Stratification by Mutational Landscape Reveals Differential Immune Infiltration and Predicts the Recurrence and Clinical Outcome of Cervical Cancer

Abstract

Cervical cancer (CC) is the second most common cancer of female reproductive system. However, satisfactory prognostic model for CC remains to be established. In this study, we perform whole-exome sequencing on formalin-fixed and paraffin-embedded tumor specimens extracted from 67 recurrent and 28 matched non-recurrent CC patients. As a result, four core mutated genes (i.e., DCHS2 , DNAH10 , RYR1 , and WDFY4 ) that are differentially presented in recurrent and non-recurrent CC patients are screened out to construct a recurrence-free related score (RRS) model capable of predicting CC prognosis in our cohort, which is further confirmed in TCGA CESC cohort. Moreover, combining tumor mutational burden (TMB) and RRS into an integrated RRS/TMB model enables better stratification of CC patients with distinct prognosis in both cohorts. Increased infiltration of multiple immune cell types, enriched interferon signaling pathway, and elevated cytolytic activity are evident in tumors from patients with a higher RRS and/or a higher TMB. In summary, this study establishes a novel mutation-based prognostic model for CC, the predictive value of which can be attributable to immunological mechanisms. This study will provide insight into the utilization of mutational analysis in guiding therapeutic strategies for CC patients.

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