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@ARTICLE{Tan:288845,
      author       = {C. L. Tan$^*$ and K. Lindner$^*$ and T. Boschert$^*$ and Z.
                      Meng$^*$ and A. Rodriguez Ehrenfried$^*$ and A. De Roia$^*$
                      and G. Haltenhof$^*$ and A. Faenza and F. Imperatore and L.
                      Bunse$^*$ and J. M. Lindner and R. Harbottle$^*$ and M.
                      Ratliff and R. Offringa$^*$ and I. Poschke$^*$ and M.
                      Platten$^*$ and E. Green$^*$},
      title        = {{P}rediction of tumor-reactive {T} cell receptors from
                      sc{RNA}-seq data for personalized {T} cell therapy.},
      journal      = {Nature biotechnology},
      volume       = {43},
      number       = {1},
      issn         = {1087-0156},
      address      = {New York, NY},
      publisher    = {Springer Nature},
      reportid     = {DKFZ-2024-00492},
      pages        = {134-142},
      year         = {2025},
      note         = {#EA:D170#LA:D170# / HI-TRON / 2025 Jan;43(1):134-142},
      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.},
      cin          = {D170 / HD01 / D200 / F160 / D420},
      ddc          = {660},
      cid          = {I:(DE-He78)D170-20160331 / I:(DE-He78)HD01-20160331 /
                      I:(DE-He78)D200-20160331 / I:(DE-He78)F160-20160331 /
                      I:(DE-He78)D420-20160331},
      pnm          = {314 - Immunologie und Krebs (POF4-314)},
      pid          = {G:(DE-HGF)POF4-314},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38454173},
      doi          = {10.1038/s41587-024-02161-y},
      url          = {https://inrepo02.dkfz.de/record/288845},
}