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| 024 | 7 | _ | |a 10.1038/s41587-024-02161-y |2 doi |
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| 024 | 7 | _ | |a 1546-1696 |2 ISSN |
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| 037 | _ | _ | |a DKFZ-2024-00492 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 660 |
| 100 | 1 | _ | |a Tan, Chin Leng |0 P:(DE-He78)c78afd4e9332b1120bda149010bb3633 |b 0 |e First author |u dkfz |
| 245 | _ | _ | |a Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy. |
| 260 | _ | _ | |a New York, NY |c 2025 |b Springer Nature |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1737362748_16472 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 500 | _ | _ | |a #EA:D170#LA:D170# / HI-TRON / 2025 Jan;43(1):134-142 |
| 520 | _ | _ | |a 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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| 700 | 1 | _ | |a Haltenhof, Gordon |0 P:(DE-He78)b37319d508946e75e8db47f8325eea5d |b 6 |u dkfz |
| 700 | 1 | _ | |a Faenza, A. |b 7 |
| 700 | 1 | _ | |a Imperatore, F. |0 0000-0001-5883-4727 |b 8 |
| 700 | 1 | _ | |a Bunse, Lukas |0 P:(DE-He78)e579130c57e8c686ed1c2dedfa595985 |b 9 |u dkfz |
| 700 | 1 | _ | |a Lindner, J. M. |b 10 |
| 700 | 1 | _ | |a Harbottle, Richard |0 P:(DE-He78)15dff5647002b4dcfe892b251cd14b62 |b 11 |u dkfz |
| 700 | 1 | _ | |a Ratliff, M. |b 12 |
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| 700 | 1 | _ | |a Poschke, Isabel |0 P:(DE-He78)e9c55f46b4b06cf835834ee7e3e00db8 |b 14 |u dkfz |
| 700 | 1 | _ | |a Platten, Michael |0 P:(DE-He78)5ef8651b0f857b9c640aa5b1498c43b5 |b 15 |e Last author |u dkfz |
| 700 | 1 | _ | |a Green, Edward |0 P:(DE-He78)9b97ca569bcb00dfda69382bc7261700 |b 16 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.1038/s41587-024-02161-y |0 PERI:(DE-600)1494943-X |n 1 |p 134-142 |t Nature biotechnology |v 43 |y 2025 |x 1087-0156 |
| 856 | 4 | _ | |u https://inrepo02.dkfz.de/record/288845/files/s41587-024-02161-y.pdf |
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