Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy - Tan, Chin Leng; Lindner, K.; Boschert, T.; Meng, Zibo; Rodriguez Ehrenfried, Aaron; De Roia, Alice; Haltenhof, G.; Faenza, A.; Imperatore, F.; Bunse, Lukas; Lindner, J. M.; Harbottle, R. P.; et al. (Nature biotechnology, 2025)
The identification of patient-derived, tumor-reactive T cell receptors (TCRs) as a basis for personalized transgenic T cell therapies remains a time- and cost-intensive endeavor. Current approaches to identify tumor-reactive TCRs analyze tumor mutations to predict T cell activating (neo)antigens and use these to either enrich tumor infiltrating lymphocyte (TIL) cultures or validate individual TCRs for transgenic autologous therapies. Here we combined high-throughput TCR cloning and reactivity validation to train predicTCR, a machine learning classifier that identifies individual tumor-reactive TILs in an antigen-agnostic manner based on single-TIL RNA sequencing. PredicTCR identifies tumor-reactive TCRs in TILs from diverse cancers better than previous gene set enrichment-based approaches, increasing specificity and sensitivity (geometric mean) from 0.38 to 0.74. By predicting tumor-reactive TCRs in a matter of days, TCR clonotypes can be prioritized to accelerate the manufacture of personalized T cell therapies.