Multi-omics multi-scale big data analytics for cancer genomics

Abstract

Cancer research is emerging as a complex orchestration of genomics, data-sciences, and network-sciences. For improving cancer diagnosis and treatment strategies, data across multiple scales, from molecules like DNA, RNA, metabolites, to the population, need to be integrated. This requires handling of large volumes of high complexity “Omics” data, requiring powerful computational algorithms and mathematical tools. Here we present an integrative analytics approach for cancer genomics. This approach takes the multi-scale biological interactions as key considerations for model development. We demonstrate the use of this approach on a publicly available lung cancer dataset collected for 109 individuals from an 18 years long clinical study. From this data, we discovered novel disease markers and drug targets that were validated using peer-reviewed literature. These results demonstrate the power of big data analytics for deriving disease actionable insight.