Supplementary Figure 4 from Systems Analysis of the NCI-60 Cancer Cell Lines by Alignment of Protein Pathway Activation Modules with “-OMIC” Data Fields and Therapeutic Response Signatures

Federici, Giulia; Gao, Xi; Slawek, Janusz; Arodz, Tomasz; Shitaye, Amanuel; Wulfkuhle, Julia D.; De Maria, Ruggero; Liotta, Lance A.; Petricoin, Emanuel F.

doi:10.1158/1541-7786.22509436.v1

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Supplementary Figure 4 from Systems Analysis of the NCI-60 Cancer Cell Lines by Alignment of Protein Pathway Activation Modules with “-OMIC” Data Fields and Therapeutic Response Signatures

journal contribution

posted on 2023-04-03, 16:02 authored by Giulia Federici, Xi Gao, Janusz Slawek, Tomasz Arodz, Amanuel Shitaye, Julia D. Wulfkuhle, Ruggero De Maria, Lance A. Liotta, Emanuel F. Petricoin

PDF file - 1038K, Systems networks of AKT (A), apoptosis (B), IGF-1R (C) and mTOR (D) pathway activation. Node color: Violet - node representing the Pathway Score; Blue - phosphoproteins (linked to pathway score or to other proteins/genes at the protein level); Yellow - genes (linked to Pathway Score or to other proteins/genes at the mRNA level); Orange - microRNA; Dark green - drug; Light green - metabolite; Gray - other. Node shape: Diamond - Pathway Score node, or phosphoprotein that is used in calculating the Pathway Score; Circle - other nodes. Edge color & label: Brown - relationship inferred based on phosphoprotein level (either with level other phosphoprotein, or with Pathway Score); Gray - relationship inferred based on gene mRNA expression (either with mRNA of other gene, or with Pathway Score); Dark green - phosphoprotein level or gene expression (mRNA) is significantly correlated with drug's response measured as -log(GI50); Light green - phosphoprotein level or gene expression (mRNA) is significantly correlated with metabolite concentration; Red - phosphoprotein level or gene expression (mRNA) is significantly correlated with mutation of other gene. Arrow points from mutation gene to the mRNA gene; Pink - gene expression (mRNA) is significantly correlated with methylation of other gene. Arrow points from methylation gene to the mRNA gene; Dark blue - gene expression (mRNA) is significantly correlated with copy number of other gene. Arrow points from copy number gene to the mRNA gene; Orange - gene expression (mRNA) is significantly correlated with expression of microRNA. Edge line style: Solid - positive correlation; Dashed - negative correlation.

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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.