Evidence map›Paper›PMID 40007544›Full record

ArticleFrontiers in immunology2025

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.

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

Abstract read
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Noninvasive Diagnosis of Kidney Allograft Rejection.Journal of the American Society of Nephrology : JASN · 2025
    Review
  9. Review
  10. Article
  11. The Progress and Challenges of Implementing HLA Molecular Matching in Clinical Practice.Transplant international : official journal of the European Society for Organ Transplantation · 2025
    Review
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Matthias NiemannResearch and Development, PIRCHE AG, Berlin, Germany.
Benedict M MaternResearch and Development, PIRCHE AG, Berlin, Germany.
Gaurav GuptaDepartment of Internal Medicine, Virginia Commonwealth University, Richmond, VA, United States.
Bekir TanrioverDivision of Nephrology, The University of Arizona, Tucson, AZ, United States.
Fabian HalleckDepartment of Nephrology and Medical Intensive Care, Charité Universitätsmedizin Berlin, Berlin, Germany.
Klemens Budde *Department of Nephrology and Medical Intensive Care, Charité Universitätsmedizin Berlin, Berlin, Germany.
Eric Spierings *Center for Translational Immunology, University Medical Center, Utrecht, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: 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. Methods: 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. Results: 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. Discussion: 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.

Indexed as

AlgorithmsEpitopes, B-LymphocyteEpitopes, T-LymphocyteGraft RejectionHistocompatibility TestingHLA AntigensKidney TransplantationFemaleGraft SurvivalHistocompatibilityHumansMaleMiddle AgedRisk AssessmentEpitopes, B-LymphocyteEpitopes, T-LymphocyteHLA Antigensclinical prediction modelepitope matchingkidney transplantationmolecular matchingPIRCHESnowXGBoost

Identifiers

PMID40007544
PMCPMC11850546

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.