A Sequenced Metabolic–Immune Framework for KEAP1-Mutant Non-Small-Cell Lung Cancer: A Computational Model of Pre-emption, Evolutionary Steering, and Immune Integration

Six minimal computational models examine KEAP1-mutant non-small-cell lung cancer as an evolving system rather than a fixed target. Across ODE population dynamics, a reinforcement-learning dosing controller, an evolutionary-steering model under realistic sensor noise, a metabolic pre-emption model, a plasticity-escape model, and a three-phase immune-integration model, the work argues that the sequencing and timing of interventions — and the quality of the information guiding them — govern tumour control more than the potency of any single agent. The models motivate a sequenced three-phase protocol (metabolic pre-conditioning of immune effectors; a metabolic trigger timed to immunogenic cell death with checkpoint blockade; durable immune-memory surveillance) whose components are supported by recent clinical and preclinical evidence but which has not been tested end-to-end. This is a computational hypothesis authored by an independent researcher without formal oncology training; parameters are illustrative and not fitted to patient data. It is offered as a falsifiable hypothesis for expert critique, not as validated biology or clinical guidance. Source code (six Python scripts) is included.