Molecular diagnostics of gliomas: state of the art

Clinical sequencing reveals diagnostic, therapeutic, and prognostic biomarkers for adult-type diffuse gliomas

Diffuse gliomas in adults are highly infiltrative and largely incurable. Whole exome sequencing (WES) has been demonstrated very useful in genetic analysis. Here WES was performed to characterize genomic landscape of adult-type diffuse gliomas to discover the diagnostic, therapeutic and prognostic biomarkers. Somatic and germline variants of 66 patients with adult-type diffuse gliomas were detected by WES based on the next-generation sequencing. TCGA and CGGA datasets were included to analyze the integrated diagnosis and prognosis. Among 66 patients, the diagnosis of 9 cases was changed, in which 8 cases of astrocytoma were corrected into IDH-wildtype glioblastoma (GBM), and 1 oligodendroglioma without 1p/19q co-deletion into astrocytoma. The distribution of mutations including ATRX/TP53 differed in three cohorts. The genetic mutations in GBM mainly concentrated on the cell cycle, PI3K and RTK pathways. The mutational landscape of astrocytoma was more similar to that of GBM, with the highest frequency in germline variants. Patients with IDH-mutant astrocytoma harboring SNVs of PIK3CA and PIK3R1 showed a significantly worse overall survival (OS) than wild-type patients. AEBP1 amplification was associated with shorter OS in GBM. Our study suggests that clinical sequencing can recapitulate previous findings, which may provide a powerful approach to discover diagnostic, therapeutic and prognostic markers for precision medicine in adult-type diffuse gliomas.

Radiogenomics-Based Risk Prediction of Glioblastoma Multiforme with Clinical Relevance

Glioblastoma multiforme (GBM)is the most common and aggressive primary brain tumor. Although temozolomide (TMZ)-based radiochemotherapy improves overall GBM patients' survival, it also increases the frequency of false positive post-treatment magnetic resonance imaging (MRI) assessments for tumor progression. Pseudo-progression (PsP) is a treatment-related reaction with an increased contrast-enhancing lesion size at the tumor site or resection margins miming tumor recurrence on MRI. The accurate and reliable prognostication of GBM progression is urgently needed in the clinical management of GBM patients. Clinical data analysis indicates that the patients with PsP had superior overall and progression-free survival rates. In this study, we aimed to develop a prognostic model to evaluate the tumor progression potential of GBM patients following standard therapies. We applied a dictionary learning scheme to obtain imaging features of GBM patients with PsP or true tumor progression (TTP) from the Wake dataset. Based on these radiographic features, we conducted a radiogenomics analysis to identify the significantly associated genes. These significantly associated genes were used as features to construct a 2YS (2-year survival rate) logistic regression model. GBM patients were classified into low- and high-survival risk groups based on the individual 2YS scores derived from this model. We tested our model using an independent The Cancer Genome Atlas Program (TCGA) dataset and found that 2YS scores were significantly associated with the patient's overall survival. We used two cohorts of the TCGA data to train and test our model. Our results show that the 2YS scores-based classification results from the training and testing TCGA datasets were significantly associated with the overall survival of patients. We also analyzed the survival prediction ability of other clinical factors (gender, age, KPS (Karnofsky performance status), normal cell ratio) and found that these factors were unrelated or weakly correlated with patients' survival. Overall, our studies have demonstrated the effectiveness and robustness of the 2YS model in predicting the clinical outcomes of GBM patients after standard therapies.

