Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

SUMMARY The Cancer Genome Atlas Network recently catalogued recurrent genomic abnormalities in glioblastoma (GBM). We describe a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical and Mesenchymal subtypes and integrate multi-dimensional genomic data to establish patterns of somatic mutations and DNA copy number. Aberrations and gene expression of EGFR, NF1, and PDGFRA/IDH1 each define Classical, Mesenchymal, and Proneural, respectively. Gene signatures of normal brain cell types show a strong relation between subtypes and different neural lineages. Additionally, response to aggressive therapy differs by subtype with greatest benefit in Classical and no benefit in Proneural. We provide a framework that unifies transcriptomic and genomic dimensions for GBM molecular stratification with important implications for future studies.