Breast Cancer Research and Treatment

, Volume 135, Issue 3, pp 705–713 | Cite as

Diagnostic values of GHSR DNA methylation pattern in breast cancer

  • Sandeep Kumar Botla
  • Amin Moghaddas Gholami
  • Mahdi Malekpour
  • Evgeny A. Moskalev
  • Mahdi Fallah
  • Pouria Jandaghi
  • Ali Aghajani
  • Irina S. Bondar
  • Ramesh Omranipour
  • Fatemeh Malekpour
  • Abbas Mohajeri
  • Azin Jahangiri Babadi
  • Özgür Sahin
  • Vladimir V. Bubnov
  • Hossein Najmabadi
  • Jörg D. Hoheisel
  • Yasser Riazalhosseini
Preclinical Study

Abstract

DNA methylation patterns have been recognised as cancer-specific markers with high potential for clinical applications. We aimed at identifying methylation variations that differentiate between breast cancers and other breast tissue entities to establish a signature for diagnosis. Candidate genomic loci were analysed in 117 fresh-frozen breast specimens, which included cancer, benign and normal breast tissues from patients as well as material from healthy individuals. A cancer-specific DNA methylation signature was identified by microarray analysis in a test set of samples (n = 52, p < 2.1 × 10−4) and its performance was assessed through bisulphite pyrosequencing in an independent validation set (n = 65, p < 1.9 × 10−7). The signature is associated with SFRP2 and GHSR genes, and exhibited significant hypermethylation in cancers. Normal-appearing breast tissues from cancer patients were also methylated at these loci but to a markedly lower extent. This occurrence of methylated DNA in normal breast tissue of cancer patients is indicative of an epigenetic field defect. Concerning diagnosis, receiver operating characteristic curves and the corresponding area under the curve (AUC) analysis demonstrated a very high sensitivity and specificity of 89.3 and 100 %, respectively, for the GHSR methylation pattern (AUC >0.99). To date, this represents the DNA methylation marker of the highest sensitivity and specificity for breast cancer diagnosis. Functionally, ectopic expression of GHSR in a cell line model reduced breast cancer cell invasion without affecting cell viability upon stimulation of cells with ghrelin. Our data suggest a link between epigenetic down-regulation of GHSR and breast cancer cell invasion.

Keywords

DNA methylation Breast cancer Diagnosis GHSR Epigenetics 

Notes

Acknowledgments

This study was supported by Bundesministerium für Bildung und Forschung as part of the NGFN programme (Grant Number 01GS08117). The authors thank Verena Beier and Neeme Tõnisson for discussions, and Achim Stephan for technical support.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10549_2012_2197_MOESM1_ESM.doc (1 mb)
Supplementary material 1 (DOC 1026 kb)

