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:

MLL3 loss drives metastasis by promoting a hybrid epithelial–mesenchymal transition state

Nature Cell Biology volume 25, pages 145–158 (2023)Cite this article

Subjects

Abstract

Phenotypic plasticity associated with the hybrid epithelial–mesenchymal transition (EMT) is crucial to metastatic seeding and outgrowth. However, the mechanisms governing the hybrid EMT state remain poorly defined. Here we showed that deletion of the epigenetic regulator MLL3, a tumour suppressor frequently altered in human cancer, promoted the acquisition of hybrid EMT in breast cancer cells. Distinct from other EMT regulators that mediate only unidirectional changes, MLL3 loss enhanced responses to stimuli inducing EMT and mesenchymal–epithelial transition in epithelial and mesenchymal cells, respectively. Consequently, MLL3 loss greatly increased metastasis by enhancing metastatic colonization. Mechanistically, MLL3 loss led to increased IFNγ signalling, which contributed to the induction of hybrid EMT cells and enhanced metastatic capacity. Furthermore, BET inhibition effectively suppressed the growth of MLL3-mutant primary tumours and metastases. These results uncovered MLL3 mutation as a key driver of hybrid EMT and metastasis in breast cancer that could be targeted therapeutically.

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: MLL3 deletion enhances breast tumour metastasis.
Fig. 2: MLL3 deletion promotes metastatic colonization.
Fig. 3: MLL3 loss facilitates the acquisition of a hybrid EMT state.
Fig. 4: Loss of MLL3 potentiates the multi-organ metastasis-initiating ability of hybrid E/M cells.
Fig. 5: MLL3 loss enables epithelial cells to gain mesenchymal features.
Fig. 6: MLL3 loss enhances tumour progression and metastasis through activation of the IFNγ pathway.
Fig. 7: MLL3 loss increased H3K27ac levels in IFNγ response gene enhancers.
Fig. 8: MLL3-mutant cells are sensitive to BET inhibition.

Similar content being viewed by others

Data availability

RNA-seq, ChIP–seq, ATAC-seq and microarray data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under accession code GSE171447. The human reference genome (GRCh38) was used as a reference genome for the alignment. The human breast cancer data were derived from the TCGA Research Network: http://cancergenome.nih.gov/. Source data are provided with this paper. The dataset derived from this resource that supports the findings of this study is available in the details of source data. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

The code for in-house scripts used in this study was deposited into the following link: https://github.com/zcmit/EinsteinMed/tree/main/Cui_etal.2022.

References

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

    Article  CAS  Google Scholar 

  2. Steeg, P. S. Targeting metastasis. Nat. Rev. Cancer 16, 201–218 (2016).

    Article  CAS  Google Scholar 

  3. Esposito, M., Ganesan, S. & Kang, Y. Emerging strategies for treating metastasis. Nat. Cancer 2, 258–270 (2021).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  6. Tsai, J. H. & Yang, J. Epithelial–mesenchymal plasticity in carcinoma metastasis. Genes Dev. 27, 2192–2206 (2013).

    Article  CAS  Google Scholar 

  7. Brabletz, T. To differentiate or not—routes towards metastasis. Nat. Rev. Cancer 12, 425–436 (2012).

    Article  CAS  Google Scholar 

  8. Tsai, J. H., Donaher, J. L., Murphy, D. A., Chau, S. & Yang, J. Spatiotemporal regulation of epithelial–mesenchymal transition is essential for squamous cell carcinoma metastasis. Cancer Cell 22, 725–736 (2012).

    Article  CAS  Google Scholar 

  9. Ocaña, O. H. et al. Metastatic colonization requires the repression of the epithelial–mesenchymal transition inducer Prrx1. Cancer Cell 22, 709–724 (2012).

    Article  Google Scholar 

  10. Williams, E. D., Gao, D., Redfern, A. & Thompson, E. W. Controversies around epithelial–mesenchymal plasticity in cancer metastasis. Nat. Rev. Cancer 19, 716–732 (2019).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  12. Lu, W. & Kang, Y. Epithelial–mesenchymal plasticity in cancer progression and metastasis. Dev. Cell 49, 361–374 (2019).

