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Metabolic rewiring in glioblastoma microenvironment: History
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Subjects: Oncology
Contributor: Assunta Virtuoso

Brain cancer cells exist with the glial cells in a functional syncytium based on a continuous metabolic rewiring, which supports the tumor growth. Standard glioma therapies do not account for the effects of the glial cells within the tumor microenvironment and this may be a possible reason for the lack of improvements in patients with high-grade gliomas therapies. Cell metabolism and bioenergetic fitness depend on the availability of nutrients and interactions in the microenvironment. It is strictly related to the cell location in the tumor mass, proximity to blood vessels, biochemical gradients, and tumor evolution. Bioinformatic and computational studies help to disengage the complexity of the metabolism in modules. Based on the literature findings, we identified eight main metabolic modules that can be considered during the disease progression, and their fluxes could be integrated to establish targeted time-dependent therapies.

  • microglia
  • astrocytes
  • high-grade glioma
  • cross-talk
  • hypoxia
  • metabolism
  • oxidative phosphorylation
  • glycolysis
  • modules
  • disease progression

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1. Introduction

Glioblastoma (GBM) classified as a grade IV astrocytoma is one of the most aggressive tumors to treat [1]. The current treatment consists of surgery followed by radiotherapy and adjuvant chemotherapy with temozolomide. The removal of the tumor bulk can be challenging due to localization (difficult surgical access, adjacent spared eloquent regions) and size with many subtypes of GBM found to be radio/chemo-resistant [2][3]. Moreover, GBM shows a very high cellular, epigenetic, and genetic heterogeneity, making therapeutic approaches difficult [4]. Eventually, GBM infiltrates brain tissue distant from the initial tumor mass, which frequently could not be identified because of the high migratory potential of GBM’s cells.
Recent studies elucidated the mechanisms of the crosstalk between the brain tumor and its environment since the GBM has never been described in extra-cranial sites. Neoplastic cells in the brain interact with the resident cells (neurons, astrocytes, microglia, oligodendrocytes) entangled in the extracellular matrix (ECM) and the vasculature, altering the neurovascular unit [5]. GBM is electrically and synaptically integrated into neural circuits [6][7] and glial cells play a prominent role in the progression of cancer, while neuronal progenitors’ dysfunction or de-differentiated mature cells may be involved in gliomagenesis [8][9][10][11]. The study of the microenvironment in the peritumoral tissue is an appealing target to sensibilize cancer cells to current and future therapies. The intercellular communication between glioma and the brain mainly occurs through nanovesicles or non-vesicular-mediated secretion. The nanovesicles contain DNA, RNA, and proteins and are taken up by immune cells as well as astrocytes and oligodendrocytes, developing somatic and epigenetic signaling to foster tumor progression. GBM affects central nervous system (CNS) elements also by cell–cell interactions [12][13][14], involving relevant plasticity in cell morphology, functions, and the bioenergetic machinery [15]. Cellular metabolism is important for region-specific neuronal toxicity in neurodegeneration [16] prompting the question of whether the metabolic changes could be the cause or the consequence of tumoral growth. Tissue context shapes the tumor metabolic adaptations, and the most favorable are selected in a specific environment. Mutations encoding for Epidermal Growth Factor Receptor (EGFR) and the isocitrate dehydrogenases (IDH1 and IDH2) are the most common GBM-mutated metabolic genes. Amplified EGFR involves pathways to control glioma glycolysis and lipogenesis, while IDH mutations link the metabolism rewiring to epigenetic regulation  [17]. Mitochondrial crucial involvement in cancer pathophysiology was described by Otto Warburg at the beginning of the 20th century [18]. According to Warburg’s hypothesis, tumor-associated mutations might not be sufficient to induce malignant transformation if cells’ mitochondria are healthy [19]. Mitochondria and the bioenergetic machinery became a hot spot in cancer research and several compounds are currently under investigation to modulate the GBM metabolism [20][21]. However, little is known about the metabolic differences between stromal cells in the tumor tissue and cancer cells, and how these differences could interfere with the therapeutic targeting of the metabolic pathways [22]. Recent data on the CNS structure prompted the study of tumor metabolic rewiring as the expression of the networking modular activity of the different components, and not only of the cancer cells [16][23]. Systems metabolomics data analyzed by artificial intelligence techniques could be of great implementation to this aim [24][25]. 

