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 prognosis1. To comprehensively characterize the HGSOC ascites ecosystem, we used single-cell RNA sequencing to profile ~11,000 cells from 22 ascites specimens from 11 patients with HGSOC. We found significant inter-patient variability in the composition and functional programs of ascites cells, including immunomodulatory fibroblast sub-populations and dichotomous macrophage populations. We 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 cells2. 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. Our work contributes to resolving the HSGOC landscape3,4,5 and provides a resource for the development of novel therapeutic approaches.
This is a preview of subscription content
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Processed data are available from the Gene Expression Omnibus (GSE146026) and raw data are available via the Broad Institute Data Use Oversight System (https://duos.broadinstitute.org/#/home). Detailed instructions on establishing a Data Use Oversight System account can be found at https://duos.broadinstitute.org/#/home. Source data for Extended Data Fig. 8 are provided with the paper.
Specific code will be made available upon request (without restrictions) to email@example.com. Code for inference of CNAs is available at https://github.com/broadinstitute/inferCNV.
Matulonis, U. A. et al. Ovarian cancer. Nat. Rev. Dis. Primers 2, 16061 (2016).
Cancer Genome Atlas Research Network Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).
Winterhoff, B. J. et al. Single cell sequencing reveals heterogeneity within ovarian cancer epithelium and cancer associated stromal cells. Gynecol. Oncol. 144, 598–606 (2017).
Shih, A. J. et al. Identification of grade and origin specific cell populations in serous epithelial ovarian cancer by single cell RNA-seq. PLoS ONE 13, e0206785 (2018).
Hu, Z. et al. The repertoire of serous ovarian cancer non-genetic heterogeneity revealed by single-cell sequencing of normal fallopian tube epithelial cells. Cancer Cell 37, 226–242.e7 (2020).
Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2016. CA Cancer J. Clin. 66, 7–30 (2016).
Patch, A.-M. et al. Whole-genome characterization of chemoresistant ovarian cancer. Nature 521, 489–494 (2015).
Ahmed, N. & Stenvers, K. L. Getting to know ovarian cancer ascites: opportunities for targeted therapy-based translational research. Front. Oncol. 3, 256 (2013).
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
Tirosh, I. et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016).
Venteicher, A. S. et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science 355, eaai8478 (2017).
Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624.e24 (2017).
Izar, B. & Rotem, A. GILA, a replacement for the soft-agar assay that permits high-throughput drug and genetic screens for cellular transformation. Curr. Protoc. Mol. Biol. 116, 28.8.1–28.8.12 (2016).
Peterson, V. M. et al. Ascites analysis by a microfluidic chip allows tumor-cell profiling. Proc. Natl Acad. Sci. USA 110, E4978–E4986 (2013).
Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).
Trombetta, J. J. et al. Preparation of single-cell RNA-seq libraries for next generation sequencing. Curr. Protoc. Mol. Biol. 107, 4.22.1–4.22.17 (2014).
Slyper, M. et al. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat. Med. 26, 792–802 (2020).
Cirri, P. & Chiarugi, P. Cancer associated fibroblasts: the dark side of the coin. Am. J. Cancer Res. 1, 482–497 (2011).
Öhlund, D. et al. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer. J. Exp. Med. 214, 579–596 (2017).
Iliopoulos, D., Hirsch, H. A. & Struhl, K. An epigenetic switch involving NF-κB, Lin28, let-7 microRNA, and IL6 links inflammation to cell transformation. Cell 139, 693–706 (2009).
Aran, D., Sirota, M. & Butte, A. J. Systematic pan-cancer analysis of tumour purity. Nat. Commun. 6, 8971 (2015).
Silva, I. A. et al. Aldehyde dehydrogenase in combination with CD133 defines angiogenic ovarian cancer stem cells that portend poor patient survival. Cancer Res. 71, 3991–4001 (2011).
Wu, X. et al. AXL kinase as a novel target for cancer therapy. Oncotarget 5, 9546–9563 (2014).
Biton, M. et al. T helper cell cytokines modulate intestinal stem cell renewal and differentiation. Cell 175, 1307–1320.e22 (2018).
Smillie, C. S. et al. Intra- and inter-cellular rewiring of the human colon during ulcerative colitis. Cell 178, 714–730.e22 (2019).
Miao, Y. et al. Adaptive immune resistance emerges from tumor-initiating stem cells. Cell 177, 1172–1186.e14 (2019).
Rodig, S. J. et al. MHC proteins confer differential sensitivity to CTLA-4 and PD-1 blockade in untreated metastatic melanoma. Sci. Transl. Med. 10, eaar3342 (2018).
Liu, J. F. et al. Establishment of patient-derived tumor xenograft models of epithelial ovarian cancer for preclinical evaluation of novel therapeutics. Clin. Cancer Res. 23, 1263–1273 (2016).
