Long-term comparative effectiveness of tepotinib versus standard of care for MET exon 14 skipping-mutated non-small cell lung cancer

Background: Tepotinib received Food and Drug Administration approval for advanced non-small cell lung cancer (aNSCLC) with MET exon 14 skipping mutation (METex14m) based on a single-arm trial. However, the comparative effectiveness between tepotinib and standard of care (SoC) remains unclear. Objectives: We aim to assess the long-term comparative effectiveness between first-line tepotinib and SoC. Design: This retrospective comparative effectiveness study was conducted based on a matching-adjusted indirect comparison using aggregate data for Asian patients of the VISION trial for the tepotinib arm, and data of patients with METex14m who received SoC from Samsung Medical Center data in January 2008 and February 2024 for the SoC arm. Methods: Hazard ratio (HR) and its confidence intervals (CIs) for progression-free survival (PFS) and overall survival (OS) were assessed through a Cox proportional hazards model. A partitioned survival model was run for a lifetime using four different scenarios to estimate long-term quality-adjusted life-years (QALYs), which reflect patients' health-related quality of life (HRQoL). Results: = 44) arm received subsequent METex14-targeting therapy. Tepotinib significantly improved PFS compared to SoC (HR (95% CI), 0.38 (0.22-0.63)). The PFS improvement was more pronounced when compared to the chemotherapy subgroup (HR (95% CI), 0.20 (0.10-0.38)) and was also evident in the immunotherapy-containing regimen subgroup (HR (95% CI), 0.55 (0.29-1.03)). Although OS did not differ significantly (HR (95% CI), 1.04 (0.56-1.93)), the partitioned survival model yielded an additional 0.2409-0.3039 QALYs in the tepotinib arm, showing HRQoL benefits of tepotinib over SoC. Conclusion: The significant PFS benefit of first-line tepotinib over SoC extended to long-term HRQoL gains, indicating tepotinib is considered one of the first-line treatment options. Future studies based on multicenter data or head-to-head trials would be able to verify the efficacy.