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| Home > Publications database > Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy. |
| Home > Publications database > Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy. |
| Journal Article | DKFZ-2024-00492 |
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2025
Springer Nature
New York, NY
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Please use a persistent id in citations: doi:10.1038/s41587-024-02161-y
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.
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