    Article  CAS  Google Scholar 

  13. Pastushenko, I. & Blanpain, C. EMT transition states during tumor progression and metastasis. Trends Cell Biol. 29, 212–226 (2019).

    Article  CAS  Google Scholar 

  14. Tripathi, S., Levine, H. & Jolly, M. K. The physics of cellular decision making during epithelial–mesenchymal transition. Annu. Rev. Biophys. 49, 1–18 (2020).

    Article  CAS  Google Scholar 

  15. Kroger, C. et al. Acquisition of a hybrid E/M state is essential for tumorigenicity of basal breast cancer cells. Proc. Natl Acad. Sci. USA 116, 7353–7362 (2019).

    Article  CAS  Google Scholar 

  16. Bierie, B. et al. Integrin-β4 identifies cancer stem cell-enriched populations of partially mesenchymal carcinoma cells. Proc. Natl Acad. Sci. USA 114, E2337–e2346 (2017).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  18. Aiello, N. M. et al. EMT subtype influences epithelial plasticity and mode of cell migration. Dev. Cell 45, 681–695.e684 (2018).

    Article  CAS  Google Scholar 

  19. Pastushenko, I. et al. Fat1 deletion promotes hybrid EMT state, tumour stemness and metastasis. Nature 589, 448–455 (2021).

    Article  CAS  Google Scholar 

  20. Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).

    Article  CAS  Google Scholar 

  21. Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).

    Article  CAS  Google Scholar 

  22. TCGA Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

  23. Ciriello, G. et al. Comprehensive molecular portraits of invasive lobular breast cancer. Cell 163, 506–519 (2015).

    Article  CAS  Google Scholar 

  24. Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and Mutations. Cell 174, 1034–1035 (2018).

    Article  CAS  Google Scholar 

  25. Razavi, P. et al. The genomic landscape of endocrine-resistant advanced breast cancers. Cancer Cell 34, 427–438.e426 (2018).

    Article  CAS  Google Scholar 

  26. Wang, L. et al. Resetting the epigenetic balance of Polycomb and COMPASS function at enhancers for cancer therapy. Nat. Med. 24, 758–769 (2018).

    Article  CAS  Google Scholar 

  27. Chen, C. et al. MLL3 is a haploinsufficient 7q tumor suppressor in acute myeloid leukemia. Cancer Cell 25, 652–665 (2014).

    Article  Google Scholar 

  28. Lee, J. et al. A tumor suppressive coactivator complex of p53 containing ASC-2 and histone H3-lysine-4 methyltransferase MLL3 or its paralogue MLL4. Proc. Natl Acad. Sci. USA 106, 8513–8518 (2009).

    Article  CAS  Google Scholar 

  29. Zhang, Z. et al. Mammary-stem-cell-based somatic mouse models reveal breast cancer drivers causing cell fate dysregulation. Cell Rep. 16, 3146–3156 (2016).

    Article  CAS  Google Scholar 

  30. Biankin, A. V. et al. Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature 491, 399–405 (2012).

    Article  CAS  Google Scholar 

  31. Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).

    Article  CAS  Google Scholar 

  32. Waldron, L. et al. Expression profiling of archival tumors for long-term health studies. Clin. Cancer Res. 18, 6136–6146 (2012).

    Article  CAS  Google Scholar 

  33. Esposito, M. et al. Bone vascular niche E-selectin induces mesenchymal–epithelial transition and Wnt activation in cancer cells to promote bone metastasis. Nat. Cell Biol. 21, 627–639 (2019).

    Article  CAS  Google Scholar 

  34. Pattabiraman, D. R. et al. Activation of PKA leads to mesenchymal-to-epithelial transition and loss of tumor-initiating ability. Science 351, aad3680 (2016).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  36. Singh, S. et al. Loss of ELF5-FBXW7 stabilizes IFNGR1 to promote the growth and metastasis of triple-negative breast cancer through interferon-γ signalling. Nat. Cell Biol. 22, 591–602 (2020).

    Article  CAS  Google Scholar 

  37. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  Google Scholar 

  38. Gala, K. et al. KMT2C mediates the estrogen dependence of breast cancer through regulation of ERα enhancer function. Oncogene 37, 4692–4710 (2018).

