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Genome-wide CRISPR screen identifies PRC2 and KMT2D-COMPASS as regulators of distinct EMT trajectories that contribute differentially to metastasis

Abstract

Epithelial–mesenchymal transition (EMT) programs operate within carcinoma cells, where they generate phenotypes associated with malignant progression. In their various manifestations, EMT programs enable epithelial cells to enter into a series of intermediate states arrayed along the E–M phenotypic spectrum. At present, we lack a coherent understanding of how carcinoma cells control their entrance into and continued residence in these various states, and which of these states favour the process of metastasis. Here we characterize a layer of EMT-regulating machinery that governs E–M plasticity (EMP). This machinery consists of two chromatin-modifying complexes, PRC2 and KMT2D-COMPASS, which operate as critical regulators to maintain a stable epithelial state. Interestingly, loss of these two complexes unlocks two distinct EMT trajectories. Dysfunction of PRC2, but not KMT2D-COMPASS, yields a quasi-mesenchymal state that is associated with highly metastatic capabilities and poor survival of patients with breast cancer, suggesting that great caution should be applied when PRC2 inhibitors are evaluated clinically in certain patient cohorts. These observations identify epigenetic factors that regulate EMP, determine specific intermediate EMT states and, as a direct consequence, govern the metastatic ability of carcinoma cells.

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Fig. 1: HMLER epithelial cells contain two subpopulations with different EMP.
Fig. 2: CRISPR screening identifies PRC2 and KMT2D-COMPASS as regulators of EMP.
Fig. 3: Knocking out PRC2 or KMT2D-COMPASS generates two distinct (quasi-)mesenchymal cell states.
Fig. 4: EED-KO quasi-mesenchymal cells and KMT2D-KO highly mesenchymal cells show different abilities of metastatic colonization.
Fig. 5: PRC2 dysfunction is associated with poor prognosis of patients with breast cancer.
Fig. 6: Transient inhibition of PRC2 is sufficient to generate a metastatic, quasi-mesenchymal cell state.

Data availability

Bulk and scRNA-seq data and CUT&RUN data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under accession code GSE158115. Human genome annotation data were obtained from Ensembl (https://useast.ensembl.org/Homo_sapiens/Info/Index). Clinical and normalized RNA-seq gene expression data for primary BRCA profiles as part of TCGA were obtained using Firehose (http://firebrowse.org/?cohort=BRCA). Mutation profiles of PRC2 and KMT2D-COMPASS component genes were obtained from cBioportal (https://www.cbioportal.org). Gene expression data of circulating tumour cells from breast cancer patients were from the GSE111065 dataset. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

All the code will be available on reasonable request, including but not limited to the following: scRNA-seq analysis, bulk RNA-seq analysis and CUT&RUN data analysis.

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Acknowledgements

We thank S. Henikoff for providing pA-MNase and yeast DNA for the CUT&RUN experiment. We are grateful to R. Goldsby, O. Rozenblatt-Rosen, G. Bell and all members of the R.A.W. laboratory for discussion and suggestions. We thank the Flow Cytometry Core Facility, the Genome Technology Core, Bioinformatics and Research Computing Core at Whitehead Institute, and MIT Koch Institute Histology Facility for technical assistance. This research was supported by MIT Stem Cell Initiative, the Breast Cancer Research Foundation, the Advanced Medical Research Foundation and the Ludwig Center for Molecular Oncology, National Cancer Institute Program R01-CA078461 (R.A.W.), R35-CA220487 (R.A.W.) and Susan G. Komen Postdoctoral Fellowship no. PDF15301255 (Y.Z.). A.W.L. was supported by an American Cancer Society—New England Division—Ellison Foundation Postdoctoral Fellowship (PF-15-131-01-CSM) and a postdoctoral fellowship from the Ludwig Center for Molecular Oncology at MIT. M.M.W. was supported by the David H. Koch Graduate Fellowship. T.B.-O. is funded by the Scientific and Technological Research Council of Turkey (TUBITAK#216S461). T.B.-O., T.T.O. and N.A.L. are funded by the Koç University Research Center for Translational Medicine (KUTTAM), funded by the Presidency of Strategy and Budget of Turkey. J.A.L. is the D.K. Ludwig Professor for cancer research. R.A.W. is an American Cancer Society research professor and a Daniel K. Ludwig Foundation cancer research professor.

