Novel transcriptomic signatures associated with premature kidney allograft failureResearch in context
Petra Hruba,
Jiri Klema,
Anh Vu Le,
Eva Girmanova,
Petra Mrazova,
Annick Massart,
Dita Maixnerova,
Ludek Voska,
Gian Benedetto Piredda,
Luigi Biancone,
Ana Ramirez Puga,
Nurhan Seyahi,
Mehmet Sukru Sever,
Laurent Weekers,
Anja Muhfeld,
Klemens Budde,
Bruno Watschinger,
Marius Miglinas,
Ivan Zahradka,
Marc Abramowicz,
Daniel Abramowicz,
Ondrej Viklicky
Affiliations
Petra Hruba
Transplant Laboratory, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
Jiri Klema
Department of Computer Science, Czech Technical University, Prague, Czech Republic
Anh Vu Le
Department of Computer Science, Czech Technical University, Prague, Czech Republic
Eva Girmanova
Transplant Laboratory, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
Petra Mrazova
Transplant Laboratory, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
Annick Massart
Antwerp University Hospital and Antwerp University, Antwerp, Belgium
Dita Maixnerova
Department of Nephrology, 1st Faculty of Medicine and General Faculty Hospital, Prague, Czech Republic
Ludek Voska
Department of Clinical and Transplant Pathology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
Gian Benedetto Piredda
Department of Kidney Disease Medicine of Renal Transplantation, G.Brotzu Hospital Cagliari, Italy
Luigi Biancone
Department of Medical Sciences, University of Torino, Torino, Italy
Ana Ramirez Puga
Hospital Universitario Insular de Gran Canaria, Servicio de nefrología, Spain
Nurhan Seyahi
Istanbul University, Cerrahpasa Medical Faculty, Nephrology, Istanbul, Turkey
Mehmet Sukru Sever
Istanbul University, Istanbul School of Medicine, Internal Medicine, Nephrology, Istanbul, Turkey
Laurent Weekers
Department of Nephrology, CHU of Liege, Liège, Belgium
Anja Muhfeld
Department of Nephrology, Uniklinik RWTH Aachen, Aachen, Germany
Klemens Budde
Charité – Universitätsmedizin Berlin, Medizinische Klinik mit Schwerpunkt Nephrologie und Internistische Intensivmedizin, Berlin, Germany
Bruno Watschinger
Department of Internal Medicine III, Nephrology, Medical University Vienna / AKH Wien, Vienna, Austria
Marius Miglinas
Faculty of Medicine, Nephrology Center, Vilnius University Hospital Santaros Klinikos, Vilnius University, Vilnius, Lithuania
Ivan Zahradka
Department of Nephrology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
Marc Abramowicz
Genetic Medicine and Development, Faculty of Medicine, University of Geneva, Rue Michel Servet 1, 1206 Geneva, Switzerland
Daniel Abramowicz
Antwerp University Hospital and Antwerp University, Antwerp, Belgium
Ondrej Viklicky
Transplant Laboratory, Institute for Clinical and Experimental Medicine, Prague, Czech Republic; Department of Nephrology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic; Corresponding author. Department of Nephrology, Transplant Centre, Institute for Clinical and Experimental Medicine, Videnska 1958/9, Prague 14021, Czech Republic.
Summary: 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.