Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170

Abstract

We analyzed 3,872 common genetic variants across the ESR1 locus (encoding estrogen receptor α) in 118,816 subjects from three international consortia. We found evidence for at least five independent causal variants, each associated with different phenotype sets, including estrogen receptor (ER+ or ER) and human ERBB2 (HER2+ or HER2) tumor subtypes, mammographic density and tumor grade. The best candidate causal variants for ER tumors lie in four separate enhancer elements, and their risk alleles reduce expression of ESR1, RMND1 and CCDC170, whereas the risk alleles of the strongest candidates for the remaining independent causal variant disrupt a silencer element and putatively increase ESR1 and RMND1 expression.

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Figure 1: Association results for all SNPs with six phenotypes.
Figure 2: ER expression and allelic imbalance correlate with signal 1 SNPs.
Figure 3: Chromatin interactions across the 6q25.1 risk region.
Figure 4: Risk alleles reduce ESR1 and RMND1 promoter activity.
Figure 5: GATA3 and CTCF binding in vivo.

References

  1. 1

    Zheng, W. et al. Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1. Nat. Genet. 41, 324–328 (2009).

    CAS  Article  Google Scholar 

  2. 2

    Turnbull, C. et al. Genome-wide association study identifies five new breast cancer susceptibility loci. Nat. Genet. 42, 504–507 (2010).

    CAS  Article  Google Scholar 

  3. 3

    Antoniou, A.C. et al. Common alleles at 6q25.1 and 1p11.2 are associated with breast cancer risk for BRCA1 and BRCA2 mutation carriers. Hum. Mol. Genet. 20, 3304–3321 (2011).

    CAS  Article  Google Scholar 

  4. 4

    Lindström, S. et al. Common variants in ZNF365 are associated with both mammographic density and breast cancer risk. Nat. Genet. 43, 185–187 (2011).

    Article  Google Scholar 

  5. 5

    Stacey, S.N. et al. Ancestry-shift refinement mapping of the C6orf97-ESR1 breast cancer susceptibility locus. PLoS Genet. 6, e1001029 (2010).

    Article  Google Scholar 

  6. 6

    Hein, R. et al. Comparison of 6q25 breast cancer hits from Asian and European genome wide association studies in the Breast Cancer Association Consortium (BCAC). PLoS One 7, e42380 (2012).

    CAS  Article  Google Scholar 

  7. 7

    Edwards, S.L., Beesley, J., French, J.D. & Dunning, A.M. Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. 93, 779–797 (2013).

    CAS  Article  Google Scholar 

  8. 8

    Mavaddat, N. et al. Pathology of breast and ovarian cancers among BRCA1 and BRCA2 mutation carriers: results from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Cancer Epidemiol. Biomarkers Prev. 21, 134–147 (2012).

    CAS  Article  Google Scholar 

  9. 9

    Spencer, A.V., Cox, A. & Walters, K. Comparing the efficacy of SNP filtering methods for identifying a single causal SNP in a known association region. Ann. Hum. Genet. 78, 50–61 (2014).

    CAS  Article  Google Scholar 

  10. 10

    Cai, Q. et al. Replication and functional genomic analyses of the breast cancer susceptibility locus at 6q25.1 generalize its importance in women of Chinese, Japanese, and European ancestry. Cancer Res. 71, 1344–1355 (2011).

    CAS  Article  Google Scholar 

  11. 11

    Li, Q. et al. Integrative eQTL-based analyses reveal the biology of breast cancer risk loci. Cell 152, 633–641 (2013).

    CAS  Article  Google Scholar 

  12. 12

    Corradin, O. et al. Combinatorial effects of multiple enhancer variants in linkage disequilibrium dictate levels of gene expression to confer susceptibility to common traits. Genome Res. 24, 1–13 (2014).

    CAS  Article  Google Scholar 

  13. 13

    Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

    CAS  Article  Google Scholar 

  14. 14

    French, J.D. et al. Functional variants at the 11q13 risk locus for breast cancer regulate cyclin D1 expression through long-range enhancers. Am. J. Hum. Genet. 92, 489–503 (2013).

    CAS  Article  Google Scholar 

  15. 15

    Ghoussaini, M. et al. Evidence that breast cancer risk at the 2q35 locus is mediated through IGFBP5 regulation. Nat. Commun. 4, 4999 (2014).

    Article  Google Scholar 

  16. 16

    Glubb, D.M. et al. Fine-scale mapping of the 5q11.2 breast cancer locus reveals at least three independent risk variants regulating MAP3K1. Am. J. Hum. Genet. 96, 5–20 (2015).

