The incidence of endometrial cancer is on the rise each year reaching over 60,000 new cases for 2017. As risk factors remain prevalent in both adults and children, more and more women including those of younger age will develop endometrial cancer. Studies demonstrate an obvious race disparity in endometrial cancer where black women present with more aggressive disease and a higher death rate compared to white women. While reasons for this are multifactorial, the biological etiology remains to be studied. In this project, we will collect endometrial tumors and its associated clinical information from black and white women. Exome sequencing will be performed and along with clinical data, bioinformatics analysis will be done to determine the racial differences in tumor genomics. In addition, it is unknown whether the tumors in black women respond to progestin similarly to white women. This is an important piece of the puzzle as young black women have increased severity of disease than white women. Endometrial tumor spheroids will be used to determine progestin response of the tumors ex vivo and with information from exome sequencing, a predictive signature will be generated, which may or may not be different in black women. Finally, an alternative to progestin therapy will be tested ex vivo as an improvement to progestin therapy. This research will increase our understanding of racial differences in endometrial cancer and their response to existing progestin treatments. The impact of our studies will be significant, as we obtain evidence and design tools to improve progestin therapy in endometrial cancer in both black and white women in a personalized manner. !
Racial disparity is present in endometrial cancer with black women exhibiting increased aggressive disease and a higher death rate compared to white women. We will investigate the biological etiology through tumor genomics, and will also explore racial disparity for progestin response given that young black women exhibit advanced disease with worse prognosis compared to young white women. We will use customized bioinformatics tools to mine the sequencing data along with clinical information and use novel ex vivo models to determine progestin response to generate signatures for responders and non-responders.
