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000288845 1001_ $$0P:(DE-He78)c78afd4e9332b1120bda149010bb3633$$aTan, Chin Leng$$b0$$eFirst author$$udkfz
000288845 245__ $$aPrediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy.
000288845 260__ $$aNew York, NY$$bSpringer Nature$$c2025
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000288845 500__ $$a#EA:D170#LA:D170# / HI-TRON / 2025 Jan;43(1):134-142
000288845 520__ $$aThe identification of patient-derived, tumor-reactive T cell receptors (TCRs) as a basis for personalized transgenic T cell therapies remains a time- and cost-intensive endeavor. Current approaches to identify tumor-reactive TCRs analyze tumor mutations to predict T cell activating (neo)antigens and use these to either enrich tumor infiltrating lymphocyte (TIL) cultures or validate individual TCRs for transgenic autologous therapies. Here we combined high-throughput TCR cloning and reactivity validation to train predicTCR, a machine learning classifier that identifies individual tumor-reactive TILs in an antigen-agnostic manner based on single-TIL RNA sequencing. PredicTCR identifies tumor-reactive TCRs in TILs from diverse cancers better than previous gene set enrichment-based approaches, increasing specificity and sensitivity (geometric mean) from 0.38 to 0.74. By predicting tumor-reactive TCRs in a matter of days, TCR clonotypes can be prioritized to accelerate the manufacture of personalized T cell therapies.
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000288845 7001_ $$0P:(DE-He78)9c599f876c762bc78d289674c15ba4a5$$aLindner, Katharina$$b1$$udkfz
000288845 7001_ $$0P:(DE-He78)ad480298366c394fb2a1817ce6cb4801$$aBoschert, Tamara$$b2$$udkfz
000288845 7001_ $$0P:(DE-He78)a7ebe54d5cb6cfa15c663da9ce1f9ab2$$aMeng, Zibo$$b3$$udkfz
000288845 7001_ $$0P:(DE-He78)c083248353e32708b98dc20cb621c06a$$aRodriguez Ehrenfried, Aaron$$b4$$udkfz
000288845 7001_ $$0P:(DE-He78)3735edce41649376fec34cf8b3a1843d$$aDe Roia, Alice$$b5$$udkfz
000288845 7001_ $$0P:(DE-He78)b37319d508946e75e8db47f8325eea5d$$aHaltenhof, Gordon$$b6$$udkfz
000288845 7001_ $$aFaenza, A.$$b7
000288845 7001_ $$00000-0001-5883-4727$$aImperatore, F.$$b8
000288845 7001_ $$0P:(DE-He78)e579130c57e8c686ed1c2dedfa595985$$aBunse, Lukas$$b9$$udkfz
000288845 7001_ $$aLindner, J. M.$$b10
000288845 7001_ $$0P:(DE-He78)15dff5647002b4dcfe892b251cd14b62$$aHarbottle, Richard$$b11$$udkfz
000288845 7001_ $$aRatliff, M.$$b12
000288845 7001_ $$0P:(DE-He78)81ae96953d6149e4307057d71a190019$$aOffringa, Rienk$$b13$$udkfz
000288845 7001_ $$0P:(DE-He78)e9c55f46b4b06cf835834ee7e3e00db8$$aPoschke, Isabel$$b14$$udkfz
000288845 7001_ $$0P:(DE-He78)5ef8651b0f857b9c640aa5b1498c43b5$$aPlatten, Michael$$b15$$eLast author$$udkfz
000288845 7001_ $$0P:(DE-He78)9b97ca569bcb00dfda69382bc7261700$$aGreen, Edward$$b16$$eLast author$$udkfz
000288845 773__ $$0PERI:(DE-600)1494943-X$$a10.1038/s41587-024-02161-y$$n1$$p134-142$$tNature biotechnology$$v43$$x1087-0156$$y2025
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