%0 Journal Article
%A Tan, Chin Leng
%A Lindner, Katharina
%A Boschert, Tamara
%A Meng, Zibo
%A Rodriguez Ehrenfried, Aaron
%A De Roia, Alice
%A Haltenhof, Gordon
%A Faenza, A.
%A Imperatore, F.
%A Bunse, Lukas
%A Lindner, J. M.
%A Harbottle, Richard
%A Ratliff, M.
%A Offringa, Rienk
%A Poschke, Isabel
%A Platten, Michael
%A Green, Edward
%T Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy.
%J Nature biotechnology
%V 43
%N 1
%@ 1087-0156
%C New York, NY
%I Springer Nature
%M DKFZ-2024-00492
%P 134-142
%D 2025
%Z #EA:D170#LA:D170# / HI-TRON / 2025 Jan;43(1):134-142
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:38454173
%R 10.1038/s41587-024-02161-y
%U https://inrepo02.dkfz.de/record/288845