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.

MSK1 regulates luminal cell differentiation and metastatic dormancy in ER+ breast cancer

Nature Cell Biology volume 20, pages 211–221 (2018)Cite this article

Subjects

A Publisher Correction to this article was published on 19 April 2018

This article has been updated

Abstract

For many patients with breast cancer, symptomatic bone metastases appear after years of latency. How micrometastatic lesions remain dormant and undetectable before initiating colonization is unclear. Here, we describe a mechanism involved in bone metastatic latency of oestrogen receptor-positive (ER+) breast cancer. Using an in vivo genome-wide short hairpin RNA screening, we identified the kinase MSK1 as an important regulator of metastatic dormancy in breast cancer. In patients with ER+ breast cancer, low MSK1 expression associates with early metastasis. We show that MSK1 downregulation impairs the differentiation of breast cancer cells, increasing their bone homing and growth capacities. MSK1 controls the expression of genes required for luminal cell differentiation, including the GATA3 and FOXA1 transcription factors, by modulating their promoter chromatin status. Our results indicate that MSK1 prevents metastatic progression of ER+ breast cancer, suggesting that stratifying patients with breast cancer as high or low risk for early relapse based on MSK1 expression could improve prognosis.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Fig. 1: Tumour mass latent ER+ breast cancer bone metastasis model.
Fig. 2: Genome-wide shRNA screen identifies MSK1 as a dormancy regulator in latent ER+ breast cancer bone metastasis.
Fig. 3: MSK1 loss is associated with early relapse in patients with ER+ breast cancer.
Fig. 4: MSK1 loss enhances early bone homing and metastasis in ER+ breast cancer.
Fig. 5: MSK1 promotes luminal gene expression and impairs metastatic traits.
Fig. 6: MSK1 positively regulates H3S10ph and H3S28ph at promoters of luminal transcription factors.
Fig. 7: MSK1 positively regulates H3K9ac and H3K27ac at promoters of luminal transcription factors.

Change history

  • 19 April 2018

    In the version of this Article originally published, the boxes framing the two plots in Fig. 1g were misaligned from the axes due to a technical error. This has now been corrected in all versions of the Article.

References

  1. Kennecke, H. et al. Metastatic behavior of breast cancer subtypes. J. Clin. Oncol. 28, 3271–3277 (2010).

    Article  PubMed  Google Scholar 

  2. Janni, W. J. et al. Pooled analysis of the prognostic relevance of circulating tumor cells in primary breast cancer. Clin. Cancer Res. 22, 2583–2593 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Ding, L. et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature 464, 999–1005 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Prat, A., Ellis, M. J. & Perou, C. M. Practical implications of gene-expression-based assays for breast oncologists. Nat. Rev. Clin. Oncol. 9, 48–57 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Pavlovic, M. et al. Enhanced MAF oncogene expression and breast cancer bone metastasis. J. Natl. Cancer Inst. 107, djv256 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Gomis, R. R. & Gawrzak, S. Tumor cell dormancy. Mol. Oncol. 11, 62–78 (2017).

  7. Sosa, M. S., Bragado, P. & Aguirre-Ghiso, J. A. Mechanisms of disseminated cancer cell dormancy: an awakening field. Nat. Rev. Cancer 14, 611–622 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bragado, P. et al. TGF-β2 dictates disseminated tumour cell fate in target organs through TGF-β-RIII and p38α/β signalling. Nat. Cell. Biol. 15, 1351–1361 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Vicent, G. P. et al. Induction of progesterone target genes requires activation of Erk and Msk kinases and phosphorylation of histone H3. Mol. Cell. 24, 367–381 (2006).

    Article  CAS  PubMed  Google Scholar 

  10. Deak, M., Clifton, A. D., Lucocq, L. M. & Alessi, D. R. Mitogen- and stress-activated protein kinase-1 (MSK1) is directly activated by MAPK and SAPK2/p38, and may mediate activation of CREB. EMBO J. 17, 4426–4441 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Reyskens, K. M. & Arthur, J. S. Emerging roles of the mitogen and stress activated kinases MSK1 and MSK2. Front. Cell. Dev. Biol. 4, 56 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Naqvi, S. et al. Characterization of the cellular action of the MSK inhibitor SB-747651A. Biochem. J. 441, 347–357 (2012).

    Article  CAS  PubMed  Google Scholar 

  13. Merlos-Suarez, A. et al. The intestinal stem cell signature identifies colorectal cancer stem cells and predicts disease relapse. Cell. Stem Cell. 8, 511–524 (2011).

