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Updated: Jul 27, 2025

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
Published on: September 13, 2022
Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma
Abstract:
Glioblastoma (GBM) is an aggressive primary brain cancer that currently has minimally effective treatments. Like other cancers, immunosuppression by the PD-L1-PD-1 immune checkpoint complex is a prominent axis by which glioma cells evade the immune system. Myeloid-derived suppressor cells (MDSCs), which are recruited to the glioma microenviroment, also contribute to the immunosuppressed GBM microenvironment by suppressing T cell functions. In this paper, we propose a GBM-specific tumor-immune ordinary differential equations model of glioma cells, T cells, and MDSCs to provide theoretical insights into the interactions between these cells. Equilibrium and stability analysis indicates that there are unique tumorous and tumor-free equilibria which are locally stable under certain conditions. Further, the tumor-free equilibrium is globally stable when T cell activation and the tumor kill rate by T cells overcome tumor growth, T cell inhibition by PD-L1-PD-1 and MDSCs, and the T cell death rate. Bifurcation analysis suggests that a treatment plan that includes surgical resection and therapeutics targeting immune suppression caused by the PD-L1-PD1 complex and MDSCs results in the system tending to the tumor-free equilibrium. Using a set of preclinical experimental data, we implement the Approximate Bayesian Computation (ABC) rejection method to construct probability density distributions that estimate model parameters. These distributions inform an appropriate search curve for global sensitivity analysis using the extended Fourier Amplitude Sensitivity Test (eFAST). Sensitivity results combined with the ABC method suggest that parameter interaction is occurring between the drivers of tumor burden, which are the tumor growth rate and carrying capacity as well as the tumor kill rate by T cells, and the two modeled forms of immunosuppression, PD-L1-PD-1 immune checkpoint and MDSC suppression of T cells. Thus, treatment with an immune checkpoint inhibitor in combination with a therapeutic targeting the inhibitory mechanisms of MDSCs should be explored.
Insights
This study models glioblastoma (GBM) tumor-immune interactions. Combining immune checkpoint inhibitors with therapies targeting myeloid-derived suppressor cells (MDSCs) may lead to tumor eradication.
Area of Science:
- Oncology
- Immunology
- Mathematical Biology
Background:
- Glioblastoma (GBM) is an aggressive brain cancer with limited treatment options.
- Immune evasion via PD-L1-PD-1 and myeloid-derived suppressor cells (MDSCs) is key in GBM.
- Understanding these complex interactions is crucial for developing effective therapies.
Approach:
- Developed a GBM-specific ordinary differential equations model simulating glioma cells, T cells, and MDSCs.
- Performed equilibrium, stability, and bifurcation analyses to understand system dynamics.
- Utilized Approximate Bayesian Computation (ABC) and extended Fourier Amplitude Sensitivity Test (eFAST) for parameter estimation and sensitivity analysis.
Key Points:
- Model predicts unique tumorous and tumor-free equilibria, with tumor-free stability dependent on T cell efficacy and immunosuppression levels.
- Bifurcation analysis suggests combined surgical resection and targeted immunosuppression therapies promote tumor clearance.
- Sensitivity analysis revealed significant interactions between tumor growth drivers and immunosuppressive mechanisms.
Conclusions:
- Targeting both PD-L1-PD-1 immune checkpoints and MDSC-mediated immunosuppression concurrently with existing treatments is a promising therapeutic strategy for GBM.
- Mathematical modeling provides valuable theoretical insights into GBM tumor-immune dynamics.
- Further preclinical and clinical investigations into combination therapies are warranted.

