Network analysis of cancer PSB 2014 Poster

<p>Cancer is, in general, a complex disease; it operates on pathways and systems, not solely on the individual components of those systems (genes or proteins). We have gathered global proteomic and phosphoproteomic data from a set of tumors with existing genomic data (sequencing, methylation, miRNA and mRNA expression) associated with a range of survival phenotypes. We report on our recent progress in analyzing proteomic, phosphoproteomic, and accompanying genomic data in terms of pathways that are significantly associated with breast and ovarian cancer progression and mortality. We show that proteomics and phosphoproteomics data both highlight a set of distinct and overlapping pathways with more traditional gene expression data. Further we use novel analysis approaches to integrate proteomic and genomic data to identify pathway activation and show that data integration improves resolution of activated pathways in short- and long- term survivors and in different clinical and molecularly-defined subtypes. We employ data integration and network analysis approaches to identify novel potential drivers of cancer progression. Our results provide a number of intriguing predictions of pathway and protein drivers of breast and ovarian cancer progression.