Pan, M.; Tan, Y.; Tracy, W. Y.; Hu, J.; Fleming, J.; Hu, H.; Yang, Z.; Zhan, X.; Li, B.

The interaction between Human leukocyte antigens (HLA) 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 inference method, and performed a comprehensive analysis of HLA-TCR cross-reactivity. This method is built upon clustering over 47 million TCRs to identify over 9 million significant 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. This analysis revealed novel disease-susceptibilities missed by single-HLA enrichment analyses, especially in the Black populations. Finally, we demonstrated that THNet prioritized optimal HLA mismatch candidates for organ transplantation, thereby improving patient survival rates. Our investigation of HLA-TCR cross-reactive network might provide useful insights for autoimmune risk prediction and better transplantation outcomes.