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Enhancer mapping uncovers phenotypic heterogeneity and evolution in patients with luminal breast cancer

Nature Medicine volume 24, pages 1469–1480 (2018)Cite this article

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Abstract

The degree of intrinsic and interpatient phenotypic heterogeneity and its role in tumor evolution is poorly understood. Phenotypic drifts can be transmitted via inheritable transcriptional programs. Cell-type specific transcription is maintained through the activation of epigenetically defined regulatory regions including promoters and enhancers. Here we have annotated the epigenome of 47 primary and metastatic estrogen-receptor (ERα)-positive breast cancer clinical specimens and inferred phenotypic heterogeneity from the regulatory landscape, identifying key regulatory elements commonly shared across patients. Shared regions contain a unique set of regulatory information including the motif for transcription factor YY1. We identify YY1 as a critical determinant of ERα transcriptional activity promoting tumor growth in most luminal patients. YY1 also contributes to the expression of genes mediating resistance to endocrine treatment. Finally, we used H3K27ac levels at active enhancer elements as a surrogate of intra-tumor phenotypic heterogeneity to track the expansion and contraction of phenotypic subpopulations throughout breast cancer progression. By tracking the clonality of SLC9A3R1-positive cells, a bona fide YY1-ERα-regulated gene, we show that endocrine therapies select for phenotypic clones under-represented at diagnosis. Collectively, our data show that epigenetic mechanisms significantly contribute to phenotypic heterogeneity and evolution in systemically treated breast cancer patients.

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Fig. 1: Assessment of inter- and intratumour epigenetic heterogeneity.
Fig. 2: Clonal and subclonal regulatory regions contain distinct regulatory information.
Fig. 3: YY1 identifies a dominant phenotypic clone in ERα BC.
Fig. 4: YY1 marks critical enhancers in BC cells.
Fig. 5: Epigenomic mapping predicts the size of phenotypic clones in patients.
Fig. 6: Endocrine treatment shapes phenotypic evolution.

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Acknowledgements

The authors acknowledge and thanks all patients and their families for support and for donating research samples. The authors thank the Breast Cancer Now Tissue Bank (project TR0121), Imperial Tissue Bank and the LEGACY study for contributing tissues. The authors acknowledge infrastructure support from the Cancer Research UK Imperial Centre, the Imperial Experimental Cancer Medicine Centre and the National Institute for Health Research Imperial Biomedical Research Centre. L.M. was supported by a CRUK fellowship (C46704/A23110) and an Imperial Junior Fellowship (G53019). D.P. was supported by a Wellcome Trust PhD studentship (103034/Z/13/Z). G.C. was supported by a Marie Skłodowska Curie Training Grant (642691, EpiPredict). G.P. was supported by AIRC IG 2016-18696. The authors thank J.A. Buendia, L. Watson and J. Carrol for constructive comments on the manuscript.

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Author notes

  1. These authors contributed equally: Darren K. Patten, Giacomo Corleone.

Authors and Affiliations

  1. Department of Surgery and Cancer, The Imperial Centre for Translational and Experimental Medicine, Imperial College London, London, UK

    Darren K. Patten, Giacomo Corleone, Ylenia Perone, Neil Slaven, Iros Barozzi, Raul C. Coombes & Luca Magnani

  2. MTA TTK Lendület Cancer Biomarker Research Group, Institute of Enzymology, Hungarian Academy of Sciences, Budapest, Hungary

    Balázs Győrffy & Lőrinc Sándor Pongor

  3. Semmelweis University, 2nd Deptartment of Pediatrics, Budapest, Hungary

    Balázs Győrffy

  4. Department of Biochemistry and Molecular Biology, Genomic Medicine and Bioinformatic Core Facility, University of Debrecen, Debrecen, Hungary

    Edina Erdős

  5. Department of Genetics and Genome Sciences, Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA

    Alina Saiakhova & Peter Scacheri

  6. Department of Breast and General Surgery, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK

    Kate Goddard

  7. Department of Pathology, European Institute of Oncology, Milan, Italy

    Andrea Vingiani

  8. Centre for Pathology, Department of Medicine, Imperial College London, London, UK

    Sami Shousha & Dimitri J. Hadjiminas

  9. IMED Biotech Unit, AstraZeneca, Cambridge, UK

    Gaia Schiavon

  10. Department of Breast Surgery, The Royal Marsden NHS Foundation Trust, Sutton, UK

    Peter Barry

  11. Institute of Translational Medicine University of Liverpool, Clatterbridge Cancer Centre, NHS Foundation Trust, and Royal Liverpool University Hospital, Liverpool, Merseyside, UK

    Carlo Palmieri

  12. Pathology Department, Fondazione IRCCS Istituto Nazionale Tumori and University of Milan, School of Medicine, Milan, Italy

    Giancarlo Pruneri

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Contributions

L.M. conceived the study. D.K.P., E.E., N.S. and Y.P. performed the experiments. L.M., G.C., B.G., A.S., L.S.P., I.B. and P.S. developed and performed bioinformatic analyses. K.G. organized tissue collection. D.J.H., G.S., P.B., C.P. and R.C.C. recruited patients and supplied tissues. S.S. performed pathology assessment of ChIP–seq processed samples. G.P. provided matched material. A.V. and G.P. performed IHC staining and scoring. All authors read and approved the manuscript.

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Correspondence to Luca Magnani.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–19 and Supplementary Methods

Reporting Summary

Supplementary Table 1

Clinical characteristics of the patient dataset

Supplementary Table 2

Summary statistics for ChIP-seq in clinical samples

Supplementary Dataset

Coding mutations at cancer genes

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Patten, D.K., Corleone, G., Győrffy, B. et al. Enhancer mapping uncovers phenotypic heterogeneity and evolution in patients with luminal breast cancer. Nat Med 24, 1469–1480 (2018). https://doi.org/10.1038/s41591-018-0091-x

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