Career Goals: Ovarian cancer affects 1 in 57 women and more than 25,000 new women in the United States are diagnosed with ovarian cancer each year. Unfortunately, women affected with late-stage disease, approximately two-thirds of all diagnosed cases, only have a 20% chance of survival after 5 years. This means that more than 16,000 women will die this year in the U.S. alone. During my postdoctoral fellowship, I was touched by many stories of women who battled for their lives against ovarian cancer. These interactions focused my longstanding interest to find pharmacological treatments for a variety of diseases into finding more effective treatment for ovarian cancer. My research on ovarian cancer is focused on understanding how normal ovary cells become cancerous, finding markers that will allow better diagnosis of ovarian cancer, and finding better ways to selectively kill ovarian cancer cells in patients. I have achieved the goal of being appointed a new assistant professor at the University of Minnesota. By leveraging my new position and the funding from the Ovarian Cancer Academy, I hope to become an independent and productive scientist that can bridge basic science and clinical science of ovarian cancer.

Scientific Objectives and Rationale: My initial contribution to the research in the ovarian cancer field was the discovery that because ovarian cancer cells are rapidly multiplying, these cells seem to increase certain cellular processes that are important for recycling and/or disposing used parts of the cells. My studies showed interfering with the ability of ovarian cancer cells caused ovarian cancer cells to die whereas normal ovarian cells were not affected.

Based on our initial studies, we propose the following scientific objectives: First, we will study if detecting an aberrant amount of proteins important for recycling/disposing of cellular components can be used to differentially diagnose types of ovarian cancer and clinical prognosis of the patient. Second, we will use cell lines, animal models, and cancer biopsies from patients to determine if interfering with the functions of the aberrantly expressed proteins can selectively kill ovarian cancer cells. Since we are using compounds that are approved for human use, such studies will lead to clinical testing. Finally, we will use state-of-the-art techniques to identify other proteins that are aberrantly regulated in cancer cells. Such a study could lead to new therapeutic targets.

Ultimate Applicability of Research: The grim prognosis associated with ovarian cancer is due to both the lack of early detection and the lack of intervention strategies to prevent the progression of this disease. This research will focus on finding new biomarkers capable helping doctors in diagnosing the disease as soon as possible, thus enormously increasing the chances of survival of ovarian cancer patients. These same biomarkers would also guide doctors in choosing an appropriate therapeutic approach allowing for more optimal intervention and treatment strategies. The second implication is that with the use of specific molecules that, alone or in combination, are capable of selectively interfering with the function of the aberrantly expressed proteins, in our case proteasome and histone deacetylase, we may be able to devise new, powerful therapeutic approaches for ovarian cancer patients. Because this new combination is based on the use of molecules that have already been considered safe and approved by the Food and Drug Administration (FDA), our results would readily move from the bench to the bed-side. Taken together, the results from this research could improve patient tumor staging, treatment, and outcome, resulting in a much improved prognosis and cure for ovarian cancer patients.

We will also work toward the identification of a new, personalized therapeutic approach. The concept is already being applied for currently existing therapies and our studies will evaluate whether such concepts can be applied for our treatment approaches. This approach may eventually allow evaluation of individual patients for the efficiency of various treatment options. Thus, we will know "in real-time" which is the best and safest therapeutic option for each patient, avoiding unsafe and time-consuming "trial and error" approaches.