Blaskovich, M. A. et al. Discovery of JSI-124 (cucurbitacin I), a selective Janus kinase/signal transducer and activator of transcription 3 signaling pathway inhibitor with potent antitumor activity against human and murine cancer cells in mice. Cancer Res. 63, 1270–1279 (2003).
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
Dijkgraaf, E. M. et al. Chemotherapy alters monocyte differentiation to favor generation of cancer-supporting M2 macrophages in the tumor microenvironment. Cancer Res. 73, 2480–2492 (2013).
Axelrod, M. L., Cook, R. S., Johnson, D. B. & Balko, J. M. Biological consequences of MHC-II expression by tumor cells in cancer. Clin. Cancer Res. 25, 2392–2402 (2019).
Jiménez-Sánchez, A. et al. Heterogeneous tumor-immune microenvironments among differentially growing metastases in an ovarian cancer patient. Cell 170, 927–938.e20 (2017).
Zhang, A. W. et al. Interfaces of malignant and immunologic clonal dynamics in ovarian cancer. Cell 173, 1755–1769.e22 (2018).
Duan, Z. et al. Signal transducers and activators of transcription 3 pathway activation in drug-resistant ovarian cancer. Clin. Cancer Res. 12, 5055–5063 (2006).
Kommoss, S. et al. Bevacizumab may differentially improve ovarian cancer outcome in patients with proliferative and mesenchymal molecular subtypes. Clin. Cancer Res. 23, 3794–3801 (2017).
Calon, A. et al. Stromal gene expression defines poor-prognosis subtypes in colorectal cancer. Nat. Genet. 47, 320–329 (2015).
Pitt, J. M. et al. Targeting the tumor microenvironment: removing obstruction to anticancer immune responses and immunotherapy. Ann. Oncol. 27, 1482–1492 (2016).
Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).
Rotem, A. et al. Alternative to the soft-agar assay that permits high-throughput drug and genetic screens for cellular transformation. Proc. Natl Acad. Sci. USA 112, 5708–5713 (2015).
Gazdar, A. F. & Oie, H. K. Re: growth of cell lines and clinical specimens of human non-small cell lung cancer in a serum-free defined medium. Cancer Res. 46, 6011–6012 (1986).
Davidowitz, R. A. et al. Mesenchymal gene program-expressing ovarian cancer spheroids exhibit enhanced mesothelial clearance. J. Clin. Invest. 124, 2611–2625 (2014).
Iwanicki, M. P. et al. Mutant p53 regulates ovarian cancer transformed phenotypes through autocrine matrix deposition. JCI Insight 1, e86829 (2016).
Nelson, E. A. et al. Nifuroxazide inhibits survival of multiple myeloma cells by directly inhibiting STAT3. Blood 112, 5095–5102 (2008).
Lin, J.-R. et al. Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. eLife 7, e31657 (2018).
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
We thank the patients and their families. We also thank: L. MacDonald, A. McKelvey, J. Gelissen, J. Jacob, L. Frackiewicz, L. Dellostritto, K. Helvie and N. Straub from the Division of Gynecologic Oncology and the Center for Cancer Genomics at the Dana-Farber Cancer Institute for coordinating the clinical samples; members of the Belfer Center for Applied Cancer Science at the Dana-Farber Cancer Institute (particularly S. Palakurthi and P. Kirschmeier) for assistance with the PDX experiments; Jennifer Guerriero for helpful discussions; and L. Gaffney for help with the artwork. This work was supported by a Marsha Rivkin Scientific Scholar Award (to B.I. and E.H.S.), a Susan F. Smith Center for Women’s Cancers Cancer Research Award (to B.I. and E.H.S.), the Wong Family Award for Translational Cancer Research (to B.I.), the Ludwig Center for Cancer Research at Harvard (to B.I.) and at MIT (to A.Regev), the Burroughs Wellcome Fund Career Award for Medical Scientists (to B.I.), the Dana-Farber Cancer Institute Claudia Adams Barr Program for Innovative Cancer Research (to B.I.), K08CA222663 from the National Cancer Institute (to B.I.), the Israel Cancer Research Fund (to I.T.), the STARR Cancer Consortium (to I.T. and A.Regev), the Human Frontier Science Program (to I.T.), the Mexican Friends New Generation (to I.T.), the Benoziyo Endowment Fund for the Advancement of Science (to I.T.), grant U54CA225088 from the National Cancer Institute (to P.K.S. and B.I.), the Koch Institute Support (core) grant P30CA14051 from the National Cancer Institute (to A.Regev), grant R33-CA202820 (to A.Regev and A.Rotem) and the Klarman Cell Observatory (A.Regev). A.Regev is an investigator of the Howard Hughes Medical Institute.
