A single-cell landscape of high-grade serous ovarian cancer
Izar B, Tirosh I, Stover EH, Wakiro I, Cuoco MS, Alter I, Rodman C, Leeson R, Su MJ, Shah P, Iwanicki M, Walker SR, Kanodia A, Melms JC, Mei S, Lin JR, Porter CBM, Slyper M, Waldman J, Jerby-Arnon L, Ashenberg O, Brinker TJ, Mills C, Rogava M, Vigneau S, Sorger PK, Garraway LA, Konstantinopoulos PA, Liu JF, Matulonis U, Johnson BE, Rozenblatt-Rosen O, Rotem A, Regev A. A single-cell landscape of high-grade serous ovarian cancer. Nat Med. 2020 Aug;26(8):1271-1279. doi: 10.1038/s41591-020-0926-0. Epub 2020 Jun 22. PMID: 32572264; PMCID: PMC7723336.
Intra-tumor heterogeneity of tumor and associated cells can drive treatment resistance in ovarian cancer. Ascites are malignant abdominal fluid collections that develop in women with advanced high-grade serous ovarian cancer (HGSOC) and can be treatment resistant with a poor prognosis. Here they study ascite expression profiles to see how they may contribute to the disease.
They identified 18 cell clusters of epithelial cells, macrophages, cancer-associated fibroblasts (CAFs), dentritic cells, T cells and erythrocytes. Immune cells were the most abundant component of the samples which is a problem for analysis in malignant effusions.
A subest of CAFs in non-malignant cells expressed immunomodulatory programs with pro-inflammatory signalling. This may contribute to tumor growth promotion and drug resistance.
The TCGA described four subtypes of ovarian cancer: differentiated, profliferative, mesenchymal and immmunoreactive. Their clustering showed a differentiated and profliferative signature, but not the mesenchymal and immmunoreactive ones. They suggest these subtypes may represent the intra-tumoral abundance of CAFs and macrophages rather than a tumor subtype. Bulk RNA-based classification may reflect the tumor ecosystem better than they do the tumor cell subtypes. They suggest increased CAF infiltration may contribute to the poor treatment response.
They then examined how expression programs varied among each sample’s tumor cells. Using NMF they identified 35 modules with co-varying gene expression. They had different functions from cell cycle to inflammation and stress. They also had stemness and mesenchymal markers. Comparing modules across patients to find shared cancer cell programs between them they found cell cycle modules and programs dominated by immune and inflammation genes.
CAFs highly expressed genes for ligands that activate the JAK/STAT pathway and signalling genes showed similar patterns in both malignant and non-malignant cells. Cancer cell sub-populations expressed immune-related programs potentially downstream of the JAK/STAT pathway. This shared activation of JAK/STAT between cancer cells and CAFs may contribute to malignant pathogenesis and drug resistance. They determined the impact of JAK/STAT inhibition with and drug screen. JSI-124 showed anti-tumor activity and reduced disease burden in the models.
While most variation between the cancer cells was inter-patient, there was intra-patient variation, some of which was consistent across multiple patients. There were subsets of malignant cells expressing the MHC class II program and could be associated with an increase in tumor-infiltrating lymphocyte numbers, and improved prognosis and treatment response. They hypothesize that interactions between CAFs and macrophages in the ascites ecosystem regulate or enhance cancre cell-autonomous programs as seen n the JAK/STAT signalling pathway.
A larger patient cohort would provide tests of generality for programs they identified in only one patient.