New hints towards a precision medicine strategy for IDH wild-type glioblastoma

GPR65 contributes to constructing immunosuppressive microenvironment in glioma

Glioma, especially glioblastoma patients, present highly heterogeneous and immunosuppressive microenvironment, leading to their poor response to treatment and survival. Targeting the tumor microenvironment is considered a promising therapeutic strategy. M2 macrophages are highly infiltrated in glioma tissue, even up to 50% of the total number of bulk tissue cells. Here, we identified GPR65 as the hub gene of the M2 macrophage-related module in glioma through WGCNA analysis. The expression and prognosis analysis suggested that GPR65 was positively correlated with the malignancy and poor prognosis of glioma, and the heterogeneity analysis found that GPR65 was highly expressed in the vascular proliferation area of glioma, which matched the spatial expression characteristics of M2 macrophages. We further verified that GPR65 was highly expressed in macrophages but not tumor cells in the glioma microenvironment by single-cell data analysis and immunofluorescence. Most importantly, we found that inhibition of GPR65 was sufficient to reduce macrophages' polarization response to glioma cell and break the malignant cooperation with glioma cells. Our study reports the expression characteristics and malignant behavior of GPR65 in the glioma microenvironment, which provides a new alternative target of treatment to glioma microenvironment.

Tumour microenvironment programming by an RNA-RNA-binding protein complex creates a druggable vulnerability in IDH-wild-type glioblastoma

Patients with IDH-wild-type glioblastomas have a poor five-year survival rate along with limited treatment efficacy due to immune cell (glioma-associated microglia and macrophages) infiltration promoting tumour growth and resistance. To enhance therapeutic options, our study investigated the unique RNA-RNA-binding protein complex LOC-DHX15. This complex plays a crucial role in driving immune cell infiltration and tumour growth by establishing a feedback loop between cancer and immune cells, intensifying cancer aggressiveness. Targeting this complex with blood-brain barrier-permeable small molecules improved treatment efficacy, disrupting cell communication and impeding cancer cell survival and stem-like properties. Focusing on RNA-RNA-binding protein interactions emerges as a promising approach not only for glioblastomas without the IDH mutation but also for potential applications beyond cancer, offering new avenues for developing therapies that address intricate cellular relationships in the body.

A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring

Here, we establish a CT-radiomics based method for application in invasive, orthotopic rodent brain tumour models. Twenty four NOD/SCID mice were implanted with U87R-Luc2 GBM cells and longitudinally imaged via contrast enhanced (CE-CT) imaging. Pyradiomics was employed to extract CT-radiomic features from the tumour-implanted hemisphere and non-tumour-implanted hemisphere of acquired CT-scans. Inter-correlated features were removed (Spearman correlation > 0.85) and remaining features underwent predictive analysis (recursive feature elimination or Boruta algorithm). An area under the curve of the receiver operating characteristic curve was implemented to evaluate radiomic features for their capacity to predict defined outcomes. Firstly, we identified a subset of radiomic features which distinguish the tumour-implanted hemisphere and non- tumour-implanted hemisphere (i.e, tumour presence from normal tissue). Secondly, we successfully translate preclinical CT-radiomic pipelines to GBM patient CT scans (n = 10), identifying similar trends in tumour-specific feature intensities (E.g. 'glszm Zone Entropy'), thereby suggesting a mouse-to-human species conservation (a conservation of radiomic features across species). Thirdly, comparison of features across timepoints identify features which support preclinical tumour detection earlier than is possible by visual assessment of CT scans. This work establishes robust, preclinical CT-radiomic pipelines and describes the application of CE-CT for in-depth orthotopic brain tumour monitoring. Overall we provide evidence for the role of pre-clinical 'discovery' radiomics in the neuro-oncology space.

The TGF-β Family in Glioblastoma

Members of the transforming growth factor β (TGF-β) family have been implicated in the biology of several cancers. In this review, we focus on the role of TGFβ and bone morphogenetic protein (BMP) signaling in glioblastoma. Glioblastoma (GBM) is the most common malignant brain tumor in adults; it presents at a median age of 64 years, but can occur at any age, including childhood. Unfortunately, there is no cure, and even patients undergoing current treatments (surgical resection, radiotherapy, and chemotherapy) have a median survival of 15 months. There is a great need to identify new therapeutic targets to improve the treatment of GBM patients. TGF-βs signaling promotes tumorigenesis in glioblastoma, while BMPs suppress tumorigenic potential by inducing tumor cell differentiation. In this review, we discuss the actions of TGF-βs and BMPs on cancer cells as well as in the tumor microenvironment, and their use in potential therapeutic intervention.

