PROJECT SUMMARY/ABSTRACT The goal of this proposal is to improve treatment selection and survival in women with advanced ovarian cancer. Ovarian cancer is the leading cause of deaths from gynecologic cancers in the U.S. Most women present with advanced disease, with distant spread at the time of diagnosis. Even so, more than 1 in 10 of such women will survive >10 years after an initial diagnosis, usually with periodic recurrences. The possibility of long-term survival underscores the paramount importance of each treatment decision. Determining the best treatment strategy for an individual patient is difficult. For women with newly diagnosed, advanced ovarian cancer, both surgery and chemotherapy are recommended. However, many women fail to complete both, rates of death from surgery are high, and chemotherapy delivery is often limited by toxicities. New therapies that are designed to target cells at the molecular level (“PARP inhibitors”) have recently been approved for use, but their benefits vary based on the presence of specific tumor mutations, and their costs exceed $150,000/year. Carefully tailored decisions about the sequence of surgery and chemotherapy, types of chemotherapy, and the way chemotherapy is delivered, could improve long-term outcomes and reduce costs. To address this problem, we will build a simulation modeling framework that projects the outcomes of women treated for advanced ovarian cancer, and use it to identify personalized treatment approaches. We have built a preliminary model that projects outcomes for women with newly diagnosed, advanced ovarian cancer. We will extend our model to include detailed patient and tumor characteristics – age, comorbidities, stage, and mutation status – that influence survival, as well as new therapies and toxicities (Aim 1.1). We will also simulate the treatment of recurrent cancer (Aim 1.2). Using our new modeling framework, we will identify tailored treatment approaches that optimize survival (Aim 2.1), minimize treatment toxicities (Aim 2.2), and are cost-effective (Aim 3). Finally, we will identify future studies that are likely to have the greatest impact in improving treatment decisions (Aim 4). To ensure that our findings are accessible to patients, physicians, and policymakers, we will create an online, interactive version of our modeling framework that can project outcomes, quantify trade-offs, and support decision-making in real time. The proposed research will result in: 1) personalized treatment recommendations; 2) real-time guidance for decision-making; 3) the capacity to rapidly weigh benefits of new therapies with long-term risks and costs; and 4) prioritization of future research. The knowledge gained will provide new opportunities to improve treatment selection and survival in ovarian cancer.