References

  1. 1.
    Dua RS, Isacke CM, Gui GP (2006) The intraductal approach to breast cancer biomarker discovery. J Clin Oncol 24(7):1209–1216PubMedCrossRefGoogle Scholar
  2. 2.
    Andre F, Michiels S, Dessen P, Scott V, Suciu V, Uzan C, Lazar V, Lacroix L, Vassal G, Spielmann M, Vielh P, Delaloge S (2009) Exonic expression profiling of breast cancer and benign lesions: a retrospective analysis. Lancet Oncol 10(4):381–390PubMedCrossRefGoogle Scholar
  3. 3.
    Brena RM, Plass C, Costello JF (2006) Mining methylation for early detection of common cancers. PLoS Med 3(12):e479PubMedCrossRefGoogle Scholar
  4. 4.
    Jones PA, Takai D (2001) The role of DNA methylation in mammalian epigenetics. Science 293(5532):1068–1070PubMedCrossRefGoogle Scholar
  5. 5.
    Jones PA, Baylin SB (2007) The epigenomics of cancer. Cell 128(4):683–692PubMedCrossRefGoogle Scholar
  6. 6.
    Vargo-Gogola T, Rosen JM (2007) Modelling breast cancer: one size does not fit all. Nat Rev Cancer 7(9):659–672PubMedCrossRefGoogle Scholar
  7. 7.
    Gal-Yam EN, Saito Y, Egger G, Jones PA (2008) Cancer epigenetics: modifications, screening, and therapy. Annu Rev Med 59:267–280PubMedCrossRefGoogle Scholar
  8. 8.
    Esteller M (2008) Epigenetics in cancer. N Engl J Med 358(11):1148–1159PubMedCrossRefGoogle Scholar
  9. 9.
    Veeck J, Ropero S, Setien F, Gonzalez-Suarez E, Osorio A, Benitez J, Herman JG, Esteller M (2010) BRCA1 CpG island hypermethylation predicts sensitivity to poly(adenosine diphosphate)-ribose polymerase inhibitors. J Clin Oncol 28(29):e563–e564 author reply e565–566PubMedCrossRefGoogle Scholar
  10. 10.
    Zhang Y, Rohde C, Tierling S, Jurkowski TP, Bock C, Santacruz D, Ragozin S, Reinhardt R, Groth M, Walter J, Jeltsch A (2009) DNA methylation analysis of chromosome 21 gene promoters at single base pair and single allele resolution. PLoS Genet 5(3):e1000438PubMedCrossRefGoogle Scholar
  11. 11.
    Hoheisel JD (2006) Microarray technology: beyond transcript profiling and genotype analysis. Nat Rev Genet 7(3):200–210PubMedCrossRefGoogle Scholar
  12. 12.
    Riazalhosseini Y, Hoheisel J (2008) Do we use the appropriate controls for the identification of informative methylation markers for early cancer detection? Genome Biol 9(11):405PubMedCrossRefGoogle Scholar
  13. 13.
    Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):R80PubMedCrossRefGoogle Scholar
  14. 14.
    Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:Article3Google Scholar
  15. 15.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc 57(1):289–300Google Scholar
  16. 16.
    Fellenberg K, Hauser NC, Brors B, Neutzner A, Hoheisel JD, Vingron M (2001) Correspondence analysis applied to microarray data. Proc Natl Acad Sci USA 98(19):10781–10786PubMedCrossRefGoogle Scholar
  17. 17.
    Moskalev EA, Zavgorodnij MG, Majorova SP, Vorobjev IA, Jandaghi P, Bure IV, Hoheisel JD (2011) Correction of PCR-bias in quantitative DNA methylation studies by means of cubic polynomial regression. Nucleic Acids Res 39(11):e77PubMedCrossRefGoogle Scholar
  18. 18.
    Perez RP, Hamilton TC, Ozols RF, Young RC (1993) Mechanisms and modulation of resistance to chemotherapy in ovarian cancer. Cancer 71(4 Suppl):1571–1580PubMedCrossRefGoogle Scholar
  19. 19.
    Ordway JM, Budiman MA, Korshunova Y, Maloney RK, Bedell JA, Citek RW, Bacher B, Peterson S, Rohlfing T, Hall J, Brown R, Lakey N, Doerge RW, Martienssen RA, Leon J, McPherson JD, Jeddeloh JA (2007) Identification of novel high-frequency DNA methylation changes in breast cancer. PLoS ONE 2(12):e1314PubMedCrossRefGoogle Scholar
  20. 20.
    Huang TH, Perry MR, Laux DE (1999) Methylation profiling of CpG islands in human breast cancer cells. Hum Mol Genet 8(3):459–470PubMedCrossRefGoogle Scholar
  21. 21.
    Euhus DM, Bu D, Milchgrub S, Xie X-J, Bian A, Leitch AM, Lewis CM (2008) DNA methylation in benign breast epithelium in relation to age and breast cancer risk. Cancer Epidemiol Biomark Prev 17(5):1051–1059CrossRefGoogle Scholar
  22. 22.
    Lewis CM, Cler LR, Bu DW, Zochbauer-Muller S, Milchgrub S, Naftalis EZ, Leitch AM, Minna JD, Euhus DM (2005) Promoter hypermethylation in benign breast epithelium in relation to predicted breast cancer risk. Clin Cancer Res 11(1):166–172PubMedGoogle Scholar
  23. 23.
    Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger DJ, Shen H, Campan M, Noushmehr H, Bell CG, Maxwell AP, Savage DA, Mueller-Holzner E, Marth C, Kocjan G, Gayther SA, Jones A, Beck S, Wagner W, Laird PW, Jacobs IJ, Widschwendter M (2010) Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res 20(4):440–446PubMedCrossRefGoogle Scholar
  24. 24.
    Yan PS, Venkataramu C, Ibrahim A, Liu JC, Shen RZ, Diaz NM, Centeno B, Weber F, Leu Y-W, Shapiro CL, Eng C, Yeatman TJ, Huang THM (2006) Mapping geographic zones of cancer risk with epigenetic biomarkers in normal breast tissue. Clin Cancer Res 12(22):6626–6636PubMedCrossRefGoogle Scholar
  25. 25.
    Braakhuis BJ, Tabor MP, Kummer JA, Leemans CR, Brakenhoff RH (2003) A genetic explanation of Slaughter’s concept of field cancerization: evidence and clinical implications. Cancer Res 63(8):1727–1730PubMedGoogle Scholar
  26. 26.
    Shen L, Kondo Y, Rosner GL, Xiao L, Hernandez NS, Vilaythong J, Houlihan PS, Krouse RS, Prasad AR, Einspahr JG, Buckmeier J, Alberts DS, Hamilton SR, Issa JP (2005) MGMT promoter methylation and field defect in sporadic colorectal cancer. J Natl Cancer Inst 97(18):1330–1338PubMedCrossRefGoogle Scholar
  27. 27.
    Wang LS, Arnold M, Huang YW, Sardo C, Seguin C, Martin E, Huang TH, Riedl K, Schwartz S, Frankel W, Pearl D, Xu Y, Winston J 3rd, Yang GY, Stoner G (2011) Modulation of genetic and epigenetic biomarkers of colorectal cancer in humans by black raspberries: a phase I pilot study. Clin Cancer Res 17(3):598–610PubMedCrossRefGoogle Scholar
  28. 28.
    Wolff EM, Chihara Y, Pan F, Weisenberger DJ, Siegmund KD, Sugano K, Kawashima K, Laird PW, Jones PA, Liang G (2010) Unique DNA methylation patterns distinguish noninvasive and invasive urothelial cancers and establish an epigenetic field defect in premalignant tissue. Cancer Res 70(20):8169–8178PubMedCrossRefGoogle Scholar
  29. 29.
    Hill VK, Ricketts C, Bieche I, Vacher S, Gentle D, Lewis C, Maher ER, Latif F (2011) Genome-wide DNA methylation profiling of CpG islands in breast cancer identifies novel genes associated with tumorigenicity. Cancer Res 71(8):2988–2999PubMedCrossRefGoogle Scholar
  30. 30.
    Bailey VJ, Zhang Y, Keeley BP, Yin C, Pelosky KL, Brock M, Baylin SB, Herman JG, Wang TH (2010) Single-tube analysis of DNA methylation with silica superparamagnetic beads. Clin Chem 56(6):1022–1025PubMedCrossRefGoogle Scholar
  31. 31.
    Evron E, Dooley WC, Umbricht CB, Rosenthal D, Sacchi N, Gabrielson E, Soito AB, Hung DT, Ljung B, Davidson NE, Sukumar S (2001) Detection of breast cancer cells in ductal lavage fluid by methylation-specific PCR. Lancet 357(9265):1335–1336PubMedCrossRefGoogle Scholar
  32. 32.
    Fackler MJ, Malone K, Zhang Z, Schilling E, Garrett-Mayer E, Swift-Scanlan T, Lange J, Nayar R, Davidson NE, Khan SA, Sukumar S (2006) Quantitative multiplex methylation-specific PCR analysis doubles detection of tumor cells in breast ductal fluid. Clin Cancer Res 12(11 Pt 1):3306–3310PubMedCrossRefGoogle Scholar
  33. 33.
    Suijkerbuijk KP, van der Wall E, Meijrink H, Pan X, Borel Rinkes IH, Ausems MG, van Diest PJ (2010) Successful oxytocin-assisted nipple aspiration in women at increased risk for breast cancer. Fam Cancer 9(3):321–325PubMedCrossRefGoogle Scholar
  34. 34.
    Komenaka IK, Ditkoff BA, Joseph KA, Russo D, Gorroochurn P, Ward M, Horowitz E, El-Tamer MB, Schnabel FR (2004) The development of interval breast malignancies in patients with BRCA mutations. Cancer 100(10):2079–2083PubMedCrossRefGoogle Scholar
  35. 35.
    Suijkerbuijk KP, van Diest PJ, van der Wall E (2011) Improving early breast cancer detection: focus on methylation. Ann Oncol 22(1):24–29PubMedCrossRefGoogle Scholar
  36. 36.
    Lieske B, Ravichandran D, Wright D (2006) Role of fine-needle aspiration cytology and core biopsy in the preoperative diagnosis of screen-detected breast carcinoma. Br J Cancer 95(1):62–66PubMedCrossRefGoogle Scholar
  37. 37.
    Meunier M, Clough K (2002) Fine needle aspiration cytology versus percutaneous biopsy of nonpalpable breast lesions. Eur J Radiol 42(1):10–16PubMedCrossRefGoogle Scholar
  38. 38.
    Vimpeli SM, Saarenmaa I, Huhtala H, Soimakallio S (2008) Large-core needle biopsy versus fine-needle aspiration biopsy in solid breast lesions: comparison of costs and diagnostic value. Acta Radiol 49(8):863–869PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2012

