Skip to main content

Macrophages and microglia: the cerberus of glioblastoma

Abstract

Glioblastoma (GBM) is the most aggressive and deadliest of the primary brain tumors, characterized by malignant growth, invasion into the brain parenchyma, and resistance to therapy. GBM is a heterogeneous disease characterized by high degrees of both inter- and intra-tumor heterogeneity. Another layer of complexity arises from the unique brain microenvironment in which GBM develops and grows. The GBM microenvironment consists of neoplastic and non-neoplastic cells. The most abundant non-neoplastic cells are those of the innate immune system, called tumor-associated macrophages (TAMs). TAMs constitute up to 40% of the tumor mass and consist of both brain-resident microglia and bone marrow-derived myeloid cells from the periphery. Although genetically stable, TAMs can change their expression profiles based upon the signals that they receive from tumor cells; therefore, heterogeneity in GBM creates heterogeneity in TAMs. By interacting with tumor cells and with the other non-neoplastic cells in the tumor microenvironment, TAMs promote tumor progression. Here, we review the origin, heterogeneity, and functional roles of TAMs. In addition, we discuss the prospects of therapeutically targeting TAMs alone or in combination with standard or newly-emerging GBM targeting therapies.

Main

Glioblastoma (GBM) develops in the complex tumor microenvironment (TME) of the brain. The GBM TME consists of extracellular matrix (ECM), interstitial fluid, and non-neoplastic cells, including both immune and non-immune cells that are compartmentalized in anatomically-distinct regions referred to as tumor niches, where the most therapy-resistant glioma stem cells (GSCs) are localized [1]. The various non-neoplastic cells closely interact with neoplastic cells and with each other in the TME by secreting growth factors, chemokines, cytokines, ECM constituents, angiogenic molecules, and factors that induce vascular permeability, creating strong interdependence that drives tumor progression. Such strong interdependence has been demonstrated between glioma cells and the most abundant non-neoplastic immune infiltrates in the TME called tumor-associated macrophages (TAMs). TAMs support tumor proliferation, regulate immunosuppression, contribute to cerebral edema, and enhance GBM resistance to chemo- and radiotherapy (RT) [2,3,4]. Therefore, they have been established as viable targets for adjuvant therapy for GBM. To develop successful TAM-targeted therapies, a thorough understanding of the inter- and intra-tumoral heterogeneity and plasticity of TAMs is required.

Current state of TAM ontology

The most abundant non-neoplastic cells in the TME of GBM are TAMs, which constitute ~ 40% of the tumor mass [1, 4,5,6]. TAMs are a diverse population consisting of both brain-resident microglia and bone marrow-derived macrophages (BMDMs) (Fig. 1). Microglia is a unique myeloid population of the central nervous system (CNS). They are mononuclear phagocytic cells, which are often referred to as CNS-specific macrophages in the literature. Macrophages are known to maintain tissue homeostasis during development and in physiology; they also constitute the innate immune myeloid cells [7, 8]. Macrophages are found dispersed throughout the mammalian body. Their functions are highly plastic and depend upon the tissue in which they reside [9,10,11,12]. Microglia were shown to exclusively originate in the yolk sac from erythro-myeloid progenitor cells (EMPs) during embryogenesis [13] and to present high longevity (15 months on average) and low self-renewal [14,15,16,17]. This traditional view has been challenged by recent data, which used Ccr2-CreER for lineage tracing to demonstrate that a 25% of microglia in the brains of P2-P24 mice are derived from a hematopoietic-monocytic population [18, 19]. Details regarding the ontogenesis of various myeloid cells in the brain are provided in greater detail in a previous review [4]. Furthermore, additional new studies showed transcriptomic heterogeneity of microglia in various regions of mouse brain, suggesting the existence of a variety of microglial sub-phenotypes depending upon their topological distribution and protein expression levels in both the mouse [20] and human [21] brain. In addition, it was shown that transcriptional states of human microglia are determined by their spatial distribution, and they further change with aging and brain-tumor pathology [22, 23]. Recent fate-mapping studies also revealed the presence of non-parenchymal macrophages at CNS interfaces (otherwise called border-associated macrophages, BAMs), like meningeal and perivascular macrophages, whose ontogeny is similar to microglia [24]. BAMs are also present in the choroid plexus, where they are further classified as stromal macrophages and Kolmer’s epiplexus cells, whose ontogeny is more complex: both have been shown to originate during embryogenesis from EMPs, but only the former present high self-renewal from BM monocytes postnatally [25]. Single-cell transcriptional studies revealed that each of these macrophages have a distinct phenotype, transcriptional signature, and signaling requirements [26].

Fig. 1
figure1

Myeloid cell lineage and morphology in healthy brain and glioblastoma. Microglia arise from yolk sac progenitors in the embryonic stage and reside permanently in the brain parenchyma. They appear as highly-ramified cells, but they rapidly change their morphology into an amoeboid-like shape in glioblastoma. Monocytes originate from the bone marrow and circulate in the blood, until they invade the brain when a glioblastoma emerges and differentiate into macrophages. They are round shaped while in circulation and upon infiltration into glioblastoma, making them morphologically indistinguishable from microglia. Images are taken from the murine glioblastoma model generated by using RCAS/tv-a, a somatic cell type-specific technology [41]. Reciprocal bone-marrow chimeras generated by using Cx3cr1-GFP mice allowed generation of only GFP-labeled bone marrow-derived macrophages or GFP-labeled microglia for imaging. Blood vessels are visualized with TRICT-dextran. Scale bars, 30 and 50 µm [41]

In the periphery, tissue macrophages originate from a hematopoietic stem cell (HSC) pool during embryogenesis in the fetal liver [13, 27]. At embryonic day 12.5 (E12.5), HSCs generate fetal monocytes, which present as two populations: Ly6C+CX3CR1int and Ly6CCX3CR1high, with both expressing the C–C chemokine receptor type 2 (CCR2) [13, 28]. Lineage tracing experiments using inducible or constitutive Cre-reporter mice demonstrated that the Ly6C+ population is actually a mandatory precursor of the Ly6C population, which exhibits restricted lifespan [27]. Additionally, studies have shown that the Ly6C+ monocytic population is the one that emigrates from the fetal liver into the blood, resulting in the downregulation of Ly6C and the initial expression of CX3CR1, which culminates in the cells infiltrating the tissue and differentiating into macrophages [7]. With few exceptions, like splenocytes [29] and skin or gut macrophages, these cells remain in the tissue postnatally with longevity and limited self-renewal [12, 30, 31].

Postnatally and during adult life, hematopoiesis takes place in the BM and also in the spleen, with the generation of Ly6C+ monocytes that extravasate the BM in response to monocyte chemoattractant proteins (MCPs) and circulate in the blood. In healthy conditions, monocytes have exceedingly short circulation half-lives, which are ~ 19 h for Ly6C+ and ~ 2.2 days for Ly6C [27], unless a pathological insult occurs, such as blood–brain barrier (BBB) disruption during a brain tumor or inflammation. In such occasions, monocytes extravasate and populate the inflamed brain tissue, where they differentiate into BMDMs [12, 32, 33].

Microglia present a highly-ramified morphology in surveillance mode, and when activated, they rapidly change their morphology into an amoeboid state [34]. BMDMs, however, are round-shaped like activated microglia, making them indistinguishable in histological sections. When lineage tracing is not used, these two cells can be discriminated by using differential expression of the CD11b/CD45 markers (CD45 is low in microglia and high in macrophages), together with the Ly6C and Ly6G markers (CD11b+CD45lowLy6CLy6G for microglia and CD11b+CD45highLy6ClowLy6G for macrophages) [35]. Bowman and colleagues used genetically-engineered mouse models (GEMMs) to track the ontogeny of myeloid cells, such as the hematopoietic tracking system Flt3:Cre;Rosa26:mTmG, and were able to illustrate BMDM contribution to the TAM population in brain tumors [36]. This model allowed not only confirmation of the ontogeny of microglia and monocytes, but also definition of the immune tumor composition when combined with different glioma mouse models, such as GEMMs and GL261-cell line-based models [36]. Previous works from our laboratory used a similar approach to define the TAM composition in murine GBM GEMMs [35]. To this end, we used Cx3cr1GFP/+; Ccr2RFP/+ knock-in mice combined with a GEMM of PDGFB-driven GBM and showed that 85% of TAMs are BMDMs, which are predominantly localized in perivascular areas of the tumor, while microglia were peri-tumoral. These findings were confirmed for human GBM by using high-dimensional unbiased single-cell RNA-sequencing from fresh GBM patient samples [37]. In addition, the authors argued against status quo therapeutic strategies that target TAMs indiscriminately, and instead suggested favoring strategies that specifically target immunosuppressive blood-derived macrophages [37]. Using RNA-sequencing, it was revealed the different transcriptional patterns of infiltrating and resident TAMs that result in differential functions that can be manipulated for therapeutic strategies. Specifically, metabolism and pro-inflammatory cytokine-related genes are enriched in tumor-associated microglia, while cellular migration is upregulated in TAMs [35]. Various mechanisms were proposed for monocyte migration into GBM. For instance, MCP-1, and partially MCP-3, are known to promote monocyte infiltration in inflammatory conditions and glioma [38,39,40], where they differentiate into BMDMs. However, other MCPs that cluster on the same genomic locus, such as MCP-2 and MCP-5, cannot be excluded from playing an active and perhaps redundant role in this process, which is probably why single chemokine-targeted therapies have been unsuccessful in both blocking monocyte infiltration and treating tumors.

More recently, by performing in vivo 2-photon imaging, one study was able to define TAM morphology and localization, and to trace their infiltration and differentiation dynamics. This study also demonstrated that monocyte infiltration is not only driven by increased chemokine gradient in tumors, but it is also influenced by disruption of the BBB [41]. Two-photon imaging provides the ability to distinguish the different morphology, round for macrophages and ramified for microglia, as well as to define their temporal and spatial localizations. Novel markers unique for microglia have been proposed such as SALL1, TMEM119 or SIGLEC-H [42,43,44], or P2RY12/SLC2A5/FCRLS for microglia and GDA/EMILIN2/HP/SELL for macrophages [45]; so far, only the latter have been tested in mouse and their expression, although faithful in healthy CNS, changes in the context of tumors.

Activation and heterogeneity of TAMs

As already stated above, TAMs are a heterogeneous population, based not only upon their origin and localization within the tumor, but also on their functions. Initially, upon activation, TAMs were classified into two different phenotypes: (1) a pro-inflammatory M1 phenotype/polarization, characterized by the classical activation of immune receptors TLR2/4 and the production of pro-inflammatory cytokines, including TNF and IL-1β, and (2) the anti-inflammatory M2 phenotype/ polarization, with the production of ARG1, IL-10, and IL-4 [46]. Historically, in the context of GBM, TAMs were considered to possess an M2-like phenotype [47]. However, transcriptional analyses have shown that this classification is an oversimplification of the otherwise complex biology of these cells [48]. In fact, microglia and macrophages share both M1 and M2 phenotypes in the setting of murine brain tumors [49]. For example, ARG1 and IL-1β were found to be enriched in both tumor-associated microglia and macrophages [35]. In human GBM, microglia and macrophages more resemble the expression profile of non-polarized M0 macrophages [50]. Since the challenges to discriminate microglia from macrophages in human GBM still exist, TAMs as a whole were used to characterize their intra-tumor heterogeneity. Thus, single-cell transcriptomic analysis and cytometry by time of flight (CyTOF) in human GBM and control tissues revealed that TAMs are not only heterogeneous, but they also show M1-like genes, such as SPP1, APOE, or CD74, as well as M2-like genes, including HLA-DR and CD163. Interestingly, the authors also compared this TAM phenotype to disease-associated microglia (DAMs) and found a similar transcriptional spectrum to neurodegenerative and neuroinflammatory diseases [23]. A recent meta-analysis of available single-cell and bulk RNA sequencing of human patient GBM samples suggests a dynamic identity (with the presence of M0, M1, and M2 states) of TAMs, with a more pro-inflammatory phenotype in the tumor core versus a more anti-inflammatory state in the periphery. Interestingly, this study also proposed specific region-associated functions of TAMs: an increased activity of the PD-1 signaling pathway was observed in the tumor core, versus stronger NF-kB signaling in the tumor periphery [51]. The studies mentioned above clearly illustrate that the simple M1/M2 dichotomy is no longer applicable and should not be used in GBM.

Temporal and spatial localization are additional differences shared by tumor-associated microglia and macrophages. Microglia are found to be prominent in the peri-tumoral areas, while macrophages are dispersed inside the tumor bulk, more in the perivascular area [35, 41]. Lineage tracing experiments using GEMMs showed early infiltration of tumor-associated monocytes and their differentiation into tissue macrophages [35]. Migratory capacity is yet another parameter in which they differ: monocytes are highly motile and display both straight and random movements, but upon tumor entry, they rapidly differentiate into macrophages and display limited movement, which may explain their localization in close proximity to blood vessels. Microglia, on the other hand, are more stationary, as they extend and retract their processes and have larger mean surface area and higher numbers of branches when compared to macrophages, which may contribute to their localization in peri-tumoral areas [41]. Recent high-resolution single-cell RNA-sequencing (scRNA-seq) in both human and murine brain tissue has provided evidence for high heterogeneity within microglial populations in healthy conditions [22, 23, 52], so it will be interesting to see whether these regional and age-dependent heterogeneities existing in healthy brain will have a similar impact in the context of brain tumors.

