Abstract

In cancer, the epithelial-to-mesenchymal transition (EMT) is associated with tumour stemness, metastasis and resistance to therapy. It has recently been proposed that, rather than being a binary process, EMT occurs through distinct intermediate states. However, there is no direct in vivo evidence for this idea. Here we screen a large panel of cell surface markers in skin and mammary primary tumours, and identify the existence of multiple tumour subpopulations associated with different EMT stages: from epithelial to completely mesenchymal states, passing through intermediate hybrid states. Although all EMT subpopulations presented similar tumour-propagating cell capacity, they displayed differences in cellular plasticity, invasiveness and metastatic potential. Their transcriptional and epigenetic landscapes identify the underlying gene regulatory networks, transcription factors and signalling pathways that control these different EMT transition states. Finally, these tumour subpopulations are localized in different niches that differentially regulate EMT transition states.

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Acknowledgements

We thank the ULB animal facility, the ULB genomic core facility (F. Libert and A. Lefort), G. Berx (Ghent University) and M. Mazzone (VIB-KUL) for MMTV-PyMT mice. I.P. is supported by TELEVIE. P.A.S. is supported by FNRS. A.S., D.B. and T.V. are supported by KULeuven SymBioSys, Stiching Tegen Kanker, FWO postdoctoral fellowship #12W7318N and Marie Sklodowska-Curie fellowship #12O5617N. The Department of Pathology acknowledges Fonds Yvonne Boel. The PDX platform is supported by Fonds Erasme. C.Bla. is supported by WELBIO, FNRS, Fondation Contre le Cancer, ULB Foundation, European Research Council, Worldwide Cancer Research and the Foundation Baillet Latour.

Reviewer information

Nature thanks B. E. Bernstein, E. Thompson and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors jointly supervised this work: Panagiota A. Sotiropoulou, Cédric Blanpain.

Affiliations

  1. Laboratory of Stem Cells and Cancer, Université Libre de Buxelles, Brussels, Belgium

    • Ievgenia Pastushenko
    • , Audrey Brisebarre
    • , Marco Fioramonti
    • , Tatiana Revenco
    • , Soufiane Boumahdi
    • , Alexandra Van Keymeulen
    • , Virginie Moers
    • , Sophie Lemaire
    • , Christine Dubois
    • , Florian De Cock
    • , Samuel Scozzaro
    • , Panagiota A. Sotiropoulou
    •  & Cédric Blanpain
  2. Department of Human Genetics, University of Leuven, Leuven, Belgium

    • Alejandro Sifrim
    • , Daniel Brown
    •  & Thierry Voet
  3. Sanger Institute-EBI Single-Cell Genomics Centre, Wellcome Trust Sanger Institute, Hinxton, UK

    • Alejandro Sifrim
    •  & Thierry Voet
  4. Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, Leuven, Belgium

    • Daniel Brown
    •  & Jean-Christophe Marine
  5. Pathology Department, Erasme University Hospital, Université Libre de Bruxelles, Brussels, Belgium

    • Sarah De Clercq
    • , Esmeralda Minguijón
    • , Nicky D’Haene
    •  & Isabelle Salmon
  6. DIAPath, The Center for Microscopy and Molecular Imaging, Université Libre de Bruxelles, Gosselies, Belgium

    • Cédric Balsat
  7. Thoracic Surgery, Erasme University Hospital, Université Libre de Bruxelles, Brussels, Belgium

    • Youri Sokolow
  8. Gastroenterology Department, Hospital Clínico Universitario “Lozano Blesa”, IIS Aragon, Zaragoza, Spain

    • Federico Sopena
  9. CIBERehd, IIS Aragón, University of Zaragoza, Zaragoza, Spain

    • Angel Lanas
  10. Laboratory for Molecular Cancer Biology, Department of Oncology, KU Leuven, Leuven, Belgium

    • Jean-Christophe Marine
  11. WELBIO, Université Libre de Bruxelles, Bruxelles, Belgium

    • Cédric Blanpain

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Contributions

I.P. and C.Bla. designed the experiments and performed data analysis. I.P. performed most of the biological experiments. P.A.S. performed part of the FACS analysis and histological characterization. A.B. performed the bioinformatic analysis. A.S., D.B. and T.V. performed scRNA-seq and analysis. M.F., A.V.K and J.-C.M. helped with breast cancer experiments. T.R. performed CTC experiments. S.B. helped with ATAC-seq. I.S., N.D’H., S.D.C., E.M., C.Bal., Y.S., A.L. and F.S. provided human samples. C.D. performed FACS sorting. V.M., S.L., F.D.C. and S.S. performed immunostaining, blocking antibody injections and follow-up of the mice. All authors read and approved the final manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Cédric Blanpain.

