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

• Published in: Bioinformatics advances 2022, Vol.2 (1), p.vbac092
• Main Authors: Cesaro, Giulia, Milia, Mikele, Baruzzo, Giacomo, Finco, Giovanni, Morandini, Francesco, Lazzarini, Alessio, Alotto, Piergiorgio, da Cunha Carvalho de Miranda, Noel Filipe, Trajanoski, Zlatko, Finotello, Francesca, Di Camillo, Barbara
• Format: Article
• Language: English
• Subjects: Original Paper
• ISSN: 2635-0041; 2635-0041
• DOI: 10.1093/bioadv/vbac092

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. 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. 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 data are available at online.