The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine

Abstract

Precision cancer medicine promises to tailor clinical decisions to patients using genomic information. Indeed, successes of drugs targeting genetic alterations in tumors, such as imatinib that targets BCR-ABL in chronic myelogenous leukemia, have demonstrated the power of this approach. However, biological systems are complex, and patients may differ not only by the specific genetic alterations in their tumor, but also by more subtle interactions among such alterations. Systems biology and more specifically, network analysis, provides a framework for advancing precision medicine beyond clinical actionability of individual mutations. Here we discuss applications of network analysis to study tumor biology, early methods for N-of-1 tumor genome analysis, and the path for such tools to the clinic.

Plasma proteomic approach in patients with heart failure: insights into pathogenesis of disease progression and potential novel treatment targets

“…Network analysis is a powerful tool for studying biological systems in health and disease that helps identify activated pathways, driven genes or mutations in the disease. In addition, network analysis can identify subtypes of the disease, classify patients as well as discover novel prognostic biomarkers and new targets for therapy that might support precision medicine.…”

Biological Random Walks: Integrating heterogeneous data in disease gene prioritization

“…A key property of the underlying molecular network of interactions is that disease proteins are not found to be uniformly scattered across the interactome, but they tend to interact with one another confined in one or several subgraphs called disease modules [23]. In fact, disease proteins are prone to participate in common biological activities such as, for example, genome maintenance, cell differentiation or growth signaling, which are the most relevant pathways in carcinogenesis [26]. Consequently, the module property also reflects the biological feature that disease proteins are often localized on specific biological compartments (pathway, cellular space, or tissue).…”

Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine

“…However, in practice, it is not cost-effective and often entirely infeasible to obtain replicate samples from the clinic procedures. Since DEG analysis methods were validated using replicates (3,4), there remains a need to learn how well a DEG method designed for identifying differential expression would perform in realworld conditions and when replicates are unavailable (ss-DEG Methods).…”

“…Novel methodological advances designed with single subjects in mind have begun to be proposed (3,4). While accurately discovering DEGs between two RNA-Seq samples remains a challenge and insufficiently studied (3,4), methods identifying differentially expressed gene sets and pathways between two transcriptomes applicable to single-subject studies have been reproducibly demonstrated as feasible (3,4) in simulations(5), retrospective studies in distinct datasets (5-10), cellular assays (11,12), as well as in one clinical classifier (13) (details in supplement Table S1). These comprehensive validations of gene set/pathway-level methods established the feasibility of single-subject interpretation of the transcriptomes and stimulate further investigations to improve more precise methods for determining the underlying differentially expressed genes.…”