Ripretinib versus sunitinib in imatinib-resistant gastro-intestinal stromal tumor with KIT Exon 11 mutations: A systematic review and meta-analysis.

Abstract

e23518 Background: Gastrointestinal Stromal Tumor(GIST) is the most common GI tract sarcoma, with imatinib serving as the standard first-line therapy. However, resistance develops in the majority of cases due to secondary mutations, necessitating second-line treatment with sunitinib. Sunitinib’s efficacy is limited due to diverse mutations especially in KIT and PDGFRA. Emerging evidence suggests that ripretinib may demonstrate superior efficacy in KIT-mutated GIST in this setting. This study aims to evaluate the efficacy of ripretinib versus sunitinib in patients with KIT Exon 11 mutations, stratified by co-occurring secondary mutations. This is the first meta-analysis comparing these agents in this context. Methods: We conducted a systematic search of PubMed, Embase, and Cochrane databases to identify studies comparing efficacy of ripretinib and sunitinib. The analysis focused on progression-free survival (PFS) and overall survival (OS) as primary endpoints. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using R version 4.4.2 with a random-effects model. Results: A total of 5 studies with 583 patients were included. The random effects analysis showed a better efficacy of ripretinib in Exon 11 + 17/18 mutations with PFS: 0.22 (95% CI: 0.12–0.41) and OS: 0.33 (95% CI: 0.17–0.64), while sunitinib had better PFS: 3.67 (95% CI: 1.90–7.08) and OS: 1.84 (95% CI: 1.01–3.33) in Exon 11 + 13/14 mutations. Conclusions: The efficacy of ripretinib and sunitinib varies across mutation subsets in imatinib-resistant GIST. Ripretinib’s ability to target both the ATP-binding pocket and the activation loop of KIT offers superior efficacy in Exon 11 + 17/18 mutations, while sunitinib retains efficacy in Exon 11 + 13/14 mutations. These findings underscore the importance of repeat molecular profiling in imatinib-refractory cases to personalize treatment selection. Tailored therapies based on mutation subsets can significantly improve patient outcomes.