Center for Computational Biology Seminar: Topics in Cancer Genomics: Mark Gerstein, Professor, Yale University

Seminar: Biosystems and Computational Biology: CS | December 12 | 2-3 p.m. | 125 Li Ka Shing Center

 Center for Computational Biology

Title: Topics in Cancer Genomics

Abstract:
My talk will focus on how to leverage thousands of cancer genomes and functional genomics datasets to discover disease-associated regulators and variations. First, I will go over the ENCODE annotation related to the cancer genome. I will show how extended gene annotation allows us to place oncogenic transformations in the context of a large cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Second, I will look at how these can be recast into a comprehensive regulatory network of both transcription factors and RNA-binding proteins (TFs and RBPs). I will showcase their value by highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of the well-known oncogenic TF MYC. Third, I will describe a workflow to prioritize key elements and variants. I will showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS (LARVA, MOAT & uORF tools). Finally, I will put all these methods together through application to the PCAWG dataset. In this analysis, we integrate genomic annotations and predicted functional impact scores to quantify the overall burdening of various elements in cancer genomes. We also show how the overall functional burdening of various genomic elements correlates with patient survival time and tumor clonality. Finally, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (~12% additive variance) for predicting cancerous phenotypes, beyond identified driver mutations. Furthermore, this framework allowed us to estimate potential weak-driver mutations in samples lacking any well-characterized driver alterations.

Bio:
Mark Gerstein is the Albert L Williams professor of Biomedical Informatics at Yale University. He is the co-director of the Yale Computational Biology & Bioinformatics Program and the Center for Biomedical Data Science. He has appointments in the Departments of Molecular Biophysics & Biochemistry, Computer Science and Statistics & Data Science. He received his AB in physics summa cum laude from Harvard College and his PhD in chemistry from Cambridge. He did post-doctoral work at Stanford and took up his post at Yale in early 1997. Since then, he has published appreciably in the scientific journals, with >550 publications in total, including a number of them in prominent venues, such as Science, Nature, and Scientific American. (His current publication list is at http://papers.gersteinlab.org. His research is focused on biomedical data science, and he is particularly interested in data mining, macromolecular geometry & simulation, human-genome annotation, disease genomics and genomic privacy.

 All Audiences

 All Audiences

 Light refreshments will be provided at reception following the talk from 3:00 - 3:30pm, 125 LKS foyer.

 ccbadmin@berkeley.edu