Author information

Authors and Affiliations

Authors

Contributions

Y.Z. conceived the project, designed and performed the experiments, analysed data and prepared the manuscript with input from all the authors. J.L.D. provided technical support to Y.Z. S.D. and Y.Z. performed CUT&RUN experiments. X.L., F.R., S.D. and Y.Z. performed mouse surgeries and tumour growth monitoring. A.W.L. provided technical support in tumoursphere assays and edited the manuscript. J.A.K., Y.Z., M.H. and A.R. designed, performed and analysed the scRNA-seq experiments. P.T., M.T. and I.T. provided technical support in bioinformatic analysis. H.R.K., M.K., O.Y.-B., N.A.L., T.T.O. and T.B.-O. provided experimental design input and technical support for CRISPR screening experiments. M.M.W. and J.A.L. provided technical support in histology studies. R.A.W. designed and supervised this study and edited the manuscript.

Corresponding authors

Correspondence to Yun Zhang or Robert A. Weinberg.

Ethics declarations

Competing interests

A.R. is a co-founder and equity holder of Celsius Therapeutics, an equity holder of Immunitas and was a SAB member of Neogene Therapeutics, Thermo Fisher Scientific, Asimov and Syros Pharmaceuticals until 31 July 2020. Since 1 August 2020, A.R. has been an employee of Genentech, a member of the Roche group. R.A.W. has a consulting agreement with Verastem Inc., together with holding shares of this company. The other authors declare no competing interests.

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Nature Cell Biology thanks Binhua Zhou, Xiang Zhang and the other, anonymous, reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 HMLER epithelial cells show differential EMP which is associated with different TGF-β responses.

a,b, Flow cytometry of the CD44 and CD104 cell-surface staining of HMLER cells (a) and Bright-phase microscopy (b) of FACS-sorted CD44hi mesenchymal cells and CD44lo epithelial cells. Scale bar, 20 𝜇m. n = 3 biologically independent experiments. c, Immunofluorescence staining shows adherent junction protein E-cadherin in FACS-sorted CD44hi mesenchymal cells and CD44lo epithelial cells. Scale bar, 20 𝜇m. n = 2 biologically independent experiments. d, Flow cytometry of the CD44 and CD104 cell-surface staining using CD44lo epithelial population sorted from C1 and C2 cells. Data were collected at 1 and 5 days after sorting. e, UMAP plots showing expression levels of epithelial marker genes EPCAM, DSP and mesenchymal marker genes CDH2, ZEB1, ZEB2 and PRRX1 in HMLER/C1/C2 cells. f, mRNA expression levels of TGFB1, TGFBR2, TGFBR1, SMAD2, SMAD3 and SMAD4 in C1, and C2-Epi cells. n=3. n.s., not significant. g. ELISA assay shows TGF-β1 protein secreted by C1 and C2-Epi cells. n=3. **, p = 0.009. h, Immunoblot of phosphor-Smad2 and total Smad2 in C1 and C2-Epi cells, as well as C1 cells treated with DMSO or SB-431542 (5 𝜇M). GAPDH as loading control. n = 2 biologically independent experiments. i, Normalized cell number of C1 and C2-Epi cells after five-day culture in control, TGF-β (2 ng/ml) and SB-431542 (5 𝜇M) treated conditions. n=6. *, p = 0.03; ***, p < 0.001. j, Percentage of CD44hi mesenchymal population of C1 and C2-Epi cells after five-day culture in control, TGF-β (2 ng/ml) and SB-431542 (5 𝜇M) treated conditions. n=3. ***, p < 0.001. Statistical analysis was performed using unpaired two-tailed Student t-tests (f,g) or one-way ANOVA followed by Tukey multiple-comparison analysis (i,j). Data are presented as mean ± SEM. Numerical source data are provided.

Source data

Extended Data Fig. 2 CRISPR screening identifies EMP regulators.

a, Gating strategies used in FACS analysis and the CRISPR screens. One C2-Epi initiated primary tumor was used as an example. b, Flow cytometry of the CD44 and EpCAM cell-surface staining of HMLER cells, demonstrating CD44hi mesenchymal cell population does not express EpCAM. c, EpCAM-based magnetic-activated cell sorting (MACS) enriches CD44lo epithelial cells in MACS-EpCAMpos population and CD44hi mesenchymal cells in MACS-EpCAMneg population. d, A summary of EPIKOL sgRNA library content. e, Diagram of the EPIKOL CRISPR screening using non-convertible C1 cells to identify possible regulators of E–M plasticity. f, List of significantly enriched GO cellular components terms from the EPIKOL CRISPR screening. Numerical source data are provided.

