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Glioblastoma (GBM) is a common and deadly brain tumor with late diagnoses and poor prognoses. Machine learning (ML) is an emerging tool that can create highly accurate diagnostic and prognostic prediction models. This paper aimed to systematically search the literature on ML for GBM metabolism and assess recent advancements. A literature search was performed using predetermined search terms. Articles describing the use of an ML algorithm for GBM metabolism were included. Ten studies met the inclusion criteria for analysis: diagnostic (n = 3, 30%), prognostic (n = 6, 60%), or both (n = 1, 10%). Most studies analyzed data from multiple databases, while 50% (n = 5) included additional original samples. At least 2536 data samples were run through an ML algorithm. Twenty-seven ML algorithms were recorded with a mean of 2.8 algorithms per study. Algorithms were supervised (n = 24, 89%), unsupervised (n = 3, 11%), continuous (n = 19, 70%), or categorical (n = 8, 30%). The mean reported accuracy and AUC of ROC were 95.63% and 0.779, respectively. One hundred six metabolic markers were identified, but only EMP3 was reported in multiple studies. Many studies have identified potential biomarkers for GBM diagnosis and prognostication. These algorithms show promise; however, a consensus on even a handful of biomarkers has not yet been made.

Glioblastoma multiforme (GBM) is the most common primary CNS neoplasm, and continues to have a dismal prognosis. A widely-used approach to the mathematical modeling of GBM involves utilizing a reaction-diffusion model of cell density as a function of space and time, which accounts for both the infiltrative nature of the tumor using a diffusion term, and the proliferation of tumor cells using a proliferation term. The current paper extends the standard models by incorporating an advection term to account for the so-called 'cell streaming' which is often seen with GBM, where some of the tumor cells seem to stream widely along the white matter pathways. The current paper introduces a bicompartmental GBM model in the form of coupled partial differential equations with a component of dispersive cells. The parameters needed for this model are explored. It is shown that this model can account for the rapid distant dispersal of GBM cells in the CNS, as well as such phenomena as multifocal gliomas with tumor foci distant from the core tumor site. The model suggests a higher percentage of tumor cells below the threshold of MRI images in comparison to the standard model. By incorporating an advection component, the proposed model is able to account for phenomena such as multicentric gliomas and rapid distant dispersion of a small fraction of tumor cells throughout the CNS, features important to the prognosis of GBM, but not easily accounted for by current models.

Glioblastoma multiforme (GBM) is the most common primary CNS neoplasm, and continues to have a dismal prognosis. A widely-used approach to the mathematical modeling of GBM involves utilizing a reaction-diffusion model of cell density as a function of space and time, which accounts for both the infiltrative nature of the tumor using a diffusion term, and the proliferation of tumor cells using a proliferation term. The current paper extends the standard models by incorporating an advection term to account for the so-called 'cell streaming' which is often seen with GBM, where some of the tumor cells seem to stream widely along the white matter pathways. The current paper introduces a bicompartmental GBM model in the form of coupled partial differential equations with a component of dispersive cells. The parameters needed for this model are explored. It is shown that this model can account for the rapid distant dispersal of GBM cells in the CNS, as well as such phenomena as multifocal gliomas with tumor foci distant from the core tumor site. The model suggests a higher percentage of tumor cells below the threshold of MRI images in comparison to the standard model. By incorporating an advection component, the proposed model is able to account for phenomena such as multicentric gliomas and rapid distant dispersion of a small fraction of tumor cells throughout the CNS, features important to the prognosis of GBM, but not easily accounted for by current models.

Glioblastoma is a devastating disease with poor prognosis. Few effective chemotherapeutics are currently available, and much effort has been expended to identify new drugs capable of slowing tumor progression. The phase 0 trial design was developed to facilitate early identification of promising agents for cancer that should undergo accelerated approval. This design features an early in-human study that enrolls a small number of patients who receive subtherapeutic doses of medication with the goals of describing pharmacokinetics through drug blood level measurements and determining intratumoral concentrations of the investigational compound as well as pharmacodynamics by studying the biochemical and physiological effects of drugs. In neuro-oncology, however, the presence of the blood-brain barrier and difficulty in obtaining brain tumor tissue warrant a separate set of considerations. In this paper, we critically reviewed the protocols used in all brain tumor related in-human phase 0 and phase 0-like ("window of opportunity") studies between 1993 and 2018, as well as ongoing clinical trials, and identified major challenges in trial design as applied to central nervous system tumors that include surgical specimen collection and storage, brain tumor drug level analysis, and confirmation of drug action. We therefore propose that phase 0 trials in neuro-oncology should include (i) only patients in whom a resection of the tumor is planned, (ii) use of clinical doses of an investigational agent, (iii) tissue sampling from enhancing and non-enhancing portions of the tumor, and (iv) assessment of drug-specific target effects. Standardization of clinical protocols for phase 0/window of opportunity studies can help accelerate the development of effective treatments for glioblastoma.

