Mathematical model of tumor-macrophage dynamics in glioma to advance myeloid-targeted therapies.

Recent biological research has highlighted the relevance of myeloid-cell populations in glioma growth, with a particular role played by tumor-associated macrophages (TAMs), which comprise resident microglia and monocyte-derived macrophages. Additionally, radiation therapy, the most common treatment for gliomas, significantly alters the tumor microenvironment, affecting TAMs and contributing to tumor recurrence. Promising preclinical studies have identified and developed drugs targeting TAMs. The development and combined deployment of these therapies require in silico techniques that enable us to optimize their outcomes. To do so, we need mathematical models of glioma growth and therapy response that explicitly incorporate TAMs-an often overlooked component in existing models. Here, we present a dynamical model of glioma growth driven by tumor-immune interactions. The model was parametrized using published data from mice experiments, including responses to ionizing radiation. We used this model to investigate glioma progression under radiotherapy combined with three treatments targeting distinct aspects of TAM biology. Simulations revealed that anti-CD47 enhanced the otherwise weak phagocytic activity, extending the upper tail of the survival curve. α-CD49d, which limits monocyte trafficking after irradiation, offered consistent survival benefits across digital twins of mice. Finally, CSF-1R inhibitors, which block the primary growth factor regulating TAM function, resulted in the largest overall survival improvement in silico. Our results aligned well with experimental evidence, suggesting that the model may help inform the optimization of myeloid cell-targeted immunotherapies, including their timing, dosage, and combination with radiation therapy, with potential relevance for improving glioma treatment strategies.