B.I. is a consultant for Merck and Volastra Therapeutics. L.A.G. is an employee of Genentech. L.A.G. was previously an employee of Eli Lilly. L.A.G. was a paid consultant for Novartis, Foundation Medicine and Boehringer Ingelheim, held equity in Foundation Medicine and was a recipient of a grant from Novartis. P.K.S. is a member of the SAB or Board of Directors and holds equity in Applied Biomath, Glencoe Software and RareCyte. A.Rotem is a consultant to eGenesis, a member of the SAB in NucleAI and an holds equity in Celsius. A.Regev is a SAB member of Thermo Fisher Scientific, Neogene Therapeutics, Asimov and Syros Pharmaceuticals, a cofounder of and equity holder in Celsius Therapeutics, and an equity holder in Immunitas Therapeutics. B.I., I.T., E.H.S., L.A.G., O.R.-R., A.Rotem and A.Regev have filed a provisional patent for the use of JSI-124 for the treatment of ovarian cancer. The other authors have no conflicts of interest to declare.
Peer review information Saheli Sadanand was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a, Timing (x axis, days) of therapies (color blocks) and sample collection (arrows) in each patient (y axis). b, Cell type composition does not group samples by treatment history. Proportion (color bar) of the four major cell types (columns) in each of the ascites samples (rows) profiled by droplet-based scRNA-seq. c, Cell intrinsic profiles do not group samples by treatment history. Pearson correlation coefficient (color bar) between the mean profiles of cancer cells (left), CAF (middle) or macrophages (right) of each pair of samples (rows, columns) profiled by droplet-based scRNA-seq and having at least 20 cells in each type.
Extended Data Fig. 2 Clustering and characterization of malignant and non-malignant cell clusters in patient ascites by droplet scRNA-seq.
a, t-stochastic neighborhood embedding (tSNE) of 9,609 droplet-based scRNA-seq profiles from 8 samples (as in Fig. 1b), 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). (d-f) 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.
a, Pearson correlation coefficient (color bar) between the average expression profiles of 302 cluster marker genes in cells in clusters defined from either droplet-based or plate-based scRNA-seq (rows, columns; ordered by hierarchical clustering). b, Pearson correlation coefficient of the mean profile of cell type specific clusters comparing droplet based and plate-based scRNA-seq.
Average relative copy number (color bar) in each chromosomal position (y axis) based on the average expression of the 100 genes surrounding that position (ref. 9) in each cell in the malignant cell clusters 1-6 (x axis), compared to non-cancer clusters used as a reference, when using the original data (left) or when randomly ordering the genes across the genome and repeating the analysis (right), as control.
Extended Data Fig. 5 Mesenchymal and immunoreactive TCGA subtypes reflect CAFs and macrophages by comparison to droplet based scRNA-seq profiles.
Subtype score (color bar), based on average expression of subtype-specific genes (Methods) of each cluster from the droplet-based scRNA-seq dataset (rows) for each of four TCGA subtypes (column). Only clusters with > 10 cells are represented in this figure.
a,b, Intra-tumoral expression modules in patients 7 and 5. Relative expression (color bar, Methods) of the top 30 module-specific genes (rows) in each module (ordered by module, dashed vertical lines), as defined by NMF (Methods) across all cancer cells (columns; ordered by hierarchical clustering) from patients 7 (a), or 5 (b, same as Fig. 3a–c). Selected genes are annotated. c, Co-variation of stemness related genes in patient 7. Relative expression of three putative stemness markers (rows) in cells from patient 7, rank ordered by the cell’s average expression of the three markers. d,e, Stemness related co-varying module present in patient 7 but not patient 8. Relative expression of the stemness score of patient 7 (top 20 genes (row) positively (top) or negatively (bottom)) correlated with the average expression of the three stemness genes in (c) in either cells from patient 7 (d) or patient 8 cells (e), with cells ordered by their average expression of the putative stemness score. (f) Stemness program is not detected in other ascites and primary tumor samples from our test cohort. Number of cells (y axis) expressing increasing numbers (x axis) of genes defining the stemness program from Patient 7 (CD24, CD133 (PROM1) and ALDH1A3) in patient cohort 3 (red) or expressing control genes with similar expression pattern in 10,000 simulations (g) Identification of cells expressing MHC Class II as cancer cells. Expression (color bar, log(TP100K + 1)) of MHC Class II program, epithelial (cancer cell) markers, and macrophage markers (rows) in cancer cells (defined by marker expression and CNA) and macrophages (columns). Top panel: CNA signal, defined as the square of the inferred copy-number log-ratios, averaged across all genes. (h–j) MHC-II, cytokine and interferon programs are detected in other ascites and primary tumor samples from our test cohort. As in (f) for the three major immune programs defined as (h) MHC Class II (core genes (CD74, HLA-DRA, HLA-DRB1, HLA-DRB5, HLA-DMA, HLA-DPA1), (i) cytokines (core genes TNF, CXCL8, IL32, ICAM1, CCL2, CCL20, NFKBIA); and (j) interferon (IFN) program (core genes IFI6, IFI44, IFIT1, IFIT3, ISG15, MX1). Error bars: SD, *=p < 0.05, **=p < 0.001; empirical p-value is the fraction of simulations in which an equal number of stemness-program genes are detected as expressed.
