Signal Transduction Score Flow Algorithm for Cytotoxic Drug Response
22/01/2013 - Room C209 at 14:00 - External Seminar
(Department of Molecular Biology and Genetics, Faculty of Science, Bilkent University)
Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer mortality and the third most common malignancy in human cancers. HCC has very limited treatment options due to its heterogeneous, multi-step, slow-progressing and chemo-resistant nature. There exists a single multikinase inhibitor, Sorafenib, which is approved for hepatocellular carcinoma treatment. Sorafenib prolongs median survival rate and the time to progression by nearly three months in patients with HCC. Therefore, there is a need for new liver-cancer-specific agents used alone or in combination with Sorafenib. Systems biology analysis of parallel or alternative cellular pathways are essential for discovery of novel drug targets, feedback loops and possible side-effects. We describe a novel model- and data-driven hybrid approach, called signal transduction score flow algorithm, which allows quantitative visualization of cyclic cell signaling pathways that are leading to ultimate cell responses such as survival, migration or death. The score flow algorithm translates a signaling pathway into a directed graph and maps experimental data, including negative and positive feedbacks, onto gene nodes as scores. The algorithm then computationally traverses the signaling pathway until a pre-defined biological target response is attained. Initially, experimental data-driven enrichment scores of the genes are computed in a pathway, then a heuristic approach is applied using the gene score partition as a solution for protein node stoichiometry during the dynamic scoring of the pathway of interest. Incorporation of a score partition during the signal flow and cyclic feedback loops in the signaling pathway significantly improves the usefulness of this model, as compared to other approaches. Evaluation of the score flow algorithm using both transcriptome and ChIP-seq data-generated signaling pathways showed good correlation with expected cellular behavior on both KEGG and manually generated pathways. Implementation of the algorithm as a Cytoscape plug-in allows interactive visualization and analysis of KEGG pathways as well as user-generated and curated Cytoscape pathways. Moreover, the algorithm accurately predicts gene-level and global impacts of single or multiple in silico gene knockouts, which mimics drug treatments.