Glioblastoma (GBM) is a fast-growing type of malignant primary brain tumor. To explore the mechanisms in GBM, complex biological networks are used to reveal crucial changes among different biological states, which reflect on the development of living organisms. It is critical to discover the kernel differential subgraph (KDS) that leads to drastic changes. However, identifying the KDS is similar to the Steiner Tree problem that is an NP-hard problem. In this paper, we developed a criterion to explore the KDS (CKDS), which considered the connectivity and scale of KDS, the topological difference of nodes and function relevance between genes in the KDS. The CKDS algorithm was applied to simulated datasets and three single-cell RNA sequencing (scRNA-seq) datasets including GBM, fetal human cortical neurons (FHCN) and neural differentiation. Then we performed the network topology and functional enrichment analyses on the extracted KDSs. Compared with the state-of-art methods, the CKDS algorithm outperformed on simulated datasets to discover the KDSs. In the GBM and FHCN, seventeen genes (one biomarker, nine regulatory genes, one driver genes, six therapeutic targets) and KEGG pathways in KDSs were strongly supported by literature mining that they were highly interrelated with GBM. Moreover, focused on GBM, there were fifteen genes (including ten regulatory genes, three driver genes, one biomarkers, one therapeutic target) and KEGG pathways found in the KDS of neural differentiation process from activated neural stem cells (aNSC) to neural progenitor cells (NPC), while few genes and no pathway were found in the period from NPC to astrocytes (Ast). These experiments indicated that the process from aNSC to NPC is a key differentiation period affecting the development of GBM. Therefore, the CKDS algorithm provides a unique perspective in identifying cell-type-specific genes and KDSs.

Glioblastoma (GBM) is a fast-growing type of malignant primary brain tumor. To explore the mechanisms in GBM, complex biological networks are used to reveal crucial changes among different biological states, which reflect on the development of living organisms. It is critical to discover the kernel differential subgraph (KDS) that leads to drastic changes. However, identifying the KDS is similar to the Steiner Tree problem that is an NP-hard problem. In this paper, we developed a criterion to explore the KDS (CKDS), which considered the connectivity and scale of KDS, the topological difference of nodes and function relevance between genes in the KDS. The CKDS algorithm was applied to simulated datasets and three single-cell RNA sequencing (scRNA-seq) datasets including GBM, fetal human cortical neurons (FHCN) and neural differentiation. Then we performed the network topology and functional enrichment analyses on the extracted KDSs. Compared with the state-of-art methods, the CKDS algorithm outperformed on simulated datasets to discover the KDSs. In the GBM and FHCN, seventeen genes (one biomarker, nine regulatory genes, one driver genes, six therapeutic targets) and KEGG pathways in KDSs were strongly supported by literature mining that they were highly interrelated with GBM. Moreover, focused on GBM, there were fifteen genes (including ten regulatory genes, three driver genes, one biomarkers, one therapeutic target) and KEGG pathways found in the KDS of neural differentiation process from activated neural stem cells (aNSC) to neural progenitor cells (NPC), while few genes and no pathway were found in the period from NPC to astrocytes (Ast). These experiments indicated that the process from aNSC to NPC is a key differentiation period affecting the development of GBM. Therefore, the CKDS algorithm provides a unique perspective in identifying cell-type-specific genes and KDSs.