Extended data figures and tables

  1. Extended Data Fig. 1 Screening of cell surface markers homogeneously and heterogeneously expressed in Epcam+ and Epcam TCs in skin SCCs.

    a, b, FACS profile of the isotype control and homogeneously or heterogeneously expressed markers in Epcam+ (a) and Epcam (b) TCs using BD lyoplate panel. c, Screening of all markers found to be heterogeneously expressed in at least one tumour in the initial BD lyoplate screen on a much larger cohort of tumours. Epcam TCs were more heterogeneous than Epcam+ TCs and CD51, CD61 and CD106 were heterogeneously expressed in at least 75% of the tumours analysed and therefore were selected and used in combination for further experiments.

  2. Extended Data Fig. 2 Gating strategy for FACS analysis and cell sorting of the different tumour subpopulations.

    a, FACS plots showing the gating strategy used to FACS-isolate Epcam+ and Epcam cells from YFP+ tumours (Lgr5CreER/KrasG12D/p53KO and K8CreER/Pik3ca/p53KO). b, FACS plots showing the six different subpopulations of Epcam TCs: EpcamCD106CD51CD61 (triple-negative), EpcamCD106+CD51CD61, EpcamCD106CD51+CD61, EpcamCD106+CD51+CD61, EpcamCD106CD51+CD61+ and EpcamCD106+CD51+CD61+ (triple-positive) populations. ch, Post-sort purity plots for triple-negative (c), CD106+ (d), CD51+ (e), CD106+CD51+ (f), CD51+CD61+ (g) and triple-positive (h) subpopulations (n = 1). i, FACS plots showing the gating strategy used to FACS-isolate Epcam+ and Epcam cells from MMTV-PyMT mammary tumours.

  3. Extended Data Fig. 3 Epithelial, hybrid and mesenchymal marker expression by scRNA-seq.

    ae, Dimensionality reduction of scRNA-seq data using PCA colouring for pure epithelial genes Esrp1 and Ovol1 (a), hybrid epithelial genes Krt5 and Trp63 (b), hybrid mesenchymal genes Prrx1, Snai1, Zeb1, Zeb2, Pdgfra and Col3a1 (c, d) and pure mesenchymal genes Aspn and Mmp19 (e). Every dot represents a single cell and its shape represents the FACS sorting criteria (Epcam+ or Epcam). The colour scale represents the normalized expression of the respective genes. Pure epithelial genes are expressed only in Epcam+ TCs, hybrid epithelial genes in both Epcam+ and hybrid Epcam TCs, hybrid mesenchymal genes in hybrid Epcam TCs and highly mesenchymal Epcam TCs, and pure mesenchymal genes only in highly mesenchymal Epcam TCs. ae, n = 66 Epcam+ and n = 277 Epcam cells from one tumour. f, Immunostaining showing the co-expression of Epcam and E-cadherin in the majority of epithelial TCs. Rare Epcam + cells are E-cadherin (arrowheads). Scale bars, 50 μm.

  4. Extended Data Fig. 4 EMT transition states in Pik3caH1047R/p53KO metaplastic mammary tumours.

    a, Percentage of Epcam+ cells in the two groups of tumours that differ by the frequency of the different CD106, CD51, CD61 subpopulations in metaplastic-like breast tumours resulting from activation of the oncogenic mutation Pik3caH1047R and p53 deletion in luminal cells of the mammary gland (n = 11 biological replicates, mean ± s.e.m.). b, FACS profile showing the six different CD106, CD51, CD61 subpopulations in the most frequent group 1 mixed tumours containing Epcam+ and Epcam TCs. c, d, Distribution of the different tumour subpopulations in group 1 (c; mixed tumours with Epcam+ and Epcam TCs) and in group 2 (d; mesenchymal Epcam TCs) (n = 10 biological replicates, mean ± s.e.m.). e, Immunostaining of K14 and vimentin in cystospin of FACS-isolated YFP+Epcam+, YFP+Epcam triple-negative and YFP+Epcam triple-positive populations. Scale bars, 20 μm; n = 3 biological replicates. f, Proportions of cells expressing K14 and vimentin based on cytospin counts; n = 3 biological replicates. g, Number of lung metastases arising from the injection of 2,000 YFP+ TCs from different EMT subpopulations. Dots represent the number of metastases per condition and per mouse (n = 2). h, qRT–PCR showing the downregulation of epithelial markers and the upregulation of EMT markers in the different Epcam tumour subpopulations. Histogram represents mean ( + s.e.m.) fold-change in gene expression compared to Epcam+ tumour cells for mesenchymal genes and compared to triple-positive tumour cells for epithelial genes (n = 7 Epcam+, n = 3 triple-negative, n = 5 CD106+, n = 3 CD51+, n = 6 CD106+CD51+, n = 3 CD51+CD61+, n = 6 triple-positive, all are biological replicates). il, Immunostaining showing the expression of K14 and vimentin, YFP and K14, YFP and vimentin, or YFP and CD61 in area 1 (K14+Vim CD61; i), area 2 (K14+Vim+CD61; j), area 3 (K14+Vim+CD61+; k) and area 4 (K14Vim+CD61+; l). Scale bars, 50 μm. mp, Immunostaining showing the expression of YFP and CD45, CD68, CD31 or Lyve1 in area 1 (m), area 2 (n), area 3 (o) and area 4 (p), showing that immune infiltration and the density of vascular and lymphatic vessels are associated with late stages of EMT. Scale bars, 50 μm, n = 3 biological replicates. In hair follicle EMT-derived tumours, the chromatin and transcriptional landscape of the cancer cell of origin prime the tumour to undergo EMT during tumorigenesis; in Pik3ca/p53KO mammary metaplastic tumours this is not always the case. Metaplastic EMT tumours are more frequent in luminal-derived tumours than in basal/myoepithelial cells, which under physiological conditions present much more EMT-like features do than luminal cells. Source data

