Breast Cancer Research and Treatment

, Volume 166, Issue 2, pp 559–568 | Cite as

FOXA1 hypermethylation: link between parity and ER-negative breast cancer in African American women?

  • Allyson C. Espinal
  • Matthew F. Buas
  • Dan Wang
  • David Ting-Yuan Cheng
  • Lara Sucheston-Campbell
  • Qiang Hu
  • Li Yan
  • Rochelle Payne-Ondracek
  • Eduardo Cortes
  • Li Tang
  • Zhihong Gong
  • Gary Zirpoli
  • Thaer Khoury
  • Song Yao
  • Angela Omilian
  • Kitaw Demissie
  • Elisa V. Bandera
  • Song Liu
  • Christine B. Ambrosone
  • Michael J. Higgins
Epidemiology
  • 321 Downloads

Abstract

Background

Reproductive factors, particularly parity, have differential effects on breast cancer risk according to estrogen receptor (ER) status, especially among African American (AA) women. One mechanism could be through DNA methylation, leading to altered expression levels of genes important in cell fate decisions.

Methods

Using the Illumina 450K BeadChip, we compared DNA methylation levels in paraffin-archived tumor samples from 383 AA and 350 European American (EA) women in the Women’s Circle of Health Study (WCHS). We combined 450K profiles with RNA-seq data and prioritized genes based on differential methylation by race, correlation between methylation and gene expression, and biological function. We measured tumor protein expression and assessed its relationship to DNA methylation. We evaluated associations between reproductive characteristics and DNA methylation using linear regression.

Results

410 loci were differentially methylated by race, with the majority unique to ER− tumors. FOXA1 was hypermethylated in tumors from AA versus EA women with ER− cancer, and increased DNA methylation correlated with reduced RNA and protein expression. Importantly, parity was positively associated with FOXA1 methylation among AA women with ER− tumors (P = 0.022), as was number of births (P = 0.026), particularly among those who did not breastfeed (P = 0.008). These same relationships were not observed among EA women, although statistical power was more limited.

Conclusions

Methylation and expression of FOXA1 is likely impacted by parity and breastfeeding. Because FOXA1 regulates a luminal gene expression signature in progenitor cells and represses the basal phenotype, this could be a mechanism that links these reproductive exposures with ER− breast cancer.

Keywords

Breast cancer African American DNA methylation FOXA1 ER− breast cancer Disparities 

Supplementary material

10549_2017_4418_MOESM1_ESM.pdf (356 kb)
Supplementary material 1 (PDF 356 kb)

