Ovarian cancer is the fifth most common form of cancer in women in the United States, accounting for 4% of the total number of cancer cases and 25% of those cases occur in the female genital tract. Because of its low cure rate, it is responsible for 5% of all cancer deaths in women. It was estimated that 13,000 deaths was caused by ovarian cancer in the year 2001. A majority of ovarian cancer cases are detected at an advanced stage (where metastases are present beyond the ovaries) and are rarely curable. Although 80% of advanced ovarian cancers respond to primary treatment with surgery and chemotherapy, the disease usually recurs and is ultimately fatal. Though most patients die within 2 years of diagnosis, a subset of patients develop a more chronic form of ovarian cancer, and may survive 5 years or more with treatment. It is possible that patients with indolent cancer should be monitored and treated differently from patients with rapidly progressing ovarian cancer. At this point, clinicians do not have the tools to predict the clinical course of disease. The proposed studies seek to develop a molecular characterization for this purpose. We propose to apply the newly established cDNA array comparative genomic hybridization (CGH) technique to generate DNA copy number abnormality (CNA) profiles on ovarian cancer samples collected from patients entered into the Gynecologic Oncology Group (GOG) 9404 clinical trial, which has thorough information regarding patient outcome following primary cytoreductive surgery and platinum-based first-line chemotherapy. Using the cDNA array as a platform, we have identified cyclin E amplification and over-expression in a majority of ovarian tumor tissue. Furthermore, using the specimens from the GOG protocol 9404, we have demonstrated that cyclin E is a prognostic marker for ovarian cancer. Based on these promising preliminary data, we propose (1) to identify DNA copy number abnormalities in ovarian cancer by cDNA array, (2) to develop a genetic prognostic model for ovarian cancer utilizing the data from patients entered on Gynecologic Oncology Group (GOG) protocol 9404 by correlating cDNA array data with clinical end points such as tumor site, histological subtype and grade, chemoresponse, and long-term survival, and (3) to identify and validate candidate genes with prognostic values by fluorescence in situ hybridization (FISH), quantitative PCR, and immunohistochemistry. The correlation study on CNA profiles and clinical outcome data will not only provide insights into the biological basis of the prognostic associations, but also identify prognostic markers for stratifying patients in future clinical trials to assess the role of chemotherapy in the treatment of ovarian cancer.
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