Glioblastoma (GBM) is a fast-growing type of malignant primary brain tumor. To explore the mechanisms in GBM, complex biological networks are used to reveal crucial changes among different biological states, which reflect on the development of living organisms. It is critical to discover the kernel differential subgraph (KDS) that leads to drastic changes. However, identifying the KDS is similar to the Steiner Tree problem that is an NP-hard problem. In this paper, we developed a criterion to explore the KDS (CKDS), which considered the connectivity and scale of KDS, the topological difference of nodes and function relevance between genes in the KDS. The CKDS algorithm was applied to simulated datasets and three single-cell RNA sequencing (scRNA-seq) datasets including GBM, fetal human cortical neurons (FHCN) and neural differentiation. Then we performed the network topology and functional enrichment analyses on the extracted KDSs. Compared with the state-of-art methods, the CKDS algorithm outperformed on simulated datasets to discover the KDSs. In the GBM and FHCN, seventeen genes (one biomarker, nine regulatory genes, one driver genes, six therapeutic targets) and KEGG pathways in KDSs were strongly supported by literature mining that they were highly interrelated with GBM. Moreover, focused on GBM, there were fifteen genes (including ten regulatory genes, three driver genes, one biomarkers, one therapeutic target) and KEGG pathways found in the KDS of neural differentiation process from activated neural stem cells (aNSC) to neural progenitor cells (NPC), while few genes and no pathway were found in the period from NPC to astrocytes (Ast). These experiments indicated that the process from aNSC to NPC is a key differentiation period affecting the development of GBM. Therefore, the CKDS algorithm provides a unique perspective in identifying cell-type-specific genes and KDSs.

Immune checkpoint blockade (ICB) is currently evaluated in patients with glioblastoma (GBM), based on encouraging clinical data in other cancers, and results from studies with the methylcholanthrene-induced GL261 mouse glioma. In this paper, we describe a novel model faithfully recapitulating some key human GBM characteristics, including low mutational load, a factor reported as a prognostic indicator of ICB response. Consistent with this observation, SB28 is completely resistant to ICB, contrasting with treatment sensitivity of the more highly mutated GL261. Moreover, SB28 shows features of a poorly immunogenic tumor, with low MHC-I expression and modest CD8+ T-cell infiltration, suggesting that it may present similar challenges for immunotherapy as human GBM. Based on these key features for immune reactivity, SB28 may represent a treatment-resistant malignancy likely to mirror responses of many human tumors. We therefore propose that SB28 is a particularly suitable model for optimization of GBM immunotherapy.

The Rho GTPase family belongs to the Ras superfamily and includes approximately 20 members in humans. Rho GTPases are important in the regulation of diverse cellular functions, including cytoskeletal dynamics, cell motility, cell polarity, axonal guidance, vesicular trafficking, and cell cycle control. Changes in Rho GTPase signaling play an essential regulatory role in many pathological conditions, such as cancer, central nervous system diseases, and immune system-dependent diseases. The posttranslational modification of Rho GTPases (i.e., prenylation by mevalonate pathway intermediates) and GTP binding are key factors which affect the activation of this protein. In this paper, two essential and simple methods are provided to detect a broad range of Rho GTPase prenylation and GTP binding activities. Details of the technical procedures that have been used are explained step by step in this manuscript.

Natural killer (NK) cells are functionally suppressed in the glioblastoma multiforme (GBM) tumor microenvironment. We have recently shown that survival and differentiation of cancer stem-like cells (CSCs)/poorly differentiated tumors are controlled through two distinct phenotypes of cytotoxic and non-cytotoxic/split anergized NK cells, respectively. In this paper, we studied the function of NK cells against brain CSCs/poorly differentiated GBM and their NK cell-differentiated counterparts. Brain CSCs/poorly differentiated GBM, differentiated by split anergized NK supernatants (supernatants from NK cells treated with IL-2 + anti-CD16mAb) expressed higher levels of CD54, B7H1 and MHC-I and were killed less by the NK cells, whereas their CSCs/poorly differentiated counterparts were highly susceptible to NK cell lysis. Resistance to NK cells and differentiation of brain CSCs/poorly differentiated GBM by split anergized NK cells were mediated by interferon (IFN)-γ and tumor necrosis factor (TNF)-α. Brain CSCs/poorly differentiated GBM expressed low levels of TNFRs and IFN-γRs, and when differentiated and cultured with IL-2-treated NK cells, they induced increased secretion of pro-inflammatory cytokine interleukin (IL)-6 and chemokine IL-8 in the presence of decreased IFN-γ secretion. NK-induced differentiation of brain CSCs/poorly differentiated GBM cells was independent of the function of IL-6 and/or IL-8. The inability of NK cells to lyse GBM tumors and the presence of a sustained release of pro-inflammatory cytokines IL-6 and chemokine IL-8 in the presence of a decreased IFN-γ secretion may lead to the inadequacy of NK cells to differentiate GBM CSCs/poorly differentiated tumors, thus failing to control tumor growth.