2. Cancer Metabolic Strategies and the Role of the Microenvironment in Glioblastoma

2.1. The Warburg Effect and the Reverse

Since 2016, the WHO classified two forms of GBM—(i) IDH-wildtype (wGBM) and (ii) IDH-mutant (mGBM). IDH is an enzyme that catalyzes the oxidative decarboxylation of isocitrate to produce α-ketoglutarate (α-KG) and carbon dioxide (CO2) in the tricarboxylic acid (TCA) cycle. The wGBM is more frequently a de novo tumor, the mGBM is a GBM developed by a lower grade glioma [26], thus supporting the GBM hypothesis as a metabolic disorder. Warburg demonstrated that “respiration” in cancer cells is impaired, even in the presence of oxygen [18]. Cancer cells selectively extract nutrients from the extracellular space and upregulate GLUT3 to promote tumorigenic properties [27] via glycolysis. This process is called “aerobic glycolysis”. However, cancer cells survive in glucose-starvation conditions [28], undergoing a metabolic rewire, that allows them to proliferate, invade, and resist therapies. Indeed, GBM cells show to be highly plastic to changes in nutritional supply [29]: the mitochondrial dysfunction (Warburg effect) is not a general feature of all the cancer cells within the tumor mass, as the existence of the oxidative cancer cells was proved. The multi-omic analysis helped to define a new classification of the GBM into four subtypes that embody metabolic and developmental features. The mitochondrial GBM subtype mainly contains oxidative cancer cells, which decrease the glycolysis and rely on oxidative phosphorylation (OXPHOS) [30][31]. This is known as “the reverse Warburg effect”. The Warburg effect and the reverse Warburg effect are adaptive mechanisms made by cells according to the phase of the cell cycle and the composition of the tumor microenvironment [32]. The Warburg effect occurs in normal cells during the early embryogenesis when the oxygen availability is low [33]. Evidence also shows the case of tumor cells that have both impaired glycolysis pathway and ATP (adenosine triphosphate) production with OXPHOS. In this case, the production of high-energy phosphate would be supported by glutaminolysis. The glutamine-derived succinate can provide adequate ATP through mitochondrial substrate-level phosphorylation (mSLP) to sustain GBM growth when OXPHOS is off [34].

2.2. The Functional Symbiosis

Two main niches are distinguished in the tumor: (i) the perivascular niche and (ii) the hypoxic niche. Expanding tumor cells reside in the peripheral and vascularized part of the tumor, while cancer stem cells have been found mostly in the hypoxic core [35], and show a low proliferation rate in quiescence. Mature cancer cells and cancer stem cells seem to exist in a functional symbiosis [36][37]. Cancer cells at the perivascular niches (where the oxygen and nutrient levels are high) spare the glucose for the cancer stem cells in the hypoxic area. Hypoxia induces the hypoxia-inducing factor 1 (HIF-1), which is involved in several functions, including the overexpression of the genes for the glycolytic pathway, the stem-like phenotype, and the self-renewal of neural precursors [38][39][40]. These cells would accelerate glucose consumption, releasing lactate. Lactate could be converted to pyruvate and used for OXPHOS in the perivascular niche, fulfilling the ATP and biosynthesis recognized need, limiting the microenvironment acidification. Relevant to this, OXPHOS metabolism is a hallmark in the differentiated state of the tumor [41]. The hypoxic tumor cells can also use carbon skeletons from glycolysis and glutamine through a truncated TCA cycle followed by mitochondrial reductive carboxylation of αKG, which does not depend on the oxygen availability. An active truncated TCA cycle can be due to the need for the biosynthesis of precursors such as lipids or nucleic acids, as well as antioxidant glutathione synthesis, rather than oxidative metabolism [42]. The symbiotic hypothesis for GBM is supported by data showing that the mitochondrial biogenesis via cyclic adenosine monophosphate (cAMP) and the metabolic switch to OXPHOS drives the differentiation of tumor cells, while the anaerobic utilization of high energy substrates such as pyruvate and lactate is associated with the expression of genes for stemness features [43][44].

