Select your timezone:

Kidney - Diagnostics 2

Wednesday September 14, 2022 - 08:00 to 09:30

Room: C2

401.8 Dissecting the impact of molecular T-cell HLA mismatches in kidney graft failure: A retrospective cohort study

William Lemieux, Canada

CORE, RI-MUHC, McGill University


Dissecting the impact of molecular T-cell HLA mismatches in kidney graft failure: a retrospective cohort study

William Lemieux1,2, David Fleisher3, Yi Archer Yang3, Matthias Nieman4, Antoine Lewin2,5, Ruth Sapir-Pichhadze1,6,7.

1Centre for Outcomes Research and Evaluation (CORE), Research Institute of McGill University Health Centre, Montreal, QC, Canada; 2Medical Affairs & Innovation, Héma-Québec, Montreal, QC, Canada; 3Department of Mathematics and Statistics, McGill University, Montreal, QC, Canada; 4PIRCHE AG, Berlin, Germany; 5Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada; 6Division of Nephrology and the Multi-Organ Transplant Program, Royal Victoria Hospital, McGill University Health Centre, Montreal, QC, Canada; 7Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada

Introduction: Kidney transplantation is the optimal treatment in end-stage kidney disease. Despite the growing understanding of the intricacies involved in rejection, de-novo donor specific antibody development continues to trouble patients undergoing kidney transplantation. One of the recent advances in solid organ transplantation has been the definition of molecular mismatch in Human Leukocyte Antigens (HLA). While not fully integrated in standard clinical care, cumulative molecular mismatch has shown promise in predicting transplant outcomes. Recently, however, our group has demonstrated that a subset of B-cell molecular mismatches (eplets) carry more risk than others. In this analysis, we sought to expand on our recent effort and study whether certain T-cell molecular mismatches (TcEMM) were highly predictive of death-censored graft failure (DCGF).

Method: We studied a retrospective cohort of kidney donor:recipient pairs from the Scientific Registry of Transplant Recipients (2000-2015). Allele level HLA-A, B, C, DRB1 and DQB1 types were imputed from serologic types using the NMDP algorithm. TcEMMs were then estimated using the PIRCHE-II algorithm. Multivariable models adjusting for donor, recipient and transplant characteristics assessed the association between single TcEMM and DCGF. Also, we fit multivariable LASSO penalized regression models to discriminate the TcEMMs most predictive of DCGF. To identify co-expressed TcEMM profiles, we applied weighted correlation network analysis (WGCNA).

Results: After applying the exclusion criteria, a total of 118,309 donor:recipient pairs were studied. We identified 1,935 unique TcEMMs between donor and recipient paires at the population level by the PIRCHE-II algorithm. Each TcEMM was represented in 1 to 26,735 of the 118,309 pairs (median donor:recipient pair number of 1080). Models fitted to identify single TcEMM independently associated with DCGF found 218 such TcEMM. The LASSO penalized regression model identified an even smaller subset of 186 TcEMMs, of which 56 and 30 TcEMM were derived from HLA Class I and II, respectively, were statistically significant and deemed highly predictive of DCGF by the post-selection inference procedure. Since the co-expressed TcEMMs can serve as confounders, we also describe profiles of co-expressed TcEMMs.

Conclusion: In this analysis, we identified subsets of TcEMMs highly predictive of DCGF as well as profiles of co-expressed mismatches. Identification of these TcEMMs as determinants of immune injury could serve to inform allocation schemes and immunosuppression regimens with further experimental validation.

This research was enabled in part by support provided by Calcul Québec ( and Compute Canada ( through a Resource Allocation for Research groups (RAC cna-921-ac).

Social Media Promotion Image

right-click to download

© 2024 TTS 2022