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Functional Characterization of Variants of Unknown Significance of Fibroblast Growth Factor Receptors 1-4 and
Martin Ziegler1,2,3, Nadira Khoury1,2,3, Louisa Maxine Hommerich1,2,3
1DKFZ-Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany.
Purpose:
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).
Materials And Methods:
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.
Results:
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.
Conclusion:
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.
Insights
This study identifies novel fibroblast growth factor receptor (FGFR) variants with oncogenic potential, expanding treatment options for cancer patients. Current artificial intelligence models need improvement for predicting variant pathogenicity.
Area of Science:
- Oncology
- Genetics
- Molecular Biology
Background:
- Fibroblast growth factor receptors (FGFRs) are crucial in solid cancers, with known oncogenic rearrangements and mutations.
- Many FGFR point mutations outside the tyrosine kinase domain are currently classified as variants of unknown significance (VUS).
Purpose of the Study:
- To assess the oncogenic potential of recurrent nonkinase domain FGFR VUS variants.
- To compare in vitro functional assay results with clinical data and artificial intelligence (AI) predictions for FGFR variants.
Main Methods:
- Collected recurrent nonkinase domain FGFR VUS variants from the Catalog of Somatic Mutations in Cancer.
- Performed in vitro functional assays to evaluate oncogenic potential.
- Compiled and compared published clinical/preclinical data with functional assays and AI pathogenicity predictions.
Main Results:
- Identified 12 novel FGFR extracellular variants with potential driver function.
- Found limited utility of current AI models in predicting FGFR variant pathogenicity.
- Demonstrated good targetability of FGFR nonkinase domain variants with FGFR inhibitors.
Conclusions:
- Expanded the range of FGFR variants treatable with FGFR inhibitors in clinical trials.
- Highlighted the need for enhanced AI models for accurate oncogenic variant pathogenicity prediction.
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