AUTHOR=Niemann Matthias , Matern Benedict M. , Gupta Gaurav , Tanriover Bekir , Halleck Fabian , Budde Klemens , Spierings Eric 

TITLE=Advancing risk stratification in kidney transplantation: integrating HLA-derived T-cell epitope and B-cell epitope matching algorithms for enhanced predictive accuracy of HLA compatibility

JOURNAL=Frontiers in Immunology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1548934

DOI=10.3389/fimmu.2025.1548934

ISSN=1664-3224

ABSTRACT=IntroductionThe immune-mediated rejection of transplanted organs is a complex interplay between T cells and B cells, where the recognition of HLA-derived epitopes plays a crucial role. Several algorithms of molecular compatibility have been suggested, each focusing on a specific aspect of epitope immunogenicity.MethodsConsidering reported death-censored graft survival in the SRTR dataset, we evaluated four models of molecular compatibility: antibody-verified Eplets, Snow, PIRCHE-II and amino acid matching. We have statistically evaluated their co-dependency and synergistic effects between models systematically on 400,935 kidney transplantations using Cox proportional hazards and XGBoost models.ResultsMultivariable models of histocompatibility generally outperformed univariable predictors, with a combined model of HLA-A, -B, -DR matching, Snow and PIRCHE-II yielding highest AUC in XGBoost and lowest BIC in Cox models. Augmentation of a clinical prediction model of pre-transplant parameters by molecular compatibility metrics improved model performance particularly considering long-term outcomes.DiscussionOur study demonstrates that the use of multiple specialized molecular HLA matching predictors improves prediction performance, thereby improving risk classification and supporting informed decision-making in kidney transplantation.