Clinical & Experimental Metastasis

, Volume 27, Issue 5, pp 279–293 | Cite as

Dietary fat-dependent transcriptional architecture and copy number alterations associated with modifiers of mammary cancer metastasis

  • Ryan R. Gordon
  • Michele La Merrill
  • Kent W. Hunter
  • Peter Sørensen
  • David W. Threadgill
  • Daniel Pomp
Research Paper


Breast cancer is a complex disease resulting from a combination of genetic and environmental factors. Among environmental factors, body composition and intake of specific dietary components like total fat are associated with increased incidence of breast cancer and metastasis. We previously showed that mice fed a high-fat diet have shorter mammary cancer latency, increased tumor growth and more pulmonary metastases than mice fed a standard diet. Subsequent genetic analysis identified several modifiers of metastatic mammary cancer along with widespread interactions between cancer modifiers and dietary fat. To elucidate diet-dependent genetic modifiers of mammary cancer and metastasis risk, global gene expression profiles and copy number alterations from mammary cancers were measured and expression quantitative trait loci (eQTL) identified. Functional candidate genes that colocalized with previously detected metastasis modifiers were identified. Additional analyses, such as eQTL by dietary fat interaction analysis, causality and database evaluations, helped to further refine the candidate loci to produce an enriched list of genes potentially involved in the pathogenesis of metastatic mammary cancer.


Breast cancer Causality eQTL High-fat diet Tumors 



Average metastasis density


Cis-acting eQTL


Copy number alteration


Copy number change




Expression quantitative trait loci


Genetic alteration


High fat diet


Ingenuity pathway analysis


Likeihood ratio statistic


Matched control diet


Metastasis detected at sacrifice




Polyoma middle t oncoprotein


Quantitative trait loci



This work was partially funded by grants from the NCI-MMHCC (U01CA105417) and NIDDK (DK076050), and by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.