    Article  CAS  Google Scholar 

  39. Jozwik, K. M., Chernukhin, I., Serandour, A. A., Nagarajan, S. & Carroll, J. S. FOXA1 directs H3K4 monomethylation at enhancers via recruitment of the methyltransferase MLL3. Cell Rep. 17, 2715–2723 (2016).

    Article  CAS  Google Scholar 

  40. Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).

    Article  CAS  Google Scholar 

  41. Loven, J. et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell 153, 320–334 (2013).

    Article  CAS  Google Scholar 

  42. Platanias, L. C. Mechanisms of type-I- and type-II-interferon-mediated signalling. Nat. Rev. Immunol. 5, 375–386 (2005).

    Article  CAS  Google Scholar 

  43. Cheon, H. & Stark, G. R. Unphosphorylated STAT1 prolongs the expression of interferon-induced immune regulatory genes. Proc. Natl Acad. Sci. USA 106, 9373–9378 (2009).

    Article  CAS  Google Scholar 

  44. Shu, S. et al. Response and resistance to BET bromodomain inhibitors in triple-negative breast cancer. Nature 529, 413–417 (2016).

    Article  CAS  Google Scholar 

  45. Ge, J. Y. et al. Acquired resistance to combined BET and CDK4/6 inhibition in triple-negative breast cancer. Nat. Commun. 11, 2350 (2020).

    Article  CAS  Google Scholar 

  46. Sahni, J. M. et al. Bromodomain and extraterminal protein inhibition blocks growth of triple-negative breast cancers through the suppression of aurora kinases. J. Biol. Chem. 291, 23756–23768 (2016).

    Article  CAS  Google Scholar 

  47. Schafer, J. M. et al. Targeting MYCN-expressing triple-negative breast cancer with BET and MEK inhibitors. Sci. Transl. Med. 12, eaaw8275 (2020).

  48. Shu, S. et al. Synthetic lethal and resistance interactions with BET bromodomain inhibitors in triple-negative breast cancer. Mol. Cell 78, 1096–1113.e1098 (2020).

    Article  CAS  Google Scholar 

  49. Cho, S. J. et al. KMT2C mutations in diffuse-type gastric adenocarcinoma promote epithelial-to-mesenchymal transition. Clin. Cancer Res. 24, 6556–6569 (2018).

    Article  CAS  Google Scholar 

  50. Na, F. et al. KMT2C deficiency promotes small cell lung cancer metastasis through DNMT3A-mediated epigenetic reprogramming. Nat. Cancer 3, 753–767 (2022).

    Article  CAS  Google Scholar 

  51. Zhang, Y. 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).

    Article  CAS  Google Scholar 

  52. Zitvogel, L., Galluzzi, L., Kepp, O., Smyth, M. J. & Kroemer, G. Type I interferons in anticancer immunity. Nat. Rev. Immunol. 15, 405–414 (2015).

    Article  CAS  Google Scholar 

  53. Minn, A. J. & Wherry, E. J. Combination cancer therapies with immune checkpoint blockade: convergence on interferon signaling. Cell 165, 272–275 (2016).

    Article  CAS  Google Scholar 

  54. Celià-Terrassa, T. et al. Normal and cancerous mammary stem cells evade interferon-induced constraint through the miR-199a–LCOR axis. Nat. Cell Biol. 19, 711–723 (2017).

    Article  Google Scholar 

  55. 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 

  56. Dorighi, K. M. et al. Mll3 and Mll4 facilitate enhancer RNA synthesis and transcription from promoters independently of H3K4 monomethylation. Mol. Cell 66, 568–576.e4 (2017).

  57. Ognjenovic, N. B. et al. Limiting self-renewal of the basal compartment by PKA activation induces differentiation and alters the evolution of mammary tumors. Dev. Cell 55, 544–557.e6 (2020).

  58. Sellappan, S. et al. Lineage infidelity of MDA-MB-435 cells: expression of melanocyte proteins in a breast cancer cell line. Cancer Res. 64, 3479–3485 (2004).

