Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China.
Department of Gynecology, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China.
Int Immunopharmacol. 2021 Feb;91:107274. doi: 10.1016/j.intimp.2020.107274. Epub 2020 Dec 21.
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.
浆液性卵巢癌（SOC）的治疗仍然是临床面临的挑战。基于免疫基因组特征对 SOC 进行分类对于制定免疫治疗策略很重要。我们从 TCGA-OV 中提取了 SOC 的 RNA-seq 数据。最终，我们通过无监督机器学习方法并结合免疫反应的多方面特征，根据 29 个免疫特征的免疫基因组特征，将这些样本分为高免疫（Immunity_H）组和低免疫（Immunity_L）组。高免疫组表现出较低的肿瘤纯度和更高的抗肿瘤免疫活性，以及 PDCD1、CD274 和 CTLA4 的高表达。此外，高免疫组的总生存时间和无进展间隔明显更长。差异表达基因主要富集在一些免疫反应相关的功能术语和 PI3K-AKT 信号通路中。根据 ImmuCellAI，高免疫组中各种 T 细胞亚型的丰度明显高于低免疫组。这种新的免疫分型有望为 SOC 患者的预后和免疫治疗分层提供依据。