    Article  CAS  PubMed  Google Scholar 

  14. Morales, M. et al. RARRES3 suppresses breast cancer lung metastasis by regulating adhesion and differentiation. EMBO Mol. Med. 6, 865–881 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Chou, J., Provot, S. & Werb, Z. GATA3 in development and cancer differentiation: cells GATA have it! J. Cell. Physiol. 222, 42–49 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Charafe-Jauffret, E. et al. Gene expression profiling of breast cell lines identifies potential new basal markers. Oncogene 25, 2273–2284 (2006).

    Article  CAS  PubMed  Google Scholar 

  17. Neve, R. M. et al. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell. 10, 515–527 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Pereira, B. et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nat. Commun. 7, 11479 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Cancer Genome Atlas Network.. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

    Article  Google Scholar 

  21. Bruna, A. et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167, 260–274.e22 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Soloaga, A. et al. MSK2 and MSK1 mediate the mitogen- and stress-induced phosphorylation of histone H3 and HMG-14. EMBO J. 22, 2788–2797 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Reyes, D. et al. Activation of mitogen- and stress-activated kinase 1 is required for proliferation of breast cancer cells in response to estrogens or progestins. Oncogene 33, 1570–1580 (2014).

    Article  CAS  PubMed  Google Scholar 

  24. Josefowicz, S. Z. et al. Chromatin kinases act on transcription factors and histone tails in regulation of inducible transcription. Mol. Cell. 64, 347–361 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Prat, A. et al. Prognostic significance of progesterone receptor-positive tumor cells within immunohistochemically defined luminal A breast cancer. J. Clin. Oncol. 31, 203–209 (2013).

    Article  CAS  PubMed  Google Scholar 

  26. Vermeulen, L., De Wilde, G., Van Damme, P., Vanden Berghe, W. & Haegeman, G. Transcriptional activation of the NF-κB p65 subunit by mitogen- and stress-activated protein kinase-1 (MSK1). EMBO J. 22, 1313–1324 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Aguirre-Ghiso, J. A. Models, mechanisms and clinical evidence for cancer dormancy. Nat. Rev. Cancer 7, 834–846 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Johnson, R. W. et al. Induction of LIFR confers a dormancy phenotype in breast cancer cells disseminated to the bone marrow. Nat. Cell. Biol. 18, 1078–1089 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Gao, H. et al. The BMP inhibitor Coco reactivates breast cancer cells at lung metastatic sites. Cell 150, 764–779 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Malladi, S. et al. Metastatic latency and immune evasion through autocrine inhibition of WNT. Cell 165, 45–60 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. McBryan, J. et al. Transcriptomic profiling of sequential tumors from breast cancer patients provides a global view of metastatic expression changes following endocrine therapy. Clin. Cancer Res. 21, 5371–5379 (2015).

    Article  CAS  PubMed  Google Scholar 

  32. Cejalvo, J. M. et al. Intrinsic subtypes and gene expression profiles in primary and metastatic breast cancer. Cancer Res. 77, 2213-2221 (2017).

  33. Kouros-Mehr, H., Kim, J. W., Bechis, S. K. & Werb, Z. GATA-3 and the regulation of the mammary luminal cell fate. Curr. Opin. Cell. Biol. 20, 164–170 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Asselin-Labat, M. L. et al. Gata-3 is an essential regulator of mammary-gland morphogenesis and luminal-cell differentiation. Nat. Cell. Biol. 9, 201–209 (2007).

    Article  CAS  PubMed  Google Scholar 

  35. Augello, M. A., Hickey, T. E. & Knudsen, K. E. FOXA1: master of steroid receptor function in cancer. EMBO J. 30, 3885–3894 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Mehra, R. et al. Identification of GATA3 as a breast cancer prognostic marker by global gene expression meta-analysis. Cancer Res. 65, 11259–11264 (2005).

    Article  CAS  PubMed  Google Scholar 

  37. Mair, B. et al. Gain- and loss-of-function mutations in the breast cancer gene GATA3 result in differential drug sensitivity. PLoS. Genet. 12, e1006279 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Mehta, R. J. et al. FOXA1 is an independent prognostic marker for ER-positive breast cancer. Breast Cancer Res. Treat. 131, 881–890 (2012).

