Computational Collaboratives: Connected Thinking to Correlate Genomic Variations with Cancer Risk and Outcomes

Presented by: Brian Van Ness

One of the important concepts that has emerged from the human genome project is that disease risk and clinical outcomes can be influenced by individual genetic background. Thus, while many diseases may have their unique genetic signatures, individual patient outcomes are dependent on heritable variations (SNPs) in a wide variety of genes and pathways affecting cellular functions and drug responses. Moreover, genetic variations in such global functions such as inflammation, immunity, and cellular signaling in the tumor microenvironment can have an impact on diverse clinical responses.   Our lab has developed a targeted SNP panel to determine genetic variations among cancer patients (eg. multiple myeloma, lung cancer) that focusses on genetic variations in genes within key cellular pathways. Because clinical trials often have limited sample size, we needed to develop computational approaches that go beyond the high error association analysis of univariate rank ordering. Indeed, most complex traits, including cancer risk and therapy outcomes, certainly result more from complex genetic interactions than single genetic drivers.   We addressed this by working with the Computational Science group (Vipin Kumar). Using some established methods in Support Vector Machines we developed significant predictive algorithms of survival. Further, novel methods in pathway intersections and "p-value jumps" provided clusters of SNPs that are associated with cancer risk and clusters associated with survival predictions far above random permutation testing.   The connected thinking in a project like this starts with clinical expertise in developing the clinical trials; which feeds the basic science to develop genetic approaches to assess variations in responses; followed by challenges met in analysis by computational expertise; and biologic insights developed by the team. Discussion will also include how this approach can incorporated in to research models we are developing for Healthcare IP2 - Informative, Personalized, and Predictive.

Bio:  The research in the Van Ness lab is directed at defining genetic deregulation that contributes to lymphoid malignancies, particularly multiple myeloma. Multiple myeloma results from plasma cell expansion in the bone marrow, and unfortunately is very hard to treat. This difficulty comes in part from the variability in genetic and signaling pathways that are deregulated in the plasma cells as well as the cells in the bone marrow microenvironment. The lab is developing both cell lines and mouse models to explore how different genes can influence disease progression and therapeutic response. The lab is identifying some of the complexity of gene expression through microarray expression profiling; and we have undertaken a collaborative project to target gene deregulation that will contribute to models of plasma cell malignancy in the mouse. The Van Ness lab is also working with both national and international clinical groups to correlate genetic defects with disease outcome and response to different therapies. The ultimate goal is to contribute to genetic characterization of patients that will direct individualized therapy.