Details

Autor(en) / Beteiligte

• Tan, C L
• Lindner, K
• Boschert, T
• Meng, Z
• Rodriguez Ehrenfried, A
• De Roia, A
• Haltenhof, G
• Faenza, A
• Imperatore, F
• Bunse, L
• Lindner, J M
• Harbottle, R P
• Ratliff, M
• Offringa, R
• Poschke, I
• Platten, M
• Green, E W

Titel

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

Ist Teil von

• Nature biotechnology, 2024-03

Ort / Verlag

United States

Erscheinungsjahr

2024

Beschreibungen/Notizen

• 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.