    Article  CAS  PubMed  Google Scholar 

  39. Jozwik, K. M. & Carroll, J. S. Pioneer factors in hormone-dependent cancers. Nat. Rev. Cancer 12, 381–385 (2012).

    Article  CAS  PubMed  Google Scholar 

  40. Root, D. E., Hacohen, N., Hahn, W. C., Lander, E. S. & Sabatini, D. M. Genome-scale loss-of-function screening with a lentiviral RNAi library. Nat. Methods 3, 715–719 (2006).

    Article  CAS  PubMed  Google Scholar 

  41. Ran, F. A. et al. Genome engineering using the CRISPR–Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Smyth, G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, 3 (2004).

  44. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289-300 (1995).

  45. Venkatraman, E. S. & Olshen, A. B. A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23, 657–663 (2007).

    Article  CAS  PubMed  Google Scholar 

  46. Gargiulo, G., Serresi, M., Cesaroni, M., Hulsman, D. & van Lohuizen, M. In vivo shRNA screens in solid tumors. Nat. Protoc. 9, 2880–2902 (2014).

    Article  CAS  PubMed  Google Scholar 

  47. Gautier, L., Cope, L., Bolstad, B. M. & Irizarry, R. A. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004).

    Article  CAS  PubMed  Google Scholar 

  48. Bolstad, B. M., Collin, F., Simpson, K. M., Irizarry, R. A. & Speed, T. P. Experimental design and low-level analysis of microarray data. Int. Rev. Neurobiol. 60, 25–58 (2004).

    Article  CAS  PubMed  Google Scholar 

  49. Eklund, A. C. & Szallasi, Z. Correction of technical bias in clinical microarray data improves concordance with known biological information. Genome Biol. 9, R26 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Bates, D., Mächler, M., Boker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Soft. 67, 1–48 (2015).

    Article  Google Scholar 

  51. Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in lenear mixed effects models. J. Stat. Soft. 82, 1–26 (2017).

    Article  Google Scholar 

  52. Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29, 1165–1188 (2001).

    Article  Google Scholar 

  53. Therneau, T. M., Grambsch, P. M. & Pankratz, V. S. Penalized survival models and frailty. J. Comp. Graph. Stat. 12, 156–175 (2003).

    Article  Google Scholar 

  54. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Wallden, B. et al. Development and verification of the PAM50-based Prosigna breast cancer gene signature assay. BMC Med. Genom. 8, 54 (2015).

    Article  Google Scholar 

  57. Urosevic, J. et al. Colon cancer cells colonize the lung from established liver metastases through p38 MAPK signalling and PTHLH. Nat. Cell. Biol. 16, 685–694 (2014).

    Article  CAS  PubMed  Google Scholar 

  58. Slebe, F. et al. FoxA and LIPG endothelial lipase control the uptake of extracellular lipids for breast cancer growth. Nat. Commun. 7, 11199 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Herrera-Abreu, M. T. et al. Early adaptation and acquired resistance to CDK4/6 inhibition in oestrogen receptor-positive breast cancer. Cancer Res. 76, 2301–2313 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Rinaldi, L. et al. Dnmt3a and Dnmt3b associate with enhancers to regulate human epidermal stem cell homeostasis. Cell. Stem Cell. 19, 491–501 (2016).

    Article  CAS  PubMed  Google Scholar 

  61. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. The R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2016); https://cran.r-project.org/doc/manuals/r-release/fullrefman.pdf

  65. Shaffer, J. P. Modified sequentally rejective multiple test procedures. J. Am. Stat. Assoc. 81, 826-831 (1986).

Download references

Acknowledgements

We thank V. Raker for manuscript editing and IRB Barcelona Functional Genomics (J.I. Pons and D. Fernández), Histopathology (N. Prats), Advanced Digital Microscopy (J. Colombelli) and Flow Cytometry (J. Comas) Core Facilities for assistance. S.Gawrzak, L.R., E.J.A. and K.S. were supported by La Caixa PhD fellowships. J.M.C. received a fellowship from ‘PhD4MD’, a Collaborative Research Training Programme for Medical Doctors (IDIBAPS, August Pi i Sunyer Institute for Biomedical Research and IRB Barcelona), and partial funding by the ISCIII (project: II14/00019). S.Gregorio, C.F.-P. and A.B. were funded by the Spanish Government (MINECO-Formación de personal Investigador). J.U. is an AECC (Asociación Española Contra el Cáncer) Fellow. D.K. was co-funded by FP7 Marie Curie Actions (COFUND program; grant agreement no. IRBPostPro2.0 600404); A.P. was supported by Susan Komen Foundation, SEOM, BBVA Foundation and the ISCIII–PI13/01718. J. Albanell. was supported by ISCIIi/FEDER under projects CIBERONC, PIE15/00008, PI15/00146 and Generalitat de Catalunya (2014 SGR 740). R.R.G., S.A.-B, J. Arribas. and A.R.N. are supported by the Institució Catalana de Recerca i Estudis Avançats. Support and structural funds were provided by the Generalitat de Catalunya (2014 SGR 535) to R.R.G. and A.R.N., and by the BBVA Foundation, the ISCIII/FEDER-CIBERONC, Worldwide Cancer Research (grant 15–1316), the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds (CIBEREONC and SAF2016-76008-R) to R.R.G.

