Tumor-Infiltrating Lymphocytes Are Enriched in Nonhypoxic Glioblastoma Niches

Tumor-associated microenvironment, PD-L1 expression and their relationship with immunotherapy in glioblastoma, IDH-wild type: A comprehensive review with emphasis on the implications for neuropathologists

Although novel knowledge has been acquired on the molecular landscape of glioblastoma (GBM), a relatively few steps forward have been made regarding its therapy. With the increasing use of novel immunotherapeutic drugs capable of stimulating the antitumor inflammatory response, in the last decades numerous studies aimed to characterize the tumor-associated microenvironment (TME) and its relationship with the immunogenicity of GBM. In this regard, although the tumor-associated microglia and macrophages (TAMs) and PD-L1/PD-1 axis have been emerged as one of the most relevant components of the GBM TME and one of the potential molecular pathways targetable with immunotherapy, respectively. It has been supposed that TAMs may acquire different phenotypes, switching from M1 to M2 phenotypes, with tumor-suppressive and tumor-stimulating role depending on the different surrounding conditions. PD-L1 is a type 1 transmembrane protein ligand expressed by T-cells, B-cells and antigen-presenting cells, with a main inhibitory checkpoint role on tumor immune regulation. While PD-L1 immunohistochemical expression has been extensively investigated in many cancers, its usefulness in the evaluation of GBM response rates to immunotherapy and its standardized evaluation by immunohistochemistry are still debated. The present review paper focuses on the current "state of the art" about the relationship between TME, PD-L1/PD-1 pathway and immunotherapy in GBM, also providing neuropathologists with an updated guide about the clinical trials conducted with PD-L1 and PD-1 inhibitors.

Niche deconvolution of the glioblastoma proteome reveals a distinct infiltrative phenotype within the proneural transcriptomic subgroup

Glioblastoma is often subdivided into three transcriptional subtypes (classical, proneural, mesenchymal) based on bulk RNA signatures that correlate with distinct genetic and clinical features. Potential cellular-level differences of these subgroups, such as the relative proportions of glioblastoma’s hallmark histopathologic features (e.g. brain infiltration, microvascular proliferation), may provide insight into their distinct phenotypes but are, however, not well understood. Here we leverage machine learning and reference proteomic profiles derived from micro-dissected samples of these major histomorphologic glioblastoma features to deconvolute and estimate niche proportions in an independent proteogenomically-characterized cohort. This approach revealed a strong association of the proneural transcriptional subtype with a diffusely infiltrating phenotype. Similarly, enrichment of a microvascular proliferation proteomic signature was seen within the mesenchymal subtype. This study is the first to link differences in the cellular pathology signatures and transcriptional profiles of glioblastoma, providing potential new insights into the genetic drivers and poor treatment response of specific subsets of glioblastomas.

Topographic mapping of the glioblastoma proteome reveals a triple-axis model of intra-tumoral heterogeneity

Glioblastoma is an aggressive form of brain cancer with well-established patterns of intra-tumoral heterogeneity implicated in treatment resistance and progression. While regional and single cell transcriptomic variations of glioblastoma have been recently resolved, downstream phenotype-level proteomic programs have yet to be assigned across glioblastoma's hallmark histomorphologic niches. Here, we leverage mass spectrometry to spatially align abundance levels of 4,794 proteins to distinct histologic patterns across 20 patients and propose diverse molecular programs operational within these regional tumor compartments. Using machine learning, we overlay concordant transcriptional information, and define two distinct proteogenomic programs, MYC- and KRAS-axis hereon, that cooperate with hypoxia to produce a tri-dimensional model of intra-tumoral heterogeneity. Moreover, we highlight differential drug sensitivities and relative chemoresistance in glioblastoma cell lines with enhanced KRAS programs. Importantly, these pharmacological differences are less pronounced in transcriptional glioblastoma subgroups suggesting that this model may provide insights for targeting heterogeneity and overcoming therapy resistance.

Altered Metabolism in Glioblastoma: Myeloid-Derived Suppressor Cell (MDSC) Fitness and Tumor-Infiltrating Lymphocyte (TIL) Dysfunction

The metabolism of glioblastoma (GBM), the most aggressive and lethal primary brain tumor, is flexible and adaptable to different adverse conditions, such as nutrient deprivation. Beyond glycolysis, altered lipid metabolism is implicated in GBM progression. Indeed, metabolic subtypes were recently identified based on divergent glucose and lipid metabolism. GBM is also characterized by an immunosuppressive microenvironment in which myeloid-derived suppressor cells (MDSCs) are a powerful ally of tumor cells. Increasing evidence supports the interconnection between GBM and MDSC metabolic pathways. GBM cells exert a crucial contribution to MDSC recruitment and maturation within the tumor microenvironment, where the needs of tumor-infiltrating lymphocytes (TILs) with antitumor function are completely neglected. In this review, we will discuss the unique or alternative source of energy exploited by GBM and MDSCs, exploring how deprivation of specific nutrients and accumulation of toxic byproducts can induce T-cell dysfunction. Understanding the metabolic programs of these cell components and how they impact fitness or dysfunction will be useful to improve treatment modalities, including immunotherapeutic strategies.