Meet The Speaker
“An Integrated Approach to Uncover Drivers of Cancer”
Dana Pe’er is assistant professor at the Department of Biological Sciences at Columbia and is one of the leading researchers in computational systems biology. In her Ph.D. (Computer Science) at the Hebrew University of Jerusalem (with Nir Friedman), Dana pioneered the use of machine learning to uncover the structure and function of molecular networks from genomics data, based on Bayesian networks. She subsequently did a postdoc with George Church at Harvard Medical School and there she began to work towards understanding of how genetic variation alters the regulatory network between individuals and subsequently manifests in phenotypic diversity. This is now the focus of Dana’s lab at Columbia University, where she and her team are developing methods to infer how variation in sequence modulates signal processing and is manifested in cellular phenotypes, with applications towards personalized cancer treatment. Dana is recipient of the Burroughs Wellcome Fund Career Award, NIH Directors New Innovator Award and a Packard Fellow in Science and Engineering.
Seminar Abstract
“An Integrated Approach to Uncover Drivers of Cancer”
We have developed Conexic, a novel Bayesian Network-based framework to integrate chromosomal copy number and gene expression data to detect to detect driver genes located in regions that are aberrant in tumors. We demonstrated the utility of the CONEXIC framework using a melanoma dataset, our analysis correctly identified known drivers in melanoma (such as MITF) and connected these to many of their known targets, as well as the biological processes they regulate. In addition, it predicted multiple tumor dependencies TBC1D16 and RAB27A in melanoma and showed that tumors highly expressing these genes are dependent on the same gene for growth. Additionally, gene expression in the associated modules is altered following knockdown as predicted by our model. The identity of these drivers suggests that abnormal regulation of protein trafficking is important for cell survival in melanoma and highlights the importance of protein trafficking in this malignancy.
We also present more recent results of applying extensions of CONEXIC to additional cancers, including Glioblastoma and Ovarian cancers, as well as additional phenotypes including invasion and drug resistance. Together, these results demonstrate the ability of integrative Bayesian approaches to identify novel drivers involved in proliferation, invasion and drug response in cancer.
