipient subject-level demographics by CP for both the CTOT-08 and NU biorepository patient cohorts. There were no discernable differences in demographics including type of immunosuppression between the 2 cohorts. Of the CTOT-08 subjects, 13.0% demonstrated only subAR (no TX), 57.7% demonstrated only TX (no subAR), and 29.2% demonstrated individual phenotypic instances of either subAR or TX (ie, ≥1 instance of subAR during the 24-month study). Thus, at the subject level, the prevalent incidence of ≥1 biopsy-proved instance(s) of subAR was 42.3% (107/253) versus 57.7% for TX only. Subjects in the NU biorepository did not undergo serial sampling, and thus there were only 2 groups: the sample-level prevalent incidence of subAR was 27.9% (36/129) compared with 72.1% for TX (93/129).TABLE 1Donor and recipient patient-level demographics and prevalence of clinical phenotypes for discovery and validation cohortsDemographicsCTOT-08 cohort (N = 253)NU cohort (N= 129)subAR, no TX (n = 33)TX, no subAR (n = 146)subAR and TX (n = 74)subAR, no TX (n = 36)TX, no subAR (n = 93)DonorsAge, yMean ± SD39.0 ± 15.5738.1 ± 13.4943.1 ± 13.3040.7 ± 13.6338.6 ± 13.28Range10-668-716-7113-7313-73Male sex17 (51.5)75 (51.4)39 (52.7)23 (63.9)46 (49.5)RaceWhite26 (78.8)98 (67.1)59 (79.7)23 (63.9)52 (55.9)Black or African American2 (6.1)23 (15.8)2 (2.7)5 (13.9)15 (16.1)Other1 (3.0)6 (4.1)4 (5.4)8 (22.2)25 (26.9)Unknown or not reported4 (12.1)19 (13.0)9 (12.2)01 (1.1)EthnicityHispanic or Latino5 (15.2)19 (13.0)11 (14.9)6 (16.7)21 (22.6)Not Hispanic or Latino25 (75.8)110 (75.3)56 (75.7)30 (83.3)71 (76.3)Unknown or not reported3 (9.1)17 (11.6)7 (9.5)01 (1.1)RecipientsAge, yMean ± SD50.1 ± 14.7650.2 ± 13.6953.4 ± 13.5352.1 ± 13.1553.0 ± 12.67Range19-7521-7821-7822-7225-75Male sex22 (66.7)94 (64.4)51 (68.9)22 (61.1)52 (55.9)RaceWhite23 (69.7)87 (59.6)51 (68.9)21 (58.3)49 (52.7)Black or African American6 (18.2)34 (23.3)8 (10.8)6 (16.7)18 (19.4)Other4 (12.1)11 (7.5)5 (6.8)9 (25.0)26 (28.0)Unknown or not reported014 (9.6)10 (13.5)00EthnicityHispanic or Latino2 (6.1)27 (18.5)12 (16.2)7 (19.4)15 (16.1)Not Hispanic or Latino30 (90.9)112 (76.7)57 (77.0)28 (77.8)74 (79.6)Unknown or not reported1 (3.0)7 (4.8)5 (6.8)1 (2.8)4 (4.3)Deceased donor22 (66.7)60 (41.1)26 (35.1)19 (52.8)30 (32.3)Primary reason for ESRDCystic (includes PKD)2 (6.1)13 (8.9)14 (18.9)4 (11.1)10 (10.8)Diabetes mellitus8 (24.2)30 (20.5)15 (20.3)10 (27.8)23 (24.7)Glomerulonephritis9 (27.3)47 (32.2)13 (17.6)8 (22.2)28 (30.1)Hypertension4 (12.1)29 (19.9)12 (16.2)7 (19.4)18 (19.4)Other10 (30.3)27 (18.5)20 (27.0)7 (19.4)14 (15.1)Secondary reason for ESRDCystic (includes PKD)01 (0.7)001 (1.1)Diabetes mellitus07 (4.8)1 (1.4)2 (5.6)2 (2.2)Glomerulonephritis07 (4.8)2 (2.7)3 (8.3)5 (5.4)Hypertension6 (18.2)14 (9.6)2 (2.7)4 (11.1)15 (16.1)Other09 (6.2)2 (2.7)01 (1.1)None reported27 (81.8)108 (74.0)67 (90.5)27 (75.0)69 (74.2)Recipient PRA at transplantPRA class I %n29107623693Mean ± SD7.4 ± 20.597.9 ± 20.856.9 ± 20.4820.3 ± 29.4119.5 ± 31.13Range0-1000-1000-960-890-99PRA class II %n29107613693Mean ± SD11.3 ± 29.037.6 ± 21.296.1 ± 18.5217.4 ± 31.3612.9 ± 25.54Range0-1000-1000-800-1000-100PRA single antigen cPRA %n2686463693Mean ± SD32.8 ± 42.0629.4 ± 35.8225.9 ± 35.4618.1 ± 28.5111.9 ± 28.19Range0-1000-990-1000-910-98Donor and recipient CMV statusD–, R+3 (9.1)25 (17.1)16 (21.6)11 (30.6)18 (19.4)D+, R–10 (30.3)23 (15.8)13 (17.6)7 (19.4)22 (23.7)D–, R–7 (21.2)33 (22.6)21 (28.4)5 (13.9)16 (17.2)D+, R+11 (33.3)60 (41.1)20 (27.0)13 (36.1)36 (38.7)Donor, recipient, or both not tested2 (6.1)5 (3.4)4 (5.4)01 (1.1)Use of induction therapyAlemtuzumab19 (57.6)74 (50.7)42 (56.8)29 (80.6)80 (86.0)Antithymocyte globulin12 (36.4)40 (27.4)14 (18.9)00Basiliximab3 (9.1)25 (17.1)18 (24.3)7 (19.4)11 (11.8)Use of desensitization therapyReceived any desensitization therapy09 (6.2)7 (9.5)4 (11.1)6 (6.5)Use of maintenance