  5. Extended Data Fig. 5 EMT transition states in MMTV-PyMT luminal-like mammary tumours.

    a, Percentage of Epcam+ cells in two groups of tumours that differ by the frequency of the different CD106, CD51, CD61 subpopulations in MMTV-PyMT luminal-like mammary tumours (n = 5 mice, mean ± s.e.m.). b, Distribution of the different tumour subpopulations in group 2 (mixed tumours with Epcam+ and Epcam TCs) (n = 9 tumours, n = 5 mice, mean ± s.e.m.). c, FACS profile showing the six different CD106, CD51, CD61 subpopulations in the group containing mixed tumours with Epcam+ and Epcam TCs. d, Immunostaining of K8 and vimentin in cystospin of FACS-isolated YFP+Epcam+, YFP+Epcam triple-negative and YFP+Epcam triple-positive populations. Scale bars, 20 μm, n = 2 biological replicates. e, Proportion of cells expressing K8 and vimentin based on cytospin counts (n = 2 tumours). f, Number of lung metastases arising from the injection of 10,000 TCs from the different EMT subpopulations (Epcam+, n = 5; triple-negative, n = 5; CD106+, n = 5; triple-positive, n = 4). All are biological replicates, mean + s.e.m. Two-sided Mann–Whitney test: Epcam+ versus triple-negative, P = 0.0079; Epcam+ versus CD106+, P = 0.0079; triple-negative versus triple-positive, P = 0.0238; CD106+ versus triple-positive, P = 0.0476; mean + s.e.m. g, qRT–PCR showing the downregulation of epithelial markers and the upregulation of EMT markers in the different Epcam tumour subpopulations. Histogram represents mean ± s.e.m. fold-change in gene expression compared to Epcam+ tumour cells for mesenchymal genes and compared to triple-positive tumour cells for epithelial genes (Epcam+, n = 5; triple-negative, n = 3; CD106+, n = 5; triple-positive, n = 4; all are biological replicates). hj, Immunostaining of pancytokeratin (PanKRT) and vimentin, PyMT and pancytokeratin or PyMT and vimentin in area 1 (pancytokeratin+Vim; h), area 2 (pancytokeratin+Vim+; i) and area 3 (pancytokeratinVim+; j). Scale bars, 50 μm. km, Immunostaining of PyMT and CD68 or Lyve1 in area 1 (k), area 2 (l) and area 3 (m), showing that immune infiltration and the density of lymphatic vessels are associated with late-stage EMT. Scale bars, 50 μm, n = 2 biological replicates. Source data

  6. Extended Data Fig. 6 EMT transition states in human patient-derived xenotransplantation (PDX).

    ad, Immunostaining of pancytokeratin and vimentin, human-specific marker Ku80 and pancytokeratin or Ku80 and vimentin in poorly differentiated human breast (a), lung (b) or oesophagus (c, d) PDX. In these examples, three different areas can be identified: area 1 (Ku80+pancytokeratin+vimentin), area 2 (Ku80+pancytokeratin+vimentin+) and area 3 (Ku80+pancytokeratinvimentin+), showing that TCs with different degrees of EMT can be identified in a subset of poorly differentiated human cancers. Scale bars, 50 μm. e, PDX derived from aggressive and highly metastatic pleomorphic lung carcinoma, showing the hybrid EMT state as defined by co-expression of pancytokeratin and vimentin by the majority of TCs.

