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 cancers^{1, 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 growth^4‐8, while lower overall genomic DNA methylation can lead to genomic instability; both genespecific

and overall lower genomic DNA methylation can increase cancer risk^4‐8. Recent studies have shown that genespecific

methylation differences, measured in selected genes in tumor vs. adjacent normal tissue (e.g., BRCA1^{9, 10},

OPCML^11‐13, RASSF1A^14‐17, 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).
   
    

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 (n_cases=262, n_controls= 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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