Patient-specific modeling of G2/M cell cycle dynamics reveals prognostic dynamical regimes driven by copy number alterations.

Cell cycle dysregulation is a hallmark of cancer and a major source of tumor heterogeneity. While large-scale cancer genomics studies have characterized recurrent copy number alterations affecting cell cycle regulators, how these alterations translate into patient-specific dynamical dysregulation remains poorly understood. Here, we develop a stochastic, human-specific model of the G2/M cell cycle transition and integrate somatic copy number alterations to generate patient-specific perturbations of cell cycle dynamics. Copy number profiles from breast cancer patients are quantitatively mapped to model components, enabling simulation of individualized G2/M transition behavior. Model simulations reveal that copy number alterations induce distinct dynamical regimes characterized by systematic shifts in transition timing and switching commitment. Importantly, model-derived dynamical metrics stratify patients into groups with significantly different overall survival, independently of age and molecular subtype. Patients exhibiting delayed or impaired G2/M transitions show improved survival probability, consistent with reduced proliferative capacity. These results demonstrate that mechanistic, stochastic modeling can transform static genomic alterations into clinically relevant dynamical phenotypes, highlighting the potential of systems-level approaches to bridge cancer genomics and functional tumor behavior.