Gene expression profiles to predict ovarian cancer chemo-response in the elderly

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DESCRIPTION (provided by applicant): Epithelial ovarian cancer is the most lethal gynecologic malignancy, and more than half of all new cases of ovarian cancer arise in women over age 65. Although elderly women with ovarian cancer have been shown to have a poorer overall prognosis compared to younger women, the exact molecular basis of this difference remains unclear. We have preliminary microarray data from analysis of 76 advanced serous ovarian cancers suggesting that it may be possible to identify discrete global gene expression patterns that underlie differences in epithelial ovarian cancer between elderly and younger women. Further our preliminary findings suggest that it may be possible to predict response to chemotherapy in elderly and younger women. The goal of the current proposal is to extend our preliminary findings to improve understanding of the molecular underpinnings of advanced stage epithelial ovarian cancer in elderly women and to identify gene expression profiles that may predict chemo-response, such that future therapy may be tailored to individual patients. We plan to perform microarray gene expression analysis of 64 advanced stage serous ovarian cancers from elderly and younger women. Data from this analysis will be combined with data from 76 samples previously arrayed in preliminary studies to provide a total of 140 samples (70 from elderly women and 70 from younger women). Gene expression patterns will be compared between cancers from elderly (>65 yrs age) and younger (<65 yrs age) women to identify genes that may underlie the difference survival between the 2 groups. Additionally, for both age groups, gene expression patterns will be compared between cancers from patients that demonstrated a complete- versus an incomplete-response to adjuvant platinum/taxane chemotherapy. Bayesian regression analysis will be used to develop predictive models which will be tested in leave-one-out cross validation studies. The profiles that predict response in each age group (elderly and younger) will be compared. Characterization of gene profiles that predict chemo- response in elderly women will facilitate the development of gene expression profile-directed therapies that will aid clinical-decision making in the aging population. As such, predictive profiles will enable therapies to be tailored to elderly patients, and thus improve treatment efficacy and response rates, decrease unnecessary toxicities, and importantly, enhance quality of life for aging patients with ovarian cancer.