Abstract
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.
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Data availability
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.
Code availability
Specific code will be made available upon request (without restrictions) to itay.tirosh@weizmann.ac.il. Code for inference of CNAs is available at https://github.com/broadinstitute/inferCNV.
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Acknowledgements
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.
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B.I., I.T., E.H.S., A.Rotem and A.Regev conceived of and designed the overall study. E.H.S., P.K., J.F.L. and U.M. provided samples and clinical annotation and reviewed the clinical data. C.B.M.P., M.S., J.W., L.J.-A., O.A., O.R.-R. and A.Rotem coordinated the data acquisition for HTAPP specimens. B.I., I.W., M.S.C., C.R., R.L., M.-J.S., P.S., J.C.M., T.J.B., M.R., S.V. O.R.-R., L.A.G., B.E.J. and A.Rotem coordinated and performed the sample acquisition and processing. B.I., S.V., A.Rotem and O.R.-R. oversaw sample processing. B.I., I.T., E.H.S., S.V., O.R.-R. and A.Rotem oversaw sample sequencing. I.T., B.I., E.H.S. I.A. and A.Regev performed and interpreted the computational analyses. B.I., E.H.S. and A.Rotem designed and oversaw the in vitro experiments. B.I., I.W., R.L., M.I., S.R.W., C.M., J.C.M. and A.Rotem performed and analyzed the in vitro experiments. B.I., S.M., P.K.S. and J.L. performed the IF experiments. B.I., E.H.S. and A.Rotem oversaw the in vivo experiments. B.I., I.T., E.H.S., A.Rotem and A.Regev interpreted the data. B.I., I.T., E.H.S., A.Rotem and A.Regev wrote the manuscript. All authors reviewed and approved the final manuscript.
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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.
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Extended data
Extended Data Fig. 1 Patient and sample characteristics.
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.
Extended Data Fig. 3 Consistent clusters among droplet and plate based scRNA-seq.
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.
Extended Data Fig. 4 Inferred CNA of single cells from plate based scRNA-seq profiles.
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.
Extended Data Fig. 6 A putative stemness program in Patient 7 modules.
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.
Extended Data Fig. 7 Some programs in malignant cells recur between patient ascites and PDX.
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.
Extended Data Fig. 9 Dose response of JSI-124 in 2D cultures or 3D spheroids.
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.
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Unprocessed Western Blots for Stat3.
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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
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DOI: https://doi.org/10.1038/s41591-020-0926-0
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