title: Proteogenomic convergence for understanding cancer pathways and networks.
abstract: During the past several decades, the understanding of cancer at the molecular level has been primarily focused on mechanisms on how signaling molecules transform homeostatically balanced cells into malignant ones within an individual pathway. However, it is becoming more apparent that pathways are dynamic and crosstalk at different control points of the signaling cascades, making the traditional linear signaling models inadequate to interpret complex biological systems. Recent technological advances in high throughput, deep sequencing for the human genomes and proteomic technologies to comprehensively characterize the human proteomes in conjunction with multiplexed targeted proteomic assays to measure panels of proteins involved in biologically relevant pathways have made significant progress in understanding cancer at the molecular level. It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and "diseased" states is important to capture a first glance at the overall proteomic landscape, which has been a main focus of proteomics research during the past 15-20 years. However, the research community is gradually shifting its heavy focus from that initial discovery step to protein target verification using multiplexed quantitative proteomic assays, capable of measuring changes in proteins and their interacting partners, isoforms, and post-translational modifications (PTMs) in response to stimuli in the context of signaling pathways and protein networks. With a critical link to genotypes (i.e., high throughput genomics and transcriptomics data), new and complementary information can be gleaned from multi-dimensional omics data to (1) assess the effect of genomic and transcriptomic aberrations on such complex molecular machinery in the context of cell signaling architectures associated with pathological diseases such as cancer (i.e., from genotype to proteotype to phenotype); and (2) target pathway- and network-driven changes and map the fluctuations of these functional units (proteins) responsible for cellular activities in response to perturbation in a spatiotemporal fashion to better understand cancer biology as a whole system.
authors.affiliation: Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, 31 Center Drive, MSC 2580, 20892 Bethesda, MD, USA.
authors.forename: Emily S
authors.initials: ES
authors.lastname: Boja
authors.affiliation: Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, 31 Center Drive, MSC 2580, 20892 Bethesda, MD, USA.
authors.forename: Henry
authors.initials: H
authors.lastname: Rodriguez
keywords: Mathematical and computational modeling; Omics integration; Post-translational modification; Protein identification and quantitation; Protein networks; Proteogenomics; Signaling pathways; Systems biology; Targeted proteomics