Integrated Genomic Analysis Identifies a Genetic Mutation Model As a Potential Biomarker of Response to Immune-Checkpoint Inhibitors in Melanoma

Background: Several biomarkers such as tumor mutation burden (TMB), neoantigen load (NAL), programmed cell-death receptor 1 ligand (PD‑L1) expression and lactate dehydrogenase (LDH) have been developed for predicting response to immune-checkpoint inhibitors (ICIs) in melanoma. However, some limitations including the undefined cut-off value, poor uniformity of test platform and weak reliability of prediction have restricted the broad application in clinical practice. Methods: We collected somatic mutation data and corresponding clinicopathologic information of 318 melanoma patients treated with ICIs from three independent studies, constructed and validated a genetic mutation model named as immunotherapy score (ITS) for predicting response to ICIs. Then multivariate logistic and Cox regression analyses were performed to adjust confounding factors. Furthermore, we characterized the distinct genomic patterns between patients with high and low ITS. Findings: Patients with high ITS had better durable clinical benefit and survival outcomes than patients with low ITS in three independent cohorts, as well as in the meta-cohort. Notably, the prediction capability of ITS was more robust than that of TMB. Remarkably, ITS was not only an independent predictor of ICIs therapy, but also combined with TMB or LDH to better predict response to ICIs than any single biomarker. Patients with high ITS harbored the immunotherapy-sensitive characteristics including high TMB and NAL, ultraviolet light damage, impaired DNA damage repair pathway, arrested cell cycle signaling and frequent mutations in NF1 and SERPINB3/4. Interpretation: Our findings showed that melanoma patients with high ITS harbored multiple genetic patterns of sensitivity to ICIs and favorable outcomes from immunotherapy, indicating the genetic mutation model as a potential biomarker of response to ICIs in melanoma. Funding Statement: This work was supported by the Key Project of Scientific and Technological Innovation of Zhejiang Province (No. 2018C03022), Grants from the Natural Science Foundation of Zhejiang Province (No. LQ20H160043, No.LQ18H160002) and National Natural Science Foundation of China (No. 81803107). Declaration of Interests: The authors declare that they have no conflict of interests. Ethical Approval Statement: Not required.