The promise and challenge of ovarian cancer models

Ovarian cancer claims approximately 140,200 lives each year, with an additional 225,500 patients being diagnosed annually (1). In spite of current chemotherapeutic and surgical options, this high lethality can be attributed to multiple factors, including a late stage presentation by which point the vast majority of patients have widely metastatic disease (2). This is largely due to a lack of effective early screening and detection methods. Consequently, treatment options for late stage disease are limited and patients become increasingly resistant to chemotherapy (3). It is clear that there is an urgent need for personalized therapies to improve overall survival (OS) and life quality while in treatment. As the predominance of ovarian carcinomas is histologically serous (80-85%), there is a greater research emphasis focused on this particular subtype. In North America, endometrioid tumors account for approximately 10% of ovarian carcinomas, while clear cell (5%) and mucinous (3%) carcinomas are more rare (4). In order to optimize treatment, it is important to recognize that ovarian cancer is composed of several different histotypes with unique molecular aberrations, cell of origin, and causal events. An enhanced understanding of the genomic and epigenomic landscape of these subtypes can aid in the development of new targeted agents and immunotherapeutic approaches (3,5)