Tressa Hood

Gene fusions have long been known to drive cancer. Initial discovery of gene fusions was opportunistic, and functional assessment was done individually and experimentally. There is no comprehensive systems biology approach to understanding the impact of gene fusions on the signaling networks within tumor cells. An integrative computational approach was taken to achieve a better understanding of gene fusions and their complex influence on pathways and interaction networks in the context of lung cancer. Using well-studied fusions and publicly available gene expression data, the effect of fusion events on the expression pattern of gene networks revealed unique differences in tumors with gene fusions, tumors without gene fusions, and normal samples. This approach identifies gene expression signatures associated with specific fusions, and provides a model for integrating experimental and pathway data to better understand the biology of a fusion genes and their roles in oncogenesis.