PDF file - 142K, Unsupervised hierarchical clustering representing the NCI-60 cell lines (horizontal axis) and the calculated six pathway activation module scores (vertical axis): IGF1-R, apoptosis, integrin, mTOR, AKT and EGFR. The NCI-60 cell lines have been colorized according to their tissue origin: orange - kidney, blue - lung, pink - breast, red - blood, green - colon, brown - prostate, purple - ovary, black - skin and gray - central nervous system.
ARTICLE ABSTRACT
The NCI-60 cell line set is likely the most molecularly profiled set of human tumor cell lines in the world. However, a critical missing component of previous analyses has been the inability to place the massive amounts of “-omic” data in the context of functional protein signaling networks, which often contain many of the drug targets for new targeted therapeutics. We used reverse-phase protein array (RPPA) analysis to measure the activation/phosphorylation state of 135 proteins, with a total analysis of nearly 200 key protein isoforms involved in cell proliferation, survival, migration, adhesion, etc., in all 60 cell lines. We aggregated the signaling data into biochemical modules of interconnected kinase substrates for 6 key cancer signaling pathways: AKT, mTOR, EGF receptor (EGFR), insulin-like growth factor-1 receptor (IGF-1R), integrin, and apoptosis signaling. The net activation state of these protein network modules was correlated to available individual protein, phosphoprotein, mutational, metabolomic, miRNA, transcriptional, and drug sensitivity data. Pathway activation mapping identified reproducible and distinct signaling cohorts that transcended organ-type distinctions. Direct correlations with the protein network modules involved largely protein phosphorylation data but we also identified direct correlations of signaling networks with metabolites, miRNA, and DNA data. The integration of protein activation measurements into biochemically interconnected modules provided a novel means to align the functional protein architecture with multiple “-omic” data sets and therapeutic response correlations. This approach may provide a deeper understanding of how cellular biochemistry defines therapeutic response. Such “-omic” portraits could inform rational anticancer agent screenings and drive personalized therapeutic approaches. Mol Cancer Res; 11(6); 676–85. ©2013 AACR.
