BACKGROUND: The power to predict kidney allograft outcomes based on non-invasive assays is limited. Assessment of operational tolerance (OT) patients allows us to identify transcriptomic signatures of true non-responders for construction of predictive models. METHODS: In this observational retrospective study, RNA sequencing of peripheral blood was used in a derivation cohort to identify a protective set of transcripts by comparing 15 OT patients (40% females), from the TOMOGRAM Study (NCT05124444), 14 chronic active antibody-mediated rejection (CABMR) and 23 stable graft function patients ≥15 years (STA). The selected differentially expressed transcripts between OT and CABMR were used in a validation cohort (n = 396) to predict 3-year kidney allograft loss at 3 time-points using RT-qPCR. FINDINGS: Archetypal analysis and classifier performance of RNA sequencing data showed that OT is clearly distinguishable from CABMR, but similar to STA. Based on significant transcripts from the validation cohort in univariable analysis, 2 multivariable Cox models were created. A 3-transcript (ADGRG3, ATG2A, and GNLY) model from POD 7 predicted graft loss with C-statistics (C) 0.727 (95% CI, 0.638-0.820). Another 3-transcript (IGHM, CD5, GNLY) model from M3 predicted graft loss with C 0.786 (95% CI, 0.785-0.865). Combining 3-transcripts models with eGFR at POD 7 and M3 improved C-statistics to 0.860 (95% CI, 0.778-0.944) and 0.868 (95% CI, 0.790-0.944), respectively. INTERPRETATION: Identification of transcripts distinguishing OT from CABMR allowed us to construct models predicting premature graft loss. Identified transcripts reflect mechanisms of injury/repair and alloimmune response when assessed at day 7 or with a loss of protective phenotype when assessed at month 3. FUNDING: Supported by the Ministry of Health of the Czech Republic under grant NV19-06-00031.

Antwerp University Hospital and Antwerp University Antwerp Belgium

Charité Universitätsmedizin Berlin Medizinische Klinik mit Schwerpunkt Nephrologie und Internistische Intensivmedizin Berlin Germany

Department of Clinical and Transplant Pathology Institute for Clinical and Experimental Medicine Prague Czech Republic

Department of Computer Science Czech Technical University Prague Czech Republic

Department of Internal Medicine 3 Nephrology Medical University Vienna AKH Wien Vienna Austria

Department of Kidney Disease Medicine of Renal Transplantation G Brotzu Hospital Cagliari Italy

Department of Medical Sciences University of Torino Torino Italy

Department of Nephrology 1st Faculty of Medicine and General Faculty Hospital Prague Czech Republic

Department of Nephrology CHU of Liege Liège Belgium

Department of Nephrology Institute for Clinical and Experimental Medicine Prague Czech Republic

Department of Nephrology Uniklinik RWTH Aachen Aachen Germany

Faculty of Medicine Nephrology Center Vilnius University Hospital Santaros Klinikos Vilnius University Vilnius Lithuania

Genetic Medicine and Development Faculty of Medicine University of Geneva Rue Michel Servet 1 1206 Geneva Switzerland

Hospital Universitario Insular de Gran Canaria Servicio de nefrología Spain

Istanbul University Cerrahpasa Medical Faculty Nephrology Istanbul Turkey

Istanbul University Istanbul School of Medicine Internal Medicine Nephrology Istanbul Turkey

Transplant Laboratory Institute for Clinical and Experimental Medicine Prague Czech Republic

Transplant Laboratory Institute for Clinical and Experimental Medicine Prague Czech Republic; Department of Nephrology Institute for Clinical and Experimental Medicine Prague Czech Republic

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Hariharan S., Israni A.K., Danovitch G. Long-term survival after kidney transplantation. N Engl J Med. 2021;385:729–743. PubMed

Sellarés J., de Freitas D.G., Mengel M., et al. Understanding the causes of kidney transplant failure: the dominant role of antibody-mediated rejection and nonadherence. Am J Transplant. 2012;12:388–399. PubMed

