Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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

Nature Cell Biology volume 24, pages 554–564 (2022)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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.

References

  1. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. McGranahan, N. & Swanton, C. Clonal heterogeneity and tumor evolution: past, present and the future. Cell 168, 613–628 (2017).

    Article  CAS  PubMed  Google Scholar 

  5. Meacham, C. E. & Morrison, S. J. Tumour heterogeneity and cancer cell plasticity. Nature 501, 328–337 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Flavahan, W. A., Gaskell, E. & Bernstein, B. E. Epigenetic plasticity and the hallmarks of cancer. Science 357, eaal2380 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Nieto, M. A., Huang, R. Y.-J., Jackson, R. A. & Thiery, J. P. EMT: 2016. Cell 166, 21–45 (2016).

    Article  CAS  PubMed  Google Scholar 

  8. Yang, J. et al. Guidelines and definitions for research on epithelial–mesenchymal transition. Nat. Rev. Mol. Cell Biol. 21, 341–352 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Lambert, A. W., Pattabiraman, D. R. & Weinberg, R. A. Emerging biological principles of metastasis. Cell 168, 670–691 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Aiello, N. M. & Kang, Y. Context-dependent EMT programs in cancer metastasis. J. Exp. Med. 216, 1016–1026 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Mani, S. A. et al. The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell 133, 704–715 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Lambert, A. W. & Weinberg, R. A. Linking EMT programmes to normal and neoplastic epithelial stem cells. Nat. Rev. Cancer 21, 325–338 (2021).

    Article  CAS  PubMed  Google Scholar 

  13. Singh, A. & Settleman, J. EMT, cancer stem cells and drug resistance: an emerging axis of evil in the war on cancer. Oncogene 29, 4741–4751 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Vega, S. et al. Snail blocks the cell cycle and confers resistance to cell death. Genes Dev. 18, 1131–1143 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Saxena, M., Stephens, M. A., Pathak, H. & Rangarajan, A. Transcription factors that mediate epithelial–mesenchymal transition lead to multidrug resistance by upregulating ABC transporters. Cell Death Dis. 2, e179 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Dongre, A. et al. Epithelial-to-mesenchymal transition contributes to immunosuppression in breast carcinomas. Cancer Res. 77, 3982–3989 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kudo-Saito, C., Shirako, H., Takeuchi, T. & Kawakami, Y. Cancer metastasis is accelerated through immunosuppression during Snail-induced EMT of cancer cells. Cancer Cell 15, 195–206 (2009).

    Article  CAS  PubMed  Google Scholar 

  18. Chen, L. et al. Metastasis is regulated via microRNA-200/ZEB1 axis control of tumour cell PD-L1 expression and intratumoral immunosuppression. Nat. Commun. 5, 5241 (2014).

    Article  CAS  PubMed  Google Scholar 

  19. Pastushenko, I. et al. Identification of the tumour transition states occurring during EMT. Nature 556, 463–468 (2018).

    Article  CAS  PubMed  Google Scholar 

  20. Jolly, M. K. et al. Hybrid epithelial/mesenchymal phenotypes promote metastasis and therapy resistance across carcinomas. Pharm. Ther. 194, 161–184 (2019).

    Article  CAS  Google Scholar 

  21. Yuan, S., Norgard, R. J. & Stanger, B. Z. Cellular plasticity in cancer. Cancer Discov. 9, 837–851 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Fischer, K. R. et al. Epithelial-to-mesenchymal transition is not required for lung metastasis but contributes to chemoresistance. Nature 527, 472–476 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zheng, X. et al. Epithelial-to-mesenchymal transition is dispensable for metastasis but induces chemoresistance in pancreatic cancer. Nature 527, 525–530 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ye, X. et al. Upholding a role for EMT in breast cancer metastasis. Nature 547, E1–E3 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Aiello, N. M. et al. Upholding a role for EMT in pancreatic cancer metastasis. Nature 547, E7–E8 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Li, Y. et al. Genetic fate mapping of transient cell fate reveals N-cadherin activity and function in tumor metastasis. Dev. Cell 54, 593–607 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. Bornes, L. et al. Fsp1-mediated lineage tracing fails to detect the majority of disseminating cells undergoing EMT. Cell Rep. 29, 2565–2569 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Oft, M. et al. TGF-β1 and Ha-Ras collaborate in modulating the phenotypic plasticity and invasiveness of epithelial tumor cells. Genes Dev. 10, 2462–2477 (1996).

