Quantitative and large-scale investigation of human TCR-HLA cross-reactivity

The interaction between human leukocyte antigens (HLAs) and T cell receptor (TCR) is essential for adaptive immune recognition. While it is known that one TCR can map to multiple HLA alleles, the extent of this cross-reactivity remains poorly understood. Here, we introduce THNet, a TCR-based HLA similarity network, and present a comprehensive analysis of HLA-TCR cross-reactivity, which is built upon more than 9 million significantly associated HLA-TCR pairs. We created similarity networks for both class I and class II HLA alleles, illustrating how peptide cross-presentation contributes to HLA-TCR cross-reactivity. Our analysis revealed disease susceptibilities missed by single-HLA enrichment analyses, especially in the Black population. Last, we demonstrated that THNet can prioritize optimal HLA mismatch candidates across different transplantation contexts, supporting its potential utility in donor selection strategies. In summary, our investigation of the HLA-TCR cross-reactive network provides useful insights into autoimmune risk prediction and improved transplantation outcomes.

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Publication Details

Journal
Science Advances
Published
2025-10-24
DOI
https://doi.org/10.1126/sciadv.adx1751
Citations
2
Primary Topic
T-cell and B-cell Immunology
Type
article
Field-Weighted Citation Impact
1.59
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article

Quantitative and large-scale investigation of human TCR-HLA cross-reactivity

Julia Fleming, Yizhou Tracy Wang, Hailong Hu, Jing Hu et al.
2 citations
Science Advances
T-cell and B-cell Immunology
1.59
article

Quantitative and large-scale investigation of human TCR-HLA cross-reactivity

Julia Fleming, Yizhou Tracy Wang, Hailong Hu, Jing Hu, Mingyao Pan, Bo Li, Xiaowei Zhan, Ziyang Song, Yuhao Tan
article en
2 citations

Abstract

The interaction between human leukocyte antigens (HLAs) and T cell receptor (TCR) is essential for adaptive immune recognition. While it is known that one TCR can map to multiple HLA alleles, the extent of this cross-reactivity remains poorly understood. Here, we introduce THNet, a TCR-based HLA similarity network, and present a comprehensive analysis of HLA-TCR cross-reactivity, which is built upon more than 9 million significantly associated HLA-TCR pairs. We created similarity networks for both class I and class II HLA alleles, illustrating how peptide cross-presentation contributes to HLA-TCR cross-reactivity. Our analysis revealed disease susceptibilities missed by single-HLA enrichment analyses, especially in the Black population. Last, we demonstrated that THNet can prioritize optimal HLA mismatch candidates across different transplantation contexts, supporting its potential utility in donor selection strategies. In summary, our investigation of the HLA-TCR cross-reactive network provides useful insights into autoimmune risk prediction and improved transplantation outcomes.

Science AdvancesVol. 11(43)
Children's Hospital of Philadelphia (US), Southwestern Medical Center (US), University of Pennsylvania (US), The University of Texas Southwestern Medical Center (US)
Openalex Percentile: Top 14%
T-cell and B-cell Immunology
1.59
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