Skip to Main Content
Ovarian cancer is the leading cause of death in gynecological cancers. Carboplatinum-based therapy is the standard treatment choice for ovarian cancer. However, a majority of the patients develop resistance to carboplatinum fairly rapidly hence there is a clinical need for early predictors of carboplatinum resistance. While there are a few indicative gene markers, they have poor sensitivity and specificity in predicting response accurately. It is essential that multiple high throughput molecular profiling modalities are integrated and investigated to provide a full picture of the ongoing processes. Here, we propose a methodology to identify central players in platinum resistance from a list of stratifying genes using a data-driven approach. We have used correlation of DNA methylation and gene expression data and applied network based features to identify the influence of DNA methylation on gene expression. This provides interpretive analysis and is complementary to the biological pathway-enrichment approaches. We suggest that our method, based on network structure properties, adds a useful layer to multi-modal evidence integration to focus on the key processes and interactions in resistance mechanisms.