Author information

Author notes

  1. Anna Arnal-Estapé

    Present address: Department of Pathology, Yale University School of Medicine, Yale, CT, USA

Authors and Affiliations

  1. Oncology Program, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain

    Sylwia Gawrzak, Lorenzo Rinaldi, Sara Gregorio, Enrique J. Arenas, Fernando Salvador, Jelena Urosevic, Cristina Figueras-Puig, Ivan del Barco Barrantes, Juan Miguel Cejalvo, Marc Guiu, Aikaterini Symeonidi, Anna Bellmunt, Daniela Kalafatovic, Anna Arnal-Estapé, Esther Fernández, Barbara Müllauer, Rianne Groeneveld, Konstantin Slobodnyuk, Angel R. Nebreda, Salvador Aznar Benitah & Roger R. Gomis

  2. CIBERONC, Madrid, Spain

    Jelena Urosevic, Federico Rojo, Marc Guiu, Joaquín Arribas, Ana Lluch, Violeta Serra, Joan Albanell & Roger R. Gomis

  3. Cancer Research Program, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain

    Federico Rojo, Ana Rovira & Joan Albanell

  4. Pathology Department, IIS-Fundación Jimenez Diaz, Madrid, Spain

    Federico Rojo

  5. Translational Genomics and Targeted Therapeutics, Institut d’Investigacions Biomèdiques Pi i Sunyer-IDIBAPS, Barcelona, Spain

