Phage display enables machine learning discovery of cancer antigen-specific TCRs.

T cells targeting epitopes in infectious diseases or cancer play a central role in spontaneous and therapy-induced immune responses. Epitope recognition is mediated by the binding of the T cell receptor (TCR), and TCRs recognizing clinically relevant epitopes are promising for T cell-based therapies. Starting from a TCR targeting the cancer-testis antigen NY-ESO-1 epitope, we built large phage display libraries of TCRs with randomized complementary determining region 3 of the β chain. The TCR libraries were panned against NY-ESO-1, which enabled us to collect thousands of epitope-specific TCR sequences. Leveraging these data, we trained a machine learning TCR-epitope interaction predictor and identified several epitope-specific TCRs from TCR repertoires. Cellular assays revealed that the predicted TCRs displayed activity toward NY-ESO-1 and no detectable cross-reactivity. Our work demonstrates how display technologies combined with TCR-epitope interaction predictors can effectively leverage large TCR repertoires for TCR discovery.