Currently, standard care for patients with advanced stage epithelial ovarian cancer includes primary surgical cytoreduction followed by primary platinum/taxane chemotherapy. Although the majority of patients initially experience a complete clinical response, a minority will have unresponsive or progressive disease despite therapy, and most will experience a recurrence following primary treatment. Patients with such """"""""platinum resistant"""""""" disease are treated with salvage chemotherapy and have a poor prognosis. The goal of the work described in this proposal is to identify patterns of gene expression that predict response to chemotherapy for advanced stage ovarian cancers. We also aim to further characterize the role of the genes that contribute to chemosensitivity. The R21 retrospective phase of this proposal aims to develop gene expression profiles that predict response to primary and salvage chemotherapy. This will be accomplished by a retrospective microarray analysis of ovarian cancers obtained from the tumor banks of the H. Lee Moffitt Cancer Center and Duke University Medical Center. Computational tools will be developed to define gene profiles that predict response to therapy. The gene expression signatures will be validated and refined in the R33 sponsored prospective clinical trial. Prospectively collected ovarian samples will be arrayed and the clinical response to primary and salvage therapy observed. Additionally, we aim to explore opportunities to improve our ability to predict response to salvage therapy by performing microarray expression analysis of recurrent ovarian cancer biopsy (or ascites) samples obtained prior to the initiation of salvage therapies. To extend our array findings, genes and gene pathways involved in the predictive model will be subject to additional analysis by advanced bioinformatics tools and quantitative PCR in a larger number of ovarian cancers. The ability to predict response to chemotherapy for ovarian cancer will enable tailored therapeutic regimens to be established for individual patients on the basis of cancer expression profiles. As such, response rates can be improved, toxic agents avoided, bone marrow spared, and quality of life enhanced. Ultimately, defining the biologic underpinnings of response to therapy will facilitate the development of more active agents that may improve cure rates for ovarian cancer.