    Juan Miguel Cejalvo, Montse Muñoz & Aleix Prat

  6. Experimental Therapeutics, Vall d’Hebron Insitute of Oncology, Barcelona, Spain

    Marta Palafox & Violeta Serra

  7. Biostatistics and Bioinformatics Unit, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain

    Antonio Berenguer-Llergo & Camille Stephan-Otto Attolini

  8. Department of Oncology, Vall d’Hebrón University Hospital, Barcelona, Spain

    Cristina Saura

  9. Vall d’Hebron Institute of Oncology, Barcelona, Spain

    Cristina Saura, Joaquín Arribas & Javier Cortes

  10. Universitat Autònoma de Barcelona, Bellaterra, Spain

    Joaquín Arribas

  11. ICREA, Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain

    Joaquín Arribas, Angel R. Nebreda, Salvador Aznar Benitah & Roger R. Gomis

  12. Ramon y Cajal University Hospital, Madrid, Spain

    Javier Cortes

  13. Medical Oncology Service, Hospital del Mar, Barcelona, Spain

    Ana Rovira & Joan Albanell

  14. Department of Oncology, Hospital Clinic de Barcelona, Barcelona, Spain

    Montse Muñoz & Aleix Prat

  15. Department of Oncology and Hematology, Hospital Clínico Universitario, Valencia, Spain

    Ana Lluch

  16. University of Valencia, Valencia, Spain

    Ana Lluch

  17. INCLIVA, Instituto de Investigación Sanitaria, Valencia, Spain

    Ana Lluch

  18. Universitat Pompeu Fabra, Barcelona, Spain

    Joan Albanell

  19. Universitat de Barcelona, Barcelona, Spain

    Roger R. Gomis

Authors
  1. Sylwia Gawrzak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lorenzo Rinaldi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sara Gregorio
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Enrique J. Arenas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fernando Salvador
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jelena Urosevic
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Cristina Figueras-Puig
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Federico Rojo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ivan del Barco Barrantes
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Juan Miguel Cejalvo
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Marta Palafox
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Marc Guiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Antonio Berenguer-Llergo
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Aikaterini Symeonidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Anna Bellmunt
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Daniela Kalafatovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Anna Arnal-Estapé
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Esther Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Barbara Müllauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Rianne Groeneveld
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Konstantin Slobodnyuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Camille Stephan-Otto Attolini
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Cristina Saura
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Joaquín Arribas
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Javier Cortes
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Ana Rovira
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Montse Muñoz
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Ana Lluch
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Violeta Serra
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Joan Albanell
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Aleix Prat
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Angel R. Nebreda
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Salvador Aznar Benitah
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Roger R. Gomis
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.Gawrzak designed and performed the experiments and analysed the data. L.R. performed the ChIP–seq experiments and analysed the data. E.J.A. F.S., J.U., C.F.-P., I.d.B.B., B.M., D.K., R.G., S.Gregorio, K.S., A.B., E.F. and M.G. contributed to the experiments. A.A.-E. isolated DBM cell line. F.R., A.L., A.R., M.M., J.M.C., A.P. and J. Albanell. contributed to building and analysing the tissue micro-array. M.P., C.S., J. Arribas., J.C. and V.S. contributed the PDX generation and analyses. A.B.-L. and C.S.-O.A. analysed the public transcriptomic data sets of the breast cancer human samples and statistics. S.A.-B. and A.R.N. participated in data analyses. R.R.G. conceived the project, designed and analysed the data, and supervised the overall project. S.Gawrzak and R.R.G. wrote the manuscript.

Corresponding author

Correspondence to Roger R. Gomis.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

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

Integrated supplementary information

Figure Supplementary 1 Characterization of ER+ breast cancer tumour mass dormancy model suitable for shRNA screen.

(a) Kaplan-Meier analysis of bone metastasis-free survival in a DBM cells xenograft (n = 12 mice). (b) Representative bioluminescence images showing bone metastasis progression in DBM xenograft (top), and quantification of BLI signal in bone metastatic lesion (n = 5 limbs with metastatic lesions) (bottom). (c) Kaplan–Meier analysis of bone metastasis-free survival in a BoM2 cell xenograft (n = 17 mice). (d) Representative bioluminescence images showing bone metastasis progression in BoM2 xenograft (top), and quantification of bone metastatic lesion BLI signal (bottom) (n = 10 limbs with metastatic lesions). (e) CGH analysis of DBM and parental cells. Genetic gains and losses in DBM cells, for each chromosome, are depicted with red and green color bars respectively. (f) Quantification of ex vivo BLI signal in bones isolated during homing (n = 8 limbs), latency (n = 7 limbs), metastasis (n = 8 limbs). Two-tailed Mann Whitney test. (g) Quantification of osteolythic area from X-ray scans in bones isolated during homing (n = 3 limbs) and metastasis (n = 5 limbs). Two-tailed Mann–Whitney test. Experiment was performed once. (h) Analysis of bone samples with detected micrometastatic lesions or single DTC by means of human ER_ IHC during homing (i) Quantification of phospho-P38-positive cells in latent lesions (n = 20 ROIs from 4 limbs) and metastatic lesions (n = 25 ROIs from 3 limbs). Two-tailed Mann-Whitney test (left). Representative image of phospho P38 staining in lesions during latency or metastatic phase (right). Scale bar, 50 µm. (j) Quantification of phospho-SMAD2-positive cells in latent lesions (n = 21 ROIs from 4 limbs) and metastatic lesions (n = 42 ROIs from 3 limbs). Two-tailed Mann-Whitney test. Experiment was performed once. Panels (b) and (d) show data as mean ± SEM, and panels (f), (g) and (i-j), as whisker plots: mid-line, median; box, 25 – 75 percentile; whisker, min-max. ns (nonsignificant) P > 0.05, * P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001. Statistics source data and P values are provided in Supplementary Table 9.

Supplementary Figure 2 Clinical and in vivo validation of MSK1 as regulator of homing, tumor mass dormancy and metastasis in ER+ breast cancer.

