Trajectory Landscapes for Therapeutic Strategy Design in Agent-Based Tumor Microenvironment Models

Multiplex tissue imaging (MTI) enables high-dimensional, spatially resolved measurements of the tumor microenvironment (TME), but most clinical datasets are temporally undersampled and longitudinally limited, restricting direct inference of underlying spatiotemporal dynamics and effective intervention timing. Agent-based models (ABMs) provide mechanistic, stochastic simulators of TME evolution; yet their high-dimensional state space and uncertain parameterization make direct control design challenging. This work presents a reduced-order, simulation-driven framework for therapeutic strategy design using ABM-derived trajectory ensembles. Starting from a nominal ABM, we systematically perturb biologically plausible parameters to generate a set of simulated trajectories and construct a low-dimensional trajectory landscape describing TME evolution. From time series of spatial summary statistics extracted from the simulations, we identify a switched Markov State Model (MSM) that captures metastable states and the transitions between them, and whose modes are indexed by the parameter regimes most predictive of terminal-state outcome within the sampled ensemble. To connect simulation dynamics with clinical observations, we map patient MTI snapshots onto the landscape and assess concordance with observed spatial phenotypes and clinical outcomes. We further show that conditioning the MSM on the most predictive parameters yields group-specific transition models to formulate a finite-horizon Markov Decision Process (MDP) and use it for reachability analysis and finite-horizon intervention design. The resulting framework enables simulation-grounded therapeutic policy design for partially observed biological systems without requiring longitudinal patient measurements, taking a step towards adaptive, state-aware therapeutic strategies in oncology.