Glioblastoma preclinical models: Strengths and weaknesses

Glioblastoma multiforme is a highly malignant brain tumor with significant intra- and intertumoral heterogeneity known for its aggressive nature and poor prognosis. The complex signaling cascade that regulates this heterogeneity makes targeted drug therapy ineffective. The development of an optimal preclinical model is crucial for the comprehension of molecular heterogeneity and enhancing therapeutic efficacy. The ideal model should establish a relationship between various oncogenes and their corresponding responses. This review presents an analysis of preclinical in vivo and in vitro models that have contributed to the advancement of knowledge in model development. The experimental designs utilized in vivo models consisting of both immunodeficient and immunocompetent mice induced with intracranial glioma. The transgenic model was generated using various techniques, like the viral vector delivery system, transposon system, Cre-LoxP model, and CRISPR-Cas9 approaches. The utilization of the patient-derived xenograft model in glioma research is valuable because it closely replicates the human glioma microenvironment, providing evidence of tumor heterogeneity. The utilization of in vitro techniques in the initial stages of research facilitated the comprehension of molecular interactions. However, these techniques are inadequate in reproducing the interactions between cells and extracellular matrix (ECM). As a result, bioengineered 3D-in vitro models, including spheroids, scaffolds, and brain organoids, were developed to cultivate glioma cells in a three-dimensional environment. These models have enabled researchers to understand the influence of ECM on the invasive nature of tumors. Collectively, these preclinical models effectively depict the molecular pathways and facilitate the evaluation of multiple molecules while tailoring drug therapy.

GNA13 inhibits glioblastoma metastasis via the ERKs/FOXO3 signaling pathway

Glioblastoma (GBM) is a malignant tumor characterized by poor prognosis and low overall survival (OS) rate. Identification of novel biological markers for the diagnosis and treatment of GBM is crucial to developing interventions to improve patient survival. GNA13, a member of the G12 family, has been reported to play important roles in a variety of biological processes involved in tumorigenesis and development. However, its role in GBM is currently unknown. Here, we explored the expression patterns and functions of GNA13 in GBM, as wells its impact on metastasis process. Results showed that GNA13 was downregulated in GBM tissues and correlated with poor prognosis of GBM. Downregulation of GNA13 promoted the migration, invasion and proliferation of GBM cells; whereas its overexpression abolished these effects. Western blots revealed that GNA13 knockdown and overexpression upregulated and inhibited the phosphorylation of ERKs, respectively. Moreover, GNA13 was the upstream of ERKs signaling to regulating ERKs phosphorylation level. Furthermore, U0126 alleviated the metastasis effect induced by GNA13 knockdown. Bioinformatics analyses and qRT-PCR experiments demonstrated that GNA13 could regulate FOXO3, a downstream signaling molecule of ERKs pathway. Overall, our results demonstrate that GNA13 expression is negatively correlated with GBM and can suppress tumor metastasis by inhibiting the ERKs signaling pathway and upregulating FOXO3 expression.

An overview of glioblastoma multiforme and temozolomide resistance: can LC-MS-based proteomics reveal the fundamental mechanism of temozolomide resistance?

Glioblastoma multiforme (GBM) is a primary type of lethal brain tumor. Over the last two decades, temozolomide (TMZ) has remained the primary chemotherapy for GBM. However, TMZ resistance in GBM constitutes an underlying factor contributing to high rates of mortality. Despite intense efforts to understand the mechanisms of therapeutic resistance, there is currently a poor understanding of the molecular processes of drug resistance. For TMZ, several mechanisms linked to therapeutic resistance have been proposed. In the past decade, significant progress in the field of mass spectrometry-based proteomics has been made. This review article discusses the molecular drivers of GBM, within the context of TMZ resistance with a particular emphasis on the potential benefits and insights of using global proteomic techniques.

Knock-down of PGM1 inhibits cell viability, glycolysis, and oxidative phosphorylation in glioma under low glucose condition via the Myc signaling pathway

PGM1 is an essential enzyme for glucose metabolism and is involved in cell viability, proliferation, and metabolism. However, the regulatory role of PGMI in glioma progression and the relation between gliomas and PGM1 expression are still unclear. This study aimed to explore the role of PGM1 in glycolysis and oxidative phosphorylation in glioma. Correlation and enrichment analyses of PGM1 in glioma cells were explored in TCGA database and two hospital cohorts. The cell viability, glycolysis, and oxidative phosphorylation were investigated in PGM1 knock-down and overexpression situations. Higher PGM1 expression in glioma patients was associated with a poor survival rate. However, knock-down of PGM1 reduced glioma cell viability, glycolysis, and oxidative phosphorylation under low glucose condition. Moreover, it suppressed tumor growth in vivo. On the other hand, PGM1 overexpression promoted glioma cell viability, glycolysis, and oxidative phosphorylation under low glucose condition by a Myc positive feedback loop. Glioma patients with higher PGM1 expression were associated with poor survival rates. Additionally, PGM1 could promote glioma cell viability, glycolysis, and oxidative phosphorylation under low glucose condition via a myc-positive feedback loop, suggesting PGM1 could be a potential therapeutic target for gliomas.

Identification, validation and biological characterisation of novel glioblastoma tumour microenvironment subtypes: implications for precision immunotherapy

BACKGROUND

New precision medicine therapies are urgently required for glioblastoma (GBM). However, to date, efforts to subtype patients based on molecular profiles have failed to direct treatment strategies. We hypothesised that interrogation of the GBM tumour microenvironment (TME) and identification of novel TME-specific subtypes could inform new precision immunotherapy treatment strategies.

