Digital Twin-Based Multiscale Models for Biomarker Discovery in Kinase and Phosphatase Tumorigenic Processes

Digital twin is a mathematical model that virtually represents a physical object or process and predicts its behavior at future time points. These simulation models enable a deeper understanding of tumorigenic processes and improve biomarker discovery in cancer research. Tumor microenvironment is marked by dysregulated signaling pathways, where kinases and phosphatases serve as critical regulators and promising sources for biomarker discovery. These enzymes operate within multiscale and context-dependent processes where spatial and temporal coordination determine cellular outcomes. Digital Twin technology provides a platform for multimodal and multiscale modeling of kinase and phosphatase processes at the patient-specific level. These models have the potential to transform biomarker validation processes, enhance the prediction of therapeutic responses, and support precision decision-making. In this review, we present the major alterations affecting kinases and phosphatase functions within the tumor microenvironment and their clinical relevance as biomarkers, and we address how digital twins in oncology can augment and refine each stage of the biomarker discovery pipeline. Introducing this emerging technology for cancer biomarker discovery will assist in accelerating its adoption and translation into precision diagnostics and targeted therapies.