Gene expression analyses have started characterizing TAM composition, and subsequently their functions, in different GBM subtypes, with the aim of finding better therapies. By using nanostring analysis, human-established GBM subtypes (Proneural (PN), Mesenchymal (MES), and Classical (CL), see BOX 1) were examined for expression of AIF1 (encoding IBA1) as a marker for TAMs. It was found that AIF1 expression is higher in MES GBM compared to PN and CL. Additionally, AIF1 expression was found to influence the survival rate with regard to the subtype analyzed: higher AIF1 levels in MES are associated with longer survival, while in PN tumors survival is shorter when AIF1 expression is high [53]. This was consistent with previous studies that additionally related higher TAM infiltration to the NF1 gene mutation [54, 55]. In addition, Bhat and colleagues proposed that PN tumors transitioning into MES are associated with increased TAMs and activation of the TNF/NF-κb signaling pathway [56]. Similarly, due to TAM inter- and intra-tumoral heterogeneity, specific signaling important for TAMs in one model may not be relevant in other models. A classic example is CSF1R inhibition, which caused TAM reduction in other solid tumors [57, 58] and resulted in the elimination of ~ 95% of microglia in naïve mice [59], and while proving effective in targeting TAMs in PDGFB-driven adult glioma mouse models, it was not effective in reducing their number, but instead it changed their expression profile [60]. These data suggest that while CSF1R is not important for TAM survival in PDGFB-driven tumors, its importance for TAMs in other subtypes remains to be determined, especially in light of the recent data showing that various driver mutations in adult GBM models can create different numbers, expression profiles, and compositions of TAMs, similar to what is observed in human GBM [50, 53, 55]. In addition, it was recently demonstrated that in the most common malignant pediatric brain tumor, medulloblastoma, CSF1R has an anti-tumoral affect, and targeting it resulted in accelerated tumor growth [61]. Whether these differing results reflect GBM subtype-specific responses to blocking CSF1R+ TAMs, as observed in recent GBM anti-angiogenic trials [62], remains to be determined. A recent study used an integrated panel of RNA-seq, flow cytometry, protein arrays, and culture assays to characterize the differences in TMEs, specifically in immune composition and myeloid cells in primary and metastatic brain tumors [63]. It would be interesting to see how the conclusions from this study would be affected by analysis of the use of dexamethasone (DEX), since the vast majority of patients with both primary and metastatic brain tumors develop vasogenic edema, which is managed primarily with DEX. Whether DEX was used in the so-called “control brain” samples is another critical question [63]. In light of recent evidence showing that DEX targets both adaptive and major inflammatory pathways in tumor-associated myeloid cells in GBM, caution should be exercised in the interpretation of immune profiling studies from both fresh patient primary and metastatic brain-tumor samples [3]. Using GBM GEMMs that are based upon human GBM-specific driver mutations, it was shown that TAM numbers, compositions, and expression profiles are dictated by driver mutations, further highlighting the intra- and inter-tumor heterogeneity of these cells [41, 48, 64], similar to what was shown in human GBM subtypes [50, 53, 55]. Isocitrate dehydrogenase (IDH) mutations (mainly in the IDH1 and IDH2 genes) have been shown to influence immune TME behaviour in both low- and high-grade gliomas [65,66,67,68]. For instance, Berghoff et al. showed that IDH mutation in GBM patients leads to less lymphocyte infiltration and lower PD-L1 expression, partly due to different levels of methylation of the PD-L1 gene promoter [65]. By using in vitro and in vivo glioma models, Bunse and colleagues showed that the glioma cell-derived oncometabolite (R)-2-hydrixyglutarate (R-2HG) is able to suppress CD8+ cell activity by interfering with the calcium-dependent transcriptional activity of nuclear factor of activated T cells (NFAT) and inhibiting T-cell receptor (TCR) signalling [66]. Mutated IDH1 reduces leukocyte chemotaxis in a murine IDH1-mutated GBM model [68]. In low-grade gliomas (LGGs), IDH1 mutation in a murine model reduced T-cell infiltration and decreased the expression levels of CXCL10 and its regulator STAT1 [67]. In vitro studies also showed that IDH1 mutation-derived transcriptional silencing of the ligand NKG2DL led to NK cell tolerance toward the tumor and lack of tumor-cell lysis by the innate immune cells [69].

Overall, these results support the concept of distinct and molecularly heterogeneous TAMs in GBM subtypes, warranting further investigation into the biological relevance of TAM heterogeneity in various GBM subtypes. In addition, studies have clearly demonstrated that various driver mutations have significant impacts on both TAM function and composition. The redundant approach of studying TAMs in various GBM subtypes will help us to better define common TAM-derived mechanisms that can be pan-GBM targets and/or to identify unique TAM targets for a given subtype.

TAMs promote tumor progression

It is now clear that TAMs play a major role in supporting glioma growth, invasion, and survival. It is still under debate, however, whether the tumor cells first reprogram the immune cells to a pro-tumorigenic phenotype, or vice versa, if the immune cells directly induce tumor growth. Nonetheless, the crosstalk between tumor cells and TAMs is clear but complex (Fig. 2).

Fig. 2
figure2

Tumor-associated macrophage (TAM)-glioma cell crosstalk creates a strong interdependence that leads to increased TAM recruitment, which supports tumor growth. Glioma cells—including both glioma non-stem cells and glioma stem cells—release chemoattractant molecules (Periostin, SSP1, GM-CSF, SDF-1, ATP, GDNF, CX3CL1, LOX, HGF/SF, MCP-1, MCP-3, CSF-1, Versican, and IL-33) that recruit and reprogram TAMs. In turn, TAMs release factors (IL-6, IL-1β, EGF, TGF-β, STI1 and PTN) that promote tumor progression

TAMs have been shown to release a plethora of factors that stimulate tumor growth and invasion, including TGF-β, IL-1β, IL-6, stress-inducible protein 1 (STI1), and epidermal growth factor (EGF) by acting on GSCs in the perivascular niche and glioma cells. TGF-β, for instance, is secreted by TAMs and causes the production of matrix metalloproteinase 9 (MMP9), which disrupts the ECM and thus increases invasion of GSCs [70]. IL-1β overexpression in TAMs leads to p38 MAPK pathway activation and CCL2 (or MCP-1) production, which promote GSC proliferation [71].

In addition, TAM-induced release of STI1 and EGF promotes GBM growth and invasion [72, 73]. TAMs have been shown to increase the expression of PDGFRB on glioma cells, which stimulates their migratory capacity and consequently tumor progression [74]. Loss of osteopontin (SPP1) in TAMs, but not glioma cells, enhances tumor progression [75]. TGF-β2 expressed by TAMs induces MMP-2 expression and blocks the tissue-inhibitor metalloproteinases 2 (TIMP-2), promoting tumor invasion [76]. CCL8 (or MCP-2) was found to be highly expressed by TAMs and to be correlated with poor survival. In a murine GBM model, CCL8 was shown to promote GBM invasion and to increase tumor cell stemness via the ERK1/2 signaling pathway [77].

As innate immune cells, TAMs express Toll-like receptors (TLRs), which are pathogen recognition receptors that have active roles in tumor growth. For instance, expression of TLR2 is increased in TAMs, and its ablation results in better survival [78]. TLR2 promotes TAM production of MMP14, which is essential for MMP2 release and glioma invasion, and the microglial expression of the metalloprotease MT1-MMP, which cleaves the pro-MMP2 into its active form, resulting in tumor invasion [79, 80]. TLR2 has also been found to regulate MMP9 production by microglia to promote glioma growth and invasion [81]. Another TLR that plays a role in GBM is TLR4, which regulates TAM IL-6 secretion, resulting in increased GSC proliferation [82].

Additionally, TAMs express aryl hydrocarbon receptor (AHR), which has been shown to have autocrine and paracrine effects. In fact, AHR promotes CCR2 expression in TAMs and drives their infiltration into the tumor site, as well as CD39 expression, promoting CD8 T-cell dysfunction [83, 84]. At the same time, tumor cells release chemoattractant proteins that recruit TAMs, such as CCL2, CSF-1, granulocyte–macrophage colony stimulating factor (GM-CSF), hepatocyte growth factor or scatter factor (HGF/SF), stroma-derived factor 1 (SDF-1), and glial cell-derived neurotrophic factor (GDNF). GBM cells express high levels of CCL2, which recruits TAMs and promotes tumor growth [35]. However, other studies showed that CCL7 (or MCP-3), rather than CCL2, acts as a TAM chemoattractant [40]. Glioma-derived IL-33, in its secreted form, is found to be associated with TAM recruitment and infiltration [85]. Also, CSF1 (alternatively called M-CSF) promotes TAM motility and has been shown to induce the switch to a more immunosuppressive phenotype (see above [72, 86]). GM-CSF and lysyl oxidase (LOX) secreted by glioma cells promote TAM recruitment [86, 87]. However, LOX is secreted only when GBM lacks PTEN expression [87]. The role of CX3CR1 in GBM is more controversial, which can be partially attributed to the different models used in various studies. Cx3cr1 deletion was shown to have no impact on TAM recruitment and survival of tumor-bearing mice [88], while our previous studies showed that Cx3cr1 loss, indeed, did not affect tumor-associated microglial migration in the peritumoral areas, but increased infiltration in the perivascular areas by upregulating IL-1β expression, resulting in increased CCL2 expression in a PN GBM murine model [71]. Glioma-derived HGF/SF is postulated to be a potent chemoattractant acting upon the tyrosine kinase receptor (c-MET) expressed by monocytes and microglia [89]. SDF-1 and GDNF are other proposed factors that have been shown to promote TAM accumulation inside the tumor [90, 91]. SPP1 released by tumor cells increased TAM infiltration and immunosuppression [92]. Periostin produced by GSCs was responsible for BMDM recruitment into the perivascular niche and enhanced glioma progression [93].

Recent endogenous ligand screenings have revealed several new molecules released by glioma cells that interact with TLRs in TAMs and promote tumor growth. Among them, Versican promotes tumor expansion via TLR2 and upregulation of MT1-MMP and MMP9 expression [78]. The cell adhesion molecule Tenascin C (TNC) has been shown to positively regulate tumor proliferation via TAMs [94]. However, very recently, it was shown that TNC increases glioma cell phagocytosis when CD47 surface marker is lost in a GBM xenograft mouse model, and the authors correlated the increased TNC levels to a potential defensive mechanism by the immune system against the tumor [95]. In addition, multiple novel mechanisms were identified specific to GSC-TAM interactions. For instance, pleiotrophin (PTN) secreted by TAMs binds to PTPRZ1 on GSCs, resulting in their activation and consequently tumor growth [96]. Additionally, GSC-secreted WISP1 has been shown to support the pro-tumorigenic phenotype of TAMs by promoting the survival of both GSCs and TAMs [97].

TAMs contribute to tumor survival against radio- and chemotherapies by initiating regenerative programs, such as wound healing, supporting tumor relapse; however, the molecular mechanisms underlying these programs are still under investigation [4, 98, 99]. The hypoxia-inducible factor 1 (HIF-1) signalling pathway has been proposed to be involved in resistance to RT; its radio-induced expression is responsible for the activation of SDF-1 and its cognate receptor CXCR4 [100], leading to monocyte recruitment as well as angiogenesis and tumor recurrence [101]. RT has the capacity to increase the recruitment of monocytes via CSF-1 [102]. Inhibition of CSF-1R enhances the efficacy of the multi-targeted kinase inhibitors vatalanib and dovitinib in a PN mouse model of GBM [103].

All of the studies mentioned above were performed using various murine and human GBM lines and models, without considering the genetic makeup of the tumors, making it difficult to determine whether each identified target is a pan-TAM target or is specific for a given model. Understanding this is vital in light of recent examples of specific TAM targeting therapies such as through CSF1R inhibition, which has proven to be extremely effective in targeting TAMs in a PDGFB-driven adult glioma model [60], but failed to demonstrate effectiveness in a clinical trial with unselected adult recurrent GBM patients [104]. Additionally, we have recently shown that CSF1R has anti-tumoral effects, and targeting it results in accelerated tumor growth in the most common malignant pediatric brain tumor, medulloblastoma [61].

In addition, in all of the studies mentioned above, there were no assessments as to whether the effects driven by TAMs were due to microglia and/or macrophages, which is very important considering that targeting monocytes in the circulation will bypass BBB limitations associated with microglia-targeted therapies.

TAMs and immunosuppression

Immune surveillance is hampered in GBM, most likely due to multiple mechanisms. Impaired phagocytosis has been proposed as one such mechanism. Glioma cells induce the expression of the surface protein CD47 (“don’t eat me” signal), which inhibits TAM phagocytic activity [105].

The signal transducer and activator of transcription factor 3 (STAT3) signalling pathway is upregulated in TAMs and is responsible for the production of tumor-promoting inflammatory molecules, such as IL-10 and IL-6, as well as inhibition of the tumor-suppressive inflammatory molecule TNF [106]. Upregulated expression of STAT3 has been found to be mediated by the interaction between the GBM molecule S100 calcium binding protein B (S100B) and the receptor for advanced glycation end products (RAGE) expressed on TAMs [107].

One emerging hypothesis explaining the immunosuppressed TME in GBM is that TAMs convert tumor-infiltrating T cells from active to ineffective via regulation of immune checkpoints. A contributing factor to this hypothesis is the fact that TAMs constitute up to 40% of the tumor mass in both human and mouse models, in contrast to low levels of T-cell infiltration [50, 71, 108]. Immune checkpoints are negative regulatory pathways that function to inhibit T-cell activation and proliferation, therefore maintaining self-tolerance and controlling the duration and degree of the immune response. Among the immune checkpoints, the most relevant are the cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4), the programmed cell death-1 (PD-1), the Fas receptor, and T-cell immunoglobulin and mucin domain-containing protein 3 (TIM3) [109].

CTLA-4 is usually expressed by activated T cells, and its role is to decrease T-cell responsiveness, preventing autoimmunity [109]. The co-stimulatory molecules CD80/CD86, expressed by TAMs, interact with CTLA-4, causing reduced T-cell activation [110, 111]. PD-1 is highly expressed by Treg cells, whose function is to inhibit T-cell activation and its cytotoxic activity [112,113,114]. TAMs promote Treg activity by binding to PD-1 via PD-L1 [106]. The Fas receptor, expressed on CD8+ T cells, binds to the Fas ligand expressed by TAMs, and their interaction induces lymphocytic apoptosis [115]. CD8+ T cells also express TIM3, whose ligand is galectin 9 expressed by TAMs, resulting in the inhibition of antitumoral T helper 1 cell response [116].