Source data

Extended Data Fig. 3 PRC2 and KMT2D-COMPASS regulate EMP.

a, Sanger sequencing demonstrate complete knock-out of ASH2L, EED and KMT2D genes in the corresponding clonal cells. b, Percentage of CD44hi mesenchymal population in C1 cells transduced with sgRNAs targeting SETD1A, SETD1B, KMT2A, KMT2B, KMT2C and KMT2D respectively. n=3. ***, p<0.001. Statistical analysis was performed using one-way ANOVA followed by Dunnett multiple-comparison analysis. Data are presented as mean ± SEM. c, Flow cytometry analysis shows the CD44 and CD104 cell-surface staining of sorted epithelial subpopulation from C1-sgEED and C1-sgKMT2D cells (left) and the quantification of CD44hi mesenchymal population in different culture conditions (right). Cells were cultured in control (DMSO) or SB-431542 (5 𝜇M) treated condition in vitro for 5 days. n=3. **, p = 0.001 (C1-sgEED-Epi), 0.007 (C1-sgKMT2D-Epi). Statistical analysis was performed using unpaired two-tailed Student t-tests. Data are presented as mean ± SEM. d, Flow cytometry of the CD44 cell-surface staining of C3-sgControl, C3-sgEED and C3-sgKMT2D cells at the population level. e, Flow cytometry of the EpCAM cell-surface staining of HCC827-sgControl, HCC827-sgEED and HCC827-sgKMT2D cells at the population level. f. Flow cytometry of cell-surface EpCAM in SUM149D2-sgControl, SUM149D2-sgEED and SUM149D2-sgKMT2D cells at the population level. g, Immortalized but not transformed HMLE epithelial cells contain convertible (nrc-4) and non-convertible (nrc-1) single cell clones. RAS transformation promotes EMT in convertible clone but not in non-convertible clone. h, Immunoblot of E-cadherin, N-cadherin, and ZEB1 in representative HMLE clones before and after RAS oncogene transformation. GAPDH as loading control. n = 2 biologically independent experiments. i, Flow cytometry of the CD44 and CD104 cell-surface staining of HMLE-nrc-1-sgControl, HMLE-nrc-1-sgEED and HMLE-nrc-1-sgKMT2D cells in control or TGF-β treated (2 ng/ml) conditions for 7 days. HMLE-nrc-1 is a clonal cell population generated from HMLE that stably reside in an epithelial state. Numerical source data are provided.

Source data

Extended Data Fig. 4 PRC2 directly binds to the promoters of several EMT-TF genes and KMT2D-KO changes H3K27me3 genomic distribution.

a, Heatmap showing the global binding pattern of PRC2 (as measured by EZH2 CUT&RUN profiles) at promoter regions in C1-sgControl, C1-sgEED-Epi and C1-sgKMT2D-Epi cells. b, Immunoblot of H3K27me3 and H3K3me1/2/3 in C1-sgControl, C1-sgEED-Epi and C1-KMT2D-Epi cells. Total H3 as loading control. n = 2 biologically independent experiments. c, Majority of PRC2 direct target genes were upregulated after EED knockout. d, Ectopic expression of EMT-TF ZEB1 is sufficient to activate an EMT program in C1 cells. e, Heatmap displaying the global COMPASS (as measured by ASH2L CUT&RUN profiles) occupancy in C1-sgControl, C1-sgEED-Epi, and C1-sgKMT2D-Epi cells. f, Heatmap showing mRNA expression levels of the 413 PRC2 direct genes. g, Heatmap showing all H3K27me3 peaks in C1-sgControl, C1-sgEED-Epi and C1-sgKMT2D-Epi cells. h, Average H3K27me3 signal of all H3K27me3 peaks in C1-sgControl, C1-sgEED-Epi and C1-sgKMT2D-Epi cells. i, Heatmap showing the top 2000 H3K27me3 peaks in C1-sgControl cells and the H3K27me3 signals in these same regions in C1-sgEED-Epi and C1-sgKMT2D-Epi cells. j, Average H3K27me3 signal of the top 2000 H3K27me3 peaks in C1-sgControl cells and average H3K27me3 signal in these regions in C1-sgEED-Epi and C1-sgKMT2D-Epi cells.

