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

TL;DR

This paper introduces a reduced-order, simulation-driven framework that uses agent-based tumor microenvironment models to design therapeutic strategies by analyzing trajectory landscapes and Markov models, enabling treatment planning without extensive longitudinal data.

Contribution

It presents a novel approach combining ABM-derived trajectory landscapes with Markov State Models to inform therapeutic decision-making in tumor microenvironments.

Findings

Constructed low-dimensional landscapes from simulated TME trajectories.

Mapped clinical MTI data onto the landscape for phenotype assessment.

Formulated a Markov Decision Process for treatment scheduling.

Abstract

Multiplex tissue imaging (MTI) enables high- dimensional, spatially resolved measurements of the tumor microenvironment (TME), but most clinical datasets are tempo- rally undersampled and longitudinally limited, restricting direct inference of underlying spatiotemporal dynamics and effective intervention timing. Agent-based models (ABMs) provide mech- anistic, 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 learn a probabilistic Markov State Model (MSM) that captures metastable states and the transitions between them. 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 dominant governing parameters yields group-specific transition models to formulate a finite-horizon Markov Decision Process (MDP) for treatment scheduling. The resulting framework enables simulation-grounded therapeutic policy design for partially observed biological systems without requiring longitudinal patient measurements.