(a) In vitro cell growth curve of shControl and shMSK1 DBM cells. Mixed-effect linear model in which the response was square-root transformed, n = 3 independent experiments were modeled as random effect and group, time point and their interaction were included as explanatory variables. (b) MSK1 KO generation by CRISPR/Cas9 genome editing. Five target sequences were located in exons 5, 6, 8, 9 or 13 (green), which overlap with residues critical for MSK1 catalytic and kinase activity (violet circles). The 20-nt target sequence in exon 5 (blue);the 3-nt PAM sequence(red); guide RNA is composed of complementary target sequence. The cleavage site is located between the 3rd and 4th nucleotides upstream from PAM (arrows). (c) Representative bioluminescence images showing bone metastasis progression of shControl and shMSK1 DBM cells. Experiment was repeated two times independently with similar results. (d) Bone metastasis incidence of ZR-75.1 cells infected with shControl (n=14 limbs) and shMSK1#1 (n=14 limbs) (left). Western blot analysis of MSK1, MSK2 and tubulin in ZR-75.1 cells (right). Western blot was repeated two times independently with similar results (e) Time-matched quantification of in vivo BLI signal in hind limbs during homing, latency and metastasis after injection of DBM shControl (n=4 limbs), shMSK1#1 (n=12 limbs) or shMSK1#2 (n=24 limbs). Two-tailed Wald tests from a linear model in which the response was log-transformed and group, status and their interaction were included as explanatory variables. (f) Time-matched quantification of apoptotic signal (Z-DEVD) normalized to lesion size (LUC) in hind limbs during homing, latency, metastasis after injection of DBM shControl (nh=6 limbs, nl=4 limbs, nm=3 limbs), shMSK1#1 (nh=6 limbs, nl=4 limbs, nm=4 limbs) or shMSK1#2 (nh=7 limbs, nl=4 limbs, nm=4 limbs). Two-tailed Wald test from a linear model in which the response was log-transformed and group, status and their interaction were included as explanatory variables. (g) Analysis of bone samples with detected metastatic lesions or single DTC by means of human ERα IHC (n=25 ROIs from 5 limbs). Panel (a) show data as mean ± SEM from three independent experiments (d,f) show data as whisker plots: mid-line, median; box, 25 – 75 percentile; whisker, min-max. ns (nonsignificant) P>0.05, * P≤0.05, ** P≤0.01, *** P≤0.001. Schaffer method was used for P-value correction when three or more groups are compared. Statistics source data P values are provided in Supplementary Table 9. Unprocessed original scans of blots are shown in Supplementary Fig. 8.

Supplementary Figure 3 Angiogenesis in vitro and in vivo is unaffected by MSK1 levels in breast cancer cells.

(a) mRNA levels fold change of angiogenesis-associated genes ANGPT1, ANGPT2, ANGPTL4 and VEGF in DBM, cells upon MSK1 downregulation, n = 4. Two-tailed Wald test from a linear model in which group was included as explanatory variable. (b) mRNA levels fold change of angiogenesis-associated genes ANGPT1, ANGPT2, ANGPTL4 and VEGF in DBM, cells upon MSK1 knockout, n = 4. Two-tailed Student’s t-test. (c) Quantification of in vitro endothelial HUVEC cell tube formation assay upon treatment with conditioned medium derived from shControl, shMSK1#1 and shMSK1#2 DBM cells, n = 3. Multiple t-test across points comparing each condition to control (d) Quantification of in vitro endothelial HUVEC cell tube formation assay upon treatment with conditioned medium derived from wild-type (WT) and MSK1 knockout (KO) DBM cells, n = 3. Multiple t-test across points comparing each condition to control (e) Quantification of number of CD31 positive vessels in metastatic lesions formed by shControl (n = 12), shMSK1#1 (n = 13) and shMSK1#2 (n = 18) DBM cells (left). Representative images of CD31 staining (right). Scale bar, 50 µm. Two-tailed Wald test from a linear model in which group was included as explanatory variable. Experiment was performed once (f) Quantification of EdU-positive cells in latent lesions formed by DBM shControl (n = 19 ROIs from 3 limbs) and shMSK1#2 cells (n = 19 ROI from 3 limbs) and metastatic lesions formed by DBM shControl (n = 22 ROIs from 2 limbs) and shMSK1#2 (n = 11 ROI from 2 limbs). Two-tailed Mann–Whitney test. (g) Representative image of EdU staining in lesions during latency or metastatic phase. Experiment was performed once. Panels (a-d) show data as mean ± SEM from three biologically independent samples each of which is an average from three technical replicates and panel (e-g) shows data as whisker plots: mid-line, median; box, 25 – 75 percentile; whisker, min-max. ns (nonsignificant) P > 0.05, * P ≤ 0.05, ** P ≤ 0.01. Schaffer method was used for P-value correction when three or more groups are compared. Statistics source data and P values are provided in Supplementary Table 9.

Supplementary Figure 4 MSK1 is dispensable for in vitro survival in hypoxia, adhesion and migration, but inhibits tumor initiation.