2.3. The Role of the Microenvironment

Cancer-induced metabolic alterations within the microenvironment play a key role in tumor maintenance or else may be involved in carcinogenesis. Interestingly, oxidative stress could be the central core of metabolic rewiring. The reactive oxygen species (ROS) diffuse from cancer cells to stromal cells, which in turn result in oxidative stress. The oxidative stress induces a metabolic shift mainly through the activation of HIF-1 and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB). These transcription factors stimulate the angiogenesis to increase oxygen availability [45][46], and the conversion into a perivascular niche. They trigger mitochondrial dysfunction and aerobic glycolysis, autophagy, and lysosomal degradation with the release of high-energy substrates such as pyruvate and lactate. Proliferative cancer stem cells would take up these molecules and use them for OXPHOS; yet, the reverse Warburg effect occurred [47].  GBM-associated stromal cells (GASCs) functionally remind the cancer-associated fibroblasts (CAFs) described in the stroma of carcinomas, promoting the tumor in vivo and in vitro [48]. Stromal cells such as non-neoplastic-astrocytes in contact with GBM could transfer mitochondrial DNA and mitochondria to the tumor cells. A recent study reported that mitochondrial transplantation, from healthy astrocytes, redirects the aerobic respiration in glioma cells, attenuates the Warburg effect, and may enhance radiosensitivity [49]. Microglia, and macrophages, rapidly respond to alteration of the CNS homeostasis, including brain tumors. Their role in GBM biology is controversial. The depletion of microglia impairs glioma growth and invasiveness both in organotypic slice culture and in vivo tumor models [1][51][52], while the “natural” microglia and macrophages induce the glioma cell cycle arrest and differentiation in culture [52]. Single-cell profiling obtained combining gene expression, sequencing data, and protein levels reveal the complexity and the heterogeneity of glial cell functioning states related to GBMs. Variation in oxidative phosphorylation (OXPHOS) rate and regulation appears to be the most important contributor to the metabolic and functional heterogeneity among malignant and non-malignant cells. Of note, OXPHOS activity is correlated with both glycolysis and response to hypoxia in almost all cell types and might be responsible for adapting to environmental factors[54]. Therefore, control of the metabolism both in cancer and host cells is a promising approach. Bioinformatic and computational studies help to disengage the complexity of the metabolism in modules [55]. Metabolic modules comprise a conserved sequence of chemical reactions for the transformation of a defined substrate. Metabolic modules present a comprehensive summary of the major metabolic activities and fulfill the production/usage of the main classes of metabolites (nucleotides, carbohydrates, lipids, and amino acids)[56]. Based on literature evidence, we identified eight main metabolic modules: glucose oxidation (glycolysis, GLU), anaerobic fermentation and lactate production (FER), pentose phosphate pathway (PPP), (non-essential) amino acid pool (AA) such as glutamate, OXPHOS, fatty acid oxidation (FAO), ROS production, glycogenolysis (GLYC). Each module could be analyzed separately in 3D models of GBM-neuro/glial interactions for every phase of the disease and could be integrated to form a feasible flux in the whole network. Integrated metabolic modules may help to identify the key factors for controlling the metabolism in targeted, time-dependent therapies.
 
 

This entry is adapted from the peer-reviewed paper 10.3390/ijms22073301

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