School of Mathematics and Statistics, University of Sydney
Rapidly emerging tools in networks research offer new platforms to understand the complex relationships among different molecular phenotypes. This study offers a framework that explores network information through co-analysis of somatic mutations and gene expression profiles. This is achieved by considering the networks generated by testing for differences in expression in direct association with specific mutated genes. I will relate our findings among 19 examined cancers to identify commonalities and differences as well as their characteristics. Our pan-cancer application of this approach suggests that while mutations are frequently common among cancer types, the impact they have on the surrounding networks via gene expression changes varies. Despite this finding, there are some cancers for which mutation-associated network behaviour appears to be similar: suggesting a potential framework for uncovering related cancers. This framework for understanding relationships among cancers has been integrated into an interactive R Shiny application, PAn Cancer Mutation Expression Networks (PACMEN), containing dynamic and static network visualization of the estimated mutation-expression networks. PACMEN also features tools for further examination of network topology characteristics among cancers.