Applying Genomics and Other High Throughput Technologies is the thematic area that this application addresses. Ovarian cancer is the most lethal gynecologic malignancy. It has a high response rate to initial combined platinum taxane chemotherapy following debulking surgery. However, the vast majority of these women will have their cancer recur within 12 to 24 months after diagnosis and will die of progressively chemotherapy-resistant tumor. No improvement has been made to improve overall survival over the past decade. Multiple prognostic and predictive markers were identified in the past few years but none of them have satisfactory predictive values. Using genomic technologies, we and others including the Cancer Genome Atlas Project have recently generated transcriptome signatures which purport to stratify patients according to survival or predict response to chemotherapy. However, none of signatures have been validated on the transcript levels and only samples from a single institution were used for each study. The clinical significance of these profiles remains unknown. In this application, we propose to perform a comprehensive survey of published ovarian cancer signatures, including a cross-study validation using existing publicly available data, tuning of available signatures and validation of signatures using DASL technology on multi-center clinical trial GOG218 specimens. This approach will provide us with robust prognostic and predictive signatures, which will enable physicians to identify patients who will not respond to standard therapy, allow for the avoidance of unnecessary toxicity and the opportunity to be treated with experimental therapy. In addition, the signatures of resistance will certainly contain genes, which are functionally important to the resistant phenotype. These genes will be important therapeutic targets for the reversal of the resistant phenotype. We expect this proposal to radically change the clinical management of ovarian cancer patients, who should benefit greatly and immediately from the results of this project.
The proposed study seek to perform a comprehensive survey of published ovarian cancer signatures, including a cross-study validation using existing publicly available data, tuning of available signatures and validation of signatures using specimens from a multi-center clinical trial GOG218. This approach will provide us with robust prognostic and predictive signatures, which will revolutionize ovarian cancer treatment strategies.
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