SR2-6: Genome Data for the Masses: Presentation of TCGA and IGC Breast Tumor Data.

Abstract

Abstract Large-scale cancer genomics projects will sequence tens of thousands of tumors in the next few years, along with matched normal tissue samples. Among these are The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the International Cancer Genomics Consortium (ICGC). TCGA alone plans to analyze 500 clinically characterized samples from each of 20 different cancer types, detecting frequently mutated genes, common copy number variants, rearrangements, altered gene expression, and methylation changes. These data will provide an exceptional resource for identifying new diagnostic targets and predictors of response. As a data analysis center and the primary sequence database for TCGA, we have built on technology developed for the UCSC Genome Browser to develop a cancer genome analysis pipeline and a cancer genomics browser (genome-cancer.ucsc.edu). These interpret cancer genomics data to aid in the identification of new targets. We reconstruct changes in tumor genomes and their expressed transcripts from tumor sequencing data (DNA, RNA, methylation) and use an approach based on factor graphs, called PARADIGM, to map multiple data types into a single coherent pathway model that includes thousands of genes and interactions for higher-level interpretation. By transforming raw genomic data to pathway activity levels, PARADIGM provides a comprehensible window into the data that can be coupled to predictors of response to improve accuracy. This paves the way to form hypotheses that clinical investigators can test in cell-line models and later explore in clinical trials. Today's concurrent cancer genomic projects provide an exceptional opportunity to study the molecular nature of cancer on an enormous scale never before possible. A cancer genomics data repository that hosts multiple large projects and makes them accessible through a general cancer genomics browser (with effective data access control) will leverage each study and enable cross-tumor cancer research. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr SR2-6.