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A stemness-related ZEB1–MSRB3 axis governs cellular pliancy and breast cancer genome stability

Abstract

Chromosomal instability (CIN), a feature of most adult neoplasms from their early stages onward, is a driver of tumorigenesis. However, several malignancy subtypes, including some triple-negative breast cancers, display a paucity of genomic aberrations, thus suggesting that tumor development may occur in the absence of CIN. Here we show that the differentiation status of normal human mammary epithelial cells dictates cell behavior after an oncogenic event and predetermines the genetic routes toward malignancy. Whereas oncogene induction in differentiated cells induces massive DNA damage, mammary stem cells are resistant, owing to a preemptive program driven by the transcription factor ZEB1 and the methionine sulfoxide reductase MSRB3. The prevention of oncogene-induced DNA damage precludes induction of the oncosuppressive p53-dependent DNA-damage response, thereby increasing stem cells' intrinsic susceptibility to malignant transformation. In accord with this model, a subclass of breast neoplasms exhibit unique pathological features, including high ZEB1 expression, a low frequency of TP53 mutations and low CIN.

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Figure 1: Cellular hierarchy of the normal human mammary gland.
Figure 2: Mammary stem cells exhibit low levels of DNA damage after RAS transduction.
Figure 3: Stemness-related expression of ZEB1 prevents oncogene-induced DNA damage in normal and cancer cells.
Figure 4: The methionine sulfoxide reductase MSRB3 is required for ZEB1-dependent protection against oncogene-induced DNA damage.
Figure 5: The ZEB1–MSRB3 axis is associated with low CNA in breast cancer.
Figure 6: ZEB1 expression fosters malignant transformation and prevents the onset of chromosomal instability.

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Sequence Read Archive

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Gene Expression Omnibus

References

  1. 1

    Negrini, S., Gorgoulis, V.G. & Halazonetis, T.D. Genomic instability: an evolving hallmark of cancer. Nat. Rev. Mol. Cell Biol. 11, 220–228 (2010).

    Article  CAS  Google Scholar 

  2. 2

    Halazonetis, T.D., Gorgoulis, V.G. & Bartek, J. An oncogene-induced DNA damage model for cancer development. Science 319, 1352–1355 (2008).

    Article  CAS  Google Scholar 

  3. 3

    Maser, R.S. & DePinho, R.A. Connecting chromosomes, crisis, and cancer. Science 297, 565–569 (2002).

    Article  CAS  Google Scholar 

  4. 4

    Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

    Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 6

    Gorgoulis, V.G. et al. Activation of the DNA damage checkpoint and genomic instability in human precancerous lesions. Nature 434, 907–913 (2005).

    Article  CAS  Google Scholar 

  7. 7

    Bartkova, J. et al. DNA damage response as a candidate anti-cancer barrier in early human tumorigenesis. Nature 434, 864–870 (2005).

    Article  CAS  Google Scholar 

  8. 8

    Di Micco, R. et al. Oncogene-induced senescence is a DNA damage response triggered by DNA hyper-replication. Nature 444, 638–642 (2006).

    Article  CAS  Google Scholar 

  9. 9

    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 

  10. 10

    Dawson, S.J., Rueda, O.M., Aparicio, S. & Caldas, C. A new genome-driven integrated classification of breast cancer and its implications. EMBO J. 32, 617–628 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Prat, A. et al. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res. 12, R68 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Prat, A. & Perou, C.M. Deconstructing the molecular portraits of breast cancer. Mol. Oncol. 5, 5–23 (2011).

    Article  CAS  Google Scholar 

  13. 13

    Weigelt, B. et al. Metaplastic breast carcinomas display genomic and transcriptomic heterogeneity. Mod. Pathol. 28, 340–351 (2015).

    Article  CAS  Google Scholar 

  14. 14

    Lim, E. et al. Aberrant luminal progenitors as the candidate target population for basal tumor development in BRCA1 mutation carriers. Nat. Med. 15, 907–913 (2009).

    Article  CAS  Google Scholar 

  15. 15

    Keller, P.J. et al. Defining the cellular precursors to human breast cancer. Proc. Natl. Acad. Sci. USA 109, 2772–2777 (2012).

    Article  Google Scholar 

  16. 16

    Eirew, P. et al. Aldehyde dehydrogenase activity is a biomarker of primitive normal human mammary luminal cells. Stem Cells 30, 344–348 (2012).

