[en] 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.
Disciplines :
Immunology & infectious disease
Author, co-author :
Hruba, Petra; Transplant Laboratory, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
Klema, Jiri; Department of Computer Science, Czech Technical University, Prague, Czech Republic.
Le, Anh Vu; Department of Computer Science, Czech Technical University, Prague, Czech Republic.
Girmanova, Eva; Transplant Laboratory, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
Mrazova, Petra; Transplant Laboratory, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
Massart, Annick; Antwerp University Hospital and Antwerp University, Antwerp, Belgium.
Maixnerova, Dita; Department of Nephrology, 1st Faculty of Medicine and General Faculty Hospital, Prague, Czech Republic.
Voska, Ludek; Department of Clinical and Transplant Pathology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
Piredda, Gian Benedetto; Department of Kidney Disease Medicine of Renal Transplantation, G.Brotzu Hospital Cagliari, Italy.
Biancone, Luigi; Department of Medical Sciences, University of Torino, Torino, Italy.
Puga, Ana Ramirez; Hospital Universitario Insular de Gran Canaria, Servicio de nefrología, Spain.
Seyahi, Nurhan; Istanbul University, Cerrahpasa Medical Faculty, Nephrology, Istanbul, Turkey.
Sever, Mehmet Sukru; Istanbul University, Istanbul School of Medicine, Internal Medicine, Nephrology, Istanbul, Turkey.
Weekers, Laurent ; Centre Hospitalier Universitaire de Liège - CHU > > Service de néphrologie
Muhfeld, Anja; Department of Nephrology, Uniklinik RWTH Aachen, Aachen, Germany.
Budde, Klemens; Charité - Universitätsmedizin Berlin, Medizinische Klinik mit Schwerpunkt Nephrologie und Internistische Intensivmedizin, Berlin, Germany.
Watschinger, Bruno; Department of Internal Medicine III, Nephrology, Medical University Vienna / AKH Wien, Vienna, Austria.
Miglinas, Marius; Faculty of Medicine, Nephrology Center, Vilnius University Hospital Santaros Klinikos, Vilnius University, Vilnius, Lithuania.
Zahradka, Ivan; Department of Nephrology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
Abramowicz, Marc; Genetic Medicine and Development, Faculty of Medicine, University of Geneva, Rue Michel Servet 1, 1206 Geneva, Switzerland.
Abramowicz, Daniel; Antwerp University Hospital and Antwerp University, Antwerp, Belgium.
Viklicky, Ondrej; Transplant Laboratory, Institute for Clinical and Experimental Medicine, Prague, Czech Republic, Department of Nephrology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic. Electronic address: ondrej.viklicky@ikem.cz.
Hariharan, S., Israni, A.K., Danovitch, G., Long-term survival after kidney transplantation. N Engl J Med 385 (2021), 729–743.
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 12 (2012), 388–399.
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), 9, 2022, 842419.
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 120 (2010), 1848–1861.
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 120 (2010), 1836–1847.
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 104 (2007), 15448–15453.
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 31 (2016), 1002–1013.
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 20 (2020), 2318–2331.
Love, M.I., Huber, W., Anders, S., Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 15, 2014, 550.
Chen, E.Y., Tan, C.M., Kou, Y., et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics, 14, 2013, 128.
Friedman, J., Hastie, T., Tibshirani, R., Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33 (2010), 1–22.
Zararsiz, G., Goksuluk, D., Klaus, B., et al. voomDDA: discovery of diagnostic biomarkers and classification of RNA-seq data. PeerJ, 5, 2017, e3890.
Guyon, I., Weston, J., Barnhill, S., Vapnik, V., Gene selection for cancer classification using support vector machines. Mach Learn 46 (2002), 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, 12, 2011, 77.
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, 2, 2017, e94197.
Eugster, M.J.A., Leisch, F., From spider-man to hero — archetypal analysis in R. J Stat Software 30 (2009), 1–23.
Champely, S., Ekstrom, C., Dalgaard, P., et al. pwr: basic functions for power analysis. published online March 17 https://CRAN.R-project.org/package=pwr, 2020. (Accessed 16 January 2023)
Viklicky, O., Krystufkova, E., Brabcova, I., et al. B-cell-related biomarkers of tolerance are up-regulated in rejection-free kidney transplant recipients. Transplantation 95 (2013), 148–154.
Fox, J., Weisberg, S., Price, B., et al. car: Companion to applied regression. published online Oct 19 https://CRAN.R-project.org/package=car, 2022. (Accessed 16 January 2023)
Therneau, T.M., Until 2009 TL (original S->R port and R maintainer, Elizabeth A, Cynthia C. survival: survival Analysis. published online March 12 https://cran.r-project.org/web/packages/survival/index.html, 2023. (Accessed 16 June 2023)
Heagerty, P.J., Saha-chaudhuri packaging by P. survivalROC: time-dependent ROC curve estimation from censored survival data. published online Dec 5 https://CRAN.R-project.org/package=survivalROC, 2022. (Accessed 16 January 2023)
Keilwagen, J., Grau, J., PRROC: precision-recall and ROC curves for weighted and unweighted data. published online June 19 https://CRAN.R-project.org/package=PRROC, 2018. (Accessed 21 April 2023)
Thiele, C., Cutpointr: determine and evaluate optimal cutpoints in binary classification tasks. published online April 13 https://cran.r-project.org/web/packages/cutpointr/index.html, 2022. (Accessed 16 June 2023)
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 122 (2012), 327–336.
Lämmermann, T., Afonso, P.V., Angermann, B.R., et al. Neutrophil swarms require LTB4 and integrins at sites of cell death in vivo. Nature 498 (2013), 371–375.
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 87 (2015), 984–995.
Choi, J.-W., Kim, Y.-H., Oh, J.W., Comparative analyses of signature genes in acute rejection and operational tolerance. Immune Netw 17 (2017), 237–249.
Massart, A., Danger, R., Olsen, C., et al. An exome-wide study of renal operational tolerance. Front Med, 9, 2022, 976248, 10.3389/fmed.2022.976248.
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, 11, 2021, e052138.
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, 366, 2019, l4923.
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 62 (2001), 21–31.
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 7 (2007), 423–433.
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 10 (2010), 1812–1822.
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 95 (2019), 188–198.
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 90 (2011), 735–740.
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, 4, 2013, e853.
Fang, W., Wang, Z., Li, Q., et al. Gpr97 exacerbates AKI by mediating Sema3A signaling. J Am Soc Nephrol 29 (2018), 1475–1489.
Kimura, T., Takabatake, Y., Takahashi, A., et al. Autophagy protects the proximal tubule from degeneration and acute ischemic injury. J Am Soc Nephrol 22 (2011), 902–913.
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 21 (2006), 1689–1696.
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 78 (2010), 503–513.
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 91 (2017), 183–195.
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 29 (2016), 540–548.
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 16 (2016), 3443–3457.
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, 58, 2020, 102899.
Roedder, S., Li, L., Alonso, M.N., et al. A three-gene assay for monitoring immune quiescence in kidney transplantation. J Am Soc Nephrol 26 (2015), 2042–2053.
