We develop approaches that balance and inter-connect quantitative and biological knowledge. Many acknowledge that no field has generated higher expectations, deeper frustrations, and more “translation anxiety” than advances in human genomics. Early on, we among others have highlighted the role of rigorous epidemiological and statistical approaches in improving the prospect of genomics and big data in personalized medicine. Our approach, termed “Systems Epidemiology” (Lund & Dumeaux, 2008 CEBP), proposes to integrate human -omics data with measurements from observational epidemiologic studies to better characterize the diverse range of factors influencing complex diseases, and help infer causation and support evidence-based research (Lund & Dumeaux, 2010 Int J Epi). In line with these concepts, we supported the development of a large biobank within the Norwegian Women and Cancer Study (Dumeaux et al, 2008 BCR).
Also, critical to these efforts is the development of computational methodologies that support the integration and interpretation of these complex “real-life” data. Specifically, we have developed novel methodologies for the sensitive detection of low amplitude changes in blood profiles across healthy individuals (developed in PloS Genetics 2010), for identifying genes, pathways, and processes that co-vary and interact across tissues and environments, for predicting activation of molecular pathways in a single-patient manner satisfying clinical practice constraints associated with personalized medicine, as well as other methodologies within collaborative manuscripts (Huttenhower et al, 2009 Genome Research; Barutcuoglu Z et al, 2009 Bioinformatics)
Relevant recent papers:
Interactions between the tumor and the blood systemic response of breast cancer patients
Detecting gene signature activation in breast cancer in an absolute, single-patient manner.
Building applications for interactive data exploration in systems biology.
Relevant recent software packages:
MIxT: system designed for exploring and comparing transcriptional profiles from two or more matched tissues across individuals.
Tumor-blood interactions in breast cancer patients
Tumor epithelium-stroma interactions in breast cancer