(a) Flow-cytometry quantification of apoptosis and cell death after 72 h of hypoxic conditions, n = 4. Representative plots of flow cytometric analysis of annexin V and PI staining in cells infected with shControl or shMSK1 are shown. Apoptotic cells, top-right quadrants; dead cells, top-left quadrants. Two-tailed Wald test from a linear model in which the response was log-transformed and group, status and their interaction were included as explanatory variables. (b) Cell adhesion to collagen, fibronectin and matrigel in DBM cells infected with shControl or shMSK1, n = 3. Two-tailed Wald test from a linear model in which the response was log-transformed and group was included as explanatory variable. (c) Cell migration of shControl or shMSK1 DBM cells, n = 3. Two-tailed Wald test from a linear model in which the response was log-transformed and group was included as explanatory variable. (d) Cell invasion of shControl or shMSK1 infected DBM cells, n = 3. Two-tailed Wald test from a linear model in which the response was log-transformed and group was included as explanatory variable. (e) mRNA levels fold change of CXCL12 upon MSK1 downregulation (n = 5) or knockout (n = 4). Wald test from a linear model in which group was included as explanatory variable. (f) Quantification of CXCR4 positive cells upon downregulation and knockout of MSK1, n = 8. Two-tailed Wald tests from a linear model in which the response was transformed (power 4) and group was included as explanatory variable. (g) Representative images of CXCR4 and MSK1 staining of shControl, shMSK1#1, shMSK1#2, WT and KO DBM cells. Scale bar, 50 µm. Experiment was performed once. (h) Fold-change of oncospheres in 3D in DBM cells upon 72-h treatment with MSK inhibitor (10 µM) (left), n = 5. Representative image of 3D oncospheres treated with DMSO (control) or MSK1 inhibitor (right). Scale bar, 500 µm. Two-tailed Wald test from a linear model in which the response was log-transformed and group was included as explanatory variable. (i) Fold-change of second-generation oncosphere formation in DBM cells with (n = 4) or without (n = 6) p38 inhibitor PH-797804 (2 µM). Two-tailed Student’s t-test test. (j) mRNA levels fold change of p38 and MSK1 genes, upon p38 depletion, n = 3. Two-tailed Student’s t-test. Panels (a-f) and (h-j) show data as mean ± SEM from minimum three biologically independent samples. ns (nonsignificant), * P ≤ 0.05, ** P ≤ 0.01. Schaffer method was used for P-value correction when three or more groups are compared. Statistics source data and P values are provided in Supplementary Table 9.

Supplementary Figure 5 MSK1 loss decreases the expression of luminal genes in ER+ breast cancer, but maintains the luminal subtype.

(a) Gene set enrichment analysis (GSEA) plots of a subset of the luminal (top) and basal (bottom) gene sets adapted from (Neve et al Cancer Cell 2006 and Charafe-Jauffret et al Oncogene 2006) and their correlation with MSK1 expression in ER+ breast cancer patients (n = 370 patient samples). NES, normalized enrichment score; NOMp, nominal P value (b) Representative images of oestrogen receptor (ERα) and MSK1 immunohistochemistry in metastatic lesions upon MSK1 knockdown and knockout. Percentage of positive cells for each lesion was quantified from shControl (n = 6), shMSK1#1 (n = 4), shMSK1#2 (n = 4), MSK1 WT (n = 3) and MSK1 KO (n = 3) biologically independent samples (lesions), and at least 8 × 102 cells were assessed. Scale bar, 50 µm. (c) Heatmap of the log2 transformed gene expression for PAM50 signatures in T47D, DBM, DBM WT and DBM MSK1 KO cells. Green and red colors represent higher and lower expression levels, respectively. The samples are further categorized into molecular subtype. (d) Proliferation assay of DBM WT and DBM MSK1 KO cells treated with increasing doses of tamoxifen, n = 3. Panels (b and d) and show data as mean ± SEM from three biologically independent samples. Statistics source data are provided in Supplementary Table 9.

Supplementary Figure 6 MSK1 loss impairs luminal gene expression and increases metastatic traits.