Natural killer (NK) cells are functionally suppressed in the glioblastoma multiforme (GBM) tumor microenvironment. We have recently shown that survival and differentiation of cancer stem-like cells (CSCs)/poorly differentiated tumors are controlled through two distinct phenotypes of cytotoxic and non-cytotoxic/split anergized NK cells, respectively. In this paper, we studied the function of NK cells against brain CSCs/poorly differentiated GBM and their NK cell-differentiated counterparts. Brain CSCs/poorly differentiated GBM, differentiated by split anergized NK supernatants (supernatants from NK cells treated with IL-2 + anti-CD16mAb) expressed higher levels of CD54, B7H1 and MHC-I and were killed less by the NK cells, whereas their CSCs/poorly differentiated counterparts were highly susceptible to NK cell lysis. Resistance to NK cells and differentiation of brain CSCs/poorly differentiated GBM by split anergized NK cells were mediated by interferon (IFN)-γ and tumor necrosis factor (TNF)-α. Brain CSCs/poorly differentiated GBM expressed low levels of TNFRs and IFN-γRs, and when differentiated and cultured with IL-2-treated NK cells, they induced increased secretion of pro-inflammatory cytokine interleukin (IL)-6 and chemokine IL-8 in the presence of decreased IFN-γ secretion. NK-induced differentiation of brain CSCs/poorly differentiated GBM cells was independent of the function of IL-6 and/or IL-8. The inability of NK cells to lyse GBM tumors and the presence of a sustained release of pro-inflammatory cytokines IL-6 and chemokine IL-8 in the presence of a decreased IFN-γ secretion may lead to the inadequacy of NK cells to differentiate GBM CSCs/poorly differentiated tumors, thus failing to control tumor growth.

Initial enthusiasm after promising Phase II trials for treating recurrent glioblastomas with the antiangiogenic drug bevacizumab—a neutralizing antibody targeting vascular endothelial growth factor—was tempered by recent Phase III trials showing no efficacy for treating newly diagnosed glioblastomas. As a result, there is uncertainty about the appropriate indications for the use of bevacizumab in glioblastoma treatment. There are also concerns about the effects of bevacizumab on wound healing that neurosurgeons must be aware of. In addition, biochemical evidence suggests a percentage of tumors treated with bevacizumab for an extended period of time will undergo transformation into a more biologically aggressive and invasive phenotype with a particularly poor prognosis. Despite these concerns, there remain numerous examples of radiological and clinical improvement after bevacizumab treatment, particularly in patients with recurrent glioblastoma with limited therapeutic options. In this paper, the authors review clinical results with bevacizumab for glioblastoma treatment to date, ongoing trials designed to address unanswered questions, current clinical indications based on existing data, neurosurgical implications of bevacizumab use in patients with glioblastoma, the current scientific understanding of the tumor response to short- and long-term bevacizumab treatment, and future studies that will need to be undertaken to enable this treatment to fulfill its therapeutic promise for glioblastoma.

Major discoveries in the biology of nervous system tumors have raised the question of how non-histological data such as molecular information can be incorporated into the next World Health Organization (WHO) classification of central nervous system tumors. To address this question, a meeting of neuropathologists with expertise in molecular diagnosis was held in Haarlem, the Netherlands, under the sponsorship of the International Society of Neuropathology (ISN). Prior to the meeting, participants solicited input from clinical colleagues in diverse neuro-oncological specialties. The present "white paper" catalogs the recommendations of the meeting, at which a consensus was reached that incorporation of molecular information into the next WHO classification should follow a set of provided "ISN-Haarlem" guidelines. Salient recommendations include that (i) diagnostic entities should be defined as narrowly as possible to optimize interobserver reproducibility, clinicopathological predictions and therapeutic planning; (ii) diagnoses should be "layered" with histologic classification, WHO grade and molecular information listed below an "integrated diagnosis"; (iii) determinations should be made for each tumor entity as to whether molecular information is required, suggested or not needed for its definition; (iv) some pediatric entities should be separated from their adult counterparts; (v) input for guiding decisions regarding tumor classification should be solicited from experts in complementary disciplines of neuro-oncology; and (iv) entity-specific molecular testing and reporting formats should be followed in diagnostic reports. It is hoped that these guidelines will facilitate the forthcoming update of the fourth edition of the WHO classification of central nervous system tumors.

