The rationale for early detection of epithelial ovarian cancer (EOC) is the relationship between stage at diagnosis and survival. Whereas the small proportion of patients with accurately diagnosed stage I disease have 5-year survival rates in excess of 90%, the survival rate for women with advanced disease is only 15-20%. Therefore, the goal of cancer risk assessment and screening is to detect early-stage EOC in the pre-clinical phase of disease, such that subsequent treatment will reduce disease morbidity and mortality. At present, there is no early detection strategy in EOC that results in sufficiently high specificity and positive predictive value (PPV) to be of widespread use to either the general or high-risk populations. Consequently, novel approaches such as functional genomics, proteomics and metabonomics should be explored to distinguish individuals with early stage EOC from healthy individuals. """"""""Metabonomics"""""""" uses high-resolution nuclear magnetic resonance (1H-NMR) of serum to profile thousands of low-molecular-weight metabolites. The resulting complex multiparametric data sets are mined using pattern recognition methods. We hypothesize that serum metabonomics will detect early stage EOC in both the general and high-risk populations. In order to test this hypothesis, we propose three specific aims: (i) To compare 1HNMR based metabonomic patterns in women with stage 1 ovarian cancer, healthy age-matched controls, and women with other gynecologic conditions, (ii) To investigate the ability of 1H-NMR metabonomics to detect early EOC in women at high-risk for ovarian cancer (from the Gilda Radner Familial Ovarian Cancer Registry, Buffalo, NY), (iii) To explore the identification of the low-molecular weight metabolites that fall within the discriminator classes responsible for differences in metabonomic profiles between EOC and controls. The proposed studies may lead to the identification of novel biomarkers for early detection of EOC, that will be evaluated further in prospective studies for early detection of EOC in general and high-risk populations.
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