Therapeutic Targeting of Glioblastoma and the Interactions with Its Microenvironment

Abstract

Glioblastoma (GBM) is the most common primary malignant brain tumour, and it confers a dismal prognosis despite intensive multimodal treatments. Whilst historically, research has focussed on the evolution of GBM tumour cells themselves, there is growing recognition of the importance of studying the tumour microenvironment (TME). Improved characterisation of the interaction between GBM cells and the TME has led to a better understanding of therapeutic resistance and the identification of potential targets to block these escape mechanisms. This review describes the network of cells within the TME and proposes treatment strategies for simultaneously targeting GBM cells, the surrounding immune cells, and the crosstalk between them.

Keywords: glioblastoma (GBM); heterogeneity; microglia; myeloid cells; therapeutic targeting; tumour associated macrophages (TAMs); tumour microenvironment (TME).

Figure 1

The complexity of the glioblastoma tumour microenvironment and the differences in techniques used to analyse it. (A) Schematic representation of the multiple cells composing the microenvironment of GBM. (B) Detailed representation of tumour microtubes and the different elements exchanged through this system. (C) Illustration of recently identified subpopulations of myeloid cells (MARCO^hi tumour-associated macrophages (TAM), CD73^hi TAM, HMOX1⁺ microglia, and high-grade glioma (HGG)-associated microglia) and their associated primary biological functions. (D) Representation of reactive astrocytes expressing GFAP and PD-L1 and secreting the immunosuppressive cytokines IL-10 and TGF-β. (E) UMAP plot indicating the diversity of cell populations in GBM. Single-nucleus RNA-Seq data were obtained from GBM-CPTAC [126], processed using Seurat [127], and mapped to GBmap reference cell types [128] using Azimuth [129]. (F) Illustrative representation of spatial transcriptomics with single-cell resolution offered by new technological advances. This allows the identification of the complexity of the GBM TME by using gene expression to identify the different cells while keeping the biological architecture. The figure was created with BioRender.com, accessed on 16 October 2023.