Igor Jurisica, Canada Research Chair in Integrative Computational Biology, University of Toronto

Integrative network analysis of prognostic markers in lung cancer. (in collaboration with M. S. Tsao, F. Shepherd, D. Strumpf, C.Q. Zhang)

Despite the introduction of diverse and powerful chemotherapeutic agents over the past two decades, many cancers remain diseases with devastating mortality rates. The accumulation of data from systematic high-throughput experiments has brought the potential to construct models of how biological systems work at the cell or whole organism level. How to integrate multiple information levels to achieve this task is not trivial, and we discuss some of the possible approaches.

Researchers, clinicians and biological methods all have specific biases. Many data sets provide useful, but not always fully accurate information on molecular cancer profiles, and we are attempting to interpret context from aggregated interactomes.

Analyzing over 1,400 NSCLC samples from 27 studies implicated 732 genes in the prediction of outcome and response to treatment. Identified genes were mapped to I2D protein-protein interaction database to generate a lung cancer network, which was further annotated using KEGG pathways, GO, and GeneCards, and analyzed in NAViGATor. We applied graph theory to determine the most significant subset of proteins and pathways within the network.