    Article  CAS  Google Scholar 

  59. Dontu, G. et al. In vitro propagation and transcriptional profiling of human mammary stem/progenitor cells. Genes Dev. 17, 1253–1270 (2003).

    Article  CAS  Google Scholar 

  60. Cui, J. et al. New use of an old drug: inhibition of breast cancer stem cells by benztropine mesylate. Oncotarget 8, 1007–1022 (2017).

    Article  Google Scholar 

  61. Levin-Allerhand, J. A., Sokol, K. & Smith, J. D. Safe and effective method for chronic 17β-estradiol administration to mice. Contemp. Top. Lab. Anim. Sci. 42, 33–35 (2003).

    CAS  Google Scholar 

  62. Dixon, G. et al. QSER1 protects DNA methylation valleys from de novo methylation. Science 372, eabd0875 (2021).

    Article  CAS  Google Scholar 

  63. Yang, D. et al. CRISPR screening uncovers a central requirement for HHEX in pancreatic lineage commitment and plasticity restriction. Nat. Cell Biol. 24, 1064–1076 (2022).

    Article  CAS  Google Scholar 

  64. Lee, J. E. et al. H3K4 mono- and di-methyltransferase MLL4 is required for enhancer activation during cell differentiation. eLife 2, e01503 (2013).

    Article  Google Scholar 

  65. Hong, S. et al. Identification of JmjC domain-containing UTX and JMJD3 as histone H3 lysine 27 demethylases. Proc. Natl Acad. Sci. USA 104, 18439–18444 (2007).

    Article  CAS  Google Scholar 

  66. Wang, C. et al. Enhancer priming by H3K4 methyltransferase MLL4 controls cell fate transition. Proc. Natl Acad. Sci. USA 113, 11871–11876 (2016).

    Article  CAS  Google Scholar 

  67. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  Google Scholar 

  68. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  69. Ramirez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    Article  CAS  Google Scholar 

  70. Neph, S. et al. BEDOPS: high-performance genomic feature operations. Bioinformatics 28, 1919–1920 (2012).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgements

We thank the Flow Cytometry, Histopathology, Analytical Imaging, and Stem Cell Isolation core facilities of Albert Einstein College of Medicine for technical assistance, supported by Einstein Cancer Center Support Grant (P30 CA013330), the New York State Department of Health/NYSTEM Program (C029154) and P250 high-capacity slide scanner (1S10OD019961-01). We thank G. Xie for assistance with MLL3 western blot. This work is supported by the DOD BCRP grant W81XWH-16-1-0311, the NIH/NCI grant 1R01CA212424 and the Mary Kay Foundation grant 04-19 (to W.G). W.G. is a V Scholar of the V Foundation for Cancer Research. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Ruth L. and David S. Gottesman Institute for Stem Cell and Regenerative Medicine Research, Albert Einstein College of Medicine, Bronx, NY, USA

    Jihong Cui, Yu Liu, Piril Erler, Dayle Q. Hodge & Wenjun Guo

  2. Department of Cell Biology, Albert Einstein College of Medicine, Bronx, NY, USA

    Jihong Cui, Chi Zhang, Boris A. Bartholdy, Yu Liu, Piril Erler, Dayle Q. Hodge & Wenjun Guo

  3. National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA

    Ji-Eun Lee & Kai Ge

  4. Developmental Biology Program, Sloan Kettering Institute, New York, NY, USA

    Dapeng Yang & Danwei Huangfu

  5. Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA

    Phillip M. Galbo Jr & Deyou Zheng

  6. Departments of Neurology and Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA

    Deyou Zheng

  7. Albert Einstein Cancer Center, Albert Einstein College of Medicine, Bronx, NY, USA

    Wenjun Guo

Authors
  1. Jihong Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ji-Eun Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Boris A. Bartholdy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dapeng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Piril Erler
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Phillip M. Galbo Jr
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Dayle Q. Hodge
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Danwei Huangfu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Deyou Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Kai Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Wenjun Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.C. and W.G. conceived the study; J.C. performed the experiments, acquired and analysed data; C.Z. and D.Z. carried out the bioinformatics analyses of ChIP–seq data. B.A.B. analysed ATAC-seq data. Y.L. analysed the transcriptomics data; J.-E.L. and K.G. generated MLL3 reagents and performed ChIP–seq experiments; D.Y. and D.H. performed the histone mark ChIP–seq experiments. P.E. generated MaSC models; P.M.G. and D.Z. performed patient data analysis; D.Q.H. generated CRISPR reagents; all authors contributed to data interpretation; J.C. and W.G. wrote the manuscript with inputs from all other authors.