Major discoveries in the biology of nervous system tumors have raised the question of how non-histological data such as molecular information can be incorporated into the next World Health Organization (WHO) classification of central nervous system tumors. To address this question, a meeting of neuropathologists with expertise in molecular diagnosis was held in Haarlem, the Netherlands, under the sponsorship of the International Society of Neuropathology (ISN). Prior to the meeting, participants solicited input from clinical colleagues in diverse neuro-oncological specialties. The present "white paper" catalogs the recommendations of the meeting, at which a consensus was reached that incorporation of molecular information into the next WHO classification should follow a set of provided "ISN-Haarlem" guidelines. Salient recommendations include that (i) diagnostic entities should be defined as narrowly as possible to optimize interobserver reproducibility, clinicopathological predictions and therapeutic planning; (ii) diagnoses should be "layered" with histologic classification, WHO grade and molecular information listed below an "integrated diagnosis"; (iii) determinations should be made for each tumor entity as to whether molecular information is required, suggested or not needed for its definition; (iv) some pediatric entities should be separated from their adult counterparts; (v) input for guiding decisions regarding tumor classification should be solicited from experts in complementary disciplines of neuro-oncology; and (iv) entity-specific molecular testing and reporting formats should be followed in diagnostic reports. It is hoped that these guidelines will facilitate the forthcoming update of the fourth edition of the WHO classification of central nervous system tumors.

We present here a potential new treatment adjunct for glioblastoma. Building on murine studies, a series of papers appeared recently showing that therapeutic irradiation of the ipsilateral subventricular zone (SVZ) retards growth of more peripherally growing cortical glioblastomas in humans, suggesting a tumor trophic function for the SVZ. Further studies showed that SVZ cells migrate out towards a peripheral glioblastoma. Dopamine signaling through D3 subtype receptor indirectly drives this centrifugal migration in humans. Since psychiatry has several drugs with good D3 blocking attributes, such as fluphenazine, or perphenazine, we suggest that adding one of these D3 blocking drugs to current standard treatment of resection followed by temozolomide and irradiation might prolong survival by depriving glioblastoma of the trophic functions previously subserved by dopaminergic signaling on SVZ cells.

We present here a potential new treatment adjunct for glioblastoma. Building on murine studies, a series of papers appeared recently showing that therapeutic irradiation of the ipsilateral subventricular zone (SVZ) retards growth of more peripherally growing cortical glioblastomas in humans, suggesting a tumor trophic function for the SVZ. Further studies showed that SVZ cells migrate out towards a peripheral glioblastoma. Dopamine signaling through D3 subtype receptor indirectly drives this centrifugal migration in humans. Since psychiatry has several drugs with good D3 blocking attributes, such as fluphenazine, or perphenazine, we suggest that adding one of these D3 blocking drugs to current standard treatment of resection followed by temozolomide and irradiation might prolong survival by depriving glioblastoma of the trophic functions previously subserved by dopaminergic signaling on SVZ cells.