Authors and Affiliations

  • Sandeep Kumar Botla
    • 1
  • Amin Moghaddas Gholami
    • 2
  • Mahdi Malekpour
    • 3
    • 4
  • Evgeny A. Moskalev
    • 1
  • Mahdi Fallah
    • 5
  • Pouria Jandaghi
    • 1
    • 6
  • Ali Aghajani
    • 7
  • Irina S. Bondar
    • 8
  • Ramesh Omranipour
    • 3
  • Fatemeh Malekpour
    • 9
  • Abbas Mohajeri
    • 10
  • Azin Jahangiri Babadi
    • 1
  • Özgür Sahin
    • 11
  • Vladimir V. Bubnov
    • 12
  • Hossein Najmabadi
    • 7
  • Jörg D. Hoheisel
    • 1
  • Yasser Riazalhosseini
    • 1
    • 13
  1. 1.Division of Functional Genome AnalysisGerman Cancer Research Center (DKFZ)HeidelbergGermany
  2. 2.Chair of Proteomics and BioanalyticsTechnische Universität MünchenFreisingGermany
  3. 3.Imam Khomeini Hospital, Cancer InstituteTehran University of Medical SciencesTehranIran
  4. 4.Department of MedicineVanderbilt University Medical CenterNashvilleUSA
  5. 5.Division of Molecular Genetic EpidemiologyGerman Cancer Research Center, (DKFZ)HeidelbergGermany
  6. 6.Research Center of Medical SciencesI.A.U, Tehran Medical BranchTehranIran
  7. 7.Genetics Research CenterUniversity of Social Welfare and Rehabilitation SciencesTehranIran
  8. 8.Regional Oncology ClinicOdessaUkraine
  9. 9.Shohada Tajrish HospitalShahid Beheshti University of Medical SciencesTehranIran
  10. 10.Department of Research and DevelopmentZahravi Pharmaceutical CompanyTabrizIran
  11. 11.Division of Molecular Genome AnalysisGerman Cancer Research Center (DKFZ)HeidelbergGermany
  12. 12.Department of Genomics and ImmunologyOdessa State Medical UniversityOdessaUkraine
  13. 13.Department of Human GeneticsMcGill University and Genome Quebec Innovation CentreMontrealCanada

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