TY  - JOUR
AU  - Tan, Chin Leng
AU  - Lindner, Katharina
AU  - Boschert, Tamara
AU  - Meng, Zibo
AU  - Rodriguez Ehrenfried, Aaron
AU  - De Roia, Alice
AU  - Haltenhof, Gordon
AU  - Faenza, A.
AU  - Imperatore, F.
AU  - Bunse, Lukas
AU  - Lindner, J. M.
AU  - Harbottle, Richard
AU  - Ratliff, M.
AU  - Offringa, Rienk
AU  - Poschke, Isabel
AU  - Platten, Michael
AU  - Green, Edward
TI  - Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy.
JO  - Nature biotechnology
VL  - 43
IS  - 1
SN  - 1087-0156
CY  - New York, NY
PB  - Springer Nature
M1  - DKFZ-2024-00492
SP  - 134-142
PY  - 2025
N1  - #EA:D170#LA:D170# / HI-TRON / 2025 Jan;43(1):134-142
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:38454173
DO  - DOI:10.1038/s41587-024-02161-y
UR  - https://inrepo02.dkfz.de/record/288845
ER  -