    Article  CAS  Google Scholar 

  17. 17

    Bartkova, J. et al. Oncogene-induced senescence is part of the tumorigenesis barrier imposed by DNA damage checkpoints. Nature 444, 633–637 (2006).

    Article  CAS  Google Scholar 

  18. 18

    Bester, A.C. et al. Nucleotide deficiency promotes genomic instability in early stages of cancer development. Cell 145, 435–446 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Costantino, L. et al. Break-induced replication repair of damaged forks induces genomic duplications in human cells. Science 343, 88–91 (2014).

    Article  CAS  Google Scholar 

  20. 20

    Neelsen, K.J., Zanini, I.M., Herrador, R. & Lopes, M. Oncogenes induce genotoxic stress by mitotic processing of unusual replication intermediates. J. Cell Biol. 200, 699–708 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Serrano, M., Lin, A.W., McCurrach, M.E., Beach, D. & Lowe, S.W. Oncogenic ras provokes premature cell senescence associated with accumulation of p53 and p16INK4a. Cell 88, 593–602 (1997).

    Article  CAS  Google Scholar 

  22. 22

    Keyomarsi, K. et al. Cyclin E and survival in patients with breast cancer. N. Engl. J. Med. 347, 1566–1575 (2002).

    Article  CAS  Google Scholar 

  23. 23

    Nielsen, N.H., Arnerlöv, C., Emdin, S.O. & Landberg, G. Cyclin E overexpression, a negative prognostic factor in breast cancer with strong correlation to oestrogen receptor status. Br. J. Cancer 74, 874–880 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

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

  25. 25

    Voduc, D., Nielsen, T.O., Cheang, M.C. & Foulkes, W.D. The combination of high cyclin E and Skp2 expression in breast cancer is associated with a poor prognosis and the basal phenotype. Hum. Pathol. 39, 1431–1437 (2008).

    Article  CAS  Google Scholar 

  26. 26

    Yang, C.C. et al. Phosphorylation of EZH2 at T416 by CDK2 contributes to the malignancy of triple negative breast cancers. Am. J. Transl. Res. 7, 1009–1020 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Barton, M.C., Akli, S. & Keyomarsi, K. Deregulation of cyclin E meets dysfunction in p53: closing the escape hatch on breast cancer. J. Cell. Physiol. 209, 686–694 (2006).

    Article  CAS  Google Scholar 

  28. 28

    Dou, Q.P., Pardee, A.B. & Keyomarsi, K. Cyclin E: a better prognostic marker for breast cancer than cyclin D? Nat. Med. 2, 254 (1996).

    Article  CAS  Google Scholar 

  29. 29

    Spruck, C.H., Won, K.A. & Reed, S.I. Deregulated cyclin E induces chromosome instability. Nature 401, 297–300 (1999).

    Article  CAS  Google Scholar 

  30. 30

    Bamford, S. et al. The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br. J. Cancer 91, 355–358 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    McLaughlin, S.K. et al. The RasGAP gene, RASAL2, is a tumor and metastasis suppressor. Cancer Cell 24, 365–378 (2013).

    Article  CAS  Google Scholar 

  32. 32

    Mueller, H. et al. Potential prognostic value of mitogen-activated protein kinase activity for disease-free survival of primary breast cancer patients. Int. J. Cancer 89, 384–388 (2000).

    Article  CAS  Google Scholar 

  33. 33

    Sivaraman, V.S., Wang, H., Nuovo, G.J. & Malbon, C.C. Hyperexpression of mitogen-activated protein kinase in human breast cancer. J. Clin. Invest. 99, 1478–1483 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    von Lintig, F.C. et al. Ras activation in human breast cancer. Breast Cancer Res. Treat. 62, 51–62 (2000).

    Article  CAS  Google Scholar 

  35. 35

    Nguyen, L.V. et al. Barcoding reveals complex clonal dynamics of de novo transformed human mammary cells. Nature 528, 267–271 (2015).

    Article  CAS  Google Scholar 

  36. 36

    Al-Hajj, M., Wicha, M.S., Benito-Hernandez, A., Morrison, S.J. & Clarke, M.F. Prospective identification of tumorigenic breast cancer cells. Proc. Natl. Acad. Sci. USA 100, 3983–3988 (2003).

