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

Abstract

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-naïve 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 α-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+ TRM, and TEX cells were preferentially enriched in early-stage tumors. In addition, an immune coinhibitory receptor TIGIT was highly expressed on CD8+ 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.

Krüppel-like factor 4 regulates the cytolytic effector function of exhausted CD8 T cells

“…S12A). scRNA-seq data of high-grade serous ovarian cancer (HGSOC) have been deposited ( 43 ) and used for the subsequent analysis. CD8 T cells were filtered from the datasets and grouped into six subpopulations (LAYN-CD8, GZMK-CD8, STMN1-CD8, FOSB-CD8, KLF2-CD8, and FOXP3-CD8) on the basis of their highly or exclusively expressed genes (fig.…”

The Dual Blockade of the TIGIT and PD-1/PD-L1 Pathway as a New Hope for Ovarian Cancer Patients

“…TIGIT regulates the anticancer immune response via CD4 + Tregs which are related to tumor burden in OC patients. The dual blockade of TIGIT and PD-1/PD-L1 factors enhances the effector function of CD8 + T cells by synergistic action [ 57 , 106 , 107 , 108 ].…”

Spatial transcriptomics reveals ovarian cancer subclones with distinct tumour microenvironments

“…With these single cell profiles it is now apparent that previously reported transcriptional subtypes of HGSOC based on bulk expression measurements (mesenchymal (C1.MES), immunoreactive (C2.IMM), differentiated (C4.DIF), and proliferative (C5.PRO)) which are associated with differences in prognosis 24 largely reflect the degree of immune cell infiltration and the abundance of fibroblasts 19 , rather than inherent differences in tumour cells. To determine how these non-malignant cell types might influence tumour growth and prognosis, several groups have predicted ligand-receptor interactions between stromal, immune and tumour cell populations 23,25 . Lastly, copy number alterations can be inferred from scRNA-seq data and this strategy has been used to identify CNAs in HGSOC tumour cells 19,21 and reveal subclones with different CNAs 25 .…”

“…In addition to intratumoural clonal heterogeneity, HGSOC tumours contain a diverse range of non-malignant cell types. Recently, several single-cell RNA-sequencing (scRNA-seq) studies of primary and metastatic tumours have described the cell types that make up the HGSOC tumour microenvironment [18][19][20][21][22][23] . With these single cell profiles it is now apparent that previously reported transcriptional subtypes of HGSOC based on bulk expression measurements (mesenchymal (C1.MES), immunoreactive (C2.IMM), differentiated (C4.DIF), and proliferative (C5.PRO)) which are associated with differences in prognosis 24 largely reflect the degree of immune cell infiltration and the abundance of fibroblasts 19 , rather than inherent differences in tumour cells.…”