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Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy

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...

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Hauptverfasser: Tan, Chin Leng (VerfasserIn) , Lindner, K. (VerfasserIn) , Boschert, T. (VerfasserIn) , Meng, Zibo (VerfasserIn) , Rodríguez Ehrenfried, Aaron (VerfasserIn) , De Roia, Alice (VerfasserIn) , Haltenhof, G. (VerfasserIn) , Faenza, A. (VerfasserIn) , Imperatore, F. (VerfasserIn) , Bunse, Lukas (VerfasserIn) , Lindner, J. M. (VerfasserIn) , Harbottle, R. P. (VerfasserIn) , Ratliff, Miriam (VerfasserIn) , Offringa, Rienk (VerfasserIn) , Poschke, Isabel (VerfasserIn) , Platten, Michael (VerfasserIn) , Green, Edward W. (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 2025
In: Nature biotechnology
Year: 2025, Jahrgang: 43, Heft: 1, Pages: 134-142
ISSN:1546-1696
DOI:10.1038/s41587-024-02161-y
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41587-024-02161-y
Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41587-024-02161-y
Volltext
Verfasserangaben:C.L. Tan, K. Lindner, T. Boschert, Z. Meng, A. Rodriguez Ehrenfried, A. De Roia, G. Haltenhof, A. Faenza, F. Imperatore, L. Bunse, J.M. Lindner, R.P. Harbottle, M. Ratliff, R. Offringa, I. Poschke, M. Platten & E.W. Green
Beschreibung
Zusammenfassung: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.
Beschreibung:Online veröffentlicht: 7 März 2024
Gesehen am 31.07.2024
Beschreibung:Online Resource
ISSN:1546-1696
DOI:10.1038/s41587-024-02161-y

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