    Article  CAS  Google Scholar 

  37. 37

    Ghebeh, H. et al. Profiling of normal and malignant breast tissue show CD44high/CD24low phenotype as a predominant stem/progenitor marker when used in combination with Ep-CAM/CD49f markers. BMC Cancer 13, 289 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    Schepeler, T. et al. A high resolution genomic portrait of bladder cancer: correlation between genomic aberrations and the DNA damage response. Oncogene 32, 3577–3586 (2013).

    Article  CAS  Google Scholar 

  40. 40

    Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Morel, A.P. et al. Generation of breast cancer stem cells through epithelial-mesenchymal transition. PLoS One 3, e2888 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Brabletz, S. & Brabletz, T. The ZEB/miR-200 feedback loop: a motor of cellular plasticity in development and cancer? EMBO Rep. 11, 670–677 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Chaffer, C.L. et al. Poised chromatin at the ZEB1 promoter enables breast cancer cell plasticity and enhances tumorigenicity. Cell 154, 61–74 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Lee, A.C. et al. Ras proteins induce senescence by altering the intracellular levels of reactive oxygen species. J. Biol. Chem. 274, 7936–7940 (1999).

    Article  CAS  Google Scholar 

  45. 45

    Morel, A.P. et al. EMT inducers catalyze malignant transformation of mammary epithelial cells and drive tumorigenesis towards claudin-low tumors in transgenic mice. PLoS Genet. 8, e1002723 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 46

    Puisieux, A., Brabletz, T. & Caramel, J. Oncogenic roles of EMT-inducing transcription factors. Nat. Cell Biol. 16, 488–494 (2014).

    Article  CAS  Google Scholar 

  47. 47

    Walker, L.C., Harris, G.C., Wells, J.E., Robinson, B.A. & Morris, C.M. Association of chromosome band 8q22 copy number gain with high grade invasive breast carcinomas by assessment of core needle biopsies. Genes Chromosom. Cancer 47, 405–417 (2008).

    Article  CAS  Google Scholar 

  48. 48

    Grigoriadis, A. et al. Molecular characterisation of cell line models for triple-negative breast cancers. BMC Genomics 13, 619 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Kao, J. et al. Molecular profiling of breast cancer cell lines defines relevant tumor models and provides a resource for cancer gene discovery. PLoS One 4, e6146 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    Lehmann, B.D. et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J. Clin. Invest. 121, 2750–2767 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    Weigman, V.J. et al. Basal-like breast cancer DNA copy number losses identify genes involved in genomic instability, response to therapy, and patient survival. Breast Cancer Res. Treat. 133, 865–880 (2012).

    Article  CAS  Google Scholar 

  52. 52

    Molyneux, G. et al. BRCA1 basal-like breast cancers originate from luminal epithelial progenitors and not from basal stem cells. Cell Stem Cell 7, 403–417 (2010).

    Article  CAS  Google Scholar 

  53. 53

    Ansieau, S. et al. Induction of EMT by twist proteins as a collateral effect of tumor-promoting inactivation of premature senescence. Cancer Cell 14, 79–89 (2008).

    Article  CAS  Google Scholar 

  54. 54

    Tran, P.T. et al. Twist1 suppresses senescence programs and thereby accelerates and maintains mutant Kras-induced lung tumorigenesis. PLoS Genet. 8, e1002650 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Zhang, P. et al. ATM-mediated stabilization of ZEB1 promotes DNA damage response and radioresistance through CHK1. Nat. Cell Biol. 16, 864–875 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Comaills, V. et al. Genomic instability is induced by persistent proliferation of cells undergoing epithelial-to-mesenchymal transition. Cell Rep. 17, 2632–2647 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 57

    Chen, X., Pappo, A. & Dyer, M.A. Pediatric solid tumor genomics and developmental pliancy. Oncogene 34, 5207–5215 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 58

    Ginestier, C. et al. ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome. Cell Stem Cell 1, 555–567 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 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  PubMed  PubMed Central  Google Scholar 

  60. 60

    Kuperwasser, C. et al. Reconstruction of functionally normal and malignant human breast tissues in mice. Proc. Natl. Acad. Sci. USA 101, 4966–4971 (2004).