Betjes M.G.H., Roelen D.L., van Agteren M., Kal-van Gestel J. Causes of kidney graft failure in a cohort of recipients with a very long-time follow-up after transplantation. Front Med (Lausanne) 2022;9 PubMed PMC

Sagoo P., Perucha E., Sawitzki B., et al. Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans. J Clin Invest. 2010;120:1848–1861. PubMed PMC

Newell K.A., Asare A., Kirk A.D., et al. Identification of a B cell signature associated with renal transplant tolerance in humans. J Clin Invest. 2010;120:1836–1847. PubMed PMC

Brouard S., Mansfield E., Braud C., et al. Identification of a peripheral blood transcriptional biomarker panel associated with operational renal allograft tolerance. Proc Natl Acad Sci U S A. 2007;104:15448–15453. PubMed PMC

Massart A., Pallier A., Pascual J., et al. The DESCARTES-Nantes survey of kidney transplant recipients displaying clinical operational tolerance identifies 35 new tolerant patients and 34 almost tolerant patients. Nephrol Dial Transplant. 2016;31:1002–1013. PubMed

Loupy A., Haas M., Roufosse C., et al. The Banff 2019 kidney meeting report (I): updates on and clarification of criteria for T cell- and antibody-mediated rejection. Am J Transplant. 2020;20:2318–2331. PubMed PMC

Love M.I., Huber W., Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. PubMed PMC

Chen E.Y., Tan C.M., Kou Y., et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14:128. PubMed PMC

Friedman J., Hastie T., Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1–22. PubMed PMC

Zararsiz G., Goksuluk D., Klaus B., et al. voomDDA: discovery of diagnostic biomarkers and classification of RNA-seq data. PeerJ. 2017;5 PubMed PMC

Guyon I., Weston J., Barnhill S., Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2002;46:389–422.

Robin X., Turck N., Hainard A., et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77. PubMed PMC

Reeve J., Böhmig G.A., Eskandary F., et al. Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes. JCI Insight. 2017;2 PubMed PMC

Eugster M.J.A., Leisch F. From spider-man to hero — archetypal analysis in R. J Stat Software. 2009;30:1–23.

Champely S., Ekstrom C., Dalgaard P., et al. pwr: basic functions for power analysis. 2020. https://CRAN.R-project.org/package=pwr published online March 17.

Viklicky O., Krystufkova E., Brabcova I., et al. B-cell-related biomarkers of tolerance are up-regulated in rejection-free kidney transplant recipients. Transplantation. 2013;95:148–154. PubMed

Fox J., Weisberg S., Price B., et al. car: Companion to applied regression. 2022. https://CRAN.R-project.org/package=car published online Oct 19.

Therneau T.M. Until 2009 TL (original S->R port and R maintainer, Elizabeth A, Cynthia C. survival: survival Analysis. 2023. https://cran.r-project.org/web/packages/survival/index.html published online March 12.

Heagerty P.J. Saha-chaudhuri packaging by P. survivalROC: time-dependent ROC curve estimation from censored survival data. 2022. https://CRAN.R-project.org/package=survivalROC published online Dec 5.

Keilwagen J., Grau J. PRROC: precision-recall and ROC curves for weighted and unweighted data. 2018. https://CRAN.R-project.org/package=PRROC published online June 19. PubMed PMC

Thiele C. Cutpointr: determine and evaluate optimal cutpoints in binary classification tasks. 2022. https://cran.r-project.org/web/packages/cutpointr/index.html published online April 13.

Pillay J., Kamp V.M., van Hoffen E., et al. A subset of neutrophils in human systemic inflammation inhibits T cell responses through Mac-1. J Clin Invest. 2012;122:327–336. PubMed PMC

Lämmermann T., Afonso P.V., Angermann B.R., et al. Neutrophil swarms require LTB4 and integrins at sites of cell death in vivo. Nature. 2013;498:371–375. PubMed PMC

Baron D., Ramstein G., Chesneau M., et al. A common gene signature across multiple studies relate biomarkers and functional regulation in tolerance to renal allograft. Kidney Int. 2015;87:984–995. PubMed PMC