Despite emerging hypotheses that TAMs are immunosuppressive, there are no studies providing direct evidence supporting these assumptions. These hypotheses are mainly based upon the observation that TAMs express high levels of PD-L1 [117]. Moreover, a recent study showed that PD-1 is also expressed by TAMs in human and in a mouse model of colon cancer, and if targeted with inhibitors in vivo, it can increase tumor cell phagocytosis and consequently reduce tumor growth [118]. Thus, inhibition of PD-1 having an impact on TAMs in GBM cannot be excluded. A lack of systematic studies to test the role of TAMs in resistance to checkpoint blockade can be partially attributed to the lack of PD-L1 conditional knockout mice, the inability to inhibit monocyte infiltration from the blood circulation to tumors, and the lack of appropriate pre-clinical testing of this hypothesis using immunocompetent GEMMs of primary GBM. Immune checkpoint inhibition (ICI) resulted in an unprecedented response rate in many cancer types, including cancers in advanced metastatic stages such as melanoma and non-small cell lung cancer. Even though the data on the expression of PD-1/PD-L1 in GBM patients are largely correlative based upon immunohistochemical (IHC) antibody staining or data mining from TCGA [119,120,121,122,123], late-phase clinical trials testing ICI in randomized clinical trials in unselected recurrent GBM patients did not meet the primary endpoint of improved overall survival [124]. Genomic and transcriptomic analysis after treatment with a PD-1 inhibitor showed an immunosuppressive expression signature in non-responder patients [125]. To that end, studies have started focusing on the use of PD-1 inhibitors as neoadjuvants in GBM therapy [126, 127]. As nivolumab and pembrolizumab have shown successful outcomes when administered as adjuvants in melanoma [128, 129], researchers aimed at looking in this direction for GBM. Neoadjuvant administration of pembrolizumab, with continued adjuvant therapy post-surgery, has been shown to increase overall patient survival and to enhance local and systemic immune response in recurrent GBM patients [126]. A single-arm Phase II clinical trial of neoadjuvant nivolumab showed increased anti-tumor immune response, although limited sample size prevented drawing definite conclusion on clinical efficacy [127]. While one study did not address whether immune status may be influenced by anti-edema therapy (80% of patients enrolled in the study were currently on steroids) [127], another found no significant correlation between DEX and immune activation, while not excluding the fact that the study protocol, which excluded patients on high-dose DEX, might have contributed to the lack of correlation [126]. Unfortunately, this has not been investigated with regard to TAM functions, and it would be interesting to determine whether a neoadjuvant anti-PD-1 therapy might also boost the inflammatory response of TAMs. More recently, a subset of myeloid cells called myeloid-derived suppressor cells (MDSCs) have emerged as tumor-promoting cells, whose function is to suppress the T-cell immune response. MDSCs include a small (5% and 8% of the total myeloid population in human and mouse gliomas, respectively [130]) group of myeloid progenitors existing in three subtypes: polymorphonuclear PMN-MDSCs, monocytic M-MDSCs, and early-stage eMDSCs [130, 131]. The first two subsets present specific markers for identification, both in human and in mouse, while the eMDSCs still need to be identified and studied. PMN-MDSCs, defined by CD11b+CD14CD33+HLA-DR in human and by Cd11b+Ly6ClowLy6G+ in mouse, are morphologically and phenotypically similar to neutrophils, while M-MDSCs, defined by CD11b+CD14+ CD33+HLA-DR in human and by CD11b+Ly6ChighLy6G in mouse, are more similar to monocytes and are distinguishable only by the lack expression of MHC II [132]. The basic function of MDSCs is to suppress mainly T-cell function, and to a lesser extent NK cells and B cells [132, 133]. In the context of GBM, MDSCs have been defined as myeloid progenitors in the blood (subsets of monocytes or neutrophils) that gain immunosuppressive properties in tumor settings, and when infiltrating into tumors, they can differentiate into BMDMs and neutrophils that exhibit immunosuppressive properties. In fact, they are the source of GBM-infiltrating immunosuppressive BMDMs, since there are no macrophages in circulation [4, 5, 48, 134]. In GBM patients, elevated levels of MDSCs are found in peripheral blood, but also in the tumor tissue. The types of MDSCs differ in tumor tissue compared to peripheral blood, but the composition and the subtype specificity are still not completely clear [130, 135,136,137]. In addition, Bayik et al. suggested that the monocytic MDSCs (mMDSCs)—CD45+CD11b+Ly6C+Ly6GI-A/I-E- were enriched in the TME of male tumor-bearing animals, while granulocytic MDSCs (gMDSCs)—CD45+CD11b+Ly6C+Ly6G were enriched in the TME of female tumor-bearing animals, suggesting that MDSC subsets drive immunosuppression in a sex-dependent manner [137]. Whether the differential recruitment of subsets of MDSCs in female and male recipient animals is a result of the fact that the GL261 cell line is derived from male C3H mice [138,139,140] needs to be determined experimentally. In addition, the authors’ definition of gMDSCs is based upon multi-parameter FACS staining for CD45+CD11b+Ly6CLy6G+, and this combination defines neutrophils and does not provide evidence as to whether these cells are immunosuppressive. Recently, elegant work has provided the field with the technology to isolate and test whether myeloid cells in tumors are in fact immunosuppressive, and in the absence of performing these functional assays, the field should use caution in referring to myeloid cells as MDSCs [141]. Nevertheless, these findings are provocative and will need to be validated by using a larger panel of cell lines and GEMMs that do not require transplantation of donor tumor cells from one sex into recipients of a different sex. Recent studies have suggested that several factors, released by glioma cells but not exclusively, can induce the proliferation, differentiation, and recruitment of MDSCs, such as IL-6, GM-CSF, IL-10, prostaglandin-E2 (PGE2), VEGF, and TGF-β [131]. Another suggested regulator for MDSCs is accumulation of CCL2, which when released by glioma cells recruits CCR2-expressing MDSCs in addition to activating Tregs [142]. In summary, multiple subsets of MDSCs have been identified, and in many cases, they are defined based upon marker expression rather than by demonstration of their immunosuppressive function. Nevertheless, these studies clearly illustrate the existence of TAM and TAN subsets with immunosuppressive properties. We have not yet succeeded in depleting TAMs (neither microglia nor macrophages) in GBM; therefore, there may be TAM subsets that promote tumor growth and immunosuppression, while other subsets may function to restrain tumor growth. This has now been shown to be the case in pancreatic ductal adenocarcinoma (PDAC). For example, while numerous studies have highlighted the tumor-supportive functions of stromal cancer-associated fibroblasts (CAFs) in PDAC TME, recent work has demonstrated that stromal components may also function to restrain PDAC growth, highlighting the importance of functional heterogeneity within these cells [143,144,145]. Additionally, in a mouse model of pediatric medulloblastoma, TAMs have been proposed to have anti-tumor functions, as reduction of their infiltration decreases the overall survival time of tumor bearing-mice [61]. While there is no doubt that TAMs can be reprogrammed by the use of targeted therapies from pro-tumorigenic to an anti-tumor phenotype, to date there are no studies demonstrating intrinsic anti-tumoral functions of TAMs in GBM. However, given their anti-tumoral potential, we expect that in the near future, this picture might change.

TAMs in GBM-associated vasogenic cerebral edema

Vasogenic cerebral edema is a common and serious complication that occurs in the majority of GBM patients as a direct consequence of enhanced vascular permeability of the BBB, leading to extracellular fluid accumulation. This eventually leads reduced cerebral blood flow, hypoxia, and increased intracranial pressure (ICP), ultimately culminating in neuronal impairments and death [146]. Despite its frequency and severity, very little is known about how edema forms. Growth factors, as well as channel and matrix metalloproteinases, are implicated in brain injury-related edema [147]. The vascular endothelial growth factor (VEGF), expressed by gliomas but also by stromal cells such as astrocytes and TAMs, is known to promote angiogenesis and tumor malignancy, and to enhance BBB permeability in brain tumors [148]. The water channel Aquaporin-4 (AQ4) is expressed by astrocytes and is suggested to play an important role in VEGFA-induced GBM-associated edema [149, 150]. MMP9, expressed by endothelial cells and astrocytes, has been shown to regulate and promote BBB disruption and cerebral edema in a rat injury model [151, 152] and it has also been associated with edema in brain tumors [153, 154].

GBM-associated vasogenic cerebral edema is almost exclusively managed with the steroid DEX. While the clinical benefits of DEX can be significant, long-term use of even low doses has significant toxicities, including diabetes, myopathy, depression, and occasionally frank psychosis [155, 156]. It has also been shown that DEX compromises the efficacy of RT in GBM [156]. Furthermore, the immunosuppressant effects of DEX counteract the efficacy of several emerging immunotherapies for treating GBM [157]. DEX decreases the numbers of infiltrating CD4+ and CD8+ T cells [158], in addition to lowering the numbers of myeloid and NK cells [159]. Recently, Iorgulescu et al. showed that DEX therapy in GBM patients reduces patient survival in a dose-dependent manner and increases the resistance to ICI [159].

Although anti-VEGFA therapy is initially effective at decreasing edema at high anti-cancer doses, GBM rapidly adapts to it, leading to rapid tumor progression without improvement in overall survival [160,161,162]. Radiographic and tissue studies of patients with GBM who were treated with Avastin or Cediranib (a small-molecule VEGFR inhibitor) support the results of enhanced tumor invasiveness and increased infiltration of tumor-promoting TAMs and other CD11b+ myeloid cells [163], similar to what was also observed in a PN mouse GBM model [156]. More studies are needed to determine the effects of anti-VEGFA treatment on other immune cells and to determine whether it interferes with emerging novel immunotherapies in GBM. Clearly, new approaches for effectively treating GBM-associated edema are needed, which ideally should target the driving mechanisms of edema formation in GBM and be devoid of the deleterious effects typical of DEX and anti-VEGF-A therapy.

Historically, myeloid cells have not been known to play an integral role in BBB maintenance in the healthy brain. However, due to their perivascular localization in GBM, TAMs became of particular interest because of a recent study evaluating the mechanism driving the anti-edema effects of DEX. This study established that DEX inhibits TAM production of the pro-inflammatory cytokine IL-1β [3].

When Il-1α/β or Il-1β were genetically ablated in a mouse model of PN GBM, magnetic resonance imaging (MRI) as well as histological analyses revealed decreased cerebral edema comparable to levels achieved by DEX or anti-VEGFA treatment, suggesting that inhibition of the IL-1β-mediated pro-inflammatory pathway may be a promising alternative therapy for cerebral edema [3]. These findings are supported by the work of Sehm and colleagues, who administered Sulfasalazine, a potential inhibitor of IL-1β production in macrophages [164], and showed reduced cerebral edema in a rat glioma model [165]. These initial findings are only the first steps in a much longer journey to uncover the important role that TAMs play in the formation of GBM-associated vasogenic cerebral edema, and they also encourage extension of these investigations into further understanding the complex interactions between TAMs and endothelial cells, pericytes, and astrocytes in the perivascular area (Fig. 3).

Fig. 3
figure3

Vasogenic cerebral edema in glioblastoma. Schematic illustration of the cell populations and soluble factors that regulate glioblastoma-associated vasogenic cerebral edema in the perivascular area. Magenta color indicates the tumor, while yellow color shows edema surrounding the tumor. Edema causes additional mass effects, often exceeding the mass induced by the tumor itself. When secreted in the perivascular areas, IL-1β [3] and MMPs [79] produced by TAMs, and VEGFA [166] produced by TAMs and tumor cells, increase vascular permeability and edema formation

In addition, the potential role of other inflammatory cytokines, such as IL-6 and TNF, may be worthwhile to investigate, as they have been shown to regulate the expression of integrin β4/α5 subunits and α2, respectively [167, 168] and may alter BBB permeability. Furthermore, it would be interesting to investigate whether other MCPs, such as CCL-2 or its cognate receptor CCR2, play a role in regulating cerebral edema diffusion. Interestingly, the genetic loss of Ccr2 in a mouse model of ischemia was able to decrease vascular permeability and cerebral edema [169].

TAM interactions with the TME

In addition to the ability of TAMs to suppress T-cell function, interesting and novel TAM—T cell interactions in TME have been documented in LGGs. For example, recent studies in LGGs demonstrated that microglia/T-cell interactions are reciprocal and are essential for Nf1 optic glioma growth [170, 171]. Additionally, it has been shown that neurons produce midkine, which in turn activates T cells to produce CCL4, which induces the microglial production of CCL5, which is essential for promoting Nf1 optic glioma [172]. This study elegantly illustrates that microglia promote tumor growth not only by directly interacting with tumor cells, but also by establishing complex interactions with other cell types in the TME, in this case via the neuron/T-cell/microglia axis [172]. While these studies are performed in LGGs, it would be interesting to evaluate these interactions in GBM. Increasing evidence suggests that neutrophils, which are an integral part of the innate immune system, also play a pro-tumorigenic role in GBM, and they are often referred to as tumor-associated neutrophils (TANs) [173, 174]. Neutrophil infiltration and activation are considered to be markers of poor glioma prognosis. Increased neutrophil degranulation, elevated levels of ARG1 that suppress T-cell functions, upregulation of S100A4 expression, and increased IL-12 levels have been shown to be associated with glioma malignancy [175, 176]. In addition, depletion of TANs by using a monoclonal antibody against Ly6G resulted in increased survival of an IDH wild-type (WT) glioma mouse model [176]. Interestingly, IDH MUT, which is less aggressive than the IDH WT glioma, has low tumor TAN infiltration and it is correlated to the downregulation of chemotaxis-related genes [68]. Similarly, the most aggressive GBM subtype, MES GBM, has higher neutrophil infiltration [55]. A few studies have investigated the interactions between TAN and glioma cells, and identified IL-6 and IL-8 as tumor-secreted key factors for TAN activation [177]. However, the mechanism of neutrophil recruitment, and their interplay with TAMs in GBM growth, are still unknown. However, monocytes and neutrophils are considered to be “partners in crime”; e.g., they coordinate an effective immune response. For example, in response to microbial challenge, tissue-resident macrophages produce neutrophil chemoattractants such as CXCL1, CXCL2, and IL-1α, which results in rapid recruitment of neutrophils to the site of infection [178]. It was recently shown that neutrophils infiltrate tumors and induce ferroptosis, which results in increased tumor necrosis and promotes GBM progression [179]. This raises questions as to whether TAMs induce neutrophil infiltration to tumors similar to what they do during the microbial challenge, or if they have tumor-specific interactions, which warrants further investigation.

In addition to interacting with cells of the adaptive immune system, studies have also demonstrated that TAMs actively interact with other non-immune non-neoplastic cells in the TME. Among the non-immune cells that compose the tumor perivascular niche are endothelial cells, pericytes, and astrocytes [108, 180]. An integral part of the perivascular niche is vigorous and abnormal angiogenesis, leading to disorganized and dysfunctional blood vessels. These blood vessels can evolve into glomeruloid microvascular proliferation (GMP), in which endothelial cells and pericytes form poorly-organized and dysfunctional vascular structures reminiscent of kidney glomeruli [181]. TAMs control tumor angiogenesis by sensing hypoxia conditions, producing IL-1β and increasing VEGFA expression, which are regulators of vascular permeability and tumor angiogenesis [182]. The TAM subset called tunica interna-endothelial cell kinase 2 -Tie-2+ cells, found in the perivascular sites in GBM, produce IL-6 and recruit or promote de novo formation of endothelial cells [183]. TAMs also increase endothelial-like markers such as Willebrand factor, CD31, vascular endothelial (VE)-cadherin, and CD105 [184, 185] and cooperate with endothelial cells to form the endothelial lining of tumor blood vessels [186]. In turn, endothelial cells have been found to promote and maintain GSC proliferation by secreting IL-8 [187], while GSCs can trans-differentiate into epithelial cells, promoting tumor growth [188,189,190]. Pericytes, which surround the endothelial cells, have been shown to regulate immune functions by releasing anti-inflammatory molecules such as IL-10 and TGF-β, or by decreasing immunostimulatory molecules such as MHC II [191]. Astrocytes support GBM survival and invasion by interacting with tumor cells via gap junctions, helping the tumor evade chemotherapy or, in conditions of hypoxia, by releasing cytokines and chemokines that lead to disruption of ECM, thereby facilitating tumor cell invasion [180, 192]. In turn, glioma cells insert themselves between the endfeet of astrocytes and the endothelial wall of blood vessels, which facilitates their invasion and causes focal breach of the BBB, thus permitting molecules and cells from the circulation to enter the CNS even further from the main tumor mass [193]. Heiland et al. recently showed that CD274+ reactive astrocytes are enriched specifically in the peritumoral cortex of de novo and recurrent GBM patients. The authors further showed by using an organotypic human brain culture model that the presence of microglia is essential for the increase in reactive and tumor-promoting astrocytes in GBM, and that astrocyte-microglia complex interactions promote immunosuppressive GBM TME [194]. Interactions between astrocytes and microglia in the context of CNS injury and disease have been extensively studied. For example, it has been shown in various human neurodegenerative diseases that activated microglia produce IL-1α, TNF, and C1q, and induce the upregulation of neurotoxic A1 astrocytes that induce the death of neurons and oligodendrocytes [195]. It is plausible that IL-1α-mediated astrocyte-microglia interactions might also play a role in GBM, where neuronal death is high and IL-1 levels are upregulated [3].

