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@ARTICLE{Ziegler:302116,
      author       = {M. Ziegler$^*$ and N. Khoury$^*$ and L. M. Hommerich$^*$
                      and H. Adler$^*$ and S. Loges$^*$},
      title        = {{F}unctional {C}haracterization of {V}ariants of {U}nknown
                      {S}ignificance of {F}ibroblast {G}rowth {F}actor {R}eceptors
                      1-4 and {C}omparison {W}ith {AI} {M}odel-{B}ased
                      {P}rediction.},
      journal      = {JCO precision oncology},
      volume       = {9},
      number       = {9},
      issn         = {2473-4284},
      address      = {Alexandria, VA},
      publisher    = {American Society of Clinical Oncology},
      reportid     = {DKFZ-2025-01248},
      pages        = {e2400847},
      year         = {2025},
      note         = {#EA:A420#LA:A420#},
      abstract     = {Fibroblast growth factor receptors (FGFRs; FGFR1, FGFR2,
                      FGFR3, FGFR4) are frequently mutated oncogenes in solid
                      cancers. The oncogenic potential of FGFR rearrangements and
                      few hotspot point mutations is well established, but the
                      majority of variants resulting from point mutations
                      especially outside of the tyrosine kinase domain are
                      currently considered variants of unknown significance
                      (VUS).Recurrent nonkinase domain FGFR VUS variants were
                      collected from the Catalog of Somatic Mutations in Cancer
                      and their oncogenic potential was assessed in vitro by
                      different functional assays. We compiled published clinical
                      and preclinical data on FGFR variants and compared the data
                      with results from our functional assays and pathogenicity
                      predictions of state-of-the-art artificial intelligence (AI)
                      models.We identified 12 novel FGFR extracellular small
                      variants with potential driver function. Comparison of
                      clinical and preclinical data on FGFR variants with
                      pathogenicity predictions of state-of-the-art AI models
                      showed limited usefulness of the AI predictions. Sensitivity
                      profiles of activating FGFR variants to targeted inhibitors
                      were recorded and showed good targetability of FGFR
                      nonkinase domain variants.The collected results extend the
                      spectrum of suitable FGFR variants for potential treatment
                      with FGFR inhibitors in the context of clinical trials and
                      beyond. Current AI models for variant pathogenicity
                      prediction require further refinement for use in oncogenic
                      decision making.},
      keywords     = {Humans / Artificial Intelligence / Neoplasms: genetics /
                      Mutation / Receptors, Fibroblast Growth Factor: genetics /
                      Receptors, Fibroblast Growth Factor (NLM Chemicals)},
      cin          = {A420},
      ddc          = {610},
      cid          = {I:(DE-He78)A420-20160331},
      pnm          = {311 - Zellbiologie und Tumorbiologie (POF4-311)},
      pid          = {G:(DE-HGF)POF4-311},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40526877},
      doi          = {10.1200/PO-24-00847},
      url          = {https://inrepo02.dkfz.de/record/302116},
}