Corresponding author

Correspondence to Wenjun Guo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Cell Biology thanks Yibin Kang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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 MLL3 is one of the most frequently altered genes in breast cancer and other cancer types.

a, Top 20 mutated genes in various cancer types as described in COSMIC v91. The mutation frequencies of individual genes are shown. b, Frequencies of different MLL3 alterations in various breast cancer subtypes. Data were obtained from the TCGA dataset via cBioPortal. c, MLL3 expression levels (Log2 of normalized RNA-seq values) in normal breast tissues and various breast cancer subtypes. Data were obtained from the TCGA BRCA dataset via UCSC Cancer Browser. Values from minimum to maximum are shown by the box and whiskers. Box plots indicate median (middle line), 25th and 75th percentile (box) and 5th and 95th percentile (whiskers). d, Correlation analysis of invasion ability and MLL3 expression levels in single breast CTCs (GSE75367, analyzed using CancerSEA). P values were determined by two-tailed Student’s t-test (c). P values for d were calculated by CancerSEA. Source data are provided.

Source data

Extended Data Fig. 2 MLL3 deletion enhances breast tumor metastasis.

a, MLL3 western blot of indicated MDA-MB-231 cells (n = 3 independent experiments). b, MDA-MB-231 orthotopic tumor growth. 1 × 106 sgNT (n = 14) and sgMLL3 (#3, n = 6; #9, n = 8 xenografts) cells were injected into each NOD/SCID mouse. c, Representative H&E and human CD44 staining of lung sections from mice bearing indicated MDA-MB-231 orthotopic tumors at week 13 after injection (sgNT, n = 6, sgMLL3#3, n = 4; sgMLL3#9, n = 4 mice). d, Number of metastatic nodules in the lung as generated in b and c. e, Representative MLL3 immunofluorescence of MDA-MB-231 primary tumor and spontaneous lung metastases. Human-specific CD44 staining identifies tumor cells. f, MLL3 expression level in MDA-MB-231 primary tumors and spontaneous lung metastases, as shown in e (primary tumor, n = 3 tumors; lung metastases, n = 12 lung nodules). g, MLL3 western blot (n = 3 independent experiments). h, MDA-MB-435s primary tumor growth rate. 1 × 106 cells were injected into each NOD/SCID mouse. i, Representative H&E and human mitochondria staining of lung sections from mice bearing indicated MDA-MB-435s orthotopic tumors at week 13 after injection. Arrows point to examples of metastases. j, Number of metastatic nodules in the lung as generated in h and i (sgNT, n = 7; sgMLL3 (#3), n = 4; sgMLL3 (#9), n = 3 mice). k, MLL3 western blot (n = 2 independent experiments). l, SUM159 primary tumor growth rate (sgNT, n = 5; sgMLL3 (#3), n = 6; sgMLL3 (#9), n = 5 tumors). m, STR report of MDA-MB-435s cell authentication. All data are represented as mean ± SEM. P values were determined by two-way RM ANOVA with correction using Geisser-Greenhouse method (b), one-way ANOVA with Dunnett’s multiple comparisons test (d, j) or two-tailed Student’s t-test (f). Source data are provided.