MATERIALS AND METHODS

A refined and validated microenvironment cell population (MCP) counter method was applied to >800 GBM patient tumours (GBM-MCP-counter). Specifically, partition around medoids (PAM) clustering of GBM-MCP-counter scores in the GLIOTRAIN discovery cohort identified three novel patient clusters, uniquely characterised by TME composition, functional orientation markers and immune checkpoint proteins. Validation was carried out in three independent GBM-RNA-seq datasets. Neoantigen, mutational and gene ontology analysis identified mutations and uniquely altered pathways across subtypes. The longitudinal Glioma Longitudinal AnalySiS (GLASS) cohort and three immunotherapy clinical trial cohorts [treatment with neoadjuvant/adjuvant anti-programmed cell death protein 1 (PD-1) or PSVRIPO] were further interrogated to assess subtype alterations between primary and recurrent tumours and to assess the utility of TME classifiers as immunotherapy biomarkers.

RESULTS

TME^High tumours (30%) displayed elevated lymphocyte, myeloid cell immune checkpoint, programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 transcripts. TME^High/mesenchymal+ patients featured tertiary lymphoid structures. TME^Med (46%) tumours were enriched for endothelial cell gene expression profiles and displayed heterogeneous immune populations. TME^Low (24%) tumours were manifest as an 'immune-desert' group. TME subtype transitions upon recurrence were identified in the longitudinal GLASS cohort. Assessment of GBM immunotherapy trial datasets revealed that TME^High patients receiving neoadjuvant anti-PD-1 had significantly increased overall survival (P = 0.04). Moreover, TME^High patients treated with adjuvant anti-PD-1 or oncolytic virus (PVSRIPO) showed a trend towards improved survival.

CONCLUSIONS

We have established a novel TME-based classification system for application in intracranial malignancies. TME subtypes represent canonical 'termini a quo' (starting points) to support an improved precision immunotherapy treatment approach.

Data-driven spatio-temporal modelling of glioblastoma

Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarize the scope, drawbacks and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. By providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks. Our article thus strikes a unique balance. On the one hand, we provide a comprehensive overview of the available modelling techniques and their applications, including key mathematical expressions. On the other hand, the content is accessible to mathematicians and biomedical scientists alike to accommodate the interdisciplinary nature of cancer research.

Prognostic Prediction Model for Glioblastoma: A Ferroptosis-Related Gene Prediction Model and Independent External Validation

Glioblastoma (GBM) is the most common primary malignant intracranial tumor with a poor prognosis. Ferroptosis is a newly discovered, iron-dependent, regulated cell death, and recent studies suggest its close correlation to GBM. The transcriptome and clinical data were obtained for patients diagnosed with GBM from TCGA, GEO, and CGGA. Ferroptosis-related genes were identified, and a risk score model was constructed using Lasso regression analyses. Survival was evaluated by univariate or multivariate Cox regressions and Kaplan-Meier analyses, and further analyses were performed between the high- and low-risk groups. There were 45 ferroptosis-related different expressed genes between GBM and normal brain tissues. The prognostic risk score model was based on four favorable genes, CRYAB, ZEB1, ATP5MC3, and NCOA4, and four unfavorable genes, ALOX5, CHAC1, STEAP3, and MT1G. A significant difference in OS between high- and low-risk groups was observed in both the training cohort (p < 0.001) and the validation cohorts (p = 0.029 and 0.037). Enrichment analysis of pathways and immune cells and functioning was conducted between the two risk groups. A novel prognostic model for GBM patients was developed based on eight ferroptosis-related genes, suggesting a potential prediction effect of the risk score model in GBM.

Long Non-coding RNA KTN1-AS1 Targets miR-505 to Promote Glioblastoma Progression

Glioblastoma (GBM) is a highly malignant cancer, the prognosis of which is pretty poor. Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs, which play important roles in carcinogenesis process of many cancers including GBM. In this study, we want to clarify the expression, biological function, and molecular mechanism of lncRNA KTN1 antisense RNA 1 (KTN1-AS1) in GBM tumor progression. We found that KTN1-AS1 expression was upregulated in GBM tissues and cell lines. KTN1-AS1 played oncogenic roles to facilitate proliferation, migration, and invasion of GBM cells. Then, we revealed that miR-505 was a target of KTN1-AS1, and its expression was decreased in GBM. KTN1-AS1 contributed to GBM progression by mediating miR-505. Finally, we demonstrated that KTN1-AS1 upregulated some target oncogenes of miR-505 including ZEB2, HMGB1, and RUNX2 in GBM cells. All in all, we concluded that the highly expressed KTN1-AS1 in GBM played oncogenic roles to facilitate GBM progression by targeting miR-505.

