Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy
Chin Leng Tan, Katharina A.M. Lindner, Tamara Boschert, Zibo Meng, Aaron Rodriguez Ehrenfried, Alice De Roia, Gordon Haltenhof, A. Faenza, Francesco Imperatore, Lukas Bunse, John M. Lindner, Richard P. Harbottle, Miriam Ratliff, Rienk Offringa, Isabel Poschke, Michael Platten, Edward W. Green
Abstract
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.