Source data

Extended Data Fig. 5 EED-KO and KMT2D-KO generate distinct mesenchymal cell states.

a, UMAP plots showing expression levels of epithelial marker genes CDH1, EPCAM, DSP and mesenchymal marker genes ZEB1, ZEB2 and TWIST1 in C1-sgControl, C1-sgEED and C1-sgKMT2D cells. b, Immunoblot of EMT-TFs SNAIL, ZEB1, EMT markers E-cadherin, pan-cytokeratines and EED, KMT2D in SUM149D2-sgControl, SUM149D2-sgEED-Mes and SUM149D2-sgKMT2D-Mes cells. n = 2 biologically independent experiments.

Source data

Extended Data Fig. 6 EED-KO quasi-mesenchymal cells show elevated ability in forming metastases.

a, Growth curve of C1-sgControl, C1-sgEED-Mes and C1-sgKMT2D-Mes cells in vitro. n=3. *, p = 0.03; **, p = 0.005. n.s., not significant. b, Quantification of mammosphere formation by C1-sgControl, C1-sgEED-Mes and C1-sgKMT2D-Mes cells. n=3. ***, p<0.001. c, Differences in primary tumor-initiating ability of C1-sgControl, C1-sgEED-Mes and C1-sgKMT2D-Mes cells upon transplantation with limiting dilution into NSG mice. Tumors that arose from transplantation of 2 × 106 cells were of similar size. n=5 in each group. d,e, Representative bright-phase and fluorescence microscopy (d) and number of metastatic nodules (e) shows metastatic outgrowths in the lung of C1-sgControl, C1-sgEED-Mes and C1-sgKMT2D-Mes cells 8 weeks after fat pad implantation. n=5 in each group. ***, p<0.001. n.s., not significant. Statistical analysis was performed using one-way ANOVA followed by Tukey multiple-comparison analysis. Data are presented as mean ± SEM. Numerical source data are provided.

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Extended Data Fig. 7 PRC2 loss of function mutations and the EED-KO gene signature associate with poor prognosis in breast cancer patients.

a, OncoPrint (cBioPortal) showing patients with loss of function mutations of PRC2 component genes in Metastatic Breast Cancer Project patient cohort. b, OncoPrint (cBioPortal) showing patients with amplification of PRC2 component genes in TCGA breast patient cohort. c, Kaplan-Meier survival (log-rank Mantel-Cox test) of TCGA breast cancer patients with or without amplification of PRC2 component genes. d, A proportion of breast cancer patient-derived CTCs was associated with the EED-KO gene signature. scRNA-seq data were derived from GSE111065 dataset. Grey circles highlight CTCs associated with the EED-KO signature.

Extended Data Fig. 8 PRC2 inhibitor treatment induces a metastatic, quasi-mesenchymal cell state.

a, Time-course flow cytometry analysis of the EpCAM cell-surface staining of C1 cells treated with different combinations of TGF-β (2ng/ml), SB-431542 (5𝜇M), EED226 (10𝜇M) and Tazemetostat (TAZ) (10𝜇M). b, Immunoblot of E-cadherin, N-cadherin, Periostin in MCF10A cells treated with different combinations of TGF-β (2ng/ml), EED226 (10𝜇M) and Tazemetostat (TAZ) (10𝜇M) for 10 days. GAPDH as loading control. c,d, Flow cytometry analysis of the CD44 (c) and EpCAM (d) cell-surface staining of C1 parental cells or C1-226-Mes, C1-sgEED-Mes and C1-sgKMT2D-Mes cells upon withdrawal of PRC2 inhibitors and addition of SB-431542 (5𝜇M).

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Zhang, Y., Donaher, J.L., Das, S. et al. Genome-wide CRISPR screen identifies PRC2 and KMT2D-COMPASS as regulators of distinct EMT trajectories that contribute differentially to metastasis. Nat Cell Biol 24, 554–564 (2022). https://doi.org/10.1038/s41556-022-00877-0

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