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Regulators of genetic risk of breast cancer identified by integrative network analysis

Nature Genetics volume 48, pages 1221 (2016) | Download Citation


Genetic risk for breast cancer is conferred by a combination of multiple variants of small effect. To better understand how risk loci might combine, we examined whether risk-associated genes share regulatory mechanisms. We created a breast cancer gene regulatory network comprising transcription factors and groups of putative target genes (regulons) and asked whether specific regulons are enriched for genes associated with risk loci via expression quantitative trait loci (eQTLs). We identified 36 overlapping regulons that were enriched for risk loci and formed a distinct cluster within the network, suggesting shared biology. The risk transcription factors driving these regulons are frequently mutated in cancer and lie in two opposing subgroups, which relate to estrogen receptor (ER)+ luminal A or luminal B and ER basal-like cancers and to different luminal epithelial cell populations in the adult mammary gland. Our network approach provides a foundation for determining the regulatory circuits governing breast cancer, to identify targets for intervention, and is transferable to other disease settings.

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We thank A. Califano for helpful discussions and J. Stingl for advice and critical reading of the manuscript. We are grateful to the genomics and bioinformatics cores at the Cancer Research UK Cambridge Institute for their support. This work was funded by Cancer Research UK and the Breast Cancer Research Foundation. M.A.A.C. is funded by the National Research Council (CNPq) of Brazil. T.E.H. held a fellowship from the US Department of Defense Breast Cancer Research Program (W81XWH-11-1-0592) and is currently supported by a Royal Adelaide Hospital Career Development Fellowship (Australia). T.E.H. and W.D.T. are funded by the National Health and Medical Research Council (NHMRC) of Australia (1008349 to W.D.T. and 1084416 to W.D.T. and T.E.H.) and Cancer Australia/National Breast Cancer Foundation (627229 to W.D.T. and T.E.H.; ID: PS-15-041-W.D.T.). B.A.J.P. is a Gibb Fellow of Cancer Research UK and a National Institute for Health Research (NIHR) Senior Investigator. We would like to acknowledge the support of the University of Cambridge, Cancer Research UK and Hutchison Whampoa, Ltd.

Author information


  1. Bioinformatics and Systems Biology Laboratory, Federal University of Paraná (UFPR), Polytechnic Center, Curitiba, Brazil.

    • Mauro A A Castro
  2. Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.

    • Ines de Santiago
    • , Thomas M Campbell
    • , Courtney Vaughn
    • , Edith Ross
    • , Florian Markowetz
    • , Bruce A J Ponder
    •  & Kerstin B Meyer
  3. Department of Oncology, University of Cambridge, Hutchison/Medical Research Council (MRC) Research Centre, Cambridge, UK.

    • Ines de Santiago
    • , Thomas M Campbell
    • , Courtney Vaughn
    • , Bruce A J Ponder
    •  & Kerstin B Meyer
  4. Dame Roma Mitchell Cancer Research Laboratories, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia.

    • Theresa E Hickey
    •  & Wayne D Tilley


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M.A.A.C. and K.B.M. designed the experiments, and M.A.A.C. and I.d.S. carried out the computational analysis. T.M.C. carried out the microarray experiments, and C.V. performed the siRNA transfection and proliferation analysis. T.E.H. and W.D.T. performed AR ChIP-seq experiments. E.R. carried out copy number normalization and eQTL calling. F.M. provided computational expertise. K.B.M., M.A.A.C. and B.A.J.P. developed the ideas and wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Kerstin B Meyer.

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