Network Models in Cancer: Advances in Systems-Level Insights and Precision Oncology

In the field of cancer research, understanding the intricate complexity of tumor biology remains a challenge. Cancer is driven by numerous molecular and cellular changes, ranging from genomic mutations and epigenetic alterations to disruptions in signaling, metabolism, and cellular communications, all of which interconnect to form multiscale networks. Although significant progress has been made through reductionist approaches targeting individual genes or pathways, such strategies often overlook how these components interact dynamically, leading to emergent properties like therapy resistance and phenotypic heterogeneity. This limitation has spurred the rise of network biology, which offers an integrative and holistic perspective by modeling the relationships among diverse molecular, cellular, and microenvironmental factors. State-of-the-art network models, built from multi-omics data and fueled by advances in machine learning, systems theory, and high-throughput technologies, have begun to reveal critical nodes and interactions that underpin cancer progression and treatment responses. Recent studies have demonstrated the value of these approaches in predicting drug responses, mapping resistance mechanisms, and identifying synergistic drug combinations, emphasizing the urgent need for further methodological innovations and real-world applications.This Research Topic aims to aggregate pioneering work that leverages network models to decipher the multifaceted nature of cancer and to identify clinically actionable insights. The objective is to spotlight research that integrates complex datasets, elucidates underlying mechanisms, and facilitates the translation of network-based discoveries into improved diagnostic, prognostic, and therapeutic modalities.The Topic is limited to the intersection of network bioinformatics and cancer research, focusing on network-based methodologies, their application to different cancer types, and their translational potential. We invite submissions that address, but are not limited to, the following themes:- Construction and analysis of multi-omics and multi-scale cancer network models- Network-guided discovery of therapeutic targets, drug combinations, and resistance pathways- Dynamic, temporal, and single-cell approaches in cancer network modeling- Integrative models of the tumor microenvironment and intercellular communication- Machine learning and artificial intelligence in network inference and analysis- Patient-specific network approaches for stratification and personalized therapy design- Network pharmacology, benchmarking and standardization of cancer network pipelinesThis Research Topic welcomes Original Research, Method, Review, Mini-Review, Systematic Review, Brief Research Report, Case Report, General Commentary, Hypothesis and Theory, Opinion, Perspective, and Technology and Code articles that contribute to theoretical, methodological, and practical advances in cancer network biology, with a special emphasis on reproducibility, open data, and tool sharing. Topic editors: Premnath Dhanaraj, Lun Hu, Shan He Source: https://www.frontiersin.org/research-topics/77505/network-models-in-cancer-advances-in-systems-level-insights-and-precision-oncology