Recently, glutamatergic neurons have also been shown to regulate and advance glioma progression by secreting neuroligin-3 (NLGN3) [196, 197]. NLGN3 mediates synaptic communications between glioma cells and neurons through AMPA receptors and the glutamate signalling pathway. Also, a subset of glioma cells has been shown to respond to non-synaptic neuronal activity, reflecting an influx of extracellular potassium, which is a behaviour comparable to normal astrocytes in response to neuronal activity. Gap junction-coupled glioma networks have been observed to support tumor proliferation by amplifying changes in the extracellular ionic compartment [198]. By using electron microscopy, another research group detected a physical coupling between glioma cells and neurons via tumor microtubes (TMs). They defined three morphologically-distinct synaptic contacts responsible for different functional properties, which ultimately support tumor proliferation: i) a single synaptic contact to a glioma TM, ii) a multi-synaptic contact between both a glioma TM and a neuron, and iii) a glioma TM approaching a pre-existing neuronal synapse with contact to the synaptic cleft. By using confocal microscopy, they further identified glutamatergic AMPA receptors to be main players involved in these neuroglioma synaptic networks [199]. Whether TAMs are interacting with neurons in the TME of GBM to promote tumor growth, similar what has been shown in LGGs, will be an interesting novel research area.

Overall, these results collectively demonstrate that non-immune non-neoplastic cells in the GBM TME contribute to tumor growth, GSC phenotype, and therapy resistance not only by direct interaction with tumor cells, but also by creating complex interdependency with each other (Fig. 4).

Fig. 4
figure4

The glioblastoma microenvironment. Schematic illustration of TAM interactions with glioma cells and the non-neoplastic cells of the tumor microenvironment, including endothelial cells, pericytes, activated astrocytes, and T cells. In addition, interactions between tumor cells and neutrophils, neurons, and astrocytes are illustrated. TAMs communicate with the other non-neoplastic cells to regulate tumor progression, either by direct contact or via secreted factors

Macrophage-targeted therapies

There is sufficient evidence to show critical roles for TAMs in GBM growth, invasion, immune evasion, and edema. These studies provided rationale for single-agent or combinatorial TAM-targeted therapies as viable alternatives for GBM treatment. TAM targeting therapies are based upon two strategies: 1) altering their tumor-promoting function, often referred to as re-polarization, and 2) blocking their infiltration.

Strategies altering TAM function. The use of CSF1R small-molecule inhibitors, such as BLZ945, has been shown to decrease glioma progression by polarizing TAMs into an anti-tumor phenotype in a PN mouse model of GBM [60]. However, further pre-clinical trials studying the long-term administration of BLZ945 showed that the tumors rebounded rapidly after a dormancy phase of 4 weeks [200]. This resistance was mediated by TAMs via the secretion of insulin growth factor 1 (IGF-1) in the extracellular space after the release of IL-4, probably produced by T cells in response to the drug. IGF-1 interactions with its cognate receptor IGF-1R on the surface of tumor cells and phosphatidylinositol 3-kinase (PI3K) signaling pathway activation resulted in tumor resistance and proliferation [200]. Unfortunately, CSF1R inhibitor as a single agent failed to demonstrate effectiveness in a clinical trial with unselected adult recurrent GBM patients [104].

A recent gene therapy-based approach targeting TAMs is in Phase I/II for treatment of unmethylated O [6]-methylguanine-DNA methyltransferase (MGMT) GBM (NCT03866109). The drug is called Temferon, and it consists of autologous CD34+-enriched hematopoietic stem/progenitor cells (HPSCs) transduced with a lentiviral vector that drives the myeloid cells to produce IFN-α, an anti-tumoral cytokine. This strategy provides a promising opportunity for brain tumor therapy, as it successfully inhibited tumor growth in a mouse model of breast cancer [201].

Strategies blocking TAM recruitment. The CCL2/CCR2 axis is essential for monocyte recruitment to the tumor site, and genetic reduction of CCL2 levels prolonged the survival of GBM-bearing mice without significant effects on TAM infiltration, which can be partially explained by the redundancies in chemokine functions involved in monocyte recruitment. A few clinical trials are currently ongoing using antagonists for CCL2 and CCR2 to treat solid tumors [202]. Another approach to block TAM recruitment is to target CD47. Two recent ongoing Phase I trials are testing the efficacy of two monoclonal antibodies, IBI 188 (NCT03763149) and SRF-231 (NCT03512340), which are being used as monotherapies in patients with advanced malignant tumors and lymphomas.

Another promising target to block TAM recruitment is the SDF-1 receptor CXCR4. Several CXCR4 antagonists, such as peptide R or LY2510924, have been shown to be beneficial in GBM mouse models [203, 204]; however, they have not moved forward into clinical trials. One additional completed Phase I/II clinical study analyzed the toxicity and efficacy of CXCR4 inhibitor, Plerixafor, in GBM patients after RT and temozolomide (NCT01977677). The Phase I/II study suggested that infusion of Plerixafor was well-tolerated as an adjuvant to chemoradiation in newly-diagnosed GBM patients and improved the local control of tumor recurrence [205].

Combinatorial therapies. Considering TAM’s immunosuppressive function and despite failed efficacy of CSF1R inhibitor as a single agent in unselected adult recurrent GBM patients [104], combinatorial therapies targeting CSF1R with novel emerging immunotherapies have been initiated to determine whether there are synergistic effects. One ongoing Phase I trial is currently evaluating the combinatorial effects of the anti-CSF1R antibody, Cabiralizumab, and the anti-PD-1 antibody Nivolumab in patients with advanced malignant glioma (NCT02526017). Another Phase I trial investigates the use of the anti-CSF1R monoclonal antibody SNDX-6352, alone or in combination with an anti-PD-1 antibody, Durvalumab, in patients with solid tumors (NCT03238027).

In addition, oncolytic virotherapies have been proposed to target and reprogram TAMs, in addition to directly killing tumor cells [206]. Recent studies demonstrated that administration of IL-12-loaded nanoparticles in tumor-bearing mice is beneficial for survival and TAM re-education toward an anti-tumorigenic phenotype [207], and resulted in an ongoing Phase I trial in recurrent and progressive GBM patients using Nivolumab together with an adenovirus that induces the production of hIL-12 (NCT03636477).

Additionally, a recent study has shown that there is a CD73hi macrophage population in human GBM that persists after ICI treatment [208]. By using reverse translational studies that exploited the use of CD73−/− mice and syngeneic glioma models, they were able to show that the absence of the molecule significantly improved survival, especially after treatment with PD-1 and CTLA-4 inhibitors, suggesting a new possible target, CD73, to improve the response to therapy ICI [208].

Several novel concepts and strategies for targeting TAMs are emerging. Targeting the antigen presentation capacity of TAMs to recruit and (re)-activate anti-tumoral effector T cells is one such option. The use of stimulator of interferon gene (STING) agonists can also be a solution, as STING has shown to increase TAM production of IFN, re-educating TAMs into a pro-inflammatory phenotype, as well as to recruit and activate T cells in murine models of GBM [209] and non-small cell lung cancer [210]. Although these are initial studies, it is tempting to speculate whether the classical professional antigen-presenting cells may be used to enhance the toxic T-cell response in GBM. Continuous effort towards novel ways of TAM re-education into a tumor-suppressor phenotype is another an ongoing strategy. Nucleic acid-based strategies may be a compelling design for future studies. Recent works investigated the role of microRNAs, like miR-142-3p, which targets TGF-β, or let-7b, which activates TLR7 in TAMs, resulting in reduced glioma growth [211, 212]. However, microRNA-based therapy still has the caveat of requiring an effective and safe formulation for in vivo delivery. Another attractive direction is utilization of TAM metabolism, which may provide novel targets. Like tumor cells, TAMs have been shown to switch to aerobic glycolysis, which allows rapid energy generation during hypoxia conditions, mediated by the AKT-mToR-HIF-1α pathway. Increased glycolysis results in increased lactic acid production, which ultimately induces TAMs to express tumor suppressor factors such as ARG1 and VEGFA [213].

All of these therapies are designed to target TAMs as a whole, but as we have discussed in this review, there are essential differences in ontogeny, regional heterogeneity, and localization of microglia and macrophages in GBM that might result in their differential functions. In addition, with novel discoveries demonstrating that various genetic driver mutations can create different microglia/macrophage composition in tumors, it is becoming apparent that we need more research to further discriminate between the biological functions of microglia versus macrophages in GBM. As a field, we have not yet explicitly succeeded in depleting one versus the other, first to demonstrate whether there is a redundancy in function between microglia and macrophages, and also what any differences might be. Understanding these differences will open novel avenues of specific TAM sub-population targeting therapies, which could be tailored to tumor genotype or subtype.

Conclusions

High-grade gliomas, such as GBM, have high degrees of inter- and intra-tumor heterogeneity, with no effective therapy to date. Despite several pre-clinical and clinical trials being conducted, beneficial outcomes have been very limited. Recently, it has become clear that TAMs, including tissue-resident microglia and invading BMDMs, play critical roles in GBM growth, invasion, immune evasion, and resistance to therapy. TAMs contribute to the high levels of tumor heterogeneity and malignancy by playing a triple tumor-supporting role that can be easily compared to the mythological evil three-headed dog, Cerberus; these myeloid cells support tumor proliferation, regulate immunosuppression, and contribute to cerebral edema (Fig. 5). Each of these functions are interconnected, and to date, are still not fully understood. In a way, TAMs protect the tumor just as Cerberus guards the underworld.

Fig. 5
figure5

Tumor-associated macrophages (TAMs): The Cerberus of glioblastoma (GBM). Representation of the main functions of TAMs in promoting GBM, including contributions to tumor progression, immunosuppression, and development of vasogenic cerebral edema. TAMs can be metaphorically compared to the three-headed dog Cerberus from Greek mythology, who guards the Underworld gates, preventing the dead from living. In GBM, TAMs protect the tumor cells from dying and support their living

Although genetically stable, TAMs are very plastic cells for their ability to change their expression profile according to specific signals and interactions, which results in highly heterogeneous TAM populations in GBM. Therefore, deep immune profiling at the transcriptional and protein levels in human resected brain tumor samples will contribute to improving our understanding of this intricate puzzle of TAM heterogeneity. It will help in defining specific functional roles of TAMs in spatiotemporal organization, and it also may provide distinct TAM signatures for microglia and macrophages. Additionally, as increasing evidence shows that GBM subtypes dictate the tumor cellular components, as well as the response to therapy, subtype discrimination is one of the main parameters to consider for future studies, especially in the context of IDH mutation. However, given the fact that spatial cellular organization and heterogeneity are important in GBM, surgical tissue resection represents a limiting factor in defining the real cellular composition in human tissues. This challenge can be partially overcome by using a high number of samples, decreasing the variance, and increasing the tissue coverage, and possibly by evaluating DEX-naïve samples. Another interesting aspect to elucidate would be the role of BAMs in the onset and malignancy of GBM. Whether the different ontogeny and cell development may play a role in tumor progression can be speculated. Once specific aberrantly-expressed TAM markers are defined, whether they are novel or were identified previously, it is important to develop proper and efficient mouse models to study the molecular pathways and to start pre-clinical trials. Elucidating other molecular mechanisms involved in glioma-associated cerebral edema, besides the IL-1 pathway, is another notable strategy to find a more optimal anti-edema therapy.

Clearly, communication between TAMs and not only tumor cells, but also the TME, cannot be underestimated and requires further investigation. Finally, complementary studies, and perhaps multi-targeted therapies, need to be validated for efficiency and side effects in combination with classical therapies in multiple models to cover GBM heterogeneity. Considering all of these aspects, future research should attempt to exploit the potential of TAMs for the development of beneficial therapies against brain tumors, and particularly GBM.

References

  1. 1.

    Hambardzumyan D, Bergers G (2015) Glioblastoma: defining tumor niches. Trends Cancer 1(4):252–265

    PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Gutmann DH, Kettenmann H (2019) Microglia/brain macrophages as central drivers of brain tumor pathobiology. Neuron 104(3):442–449

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Herting CJ, Chen Z, Maximov V, Duffy A, Szulzewsky F, Shayakhmetov DM et al (2019) Tumour-associated macrophage-derived interleukin-1 mediates glioblastoma-associated cerebral oedema. Brain J Neurol 142(12):3834–3851

    Article  Google Scholar 

  4. 4.

    Hambardzumyan D, Gutmann DH, Kettenmann H (2016) The role of microglia and macrophages in glioma maintenance and progression. Nat Neurosci 19(1):20–27

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Rossi ML, Hughes JT, Esiri MM, Coakham HB, Brownell DB (1987) Immunohistological study of mononuclear cell infiltrate in malignant gliomas. Acta Neuropathol 74(3):269–277

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Morantz RA, Wood GW, Foster M, Clark M, Gollahon K (1979) Macrophages in experimental and human brain tumors. Part I. Studies of the macrophage content of experimental rat brain tumors of varying immunogenicity. J Neurosurg 50(3):298–304

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Varol C, Mildner A, Jung S (2015) Macrophages: development and tissue specialization. Annu Rev Immunol 33:643–675

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Wynn TA, Chawla A, Pollard JW (2013) Macrophage biology in development, homeostasis and disease. Nature 496(7446):445–455

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Okabe Y, Medzhitov R (2016) Tissue biology perspective on macrophages. Nat Immunol 17(1):9–17

    CAS  PubMed  Article  Google Scholar 

  10. 10.

    van Furth R, Cohn ZA (1968) The origin and kinetics of mononuclear phagocytes. J Exp Med 128(3):415–435

    PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    van Furth R, Cohn ZA, Hirsch JG, Humphrey JH, Spector WG, Langevoort HL (1972) The mononuclear phagocyte system: a new classification of macrophages, monocytes, and their precursor cells. Bull World Health Organ 46(6):845–852

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Hoeffel G, Ginhoux F (2018) Fetal monocytes and the origins of tissue-resident macrophages. Cell Immunol 330:5–15

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Hoeffel G, Chen J, Lavin Y, Low D, Almeida FF, See P et al (2015) C-Myb(+) erythro-myeloid progenitor-derived fetal monocytes give rise to adult tissue-resident macrophages. Immunity 42(4):665–678

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Ajami B, Bennett JL, Krieger C, Tetzlaff W, Rossi FM (2007) Local self-renewal can sustain CNS microglia maintenance and function throughout adult life. Nat Neurosci 10(12):1538–1543

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Gomez Perdiguero E, Klapproth K, Schulz C, Busch K, Azzoni E, Crozet L et al (2015) Tissue-resident macrophages originate from yolk-sac-derived erythro-myeloid progenitors. Nature 518(7540):547–551

    PubMed  Article  CAS  Google Scholar 

  16. 16.

    Ginhoux F, Greter M, Leboeuf M, Nandi S, See P, Gokhan S et al (2010) Fate mapping analysis reveals that adult microglia derive from primitive macrophages. Science (New York, NY) 330(6005):841–845

    CAS  Article  Google Scholar 

  17. 17.

    Kierdorf K, Erny D, Goldmann T, Sander V, Schulz C, Perdiguero EG et al (2013) Microglia emerge from erythromyeloid precursors via Pu.1- and Irf8-dependent pathways. Nature Neurosci 16(3):273–280

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Chen HR, Sun YY, Chen CW, Kuo YM, Kuan IS, Tiger Li ZR et al (2020) Fate mapping via CCR2-CreER mice reveals monocyte-to-microglia transition in development and neonatal stroke. Sci Adv 6(35):2119

    Article  CAS  Google Scholar 

  19. 19.

    De S, Van Deren D, Peden E, Hockin M, Boulet A, Titen S et al (2018) Two distinct ontogenies confer heterogeneity to mouse brain microglia. Development (Cambridge). 145:13

    Google Scholar 

  20. 20.