Source data

Extended Data Fig. 3 Loss of MLL3 promotes metastatic colonization.

a, The average number of tumor cells in each lung metastatic nodule from the mice injected with sgNT or sgMLL3 MDA-MB-231 cells at 8 or 14 days post tail vein injection (8 days: sgNT, n = 62; sgMLL3, n = 93; 14 days: sgNT, n = 61; sgMLL3, n = 60 nodules from 3 mice per group). b, H&E staining of lung sections from mice 4 weeks after tail vein injection with sgNT or sgMLL3 MDA-MB-231 cells. c, Representative Ki67 immunofluorescence staining of lung metastases in mice at day 8 or day 14 after tail-vein injection with sgNT or sgMLL3 MDA-MB-231 cells. The graph shows percentages of Ki67+ tumor cells in lung metastases from each mouse (n = 3 mice). d, Representative images and quantification of the percentage of cleaved caspase 3-positive cells in size-matched lung metastatic nodules formed by sgNT or sgMLL3 MDA-MB-231 cells 6 weeks after tail-vein injection (n = 15 tumor regions of 3 mice per group). e, Quantification of ex vivo bioluminescence signals of lung, bone, kidney and liver metastasis burdens in animals injected with sgNT (n = 9) or sgMLL3 (n = 7 mice) SUM159 cell 4 weeks after tail vein injection. f, Lung colonization of sgNT and sgMll3 p53null/Pik3caH1047R cells injected by tail vein (Set 2). Representative H&E staining and number of metastatic nodules in the left lung lobe of each animal (n = 3 per group) were shown. 500,000 luciferase-labeled cells were injected into FVB mice via the tail vein and analyzed at week 5. All data are represented as mean ± SEM. P values were determined by two-way ANOVA with Šídák’s multiple comparisons test (a, c), or two-tailed Student’s t-test (d-f). Source data are provided as a source data file.

Source data

Extended Data Fig. 4 Loss of MLL3 greatly potentiates the metastasis-initiating ability of hybrid E/M cells.

a, Bioluminescence signals of kidney metastatic burdens in animals injected with the indicated cell types via the tail vein (sgNT-CD44+CD104low, n = 3, sgNT-CD44+CD104high, n = 3, sgMLL3-CD44+CD104low, n = 5, sgMLL3-CD44+CD104high, n = 8 mice). b, Anti-human mitochondria staining showing metastatic lesions in the kidney. c, Bioluminescence signals of liver metastasis burdens in animals injected with the indicated cell types via the tail vein. d, Anti-human mitochondria staining showing metastatic lesions in the liver. e, Tumor growth curve of CD104high/CD44+ and CD104low/CD44+ WT(sgNT) or MLL3-mutant (sgMLL3) MDA-MB-231 cells in NSG mice. 2 × 105 cells were injected orthotopically into mammary gland fat pads of female NSG mice (n = 6 for each group). All data are represented as mean ± SEM. P values were determined by two-tailed Student’s t-test (a, c), or two-way ANOVA with correction using Geisser-Greenhouse method (e). Source data are provided.

Source data

Extended Data Fig. 5 MLL3 loss enables epithelial cells to gain mesenchymal features.

a, Sphere-forming efficiency of CD24– and CD24+ cells sorted from sgNT or sgMLL3 MCF7 spheres (n = 3 biological samples for each group). b, Flow cytometric analysis of CD104 expression in sgNT or sgMLL3 MCF7 spheres. c, Phase-contrast images and E-cadherin and vimentin immunofluorescence staining in sgNT and sgMLL3 T47D cells (n = 3 independent experiments). d, MLL3 western blot of sgNT and sgMLL3 HMLE cells. MLL3 is a very large protein (> 500 kDa), thus prone to degradation during western blot (n = 2 independent experiments). All data are represented as mean ± SEM. P values were determined by one-way ANOVA with Tukey’s multiple comparisons test (a). Source data are provided.

Source data

Extended Data Fig. 6 MLL3 loss upregulates the interferon-γ pathway.

