Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model

TLDR

Quantitative systems pharmacology and spatial agent‑based models are powerful tools for analyzing biological systems, yet QSP alone cannot capture the spatial heterogeneity of the tumor microenvironment. The study extends a spatial QSP model to analyze tumor growth dynamics and immunotherapy response across multiple spatio‑temporal scales. The model couples an agent‑based tumor spatial dynamics module to a QSP framework with central, tumor, lymph node, and peripheral compartments, incorporating dynamic recruitment of T cells and myeloid‑derived suppressor cells and enabling coarse‑grained whole‑tumor and agent‑based ROI analyses. Using this coupled model, the authors characterize tumor growth, identify T‑cell hotspots, quantify cell density at the invasive front, and evaluate immunotherapy effects at both whole‑tumor and ROI scales under varying growth and immune conditions.

Abstract

Quantitative systems pharmacology (QSP) models and spatial agent-based models (ABM) are powerful and efficient approaches for the analysis of biological systems and for clinical applications. Although QSP models are becoming essential in discovering predictive biomarkers and developing combination therapies through in silico virtual trials, they are inadequate to capture the spatial heterogeneity and randomness that characterize complex biological systems, and specifically the tumor microenvironment. Here, we extend our recently developed spatial QSP (spQSP) model to analyze tumor growth dynamics and its response to immunotherapy at different spatio-temporal scales. In the model, the tumor spatial dynamics is governed by the ABM, coupled to the QSP model, which includes the following compartments: central (blood system), tumor, tumor-draining lymph node, and peripheral (the rest of the organs and tissues). A dynamic recruitment of T cells and myeloid-derived suppressor cells (MDSC) from the QSP central compartment has been implemented as a function of the spatial distribution of cancer cells. The proposed QSP-ABM coupling methodology enables the spQSP model to perform as a coarse-grained model at the whole-tumor scale and as an agent-based model at the regions of interest (ROIs) scale. Thus, we exploit the spQSP model potential to characterize tumor growth, identify T cell hotspots, and perform qualitative and quantitative descriptions of cell density profiles at the invasive front of the tumor. Additionally, we analyze the effects of immunotherapy at both whole-tumor and ROI scales under different tumor growth and immune response conditions. A digital pathology computational analysis of triple-negative breast cancer specimens is used as a guide for modeling the immuno-architecture of the invasive front.