Neighborhood enrichment for the identification of antigen-specific T-cell receptors.

Abstract

Understanding T-cell receptor (TCR) specificity is not only essential for fundamental research, but could open up novel avenues for diagnostics, cancer immunotherapy, and the targeted treatment of autoimmune diseases. The immune system responds to challenges through groups of T-cells with similar TCR sequences. In recent years, searching for TCRs with an enrichment of similar sequences - neighbors - in a TCR repertoire has become a standard procedure for antigen-specific TCR identification. This study provides a systematic comparison of computational algorithms-ALICE, TCRNET, GLIPH2, and tcrdist3-that leverage neighborhood enrichment for antigen-specific TCR identification. Using published murine datasets from Lymphocytic choriomeningitis virus (LCMV) infection and novel datasets from Sputnik V vaccination and Mycobacterium tuberculosis (Mtb) infection, we evaluated the performance of these algorithms. To facilitate reproducible analysis, we developed TCRgrapher, an R library that integrates these pipelines into a user-friendly framework. TCRgrapher enables efficient identification of antigen-specific TCRs from single repertoire snapshots and supports flexible parameter customization. Our comparative analysis revealed that ALICE and TCRNET consistently outperformed GLIPH2 and tcrdist3 across most datasets, achieving higher area under precision-recall curve. While murine datasets provide valuable insights into algorithm performance, caution is advised when extrapolating these results to other species or different experimental conditions. TCRgrapher is freely available on GitHub (https://github.com/KseniaMIPT/tcrgrapher), offering researchers a robust tool for investigating TCR specificity and advancing immunological studies.