Clinically relevant glioblastoma patient-derived xenograft models to guide drug development and identify molecular signatures

Glioblastoma (GBM) heterogeneity, aggressiveness and infiltrative growth drastically limit success of current standard of care drugs and efficacy of various new therapeutic approaches. There is a need for new therapies and models reflecting the complex biology of these tumors to analyze the molecular mechanisms of tumor formation and resistance, as well as to identify new therapeutic targets. We established and screened a panel of 26 patient-derived subcutaneous (s.c.) xenograft (PDX) GBM models on immunodeficient mice, of which 15 were also established as orthotopic models. Sensitivity toward a drug panel, selected for their different modes of action, was determined. Best treatment responses were observed for standard of care temozolomide, irinotecan and bevacizumab. Matching orthotopic models frequently show reduced sensitivity, as the blood-brain barrier limits crossing of the drugs to the GBM. Molecular characterization of 23 PDX identified all of them as IDH-wt (R132) with frequent mutations in EGFR, TP53, FAT1, and within the PI3K/Akt/mTOR pathway. Their expression profiles resemble proposed molecular GBM subtypes mesenchymal, proneural and classical, with pronounced clustering for gene sets related to angiogenesis and MAPK signaling. Subsequent gene set enrichment analysis identified hallmark gene sets of hypoxia and mTORC1 signaling as enriched in temozolomide resistant PDX. In models sensitive for mTOR inhibitor everolimus, hypoxia-related gene sets reactive oxygen species pathway and angiogenesis were enriched. Our results highlight how our platform of s.c. GBM PDX can reflect the complex, heterogeneous biology of GBM. Combined with transcriptome analyses, it is a valuable tool in identification of molecular signatures correlating with monitored responses. Available matching orthotopic PDX models can be used to assess the impact of the tumor microenvironment and blood-brain barrier on efficacy. Our GBM PDX panel therefore represents a valuable platform for screening regarding molecular markers and pharmacologically active drugs, as well as optimizing delivery of active drugs to the tumor.

Brain-targeted antigen-generating nanoparticles improve glioblastoma prognosis

The exploration of multifunctional nanomedicine has prompted interest in improving glioblastoma (GBM) prognosis. In this study, we constructed tumor microenvironment (TME)-responsive magnetic therapeutic nanoparticles (BK@MTNPs) as a multifunctional drug delivery platform. It contains the following components. [Des-arg(Sheets et al., 2020 [9])]bradykinin (BK), which contributes to the transient opening of the blood-brain barrier (BBB) and targeting of GBM cells; nanoparticles (NPs) encapsulated in MTNPs, which act as an in vivo magnetic resonance (MR) imaging agent; crizotinib, which is an inhibitor of protein kinase c-Met; and the immune drug anti-PDL1 antibody. These components were loaded into BK@MTNPs for complete tumoricidal effects. Abundant glutathione in the TME can promote BK@MTNP degradation by interrupting the disulfide bonds between cysteine residues. Such BK@MTNPs support a synergistic tumoricidal effect by inducing DNA damage, activating the transcription of the tumor suppressor gene PTEN, inhibiting glioblastoma stem cell function, activating cytotoxic T lymphocytes, and reprogramming tumor-associated macrophages. BK@MTNPs showed a significant increase in antitumor activity compared with free drugs in vitro. Furthermore, in mice bearing orthotopic GBM, treatment with BK@MTNPs resulted in marked tumor inhibition and greatly extended survival time with minimal side effects. This study demonstrates the advantages of chemo-immunotherapeutic NPs accumulated in the GBM area and their effective inhibition of GBM growth, thus establishing a delivery platform to promote antitumor immunity against GBM.

Leveraging extrachromosomal DNA to fine-tune trials of targeted therapy for glioblastoma: opportunities and challenges

Glioblastoma evolution is facilitated by intratumour heterogeneity, which poses a major hurdle to effective treatment. Evidence indicates a key role for oncogene amplification on extrachromosomal DNA (ecDNA) in accelerating tumour evolution and thus resistance to treatment, particularly in glioblastomas. Oncogenes contained within ecDNA can reach high copy numbers and expression levels, and their unequal segregation can result in more rapid copy number changes in response to therapy than is possible through natural selection of intrachromosomal genomic loci. Notably, targeted therapies inhibiting oncogenic pathways have failed to improve glioblastoma outcomes. In this Perspective, we outline reasons for this disappointing lack of clinical translation and present the emerging evidence implicating ecDNA as an important driver of tumour evolution. Furthermore, we suggest that through detection of ecDNA, patient selection for clinical trials of novel agents can be optimized to include those most likely to benefit based on current understanding of resistance mechanisms. We discuss the challenges to successful translation of this approach, including accurate detection of ecDNA in tumour tissue with novel technologies, development of faithful preclinical models for predicting the efficacy of novel agents in the presence of ecDNA oncogenes, and understanding the mechanisms of ecDNA formation during cancer evolution and how they could be attenuated therapeutically. Finally, we evaluate the feasibility of routine ecDNA characterization in the clinic and how this process could be integrated with other methods of molecular stratification to maximize the potential for clinical translation of precision medicines.