    Article  CAS  PubMed  Google Scholar 

  29. Lamouille, S., Xu, J. & Derynck, R. Molecular mechanisms of epithelial–mesenchymal transition. Nat. Rev. Mol. Cell Biol. 15, 178–196 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Latil, M. et al. Cell-type-specific chromatin states differentially prime squamous cell carcinoma tumor-initiating cells for epithelial to mesenchymal transition. Cell Stem Cell 20, 191–204 (2017).

    Article  CAS  PubMed  Google Scholar 

  31. Yuan, S. et al. Global regulation of the histone mark H3K36me2 underlies epithelial plasticity and metastatic progression. Cancer Discov. 10, 854–871 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Elenbaas, B. et al. Human breast cancer cells generated by oncogenic transformation of primary mammary epithelial cells. Genes Dev. 15, 50–65 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Chaffer, C. L. et al. Normal and neoplastic nonstem cells can spontaneously convert to a stem-like state. Proc. Natl Acad. Sci. USA 108, 7950–7955 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Yedier-Bayram, O. et al. EPIKOL, a chromatin-focused CRISPR/Cas9-based screening platform, to identify cancer-specific epigenetic vulnerabilities. Preprint at bioRxiv https://doi.org/10.1101/2021.05.14.444239 (2021).

  36. Meeks, J. J. & Shilatifard, A. Multiple roles for the MLL/COMPASS family in the epigenetic regulation of gene expression and in cancer. Annu. Rev. Cancer Biol. 1, 425–446 (2017).

    Article  Google Scholar 

  37. Margueron, R. & Reinberg, D. The Polycomb complex PRC2 and its mark in life. Nature 469, 343–349 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Piunti, A. & Shilatifard, A. Epigenetic balance of gene expression by Polycomb and COMPASS families. Science 352, aad9780 (2016).

    Article  PubMed  CAS  Google Scholar 

  39. Dhar, S. S. et al. MLL4 is required to maintain broad H3K4me3 peaks and super-enhancers at tumor suppressor genes. Mol. Cell 70, 825–841 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife 6, e21856 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Michalak, E. M. et al. Canonical PRC2 function is essential for mammary gland development and affects chromatin compaction in mammary organoids. PLoS Biol. 16, e2004986 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Malanchi, I. et al. Interactions between cancer stem cells and their niche govern metastatic colonization. Nature 481, 85–89 (2011).

    Article  PubMed  CAS  Google Scholar 

  44. Fazilaty, H. A gene regulatory network to control EMT programs in development and disease. Nat. Commun. 10, 5115 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Ye, X. et al. Distinct EMT programs control normal mammary stem cells and tumour-initiating cells. Nature 525, 256–260 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Moody, S. E. et al. The transcriptional repressor Snail promotes mammary tumor recurrence. Cancer Cell 8, 197–209 (2005).

    Article  CAS  PubMed  Google Scholar 

  47. Göllner, S. et al. Loss of the histone methyltransferase EZH2 induces resistance to multiple drugs in acute myeloid leukemia. Nat. Med. 23, 69–78 (2017).

    Article  PubMed  CAS  Google Scholar 

  48. Pastushenko, I. et al. Fat1 deletion promotes hybrid EMT state, tumour stemness and metastasis. Nature 589, 448–455 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Comet, I., Riising, E. M., Leblanc, B. & Helin, K. Maintaining cell identity: PRC2-mediated regulation of transcription and cancer. Nat. Rev. Cancer 16, 803–810 (2016).

