The aim of this study is to identify novel ovarian cancer biomarkers using high throughput mass spectrometry and other proteomic techniques on serum, plasma, and urine samples from high risk women undergoing risk reducing salpingo oophorectomy (RRSO). An often used approach to identifying cancer biomarkers is to obtain samples from subjects already clinically identified as having the target cancer but prior to any treatment intervention, and compare with samples from subjects without the disease. Putative markers which separate well the cases from the controls are then proposed as candidates for further testing, especially markers which separate early stage cases from controls. High throughput mass spectroscopy coupled with non-linear statistical analyses has recently demonstrated that patterns of peaks in the spectra can separate all cases from most controls. However, two issues arise with using pre-operative samples. The first is that clinically identified early stage disease is likely to be bulky, symptomatic disease, and the markers identified may be indicators only of bulky disease late in the carcinogenesis process. The second issue is that clinically identified early stage disease is not the target disease for an early detection program. In fact, an early detection program aims to identify asymptomatic subjects in early stage disease that would have been clinically identified in late stage disease. Subjects planning on RRSO form an ideal cohort for identification of biomarkers which are sensitive to low volume, asymptomatic, early stage disease. Usually individuals who undergo RRSO are at high risk of ovarian cancer due to known BRCA mutations or a strong family history of ovarian and breast cancer. Occult ovarian cancer has been identified in approximately 10% of ovaries following RRSO. Biospecimens will be obtained from a large cohort of subjects undergoing RRSO prior to and following surgery. A comprehensive pathology review will identify the subjects with occult ovarian cancer (cases) and subjects without ovarian cancer (controls). High throughput mass spectrometry followed by non-linear statistical classification methods will be utilized to identify patterns of peaks which separate cases as much as possible from non-cases. An alternative methodology, 2D DIGE (2 dimensional digital gel electrophoresis) will also be applied to identify potential serum/plasma or urine biomarkers. Following identification of the most promising peak/spot pattern, proteins and peptides corresponding to the peaks/spots will be identified through LC-MS/MS. Monoclonal antibodies will be developed for the six most important proteins/peptides in the pattern, immunoassays developed from the antibodies, and finally tested against the remaining aliquots ofbiospecimens to verify and enhance pattern identification through further application of non-linear classification methods.
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