Comprehensive metabolomics study on the pathogenesis of anaplastic astrocytoma via UPLC-Q/TOF-MS

Anaplastic astrocytoma (AA) is a malignant carcinoma whose pathogenesis remains to be fully elucidated. System biology techniques have been widely used to clarify the mechanism of diseases from a systematic perspective. The present study aimed to explore the pathogenesis and novel potential biomarkers for the diagnosis of AA according to metabolic differences. Patients with AA (n = 12) and healthy controls (n = 15) were recruited. Serum was assayed with untargeted ultraperformance liquid chromatography-quadrupole/time-of-flight-mass spectrometry (UPLC-Q/TOF-MS) metabolomic techniques. The data were further evaluated using multivariate analysis and bioinformatic methods based on the KEGG database to determine the distinct metabolites and perturbed pathways. Principal component analysis and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) identified the significance of the distinct metabolic pattern between patients with AA and healthy controls (P < .001) in both ESI modes. Permutation testing confirmed the validity of the OPLS-DA model (permutation = 200, Q2 < 0.5). In total, 24 differentiated metabolites and 5 metabolic pathways, including sphingolipid, glycerophospholipid, caffeine, linoleic acid, and porphyrin metabolism, were identified based on the OPLS-DA model. 3-Methylxanthine, sphinganine, LysoPC(18:1), and lactosylceramide were recognized as potential biomarkers with excellent sensitivity and specificity (area under the curve > 98%). These findings indicate that the perturbed metabolic pattern related to immune regulation and cellular signal transduction is associated with the pathogenesis of AA. 3-Methylxanthine, sphinganine, LysoPC(18:1), and lactosylceramide could be used as biomarkers of AA in future clinical practice. This study provides a therapeutic basis for further studies on the mechanism and precise clinical diagnosis of AA.

A Quantitative Digital Analysis of Tissue Immune Components Reveals an Immunosuppressive and Anergic Immune Response with Relevant Prognostic Significance in Glioblastoma

Objectives: Immunostimulatory therapies using immune checkpoint blockers show clinical activity in a subset of glioblastoma (GBM) patients. Several inhibitory mechanisms play a relevant role in the immune response to GBM. With the objective of analyzing the tumor immune microenvironment and its clinical significance, we quantified several relevant immune biomarkers. Design: We studied 76 primary (non-recurrent) GBMs with sufficient clinical follow-up, including a subgroup of patients treated with a dendritic cell vaccine. The IDH-mutation, EGFR-amplification, and MGMT methylation statuses were determined. Several relevant immune biomarkers, including CD163, CD8, PD1, and PDL1, were quantified in representative selected areas by digital image analysis and semiquantitative evaluation. The percentage of each immune expression was calculated with respect to the total number of tumor cells. Results: All GBMs were wild-type IDH, with a subgroup of classical GBMs according to the EGFR amplification (44%). Morphologically, CD163 immunostained microglia and intratumor clusters of macrophages were observed. A significant direct correlation was found between the expression of CD8 and the mechanisms of lymphocyte immunosuppression, in such a way that higher values of CD8 were directly associated with higher values of CD163 (p < 0.001), PDL1 (0.026), and PD1 (0.007). In a multivariate analysis, high expressions of CD8+ (HR = 2.05, 95%CI (1.02−4.13), p = 0.034) and CD163+ cells (HR 2.50, 95%CI (1.29−4.85), p = 0.007), were associated with shorter survival durations. The expression of immune biomarkers was higher in the non-classical (non-EGFR amplified tumors) GBMs. Other relevant prognostic factors were age, receipt of the dendritic cell vaccine, and MGMT methylation status. Conclusions: In accordance with the inverse correlation between CD8 and survival and the direct correlation between effector cells and CD163 macrophages and immune-checkpoint expression, we postulate that CD8 infiltration could be placed in a state of anergy or lymphocytic inefficient activity. Furthermore, the significant inverse correlation between CD163 tissue concentration and survival explains the relevance of this type of immune cell when creating a strong immunosuppressive environment. This information may potentially be used to support the selection of patients for immunotherapy.

Integrated MRI–Immune–Genomic Features Enclose a Risk Stratification Model in Patients Affected by Glioblastoma

Simple Summary

Despite crucial scientific advances, Glioblastoma (GB) remains a fatal disease with limited therapeutic options and a lack of suitable biomarkers. The unveiled competence of the brain immune system together with the breakthrough advent of immunotherapy has shifted the present translational research on GB towards an immune-focused perspective. Several clinical trials targeting the immunosuppressive GB background are ongoing. So far, results are inconclusive, underpinning our partial understanding of the complex cancer-immune interplay in brain tumors. High throughput Magnetic Resonance (MR) imaging has shown the potential to decipher GB heterogeneity, including pathologic and genomic clues. However, whether distinct GB immune contextures can be deciphered at an imaging scale is still elusive, leaving unattained the non-invasive achievement of prognostic and predictive biomarkers. Along these lines, we integrated genetic, immunopathologic and imaging features in a series of GB patients. Our results suggest that multiparametric approaches might offer new efficient risk stratification models, opening the possibility to intercept the critical events implicated in the dismal prognosis of GB.

