Abstract Large-scale cancer genomics projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genomics Consortium (ICGC) will sequence tens of thousands of tumors in the new few years, along with matched normal blood samples and other normal tissues. TCGA alone plans to analyze 500 clinically characterized samples from each of 20 different cancer types, detecting frequently mutated genes, common copy number variants, altered gene expression and methylation changes. These data will provide an exceptional resource for identifying new diagnostic targets and predictors of response. Building on technology developed for the UCSC genome browser, we have developed a cancer genome analysis pipeline and a cancer genomics browser to interpret cancer genomics data that will aid in the identification of new targets. We use a new approach called PARADIGM, based on factor graphs, to map multiple data types into a single coherent pathway model for higher level interpretation. PARADIGM currently employs models for many hundreds of pathways from pathway libraries such as NCI pathways, Wikipathways, KEGG, etc. 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. We discuss the early results of this approach to the TCGA data.