[This page is intended to provide a study summary, the sections of which are below. Please complete these sections, as applicable. The headings below are suggested headings. You can remove inapplicable sections, or add new ones relevant to your study]
Investigator Names and Contact Information
M. Genkinger [ firstname.lastname@example.org ]
Ovarian cancer is the 5th most common cause of cancer mortality in females1. Ovarian cancer has few early symptoms, is
usually diagnosed at late stages and has the lowest five year survival rate of all gynecologic cancers1, 2. One of the main
reasons for the high fatality rate of ovarian cancer is due to the lack of a population‐based screening tool3. Thus, it is
crucial to identify modifiable risk factors and pathways of risk, such as epigenetics, that may inform screening.
Aberrant DNA methylation in specific genes can activate or silence genes, some of which may be critical to tumor
development and growth4‐8, while lower overall genomic DNA methylation can lead to genomic instability; both genespecific
and overall lower genomic DNA methylation can increase cancer risk4‐8. Recent studies have shown that genespecific
methylation differences, measured in selected genes in tumor vs. adjacent normal tissue (e.g., BRCA19, 10,
OPCML11‐13, RASSF1A14‐17, ARLTS118), are important to ovarian cancer19. Prior studies relied on in vitro systems, or in vivo
with primary tumor tissue, and not less invasively and more easily collected blood samples. However, genomic and
gene‐specific DNA methylation using plasma DNA and white blood cells (WBC) DNA has not been evaluated in
prospective epidemiologic studies with ovarian cancer risk, nor tissue with ovarian cancer survival. Measurement of
methylation in plasma DNA may be useful as a tumor marker as we may be measuring the DNA that tumors release into
bloodstream, while WBC, due to its rapid turnover, may represent an early biomarker for biological processes that
systematically influence DNA methylation. In addition, identifying modifiable factors that can modify biomarkers of risk
will be crucial. Micronutrients, such as folate and methionine, mediate the transfer of one‐carbon units, and these
micronutrients may have a direct impact on DNA repair and methylation. Thus, we propose to study the following
hypotheses within the Women’s Health Initiative (WHI), a large prospective observational study and clinical trial:
Aim 1: gene specific DNA methylation measured in tumor tissue and ovarian carcinogenesis
Aim 3: Risk Prediction of ovarian cancer using methylation markers
Aim 4: Diet and ovarian cancer risk, Understanding mediation
1. Cancer Facts & Figures 2011. American Cancer Society, 2011.
2. Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T, Thun MJ. Cancer statistics, 2008. CA Cancer J Clin 2008;58:71‐
3. Buys SS, Partridge E, Black A, Johnson CC, Lamerato L, Isaacs C, Reding DJ, Greenlee RT, Yokochi LA, Kessel B,
Crawford ED, Church TR, et al. Effect of screening on ovarian cancer mortality: the Prostate, Lung, Colorectal and Ovarian
(PLCO) Cancer Screening Randomized Controlled Trial. JAMA 2011;305:2295‐303.
4. Feinberg AP. The epigenetics of cancer etiology. Semin Cancer Biol 2004;14:427‐32.
5. Jones PA. DNA methylation and cancer. Oncogene 2002;21:5358‐60.
6. Jones PA, Baylin SB. The fundamental role of epigenetic events in cancer. Nat.Rev.Genet. 2002;3:415‐28.
7. Terry MB, Ferris JS, Pilsner R, Flom JD, Tehranifar P, Santella RM, Gamble MV, Susser E. Genomic DNA
methylation among women in a multiethnic New York City birth cohort. Cancer Epidemiol Biomarkers Prev 2008;17:2306‐
8. Ehrlich M. DNA methylation in cancer: too much, but also too little. Oncogene 2002;21:5400‐13.
9. Esteller M, Corn PG, Baylin SB, Herman JG. A gene hypermethylation profile of human cancer. Cancer Res
10. Esteller M, Silva JM, Dominguez G, Bonilla F, Matias‐Guiu X, Lerma E, Bussaglia E, Prat J, Harkes IC, Repasky
EA, Gabrielson E, Schutte M, et al. Promoter hypermethylation and BRCA1 inactivation in sporadic breast and ovarian
tumors. J Natl Cancer Inst 2000;92:564‐9.
11. Sellar GC, Watt KP, Rabiasz GJ, Stronach EA, Li L, Miller EP, Massie CE, Miller J, Contreras‐Moreira B, Scott D,
Brown I, Williams AR, et al. OPCML at 11q25 is epigenetically inactivated and has tumor‐suppressor function in epithelial
ovarian cancer. Nat Genet 2003;34:337‐43.
12. Teodoridis JM, Hall J, Marsh S, Kannall HD, Smyth C, Curto J, Siddiqui N, Gabra H, McLeod HL, Strathdee G,
Brown R. CpG island methylation of DNA damage response genes in advanced ovarian cancer. Cancer Res 2005;65:8961‐
13. Zhang J, Ye F, Chen HZ, Ye DF, Lu WG, Xie X. [Deletion of OPCML gene and promoter methylation in ovarian
epithelial carcinoma]. Zhongguo Yi Xue Ke Xue Yuan Xue Bao 2006;28:173‐7.
14. Agathanggelou A, Honorio S, Macartney DP, Martinez A, Dallol A, Rader J, Fullwood P, Chauhan A, Walker R,
Shaw JA, Hosoe S, Lerman MI, et al. Methylation associated inactivation of RASSF1A from region 3p21.3 in lung, breast
and ovarian tumours. Oncogene 2001;20:1509‐18.
15. Ibanez de Caceres I, Battagli C, Esteller M, Herman JG, Dulaimi E, Edelson MI, Bergman C, Ehya H, Eisenberg
BL, Cairns P. Tumor cell‐specific BRCA1 and RASSF1A hypermethylation in serum, plasma, and peritoneal fluid from
ovarian cancer patients. Cancer Res 2004;64:6476‐81.
16. Rathi A, Virmani AK, Schorge JO, Elias KJ, Maruyama R, Minna JD, Mok SC, Girard L, Fishman DA, Gazdar AF.
Methylation profiles of sporadic ovarian tumors and nonmalignant ovaries from high‐risk women. Clin Cancer Res
17. Yoon JH, Dammann R, Pfeifer GP. Hypermethylation of the CpG island of the RASSF1A gene in ovarian and
renal cell carcinomas. Int J Cancer 2001;94:212‐7.
18. Petrocca F, Iliopoulos D, Qin HR, Nicoloso MS, Yendamuri S, Wojcik SE, Shimizu M, Di Leva G, Vecchione A,
Trapasso F, Godwin AK, Negrini M, et al. Alterations of the tumor suppressor gene ARLTS1 in ovarian cancer. Cancer Res
19. Barton CA, Hacker NF, Clark SJ, O'Brien PM. DNA methylation changes in ovarian cancer: implications for
early diagnosis, prognosis and treatment. Gynecol Oncol 2008;109:129‐39.
20. Pfeiffer RM, Park Y, Kreimer AR, Lacey JV, Jr., Pee D, Greenlee RT, Buys SS, Hollenbeck A, Rosner B, Gail MH,
Hartge P. Risk prediction for breast, endometrial, and ovarian cancer in white women aged 50 y or older: derivation and
validation from population‐based cohort studies. PLoS Med 2013;10:e1001492.
21. Rosner BA, Colditz GA, Webb PM, Hankinson SE. Mathematical models of ovarian cancer incidence.