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| Home > Publications database > Functional Characterization of Variants of Unknown Significance of Fibroblast Growth Factor Receptors 1-4 and Comparison With AI Model-Based Prediction. |
| Home > Publications database > Functional Characterization of Variants of Unknown Significance of Fibroblast Growth Factor Receptors 1-4 and Comparison With AI Model-Based Prediction. |
| Journal Article | DKFZ-2025-01248 |
; ; ; ;
2025
American Society of Clinical Oncology
Alexandria, VA
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Please use a persistent id in citations: doi:10.1200/PO-24-00847
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.
Keyword(s): Humans (MeSH) ; Artificial Intelligence (MeSH) ; Neoplasms: genetics (MeSH) ; Mutation (MeSH) ; Receptors, Fibroblast Growth Factor: genetics (MeSH) ; Receptors, Fibroblast Growth Factor
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