Classification of serous ovarian carcinoma based on immunogenomic profiling

Abstract

Treatment of serous ovarian cancer (SOC) remains a clinical challenge. Classification of SOC based on immunogenomic profiling is important for establishing immunotherapy strategies. We extracted RNA-seq data of SOC from TCGA-OV. The samples were ultimately classified into high immune (Immunity_H) group and low immune (Immunity_L) group based on the immunogenomic profiling of 29 immune signatures by using unsupervised machine learning methods and modified by multifaceted characterization of immune response. High immune group showed the lower tumor purity and higher anti-tumor immune activity, and the higher expressions of PDCD1, CD274 and CTLA4. Furthermore, the overall survival time and the progression-free interval were significantly longer in high-immun group. The differentially expressed genes were mainly enriched in some immune response related functional terms and PI3K-AKT signaling pathway. According to ImmuCellAI, the abundance of various T cell subtypes in high immune group were significantly higher than those in low immune group. This novel immunotyping shows promise for prognostic and immunotherapeutic stratification in SOC patients.