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

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.

Quantitative Spatial Profiling of Immune Populations in Pancreatic Ductal Adenocarcinoma Reveals Tumor Microenvironment Heterogeneity and Prognostic Biomarkers

“…While the platform still requires intensive validation on external cohort, it might complement current standard pathology staging and serve as a research tool for quantitative immuno-oncology domain. The proposed platform also reveals a broad applicability and represents a novel application in the field of translational medicine, as well as prospects in initialization and parameterization of computational models (47)(48)(49)(50)(51)(52)(53).…”

Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model

“…[73][74][75][76] We have developed a spatial platform, spQSP, that merges whole-patient QSP models with agent-based models of tumor with stochastic celllevel representation of the tumor microenvironment that is capable of incorporating results of digital pathology and spatial transcriptomic. [77][78][79] In summary, QSP is a quickly developing field that has been shown to play a crucial role in drug development and QSP models are increasingly becoming a standard part of regulatory submissions. [80][81][82] The predictive power of QSP models is continually being demonstrated and constantly improving.…”

A transcriptome-informed QSP model of metastatic triple-negative breast cancer identifies predictive biomarkers for PD-1 inhibition

“…Spatial immune architecture such as the proximity of certain cell types has been shown to be associated with disease prognosis (63). Agent-based models have been developed and integrated with QSP models to explore the effects of intratumoral heterogeneity on treatment responses (64)(65)(66). Nevertheless, the QSP platform developed in this study could be integrated with such spatial models or adapted for other metastatic cancers to investigate existing and emerging treatment strategies in silico.…”