Bifurcation Based Nonlinear Feedback Control of Cancer Mutation Evolution Integrated with Deep Reinforcement Learning and Real World Clinical Mutation Profiles

Abstract

Cancer mutation evolution remains one of the principal obstacles in achieving sustainable therapeutic responses in advanced malignancies. Tumor heterogeneity, nonlinear mutation dynamics, adaptive resistance, and temporal genomic instability continuously alter treatment sensitivity during therapy. Conventional therapeutic optimization approaches often fail to capture the bifurcation behavior and nonlinear transitions associated with mutation-driven evolutionary adaptation. In recent years, the integration of mathematical oncology with intelligent control systems has opened new pathways for dynamic treatment regulation and adaptive intervention design. However, most existing studies either rely on simplified deterministic models or neglect the interaction between mutation landscape evolution and real-time therapeutic feedback learning. This study proposes a novel bifurcation-based nonlinear feedback control framework integrated with deep reinforcement learning for regulating cancer mutation evolution using real-world clinical mutation profiles. The proposed model combines nonlinear dynamical systems theory, adaptive bifurcation analysis, and Deep Deterministic Policy Gradient (DDPG) optimization to construct a closed-loop therapeutic control architecture capable of dynamically adjusting treatment intensity according to evolving mutation states. Real somatic mutation datasets derived from publicly available clinical cancer genomic repositories are incorporated into the model to improve biological realism and translational applicability. The developed framework models tumor cell populations as interacting nonlinear evolutionary subsystems characterized by mutation-dependent growth transitions, therapy-induced selective pressure, and adaptive resistance emergence. Bifurcation analysis is employed to identify critical transition thresholds associated with rapid mutation amplification and instability regions. Subsequently, a deep reinforcement learning agent learns optimal therapeutic control policies capable of suppressing unstable mutation trajectories while minimizing excessive therapeutic toxicity. Simulation analyses demonstrate that the proposed integrated strategy significantly stabilizes mutation evolution dynamics, delays resistance emergence, and improves long-term tumor suppression compared with conventional fixed-dose therapeutic protocols. Multi-parameter sensitivity analyses further reveal that adaptive nonlinear control substantially reduces oscillatory mutation expansion under heterogeneous genomic conditions. The proposed framework establishes a scalable computational paradigm for intelligent cancer therapy optimization and provides a mathematically interpretable pathway toward personalized adaptive oncology systems.