a, Expression of ISGs in the WT and MLL3-mutant MCF7 cells in adherent culture, as measured by qRT-PCR (n = 3 biological samples). b, Expression of ISGs in the WT and MLL3-mutant MCF7 cells in sphere culture, as measured by qRT-PCR (n = 3 biological samples). c, Expression of ISGs in the CD44+CD104high and CD44+CD104low MLL3-WT MDA-MB-231 cells, as measured by qRT-PCR (n = 3 biological samples with 2 technique replicates). d, Effect of inflammatory cytokines on the frequency of CD24– MCF7 cells. Cells were treated with PBS, IFNγ (10 ng/ml), TNFα (10 ng/ml) for 11 days, or IFNα (2,000 unit/ml) for 17 days in adherent culture (n = 3 biological samples). e, Kinetics of CD24– cell induction by IFNγ in sgNT or sgMLL3 MCF7 cells (n = 3 biological samples in each group). f, IFNGR1 flow cytometry measuring the CRISPR deletion efficiency (n = 3 independent experiments). g, Representative images of H&E staining, Keratin 8(KRT8)/Vimentin(Vim), smooth-muscle actin (SMA) immunofluorescence, and SNAIL immunohistochemistry of sgNT, sgMLL3, and sgMLL3/sgIFNGR1 MCF7 tumors. h, Quantification of SMA expression in sgNT, sgMLL3 and sgMLL3/sgIFNGR1 MCF7 tumors (sgNT, n = 7; sgMLL3, n = 6; and sgMLL3/sgIFNGR1, n = 7 tumors). i, Quantification of SNAIL+ cells in sgNT, sgMLL3 and sgMLL3/sgIFNGR1 MCF7 tumors (sgNT, n = 7; sgMLL3, n = 5; and sgMLL3/sgIFNGR1, n = 7 tumors). All data are represented as mean ± SEM. P values were determined by two-tailed Student’s t-test (a-c, f), one-way ANOVA with Dunnett’s or Turkey’s multiple comparisons test (d, h-i), or Ordinary two-way ANOVA (e). Source data are provided.

Source data

Extended Data Fig. 7 IFNGR1 deletion abolished the enhancement of breast cancer metastasis by MLL3 loss.

a, The induction of CD44+CD104high cells by forskolin in indicated MDA-MB-231 cells (n = 3 biological samples in each group). b, Representative images of CD104 and CD44 staining in lung metastases formed by indicated MDA-MB-231 cells at day 14 post tail-vein injection. c, Quantification of CD104 and CD44 co-staining of cells in lung metastases generated as in b (n = 3 mice in each group). d, Representative images of GFP, Keratin 8 (KRT8), and vimentin (Vim) co-staining in lung metastases generated as in b. e, Growth rates and representative bioluminescence images of NOD/SCID mice at 10 weeks after orthotopic injection of sgNT (n = 4), sgMLL3 (n = 5), or sgMLL3/sgIFNGR1 (n = 5 mice) MDA-MB-231 cells. f, Quantification of spontaneous bone metastasis in sgNT (n = 4), sgMLL3 (n = 4), and sgMLL3/sgIFNGR1 (n = 5) MDA-MB-231 tumor-bearing mice as generated in e at week 13. Metastatic index = bone photon flux/ primary tumor photon flux. g, Western blot of EMT TFs in sgNT MCF7 cells treated with indicated levels of IFNγ for 24 h (Left) or in sgNT and sgMLL3 MDA-MB-231 cells treated with 1 ng/ml IFNγ for 4 days (right) (n = 2 independent experiments). h, Western blot of EMT TFs in indicated cells (n = 2 independent experiments). i, Correlation analysis of IFNγ response genes and EMT TFs, epithelial signature genes, or mesenchymal signature genes in the BRCA TCGA patient cohort by using GEPIA2 (n = 1083 patient samples). Gene lists were provided in Supplementary Table 4. All data are represented as mean ± SEM. P values were determined by two-way ANOVA with Šídák’s multiple comparisons test (a) or with correction using Geisser-Greenhouse method (e), one-way ANOVA with Turkey’s multiple comparisons test (c, f), or Pearson’s correlation coefficient analysis (i). Source data are provided.

Source data

Extended Data Fig. 8 The effect of MLL3 loss on genome-wide changes of H3K27ac, H3K4me1 and H3K4me3 in MCF7 and MDA-MB-231 cells.

a, Heatmaps of ChIP-seq signals for the indicated histone marks and ATAC-seq signals in a 6 kb window grouped by localization at the promoter, intron, and intergenic regions in sgNT or sgMLL3 MDA-MB-231 cells. b, Western blot analysis of protein levels of H3K27ac, H3K4me1, and H3K4me3 in sgNT or sgMLL3 MCF7 and MDA-MB-231 cells (n = 2 independent experiments).