Abstract

Background: The aim of the present study was to dissect the clinical outcome of GB patients through the integration of molecular, immunophenotypic and MR imaging features. Methods: We enrolled 57 histologically proven and molecularly tested GB patients (5.3% IDH-1 mutant). Two-Dimensional Free ROI on the Biggest Enhancing Tumoral Diameter (TDFRBETD) acquired by MRI sequences were used to perform a manual evaluation of multiple quantitative variables, among which we selected: SD Fluid Attenuated Inversion Recovery (FLAIR), SD and mean Apparent Diffusion Coefficient (ADC). Characterization of the Tumor Immune Microenvironment (TIME) involved the immunohistochemical analysis of PD-L1, and number and distribution of CD3+, CD4+, CD8+ Tumor Infiltrating Lymphocytes (TILs) and CD163+ Tumor Associated Macrophages (TAMs), focusing on immune-vascular localization. Genetic, MR imaging and TIME descriptors were correlated with overall survival (OS). Results: MGMT methylation was associated with a significantly prolonged OS (median OS = 20 months), while no impact of p53 and EGFR status was apparent. GB cases with high mean ADC at MRI, indicative of low cellularity and soft consistency, exhibited increased OS (median OS = 24 months). PD-L1 and the overall number of TILs and CD163+TAMs had a marginal impact on patient outcome. Conversely, the density of vascular-associated (V) CD4+ lymphocytes emerged as the most significant prognostic factor (median OS = 23 months in V-CD4^high vs. 13 months in V-CD4^low, p = 0.015). High V-CD4+TILs also characterized TIME of MGMT^meth GB, while p53^mut appeared to condition a desert immune background. When individual genetic (MGMT^unmeth), MR imaging (mean ADC^low) and TIME (V-CD4+TILs^low) negative predictors were combined, median OS was 21 months (95% CI, 0–47.37) in patients displaying 0–1 risk factor and 13 months (95% CI 7.22–19.22) in the presence of 2–3 risk factors (p = 0.010, HR = 3.39, 95% CI 1.26–9.09). Conclusion: Interlacing MRI–immune–genetic features may provide highly significant risk-stratification models in GB patients.

Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma

Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS < 6 months) vs all, and low-risk (OS ≥ 18 months) vs all. The trained radiomic model was independently tested in the replication cohort (n = 112) and a patient-wise survival prediction index was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed area under the ROC curves (AUCs) of 0.78 (95% CI 0.70-0.85)/0.75 (95% CI 0.64-0.79) and 0.75 (95% CI 0.65-0.84)/0.63 (95% CI 0.52-0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95% CI 0.6-0.7) for clinical data improving to 0.75 (95% CI 0.72-0.79) for the combination of all omics. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM.

Immune cell gene expression signatures in diffuse glioma are associated with IDH mutation status, patient outcome and malignant cell state, and highlight the importance of specific cell subsets in glioma biology

The tumor micro-environment (TME) plays an important role in various cancers, including gliomas. We estimated immune cell type-specific gene expression profiles in 3 large clinically annotated glioma datasets using CIBERSORTx and LM22/LM10 blood-based immune signatures and found that the proportions and estimated gene expression patterns of specific immune cells significantly varied according to IDH mutation status. When IDH-WT and IDH-MUT tumors were considered separately, cluster-of-cluster analyses of immune cell gene expression identified groups with distinct survival outcomes. We confirmed and extended these findings by applying a signature matrix derived from single-cell RNA-sequencing data derived from 19 glioma tumor samples to the bulk profiling data, validating findings from the LM22/LM10 results. To link immune cell signatures with outcomes in checkpoint therapy, we then showed a significant association of monocytic lineage cell gene expression clusters with patient survival and with mesenchymal gene expression scores. Integrating immune cell-based gene expression with previously described malignant cell states in glioma demonstrated that macrophage M0 abundance significantly correlated with mesenchymal state in IDH-WT gliomas, with evidence of a previously implicated role of the Oncostatin-M receptor and macrophages in the mesenchymal state. Among IDH-WT tumors that were enriched for the mesenchymal cell state, the estimated M0 macrophage expression signature coordinately also trended to a mesenchymal signature. We also examined IDH-MUT tumors stratified by 1p/19q status, showing that a mesenchymal gene expression signature the M0 macrophage fraction was enriched in IDH-MUT, non-codeleted tumors. Overall, these results highlight the biological and clinical significance of the immune cell environment related to IDH mutation status, patient prognosis and the mesenchymal state in diffuse gliomas.

Interplay Among Metabolism, Epigenetic Modifications, and Gene Expression in Cancer

Epigenetic modifications and metabolism are two fundamental biological processes. During tumorigenesis and cancer development both epigenetic and metabolic alterations occur and are often intertwined together. Epigenetic modifications contribute to metabolic reprogramming by modifying the transcriptional regulation of metabolic enzymes, which is crucial for glucose metabolism, lipid metabolism, and amino acid metabolism. Metabolites provide substrates for epigenetic modifications, including histone modification (methylation, acetylation, and phosphorylation), DNA and RNA methylation and non-coding RNAs. Simultaneously, some metabolites can also serve as substrates for nonhistone post-translational modifications that have an impact on the development of tumors. And metabolic enzymes also regulate epigenetic modifications independent of their metabolites. In addition, metabolites produced by gut microbiota influence host metabolism. Understanding the crosstalk among metabolism, epigenetic modifications, and gene expression in cancer may help researchers explore the mechanisms of carcinogenesis and progression to metastasis, thereby provide strategies for the prevention and therapy of cancer. In this review, we summarize the progress in the understanding of the interactions between cancer metabolism and epigenetics.

