Dr. Getz will discuss how the recent revolution in sequencing technologies has enabled comprehensive characterization of many thousands of cancer genomes, for example from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). These resulting data pose new computational challenges in detecting the various genomic alterations in the cancer genomes from sequencing data, understanding the mechanisms that create them, and dealing with heterogeneous samples. In addition, he will discuss the greatest challenge is how to introduce these findings to clinical trials and standard practice.

The revolution in sequencing technologies in recent years has enabled comprehensive characterization of many thousands of cancer genomes (e.g. TCGA, ICGC). These data pose new computational challenges in two major categories:

Detecting the various genomic alterations in the cancer genomes from sequencing data, understanding the mechanisms that create them and dealing with heterogeneous samples

Interpreting the genomic events and distinguishing ones that promote cancer initiation or progression (‘drivers’) vs. ones that have little or no effect on cancer evolution (‘passengers’)

Finally, the greatest challenge is how to introduce these findings to clinical trials and standard practice.

Dr. Getz is the Director of Cancer Genome Computational Analysis at the Broad Institute of MIT and Harvard and serves as a member of an NCI Board of Scientific Advisors’ subcommittee with oversight of caBIG®. He has received many awards and honors including the most recent, the Broad Institute Exceptional Year Award. With his background and expertise in computational biology, he seeks to bring statistical methods to the analysis of genomic data. Dr. Getz is interested in developing novel tools for detecting genomic events from high throughput sequencing data and tools that distinguish ‘driver’ from ‘passenger’ alterations in the cancer genome and by that, identifying novel candidate genes, pathways, and non-coding regions that promote tumorigenesis. As an NCI grantee, he leads a team at a major TCGA genome data analysis center, which automatically analyzes data from multiple centers.