    Grabert K, Michoel T, Karavolos MH, Clohisey S, Kenneth Baillie J, Stevens MP et al (2016) Microglial brain regionâ ’dependent diversity and selective regional sensitivities to aging. Nat Neurosci 19(3):504–516

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Böttcher C, Schlickeiser S, Sneeboer MAM, Kunkel D, Knop A, Paza E et al (2019) Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry. Nat Neurosci 22(1):78–90

    PubMed  Article  CAS  Google Scholar 

  22. 22.

    Masuda T, Sankowski R, Staszewski O, Böttcher C, Amann L et al (2019) Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 566(7744):388–392

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Sankowski R, Bottcher C, Masuda T, Geirsdottir L, Sindram E et al (2019) Mapping microglia states in the human brain through the integration of high-dimensional techniques. Nat Neurosci 22(12):2098–2110

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Goldmann T, Wieghofer P, Jordao MJC, Prutek F, Hagemeyer N, Frenzel K et al (2016) Origin, fate and dynamics of macrophages at central nervous system interfaces. Nat Immunol 17(7):797–805

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Van Hove H, Martens L, Scheyltjens I, De Vlaminck K, Pombo Antunes AR, De Prijck S et al (2019) A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment. Nat Neurosci 22(6):1021–1035

    PubMed  Article  CAS  Google Scholar 

  26. 26.

    Utz SG, See P, Mildenberger W, Thion MS, Silvin A, Lutz M et al (2020) Early fate defines microglia and non-parenchymal brain macrophage development. Cell

  27. 27.

    Yona S, Kim KW, Wolf Y, Mildner A, Varol D, Breker M et al (2013) Fate mapping reveals origins and dynamics of monocytes and tissue macrophages under homeostasis. Immunity 38(1):79–91

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Geissmann F, Jung S, Littman DR (2003) Blood monocytes consist of two principal subsets with distinct migratory properties. Immunity 19(1):71–82

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    Swirski FK, Nahrendorf M, Etzrodt M, Wildgruber M, Cortez-Retamozo V, Panizzi P et al (2009) Identification of splenic reservoir monocytes and their deployment to inflammatory sites. Science (New York, NY) 325(5940):612–616

    CAS  Article  Google Scholar 

  30. 30.

    Ginhoux F, Guilliams M (2016) Tissue-resident macrophage ontogeny and homeostasis. Immunity 44(3):439–449

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Ginhoux F, Jung S (2014) Monocytes and macrophages: developmental pathways and tissue homeostasis. Nat Rev Immunol 14(6):392–404

    CAS  Article  Google Scholar 

  32. 32.

    Davies LC, Jenkins SJ, Allen JE, Taylor PR (2013) Tissue-resident macrophages. Nat Immunol 14(10):986–995

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Shi C, Pamer EG (2011) Monocyte recruitment during infection and inflammation. Nat Rev Immunol 11(11):762–774

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Kettenmann H, Hanisch U-K, Noda M, Verkhratsky A (2011) Physiology of microglia. Physiol Rev 91(2):461–553

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Chen Z, Feng X, Herting CJ, Garcia VA, Nie K, Pong WW et al (2017) Cellular and molecular identity of tumor-associated macrophages in glioblastoma. Can Res 77(9):2266–2278

    CAS  Article  Google Scholar 

  36. 36.

    Bowman RL, Klemm F, Akkari L, Pyonteck SM, Sevenich L, Quail DF et al (2016) Macrophage ontogeny underlies differences in tumor-specific education in brain malignancies. Cell Rep 17(9):2445–2459

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Müller S, Kohanbash G, Liu SJ, Alvarado B, Carrera D, Bhaduri A et al (2017) Single-cell profiling of human gliomas reveals macrophage ontogeny as a basis for regional differences in macrophage activation in the tumor microenvironment. Genome Biol 18(1):234

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  38. 38.

    Tsou CL, Peters W, Si Y, Slaymaker S, Aslanian AM, Weisberg SP et al (2007) Critical roles for CCR2 and MCP-3 in monocyte mobilization from bone marrow and recruitment to inflammatory sites. J Clin Investig 117(4):902–909

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Lu B, Rutledge BJ, Gu L, Fiorillo J, Lukacs NW, Kunkel SL et al (1998) Abnormalities in monocyte recruitment and cytokine expression in monocyte chemoattractant protein 1-deficient mice. J Exp Med 187(4):601–608

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Okada M, Saio M, Kito Y, Ohe N, Yano H, Yoshimura S et al (2009) Tumor-associated macrophage/microglia infiltration in human gliomas is correlated with MCP-3, but not MCP-1. Int J Oncol 34(6):1621–1627

    CAS  PubMed  Google Scholar 

  41. 41.

    Chen Z, Ross JL, Hambardzumyan D (2019) Intravital 2-photon imaging reveals distinct morphology and infiltrative properties of glioblastoma-associated macrophages. Proc Natl Acad Sci USA 116(28):14254–14259

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Bennett ML, Bennett FC, Liddelow SA, Ajami B, Zamanian JL, Fernhoff NB et al (2016) New tools for studying microglia in the mouse and human CNS. Proc Natl Acad Sci USA 113(12):E1738–E1746

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Gautier EL, Shay T, Miller J, Greter M, Jakubzick C, Ivanov S et al (2012) Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages. Nat Immunol 13(11):1118–1128

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Buttgereit A, Lelios I, Yu X, Vrohlings M, Krakoski NR, Gautier EL et al (2016) Sall1 is a transcriptional regulator defining microglia identity and function. Nat Immunol 17(12):1397–1406

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Haage V, Semtner M, Vidal RO, Hernandez DP, Pong WW, Chen Z et al (2019) Comprehensive gene expression meta-analysis identifies signature genes that distinguish microglia from peripheral monocytes/macrophages in health and glioma. Acta Neuropathol Commun 7(1):20

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. 46.

    Gordon S (2003) Alternative activation of macrophages. Nat Rev Immunol 3(1):23–35

    CAS  PubMed  Article  Google Scholar 

  47. 47.

    Mantovani A, Sozzani S, Locati M, Allavena P, Sica A (2002) Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes. Trends Immunol 23(11):549–555

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Chen Z, Herting CJ, Ross JL, Gabanic B, Puigdelloses Vallcorba M, Szulzewsky F et al (2020) Genetic driver mutations introduced in identical cell-of-origin in murine glioblastoma reveal distinct immune landscapes but similar response to checkpoint blockade. Glia 68(10):2148–2166

    PubMed  Article  Google Scholar 

  49. 49.

    Szulzewsky F, Pelz A, Feng X, Synowitz M, Markovic D, Langmann T et al (2015) Glioma-associated microglia/macrophages display an expression profile different from M1 and M2 polarization and highly express Gpnmb and Spp1. PLoS ONE 10(2):e0116644

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  50. 50.

    Gabrusiewicz K, Rodriguez B, Wei J, Hashimoto Y, Healy LM, Maiti SN et al (2016) Glioblastoma-infiltrated innate immune cells resemble M0 macrophage phenotype. JCI insight. 1:2

    Article  Google Scholar 

  51. 51.

    Landry AP, Balas M, Alli S, Spears J, Zador Z (2020) Distinct regional ontogeny and activation of tumor associated macrophages in human glioblastoma. Sci Rep 10(1):19542

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Hammond TR, Dufort C, Dissing-Olesen L, Giera S, Young A, Wysoker A et al (2019) Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 50(1):253–71.e6

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Kaffes I, Szulzewsky F, Chen Z, Herting CJ, Gabanic B, Velazquez Vega JE et al (2019) Human mesenchymal glioblastomas are characterized by an increased immune cell presence compared to proneural and classical tumors. Oncoimmunology 8(11):e1655360

    PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Sorensen MD, Dahlrot RH, Boldt HB, Hansen S, Kristensen BW (2018) Tumour-associated microglia/macrophages predict poor prognosis in high-grade gliomas and correlate with an aggressive tumour subtype. Neuropathol Appl Neurobiol 44(2):185–206

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    Wang Q, Hu B, Hu X, Kim H, Squatrito M, Scarpace L et al (2017) Tumor evolution of glioma-intrinsic gene expression subtypes associates with immunological changes in the microenvironment. Cancer Cell 32(1):42-56.e6

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  56. 56.

    Bhat KPL, Balasubramaniyan V, Vaillant B, Ezhilarasan R, Hummelink K, Hollingsworth F et al (2013) Mesenchymal differentiation mediated by NF-κB promotes radiation resistance in glioblastoma. Cancer Cell 24(3):331–346

    CAS  Article  Google Scholar 

  57. 57.

    Cannarile MA, Weisser M, Jacob W, Jegg A-M, Ries CH, Rüttinger D (2017) Colony-stimulating factor 1 receptor (CSF1R) inhibitors in cancer therapy. J Immunother Cancer 5(1):53

    PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Strachan DC, Ruffell B, Oei Y, Bissell MJ, Coussens LM, Pryer N et al (2013) CSF1R inhibition delays cervical and mammary tumor growth in murine models by attenuating the turnover of tumor-associated macrophages and enhancing infiltration by CD8+ T cells. Oncoimmunology 2(12):e26968

    PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Elmore MRP, Najafi AR, Koike MA, Dagher NN, Spangenberg EE, Rice RA et al (2014) Colony-stimulating factor 1 receptor signaling is necessary for microglia viability, unmasking a microglia progenitor cell in the adult brain. Neuron 82(2):380–397

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Pyonteck SM, Akkari L, Schuhmacher AJ, Bowman RL, Sevenich L, Quail DF et al (2013) CSF-1R inhibition alters macrophage polarization and blocks glioma progression. Nat Med 19(10):1264–1272

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Maximov V, Chen Z, Wei Y, Robinson MH, Herting CJ, Shanmugam NS et al (2019) Tumour-associated macrophages exhibit anti-tumoural properties in Sonic Hedgehog medulloblastoma. Nat Commun 10(1):2410

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  62. 62.

    Sandmann T, Bourgon R, Garcia J, Li C, Cloughesy T, Chinot OL et al (2015) Patients with proneural glioblastoma may derive overall survival benefit from the addition of bevacizumab to first-line radiotherapy and temozolomide: retrospective analysis of the AVAglio trial. J Clin Oncol Off J Am Soc Clin Oncol 33(25):2735–2744

    CAS  Article  Google Scholar 

  63. 63.

    Klemm F, Maas RR, Bowman RL, Kornete M, Soukup K, Nassiri S et al (2020) Interrogation of the microenvironmental landscape in brain tumors reveals disease-specific alterations of immune cells. Cell 181(7):1643–60.e17

    CAS  PubMed  Article  Google Scholar 

  64. 64.

    Herting CJ, Chen Z, Pitter KL, Szulzewsky F, Kaffes I, Kaluzova M et al (2017) Genetic driver mutations define the expression signature and microenvironmental composition of high-grade gliomas. Glia 65(12):1914–1926

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Berghoff AS, Kiesel B, Widhalm G, Wilhelm D, Rajky O, Kurscheid S et al (2017) Correlation of immune phenotype with IDH mutation in diffuse glioma. Neuro Oncol 19(11):1460–1468

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Bunse L, Pusch S, Bunse T, Sahm F, Sanghvi K, Friedrich M et al (2018) Suppression of antitumor T cell immunity by the oncometabolite (R)-2-hydroxyglutarate. Nat Med 24(8):1192–1203

    CAS  PubMed  Article  Google Scholar 

  67. 67.

    Kohanbash G, Carrera DA, Shrivastav S, Ahn BJ, Jahan N, Mazor T et al (2017) Isocitrate dehydrogenase mutations suppress STAT1 and CD8+ T cell accumulation in gliomas. J Clin Investig 127(4):1425–1437

    PubMed  Article  Google Scholar 

  68. 68.

    Amankulor NM, Kim Y, Arora S, Kargl J, Szulzewsky F, Hanke M et al (2017) Mutant idh1 regulates the tumor-associated immune system in gliomas. Genes Dev 31(8):774–786

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. 69.

    Zhang X, Rao A, Sette P, Deibert C, Pomerantz A, Kim WJ et al (2016) IDH mutant gliomas escape natural killer cell immune surveillance by downregulation of NKG2D ligand expression. Neuro Oncol 18(10):1402–1412

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. 70.

    Ye XZ, Xu SL, Xin YH, Yu SC, Ping YF, Chen L et al (2012) Tumor-associated microglia/macrophages enhance the invasion of glioma stem-like cells via TGF-beta1 signaling pathway. J Immunol (Baltimore, Ms: 1950). 189(1):444–453

    CAS  Google Scholar 

  71. 71.

    Feng X, Szulzewsky F, Yerevanian A, Chen Z, Heinzmann D, Rasmussen RD et al (2015) Loss of CX3CR1 increases accumulation of inflammatory monocytes and promotes gliomagenesis. Oncotarget 6(17):15077–15094

    PubMed  PubMed Central  Article  Google Scholar 

  72. 72.

    Coniglio SJ, Eugenin E, Dobrenis K, Stanley ER, West BL, Symons MH et al (2012) Microglial stimulation of glioblastoma invasion involves epidermal growth factor receptor (EGFR) and colony stimulating factor 1 receptor (CSF-1R) signaling. Mol Med (Cambridge, Mass) 18:519–527

    CAS  Article  Google Scholar 

  73. 73.

    Carvalho da Fonseca AC, Wang H, Fan H, Chen X, Zhang I, Zhang L et al (2014) Increased expression of stress inducible protein 1 in glioma-associated microglia/macrophages. J Neuroimmunol 274(1–2):71–77

    CAS  PubMed  Article  Google Scholar 

  74. 74.

    Wallmann T, Zhang XM, Wallerius M, Bolin S, Joly AL, Sobocki C et al (2018) Microglia induce PDGFRB expression in glioma cells to enhance their migratory capacity. iScience 9:71–83

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. 75.

    Szulzewsky F, Schwendinger N, Guneykaya D, Cimino PJ, Hambardzumyan D, Synowitz M et al (2018) Loss of host-derived osteopontin creates a glioblastoma-promoting microenvironment. Neuro Oncol 20(3):355–366

    CAS  PubMed  Article  Google Scholar 

  76. 76.

    Wick W, Platten M, Weller M (2001) Glioma cell invasion: regulation of metalloproteinase activity by TGF-beta. J Neurooncol 53(2):177–185

    CAS  PubMed  Article  Google Scholar 

  77. 77.

    Zhang X, Chen L, Dang WQ, Cao MF, Xiao JF, Lv S et al (2020) CCL8 secreted by tumor-associated macrophages promotes invasion and stemness of glioblastoma cells via ERK1/2 signaling. Lab Investig 100(4):619–629

    CAS  PubMed  Article  Google Scholar 

  78. 78.

    Hu F, Dzaye O, Hahn A, Yu Y, Scavetta RJ, Dittmar G et al (2015) Glioma-derived versican promotes tumor expansion via glioma-associated microglial/macrophages Toll-like receptor 2 signaling. Neuro Oncol 17(2):200–210

    CAS  PubMed  Article  Google Scholar 

  79. 79.

    Markovic DS, Vinnakota K, Chirasani S, Synowitz M, Raguet H, Stock K et al (2009) Gliomas induce and exploit microglial MT1-MMP expression for tumor expansion. Proc Natl Acad Sci USA 106(30):12530–12535

    CAS  PubMed  Article  Google Scholar 

  80. 80.

