Letter

Acquired CYP19A1 amplification is an early specific mechanism of aromatase inhibitor resistance in ERα metastatic breast cancer

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Accepted:
Published online:

Abstract

Tumor evolution is shaped by many variables, potentially involving external selective pressures induced by therapies1. After surgery, patients with estrogen receptor (ERα)-positive breast cancer are treated with adjuvant endocrine therapy2, including selective estrogen receptor modulators (SERMs) and/or aromatase inhibitors (AIs)3. However, more than 20% of patients relapse within 10 years and eventually progress to incurable metastatic disease4. Here we demonstrate that the choice of therapy has a fundamental influence on the genetic landscape of relapsed diseases. We found that 21.5% of AI-treated, relapsed patients had acquired CYP19A1 (encoding aromatase) amplification (CYP19A1amp). Relapsed patients also developed numerous mutations targeting key breast cancer–associated genes, including ESR1 and CYP19A1. Notably, CYP19A1amp cells also emerged in vitro, but only in AI-resistant models. CYP19A1 amplification caused increased aromatase activity and estrogen-independent ERα binding to target genes, resulting in CYP19A1amp cells showing decreased sensitivity to AI treatment. These data suggest that AI treatment itself selects for acquired CYP19A1amp and promotes local autocrine estrogen signaling in AI-resistant metastatic patients.

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Change history

  • Corrected online 31 January 2017

    In the version of this article initially published online, the names of authors Hermannus Kempe and Pernette J. Verschure were spelled incorrectly. These errors have been corrected in the print, PDF and HTML versions of this article.

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Primary accessions

Gene Expression Omnibus

References

  1. 1.

    The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).

  2. 2.

    Early Breast Cancer Trialists' Collaborative Group (EBCTCG). Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials. Lancet 378, 771–784 (2011).

  3. 3.

    et al. Adjuvant exemestane with ovarian suppression in premenopausal breast cancer. N. Engl. J. Med. 371, 107–118 (2014).

  4. 4.

    Early Breast Cancer Trialists' Collaborative Group (EBCTCG). Aromatase inhibitors versus tamoxifen in early breast cancer: patient-level meta-analysis of the randomised trials. Lancet 386, 1341–1352 (2015).

  5. 5.

    & Biological determinants of endocrine resistance in breast cancer. Nat. Rev. Cancer 9, 631–643 (2009).

  6. 6.

    et al. Chromosome-wide mapping of estrogen receptor binding reveals long-range regulation requiring the forkhead protein FoxA1. Cell 122, 33–43 (2005).

  7. 7.

    et al. Genome-wide reprogramming of the chromatin landscape underlies endocrine therapy resistance in breast cancer. Proc. Natl. Acad. Sci. USA 110, E1490–E1499 (2013).

  8. 8.

    , & Estrogen receptor mutations in breast cancer. J. Cell. Biochem. 51, 135–139 (1993).

  9. 9.

    et al. Plasma ESR1 mutations and the treatment of estrogen receptor–positive advanced breast cancer. J. Clin. Oncol. 34, 2961–2968 (2016).

  10. 10.

    et al. Activating ESR1 mutations in hormone-resistant metastatic breast cancer. Nat. Genet. 45, 1446–1451 (2013).

  11. 11.

    et al. ESR1 ligand-binding domain mutations in hormone-resistant breast cancer. Nat. Genet. 45, 1439–1445 (2013).

  12. 12.

    et al. Differential epigenetic reprogramming in response to specific endocrine therapies promotes cholesterol biosynthesis and cellular invasion. Nat. Commun. 6, 10044 (2015).

  13. 13.

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

  14. 14.

    et al. Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc. Natl. Acad. Sci. USA 104, 20007–20012 (2007).

  15. 15.

    et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).

  16. 16.

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

  17. 17.

    et al. CaSNP: a database for interrogating copy number alterations of cancer genome from SNP array data. Nucleic Acids Res. 39, D968–D974 (2011).

  18. 18.

    et al. Androgen receptor gene amplification: a possible molecular mechanism for androgen deprivation therapy failure in prostate cancer. Cancer Res. 57, 314–319 (1997).

  19. 19.

    et al. In vivo amplification of the androgen receptor gene and progression of human prostate cancer. Nat. Genet. 9, 401–406 (1995).

  20. 20.

    , , & Directed evolution of human estrogen receptor variants with significantly enhanced androgen specificity and affinity. J. Biol. Chem. 279, 33855–33864 (2004).

  21. 21.

    , , & Estrogen receptor activation function 2 (AF-2) is essential for hormone-dependent transactivation and cell transformation induced by a v-Jun DNA binding domain–estrogen receptor chimera. Biochim. Biophys. Acta 1628, 147–155 (2003).

  22. 22.

    , & Molecular simulations of aromatase reveal new insights into the mechanism of ligand binding. J. Chem. Inf. Model. 53, 2047–2056 (2013).

  23. 23.

    , & Antiestrogens and their therapeutic applications in breast cancer and other diseases. Annu. Rev. Med. 62, 217–232 (2011).

  24. 24.

    et al. Effects of resin or charcoal treatment on fetal bovine serum and bovine calf serum. Endocr. Res. 34, 101–108 (2009).

  25. 25.

    , , , & Low-coverage sequencing: implications for design of complex trait association studies. Genome Res. 21, 940–951 (2011).

  26. 26.

