UC San Diego

Despite many molecular studies showing a wide diversity of ovarian tumor types, ovarian cancer is still clinically treated as a single disease. Of five epithelial ovarian cancer subtypes, high grade serous ovarian cancer (HGSOC) has been identified as the most aggressive and constitutes roughly 60% of all malignant tumors. Aside from an almost universal loss of TP53 function, HGSOC is a highly heterogeneous disease. Several attempts to stratify this disease into more homogeneous cohorts have been made, but not applied in medical practice. Recently we developed Network-based Stratification (NBS), an unsupervised clustering method which uses somatic mutation profiles and known gene interaction networks, which successfully stratified HGSOC patients into four distinct and clinically relevant subtypes. Here we’ve developed a new supervised classifier, trained on the previously found HGSOC subtypes, that uses somatic mutation profiles of ovarian cancer to recover the ‘high-risk’ subtype and ‘standard-risk’ II-IV subtypes. We demonstrate the robustness of NBS across independent cohorts, by retrieving the NBS subtypes in a newly available HGSOC study from the International Cancer Genome Consortium (ICGC). To study the molecular characteristics of the ‘high-risk’ subtype, we’ve developed a supervised cell line classifier and classified the ovarian cell lines in the Cancer Cell Line Encyclopedia (CCLE) into ‘high-risk’ and ‘standard-risk’ subtypes. We established adequate cell line models of HGSOC and these subtypes and found the ‘high-risk’ subtype to be significantly resistant to cisplatin. Further exploration for the genes modulating response to cisplatin is underway in a whole-exome screen on a ‘high-risk’ HGSOC cell line.