Survival rates for advanced stage ovarian cancer have not changed significantly in the past 40 years and ovarian cancer remains the most lethal gynecologic cancer in women. Our goal is to change the status quo by developing new paradigms in the laboratory and efficiently translating them into the clinic.

The most common type of ovarian cancer, and the one that accounts for the majority of deaths from ovarian cancer, is serous papillary carcinoma. Approximately 20% of patients with this ovarian cancer subtype are intrinsically resistant to chemotherapy or develop chemoresistant disease within one year from initial treatment. A reliable method to identify these poor prognosis patients would facilitate their inclusion into clinical trials or personalized treatment strategies at an earlier point. One successful example of such approach is the development and validation of the OncotypeDX® and Mammaprint® assays for breast cancer, which have become the standard of care for individualized treatment decision-making in breast cancer. Unlike in breast cancer, a fully validated and clinically applied test that guides treatment decisions in the management of ovarian cancer patients does not exist.

The lack of reliable prognostic markers and curative treatment strategies for ovarian cancer inspired our work aimed at identifying molecular abnormalities that contribute to the disease, resulting in the hypothesis that these molecules will yield biomarkers for early detection, prognostication, and personalization of therapy. Our laboratory identified a gene signature that is strongly correlated with poor prognosis in ovarian cancer patients. In the proposed project, we will optimize the gene signature and develop a preliminary quantitative assay for use in the clinical setting. The development of a reliable test for the identification of high-risk patients is not only crucial to improving their clinical management but also timely because of the increased number of clinical trials using novel agents aimed at ovarian cancer and the emergence of personalized treatment strategies. Having a validated gene signature to identify patients who are unlikely to respond to standard treatment will benefit patients, physicians, and taxpayers by reducing both the human and financial costs of ineffective therapies and associated toxicities. Importantly, implementation of the predictive signature assay will provide opportunities to deliver targeted therapies directed at the underlying mechanism of the poor prognosis signature.

Our work will not stop at validating the predictive signature, as some of the genes in the signature are not only predictors of poor prognosis but also integral players in tumor progression. Their inhibition may impede tumor progression or even completely incapacitate the tumor. Such genes could serve as therapeutic targets. Current chemotherapeutic agents have been largely selected for their ability to destroy rapidly dividing cancer cells rather than the tumor infrastructure that protects the rare specialized cells that drive tumor recurrence and chemoresistance. This might explain why tumor response does not necessarily translate into increased patient survival. The poor prognosis signature genes that we identified belong to a molecular network that is required to maintain the three-dimensional structure of the tumor, similar to the poles that hold up a tent. We have shown that inhibition of one of the signature genes is effective in reducing ovarian tumor growth in mouse models. In this proposal, we will use our experience in ovarian cancer modeling to understand how the signature genes work together to maintain the tumor infrastructure and how to effectively incapacitate them. A better understanding of the components of this underlying infrastructure that drives ovarian cancer progression could reveal the "Achilles heel" of the tumor and thus have a major impact on the development of improved therapies for advanced ovarian cancer. Such preclinical studies and models are an important step toward initiating clinical trials in ovarian cancer patients. We anticipate that our studies will enable physicians to select and target the most active agents for those patients who, based on the predictive gene signature, are most likely to benefit from such targeted therapies.