Identification of clinically relevant T cell receptors for personalized T cell therapy using combinatorial algorithms.
Rémy PétremandJohanna ChiffelleSara BobisseMarta A S PerezJulien SchmidtMarion ArnaudDavid BarrasMaria Lozano-RabellaRaphaël GenoletChristophe SauvageDamien SaugyAlexandra MichelAnne-Laure Huguenin-BergenatCharlotte CaptJonathan S MooreClaudio De VitoS Intidhar Labidi-GalyLana Elias KandalaftDenarda Dangaj LanitiMichal Bassani-SternbergGiacomo OliveiraCatherine J WuSergio A QuezadaVincent ZoeteAlexandre HarariPublished in: Nature biotechnology (2024)
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.