therapySteroid24 (72.7)71 (48.6)50 (67.6)13 (36.1)27 (29.0)Tacrolimus33 (100)145 (99.3)74 (100)30 (83.3)89 (95.7)Cyclosporine3 (9.1)7 (4.8)4 (5.4)3 (8.3)2 (2.2)Azathioprine1 (3.0)001 (2.8)0MMF33 (100)143 (97.9)74 (100)35 (97.2)92 (98.9)mTOR inhibitor1 (3.0)11 (7.5)5 (6.8)3 (8.3)2 (2.2)Leflunomide02 (1.4)2 (2.7)00Belatacept01 (0.7)000CTOT-08, Clinical Trials in Organ Transplantation 08; NU, Northwestern University; subAR, subclinical acute rejection; TX, transplant excellent; ESRD, end-stage renal disease; PKD, polycystic kidney disease; CMV, cytomegalovirus; MMF, mycophenolate mofetil; mTOR, mammalian target of rapamycin.Values are given as number (%) unless otherwise specified. Of the 253 precisely phenotyped CTOT-08 subjects with stable renal function who underwent ≥1 surveillance biopsy, 33 (13.0%) demonstrated only subAR (no TX), 146 subjects (57.7%) demonstrated only TX (no subAR), and 74 (29.2%) demonstrated individual instances of either subAR or TX (ie, ≥1 instance of subAR during the 24-month study). The subAR only (no instances of TX per surveillance biopsies during the study period) and the subAR or TX groups collectively represent subjects with ≥1 episode of subAR (≥1 subAR). At the patient level, the prevalent incidence of ≥1 biopsy-proved instance(s) of subAR was 42.3% (107/253) versus 57.7% for TX only. Subjects in the NU biorepository did not undergo serial sampling, and therefore there were only 2 groups: the sample-level prevalent incidence of subAR was 27.9% (36/129) compared with 72.1% for TX (93/129). Open table in a new tab CTOT-08, Clinical Trials in Organ Transplantation 08; NU, Northwestern University; subAR, subclinical acute rejection; TX, transplant excellent; ESRD, end-stage renal disease; PKD, polycystic kidney disease; CMV, cytomegalovirus; MMF, mycophenolate mofetil; mTOR, mammalian target of rapamycin. Values are given as number (%) unless otherwise specified. Of the 253 precisely phenotyped CTOT-08 subjects with stable renal function who underwent ≥1 surveillance biopsy, 33 (13.0%) demonstrated only subAR (no TX), 146 subjects (57.7%) demonstrated only TX (no subAR), and 74 (29.2%) demonstrated individual instances of either subAR or TX (ie, ≥1 instance of subAR during the 24-month study). The subAR only (no instances of TX per surveillance biopsies during the study period) and the subAR or TX groups collectively represent subjects with ≥1 episode of subAR (≥1 subAR). At the patient level, the prevalent incidence of ≥1 biopsy-proved instance(s) of subAR was 42.3% (107/253) versus 57.7% for TX only. Subjects in the NU biorepository did not undergo serial sampling, and therefore there were only 2 groups: the sample-level prevalent incidence of subAR was 27.9% (36/129) compared with 72.1% for TX (93/129). Figure S3A illustrates the sample-level disposition of paired samples for both discovery and validation cohorts. Of 283 CTOT-08 subjects, 253 had sufficient data to define the CP of either subAR or TX (ie, no subAR). In addition, 138 NU biorepository subjects had undergone surveillance biopsies and met the clinical definitions of either subAR or TX; 129 of 138 met the strict phenotypic algorithm used for CTOT-08 subjects. Figure S3B illustrates the subject-level disposition for both the CP and the GEP used to assess the impact of each on the clinical endpoints. Figures S4A and S4B illustrate the ingenuity pathway analysis (IPA) results for the CTOT-08 (530) discovery and NU biorepository (129/138) validation cohorts, respectively, demonstrating biologic relevance of the differentially expressed genes used to populate the biomarker model (see later) and shared pathways between the discovery and validation cohorts. Also, in the CTOT-08 data set, Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to identify the T cell receptor pathway as significant (P < .0001) by