    Vinnakota K, Hu F, Ku MC, Georgieva PB, Szulzewsky F, Pohlmann A et al (2013) Toll-like receptor 2 mediates microglia/brain macrophage MT1-MMP expression and glioma expansion. Neuro Oncol 15(11):1457–1468

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. 81.

    Hu F, Ku MC, Markovic D, Dzaye O, Lehnardt S, Synowitz M et al (2014) Glioma-associated microglial MMP9 expression is upregulated by TLR2 signaling and sensitive to minocycline. Int J Cancer 135(11):2569–2578

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  82. 82.

    Dzaye O, Hu F, Derkow K, Haage V, Euskirchen P, Harms C et al (2016) Glioma stem cells but not bulk glioma cells upregulate il-6 secretion in microglia/brain macrophages via toll-like receptor 4 signaling. J Neuropathol Exp Neurol 75(5):429–440

    PubMed  Article  CAS  Google Scholar 

  83. 83.

    Zhang J, Sarkar S, Cua R, Zhou Y, Hader W, Yong VW (2012) A dialog between glioma and microglia that promotes tumor invasiveness through the CCL2/CCR2/interleukin-6 axis. Carcinogenesis 33(2):312–319

    CAS  PubMed  Article  Google Scholar 

  84. 84.

    Takenaka MC, Gabriely G, Rothhammer V, Mascanfroni ID, Wheeler MA, Chao CC et al (2019) Control of tumor-associated macrophages and T cells in glioblastoma via AHR and CD39. Nat Neurosci 22(5):729–740

    CAS  PubMed  Article  Google Scholar 

  85. 85.

    De Boeck A, Ahn BY, D’Mello C, Lun X, Menon SV, Alshehri MM et al (2020) Glioma-derived IL-33 orchestrates an inflammatory brain tumor microenvironment that accelerates glioma progression. Nat Commun 11(1):12

    Article  CAS  Google Scholar 

  86. 86.

    Sielska M, Przanowski P, Wylot B, Gabrusiewicz K, Maleszewska M, Kijewska M et al (2013) Distinct roles of CSF family cytokines in macrophage infiltration and activation in glioma progression and injury response. J Pathol 230(3):310–321

    CAS  PubMed  Article  Google Scholar 

  87. 87.

    Chen P, Zhao D, Li J, Liang X, Li J, Chang A et al (2019) Symbiotic macrophage-glioma cell interactions reveal synthetic lethality in PTEN-null glioma. Cancer Cell 35(6):868–84.e6

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  88. 88.

    Liu C, Luo D, Streit WJ, Harrison JK (2008) CX3CL1 and CX3CR1 in the GL261 murine model of glioma: CX3CR1 deficiency does not impact tumor growth or infiltration of microglia and lymphocytes. J Neuroimmunol 198(1–2):98–105

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  89. 89.

    Badie B, Schartner J, Klaver J, Vorpahl J (1999) In vitro modulation of microglia motility by glioma cells is mediated by hepatocyte growth factor/scatter factor. Neurosurgery 44(5):1073–1077

    Article  Google Scholar 

  90. 90.

    Wang SC, Hong JH, Hsueh C, Chiang CS (2012) Tumor-secreted SDF-1 promotes glioma invasiveness and TAM tropism toward hypoxia in a murine astrocytoma model. Lab Investig J Techn Methods Pathol 92(1):151–162

    CAS  Article  Google Scholar 

  91. 91.

    Ku MC, Wolf SA, Respondek D, Matyash V, Pohlmann A, Waiczies S et al (2013) GDNF mediates glioblastoma-induced microglia attraction but not astrogliosis. Acta Neuropathol 125(4):609–620

    CAS  PubMed  Article  Google Scholar 

  92. 92.

    Wei J, Marisetty A, Schrand B, Gabrusiewicz K, Hashimoto Y, Ott M et al (2019) Osteopontin mediates glioblastoma-associated macrophage infiltration and is a potential therapeutic target. J Clin Investig 129(1):137–149

    PubMed  Article  Google Scholar 

  93. 93.

    Zhou W, Ke SQ, Huang Z, Flavahan W, Fang X, Paul J et al (2015) Periostin secreted by glioblastoma stem cells recruits M2 tumour-associated macrophages and promotes malignant growth. Nat Cell Biol 17(2):170–182

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  94. 94.

    Xia S, Lal B, Tung B, Wang S, Goodwin CR, Laterra J (2016) Tumor microenvironment tenascin-C promotes glioblastoma invasion and negatively regulates tumor proliferation. Neuro Oncol 18(4):507–517

    CAS  PubMed  Article  Google Scholar 

  95. 95.

    Ma D, Liu S, Lal B, Wei S, Wang S, Zhan D et al (2019) Extracellular matrix protein tenascin C increases phagocytosis mediated by CD47 Loss of function in glioblastoma. Can Res 79(10):2697–2708

    CAS  Article  Google Scholar 

  96. 96.

    Shi Y, Ping YF, Zhou W, He ZC, Chen C, Bian BSJ et al (2017) Tumour-associated macrophages secrete pleiotrophin to promote PTPRZ1 signalling in glioblastoma stem cells for tumour growth. Nat Commun 8(1):1–17

    Article  CAS  Google Scholar 

  97. 97.

    Tao W, Chu C, Zhou W, Huang Z, Zhai K, Fang X et al (2020) Dual Role of WISP1 in maintaining glioma stem cells and tumor-supportive macrophages in glioblastoma. Nat Commun 11:1

    Google Scholar 

  98. 98.

    Mantovani A, Allavena P (2015) The interaction of anticancer therapies with tumor-associated macrophages. J Exp Med 212(4):435–445

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  99. 99.

    Prionisti I, Buhler LH, Walker PR, Jolivet RB (2019) Harnessing microglia and macrophages for the treatment of glioblastoma. Front Pharmacol 10:506

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  100. 100.

    Ceradini DJ, Kulkarni AR, Callaghan MJ, Tepper OM, Bastidas N, Kleinman ME et al (2004) Progenitor cell trafficking is regulated by hypoxic gradients through HIF-1 induction of SDF-1. Nat Med 10(8):858–864

    CAS  PubMed  Article  Google Scholar 

  101. 101.

    Wang SC, Yu CF, Hong JH, Tsai CS, Chiang CS (2013) Radiation therapy-induced tumor invasiveness is associated with SDF-1-regulated macrophage mobilization and vasculogenesis. PLoS ONE 8(8):e69182

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  102. 102.

    Stafford JH, Hirai T, Deng L, Chernikova SB, Urata K, West BL et al (2016) Colony stimulating factor 1 receptor inhibition delays recurrence of glioblastoma after radiation by altering myeloid cell recruitment and polarization. Neuro Oncol 18(6):797–806

    CAS  Article  Google Scholar 

  103. 103.

    Yan D, Kowal J, Akkari L, Schuhmacher AJ, Huse JT, West BL et al (2017) Inhibition of colony stimulating factor-1 receptor abrogates microenvironment-mediated therapeutic resistance in gliomas. Oncogene 36(43):6049–6058

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  104. 104.

    Butowski N, Colman H, De Groot JF, Omuro AM, Nayak L, Wen PY et al (2016) Orally administered colony stimulating factor 1 receptor inhibitor PLX3397 in recurrent glioblastoma: an Ivy Foundation Early Phase Clinical Trials Consortium phase II study. Neuro Oncol 18(4):557–564

    Article  Google Scholar 

  105. 105.

    Hutter G, Theruvath J, Graef CM, Zhang M, Schoen MK, Manz EM et al (2019) Microglia are effector cells of CD47-SIRPalpha antiphagocytic axis disruption against glioblastoma. Proc Natl Acad Sci USA 116(3):997–1006

    CAS  PubMed  Article  Google Scholar 

  106. 106.

    Razavi SM, Lee KE, Jin BE, Aujla PS, Gholamin S, Li G (2016) Immune evasion strategies of glioblastoma. Front Surg 3:11

    PubMed  PubMed Central  Article  Google Scholar 

  107. 107.

    Zhang L, Liu W, Alizadeh D, Zhao D, Farrukh O, Lin J et al (2011) S100B attenuates microglia activation in gliomas: possible role of STAT3 pathway. Glia 59(3):486–498

    PubMed  Article  Google Scholar 

  108. 108.

    Charles NA, Holland EC, Gilbertson R, Glass R, Kettenmann H (2011) The brain tumor microenvironment. Glia 59(8):1169–1180

    PubMed  Article  Google Scholar 

  109. 109.

    Pardoll DM (2012) The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer 12(4):252–264

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  110. 110.

    Fecci PE, Ochiai H, Mitchell DA, Grossi PM, Sweeney AE, Archer GE et al (2007) Systemic CTLA-4 blockade ameliorates glioma-induced changes to the CD4+ T cell compartment without affecting regulatory T-cell function. Clin Cancer Res Off J Am Assoc Cancer Res 13(7):2158–2167

    CAS  Article  Google Scholar 

  111. 111.

    Badie B, Schartner J, Prabakaran S, Paul J, Vorpahl J (2001) Expression of Fas ligand by microglia: possible role in glioma immune evasion. J Neuroimmunol 120(1–2):19–24

    CAS  PubMed  Article  Google Scholar 

  112. 112.

    Latchman YE, Liang SC, Wu Y, Chernova T, Sobel RA, Klemm M et al (2004) PD-L1-deficient mice show that PD-L1 on T cells, antigen-presenting cells, and host tissues negatively regulates T cells. Proc Natl Acad Sci USA 101(29):10691–10696

    CAS  PubMed  Article  Google Scholar 

  113. 113.

    Sakaguchi S (2000) Regulatory T cells: key controllers of immunologic self-tolerance. Cell 101(5):455–458

    CAS  PubMed  Article  Google Scholar 

  114. 114.

    Francisco LM, Salinas VH, Brown KE, Vanguri VK, Freeman GJ, Kuchroo VK et al (2009) PD-L1 regulates the development, maintenance, and function of induced regulatory T cells. J Exp Med 206(13):3015–3029

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  115. 115.

    Walker DG, Chuah T, Rist MJ, Pender MP (2006) T-cell apoptosis in human glioblastoma multiforme: implications for immunotherapy. J Neuroimmunol 175(1–2):59–68

    CAS  PubMed  Article  Google Scholar 

  116. 116.

    Zhu C, Anderson AC, Schubart A, Xiong H, Imitola J, Khoury SJ et al (2005) The Tim-3 ligand galectin-9 negatively regulates T helper type 1 immunity. Nat Immunol 6(12):1245–1252

    CAS  PubMed  Article  Google Scholar 

  117. 117.

    Deng L, Liang H, Burnette B, Beckett M, Darga T, Weichselbaum RR et al (2014) Irradiation and anti-PD-L1 treatment synergistically promote antitumor immunity in mice. J Clin Investig 124(2):687–695

    CAS  PubMed  Article  Google Scholar 

  118. 118.

    Gordon SR, Maute RL, Dulken BW, Hutter G, George BM, McCracken MN et al (2017) PD-1 expression by tumour-associated macrophages inhibits phagocytosis and tumour immunity. Nature 545(7655):495–499

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  119. 119.

    Garber ST, Hashimoto Y, Weathers SP, Xiu J, Gatalica Z, Verhaak RGW et al (2016) Immune checkpoint blockade as a potential therapeutic target: surveying CNS malignancies. Neuro Oncol 18(10):1357–1366

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  120. 120.

    Berghoff AS, Kiesel B, Widhalm G, Rajky O, Ricken G, Wohrer A et al (2015) Programmed death ligand 1 expression and tumor-infiltrating lymphocytes in glioblastoma. Neuro Oncol 17(8):1064–1075

    CAS  PubMed  Article  Google Scholar 

  121. 121.

    Heiland DH, Haaker G, Delev D, Mercas B, Masalha W, Heynckes S et al (2017) Comprehensive analysis of PD-L1 expression in glioblastoma multiforme. Oncotarget 8(26):42214–42225

    PubMed  PubMed Central  Article  Google Scholar 

  122. 122.

    Preusser M, Lim M, Hafler DA, Reardon DA, Sampson JH (2015) Prospects of immune checkpoint modulators in the treatment of glioblastoma. Nature Publishing Group, New York, pp 504–514

    Google Scholar 

  123. 123.

    Berghoff AS, Preusser M (2016) In search of a target: PD-1 and PD-L1 profiling across glioma types. Neuro Oncol 18(10):1331–1332

    PubMed  PubMed Central  Article  Google Scholar 

  124. 124.

    Buerki RA, Chheda ZS, Okada H (2018) Immunotherapy of primary brain tumors: facts and hopes. Clin Cancer Res 24(21):5198–5205

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  125. 125.

    Zhao J, Chen AX, Gartrell RD, Silverman AM, Aparicio L, Chu T et al (2019) Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma. Nat Med 25(3):462–469

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  126. 126.

    Cloughesy TF, Mochizuki AY, Orpilla JR, Hugo W, Lee AH, Davidson TB et al (2019) Neoadjuvant anti-PD-1 immunotherapy promotes a survival benefit with intratumoral and systemic immune responses in recurrent glioblastoma. Nat Med 25(3):477–486

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  127. 127.

    Schalper KA, Rodriguez-Ruiz ME, Diez-Valle R, López-Janeiro A, Porciuncula A, Idoate MA et al (2019) Neoadjuvant nivolumab modifies the tumor immune microenvironment in resectable glioblastoma. Nat Med 25(3):470–476

    CAS  PubMed  Article  Google Scholar 

  128. 128.

    Eggermont AMM, Blank CU, Mandala M, Long GV, Atkinson V, Dalle S et al (2018) Adjuvant pembrolizumab versus placebo in resected stage III melanoma. N Engl J Med 378(19):1789–1801

    CAS  PubMed  Article  Google Scholar 

  129. 129.

    Weber J, Mandala M, Del Vecchio M, Gogas HJ, Arance AM, Cowey CL et al (2017) Adjuvant nivolumab versus ipilimumab in resected stage III or IV melanoma. N Engl J Med 377(19):1824–1835

    CAS  PubMed  Article  Google Scholar 

  130. 130.

    Raychaudhuri B, Ireland PRJ, Ko J, Rini B, Borden EC, Garcia J et al (2011) Myeloid-derived suppressor cell accumulation and function in patients with newly diagnosed glioblastoma. Neuro Oncol 13(6):591–599

    PubMed  PubMed Central  Article  Google Scholar 

  131. 131.

    Mi Y, Guo N, Luan J, Cheng J, Hu Z, Jiang P et al (2020) The emerging role of myeloid-derived suppressor cells in the glioma immune suppressive microenvironment. Front Immunol 11:737

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  132. 132.

    Gabrilovich DI (2017) Myeloid-derived suppressor cells. Cancer Immunol Res 5(1):3–8

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  133. 133.

    Veglia F, Perego M, Gabrilovich D (2018) Myeloid-derived suppressor cells coming of age. Nat Immunol 19(2):108–119

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  134. 134.

    Chen Z, Hambardzumyan D (2018) Immune microenvironment in glioblastoma subtypes. Front Immunol 9:1004

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  135. 135.

    Dubinski D, Wölfer J, Hasselblatt M, Schneider-Hohendorf T, Bogdahn U, Stummer W et al (2016) CD4+ T effector memory cell dysfunction is associated with the accumulation of granulocytic myeloid-derived suppressor cells in glioblastoma patients. Neuro Oncol 18(6):807–818

    CAS  PubMed  Article  Google Scholar 

  136. 136.

