Computational agent-based modelling reveals the role of tumour microenvironment on the success of combination chemotherapy/immunotherapy to treat glioblastoma

Glioblastoma (GBM) is one of the most aggressive and deadly brain cancer, with a survival time of only 12 to 18 months. Unfortunately, the current standard-of-care for GBM with chemotherapy (temozolomide or TMZ) result in recurrences and treatment failure, leading to very poor therapeutic outcomes. Immunotherapies such as immune checkpoint blockade, a class of biologics that leverage the body's own defences against tumours, have shown durable benifits in many types of cancers. Immune checkpoints are proteins that prevent the immune response from being too strong (i.e. preventing destruction of cancer cells by immune cells). The blocking of immune checkpoints therefore enables tumour eradication. Though this treatment method is quite promising, recent clinical trials of immune checkpoint blockade in GBM have been largely disappointing, suggesting an absence of mechanistic understanding of the role of immune system in this disease. To address this, we need to understand the immune content of glioblastoma and the interactions between immune and cancer cells. To this end, we develop a novel agent-based model of glioblastoma dynamics that accounts for the interactions between GBM and immune cells and its effects on the treatment efficacy.