References

  1. 1.
    DeSantis CE et al (2016) Breast cancer statistics, 2015: convergence of incidence rates between black and white women. CA Cancer J Clin 66:31–42CrossRefPubMedGoogle Scholar
  2. 2.
    Palmer JR, Ambrosone CB, Olshan AF (2014) A collaborative study of the etiology of breast cancer subtypes in African American women: the AMBER consortium. Cancer Causes Control 25:309–319CrossRefPubMedGoogle Scholar
  3. 3.
    Palmer JR et al (2014) Parity, lactation, and breast cancer subtypes in African American women: results from the AMBER Consortium. J Natl Cancer Inst 106:dju237CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Martin JA, Hamilton BE, Osterman MJ, Curtin SC, Matthews TJ (2015) Births: final data for 2013. Natl Vital Stat Rep 64:1–65Google Scholar
  5. 5.
    McDowell MM, Wang C-Y, Kennedy-Stephenson J (2008) Breastfeeding in the United States: findings from the national health and nutrition examination surveys, 1999-2006. NCHS Data Brief 5:1–8Google Scholar
  6. 6.
    Huh SJ et al (2015) Age- and pregnancy-associated DNA methylation changes in mammary epithelial cells. Stem Cell Rep 4:297–311CrossRefGoogle Scholar
  7. 7.
    Izadi P, Noruzinia M, Fereidooni F, Mostakhdemine Hosseini Z, Kamali F (2014) Epigenetic marks in estrogen receptor alpha CpG island correlate with some reproductive risk factors in breast cancer. Mol Biol Rep 41:7607–7612CrossRefPubMedGoogle Scholar
  8. 8.
    Dos Santos CO, Dolzhenko E, Hodges E, Smith AD, Hannon GJ (2015) An epigenetic memory of pregnancy in the mouse mammary gland. Cell Rep 11:1102–1109CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Ghosh S et al (2014) Genome-wide DNA methylation profiling reveals parity-associated hypermethylation of FOXA1. Breast Cancer Res Treat 147:653–659CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Ambrosone CB et al (2014) Genome-wide methylation patterns provide insight into differences in breast tumor biology between American women of African and European ancestry. Oncotarget 5:237–248CrossRefPubMedGoogle Scholar
  11. 11.
    Ambrosone CB et al (2009) Conducting molecular epidemiological research in the age of HIPAA: a multi-institutional case-control study of breast cancer in African-American and European-American women. J Oncol 2009:871250CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Espinal AC et al (2017) A methodological study of genome-wide DNA methylation analyses using matched archival formalin-fixed paraffin embedded and fresh frozen breast tumors. Oncotarget 8:14821–14829PubMedPubMedCentralGoogle Scholar
  13. 13.
    Yan L et al (2012) OSAT: a tool for sample-to-batch allocations in genomics experiments. BMC Genomics 13:689CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Bibikova M et al (2011) High density DNA methylation array with single CpG site resolution. Genomics 98:288–295CrossRefPubMedGoogle Scholar
  15. 15.
    Sandoval J et al (2011) Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 6:692–702CrossRefPubMedGoogle Scholar
  16. 16.
    Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8:118–127CrossRefPubMedGoogle Scholar
  17. 17.
    Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28:882–883CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Wang D et al (2012) IMA: an R package for high-throughput analysis of Illumina’s 450K Infinium methylation data. Bioinformatics 28:729–730CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Blair JD, Price EM (2012) Illuminating potential technical artifacts of DNA-methylation array probes. Am J Hum Genet 91:760–762CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Zhang X, Mu W, Zhang W (2012) On the analysis of the illumina 450k array data: probes ambiguously mapped to the human genome. Front Genet 3:73PubMedPubMedCentralGoogle Scholar
  21. 21.
    Chen Y et al (2013) Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics 8:203–209CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol) 57:289–300Google Scholar
  23. 23.
    Allott EH et al (2016) Performance of three-biomarker immunohistochemistry for intrinsic breast cancer subtyping in the AMBER consortium. Cancer Epidemiol Biomark Prev 25:470–478CrossRefGoogle Scholar
  24. 24.
    Hirsch FR et al (2003) Epidermal growth factor receptor in non-small-cell lung carcinomas: correlation between gene copy number and protein expression and impact on prognosis. J Clin Oncol 21:3798–3807CrossRefPubMedGoogle Scholar
  25. 25.
    Bernardo GM et al (2013) FOXA1 represses the molecular phenotype of basal breast cancer cells. Oncogene 32:554–563CrossRefPubMedGoogle Scholar
  26. 26.
    Bernardo GM, Keri RA (2012) FOXA1: a transcription factor with parallel functions in development and cancer. Biosci Rep 32:113–130CrossRefPubMedGoogle Scholar
  27. 27.
    Bernardo GM et al (2010) FOXA1 is an essential determinant of ERalpha expression and mammary ductal morphogenesis. Development 137:2045–2054CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Ernst J, Kellis M (2010) Discovery and characterization of chromatin states for systematic annotation of the human genome. Nat Biotechnol 28:817–825CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Ernst J et al (2011) Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473:43–49CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Work ME et al (2014) Reproductive risk factors and oestrogen/progesterone receptor-negative breast cancer in the Breast Cancer Family Registry. Br J Cancer 110:1367–1377CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Eeckhoute J et al (2009) Cell-type selective chromatin remodeling defines the active subset of FOXA1-bound enhancers. Genome Res 19:372–380CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Cowper-Sal lari R et al (2012) Breast cancer risk-associated SNPs modulate the affinity of chromatin for FOXA1 and alter gene expression. Nat Genet 44:1191–1198CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Gross K, Wronski A, Skibinski A, Phillips S, Kuperwasser C (2016) Cell fate decisions during breast cancer development. J Dev Biol 4:4CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Allyson C. Espinal
    • 1
  • Matthew F. Buas
    • 2
  • Dan Wang
    • 3
  • David Ting-Yuan Cheng
    • 2
    • 4
  • Lara Sucheston-Campbell
    • 5
  • Qiang Hu
    • 3
  • Li Yan
    • 3
  • Rochelle Payne-Ondracek
    • 2
  • Eduardo Cortes
    • 3
  • Li Tang
    • 2
  • Zhihong Gong
    • 2
  • Gary Zirpoli
    • 2
    • 6
  • Thaer Khoury
    • 7
  • Song Yao
    • 2
  • Angela Omilian
    • 7
  • Kitaw Demissie
    • 8
  • Elisa V. Bandera
    • 9
  • Song Liu
    • 3
  • Christine B. Ambrosone
    • 2
  • Michael J. Higgins
    • 1
  1. 1.Department of Molecular and Cellular BiologyRoswell Park Cancer InstituteBuffaloUSA
  2. 2.Department of Cancer Prevention and ControlRoswell Park Cancer InstituteBuffaloUSA
  3. 3.Department of Biostatistics and BioinformaticsRoswell Park Cancer InstituteBuffaloUSA
  4. 4.Department of EpidemiologyUniversity of FloridaGainesvilleUSA
  5. 5.The Ohio State UniversityColumbusUSA
  6. 6.Massachusetts General HospitalBostonUSA
  7. 7.Department of PathologyRoswell Park Cancer InstituteBuffaloUSA
  8. 8.Department of EpidemiologyRutgers School of Public Health and Cancer Institute of New JerseyNew JerseyUSA
  9. 9.Rutgers Cancer Institute of New JerseyNew BrunswickUSA

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