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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Alloimmunity and Transplantation
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1548934

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

Provisionally accepted
Matthias Niemann Matthias Niemann 1,2*Benedict M Matern Benedict M Matern 1,3Gaurav Gupta Gaurav Gupta 4Bekir Tanriover Bekir Tanriover 5Fabian Halleck Fabian Halleck 2Klemens Budde Klemens Budde 2Eric Spierings Eric Spierings 3,6
  • 1 PIRCHE AG, Berlin, Germany
  • 2 Department of Nephrology and Medical Intensive Care, Charité University Medicine Berlin, Berlin, Brandenburg, Germany
  • 3 Center for Translational Immunology, University Medical Center Utrecht, Utrecht, Netherlands, Netherlands
  • 4 Virginia Commonwealth University, Richmond, Virginia, United States
  • 5 University of Arizona Medical Center, Tucson, Arizona, United States
  • 6 Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht, Netherlands, Netherlands

The final, formatted version of the article will be published soon.

    The 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. Considering 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. Multivariable 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. Our 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.

    Keywords: HLA, epitope, Antibodies, Deep-learning, XGBoost, Neural Network, protrusion, prediction model SRTR molecular matching

    Received: 20 Dec 2024; Accepted: 23 Jan 2025.

    Copyright: © 2025 Niemann, Matern, Gupta, Tanriover, Halleck, Budde and Spierings. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Matthias Niemann, PIRCHE AG, Berlin, Germany

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.