| Abstract: |
| Glioblastoma (GBM) progression is driven by the dynamic interplay between tumor cells, which transition between proneural (PN) and mesenchymal (MES) states, and tumor-associated macrophages (TAMs), which shift between M1 and M2 phenotypes. In this study, we developed a multiscale spatiotemporal model based on nonlocal reaction-diffusion equations to investigate the intrinsic mechanisms of GBM phenotypic transitions during both natural progression and drug treatment. The model integrates continuous tumor and TAM phenotypic shifts and their interplay via microenvironment-mediated signaling feedback loops. Through rigorous mathematical analysis, we established the well-posedness of the model and constructed an efficient numerical scheme for its solution. The model demonstrates excellent agreement with experimental observations across multiple validation metrics, confirming its biological plausibility and predictive capability. Using global sensitivity analysis and parameter stability analysis, we systematically identified and quantitatively characterized key parameters regulating tumor growth dynamics and treatment response. Finally, we evaluated the efficacy of combination therapy regimens and proposed strategic approaches for optimizing GBM treatment. Our study establishes a novel nonlocal modeling framework for investigating phenotypic plasticity in tumor and immune cells and their crosstalk, providing valuable insights for optimizing combination therapies in cancer treatment. |
|