Identification of clinically relevant T cell receptors for personalized T cell therapy using combinatorial algorithms
Rémy Pétremand, Johanna Chiffelle, Sara Bobisse, Marta A. S. Perez, Julien Schmidt, Marion Arnaud, David Barras, Maria Lozano-Rabella, Raphaël Genolet, Christophe Sauvage, Damien Saugy, Alexandra Michel, Anne-Laure Huguenin-Bergenat, Charlotte Capt, Jonathan S. Moore, Claudio De Vito, Sana Intidhar Labidi‐Galy, Lana E. Kandalaft, Denarda Dangaj Laniti, Michal Bassani‐Sternberg, Giacomo Oliveira, Catherine J. Wu, George Coukos, Vincent Zoete, Alexandre Harari
Abstract
A central challenge in developing personalized cancer cell immunotherapy is the identification of tumor-reactive T cell receptors (TCRs). By exploiting the distinct transcriptomic profile of tumor-reactive T cells relative to bystander cells, we build and benchmark TRTpred, an antigen-agnostic in silico predictor of tumor-reactive TCRs. We integrate TRTpred with an avidity predictor to derive a combinatorial algorithm of clinically relevant TCRs for personalized T cell therapy and benchmark it in patient-derived xenografts.