New Autophagy-Ferroptosis Gene Signature Predicts Survival in Glioma

Background: Ferroptosis plays an important role in glioma and significantly affects the prognosis, but the specific mechanism has not yet been elucidated. Recent studies suggest that autophagy regulates the process of ferroptosis. This study aimed to find potential autophagy-ferroptosis genes and explore the prognostic significance in glioma.

Methods: Ferroptosis and autophagy genes were obtained from two online databases (zhounan.org/ferrdb and autophagy.lu/). The RNAseq data and clinical information were obtained from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn/). Univariate, multivariate, lasso and Cox regression analysis screened out prognosis-related genes, and a risk model was constructed. Receiver operating characteristic (ROC) curve analysis evaluated the predictive efficiency of the model. Finally, a nomogram was constructed to more accurately predict the prognosis of glioma.

Results: We developed a Venn diagram showing 23 autophagy-ferroptosis genes. A total of 660 cases (including RNA sequences and complete clinical information) from two different cohorts (training group n = 413, verification group n = 247) of the CGGA database was acquired. Cohorts were screened to include five prognosis-related genes (MTOR, BID, HSPA5, CDKN2A, GABARAPLA2). Kaplan-Meier curves showed that the risk model was a good prognostic indicator (p < 0.001). ROC analysis showed good efficacy of the risk model. Multivariate Cox analysis also revealed that the risk model was suitable for clinical factors related to prognosis, including type of disease (primary, recurrence), grade (III-IV), age, temozolomide treatment, and 1p19q state. Using the five prognosis-related genes and the risk score, we constructed a nomogram assessed by C-index (0.7205) and a calibration plot that could more accurately predict glioma prognosis.

Conclusion: Using a current database of autophagy and ferroptosis genes, we confirmed the prognostic significance of autophagy-ferroptosis genes in glioma, and we constructed a prognostic model to help guide treatment for high grade glioma in the future.

Glioblastoma pharmacotherapy: A multifaceted perspective of conventional and emerging treatments (Review)

Due to its localisation, rapid onset, high relapse rate and resistance to most currently available treatment methods, glioblastoma multiforme (GBM) is considered to be the deadliest type of all gliomas. Although surgical resection, chemotherapy and radiotherapy are among the therapeutic strategies used for the treatment of GBM, the survival rates achieved are not satisfactory, and there is an urgent need for novel effective therapeutic options. In addition to single-target therapy, multi-target therapies are currently under development. Furthermore, drugs are being optimised to improve their ability to cross the blood-brain barrier. In the present review, the main strategies applied for GBM treatment in terms of the most recent therapeutic agents and approaches that are currently under pre-clinical and clinical testing were discussed. In addition, the most recently reported experimental data following the testing of novel therapies, including stem cell therapy, immunotherapy, gene therapy, genomic correction and precision medicine, were reviewed, and their advantages and drawbacks were also summarised.

A Fast and Efficient Approach to Obtaining High-Purity Glioma Stem Cell Culture

Glioma is the most common and malignant primary brain tumor. Patients with malignant glioma usually have a poor prognosis due to drug resistance and disease relapse. Cancer stem cells contribute to glioma initiation, progression, resistance, and relapse. Hence, quick identification and efficient understanding of glioma stem cells (GSCs) are of profound importance for therapeutic strategies and outcomes. Ideally, therapeutic approaches will only kill cancer stem cells without harming normal neural stem cells (NSCs) that can inhibit GSCs and are often beneficial. It is key to identify the differences between cancer stem cells and normal NSCs. However, reports detailing an efficient and uniform protocol are scarce, as are comparisons between normal neural and cancer stem cells. Here, we compared different protocols and developed a fast and efficient approach to obtaining high-purity glioma stem cell by tracking observation and optimizing culture conditions. We examined the proliferative and differentiative properties confirming the identities of the GSCs with relevant markers such as Ki67, SRY-box containing gene 2, an intermediate filament protein member nestin, glial fibrillary acidic protein, and s100 calcium-binding protein (s100-beta). Finally, we identified distinct expression differences between GSCs and normal NSCs including cyclin-dependent kinase 4 and tumor protein p53. This study comprehensively describes the features of GSCs, their properties, and regulatory genes with expression differences between them and normal stem cells. Effective approaches to quickly obtaining high-quality GSCs from patients should have the potential to not only help understand the diseases and the resistances but also enable target drug screening and personalized medicine for brain tumor treatment.

A Systematic Review of Glioblastoma-Targeted Therapies in Phases II, III, IV Clinical Trials

Glioblastoma (GBM), the most frequent and aggressive glial tumor, is currently treated as first line by the Stupp protocol, which combines, after surgery, radiotherapy and chemotherapy. For recurrent GBM, in absence of standard treatment or available clinical trials, various protocols including cytotoxic drugs and/or bevacizumab are currently applied. Despite these heavy treatments, the mean overall survival of patients is under 18 months. Many clinical studies are underway. Based on clinicaltrials.org and conducted up to 1 April 2020, this review lists, not only main, but all targeted therapies in phases II-IV of 257 clinical trials on adults with newly diagnosed or recurrent GBMs for the last twenty years. It does not involve targeted immunotherapies and therapies targeting tumor cell metabolism, that are well documented in other reviews. Without surprise, the most frequently reported drugs are those targeting (i) EGFR (40 clinical trials), and more generally tyrosine kinase receptors (85 clinical trials) and (ii) VEGF/VEGFR (75 clinical trials of which 53 involving bevacizumab). But many other targets and drugs are of interest. They are all listed and thoroughly described, on an one-on-one basis, in four sections related to targeting (i) GBM stem cells and stem cell pathways, (ii) the growth autonomy and migration, (iii) the cell cycle and the escape to cell death, (iv) and angiogenesis.

