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@ARTICLE{Jassim:302324,
      author       = {A. Jassim and B. V. Nimmervoll and S. Terranova and E.
                      Nathan and L. Hu and J. T. Taylor and K. E. Masih and L.
                      Ruff and M. Duarte and E. Cooper and G. Katyal and M.
                      Akhbari and R. J. Gilbertson and J. C. Coleman and J. S.
                      Toker and C. Terhune and G. Balmus and S. P. Jackson and H.
                      Liu and T. Jiang and M. D. Taylor and K. Hua and J. E.
                      Abraham and M. G. Filbin and A. Hill$^*$ and A. Patrizi$^*$
                      and N. Dani and A. Regev and M. K. Lehtinen and R. J.
                      Gilbertson},
      title        = {{G}ene context drift identifies drug targets to mitigate
                      cancer treatment resistance.},
      journal      = {Cancer cell},
      volume       = {nn},
      issn         = {1535-6108},
      address      = {Cambridge, Mass.},
      publisher    = {Cell Press},
      reportid     = {DKFZ-2025-01319},
      pages        = {nn},
      year         = {2025},
      note         = {epub},
      abstract     = {Cancer treatment often fails because combinations of
                      different therapies evoke complex resistance mechanisms that
                      are hard to predict. We introduce REsistance through COntext
                      DRift (RECODR): a computational pipeline that combines
                      co-expression graph networks of single-cell RNA sequencing
                      profiles with a graph-embedding approach to measure changes
                      in gene co-expression context during cancer treatment.
                      RECODR is based on the idea that gene co-expression context,
                      rather than expression level alone, reveals important
                      information about treatment resistance. Analysis of tumors
                      treated in preclinical and clinical trials using RECODR
                      unmasked resistance mechanisms -invisible to existing
                      computational approaches- enabling the design of highly
                      effective combination treatments for mice with choroid
                      plexus carcinoma, and the prediction of potential new
                      treatments for patients with medulloblastoma and
                      triple-negative breast cancer. Thus, RECODR may unravel the
                      complexity of cancer treatment resistance by detecting
                      context-specific changes in gene interactions that determine
                      the resistant phenotype.},
      keywords     = {DNA repair (Other) / cancer (Other) / choroid plexus
                      (Other) / choroid plexus carcinoma (Other) / combination
                      therapy (Other) / graph networks (Other) / machine learning
                      (Other) / radiation (Other) / treatment resistance (Other) /
                      triple-negative breast cancer (Other)},
      cin          = {A320},
      ddc          = {610},
      cid          = {I:(DE-He78)A320-20160331},
      pnm          = {311 - Zellbiologie und Tumorbiologie (POF4-311)},
      pid          = {G:(DE-HGF)POF4-311},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40578362},
      doi          = {10.1016/j.ccell.2025.06.005},
      url          = {https://inrepo02.dkfz.de/record/302324},
}