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B0967
Title: Identifying cancer driver genes from differential co-expression networks Authors:  Tyler Grimes - University of North Florida (United States) [presenting]
Abstract: The underlying driver of cancer stems from somatic mutations in the genome that change the function of gene products. However, not all mutations are associated with cancer progression. Rather, such ``passenger'' mutations are a symptom of DNA instability. Only a small portion of mutations are actual ``drivers'' that are responsible for disease progression. A methodology is proposed for analyzing gene expression data, to identify driver genes by considering both the functional changes of genes and the clinical relevance of those changes. Functional changes are identified by performing a differential network analysis, which compares the structure of gene-gene associations across different stages of cancer. Genes that are differentially connected indicate a change in their functional activity along with cancer progression. Clinical relevance of these differential connections is assessed using a survival model to predict overall survival. Potential driver genes are identified for Neuroblastoma and Breast cancer patient populations. We identify regulatory pathways that are both differentially connected and whose expression profile is predictive of overall survival. We plan to analyze additional cancers from the TCGA data repository and perform a meta-analysis to identify any pan-cancer driver genes or biomarkers.