a, Congruent cancer cell profiles between patient and PDX cells. Left: Pearson correlation coefficient (color bar) between mean profiles (rows, columns) among major cell types discovered by plate-based scRNA-seq (cancer cells, macrophages and CAFs) in patient samples and three patient-derived xenograft models (DF20, DF68 and DF101). Right: Distribution of Pearson correlation coefficient (x axis) between different subsets. n = 27 (8 patient samples and 19 PDX samples). b–d, Intra-tumoral expression modules. Relative expression (color bar, Methods) of the top 30 module-specific genes (rows) in each module (ordered by module, dashed horizontal lines), as defined by NMF (Methods) across all cancer cells (columns; ordered by hierarchical clustering) from PDX models DF20 (b), DF68 (c), and DF101 (d). Selected genes are annotated. Top bar (b, c): cell of origin from individual mice. e,f, Cell cycle and inflammatory/immune programs recur across PDX models. (e) Number of top genes (color bar) shared between pairs of patients (rows, ordered as in Fig. 3e) and PDX (columns; ordered by hierarchical clustering) modules. Top: origin of each PDX module. (f) Module membership in the top 30 (black) or 50 (grey) of selected genes (rows) from cell cycle (top), immune-related (middle), and other (bottom) modules across all modules (columns), ordered as in (e). All genes included were shared between a corresponding PDX module and patient ascites module. g, Cytokine and MHC-II programs are only identified in patient samples. Median expression (x axis) and % of outlier highly expressing cells (y axis; average log2(TPM + 1)>5 and more than 2 SD larger than the mean of all cells) of the cytokine (left) and MHC-II (right) programs in each patient (black) and PDX (blue) samples. N = 25 (6 patient samples and 19 PDX samples).
Extended Data Fig. 8 Prominent expression of JAK-STAT pathway genes and on-target activity of JSI-124 against STAT3.
a, Prominent expression of JAK-STAT pathway genes. Mean gene expression (x axis, log2(TPM + 1)) and percentage of expressing cells (y axis) across the entire cell’s transcriptomes with highlighted signaling genes in patient cancer cells (top left), PDX models (top right), patient-derived CAFs (bottom left) and macrophages (bottom right). Black curve: LOWESS regression curve. Dark and light blue: top 5 and 10 percentiles calculated in a moving average of 200 genes. b, STAT3 activity induced by Oncostatin M. Relative (mean) luciferase activity (y axis) in Heya8 ovarian cancer cells transfected with a STAT3 responsive reporter that were stimulated with OSM to activate STAT3 for 6 h or untreated with either 1 h pre-treatment with JSI-124 (1 μM) or vehicle (x axis) for 1 h. p = 0.09, t test. Error bars: SD. c, JSI-124 treatment reduced pSTAT3. Cropped immunoblot (representative of duplicates; uncropped available in Source Data) of STAT3 and phosphorylated (p-)STAT3 from cells treated with 1μM JSI-124 for the indicated hours (bottom). Par=parental cell line, and R1 and R2 refer to two independently generated platinum-resistant cell lines.
Relative (mean) viability (y axis, relative luminescence signal compared to DMSO control) of three ovarian cancer cell lines (labels, top) grown for 4 days in either ultra-low attachment conditions eliciting formation of spheroids a, or in 2D cultures in regular plastic culture surfaces b, and treated with JSI-124, carboplatin, paclitaxel, cisplatin or olaparib at indicated doses (x axis, log µM). Error bars: SD. n = 4. Representative of biological duplicates.
About this article
Cite this article
Izar, B., Tirosh, I., Stover, E.H. et al. A single-cell landscape of high-grade serous ovarian cancer. Nat Med 26, 1271–1279 (2020). https://doi.org/10.1038/s41591-020-0926-0
Ileum tissue single-cell mRNA sequencing elucidates the cellular architecture of pathophysiological changes associated with weaning in piglets
BMC Biology (2022)
Comprehensive analysis of lncRNA-mRNAs co-expression network identifies potential lncRNA biomarkers in cutaneous squamous cell carcinoma
BMC Genomics (2022)
Single-cell transcriptomics links malignant T cells to the tumor immune landscape in cutaneous T cell lymphoma
Nature Communications (2022)
Genome Biology (2022)
High-grade serous tubo-ovarian cancer refined with single-cell RNA sequencing: specific cell subtypes influence survival and determine molecular subtype classification
Genome Medicine (2021)