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The molecular interaction pattern of lenvatinib enables inhibition of wild-type or kinase-mutated FGFR2-driven cholangiocarcinoma.

Stephan SpahnFabian KleinhenzEkaterina ShevchenkoAaron StahlYvonne RasenChristine GeislerKristina RuhmMarion KlaumuenzerThales KronenbergerStefan A LauferHolly Sundberg-MalekKhac Cuong BuiMarius HorgerSaskia BiskupHeike BantelMarkus TemplinNisar P MalekAntti PosoMichael Bitzer
Published in: Nature communications (2024)
Fibroblast growth factor receptor (FGFR)-2 can be inhibited by FGFR-selective or non-selective tyrosine kinase inhibitors (TKIs). Selective TKIs are approved for cholangiocarcinoma (CCA) with FGFR2 fusions; however, their application is limited by a characteristic pattern of adverse events or evocation of kinase domain mutations. A comprehensive characterization of a patient cohort treated with the non-selective TKI lenvatinib reveals promising efficacy in FGFR2-driven CCA. In a bed-to-bench approach, we investigate FGFR2 fusion proteins bearing critical tumor-relevant point mutations. These mutations confer growth advantage of tumor cells and increased resistance to selective TKIs but remain intriguingly sensitive to lenvatinib. In line with clinical observations, in-silico analyses reveal a more favorable interaction pattern of lenvatinib with FGFR2, including an increased flexibility and ligand efficacy, compared to FGFR-selective TKIs. Finally, the treatment of a patient with progressive disease and a newly developed kinase mutation during therapy with a selective inhibitor results in a striking response to lenvatinib. Our in vitro, in silico, and clinical data suggest that lenvatinib is a promising treatment option for FGFR2-driven CCA, especially when insurmountable adverse reactions of selective TKIs or acquired kinase mutations occur.
Keyphrases
  • tyrosine kinase
  • wild type
  • protein kinase
  • stem cells
  • multiple sclerosis
  • mesenchymal stem cells
  • binding protein
  • advanced non small cell lung cancer
  • epidermal growth factor receptor
  • data analysis