Comprehensive review maps TCR computational tools for translational immunology and cancer therapy design

The immense sequence and structural diversity of T cell receptors (TCRs) pose a significant challenge to fully comprehending adaptive immune responses. Understanding these interactions is critical for developing effective cancer immunotherapies and addressing other immune-related conditions. High-throughput sequencing technologies generate vast datasets of TCR repertoire information, but their complexity necessitates advanced computational tools for meaningful analysis, bridging the gap between raw data and actionable insights in translational immunology.

This comprehensive review systematically categorized over 40 state-of-the-art computational tools developed for diverse TCR repertoire analyses. The authors organized these tools into six primary analytical stages, creating a workflow specifically tailored for TCR analysis within the context of cancer immunotherapy. For each category, the review discussed underlying methodologies, representative tools, key applications, usability, accessibility, and identified strengths and limitations.

The review identified six primary analytical stages for TCR analysis. These stages include: (1) data acquisition, covering differences in TCR sequencing technologies and databases; (2) TCR reconstruction and inference, focusing on accurately extracting V(D)J gene usage, complementarity-determining region sequences, and α/β pairing from raw data; (3) TCR clustering, which groups receptors by similarity to characterize repertoire shifts, therapy responses, and identify cancer-associated TCR clones; (4) structural modeling of TCRs and TCR-peptide-major histocompatibility complex (MHC) to predict 3D structures; (5) TCR specificity prediction, forecasting binding to peptide-MHC complexes; and (6) functional and clinical integration. > The review highlighted significant breakthroughs alongside persistent bottlenecks that hinder the wider clinical application of these advanced computational methods in TCR-based therapies.