(a) Fold-change of mRNA levels of luminal genes in DBM MSK1 WT or MSK1 KO cells, n = 3. Two-tailed Student’s t-test. (b) Images of MSK1 and FOXA1 immunohistochemistry in overt-metastasis–bearing DBM shControl hind limbs (n = 3 limbs per group) or DBM shMSK1 hind limbs (n = 2 limbs per group). Scale bar, 50 μm. Experiment was performed once. (c) Representative images of ERα immunohistochemistry in PDX tumours. Percentage of positive cells for each PDX was quantified from two biologically independent samples (passages), and at least 4 × 103 cells were assessed. Scale bar, 50 μm. (d) Fold-change of mRNA levels of luminal genes in DBM MSK1-downregulated cells upon ectopic expression of (OE) of FOXA1 (top), GATA3 (middle) or combination of both (bottom), n = 3. Two-tailed Wald test from a linear model in which group, OE and their interaction were included as explanatory variables. Comparisons where made across each gene response comparing control versus the indicated overexpression condition. (e) Survival analysis representing the proportion of bone metastasis recurrence-free patients stratified according to MSK2 mRNA levels in ER+ breast cancer patient samples (low, n = 65; medium, n = 64; high, n = 35). Two-tailed Wald test. HR(<3y)=1.34; CI[0.47, 3.79], p=0.58 (f) Western blot analysis of MSK1, MSK2 and tubulin in MSK1 WT and KO cells upon downregulation of MSK2. Experiment was performed two times with similar results. (g) Fold-change of mRNA levels of luminal genes in DBM MSK1 KO upon MSK1 downregulation, n = 3. Statistical significance was tested by Wald test from a linear model in which group was included as explanatory variable. Panels (a), (d) and (g) show data as mean ± SEM from 3 biologically independent samples, each of which is an average from three technical replicates. ns, non-significant; P > 0.05, *P ≤ 0.05 and ****P ≤ 0.0001. Schaffer method was used for p-value correction when three or more groups are compared. Statistics source data and P values are provided in Supplementary Table 9. Unprocessed original scans of blots are shown in Supplementary Fig. 8.

Supplementary Figure 7 MSK1 acts at promoters of luminal transcription factors by modulating histone H3 phosphorylation of S10 and S28 residues and histone H3 acetylation of K9 and K27 residues.

(a) Correlation of MSK1 in ER+ patient cohort (n = 370). Dashed line indicates cut-off of 5% FDR, and positively- or negatively-correlated genes are marked in red. (b) ChIP-seq coverage-depth of H3K9ac, H3K27ac, H3K9me3 and H3K27me3 peaks in DBM shControl or shMSK1 cells. (c) ChIP-seq coverage depth of HK9me3 and H3K27me3 around TSS of luminal (n = 601) and basal genes (n = 873) in DBM shControl and shMSK1 cells. Two-tailed paired Student´s t-test. (d) ChIP-seq coverage depth of H3S10p, H3S28p at promoters of luminal transcription factors FOXA1 and GATA3. Zoom in from (7e) (e) ChIP-seq coverage depth of H3K9ac, H3K27ac at promoters of luminal transcription factors FOXA1 and GATA3. Zoom in from (7a). (f) Violin plots showing normalized peak count of input, H3K9me3 and H3K27me3 in promoters of genes bound by MSK1, n = 834. Two-tailed Paired Student´s t-test. ChIP-seq experiments were performed once. Panels (c and e) show data as violin plots that indicate the distribution of data points: mid-point, median; vertical lines 95% confidence interval ns (non-significant) P > 0.05, * P ≤ 0.05, *** P ≤ 0.001. P values are provided in the Supplementary data Supplementary Table 9.

Supplementary Figure 8 Unprocessed Western Blots.

Unprocessed Western blots from key blots in Fig: 4a, 4b, 5c and 5g, and, Supplementaty Fig: 2e, 6f. Dashed line boxes indicate the cropped areas shown in the figures.

Supplementary information

Supplementary Information

Supplementary Figures 1–8 and Supplementary Table titles and legends.

Life Sciences Reporting Summary

Supplementary Table 1

List of differentially expressed genes between T47D (parental) and DBM (derivative) cell lines.

Supplementary Table 2

Deconvolution results of shRNA screen.

Supplementary Table 3

List of genes with >1.5-fold enrichment.

Supplementary Table 4

CRISPR–Cas9 genome editing efficiency.

Supplementary Table 5

Luminal and basal gene lists used for GSEA.

Supplementary Table 6

PDX tumor details.

Supplementary Table 7

GSEA of genes significantly correlated with MSK1 in BC primary tumours.

Supplementary Table 8

Materials and reagents.

Supplementary Table 9

Statistics source data.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gawrzak, S., Rinaldi, L., Gregorio, S. et al. MSK1 regulates luminal cell differentiation and metastatic dormancy in ER+ breast cancer. Nat Cell Biol 20, 211–221 (2018). https://doi.org/10.1038/s41556-017-0021-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41556-017-0021-z

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