    Gielen PR, Schulte BM, Kers-Rebel ED, Verrijp K, Petersen-Baltussen HMJM, Ter Laan M et al (2015) Increase in both CD14-positive and CD15-positive myeloid-derived suppressor cell subpopulations in the blood of patients with glioma but predominance of CD15-positive myeloid-derived suppressor cells in glioma tissue. J Neuropathol Exp Neurol 74(5):390–400

    CAS  PubMed  Article  Google Scholar 

  137. 137.

    Bayik D, Zhou Y, Park C, Hong C, Vail D, Silver DJ et al (2020) Myeloid-derived suppressor cell subsets drive glioblastoma growth in a sex-specific manner. Cancer Discov 10(8):1210–1225

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  138. 138.

    Seligman AM, Shear MJ, Alexander L (1939) Studies in carcinogenesis: VIII experimental production of brain tumors in mice with methylcholanthrene. Am J Cancer 37(3):364

    CAS  Google Scholar 

  139. 139.

    Ausman JI, Shapiro WR, Rall DP (1970) Studies on the chemotherapy of experimental brain tumors: development of an experimental model. Can Res 30(9):2394

    CAS  Google Scholar 

  140. 140.

    Akbasak A, Oldfield EH, Saris SC (1991) Expression and modulation of major histocompatibility antigens on murine primary brain tumor in vitro. J Neurosurg 75(6):922–929

    CAS  PubMed  Article  Google Scholar 

  141. 141.

    Alghamri MS, Kamran N, Kadiyala P, Lowenstein PR, Castro MG (2020) Chapter twelve—functional assay to assess T-cell inhibitory properties of myeloid derived suppressor cells (MDSCs) isolated from the tumor microenvironment of murine glioma models. In: Galluzzi L, Rudqvist N-P (eds) Methods in enzymology, vol 632. Academic Press, London, pp 215–228

    Google Scholar 

  142. 142.

    Chang AL, Miska J, Wainwright DA, Dey M, Rivetta CV, Yu D et al (2016) CCL2 produced by the glioma microenvironment is essential for the recruitment of regulatory t cells and myeloid-derived suppressor cells. Can Res 76(19):5671–5682

    CAS  Article  Google Scholar 

  143. 143.

    Rhim AD, Oberstein PE, Thomas DH, Mirek ET, Palermo CF, Sastra SA et al (2014) Stromal elements act to restrain, rather than support, pancreatic ductal adenocarcinoma. Cancer Cell 25(6):735–747

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  144. 144.

    Ozdemir BC, Pentcheva-Hoang T, Carstens JL, Zheng X, Wu CC, Simpson TR et al (2014) Depletion of carcinoma-associated fibroblasts and fibrosis induces immunosuppression and accelerates pancreas cancer with reduced survival. Cancer Cell 25(6):719–734

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  145. 145.

    Lee JJ, Perera RM, Wang H, Wu DC, Liu XS, Han S et al (2014) Stromal response to Hedgehog signaling restrains pancreatic cancer progression. Proc Natl Acad Sci USA 111(30):E3091–E3100

    CAS  PubMed  Article  Google Scholar 

  146. 146.

    Nag S, Manias JL, Stewart DJ (2009) Pathology and new players in the pathogenesis of brain edema. Acta Neuropathol 118(2):197–217

    PubMed  Article  Google Scholar 

  147. 147.

    Michinaga S, Koyama Y (2015) Pathogenesis of brain edema and investigation into anti-edema drugs. Int J Mol Sci 16(5):9949–9975

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  148. 148.

    Carlson MRJ, Pope WB, Horvath S, Braunstein JG, Nghiemphu P, Tso C-L et al (2007) Relationship between survival and edema in malignant gliomas: role of vascular endothelial growth factor and neuronal Pentraxin 2. Clin Cancer Res 13(9):2592

    CAS  PubMed  Article  Google Scholar 

  149. 149.

    Saadoun S, Papadopoulos MC, Davies DC, Krishna S, Bell BA (2002) Aquaporin-4 expression is increased in oedematous human brain tumours. J Neurol Neurosurg Psychiatry 72(2):262–265

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  150. 150.

    Yang L, Wang X, Zhen S, Zhang S, Kang D, Lin Z (2012) Aquaporin-4 upregulated expression in glioma tissue is a reaction to glioma-associated edema induced by vascular endothelial growth factor. Oncol Rep 28(5):1633–1638

    PubMed  Article  Google Scholar 

  151. 151.

    Jia F, Pan Y-H, Mao Q, Liang Y-M, Jiang J-Y (2010) Matrix metalloproteinase-9 expression and protein levels after fluid percussion injury in rats: the effect of injury severity and brain temperature. J Neurotrauma 27(6):1059–1068

    PubMed  Article  Google Scholar 

  152. 152.

    Rosenberg GA, Navratil M (1997) Metalloproteinase inhibition blocks edema in intracerebral hemorrhage in the rat. Neurology 48(4):921–926

    CAS  PubMed  Article  Google Scholar 

  153. 153.

    Xue Q, Cao L, Chen X-Y, Zhao J, Gao L, Li S-Z et al (2017) High expression of MMP9 in glioma affects cell proliferation and is associated with patient survival rates. Oncol Lett 13(3):1325–1330

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  154. 154.

    Reszec J, Hermanowicz A, Rutkowski R, Turek G, Mariak Z, Chyczewski L (2015) Expression of MMP-9 and VEGF in meningiomas and their correlation with peritumoral brain edema. Biomed Res Int 2015:646853

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  155. 155.

    Kofman S, Garvin JS, Nagamani D, Taylor SG 3rd (1957) Treatment of cerebral metastases from breast carcinoma with prednisolone. J Am Med Assoc 163(16):1473–1476

    CAS  PubMed  Article  Google Scholar 

  156. 156.

    Pitter KL, Tamagno I, Alikhanyan K, Hosni-Ahmed A, Pattwell SS, Donnola S et al (2016) Corticosteroids compromise survival in glioblastoma. Brain J Neurol 139(Pt 5):1458–1471

    Article  Google Scholar 

  157. 157.

    Wong ET, Swanson KD (2019) Dexamethasone-Friend or Foe for patients with glioblastoma? JAMA Neurol 76(3):247–248

    PubMed  Article  Google Scholar 

  158. 158.

    Chitadze G, Fluh C, Quabius ES, Freitag-Wolf S, Peters C, Lettau M et al (2017) In-depth immunophenotyping of patients with glioblastoma multiforme: impact of steroid treatment. Oncoimmunology 6(11):e1358839

    PubMed  PubMed Central  Article  Google Scholar 

  159. 159.

    Iorgulescu JB, Gokhale PC, Speranza MC, Eschle BK, Poitras MJ, Wilkens MK et al (2020) Concurrent dexamethasone limits the clinical benefit of immune checkpoint blockade in glioblastoma. Clin Cancer Res

  160. 160.

    Gerstner ER, Chen PJ, Wen PY, Jain RK, Batchelor TT, Sorensen G (2010) Infiltrative patterns of glioblastoma spread detected via diffusion MRI after treatment with cediranib. Neuro Oncol 12(5):466–472

    CAS  PubMed  PubMed Central  Google Scholar 

  161. 161.

    Gerstner ER, Frosch MP, Batchelor TT (2010) Diffusion magnetic resonance imaging detects pathologically confirmed, nonenhancing tumor progression in a patient with recurrent glioblastoma receiving bevacizumab. J Clin Oncol Off J Am Soc Clin Oncol 28(6):e91–e93

    Article  Google Scholar 

  162. 162.

    de Groot JF, Fuller G, Kumar AJ, Piao Y, Eterovic K, Ji Y et al (2010) Tumor invasion after treatment of glioblastoma with bevacizumab: radiographic and pathologic correlation in humans and mice. Neuro Oncol 12(3):233–242

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  163. 163.

    Piao Y, Liang J, Holmes L, Zurita AJ, Henry V, Heymach JV et al (2012) Glioblastoma resistance to anti-VEGF therapy is associated with myeloid cell infiltration, stem cell accumulation, and a mesenchymal phenotype. Neuro Oncol 14(11):1379–1392

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  164. 164.

    Nielsen OH, Verspaget HW, Elmgreen J (1988) Inhibition of intestinal macrophage chemotaxis to leukotriene B4 by sulphasalazine, olsalazine, and 5-aminosalicylic acid. Aliment Pharmacol Ther 2(3):203–211

    CAS  PubMed  Article  Google Scholar 

  165. 165.

    Sehm T, Fan Z, Ghoochani A, Rauh M, Engelhorn T, Minakaki G et al (2016) Sulfasalazine impacts on ferroptotic cell death and alleviates the tumor microenvironment and glioma-induced brain edema. Oncotarget 7(24):36021–36033

    PubMed  PubMed Central  Article  Google Scholar 

  166. 166.

    Osterberg N, Ferrara N, Vacher J, Gaedicke S, Niedermann G, Weyerbrock A et al (2016) Decrease of VEGF-A in myeloid cells attenuates glioma progression and prolongs survival in an experimental glioma model. Neuro Oncol 18(7):939–949

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  167. 167.

    Milner R, Campbell IL (2006) Increased expression of the beta4 and alpha5 integrin subunits in cerebral blood vessels of transgenic mice chronically producing the pro-inflammatory cytokines IL-6 or IFN-alpha in the central nervous system. Mol Cell Neurosci 33(4):429–440

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  168. 168.

    Tigges U, Boroujerdi A, Welser-Alves JV, Milner R (2013) TNF-alpha promotes cerebral pericyte remodeling in vitro, via a switch from alpha1 to alpha2 integrins. J Neuroinflamm 10:33

    CAS  Article  Google Scholar 

  169. 169.

    Dimitrijevic OB, Stamatovic SM, Keep RF, Andjelkovic AV (2007) Absence of the chemokine receptor CCR2 protects against cerebral ischemia/reperfusion injury in mice. Stroke 38(4):1345–1353

    CAS  PubMed  Article  Google Scholar 

  170. 170.

    Pan Y, Xiong M, Chen R, Ma Y, Corman C, Maricos M et al (2018) Athymic mice reveal a requirement for T-cell-microglia interactions in establishing a microenvironment supportive of Nf1 low-grade glioma growth. Genes Dev 32(7–8):491–496

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  171. 171.

    Daginakatte GC, Gutmann DH (2007) Neurofibromatosis-1 (Nf1) heterozygous brain microglia elaborate paracrine factors that promote Nf1-deficient astrocyte and glioma growth. Hum Mol Genet 16(9):1098–1112

    CAS  PubMed  Article  Google Scholar 

  172. 172.

    Guo X, Pan Y, Xiong M, Sanapala S, Anastasaki C, Cobb O et al (2020) Midkine activation of CD8(+) T cells establishes a neuron-immune-cancer axis responsible for low-grade glioma growth. Nat Commun 11(1):2177

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  173. 173.

    Sippel TR, White J, Nag K, Tsvankin V, Klaassen M, Kleinschmidt-DeMasters BK et al (2011) Neutrophil degranulation and immunosuppression in patients with GBM: restoration of cellular immune function by targeting arginase I. Clin Cancer Res 17(22):6992–7002

    CAS  PubMed  Article  Google Scholar 

  174. 174.

    Khan S, Mittal S, McGee K, Alfaro-Munoz KD, Majd N, Balasubramaniyan V et al (2020) Role of neutrophils and myeloid-derived suppressor cells in glioma progression and treatment resistance. Int J Mol Sci 21:6

    CAS  Google Scholar 

  175. 175.

    Liang J, Piao Y, Holmes L, Fuller GN, Henry V, Tiao N et al (2014) Neutrophils promote the malignant glioma phenotype through S100A4. Clin Cancer Res 20(1):187–198

    CAS  PubMed  Article  Google Scholar 

  176. 176.

    Rahbar A, Cederarv M, Wolmer-Solberg N, Tammik C, Stragliotto G, Peredo I et al (2016) Enhanced neutrophil activity is associated with shorter time to tumor progression in glioblastoma patients. Oncoimmunology 5:2

    Google Scholar 

  177. 177.

    Hor W-S, Huang W-L, Lin Y-S, Yang B-C (2003) Cross-talk between tumor cells and neutrophils through the Fas (APO-1, CD95)/FasL system: human glioma cells enhance cell viability and stimulate cytokine production in neutrophils. J Leukoc Biol 73(3):363–368

    CAS  PubMed  Article  Google Scholar 

  178. 178.

    Prame Kumar K, Nicholls AJ, Wong CHY (2018) Partners in crime: neutrophils and monocytes/macrophages in inflammation and disease. Cell Tissue Res 371(3):551–565

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  179. 179.

    Yee PP, Wei Y, Kim SY, Lu T, Chih SY, Lawson C et al (2020) Neutrophil-induced ferroptosis promotes tumor necrosis in glioblastoma progression. Nat Commun 11(1):5424

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  180. 180.

    Quail DF, Joyce JA (2017) The Microenvironmental landscape of brain tumors. Cancer Cell 31(3):326–341

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  181. 181.

    Dvorak HF (2015) Tumors: wounds that do not heal-redux. Cancer Immunol Res 3(1):1–11

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  182. 182.

    Zhu C, Kros JM, Cheng C, Mustafa D (2017) The contribution of tumor-associated macrophages in glioma neo-Angiogenesis and implications for anti-Angiogenic strategies. Neuro Oncol 19(11):1435–1446

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  183. 183.

    De Palma M, Venneri MA, Galli R, Sergi Sergi L, Politi LS, Sampaolesi M et al (2005) Tie2 identifies a hematopoietic lineage of proangiogenic monocytes required for tumor vessel formation and a mesenchymal population of pericyte progenitors. Cancer Cell 8(3):211–226

    PubMed  Article  CAS  Google Scholar 

  184. 184.

    Schmeisser A, Garlichs CD, Zhang H, Eskafi S, Graffy C, Ludwig J et al (2001) Monocytes coexpress endothelial and macrophagocytic lineage markers and form cord-like structures in Matrigel® under angiogenic conditions. Cardiovasc Res 49(3):671–680

    CAS  PubMed  Article  Google Scholar 

  185. 185.

    Gottfried E, Kreutz M, Haffner S, Holler E, Iacobelli M, Andreesen R et al (2007) Differentiation of human tumour-associated dendritic cells into endothelial-like cells: an alternative pathway of tumour angiogenesis. Scand J Immunol 65(4):329–335

    CAS  PubMed  Article  Google Scholar 

  186. 186.

    Ribatti D, Crivellato E (2009) Immune cells and angiogenesis. J Cell Mol Med 13(9a):2822–2833

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  187. 187.

    Infanger DW, Cho Y, Lopez BS, Mohanan S, Liu SC, Gursel D et al (2013) Glioblastoma stem cells are regulated by interleukin-8 signaling in a tumoral perivascular niche. Can Res 73(23):7079–7089

    CAS  Article  Google Scholar 

  188. 188.

    Hardee ME, Zagzag D (2012) Mechanisms of glioma-associated neovascularization. Am J Pathol 181(4):1126–1141

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  189. 189.

    Soda Y, Marumoto T, Friedmann-Morvinski D, Soda M, Liu F, Michiue H et al (2011) Transdifferentiation of glioblastoma cells into vascular endothelial cells. Proc Natl Acad Sci USA 108(11):4274–4280

    CAS  PubMed  Article  Google Scholar 

  190. 190.

