Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems’ level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research

Pathway and Network Approaches for Identification of Cancer Signature Markers from Omics Data

The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin

Sparse graphical models for cancer signalling

Protein signalling networks play a key role in cellular function, and their dysregulation is central to many diseases, including cancer. Recent advances in biochemical technology have begun to allow high-throughput, data-driven studies of signalling. In this thesis, we investigate multivariate statistical methods, rooted in sparse graphical models, aimed at probing questions in cancer signalling. First, we propose a Bayesian variable selection method for identifying subsets of proteins that jointly in uence an output of interest, such as drug response. Ancillary biological information is incorporated into inference using informative prior distributions. Prior information is selected and weighted in an automated manner using an empirical Bayes formulation. We present examples of informative pathway and network-based priors, and illustrate the proposed method on both synthetic and drug response data. Second, we use dynamic Bayesian networks to perform structure learning of context-specific signalling network topology from proteomic time-course data. We exploit a connection between variable selection and network structure learning to efficiently carry out exact inference. Existing biology is incorporated using informative network priors, weighted automatically by an empirical Bayes approach. The overall approach is computationally efficient and essentially free of user-set parameters. We show results from an empirical investigation, comparing the approach to several existing methods, and from an application to breast cancer cell line data. Hypotheses are generated regarding novel signalling links, some of which are validated by independent experiments. Third, we describe a network-based clustering approach for the discovery of cancer subtypes that differ in terms of subtype-specific signalling network structure. Model-based clustering is combined with penalised likelihood estimation of undirected graphical models to allow simultaneous learning of cluster assignments and cluster-specific network structure. Results are shown from an empirical investigation comparing several penalisation regimes, and an application to breast cancer proteomic data