| Like all human cancers colorectal cancer (CRC) is a complicated disease. While a mature body of research involving CRC has implicated the putative sequence of genetic alterations that trigger the disease and sustain its progression, there is a surprising paucity of well-validated, clinically useful diagnostic markers of this disease. For prognosis or guiding therapy, single gene-based markers of CRC often have limited specificity and sensitivity. Genome-wide analyses (microarray) have been used to propose candidate patterns of gene expression that are prognostic of outcome or predict the tumor's response to a therapy regimen, however these patterns frequently do not overlap, and this has raised questions concerning their power as biomarkers. The limitation of gene expression approaches to marker discovery occurs because the change in mRNA expression across tumors is highly variable and alone accounts for a limited variability of the phenotype, e.g. cancer. It is largely unknown how the integration of proteomic data and genomic data, along with protein-protein interaction data may enhance the discovery of more quantitatively powerful biomarkers. In this work we show that a proteomics-first approach can discover significantly, differentially expressed proteins between cancer and control tissues. In turn, these targets may be integrated with mRNA and protein-protein interaction data to discover networks of proteins that are quantitatively significant discriminators of cancer versus control. Further, we show that our bioinformatic methods are extensible and robust with respect to publicly available proteomic data and public PPI datasets. Further, a proteomics-first approach for finding significant sub-networks in CRC is comparable to the same approach seeded instead with a set genes implicated as "drivers" of CRC. Finally, because these network discriminators exist at the level of the proteome, they provide an optimal basis for mechanistic validation in in vitro disease models, such as cell culture. It is thought that network-based approaches may provide improved diagnostic, prognostic, or predictive markers in CRC, and lead to improvements in molecularly targeted therapies. |
