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AS461 - Epigenetics, dietary intake and ovarian cancer risk

AS461 - Epigenetics, dietary intake and ovarian cancer risk

[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

Jeanine M. Genkinger [ ]



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

  1. To compare gene-specific DNA methylation patterns, using the Illumina EPIC array, measured in tumor tissue and adjacent non-tumor tissue pairs (npairs = 20).
  2. To examine the correlation between gene-specific DNA methylation patterns in tumor tissue, WBC and plasma DNA (npairs = 20).

Aim 2: GENOME-WIDE, Genomic and Gene-specific DNA methylation in plasma AND WBC and ovarian cancer risk
  1. To compare patterns of genome-wide DNA methylation measured using Illumina array-based techniques in WBC in ovarian cancer cases and controls (ncases=96, ncontrols=96).
  2. To examine the association between gene-specific DNA methylation patterns (using Illumina EPIC array) measured in plasma and WBC of epithelial ovarian cases and controls using a nested case-control design (ncases=262, ncontrols= 262 / 26 blind duplicates


Aim 3: Risk Prediction of ovarian cancer using methylation markers

  1. To estimate age specific absolute risks of ovarian cancer using reproductive and lifestyle characteristics, family history and a panel of methylation markers.
  2. Using existing validated models (e.g, Pfeiffer et al20, Rosner et al21), to evaluate the screening performance with and without the addition of a panel of DNA methylation markers.
  3. Using existing validated models (e.g, Pfeiffer et al20, Rosner et al21), to evaluate the screening performance of a panel of DNA methylation markers to the risk prediction model (Aim 3b) with and without the addition of the following biomarkers of CA125, human epididymis protein 4 (HE4), mesothelin, matrix metalloproteinase 7 (MMP7), SLPI, Spondin-2, and insulin-like growth factor binding protein 2 (IGFBP2).


Aim 4: Diet and ovarian cancer risk, Understanding mediation

  1. To examine the association between dietary and supplemental folate and dietary methyl status and epithelial ovarian cancer risk using a cohort design (n=161,808 women; 610 cases). 
  2. To examine whether dietary factors are associated with DNA methylation patterns measured in, plasma, and WBC (n=610 controls).


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Crawford ED, Church TR, et al. Effect of screening on ovarian cancer mortality: the Prostate, Lung, Colorectal and Ovarian

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Shaw JA, Hosoe S, Lerman MI, et al. Methylation associated inactivation of RASSF1A from region 3p21.3 in lung, breast

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BL, Cairns P. Tumor cell‐specific BRCA1 and RASSF1A hypermethylation in serum, plasma, and peritoneal fluid from

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Methylation profiles of sporadic ovarian tumors and nonmalignant ovaries from high‐risk women. Clin Cancer Res


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renal cell carcinomas. Int J Cancer 2001;94:212‐7.

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Trapasso F, Godwin AK, Negrini M, et al. Alterations of the tumor suppressor gene ARLTS1 in ovarian cancer. Cancer Res


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early diagnosis, prognosis and treatment. Gynecol Oncol 2008;109:129‐39.

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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.

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