Choi J.-W., Kim Y.-H., Oh J.W. Comparative analyses of signature genes in acute rejection and operational tolerance. Immune Netw. 2017;17:237–249. PubMed PMC

Massart A., Danger R., Olsen C., et al. An exome-wide study of renal operational tolerance. Front Med. 2022;9 doi: 10.3389/fmed.2022.976248. PubMed DOI PMC

Aubert O., Divard G., Pascual J., et al. Application of the iBox prognostication system as a surrogate endpoint in the TRANSFORM randomised controlled trial: proof-of-concept study. BMJ Open. 2021;11 PubMed PMC

Loupy A., Aubert O., Orandi B.J., et al. Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study. BMJ. 2019;366 PubMed PMC

Sarwal M.M., Jani A., Chang S., et al. Granulysin expression is a marker for acute rejection and steroid resistance in human renal transplantation. Hum Immunol. 2001;62:21–31. PubMed

Seiler M., Brabcova I., Viklicky O., et al. Heightened expression of the cytotoxicity receptor NKG2D correlates with acute and chronic nephropathy after kidney transplantation. Am J Transplant. 2007;7:423–433. PubMed

Hidalgo L.G., Sis B., Sellares J., et al. NK cell transcripts and NK cells in kidney biopsies from patients with donor-specific antibodies: evidence for NK cell involvement in antibody-mediated rejection. Am J Transplant. 2010;10:1812–1822. PubMed

Yazdani S., Callemeyn J., Gazut S., et al. Natural killer cell infiltration is discriminative for antibody-mediated rejection and predicts outcome after kidney transplantation. Kidney Int. 2019;95:188–198. PubMed

Peng Y.-M., van de Garde M.D.B., Cheng K.-F., et al. Specific expression of GPR56 by human cytotoxic lymphocytes. J Leukoc Biol. 2011;90:735–740. PubMed

Wang J.-J., Zhang L.-L., Zhang H., et al. Gpr97 is essential for the follicular versus marginal zone B-lymphocyte fate decision. Cell Death Dis. 2013;4 PubMed PMC

Fang W., Wang Z., Li Q., et al. Gpr97 exacerbates AKI by mediating Sema3A signaling. J Am Soc Nephrol. 2018;29:1475–1489. PubMed PMC

Kimura T., Takabatake Y., Takahashi A., et al. Autophagy protects the proximal tubule from degeneration and acute ischemic injury. J Am Soc Nephrol. 2011;22:902–913. PubMed PMC

Quiroga I., McShane P., Koo D.D.H., et al. Major effects of delayed graft function and cold ischaemia time on renal allograft survival. Nephrol Dial Transplant. 2006;21:1689–1696. PubMed

Pallier A., Hillion S., Danger R., et al. Patients with drug-free long-term graft function display increased numbers of peripheral B cells with a memory and inhibitory phenotype. Kidney Int. 2010;78:503–513. PubMed

Cherukuri A., Salama A.D., Carter C.R., et al. Reduced human transitional B cell T1/T2 ratio is associated with subsequent deterioration in renal allograft function. Kidney Int. 2017;91:183–195. PubMed

Svachova V., Sekerkova A., Hruba P., et al. Dynamic changes of B-cell compartments in kidney transplantation: lack of transitional B cells is associated with allograft rejection. Transpl Int. 2016;29:540–548. PubMed

Rebollo-Mesa I., Nova-Lamperti E., Mobillo P., et al. Biomarkers of tolerance in kidney transplantation: are we predicting tolerance or response to immunosuppressive treatment? Am J Transplant. 2016;16:3443–3457. PubMed PMC

Christakoudi S., Runglall M., Mobillo P., et al. Development and validation of the first consensus gene-expression signature of operational tolerance in kidney transplantation, incorporating adjustment for immunosuppressive drug therapy. EBioMedicine. 2020;58 PubMed PMC

Roedder S., Li L., Alonso M.N., et al. A three-gene assay for monitoring immune quiescence in kidney transplantation. J Am Soc Nephrol. 2015;26:2042–2053. PubMed PMC