142P Association of MUC16 mutation with survival of immune checkpoint inhibitor in patients with cancer. (15th December 2019) - Ou, Q; Lin, D; Yu, Y; Li, A; Zhang, W; Chen, Y; Liu, R; Tan, Y; Xu, M; Li, L; Yao, H
Abstract: Background: MUC16 mutation has been shown to be associated with survival outcomes in cancers, but the association of MUC16 mutation with clinical outcomes of immunotherapy remains to be explored. Here we performed clinical validation of MUC16 mutation as a predictive biomarker of immune checkpoint inhibitor (ICI) in patients with cancer. Methods: This study analyzed genomic data from the cBioPortal database. Nonsynonymous mutations of MUC16 were considered. Kaplan-Meier survival analysis and logistic regression models were applied. Primary outcomes were overall survival (OS) and progression free survival (PFS). Results: A total of 2, 129 cancer patients treated with ICI were included. The tumor mutation burden of patients with MUC16 mutations was substantially higher than in those without the mutations (Wilcoxon rank sum test, P < 0.0001). Patients with MUC16 mutations were associated with better OS benefits from ICI in non–small cell lung cancer (NSCLC) (hazard ratio [HR] 0.38, 95% CI 0.17 to 0.86) and in pancancer (HR 0.79, 95% CI 0.65 to 0.96) compared with wild-type population. Similar findings were observed in terms of PFS in NSCLC (HR 0.70, 95% CI 0.57 to 0.87) and in pancancer (HR 0.42, 95% CI 0.28 to 0.64). Conclusion: We demonstrated that MUC16 mutation can predict survival benefit from ICI therapy in cancer, and suggested that MUC16 mutation should be integrated into predictive biomarker panels for ICI therapy and be validated in future prospective clinical trials. Legal entity responsible for the study: Herui Yao. Funding: Grants from the National Natural Science Foundation of China (81372819, 81572596, U1601223), the Natural Science Foundation of Guangdong Province (2017A030313828), the Guangzhou Science and Technology Program (201704020131), the Sun Yat-Sen University Clinical Research 5010 Program (2018007). Disclosure: All authors have declared no conflicts of interest.