  7. Extended Data Fig. 7 Transcription factor binding motif enrichment in opened chromatin regions in the different EMT populations.

    a, Heat map representing unsupervised clustering of RNA-seq data from the different tumour subpopulations, showing that Epcam+ epithelial TCs, Epcam hybrid TCs and Epcam mesenchymal TCs fall into different clusters (n = 3 biological replicates). b, Representation of chromatin remodelling and associated transcription factors (TFs) during EMT based on the transcription factor motifs that were statistically enriched in ATAC-seq peaks that differed between tumour subpopulations as determined by Homer analysis. cf, Transcription factor motifs enriched in the ATAC-seq peaks that were upregulated between the indicated subpopulations as determined by Homer analysis using known or de novo motif search (asterisk) or using JASPAR motif matrix (hash symbol). gj, Transcription factor motifs enriched in the ATAC-seq peaks that were upregulated between the indicated tumour subpopulations as determined by Homer analysis using known or de novo motif search (asterisk). Green, core transcription factors; pink, epithelial transcription factors; yellow, mesenchymal transcription factors. cj, Analysis based on ATAC-seq results from different subpopulations derived from one tumour.

  8. Extended Data Fig. 8 Identification of EMT-related tumour heterogeneity by scRNA-seq.

    a, Heat map of normalized scRNA-seq expression of markers. Markers are obtained from the scRNA-seq data using SC3 with clustering parameter k = 5 and selecting genes with an area under the curve (AUC) > 0.8 and P < 0.01. Columns represent cells and rows represent genes. Colouring represents the normalized expression in the scRNA-seq data for each cell. b, Dimensionality reduction of scRNA-seq data using PCA colouring for clusters identified by SC3 clustering. Dots represent single cells; shape represents FACS sorting criteria. c, Dimensionality reduction of scRNA-seq data using PCA colouring for clusters identified by Monocle using only the top 500 genes with the highest covariability with CD51, CD61 and CD106 expression. Dots represent single cells; colour represents Monocle cluster; shape represents FACS sorting criteria. d, Pseudotime-ordering trajectory of scRNA-seq data using Monocle. The top 500 differentially expressed genes across clusters identified in a semi-supervised approach using Monocle were used to order cells in pseudotime using the DDRTree method. Dots represent single cells; colours represent assigned Monocle clusters. e, Heat map representing normalized scRNA-seq data after branched expression analysis modelling (BEAM) using Monocle for the top 100 differentially expressed genes across the branch-point. The pre-branch here is set to be the state corresponding to the Epcam+ FACS-sorted cells. Cell fates 1 and 2 represent the two branches of the pseudotime trajectory corresponding to more hybrid and mesenchymal EMT phases, respectively. Heat map colours represent gene-wise normalized expression across pseudotime. Genes are hierarchically clustered according to their expression pattern. ae, n = 66 Epcam+ and n = 277 Epcam cells from one tumour.

  9. Extended Data Fig. 9 Effect of constitutive overexpression of p63 and inhibition of TGF-β signalling in vivo on the different EMT subpopulations.

    a, Immunostaining of K14 and vimentin, YFP and K14 or YFP and vimentin in hair follicle-derived skin SCCs with constitutive overexpression of p63, showing that EMT is blocked at an early stage and only area 1 epithelial TCs (K14+vimentin) and area 2 early hybrid TCs (K14+vimentin+) can be detected. Scale bars, 50 μm, n = 5 biological replicates. b, Experimental strategy to inhibit TGF-β signalling during hair follicle-derived SCC tumorigenesis. c, Immunostaining of K14 and vimentin or YFP K14, vimentin, CD61, CD45, CD68, CD31 or Lyve1 in area 1 (K14+vimentin), area 2 (K14+vimentin+), area 3 (K14+vimentin+CD61+) and area 4 (K14vimentin+CD61+) in mice treated with control antibodies. d, Immunostaining showing the expression of K14 and vimentin, or YFP and K14, vimentin, CD61, CD45, CD68, CD31 or Lyve1 in area 1 and area 2 of mice treated with anti-TGFβ antibodies, showing that inhibition of TGF-β or Smad2 signalling blocks EMT progression at the early stages of EMT and no areas corresponding to late EMT states (area 3 and 4) could be detected. c, d, Scale bars, 50 μm, n = 3 biological replicates.

  10. Extended Data Fig. 10 Macrophage depletion inhibits EMT progression.

    a, Experimental strategy to deplete macrophages by administrating anti-Csfr1 and anti-Ccl2 antibodies. b, Immunostaining of K14 and vimentin or YFP and K14, vimentin, YFP, CD45, CD68, CD31 or Lyve1 in area 1 (K14+vimentin), area 2 (K14+vimentin+), area 3 (K14+vimentin+CD61+) and area 4 (K14vimentin+CD61+) in mice treated with control antibodies. Scale bars, 50 μm. c, Immunostaining of K14 and vimentin or YFP and K14, vimentin, CD61, CD45, CD68, CD31 or Lyve1 in area 1 and area 2 of mice treated with anti-Csf1r and anti-Ccl2 antibodies, showing that EMT is blocked at its early stages and no areas corresponding to late EMT stages (areas 3 and 4) could be detected. Scale bars, 50 μm, n = 3 biological replicates.

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