MAST: a hybrid Multi-Agent Spatio-Temporal model of tumor microenvironment informed using a data-driven approach

Abstract

Motivation: Recently, several computational modeling approaches, such as agent-based models, have been applied to study the interaction dynamics between immune and tumor cells in human cancer. However, each tumor is characterized by a specific and unique tumor microenvironment, emphasizing the need for specialized and personalized studies of each cancer scenario.

Results: We present MAST, a hybrid Multi-Agent Spatio-Temporal model which can be informed using a data-driven approach to simulate unique tumor subtypes and tumor-immune dynamics starting from high-throughput sequencing data. It captures essential components of the tumor microenvironment by coupling a discrete agent-based model with a continuous partial differential equations-based model.The application to real data of human colorectal cancer tissue investigating the spatio-temporal evolution and emergent properties of four simulated human colorectal cancer subtypes, along with their agreement with current biological knowledge of tumors and clinical outcome endpoints in a patient cohort, endorse the validity of our approach.

Availability and implementation: MAST, implemented in Python language, is freely available with an open-source license through GitLab (https://gitlab.com/sysbiobig/mast), and a Docker image is provided to ease its deployment. The submitted software version and test data are available in Zenodo at https://dx.doi.org/10.5281/zenodo.7267745.

Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Fig. 1.

Schematic representation of MAST, a hybrid multi-ABM of tumor–immune system. (A) MAST can be informed through a data-driven approach using several sources of information to model unique characteristics of the TME in a tumor. (B) It couples a discrete ABM, which simulates tumor–immune system dynamics in the TME, and a continuous PDEs-based model to simulate nutrient diffusion from vessels. (C) MAST provides tabular and graphical outputs in order to analyze spatio-temporal evolution of in silico tumor growth simulation

Fig. 2.

In silico simulation of the four CMS. Left panels (A, C, E and G) provide information on the temporal evolution of 100 simulations. In particular, in the upper subgraph, the number of not completely tumor-free (NTF) simulations in a determined instant (day), i.e. simulations having at least one cancer agent in the domain, is showed for each CMS. In the below subgraph, the time course of agent counts across NTF simulations in log10-scale is showed: continuous line represents the average count, and the shaded area represents its variability (±standard deviation). Right panels (B, D, F and H) display the spatio-temporal evolution of one simulation for each CMS. From left to right, tumor progression on Days 60, 90, 120 and 150 are represented. Legend represents color-agent association related to above representations. All graphical representations are generated using MAST