Network as Biomarker: Quantifying Transcriptional Co-expression to Stratify Cancer Clinical Phenotypes

Identifying robust biomarkers for cancer phenotypes has challenged the biological and pharmacological communities for many years, more so since the availability of screening methods that reveal the expression levels of all the genes in the genome. A host of different approaches have been used to address this lack of robustness. These methods have included a spectrum of approaches from gene enrichment analysis to network inference analysis. More recently, some methods that use the network properties of genes have demonstrated an ability to provide a more robust signature. In this review, we survey different network-as-biomarker methods used to identify various biomarkers and we discuss the critical role of networks in the progress toward personalized medicine. We also discuss the ability of the network to identify misguided processes, rather than the gene itself, as the core of distinctions among phenotypes. Discussions about the importance of the molecular pathway view and about processes (rather than the gene per se) at the core of understanding cancer are not new. However, this review focuses on the set of tools available for actually measuring the pathway, or the process, when the expression levels of their components are available.