AI Article Synopsis
- Precision oncology aims to personalize cancer treatment but relies on more than just knowing cancer mutations, as the relationships between genotype and phenotype are complex and ever-changing.
- Systems biology examines biological processes in a comprehensive way, using various techniques to model and understand the dynamic behavior of these systems and their responses to changes in the environment.
- The paper highlights how systems biology enhances precision oncology by analyzing signal transduction networks, which can help overcome resistance to therapies and improve treatment decisions using patient-specific biomarkers tied to dynamic signaling models.
Article 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699328 | PMC |
| http://dx.doi.org/10.3390/cancers13246312 | DOI Listing |
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