    Article  CAS  Google Scholar 

  61. 61

    Morgenstern, J.P. & Land, H. Advanced mammalian gene transfer: high titre retroviral vectors with multiple drug selection markers and a complementary helper-free packaging cell line. Nucleic Acids Res. 18, 3587–3596 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    Barradas, M. et al. Histone demethylase JMJD3 contributes to epigenetic control of INK4a/ARF by oncogenic RAS. Genes Dev. 23, 1177–1182 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. 63

    Jackson, D.A. & Pombo, A. Replicon clusters are stable units of chromosome structure: evidence that nuclear organization contributes to the efficient activation and propagation of S phase in human cells. J. Cell Biol. 140, 1285–1295 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. 64

    Machon, C. et al. Fully validated assay for the quantification of endogenous nucleoside mono- and triphosphates using online extraction coupled with liquid chromatography-tandem mass spectrometry. Anal. Bioanal. Chem. 406, 2925–2941 (2014).

    Article  CAS  Google Scholar 

  65. 65

    R Development Core Team. R: a Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2008).

  66. 66

    Gendoo, D.M. et al. Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer. Bioinformatics 32, 1097–1099 (2016).

    Article  CAS  Google Scholar 

  67. 67

    Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. 68

    Omberg, L. et al. Enabling transparent and collaborative computational analysis of 12 tumor types within The Cancer Genome Atlas. Nat. Genet. 45, 1121–1126 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 69

    Hoadley, K.A. et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158, 929–944 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. 70

    Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. 71

    Zack, T.I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. 72

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. 73

    Carr, D., Lewin-Koh, N. & Maechler, M. hexbin: hexagonal binning routines. (2011).

  74. 74

    Irizarry, R.A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).

    Article  Google Scholar 

  75. 75

    Irizarry, R.A. et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. 76

    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 

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Acknowledgements

The authors thank I. Treilleux and G. Clapisson (Resource Biological Center) for providing tumor samples; N. Nazaret for sequencing analysis; P. Kannouche for providing help and laboratory space for the DNA-fiber spreading analysis; I. Iacono, S. Bréjon, A. Pierrot, J. Perrossier and S. Croze for technical support; D. Nègre (CIRI/EVIR) for providing the pWPIR-GFP lentiviral vector; C. Eaves (Terry Fox Laboratory) for providing cells; and M. Tommasino (IARC) for providing vectors. We thank the CRCM animal core facility for animal housing and the CRCM flow cytometry core (M.L. Thibullt) for technical assistance. We also thank B. Manship for critical reading of the manuscript. This work was supported by funding from the Ligue Nationale contre le Cancer (EL2011.LNCC/AP and EL2016.LNCC/AIP) and the Project National Cancer Association, Lyon Integrated Research Institute in Cancer (LYRIC, INCa 4664) to A.P. This work was additionally supported by funding from the Institut National contre le Cancer (project PLBio 2015-266) to P.S., the Institut National contre le Cancer (project PLBIO12-007) to E.D., a Cancer-DGOS grant (TRANSLA11-103) to E.C.-J. and the Marseille Integrated Research Institute in Cancer (INCa 6038) to D.B.

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A.-P.M. coordinated all experiments performed on cell lines and assisted in data interpretation and preparation of figures and methods. C.C., C.P. and F.F. produced and characterized all HMEC-derived cell lines. R.M.P., E.R., P.S., E.T. and C.M.-L. performed bioinformatics and statistical analyses, and R.M.P. assisted in preparation of figures and methods. M.D.-S., N.R.-R. and F.P.-L. performed tumor analyses. V.C. coordinated aCGH analyses. J.L. and Q.W. coordinated sequencing analyses. A.W. and F.B. performed gene expression analysis. E.D. and A.T. performed DNA-fiber spreading analysis, and A.T. assisted in preparation of figures and methods. J.-L.J. provided normal breast samples. E.C.-J., C.G. and J.W. performed normal breast tissue isolation. E.C.-J., C.G. and D.B. conceived experiments on normal human mammary glands. O.C. carried out the in vivo experiments. P.F. isolated mRNA from normal mammary glands. J.G. and A.M.V. performed nucleotide quantification experiments. J.C., S.A., A.T., F.H. and A.M.V. assisted in data interpretation. D.G.C. provided critical evaluation of the manuscript and scientific content. A.P. conceived the project, designed experiments, interpreted data and wrote the manuscript. All authors read and approved the final version of the manuscript.

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Correspondence to Alain Puisieux.

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Morel, AP., Ginestier, C., Pommier, R. et al. A stemness-related ZEB1–MSRB3 axis governs cellular pliancy and breast cancer genome stability. Nat Med 23, 568–578 (2017). https://doi.org/10.1038/nm.4323

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