Network-based analysis of reverse phase protein array data

Abstract

In this paper, we introduce a computational method for constructing networks based on reverse phase protein array (RPPA) data to identify complex patterns in protein signaling. The method is applied to phosphoproteomic profiles of basal expression and activation/phosphorylation of 76 key signaling proteins in three breast cancer cell lines (MCF7, LCC1, and LCC9). Temporal RPPA data are acquired at 48h, 96h, and 144h after knocking down four genes in separate experiments. These genes are selected from a previous study as important determinants for breast cancer survival. Interaction networks between the proteins and phosphorylated proteins are constructed by analyzing the expression levels of protein pairs using a multivariate analysis of variance model. A new scoring criterion is introduced to determine the proteins that changed significantly at each of the three time points. Signaling networks are constructed based on statistically significant protein pairs selected from the RPPA data. Through a topology based analysis, we search for wiring patterns to help identify key network nodes that are associated with protein expression changes in various experimental conditions.