    CAS  Article  Google Scholar 

  17. 17

    Cowper-Sal·lari, R. et al. Breast cancer risk-associated SNPs modulate the affinity of chromatin for FOXA1 and alter gene expression. Nat. Genet. 44, 1191–1198 (2012).

    Article  Google Scholar 

  18. 18

    Ward, L.D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012).

    CAS  Article  Google Scholar 

  19. 19

    Grabe, N. AliBaba2: context specific identification of transcription factor binding sites. In Silico Biol. 2, S1–S15 (2002).

    PubMed  Google Scholar 

  20. 20

    Dunbier, A.K. et al. ESR1 is co-expressed with closely adjacent uncharacterised genes spanning a breast cancer susceptibility locus at 6q25.1. PLoS Genet. 7, e1001382 (2011).

    CAS  Article  Google Scholar 

  21. 21

    Antoniou, A.C. et al. A locus on 19p13 modifies risk of breast cancer in BRCA1 mutation carriers and is associated with hormone receptor–negative breast cancer in the general population. Nat. Genet. 42, 885–892 (2010).

    CAS  Article  Google Scholar 

  22. 22

    Haiman, C.A. et al. A common variant at the TERT-CLPTM1L locus is associated with estrogen receptor–negative breast cancer. Nat. Genet. 43, 1210–1214 (2011).

    CAS  Article  Google Scholar 

  23. 23

    McCormack, V.A. & dos Santos Silva, I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol. Biomarkers Prev. 15, 1159–1169 (2006).

    Article  Google Scholar 

  24. 24

    Varghese, J.S. et al. Mammographic breast density and breast cancer: evidence of a shared genetic basis. Cancer Res. 72, 1478–1484 (2012).

    CAS  Article  Google Scholar 

  25. 25

    Crandall, C.J. et al. Sex steroid metabolism polymorphisms and mammographic density in pre- and early perimenopausal women. Breast Cancer Res. 11, R51 (2009).

    Article  Google Scholar 

  26. 26

    Lindström, S. et al. Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk. Nat. Commun. 5, 5303 (2014).

    Article  Google Scholar 

  27. 27

    Stone, J. et al. Novel associations between common breast cancer susceptibility variants and risk-predicting mammographic density measures. Cancer Res. 75, 2457–2467 (2015).

    CAS  Article  Google Scholar 

  28. 28

    Estrada, K. et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat. Genet. 44, 491–501 (2012).

    CAS  Article  Google Scholar 

  29. 29

    Koller, D.L. et al. Meta-analysis of genome-wide studies identifies WNT16 and ESR1 SNPs associated with bone mineral density in premenopausal women. J. Bone Miner. Res. 28, 547–558 (2013).

    CAS  Article  Google Scholar 

  30. 30

    Perry, J.R. et al. Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche. Nature 514, 92–97 (2014).

    CAS  Article  Google Scholar 

  31. 31

    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).

    CAS  Article  Google Scholar 

  32. 32

    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).

    CAS  Article  Google Scholar 

  33. 33

    Janer, A. et al. An RMND1 mutation causes encephalopathy associated with multiple oxidative phosphorylation complex deficiencies and a mitochondrial translation defect. Am. J. Hum. Genet. 91, 737–743 (2012).

    CAS  Article  Google Scholar 

  34. 34

    Perry, J.J. et al. Human C6orf211 encodes Armt1, a protein carboxyl methyltransferase that targets PCNA and is linked to the DNA damage response. Cell Rep. 10, 1288–1296 (2015).

    CAS  Article  Google Scholar 

  35. 35

    Veeraraghavan, J. et al. Recurrent ESR1-CCDC170 rearrangements in an aggressive subset of oestrogen receptor–positive breast cancers. Nat. Commun. 5, 4577 (2014).

    CAS  Article  Google Scholar 

  36. 36

    Yamamoto-Ibusuki, M. et al. C6ORF97-ESR1 breast cancer susceptibility locus: influence on progression and survival in breast cancer patients. Eur. J. Hum. Genet. 23, 949–956 (2015).

    CAS  Article  Google Scholar 

  37. 37

    Chenevix-Trench, G. et al. An international initiative to identify genetic modifiers of cancer risk in BRCA1 and BRCA2 mutation carriers: the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). Breast Cancer Res. 9, 104 (2007).

