A single-cell landscape of high-grade serous ovarian cancer

Malignant abdominal fluid (ascites) frequently develops in women with advanced high-grade serous ovarian cancer (HGSOC) and is associated with drug resistance and a poor prognosis. To comprehensively characterize the HGSOC ascites ecosystem, a team led by researchers at the Dana-Farber Cancer Institute used single-cell RNA sequencing to profile ~11,000 cells from 22 ascites specimens from 11 patients with HGSOC. They found significant inter-patient variability in the composition and functional programs of ascites cells, including immunomodulatory fibroblast sub-populations and dichotomous macrophage populations. They found that the previously described immunoreactive and mesenchymal subtypes of HGSOC, which have prognostic implications, reflect the abundance of immune infiltrates and fibroblasts rather than distinct subsets of malignant cells. Malignant cell variability was partly explained by heterogeneous copy number alteration patterns or expression of a stemness program. Malignant cells shared expression of inflammatory programs that were largely recapitulated in single-cell RNA sequencing of ~35,000 cells from additionally collected samples, including three ascites, two primary HGSOC tumors and three patient ascites-derived xenograft models. Inhibition of the JAK/STAT pathway, which was expressed in both malignant cells and cancer-associated fibroblasts, had potent anti-tumor activity in primary short-term cultures and patient-derived xenograft models. This work contributes to resolving the HSGOC landscape and provides a resource for the development of novel therapeutic approaches.

Clustering and characterization of malignant and non-malignant
cell clusters in patient ascites by droplet scRNA-seq

Extended Data Fig. 2

a, t-stochastic neighborhood embedding (tSNE) of 9,609 droplet-based scRNA-seq profiles from 8 samples , colored by unsupervised cluster assignment. b, Cluster 9 is an inflammatory subset of CAFs. Comparison of the average expression (log2(TPM + 1)) of each gene in CAF cluster 9 (y axis) vs. CAF clusters 6 and 7 (x axis). Red: immunomodulatory genes. c, CAF diversity observed within a single sample. Differential expression (log2(TPM + 1)) between CAF8 and CAF6/7 cells in patient 5.1 only of the top up- and down- regulated genes from (b). (df) Two distinct macrophage programs. d, Hierarchical clustering of macrophages (rows, columns) from cluster 10 from either Patient 5.0 (left) or Patient 6 (right). Shown are the Pearson correlation coefficients (color bar) between expression profiles of macrophages, ordered by the clustering. Yellow lines highlight the separation into two main clusters. e, Left: Differential expression (log2(fold change)) for each gene (dot) between the two clusters identified in (d) for Patient 6 (x axis) or patient 5 (y axis), demonstrating high consistency. Top left corner: Pearson’s r. Genes significantly differentially up or down regulated in both patients are marked in red and blue, respectively. Middle and Right: Expression levels (color bar, log2(fold change)) of the highlighted differentially expressed genes from the left panel (rows) across macrophages from Patient 5 (middle) and Patient 6 (right) sorted by the hierarchical clustering of (d). (f) As in (e) for each other samples tested. Right panel: correlation between the average expression of cluster 1 and cluster 2 genes across cells from each of the samples tested.

Izar B et al. (2020) A Single-Cell Landscape of High-Grade Serous Ovarian Cancer. Nat Med [Online ahead of print]. [abstract]

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