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

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. Author summary: Spatial heterogeneity is a hallmark of cancer, thus the ability to quantify the complexity of the tumor microenvironment is an important goal of computational modeling. We present a hybrid computational modeling platform, spQSP, to extend quantitative systems pharmacology (QSP) models of immuno-oncology into the spatial dimension by combining them with spatial agent-based models (ABM). We focus on several methodological and biological aspects of modeling cancer. First, the coupling of deterministic ordinary differential equation based whole-patient QSP model and stochastic, spatial agent-based model of tumor. Second, we focus on the region at the edge of the tumor called the Invasive Front (IF). We introduce a quantitative definition of IF consistent with pathologists' definition and present examples of its geometry. Third, we apply the model to immunotherapy of triple-negative breast cancer and compare the simulations of spatial distribution of immune cells (e.g., CD8+ and FoxP3+ T cells) with our recent digital pathology quantitative analysis of patients' specimens. Thus, the computational platform combines the power of QSP models with spatial ABM thus opening the way to utilizing the immense information about the tumor microenvironment contained in multiplexed pathology specimens. This modeling platform enables conducting virtual clinical trials and biomarker discovery for cancer immunotherapies. [ABSTRACT FROM AUTHOR]