ObjectRecurrent malignant gliomas have inherent resistance to traditional chemotherapy. Novel therapies target specific molecular mechanisms involved in abnormal signaling and resistance to apoptosis. The proteasome is a key regulator of multiple cellular functions, and its inhibition in malignant astrocytic lines causes cell growth arrest and apoptotic cell death. The proteasome inhibitor bortezomib was reported to have very good in vitro activity against malignant glioma cell lines, with modest activity in animal models as well as in clinical trials as a single agent. In this paper, the authors describe the multiple effects of bortezomib in both in vitro and in vivo glioma models and offer a novel explanation for its seeming lack of activity.MethodsGlioma stem-like cells (GSCs) were obtained from resected glioblastomas (GBMs) at surgery and expanded in culture. Stable glioma cell lines (U21 and D54) as well as temozolomide (TMZ)-resistant glioma cells derived from U251 and D54-MG were also cultured. GSCs from 2 different tumors, as well as D54 and U251 cells, were treated with bortezomib, and the effect of the drug was measured using an XTT cell viability assay. The activity of bortezomib was then determined in D54-MG and/or U251 cells using apoptosis analysis as well as caspase-3 activity and proteasome activity measurements. Human glioma xenograft models were created in nude mice by subcutaneous injection. Bevacizumab was administered via intraperitoneal injection at a dose of 5 mg/kg daily. Bortezomib was administered by intraperitoneal injection 1 hour after bevacizumab administration in doses of at a dose of 0.35 mg/kg on days 1, 4, 8, and 11 every 21 days. Tumors were measured twice weekly.ResultsBortezomib induced caspase-3 activation and apoptotic cell death in stable glioma cell lines and in glioma stem-like cells (GSCs) derived from malignant tumor specimens Furthermore, TMZ-resistant glioma cell lines retained susceptibility to the proteasome inhibition. The bortezomib activity was directly proportional with the cells' baseline proteasome activity. The proteasome inhibition stimulated both hypoxia-inducible factor (HIF)-1α and vascular endothelial growth factor (VEGF) production in malignant GSCs. As such, the VEGF produced by GSCs stimulated endothelial cell growth, an effect that could be prevented by the addition of bevacizumab (VEGF antibody) to the media. Similarly, administration of bortezomib and bevacizumab to athymic mice carrying subcutaneous malignant glioma xenografts resulted in greater tumor inhibition and greater improvement in survival than administration of either drug alone. These data indicate that simultaneous proteasome inhibition and VEGF blockade offer increased benefit as a strategy for malignant glioma therapy.ConclusionsThe results of this study indicate that combination therapies based on bortezomib and bevacizumab might offer an increased benefit when the two agents are used in combination. These drugs have a complementary mechanism of action and therefore can be used together to treat TMZ-resistant malignant gliomas.

ObjectRecurrent malignant gliomas have inherent resistance to traditional chemotherapy. Novel therapies target specific molecular mechanisms involved in abnormal signaling and resistance to apoptosis. The proteasome is a key regulator of multiple cellular functions, and its inhibition in malignant astrocytic lines causes cell growth arrest and apoptotic cell death. The proteasome inhibitor bortezomib was reported to have very good in vitro activity against malignant glioma cell lines, with modest activity in animal models as well as in clinical trials as a single agent. In this paper, the authors describe the multiple effects of bortezomib in both in vitro and in vivo glioma models and offer a novel explanation for its seeming lack of activity.MethodsGlioma stem-like cells (GSCs) were obtained from resected glioblastomas (GBMs) at surgery and expanded in culture. Stable glioma cell lines (U21 and D54) as well as temozolomide (TMZ)-resistant glioma cells derived from U251 and D54-MG were also cultured. GSCs from 2 different tumors, as well as D54 and U251 cells, were treated with bortezomib, and the effect of the drug was measured using an XTT cell viability assay. The activity of bortezomib was then determined in D54-MG and/or U251 cells using apoptosis analysis as well as caspase-3 activity and proteasome activity measurements. Human glioma xenograft models were created in nude mice by subcutaneous injection. Bevacizumab was administered via intraperitoneal injection at a dose of 5 mg/kg daily. Bortezomib was administered by intraperitoneal injection 1 hour after bevacizumab administration in doses of at a dose of 0.35 mg/kg on days 1, 4, 8, and 11 every 21 days. Tumors were measured twice weekly.ResultsBortezomib induced caspase-3 activation and apoptotic cell death in stable glioma cell lines and in glioma stem-like cells (GSCs) derived from malignant tumor specimens Furthermore, TMZ-resistant glioma cell lines retained susceptibility to the proteasome inhibition. The bortezomib activity was directly proportional with the cells' baseline proteasome activity. The proteasome inhibition stimulated both hypoxia-inducible factor (HIF)-1α and vascular endothelial growth factor (VEGF) production in malignant GSCs. As such, the VEGF produced by GSCs stimulated endothelial cell growth, an effect that could be prevented by the addition of bevacizumab (VEGF antibody) to the media. Similarly, administration of bortezomib and bevacizumab to athymic mice carrying subcutaneous malignant glioma xenografts resulted in greater tumor inhibition and greater improvement in survival than administration of either drug alone. These data indicate that simultaneous proteasome inhibition and VEGF blockade offer increased benefit as a strategy for malignant glioma therapy.ConclusionsThe results of this study indicate that combination therapies based on bortezomib and bevacizumab might offer an increased benefit when the two agents are used in combination. These drugs have a complementary mechanism of action and therefore can be used together to treat TMZ-resistant malignant gliomas.