Grant: Project Grant

Ovarian cancer ranks fifth in cancer deaths among women, accounting for more deaths than any other cancer of the female reproductive system. The revolution in our understanding of genetic and molecular drivers of other cancers has resulted in major improvements in how such cancers are routinely managed. However, standard clinical management of ovarian cancer and patient outcomes have not seen any improvements. Recent findings, including ours, in the classification of ovarian cancer based on genetic markers have important clinical implications, including the potential for identifying new treatments. However, until the discovery of surrogate biomarkers, these sub-classifications are based on labor-intensive assays with long turn around times, limiting their routine use. Visual microscopic study of diseased tissue (referred to as histology) by pathologists has been a cornerstone in cancer diagnosis and prognostication for more than a century. The information present in histology reflects the aggregate effect of genetic alterations on cancer cells and provides a convenient visual readout of disease biology. We will build a platform for characterizing ovarian cancer, based on advanced artificial intelligence (AI) techniques, by combining genetic and histology markers. More specifically, we will build automated differential diagnostic tool for major ovarian cancer subtypes, investigate the relationship between genetic markers, histology and disease outcome, and combine these data for a comprehensive profile of each tumor. New knowledge generated from this project will shed light on the link between histology and genetic markers and identify potential biomarkers that can be rapidly and accurately tested to stratify ovarian cancer for accurate treatment selection.