Modeling Three-dimensional Invasive Solid Tumor Growth in Heterogeneous Microenvironment under Chemotherapy

TL;DR

This paper presents a hybrid 3D computational model to study invasive solid tumor growth in heterogeneous environments under chemotherapy, revealing how microenvironment heterogeneity affects treatment efficacy and tumor progression.

Contribution

The study introduces a novel hybrid 3D model combining pharmacokinetics, diffusion-reaction, and cell automaton approaches to simulate tumor growth and response to chemotherapy in heterogeneous microenvironments.

Findings

Constant chemotherapy dosing suppresses primary tumor growth more effectively.

Tumor malignancy increases in highly heterogeneous microenvironments.

Microenvironment geometry and drug dosing heterogeneity influence treatment outcomes.

Abstract

A systematic understanding of the evolution and growth dynamics of invasive solid tumors in response to different chemotherapy strategies is crucial for the development of individually optimized oncotherapy. Here, we develop a hybrid three-dimensional (3D) computational model that integrates pharmacokinetic model, continuum diffusion-reaction model and discrete cell automaton model to investigate 3D invasive solid tumor growth in heterogeneous microenvironment under chemotherapy. Specifically, we consider the effects of heterogeneous environment on drug diffusion, tumor growth, invasion and the drug-tumor interaction on individual cell level. We employ the hybrid model to investigate the evolution and growth dynamics of avascular invasive solid tumors under different chemotherapy strategies. Our simulations reproduce the well-established observation that constant dosing is generally more effective in suppressing primary tumor growth than periodic dosing, due to the resulting continuous high drug concentration. In highly heterogeneous microenvironment, the malignancy of the tumor is significantly enhanced, leading to inefficiency of chemotherapies. The effects of geometrically-confined microenvironment and non-uniform drug dosing are also investigated. Our computational model, when supplemented with sufficient clinical data, could eventually lead to the development of efficient in silico tools for prognosis and treatment strategy optimization.