Abstract 4911: Systems level network analysis of NCI60 cell lines by alignment of protein pathway activation modules with multiple -omic data fields and therapeutic response signatures

Abstract

Abstract BACKGROUND: The NCI-60 cell line set is the most extensively profiled set of human tumor cell lines worldwide and have been characterized through chromosome karyotyping, SNP, mutation analysis, miRNA profiling, transcriptional profiling, metabolomic analysis, protein expression measurements, and functional enzymatic analysis. These types of “-omic” data have been mined for correlations with the tremendous drug response data that populates the dataset and provide a great opportunity for a systems level connectivity analysis between drug response and in-depth molecular characterization. However, a missing component of previous attempts to connect datasets together has been the lack of comprehensive functional protein signaling activation analysis. METHODS: Using Reverse-phase Protein MicroArray (RPMA) analysis, we were able to measure the activation state of nearly 200 key signaling proteins representative of the main pathways involved in cell proliferation, survival, migration and adhesion in all 60 cell lines. We used the nature of the comprehensive pathway activation analysis we performed to focus our analysis on modules of interconnected kinase-substrate protein architecture for the following 7 key signaling pathways known to be involved in cancer tumorigenesis and metastasis: AKT signaling, mTOR signaling, EGFR signaling, IGF1R signaling, integrin signaling, autophagy signaling, and apoptosis signaling. These modules were then used to define a systems level view of functional interactions through the correlation of protein phosphorylation/activation states within each module and the mutational, metabolomic, miRNA, transcriptional and drug sensitivity data available.RESULTS AND DISCUSSION: The integration of individual protein pathway activation measurements into biochemically interconnected modules provided a new 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 through genetic, genomic, transcriptomic, proteomic and metabolomic signatures predicts and defines therapeutic response. Such -omic portraits could inform rational anticancer agent screenings and drive personalized therapeutic approaches. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4911. doi:1538-7445.AM2012-4911