    Article  Google Scholar 

  38. 38

    Couch, F.J. et al. Genome-wide association study in BRCA1 mutation carriers identifies novel loci associated with breast and ovarian cancer risk. PLoS Genet. 9, e1003212 (2013).

    CAS  Article  Google Scholar 

  39. 39

    Boyd, N.F. et al. Mammographic density and the risk and detection of breast cancer. N. Engl. J. Med. 356, 227–236 (2007).

    CAS  Article  Google Scholar 

  40. 40

    Dunning, A.M. et al. Association of ESR1 gene tagging SNPs with breast cancer risk. Hum. Mol. Genet. 18, 1131–1139 (2009).

    CAS  Article  Google Scholar 

  41. 41

    Barnes, D.R., Lee, A., Easton, D.F. & Antoniou, A.C. Evaluation of association methods for analysing modifiers of disease risk in carriers of high-risk mutations. Genet. Epidemiol. 36, 274–291 (2012).

    Article  Google Scholar 

  42. 42

    Antoniou, A.C. et al. A weighted cohort approach for analysing factors modifying disease risks in carriers of high-risk susceptibility genes. Genet. Epidemiol. 29, 1–11 (2005).

    Article  Google Scholar 

  43. 43

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

  44. 44

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

    CAS  Article  Google Scholar 

  45. 45

    Xiao, R. & Scott, L.J. Detection of cis-acting regulatory SNPs using allelic expression data. Genet. Epidemiol. 35, 515–525 (2011).

    Article  Google Scholar 

  46. 46

    Sherman, M.E. et al. The Susan G. Komen for the Cure Tissue Bank at the IU Simon Cancer Center: a unique resource for defining the “molecular histology” of the breast. Cancer Prev. Res. (Phila.) 5, 528–535 (2012).

    Article  Google Scholar 

Download references

Acknowledgements

We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. This study would not have been possible without the contributions of the following: A. Berchuck (OCAC), R.A. Eeles, A.A. Al Olama, Z. Kote-Jarai and S. Benlloch (PRACTICAL), C. Luccarini and the staff of the Centre for Genetic Epidemiology Laboratory, the staff of the CNIO genotyping unit, D.C. Tessier, F. Bacot, D. Vincent, S. LaBoissière, F. Robidoux and the staff of the McGill University and Génome Québec Innovation Centre, S.F. Nielsen, B.G. Nordestgaard and the staff of the Copenhagen DNA laboratory, and J.M. Cunningham, S.A. Windebank, C.A. Hilker, J. Meyer and the staff of the Mayo Clinic Genotyping Core Facility. Normal human tissues from the Susan G. Komen for the Cure Tissue Bank at the Indiana University Simon Cancer Center (Indianapolis) were used in this study. We thank the contributors, including Indiana University who collected samples used in this study, as well as the donors and their families, whose help and participation made this work possible. We also acknowledge National Institute for Health Research (NIHR) support to the Royal Marsden Biomedical Research Centre. Funding for the iCOGS infrastructure came from the European Community's Seventh Framework Programme under grant agreement 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692 and C8197/A16565), the US National Institutes of Health (NIH; CA128978, CA192393, CA116167, CA176785 and an NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201)) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112, the GAME-ON initiative), the US Department of Defense (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, the Komen Foundation for the Cure, the Breast Cancer Research Foundation and the Ovarian Cancer Research Fund. Full acknowledgments are given in the Supplementary Note.