  1. 1.
    Rohan TE, Li SQ, Hartwick R, et al. (2006) p53 Alterations and protein accumulation in benign breast tissue and breast cancer risk: a cohort study. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 15(7): 1316–1323Google Scholar
  2. 2.
    Song CG, Hu Z, Wu J et al (2006) The prevalence of BRCA1 and BRCA2 mutations in eastern Chinese women with breast cancer. J Cancer Res Clin Oncol 132(10):617–626CrossRefPubMedGoogle Scholar
  3. 3.
    Walsh T, Casadei S, Coats KH et al (2006) Spectrum of mutations in BRCA1, BRCA2, CHEK2, and TP53 in families at high risk of breast cancer. JAMA J Am Med Assoc 295(12):1379–1388CrossRefGoogle Scholar
  4. 4.
    Gordon RR, Hunter KW, Sorensen P et al (2008) Genotype X diet interactions in mice predisposed to mammary cancer. I. Body weight and fat. Mamm Genome 19(3):163–178CrossRefPubMedGoogle Scholar
  5. 5.
    Allan MF, Eisen EJ, Pomp D (2004) The M16 mouse: an outbred animal model of early onset polygenic obesity and diabesity. Obes Res 12(9):1397–1407CrossRefPubMedGoogle Scholar
  6. 6.
    Guy CT, Cardiff RD, Muller WJ (1992) Induction of mammary tumors by expression of polyomavirus middle T oncogene: a transgenic mouse model for metastatic disease. Mol Cell Biol 12(3):954–961PubMedGoogle Scholar
  7. 7.
    Gordon RR, Hunter KW, La Merrill M et al (2008) Genotype X diet interactions in mice predisposed to mammary cancer: II. Tumors and metastasis. Mamm Genome 19(3):179–189CrossRefPubMedGoogle Scholar
  8. 8.
    La Merrill M, Gordon RR, Hunter KW et al (2010) Dietary fat alters pulmonary metastasis of mammary cancers through cancer autonomous and non-autonomous changes in gene expression. Clin Exp Metastasis 27(2):107–116CrossRefPubMedGoogle Scholar
  9. 9.
    Allan MF, Eisen EJ, Pomp D (2005) Genomic mapping of direct and correlated responses to long-term selection for rapid growth rate in mice. Genetics 170(4):1863–1877CrossRefPubMedGoogle Scholar
  10. 10.
    Kuhn K, Baker SC, Chudin E et al (2004) A novel, high-performance random array platform for quantitative gene expression profiling. Genome Res 14(11):2347–2356CrossRefPubMedGoogle Scholar
  11. 11.
    Du P, Kibbe WA, Lin SM (2008) lumi: a pipeline for processing Illumina microarray. Bioinformatics 24(13):1547–1548CrossRefPubMedGoogle Scholar
  12. 12.
    Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 98(9):5116–5121CrossRefPubMedGoogle Scholar
  13. 13.
    Storey JD (2002) A direct approach to false discovery rates. J Roy Stat Soc Ser B 64:479–498CrossRefGoogle Scholar
  14. 14.
    Doss S, Schadt EE, Drake TA et al (2005) Cis-acting expression quantitative trait loci in mice. Genome Res 15(5):681–691CrossRefPubMedGoogle Scholar
  15. 15.
    Sun W, Yu T, Li KC (2007) Detection of eQTL modules mediated by activity levels of transcription factors. Bioinformatics 23(17):2290–2297CrossRefPubMedGoogle Scholar
  16. 16.
    Rhodes DR, Yu J, Shanker K et al (2004) ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 6(1):1–6PubMedGoogle Scholar
  17. 17.
    Crawford NP, Qian X, Ziogas A et al (2007) Rrp1b, a new candidate susceptibility gene for breast cancer progression and metastasis. PLoS Genet 3(11):e214CrossRefPubMedGoogle Scholar
  18. 18.
    Schadt EE, Lamb J, Yang X et al (2005) An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet 37(7):710–717CrossRefPubMedGoogle Scholar
  19. 19.
    Eccles SA, Box G, Court W et al (1994) Preclinical models for the evaluation of targeted therapies of metastatic disease. Cell Biophys 24–25:279–291PubMedGoogle Scholar
  20. 20.
    Crawford NP, Walker RC, Lukes L et al (2008) The Diasporin Pathway: a tumor progression-related transcriptional network that predicts breast cancer survival. Clin Exp Metastasis 25(4):357–369CrossRefPubMedGoogle Scholar
  21. 21.
    Yamashita S, Wakazono K, Nomoto T et al (2005) Expression quantitative trait loci analysis of 13 genes in the rat prostate. Genetics 171(3):1231–1238CrossRefPubMedGoogle Scholar
  22. 22.
    Wang SS, Schadt EE, Wang H et al (2007) Identification of pathways for atherosclerosis in mice: integration of quantitative trait locus analysis and global gene expression data. Circ Res 101(3):e11–e30CrossRefPubMedGoogle Scholar
  23. 23.
    Morgan K, Uyuni A, Nandgiri G et al (2008) Altered expression of transcription factors and genes regulating lipogenesis in liver and adipose tissue of mice with high fat diet-induced obesity and nonalcoholic fatty liver disease. Eur J Gastroenterol Hepatol 20(9):843–854CrossRefPubMedGoogle Scholar
  24. 24.
    Gong H, Guo P, Zhai Y et al (2007) Estrogen deprivation and inhibition of breast cancer growth in vivo through activation of the orphan nuclear receptor liver X receptor. Mol Endocrinol 21(8):1781–1790 (Baltimore, Md)CrossRefPubMedGoogle Scholar
  25. 25.
    Fraga MF, Ballestar E, Villar-Garea A et al (2005) Loss of acetylation at Lys16 and trimethylation at Lys20 of histone H4 is a common hallmark of human cancer. Nat Genet 37(4):391–400CrossRefPubMedGoogle Scholar
  26. 26.
    Seligson DB, Horvath S, Shi T et al (2005) Global histone modification patterns predict risk of prostate cancer recurrence. Nature 435(7046):1262–1266CrossRefPubMedGoogle Scholar
  27. 27.
    Liu Y, Tseng M, Perdreau SA et al (2007) Histone H2AX is a mediator of gastrointestinal stromal tumor cell apoptosis following treatment with imatinib mesylate. Cancer Res 67(6):2685–2692CrossRefPubMedGoogle Scholar
  28. 28.
    Lee HS, Park CB, Kim JM et al (2008) Mechanism of anticancer activity of buforin IIb, a histone H2A-derived peptide. Cancer Lett 271(1):47–55CrossRefPubMedGoogle Scholar
  29. 29.
    Sieben NL, Oosting J, Flanagan AM et al (2005) Differential gene expression in ovarian tumors reveals Dusp 4 and Serpina 5 as key regulators for benign behavior of serous borderline tumors. J Clin Oncol 23(29):7257–7264CrossRefPubMedGoogle Scholar
  30. 30.
    Chitale D, Gong Y, Taylor BS et al (2009) An integrated genomic analysis of lung cancer reveals loss of DUSP4 in EGFR-mutant tumors. Oncogene 28(31):2773–2783CrossRefPubMedGoogle Scholar
  31. 31.
    Woelfle U, Cloos J, Sauter G et al (2003) Molecular signature associated with bone marrow micrometastasis in human breast cancer. Cancer Res 63(18):5679–5684PubMedGoogle Scholar
  32. 32.
    Williams Rt, Lim JE, Harr B et al (2009) A common and unstable copy number variant is associated with differences in Glo1 expression and anxiety-like behavior. PLoS ONE 4(3):e4649CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Ryan R. Gordon
    • 1
    • 7
  • Michele La Merrill
    • 5
  • Kent W. Hunter
    • 6
  • Peter Sørensen
    • 8
  • David W. Threadgill
    • 2
    • 4
    • 7
  • Daniel Pomp
    • 1
    • 2
    • 3
    • 4
  1. 1.Department of NutritionUniversity of North Carolina Chapel HillChapel HillUSA
  2. 2.Department of GeneticsUniversity of North Carolina Chapel HillChapel HillUSA
  3. 3.Department of Cell and Molecular PhysiologyUniversity of North Carolina Chapel HillChapel HillUSA
  4. 4.Lineberger Comprehensive Cancer CenterUniversity of North Carolina Chapel HillChapel HillUSA
  5. 5.Department of Preventive MedicineMount Sinai School of MedicineNew YorkUSA
  6. 6.Laboratory of Cancer Biology & GeneticsNIH/NCIBethesdaUSA
  7. 7.Department of GeneticsNorth Carolina State UniversityRaleighUSA
  8. 8.Department of Genetics and Biotechnology, Faculty of Agricultural SciencesAarhus UniversityAarhusDenmark

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