    Wang R, Chadalavada K, Wilshire J, Kowalik U, Hovinga KE, Geber A et al (2010) Glioblastoma stem-like cells give rise to tumour endothelium. Nature 468(7325):829–833

    CAS  PubMed  Article  Google Scholar 

  191. 191.

    Valdor R, Garcia-Bernal D, Bueno C, Rodenas M, Moraleda JM, Macian F et al (2017) Glioblastoma progression is assisted by induction of immunosuppressive function of pericytes through interaction with tumor cells. Oncotarget 8(40):68614–68626

    PubMed  PubMed Central  Article  Google Scholar 

  192. 192.

    Brandao M, Simon T, Critchley G, Giamas G (2019) Astrocytes, the rising stars of the glioblastoma microenvironment. Glia 67(5):779–790

    PubMed  Article  Google Scholar 

  193. 193.

    Watkins S, Robel S, Kimbrough IF, Robert SM, Ellis-Davies G, Sontheimer H (2014) Disruption of astrocyte-vascular coupling and the blood-brain barrier by invading glioma cells. Nat Commun 5(1):1–15

    Article  CAS  Google Scholar 

  194. 194.

    Henrik Heiland D, Ravi VM, Behringer SP, Frenking JH, Wurm J, Joseph K et al (2019) Tumor-associated reactive astrocytes aid the evolution of immunosuppressive environment in glioblastoma. Nat Commun 10(1):1–12

    CAS  Article  Google Scholar 

  195. 195.

    Liddelow SA, Guttenplan KA, Clarke LE, Bennett FC, Bohlen CJ, Schirmer L et al (2017) Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541(7638):481–487

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  196. 196.

    Venkatesh HS, Johung TB, Caretti V, Noll A, Tang Y, Nagaraja S et al (2015) Neuronal activity promotes glioma growth through neuroligin-3 secretion. Cell 161(4):803–816

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  197. 197.

    Venkatesh HS, Tam LT, Woo PJ, Lennon J, Nagaraja S, Gillespie SM et al (2017) Targeting neuronal activity-regulated neuroligin-3 dependency in high-grade glioma. Nature 549(7673):533–537

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  198. 198.

    Venkatesh HS, Morishita W, Geraghty AC, Silverbush D, Gillespie SM, Arzt M et al (2019) Electrical and synaptic integration of glioma into neural circuits. Nature 573(7775):539–545

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  199. 199.

    Venkataramani V, Tanev DI, Strahle C, Studier-Fischer A, Fankhauser L, Kessler T et al (2019) Glutamatergic synaptic input to glioma cells drives brain tumour progression. Nature 573(7775):532–538

    CAS  PubMed  Article  Google Scholar 

  200. 200.

    Quail DF, Bowman RL, Akkari L, Quick ML, Schuhmacher AJ, Huse JT et al (2016) The tumor microenvironment underlies acquired resistance to CSF-1R inhibition in gliomas. Science (New York, NY). 352(6288):3018

    Article  CAS  Google Scholar 

  201. 201.

    Escobar G, Moi D, Ranghetti A, Ozkal-Baydin P, Squadrito ML, Kajaste-Rudnitski A et al (2014) Genetic engineering of hematopoiesis for targeted IFN-α delivery inhibits breast cancer progression. Sci Transl Med 6(217):217

    Article  CAS  Google Scholar 

  202. 202.

    Gschwandtner M, Derler R, Midwood KS (2019) More than just attractive: how CCL2 influences myeloid cell behavior beyond chemotaxis. Front Immunol 10:2759

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  203. 203.

    Peng SB, Zhang X, Paul D, Kays LM, Gough W, Stewart J et al (2015) Identification of LY2510924, a novel cyclic peptide CXCR4 antagonist that exhibits antitumor activities in solid tumor and breast cancer metastatic models. Mol Cancer Ther 14(2):480–490

    CAS  PubMed  Article  Google Scholar 

  204. 204.

    Mercurio L, Ajmone-Cat MA, Cecchetti S, Ricci A, Bozzuto G, Molinari A et al (2016) Targeting CXCR4 by a selective peptide antagonist modulates tumor microenvironment and microglia reactivity in a human glioblastoma model. J Exp Clin Cancer Res CR 35:55

    PubMed  Article  CAS  Google Scholar 

  205. 205.

    Thomas RP, Nagpal S, Iv M, Soltys SG, Bertrand S, Pelpola JS et al (2019) Macrophage exclusion after radiation therapy (MERT): a first in human phase I/II trial using a CXCR4 inhibitor in glioblastoma. Clin Cancer Res Off J Am Assoc Cancer Res 25(23):6948–6957

    CAS  Article  Google Scholar 

  206. 206.

    van den Bossche WBL, Kleijn A, Teunissen CE, Voerman JSA, Teodosio C, Noske DP et al (2018) Oncolytic virotherapy in glioblastoma patients induces a tumor macrophage phenotypic shift leading to an altered glioblastoma microenvironment. Neuro Oncol 20(11):1494–1504

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  207. 207.

    Wang Y, Lin YX, Qiao SL, An HW, Ma Y, Qiao ZY et al (2017) Polymeric nanoparticles promote macrophage reversal from M2 to M1 phenotypes in the tumor microenvironment. Biomaterials 112:153–163

    CAS  PubMed  Article  Google Scholar 

  208. 208.

    Goswami S, Walle T, Cornish AE, Basu S, Anandhan S, Fernandez I et al (2020) Immune profiling of human tumors identifies CD73 as a combinatorial target in glioblastoma. Nat Med 26(1):39–46

    CAS  PubMed  Article  Google Scholar 

  209. 209.

    Ohkuri T, Ghosh A, Kosaka A, Zhu J, Ikeura M, David M et al (2014) STING contributes to antiglioma immunity via triggering type I IFN signals in the tumor microenvironment. Cancer Immunol Res 2(12):1199

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  210. 210.

    Downey CM, Aghaei M, Schwendener RA, Jirik FR (2014) DMXAA causes tumor site-specific vascular disruption in murine non-small cell lung cancer, and like the endogenous non-canonical cyclic dinucleotide STING agonist, 2’3’-cGAMP, induces M2 macrophage repolarization. PLoS ONE 9(6):99988

    Article  CAS  Google Scholar 

  211. 211.

    Xu S, Wei J, Wang F, Kong L-Y, Ling X-Y, Nduom E et al (2014) Effect of miR-142–3p on the M2 macrophage and therapeutic efficacy against murine glioblastoma. J Natl Cancer Inst 106:8

    Article  CAS  Google Scholar 

  212. 212.

    Buonfiglioli A, Efe IE, Guneykaya D, Ivanov A, Huang Y, Orlowski E et al (2019) let-7 MicroRNAs regulate microglial function and suppress glioma growth through toll-like receptor 7. Cell Rep 29(11):3460–71.e7

    CAS  PubMed  Article  Google Scholar 

  213. 213.

    Colegio OR, Chu NQ, Szabo AL, Chu T, Rhebergen AM, Jairam V et al (2014) Functional polarization of tumour-associated macrophages by tumour-derived lactic acid. Nature 513(7519):559–563

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  214. 214.

    Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352(10):987–996

    CAS  PubMed  Article  Google Scholar 

  215. 215.

    Davis ME (2016) Glioblastoma: overview of disease and treatment. Clin J Oncol Nurs 20(5):1–8

    Article  Google Scholar 

  216. 216.

    Wen PY, Weller M, Lee EQ, Alexander BA, Barnholtz-Sloan JS, Barthel FP et al (2020) Glioblastoma in adults: a society for neuro-oncology (SNO) and European Society of Neuro-Oncology (EANO) consensus review on current management and future directions. Neuro-oncology

  217. 217.

    Gaspar LE, Fisher BJ, Macdonald DR, Leber DV, Halperin EC, Schold SC et al (1992) Supratentorial malignant glioma: Patterns of recurrence and implications for external beam local treatment. Int J Radiat Oncol Biol Phys 24(1):55–57

    CAS  PubMed  Article  Google Scholar 

  218. 218.

    Couturier CP, Ayyadhury S, Le PU, Nadaf J, Monlong J, Riva G et al (2020) Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy. Nat Commun 11(1):1–19

    Article  CAS  Google Scholar 

  219. 219.

    Dirkse A, Golebiewska A, Buder T, Nazarov PV, Muller A, Poovathingal S et al (2019) Stem cell-associated heterogeneity in Glioblastoma results from intrinsic tumor plasticity shaped by the microenvironment. Nat Commun 10(1):1–16

    CAS  Article  Google Scholar 

  220. 220.

    Brennan CW, Verhaak RG, McKenna A, Campos B, Noushmehr H, Salama SR et al (2013) The somatic genomic landscape of glioblastoma. Cell 155(2):462–477

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  221. 221.

    McLendon R, Friedman A, Bigner D, Van Meir EG, Brat DJ, Mastrogianakis GM et al (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455(7216):1061–1068

    CAS  Article  Google Scholar 

  222. 222.

    Verhaak RG, Hoadley KA, Purdom E, Wang V, Qi Y, Wilkerson MD et al (2010) Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17(1):98–110

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  223. 223.

    Sottoriva A, Spiteri I, Piccirillo SGM, Touloumis A, Collins VP, Marioni JC et al (2013) Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci USA 110(10):4009–4014

    CAS  Article  Google Scholar 

  224. 224.

    Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H et al (2014) Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science (New York, NY) 344(6190):1396–1401

    CAS  Article  Google Scholar 

  225. 225.

    Neftel C, Laffy J, Filbin MG, Hara T, Shore ME, Rahme GJ et al (2019) An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell 178(4):835–49.e21

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  226. 226.

    Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131(6):803–820

    PubMed  Article  Google Scholar 

  227. 227.

    Suvà ML, Tirosh I (2020) The glioma stem cell model in the era of single-cell genomics. Cancer Cell 37(5):630–636

    PubMed  Article  CAS  Google Scholar 

  228. 228.

    Gimple RC, Bhargava S, Dixit D, Rich JN (2019) Glioblastoma stem cells: lessons from the tumor hierarchy in a lethal cancer. Genes Dev 33(11–12):591–609

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  229. 229.

    Lathia JD, Gallagher J, Heddleston JM, Wang J, Eyler CE, MacSwords J et al (2010) Integrin Alpha 6 regulates glioblastoma stem cells. Cell Stem Cell 6(5):421–432

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  230. 230.

    Anido J, Sáez-Borderías A, Gonzàlez-Juncà A, Rodón L, Folch G, Carmona MA et al (2010) TGF-β receptor inhibitors target the CD44high/Id1high glioma-initiating cell population in human glioblastoma. Cancer Cell 18(6):655–668

    CAS  PubMed  Article  Google Scholar 

  231. 231.

    Son MJ, Woolard K, Nam DH, Lee J, Fine HA (2009) SSEA-1 is an enrichment marker for tumor-initiating cells in human glioblastoma. Cell Stem Cell 4(5):440–452

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  232. 232.

    Singh SK, Hawkins C, Clarke ID, Squire JA, Bayani J, Hide T et al (2004) Identification of human brain tumour initiating cells. Nature 432(7015):396–401

    CAS  PubMed  Article  Google Scholar 

Download references

Acknowledgements

We would like to thank Dr. Chris Nelson for editorial assistance and Mr. David Schumick for the illustrations. We are also grateful to Drs. Zhihong Chen and James Ross for contributing to Fig. 2.

Funding

This work was supported by NIH/NINDS R01 NS100864 (DH).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Dolores Hambardzumyan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

BOX 1

Glioblastoma: inter- and intra-tumor heterogeneity

Glioblastoma (GBM) is an incurable malignancy, despite aggressive treatment regimens. The standard of care for GBM consists of maximal surgical resection followed by radiation and concomitant and maintenance temozolomide (TMZ) administration [214]. The median survival time of GBM patients is only 15 months [214], with ~ 70% of recurrences within one year and ~ 5% survival after 5 years [215]. These tumors are chemo- and radiotherapy resistant [216]. GBM resistance can be partially attributed to the infiltrative nature of GBM cells, which makes surgical resection incomplete; remaining malignant cells in the post-surgical cavity inevitably lead to tumor regrowth, usually within 2–3 cm from the border of surgical resection [217].

An additional contributing factor for GBM resistance is the heterogeneity of these tumors within neoplastic cells, consisting of both inter- and intra-tumor heterogeneity [218, 219]. Inter-tumor heterogeneity refers to GBM from different patients with altered genotype and phenotype, while intra-tumor heterogeneity refers to genomic and biological variations within the same tumor, at both the regional and single-cell levels. Over a decade ago, The Cancer Genome Atlas (TCGA) initiative provided robust gene expression-based identification of GBM subtypes, including Proneural (PN), Mesenchymal (MES), and Classical (CL) [55, 220,221,222]. These subtypes were established based upon the dominant transcriptional patterns at the time and location of tumor resection, so they are not mutually exclusive (i.e. multiple subtypes can co-exist within a single tumor, both at the regional [223] and single-cell levels [224]). Additionally, using multiple high-dimensional approaches with the goal of defining a unified model of cellular states and genetic diversity, one study found that malignant cells in GBM exist in four main cellular states, which closely resemble distinct neural cell types, are affected by the tumor microenvironment (TME), and most importantly, are plastic [225].

It was also documented that amplification and/or mutation of the epidermal growth factor receptor (EGFR) is predominant in CL GBM, whereas neurofibromin 1 (NF1) deletions are frequent in MES GBM and platelet-derived growth factor receptor alpha (PDGFRA) gene amplification is common in PN GBM [55, 64, 222], although none of these are mutually exclusive. These studies resulted in multiple classification strategies, but in 2016 the WHO definitively classified GBM by using both histological and molecular genetic features [226]. In particular, this new classification system divides GBM into isocitrate hydrogenase-wildtype (IDH WT), where IDH is not mutated. This represents ~ 90% of the cases and occurs more frequently as primary or de novo tumors in patients over 55 years of age. IDH-mutant (IDH MUT) GBM represents the remaining ~ 10% of cases and usually arise as secondary tumors in younger people. They are associated with significantly better overall survival compared to patients with IDH WT tumors [226].

BOX 2

Glioma stem cells

Regardless of the cell of origin, many cancers are defined hierarchical structures, in the apex of which Glioma Stem Cells (GSCs) reside. These cells exhibit unique properties, self-renewal, multipotency, and have increased tumor initiation ability upon transplantation into recipient animals. They are considered to be a relatively rare population in tumors, which give rise to more-differentiated non-glioma stem cells that constitute a large proportion of tumor mass. In contrast to GSCs, non-glioma stem cells have a significantly lower ability to initiate tumors upon transplantation into recipient animals [227, 228]. However, GSC characterization is still quite challenging to date [227]. Several markers have been proposed to define this cell population, such as CD133, CD44, SSEA1, and CD49f among others [229,230,231,232], which are more resistant to chemo- and radiation therapy and are believed to be responsible for tumor recurrence (more details on GSC biology are provided in [1, 4]).

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Buonfiglioli, A., Hambardzumyan, D. Macrophages and microglia: the cerberus of glioblastoma. acta neuropathol commun 9, 54 (2021). https://doi.org/10.1186/s40478-021-01156-z

Download citation

Keywords

  • Glioblastoma
  • Macrophages
  • Microglia
  • Microenvironment
  • Heterogeneity
\