Women with endometriosis are at a two-fold increased risk of developing ovarian endometrioid adenocarcinoma and a three-fold increased risk of developing ovarian clear cell adenocarcinoma. Because of this, ovarian endometrioid and clear cell adenocarcinomas are considered endometriosis-associated ovarian cancers. Clinically important, women with endometriosis at the time of ovarian cancer staging have improved prognosis.
Multiple studies have shown that these endometriosis-associated ovarian cancers have frequent loss of function mutations in ARID1A, a putative tumor suppressor gene involved in chromatin remodeling. Using genetically engineered mouse models, the Hawkins Lab showed that conditional deletion of Arid1a in the female reproductive tract (i.e., Pgr-Cre and Amhr2-Cre) was not sufficient to initiate cancer. To determine additional molecular factors important in these cancers, the Hawkins Lab procured specimens of ovarian endometrioid adenocarcinoma from women with and without endometriosis. Using an unbiased approach, the Hawkins Lab showed that ovarian endometrioid adenocarcinomas from women with concurrent endometriosis had a distinct molecular profile compared to ovarian endometrioid adenocarcinomas from women without concurrent endometriosis. Ovarian tumors with endometriosis showed enrichment of oncogenic KRAS signaling.
The Hawkins Lab received an R03 from the National Cancer Institute to study the reproductive contributions of ARID1A and KRAS in ovarian cancer using genetically engineered mouse models and genetically modified in vitro systems.
Recently, the Hawkins lab received pilot funds from the Rivkin Center for Ovarian Cancer to develop a novel 3D bioprinted model of ovarian cancer within the endometriotic tumor microenvironment. This 3D bioprinted model uses innovative Kenzan technology available at Indiana University School of Medicine. This model will be used as a tool for target discovery and therapeutic testing. Future studies will focus on similar studies in ovarian clear cell adenocarcinomas including use of bioinformatical approaches to determine novel pharmacology therapies based on unique molecular signatures.