Phage display enables machine learning discovery of cancer antigen–specific TCRs
Giancarlo Croce, Rachid Lani, Delphine Tardivon, Sara Bobisse, Mariastella de Tiani, Maiia E. Bragina, Marta A. S. Perez, Justine Michaux, Hui Song Pak, Alexandra Michel, Talita Gehret, Julien Schmidt, Philippe Guillame, Michal Bassani‐Sternberg, Vincent Zoete, Alexandre Harari, Nathalie Rufer, Michaël Hebeisen, Steven M. Dunn, David Gfeller
Abstract
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-1 157–165 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.