The comprehensive, multidimensional molecular characterization of tumors and the individuals in which they have developed is transforming cancer definition, diagnosis, treatment, and prevention. These technologies identify the millions of variants present in normal individuals and thousands of alterations that occur during the course of tumor development. This systems-wide molecular analysis has identified a complex cacophony of inherited and acquired variation. The integration and interpretation of this complex multidimensional information into evidence exceeds raw human cognitive capacity. It presents challenges of contextualizing the data and converting it into actionable information.
Data Science has the capacity to provide the needed tools to tackle this challenge. Arizona State Universitys (ASU) Complex Adaptive Systems team is building a first generation Data Science research platform - the Next Generation Cyber Capability (NGCC). The ASU NGCC composed of hardware, software, and people transforms Big Data to information and creates the evidence necessary to enable personalized medicine. The NGCC permits data points to be evaluated in concert using Big Data analytic frameworks thereby identifying an emergent, coherent whole. Biologic network analysis represents one such promising integrative approach. These networks account for the individual heterogeneity in underlying etiology as well as the interaction of diverse events necessary to generate a complex phenotype such as cancer. Emerging collections of analytic approaches permit analysis using genome-wide data sets and established biologic networks as models.
These approaches are being applied to understand the origins and outcomes of cancer. Big Data approaches are identifying key biologic processes underpinning cancer susceptibility and oncogenesis. Novel analytic approaches are being applied to identify new strategies for intervention.