Ovarian cancer is the leading cause of gynecologic cancer death in the United States, responsible for over 22,000 cases per year and over 14,000 deaths. The most common type of ovarian cancer is High Grade Serous Ovarian Cancer (HGSOC). Unfortunately, few therapeutic advances for HGSOC have been forthcoming, and the cornerstone of treatment remains chemotherapy and surgery. Identifying therapeutic targets for this disease represents an urgent medical need.
The genomics revolution has yielded personalized treatments for many cancers which are driven by mutated proteins. But HGSOC seems to lack mutations in DNA regions that code for cancer-promoting genes, and so is not amenable to this scientific or therapeutic approach. Rather, HGSOC seems to be driven by dysfunctional gene expression. In cancer, incorrect gene expression is often mediated by regulatory genetic elements called super-enhancers, which we can detect with novel whole genome technologies.
This project aims to map the super-enhancer landscape in human HGSOC tissue samples, and well as normal cells-of-origin, and tumor cell line models. This approach will yield fundamental insights into the mechanisms by which normal cells transform into ovarian cancer cells, and reveal the genes that maintain the oncogenic state in this disease.
Genetic dependencies are genes required for survival or growth of tumor cells, and represent attractive targets for cancer drug development. Super-enhancers are known to mark genes that are dependencies in several tumors. In cancers such as ependymoma and medulloblastoma, super-enhancer analysis has already pointed to novel therapeutic opportunities. Thus, by mapping the super-enhancer landscape of HGSOC, we will uncover putative therapeutic targets, which can be rapidly feed clinical trial development for patients.
This grant is made possible in part by a generous donation from Rock ‘N Run.