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

Abstract

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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Figure 1: EVSE-based identification of 36 risk TFs.
Figure 2: Enrichment of risk TF binding sites at breast cancer GWAS loci.
Figure 3: Regulatory network for breast cancer, showing clustering of breast cancer risk.
Figure 4: Correlation of expression of targets shared by transcription factor pairs in breast tumors.
Figure 5: A tree-and-leaf representation of the correlation matrix shows two clusters of risk TFs.
Figure 6: Effects of risk TF knockdown on cell proliferation.
Figure 7: The ESR1 regulon as readout of cell state.
Figure 8: Schematic model of mammary gland cell populations.

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Gene Expression Omnibus

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Gene Expression Omnibus

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Acknowledgements

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.

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Contributions

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.

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Correspondence to Kerstin B Meyer.

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The authors declare no competing financial interests.

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Castro, M., de Santiago, I., Campbell, T. et al. Regulators of genetic risk of breast cancer identified by integrative network analysis. Nat Genet 48, 12–21 (2016). https://doi.org/10.1038/ng.3458

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