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Manuscript writing group: A.M.D., K. Michailidou, K.B.K., D. Thompson, J.D.F., K.A.P., J. Beesley, C.S.H., G.C.-T., A.C.A., D.F.E. and S.L.E. Locus SNP selection: A.M.D., C.S.H. and E.D. iCOGS genotyping, genotype calling and quality control: A.M.D., J. Beesley, C.S.H., M.G., G.C.-T., K.A.P. and D.F.E. Imputation: K. Michailidou, K.B.K., A.C.A. and D.F.E. Statistical analyses and programming: K. Michailidou, K.B.K., A.C.A. and D.F.E. Functional analysis and bioinformatics: S.L.E., J.D.F., K.M.H., S. Kaufmann, H.S., M.M.M., J.S.L., E.L.-K., M. Hills, M.J., S.D., J. Beesley, S. Kar, N.A.S.-A., R.C.S., S.C. and S.N. COGS coordination: P.H., D.F.E., J. Beesley and A.M.D. BCAC coordination: D.F.E., G.C.-T., P.D.P.P. and J. Stone. BCAC data management: M.K.B. and Q.W. CIMBA coordination: A.C.A., G.C.-T., J. Stone and F.J.C. CIMBA data management: L.M. and D.B. MODE coordination: D. Thompson, C.V. and F.J.C. Provided participant samples and phenotype information and read and approved the manuscript: A.M.D., K. Michailidou, K.B.K., D. Thompson, J.D.F., J. Beesley, C.S.H., S. Kar, K.A.P., E.L.-K., E.D., D.B., N.A.S.-A., R.C.S., K.M.H., S. Kaufmann, H.S., M.M.M., J.S.L., M. Hills, M.J., S.D., S.C., M.K.B., J.D., Q.W., J.L.H., M.C.S., A. Broeks, M.K.S., A. Lophatananon, K. Muir, M.W.B., P.A.F., I.d.-S.-S., J.P., E.J.S., I.T., B. Burwinkel, F.M., P.G., T.T., S.E.B., H.F., A.G.-N., J.I.A.P., H.A.-C., L.E., V.A., H. Brenner, A. Meindl, R.K.S., H. Brauch, U.H., K.A., C.B., H.I., K. Matsuo, N.B., T.D., A. Lindblom, S. Margolin, V.-M.K., A. Mannermaa, C.T., A.H.W., D.L., H.W., J.C.-C., A.R., P.P., P.R., J.E.O., G.G.G., R.L.M., C.A.H., B.E.H., M.S.G., S.H.T., C.H.Y., S.N., A.-L.B.-D., V.K., J. Long, W.Z., K.P., R.W., I.L.A., J.A.K., P.D., C. Seynaeve, J.F., M.E.S., K.C., H.D., A.Hollestelle, A.M.W.v.d.O., K.H., Y.-T.G., X.-O.S., A.C., S.S.C., W.B., Q.C., B.J.P., M.S., J.-Y.C., D.K., S.C.L., M. Hartman, M. Kabisch, D. Torres, A.J., J. Lubinski, P.B., S.S., C.B.A., A.E.T., C.-Y.S., P.-E.W., N.O., A.S., L.M., S.H., A. Lee, M. Kapuscinski, E.M.J., M.B.T., M.B.D., D.E.G., S.S.B., R.J., L.T., N.T., C.M.D., E.J.v.R., S.L.N., B.E., T.V.O.H., A.O., J. Benitez, R.R., J.N.W., B. Bonanni, B.P., S. Manoukian, L.P., L.O., I.K., P.A., J. Garber, M.U.R., D.F., L.I., S.E., A.K.G., N.A., D.N., K.R., N.B.-M., C. Sagne, D.S.-L., F.D., O.M.S., S. Mazoyer, C.I., K.B.M.C., K.D.L., M.d.l.H., T.C., H.N., S. Khan, A.R.M., M.J.H., M.A.R., A.K., E.O., O.D., J. Brunet, M.A.P., J. Gronwald, T.H., R.B.B., R. Laframboise, P.S., M.M., S.A., M.R.T., S.K.P., N.L., F.J.C., M. Tischkowitz, L.F., J.V., K.O., C.F.S., C.R., C.M.P., M.H.G., P.L.M., G.R., E.N.I., P.J.H., K.-A.P., M.P., A.M.M., G.G., A. Bojesen, M. Thomassen, M.A.C., S.-Y.Y., E.F., Y.L., A. Borg, A.v.W., H.E., J.R., O.I.O., P.A.G., R.L.N., S.A.G., K.L.N., S.M.D., B.K.A., G. Mitchell, B.Y.K., J. Lester, G. Maskarinec, C.W., C. Scott, J. Stone, C.A., R.T., R. Luben, K.-T.K., Å.Helland, V.H., M.D., P.D.P.P., J. Simard, P.H., M.G.-C., C.V., G.C.-T., A.C.A., D.F.E. and, S.L.E.

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Correspondence to Alison M Dunning or Douglas F Easton or Stacey L Edwards.

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A full list of members appears in the Supplementary Note.

A full list of members appears in the Supplementary Note.

A full list of members appears in the Supplementary Note.

A full list of members appears in the Supplementary Note.

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Supplementary Figures 1–15, Supplementary Tables 1–18 and Supplementary Note. (PDF 8070 kb)

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Dunning, A., Michailidou, K., Kuchenbaecker, K. et al. Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170. Nat Genet 48, 374–386 (2016). https://doi.org/10.1038/ng.3521

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