Can Systems Biology Advance Clinical Precision Oncology?

SIMPLE SUMMARY: Omics technologies and targeted drugs are revolutionizing the clinical oncology landscape, portending the promise of precision oncology. However, our abilities to define the best treatment for the individual tumor, based on its molecular characterization, are still limited. Systems biology, by studying the collective behavior of the different types of molecules involved in a biological process, allows us to reconstruct the complex behavior of biological systems and to compute the system’s response to perturbations, such as targeted therapies. This helps to dissect drug resistance phenomena, as well as to establish the best drug combinations for a specific tumor. Patient-specific biomarkers can be built from dynamical models of signaling networks and can have a greater prognostic value than conventional biomarkers. In this paper, we review current systems biology methods to highlight how they can contribute to advance clinical and translational research in precision oncology. ABSTRACT: 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.