the Gene Ontology (GO) biological process as well as the canonical T cell receptor Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway (P < .001). In both the CTOT-08 and the NU data sets, DAVID was also used to identify the B cell receptor, T cell receptor, and intereukin-2 receptor β-chain pathways as significant by the canonical KEGG pathways (P = .0002, .01, and .03, respectively). In addition, Tables S1A and S1 B and Figures S5A and S5B illustrate the preranked gene set enrichment analysis for the CTOT-08 and NU biorepository data sets, further demonstrating biologic relevance of the genes populating the model. Figure 1 illustrates the characteristics and performance of the random forests discovery model used to develop the biomarker and to select the threshold. We selected a random forests model optimizing for area under the curve (AUC; 0.85); the AUC after bootstrap was 0.84. We then selected a predicted probability threshold of 0.375 based on best overall performance, favoring specificity and negative predictive value (NPV; 87% and 88%) over sensitivity and positive predictive value (PPV; 64% and 61%, respectively). The classifier consisted of 61 probe sets that mapped to 57 genes (Figure S6). We then locked the model at the defined threshold in a blinded fashion in order to externally validate their ability to predict the phenotype of the NU biorepository samples. The results were interpreted dichotomously as either “positive” (ie, correlating with a clinical phenotype of subAR) if the probability exceeded the 0.375 threshold or “negative” (ie, correlating with TX) if ≤0.375. We first validated the classifier on 138 subject" @default.
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• W2856664011 date "2019-01-01" @default.
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• W2856664011 title "Development and clinical validity of a novel blood-based molecular biomarker for subclinical acute rejection following kidney transplant" @default.
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• W2856664011 cites W1723715242 @default.
• W2856664011 cites W1891520818 @default.
• W2856664011 cites W1981650984 @default.
• W2856664011 cites W1983275136 @default.
• W2856664011 cites W1985118286 @default.
• W2856664011 cites W2015989676 @default.
• W2856664011 cites W2016105155 @default.
• W2856664011 cites W2024692616 @default.
• W2856664011 cites W2032992413 @default.
• W2856664011 cites W2042953533 @default.
• W2856664011 cites W2044716184 @default.
• W2856664011 cites W2072252245 @default.
• W2856664011 cites W2074840731 @default.
• W2856664011 cites W2076992310 @default.
• W2856664011 cites W2087505884 @default.
• W2856664011 cites W2097795961 @default.
• W2856664011 cites W2098773501 @default.
• W2856664011 cites W2107665951 @default.
• W2856664011 cites W2109968192 @default.
• W2856664011 cites W2113878188 @default.
• W2856664011 cites W2122930177 @default.
• W2856664011 cites W2129925362 @default.
• W2856664011 cites W2130410032 @default.
• W2856664011 cites W2138450760 @default.
• W2856664011 cites W2146512944 @default.
• W2856664011 cites W2150141583 @default.
• W2856664011 cites W2169353806 @default.
• W2856664011 cites W2169639894 @default.
• W2856664011 cites W2170264508 @default.
• W2856664011 cites W2176557263 @default.
• W2856664011 cites W2295876746 @default.
• W2856664011 cites W2299602226 @default.
• W2856664011 cites W2302987379 @default.
• W2856664011 cites W2332349745 @default.
• W2856664011 cites W2467904883 @default.
• W2856664011 cites W2570751156 @default.
• W2856664011 cites W2586531649 @default.
• W2856664011 cites W2587563910 @default.
• W2856664011 cites W2588590276 @default.
• W2856664011 cites W2592945756 @default.
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• W2856664011 cites W2594388093 @default.
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