Targeted Deep Sequencing Uncovers Cryptic KIT Mutations in KIT/PDGFRA/SDH/RAS-P Wild-Type GIST

Annalisa Astolfi1, Valentina Indio2, Margherita Nannini3,4

  • 1Department of Morphology, Surgery & Experimental Medicine, University of Ferrara, Ferrara, Italy.

Insights

Many gastrointestinal stromal tumors (GIST) initially appear mutation-negative but harbor cryptic KIT mutations. Identifying these mutations in KIT/PDGFRA/SDH/RAS-pathway wild-type GIST is crucial for effective targeted therapy.

Area of Science:

  • Oncology
  • Molecular Biology
  • Genetics

Background:

  • Gastrointestinal stromal tumors (GIST) typically harbor KIT or PDGFRA mutations.
  • A subset of GISTs (~10%) lack these common mutations, presenting limited treatment options.
  • Identifying novel mutations in these refractory GIST cases is critical for therapeutic advancement.

Purpose of the Study:

  • To investigate the molecular landscape of sporadic GISTs lacking common oncogenic mutations.
  • To identify alternative genetic alterations in KIT/PDGFRA/SDH/RAS-pathway wild-type GIST.
  • To determine the clinical relevance of newly identified mutations for targeted therapy.

Main Methods:

  • Targeted next-generation sequencing (NGS) was employed on 26 KIT/PDGFRA/SDH/RAS-pathway wild-type GIST samples.
  • Findings were validated using amplicon-based targeted sequencing, immunohistochemistry, gene expression profiling, and Sanger sequencing.
  • NF1 mutations were identified and patients were excluded from further analysis.

Main Results:

  • Three patients with NF1 inactivating mutations were identified and excluded.
  • Five patients (approximately 20% of the analyzed cohort) were found to harbor cryptic KIT alterations, often at low allele fractions.
  • These low-allele-fraction KIT mutations were consistently validated across multiple molecular techniques.

Conclusions:

  • A significant proportion of GISTs initially classified as wild-type for common mutations actually harbor pathogenic KIT mutations.
  • These cryptic KIT mutations may predict responsiveness to tyrosine kinase inhibitor (TKI) therapies.
  • Centralized, second-level molecular analysis for KIT/PDGFRA wild-type GIST is recommended to optimize patient stratification for targeted treatments.

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