    , , & Changes in oestrogen receptor-α and -β during progression to acquired resistance to tamoxifen and fulvestrant (Faslodex, ICI 182,780) in MCF7 human breast cancer cells. J. Steroid Biochem. Mol. Biol. 99, 19–32 (2006).

  27. 27.

    et al. Estrogen receptor expression and function in long-term estrogen-deprived human breast cancer cells. Endocrinology 139, 4164–4174 (1998).

  28. 28.

    et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann. Oncol. 24, 2206–2223 (2013).

  29. 29.

    et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

  30. 30.

    et al. The transactivating isoforms of p63 are overexpressed in high-grade follicular lymphomas independent of the occurrence of p63 gene amplification. J. Pathol. 206, 337–345 (2005).

  31. 31.

    , , , & The volumes and transcript counts of single cells reveal concentration homeostasis and capture biological noise. Mol. Biol. Cell 26, 797–804 (2015).

  32. 32.

    et al. Identification of a population of blood circulating tumor cells from breast cancer patients that initiates metastasis in a xenograft assay. Nat. Biotechnol. 31, 539–544 (2013).

  33. 33.

    , , , & Prospective identification of tumorigenic breast cancer cells. Proc. Natl. Acad. Sci. USA 100, 3983–3988 (2003).

  34. 34.

    & Assay of aromatase activity. Methods Enzymol. 206, 477–483 (1991).

  35. 35.

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

  36. 36.

    et al. An interactive analysis and exploration tool for epigenomic data. Comput. Graph. Forum 32, 91–100 (2013).

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Acknowledgements

We thank all participants and their families. We thank A. Bardelli for his comments. We thank D. Patten for help with the exemestane study. We thank L. Watson for her help with the manuscript. We thank J.Bean for support. For these studies, S.M. and G.P. were supported by Associazione Italiana Ricerca sul Cancro (AIRC) (5x1000 campaign). L.M. was supported by the Imperial College Junior Research Fellowship. S.-P.H. was supported by Cancer Research UK (CRUK) grant C37/A18784. Y.P. was supported by CRUK PhD studentship P55374. G.C. was supported by the EpiPredict project (European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement 642691).

Author information

Author notes

    • Luca Magnani
    •  & Gianmaria Frigè

    These authors contributed equally to this work.

Affiliations

  1. Department of Surgery and Cancer, Imperial College London, London, UK.

    • Luca Magnani
    • , Raffaella Maria Gadaleta
    • , Giacomo Corleone
    • , Sung-Pil Hong
    • , Ylenia Perone
    •  & Simak Ali
  2. Department of Experimental Oncology, European Institute of Oncology, Milan, Italy.

    • Gianmaria Frigè
    •  & Saverio Minucci
  3. Hematology Unit, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.

    • Sonia Fabris
    •  & Antonino Neri
  4. Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands.

    • Hermannus Kempe
    •  & Pernette J Verschure
  5. Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.

    • Iros Barozzi
  6. Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Arcavacata di Rende, Italy.

    • Valentina Vircillo
  7. Division of Stem Cells and Cancer, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany.

    • Massimo Saini
    •  & Andreas Trumpp
  8. Institute for Stem Cell Technology and Experimental Medicine GmbH, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany.

    • Massimo Saini
    • , Andreas Trumpp
    •  & Giancarlo Pruneri
  9. Division of Pathology, European Institute of Oncology and University of Milan, School of Medicine, Milan, Italy.

    • Giuseppe Viale
  10. Department of Oncology and Hemato-oncology, University of Milano, Milan, Italy.

    • Antonino Neri
  11. Division of Medical Senology, European Institute of Oncology (IEO), Milan, Italy.

    • Marco Angelo Colleoni
  12. Department of Biosciences, University of Milano, Milan, Italy.

    • Saverio Minucci

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Contributions

L.M. conceived the study and wrote the manuscript. L.M., S.M. and G.P. planned and supervised all experiments. L.M., G.F., S.-P.H., Y.P., R.M.G., S.F., H.K., and V.V. performed experiments. G.C. and I.B. performed bioinformatics analyses. P.J.V., G.V., A.N., M.S., A.T., S.A. and M.A.C. provided reagents, samples and intellectual contribution. All authors discussed the results and commented on the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Luca Magnani or Giancarlo Pruneri or Saverio Minucci.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–11 and Supplementary Note

Excel files

  1. 1.

    Supplementary Table 1

    Estimation of CYP19A1 amplification in primary cancers using SNP arraysSNP array-based CNV analysis of several cancer studies has been performed using http://cistrome.org/CaSNP/ (see Online Methods). A summary of the results is shown.

  2. 2.

    Supplementary Table 2

    Probe lists for mutational profiling using Ion Torrent technologyBEDFILE containing the genomic coordinates of the probes used for the AmpliSeq custom panel.

  3. 3.

    Supplementary Data 1

    Ion Torrent called mutations in patients treated with tamoxifenReport summaries for each patient treated with single adjuvant tamoxifen were generated using Ion Reporter (https://ionreporter.thermofisher.com/ir/). Each tab contains the results from paired analyses (normal-metastasis). Labeling of each patient is concordant with the labeling from the main text.

  4. 4.

    Supplementary Data 2

    Ion Torrent called mutations in patients treated with aromatase inhibitorsReport summaries for each patient treated with single adjuvant aromatase inhibitors were generated using Ion Reporter (https://ionreporter.thermofisher.com/ir/). Each tab contains the results from paired analyses (normal-metastasis). Labeling of each patient is concordant with the labeling in the main text.