Single-Cell RNA Sequencing Reveals the Tissue Architecture in Human High-Grade Serous Ovarian Cancer

Purpose: The heterogeneity of high-grade serous ovarian cancer (HGSOC) is not well studied, which severely hinders clinical treatment of HGSOC. Thus, it is necessary to characterize the heterogeneity of HGSOC within its tumor microenvironment (TME). Experimental Design: The tumors of 7 treatment-naeurove patients with HGSOC at early or late stages and five age-matched nonmalignant ovarian samples were analyzed by deep single-cell RNA sequencing (scRNA-seq). Results: A total of 59,324 single cells obtained from HGSOC and nonmalignant ovarian tissues were sequenced by scRNA-seq. Among those cells, tumor cells were characterized by a set of epithelial-to-mesenchymal transition (EMT)-associated gene signatures, in which a combination of NOTCH1, SNAI2, TGFBR1, and WNT11 was further selected as a genetic panel to predict the poor outcomes of patients with HGSOC. Matrix cancer- associated fibroblasts (mCAF) expressing a-SMA, vimentin, COL3A, COL10A, and MMP11 were the dominant CAFs in HGSOC tumors and could induce EMT properties of ovarian cancer cells in the coculture system. Specific immune cell subsets such as C7-APOBEC3A M1 macrophages, CD8 thorn TRM, and TEX cells were preferentially enriched in early-stage tumors. In addition, an immune coinhibitory receptor TIGIT was highly expressed on CD8 thorn TEX cells and TIGIT blockade could significantly reduce ovarian cancer tumor growth in mouse models. Conclusions: Our transcriptomic results analyzed by scRNA-seq delineate an ecosystemic landscape of HGSOC at early or late stages with a focus on its heterogeneity with TME. The major applications of our findings are a four-EMT gene model for prediction of HGSOC patient outcomes, mCAFs' capability of enhancing ovarian cancer cell invasion and potential therapeutic value of anti-TIGIT treatment.

Automated summary

This study characterized the heterogeneity of HGSOC using single-cell RNA sequencing, revealing a gene model for predicting patient outcomes, major cell components in the tumor microenvironment, and potential immunotherapeutic value.