Abstract
BACKGROUND: Currently developed molecular markers can predict the effectiveness of cancer immunotherapy and screen beneficiaries to some extent, but they are not stable enough. Therefore, there is an urgent need for discovering novel biomarkers. At the same time, sex factor plays a vital role in the response to immunotherapy, so it is particularly important to identify sex-related molecular indicators. METHODS: We integrated a pan-cancer cohort consisting of 2348 cancer patients who received immune checkpoint inhibitors and targeted sequencing. Using somatic mutation profiles, we identified mutational signatures, molecular subtypes, and frequently mutated genes, and analyzed their relationships with immunotherapeutic outcomes. We also explored sex disparities of determined biomarkers in response to treatments. RESULTS: We found that male patients exhibited better immunotherapy outcomes and higher tumor mutational burden. A total of seven mutational signatures were identified, among which signatures 1 and 3 were associated with worse immunotherapy outcomes, while signatures 2 and 6 correlated with better outcomes. Gender-based analysis revealed that mutational signature 1 continued to show a worse immunotherapy outcome in female patients, whereas signature 6 demonstrated a better outcome in male patients. Based on mutational activities, we identified four potential molecular subtypes with gender differences and relevance to treatment outcomes. PI3K-AKT, RAS signaling pathways, and 68 significantly mutated genes were identified to be associated with immunotherapy outcomes, with nine genes (i.e., ATM, ATRX, DOT1L, EP300, EPHB1, NOTCH1, PBRM1, RBM10, and SETD2) exhibiting gender differences. Finally, we discovered co-mutated gene pairs and TP53 p.R282W mutations related to treatment outcomes, highlighting their gender-specific differences. CONCLUSION: This study identified several molecular biomarkers related to cancer immunotherapy outcomes in terms of mutational signatures, molecular subtypes, and mutated genes, and explored their gender-relatedness in order to provide clues and basis for clinical treatment efficacy evaluation and patient selection.