Glioblastoma Organoids: Pre-Clinical Applications and Challenges in the Context of Immunotherapy

Malignant brain tumors remain uniformly fatal, even with the best-to-date treatment. For Glioblastoma (GBM), the most severe form of brain cancer in adults, the median overall survival is roughly over a year. New therapeutic options are urgently needed, yet recent clinical trials in the field have been largely disappointing. This is partially due to inappropriate preclinical model systems, which do not reflect the complexity of patient tumors. Furthermore, clinically relevant patient-derived models recapitulating the immune compartment are lacking, which represents a bottleneck for adequate immunotherapy testing. Emerging 3D organoid cultures offer innovative possibilities for cancer modeling. Here, we review available GBM organoid models amenable to a large variety of pre-clinical applications including functional bioassays such as proliferation and invasion, drug screening, and the generation of patient-derived orthotopic xenografts (PDOX) for validation of biological responses in vivo. We emphasize advantages and technical challenges in establishing immunocompetent ex vivo models based on co-cultures of GBM organoids and human immune cells. The latter can be isolated either from the tumor or from patient or donor blood as peripheral blood mononuclear cells (PBMCs). We also discuss the challenges to generate GBM PDOXs based on humanized mouse models to validate efficacy of immunotherapies in vivo. A detailed characterization of such models at the cellular and molecular level is needed to understand the potential and limitations for various immune activating strategies. Increasing the availability of immunocompetent GBM models will improve research on emerging immune therapeutic approaches against aggressive brain cancer.

Targeting the RhoGEF βPIX/COOL-1 in Glioblastoma: Proof of Concept Studies

Simple Summary

Glioblastoma (GBM) is an incurable disease with a 14-month average life-expectancy following diagnosis, and clinical management has not improved in four decades. GBM mortality is due to rapid tumour growth and invasion into surrounding normal brain. Invasive cells make complete surgical removal of the tumour impossible, and result in disease relapse. Thus, it is imperative that any new treatment strategy takes these invading cells into consideration. Bevacizumab (Bev), which prevents the formation of new blood vessels, is an FDA approved therapy, but it has failed to increase overall survival in GBM and has even been shown to increase tumour invasion in some cases. Complementary anti-invasive therapies are therefore urgently required to enhance bevacizumab efficacy. We have identified βPIX/COOL-1, a RhoGEF protein which plays an important role in GBM cell invasion and angiogenesis and could be a useful target in this setting.

Abstract

Glioblastoma (GBM), a highly invasive and vascular malignancy is shown to rapidly develop resistance and evolve to a more invasive phenotype following bevacizumab (Bev) therapy. Rho Guanine Nucleotide Exchange Factor proteins (RhoGEFs) are mediators of key components in Bev resistance pathways, GBM and Bev-induced invasion. To identify GEFs with enhanced mRNA expression in the leading edge of GBM tumours, a cohort of GEFs was assessed using a clinical dataset. The GEF βPix/COOL-1 was identified, and the functional effect of gene depletion assessed using 3D-boyden chamber, proliferation, and colony formation assays in GBM cells. Anti-angiogenic effects were assessed in endothelial cells using tube formation and wound healing assays. In vivo effects of βPix/COOL-1-siRNA delivered via RGD-Nanoparticle in combination with Bev was studied in an invasive model of GBM. We found that siRNA-mediated knockdown of βPix/COOL-1 in vitro decreased cell invasion, proliferation and increased apoptosis in GBM cell lines. Moreover βPix/COOL-1 mediated endothelial cell migration in vitro. Mice treated with βPix/COOL-1 siRNA-loaded RGD-Nanoparticle and Bev demonstrated a trend towards improved median survival compared with Bev monotherapy. Our hypothesis generating study suggests that the RhoGEF βPix/COOL-1 may represent a target of vulnerability in GBM, in particular to improve Bev efficacy.

RNA m6A Modification in Cancers: Molecular Mechanisms and Potential Clinical Applications

N6-Methyladenosine (m6A) RNA modification brings a new dawn for RNA modification researches in recent years. This posttranscriptional RNA modification is dynamic and reversible, and is regulated by methylases ("writers"), demethylases ("erasers"), and proteins that preferentially recognize m6A modifications ("readers"). The change of RNA m6A modification regulates RNA metabolism in eucaryon, including translation, splicing, exporting, decay, and processing. Thereby the dysregulation of m6A may lead to tumorigenesis and progression. Given the tumorigenic role of abnormal m6A expression, m6A regulators may function as potential clinical therapeutic targets for cancers. In this review, we emphasize on the underlying mechanisms of m6A modifications in tumorigenesis and further introduce the potential m6A regulators-associated therapeutic targets for tumor therapy.