Precision Medicine: New Integrative Paradigms for Disease Modeling

New and emerging technologies generate low-cost, high-throughput assessments of molecules at the DNA, RNA, and protein levels, with streams of patient data from mobile health monitors. As yet unknown relationships between genomics, symptomatic disease, health interventions, and health outcomes reside in these huge, unstructured data archives. Multidisciplinary quantitative science combining biostatistics, bioinformatics, and computing is needed to reveal new insights for human biology, clinical medicine, and public health. Key goals include identifying mutations that initiate disease and molecular markers that predict patient response to therapy. Finding biomarkers to test in clinical trials is a step toward personalized risk modeling, personalized cancer prevention, and precision medicine, yet analyzing and interpreting these data is a significant challenge. New statistical and computational methods are needed to identify relationships between high-dimensional proteomics and genomics data, imaging data, and information about a person’s environment, diseases, clinical interventions, and health outcomes. Understanding cancer and disease heterogeneity will require new computational methods for third-generation genetic sequencing and related single-molecule technologies. The panel will discuss novel statistical methods in data-mining for cancer and health genomics, the utility of mobile apps in asthma care and research, and single-cell RNA-sequencing in ovarian cancer biomarker development.