    Article  CAS  PubMed  Google Scholar 

  50. Kleer, C. G. et al. EZH2 is a marker of aggressive breast cancer and promotes neoplastic transformation of breast epithelial cells. Proc. Natl Acad. Sci. USA 100, 11606–11611 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wassef, M. et al. Impaired PRC2 activity promotes transcriptional instability and favors breast tumorigenesis. Genes Dev. 29, 2547–2562 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Holm, K. et al. Global H3K27 trimethylation and EZH2 abundance in breast tumor subtypes. Mol. Oncol. 6, 494–506 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Serresi, M. et al. Polycomb Repressive Complex 2 is a barrier to KRAS-driven inflammation and epithelial-mesenchymal transition in non-small-cell lung cancer. Cancer Cell 29, 17–31 (2016).

    Article  CAS  PubMed  Google Scholar 

  54. Cardenas, H., Zhao, J., Vieth, E., Nephew, K. P. & Matei, D. EZH2 inhibition promotes epithelial-to-mesenchymal transition in ovarian cancer cells. Oncotarget 7, 84453–84467 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Hu, Y. & Smyth, G. K. ELDA: extreme limiting dilution analysis for comparing depleted and enriched populations in stem cell and other assays. J. Immunol. Methods 347, 70–78 (2009).

    Article  CAS  PubMed  Google Scholar 

  56. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  57. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Mi, H., Muruganujan, A., Casagrande, J. T. & Thomas, P. D. Large-scale gene function analysis with the PANTHER classification system. Nat. Protoc. 8, 1551–1566 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Griffiths, J. A., Richard, A. C., Bach, K., Lun, A. T. L. & Marioni, J. C. Detection and removal of barcode swapping in single-cell RNA-seq data. Nat. Commun. 9, 2667 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Shen, L., Shao, N., Liu, X. & Nestler, E. ngs.plot: quick mining and visualization of next-generation sequencing data by integrating genomic databases. BMC Genomics 15, 284 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160-5 (2016).

    Article  PubMed  CAS  Google Scholar 

  67. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

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

Author notes

  1. Aviv Regev

    Present address: Genentech, South San Francisco, CA, USA

Authors and Affiliations

  1. Whitehead Institute for Biomedical Research, Cambridge, MA, USA

    Yun Zhang, Joana Liu Donaher, Sunny Das, Xin Li, Ferenc Reinhardt, Jordan A. Krall, Arthur W. Lambert, Prathapan Thiru, Heather R. Keys, Mehreen Khan & Robert A. Weinberg

  2. Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Matan Hofree & Aviv Regev

  3. The David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Molly M. Wilson & Jacqueline A. Lees

  4. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Molly M. Wilson, Aviv Regev, Jacqueline A. Lees & Robert A. Weinberg

  5. Koç University School of Medicine, Rumelifeneri Yolu, Sarıyer, Istanbul, Turkey

    Ozlem Yedier-Bayram, Nathan A. Lack, Tamer T. Onder & Tugba Bagci-Onder

  6. Vancouver Prostate Center, University of British Columbia, Vancouver, British Columbia, Canada

    Nathan A. Lack

  7. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

    Michael Tyler & Itay Tirosh

  8. MIT Ludwig Center for Molecular Oncology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Robert A. Weinberg

Authors
  1. Yun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joana Liu Donaher
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sunny Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ferenc Reinhardt
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jordan A. Krall
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Arthur W. Lambert
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Prathapan Thiru
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Heather R. Keys
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Mehreen Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Matan Hofree
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Molly M. Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Ozlem Yedier-Bayram
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Nathan A. Lack
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Tamer T. Onder
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Tugba Bagci-Onder
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Michael Tyler
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Itay Tirosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Aviv Regev
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Jacqueline A. Lees
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Robert A. Weinberg
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Peer review

Peer review information

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Source data

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).

Source data

Supplementary information

Source data

Source Data Fig. 1

Unprocessed western blots.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 3

Unprocessed western blots.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 6

Unprocessed western blots.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 1

Unprocessed western blots.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 3

Unprocessed western blots.

Source Data Extended Data Fig. 4

Unprocessed western blots.

Source Data Extended Data Fig. 5

Unprocessed western blots.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 8

Unprocessed western blots.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41556-022-00877-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer