A pathway-based mutation signature to predict the clinical outcomes and response to CTLA-4 inhibitors in melanoma

Qian Wang; Xiangmei Li; Jiayue Qiu; Yalan He; Jiashuo Wu; Ji Li; Wei Liu; Junwei Han

doi:10.1016/j.csbj.2023.04.004

A pathway-based mutation signature to predict the clinical outcomes and response to CTLA-4 inhibitors in melanoma

Comput Struct Biotechnol J. 2023 Apr 11:21:2536-2546. doi: 10.1016/j.csbj.2023.04.004. eCollection 2023.

Authors

Qian Wang¹, Xiangmei Li¹, Jiayue Qiu¹, Yalan He¹, Jiashuo Wu¹, Ji Li¹, Wei Liu², Junwei Han¹

Affiliations

¹ College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
² College of Science, Heilongjiang Institute of Technology, Harbin 150050, PR China.

Abstract

Immune checkpoint inhibitor (ICI) therapy has become a powerful clinical strategy for treating melanoma. The relationship between somatic mutations and the clinical benefits of immunotherapy has been widely recognized. However, the gene-based predictive biomarkers are less stable due to the heterogeneity of cancer at the individual gene level. Recent studies have suggested that the accumulation of gene mutations in biological pathways may activate antitumor immune responses. Herein, a novel pathway mutation signature (PMS) was constructed to predict the survival and efficacy of ICI therapy. In a dataset of melanoma patients treated with anti-CTLA-4, we mapped the mutated genes into the pathways and then identified seven significant mutation pathways associated with survival and immunotherapy response, which were used to construct the PMS model. According to the PMS model, the patients in the PMS-high group showed better overall survival (hazard ratio (HR) = 0.37; log-rank test, p < 0.0001) and progression-free survival (HR = 0.52; log-rank test, p = 0.014) than those in the PMS-low group. The PMS-high patients also showed a significantly higher objective response rate to anti-CTLA-4 therapy than the PMS-low patients (Fisher's exact test, p = 0.0055), and the predictive power of the PMS model was superior to that of TMB. Finally, the prognostic and predictive value of the PMS model was validated in two independent validation sets. Our study demonstrated that the PMS model can be considered a potential biomarker to predict the clinical outcomes and response to anti-CTLA-4 therapy in melanoma patients.

Keywords: Immune checkpoint inhibitor; Pathway mutation signature; Predictive biomarker; Tumor mutation burden.