Predicting Clinical Sensitivities of PDGFRA Exon 18 Mutations to Imatinib and Avapritinib to Optimize Gastrointestinal Stromal Tumor Treatment.

The most common platelet-derived growth factor receptor α (PDGFRA) alteration in gastrointestinal stromal tumors (GIST) is the exon 18 activation loop mutation D842V, which is resistant to imatinib and other type II tyrosine kinase inhibitors (TKI) but sensitive to type I TKI avapritinib. Avapritinib is FDA-approved for first-line treatment of all PDGFRA exon 18-mutant GIST cases but is only available for D842V-mutant cases outside the United States. Non-D842V exon 18-mutant GIST cases are understudied and lack evidence-based treatment guidelines. However, there are a few previous reports describing non-D842V exon 18-mutant patients with GIST who responded well to imatinib therapy. Given that imatinib is more tolerable, globally accessible, and significantly less expensive than avapritinib, we sought to define which patients could be treated with imatinib rather than avapritinib. We assembled a cohort of more than 1,000 PDGFRA exon 18-mutant GIST cases and identified that 78% of these mutations involved a key autoinhibitory aspartic acid residue at position 842. Using cell-based models, we demonstrated that imatinib sensitivity was dependent on the amino acid class of the 842-position residue, with all hydrophobic amino acids except alanine conferring resistance. In contrast, all 842-position mutations were avapritinib-sensitive. Structural modeling supported our biochemical results and revealed how 842-position mutations induce changes that can interfere with imatinib binding. Lastly, our biochemical data were validated using imatinib response data for first-line metastatic disease; patients with predicted exon 18-sensitive mutations had longer progression-free survival than patients with predicted imatinib-resistant mutations. These results provide key evidence that should be used to guide therapy selection for PDGFRA-mutant GIST. SIGNIFICANCE: Biochemical and structural modeling of PDGFRA exon 18 842-position mutations allows for the prediction of clinical TKI responses. These data can be used to generate new treatment guidelines for PDGFRA exon 18-mutant GISTs and select optimal therapies for patients.