Source data

Extended Data Fig. 9 MLL3 loss increases H3K27ac levels in the enhancers of a subset of IFNγ pathway genes.

a, H3K4me1, UTX and MLL3/4 binding signals at enhancer regions of IFNγ response genes in sgNT and sgMLL3 MCF7 cells based on ChIP-seq. b, H3k4me1, UTX and MLL3/4 binding signals at enhancer regions of IFNγ response genes in sgNT and sgMLL3 MDA-MB-231 cells based on ChIP-seq. c, Hockey-stick plots generated by ROSE showing ranked enhancers based on H3K27ac ChIP-seq signal intensity in sgNT and sgMLL3 MDA-MB-231 cells (SE: super enhancer). d, H3K27ac and H3K4me1 binding signals at IRF1 and STAT1 gene loci in sgNT and sgMLL3 MDA-MB-231 cells. e, ChIP-qPCR analysis of H3K27ac occupancy at the IRF1 enhancer regions in sgNT and sgMLL3 MDA-MB-231 cells treated with PBS or 100 pg/ml IFNγ for 4 h. ChIP samples were analyzed by qPCR and normalized to input. The regions detected by PCR were labeled in D (P1 and P2) (n = 3 technique replicates each experiment, similar results were repeated 3 times). f, The average signal intensity of H3K27ac at enhancer regions identified by ROSE in sgNT and sgMLL3 MCF7 and MDA-MB-231 cells. g, UTX and MLL3/4 binding signals at IRF1 and STAT1 gene loci in sgNT and sgMLL3 MCF7 and MDA-MB-231 cells, respectively. All data are represented as mean ± SEM. P values were determined by one-way ANOVA with Turkey’s multiple comparisons test (e). Source data are provided.

Source data

Extended Data Fig. 10 MLL3 loss leads to increased phosphorylation and expression of STAT1 upon IFNγ stimulation.

a, Western blot analysis of pSTAT1 and STAT1 in sgNT and sgMLL3 MCF7 cells treated with 10 ng/ml IFNγ for 0.5, 1, 2, 4, or 8 h. Two experimental repeats were quantified. b, Western blot analysis of pSTAT1 and STAT1 in sgNT and sgMLL3 MCF7 spheres treated with 10 ng/ml IFNγ for 5 days (n = 3 biological independent experiments). c, Western blot analysis of pSTAT1 and STAT1 in sgNT and sgMLL3 MDA-MB-231 cells treated with 100 pg/ml IFNγ for 24 h (n = 2 independent experiments). d, Western blot analysis of pSTAT1 and STAT1 in sgNT and sgMLL3 MDA-MB-231 cells treated with 10 ng/ml IFNγ for 1, 2, or 4hrs (n = 2 biological independent experiments). All data are represented as mean ± SEM. P values were determined by two-way ANOVA with Šídák’s multiple comparisons test (a). Source data are provided.

Source data

Supplementary information

Supplementary Information

Supplementary figures for FACS gating strategies.

Reporting Summary

Supplementary Table

Supplementary Tables 1–4.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 5

Unprocessed western blots.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 7

Statistical source data.

Source Data Fig. 7

Unprocessed western blots.

Source Data Fig. 8

Statistical source data.

Extended Data Fig. 1

Statistical source data.

Extended Data Fig. 2

Statistical source data.

Extended Data Fig. 2

Unprocessed western blots.

Extended Data Fig. 3

Statistical source data.

Extended Data Fig. 4

Statistical source data.

Extended Data Fig. 5

Statistical source data.

Extended Data Fig. 5

Unprocessed western blots.

Extended Data Fig. 6

Statistical source data.

Extended Data Fig. 7

Statistical source data.

Extended Data Fig. 7

Unprocessed western blots.

Extended Data Fig. 8

Unprocessed western blots.

Extended Data Fig. 9

Statistical source data.

Extended Data Fig. 10

Statistical source data.

Extended Data Fig. 10

Unprocessed western blots.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cui, J., Zhang, C., Lee, JE. et al. MLL3 loss drives metastasis by promoting a hybrid epithelial–mesenchymal transition state. Nat Cell Biol 25, 145–158 (2023). https://doi.org/10.1038/s41556-022-01045-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41556-022-01045-0

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