Revolutionizing Glioblastoma Treatment: A Comprehensive Overview of Modern Therapeutic Approaches

Glioblastoma is the most common malignant primary brain tumor in the adult population, with an average survival of 12.1 to 14.6 months. The standard treatment, combining surgery, radiotherapy, and chemotherapy, is not as efficient as we would like. However, the current possibilities are no longer limited to the standard therapies due to rapid advancements in biotechnology. New methods enable a more precise approach by targeting individual cells and antigens to overcome cancer. For the treatment of glioblastoma, these are gamma knife therapy, proton beam therapy, tumor-treating fields, EGFR and VEGF inhibitors, multiple RTKs inhibitors, and PI3K pathway inhibitors. In addition, the increasing understanding of the role of the immune system in tumorigenesis and the ability to identify tumor-specific antigens helped to develop immunotherapies targeting GBM and immune cells, including CAR-T, CAR-NK cells, dendritic cells, and immune checkpoint inhibitors. Each of the described methods has its advantages and disadvantages and faces problems, such as the inefficient crossing of the blood-brain barrier, various neurological and systemic side effects, and the escape mechanism of the tumor. This work aims to present the current modern treatments of glioblastoma.

Prospective Prediction of Treatment Response in High-Grade Glioma Patients using Pre-Treatment Tumor ADC Value and miR-222 and miR-205 Expression Levels in Plasma

Background

Treatment response in High-grade Glioma (HGG) patients changes based on their genetic and biological characteristics. MiRNAs, as important regulators of drug and radiation resistance, and the Apparent Diffusion Coefficients (ADC) value of tumor can be used as a prognostic predictor for glioma.

Objective

This study aimed to identify some of the pre-treatment individual patient features for predicting the treatment response in HGG patients.

Material and Methods

In this prospective study, 18 HGG patients, who were candidated for chemo-radiation treatment, participated after informed consent of the patients. The investigated features were the expression level of miR-222 and miR-205 in plasma, the ADC value of tumor, Body Mass Index (BMI), and age. Treatment response was assessed, and Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to obtain a model to predict the treatment response. Mann-Whitney U test was also applied to select the variables with a significant relationship with patients' treatment response.

Results

The LASSO coefficients for miR-205, miR-222, tumor's mean ADC value, BMI, and age were 3.611, -1.683, 2.468, -0.184, and -0.024, respectively. Mann-Whitney U test results showed miR-205 and tumor's mean ADC significantly related to treatment response (P-value<0.05).

Conclusion

The miR-205 expression level of the patient in plasma and tumor's mean ADC value has the potential for prognostic predictors in HGG.

Awake craniotomy for high-grade gliomas - a prospective cohort study in a UK tertiary-centre

BACKGROUND

Studies from the UK reporting on awake craniotomy (AC) include a heterogenous group of patients which limit the evaluation of the true impact of AC in high-grade glioma (HGG) patients. This study aims to report solely the experience and outcomes of AC for HGG surgery from our centre.

METHODS

A prospective review of all patients who underwent AC for HGG from 2013 to 2019 were performed. Data on patient characteristics including but not limited to demographics, pre- and post-operative Karnofsky performance status (KPS), tumour location and volume, type of surgery, extent of resection (EOR), tumour histopathology, intra- and post-operative complications, morbidity, mortality, disease recurrence, progression-free survival (PFS) and overall survival (OS) from the time of surgery were collected.

RESULTS

Fifteen patients (6 males; 9 females; 17 surgeries) underwent AC for HGG (median age = 55 years). Two patients underwent repeat surgeries due to disease recurrence. Median pre- and post-operative KPS score was 90 (range:80-100) and 90 (range:60-100), respectively. The EOR ranges from 60 to 100 % with a minimum of 80 % achieved in 81.3 % cases. Post-operative complications include focal seizures (17.6 %), transient aphasia/dysphasia (17.6 %), permanent motor deficit (11.8 %), transient motor deficit (5.9 %) and transient sensory disturbance (5.9 %). There were no surgery-related mortality or post-operative infection. The median PFS and OS were 13 (95%CI 5-78) and 30 (95%CI 21-78) months, respectively.

CONCLUSION

This is the first study in the UK to solely report outcomes of AC for HGG surgery. Our data demonstrates that AC for HGG in eloquent region is safe, feasible and provides comparable outcomes to those reported in the literature.

CRISPR/Cas9-Mediated Gene Therapy for Glioblastoma: A Scoping Review

This scoping review examines the use of CRISPR/Cas9 gene editing in glioblastoma (GBM), a predominant and aggressive brain tumor. Categorizing gene targets into distinct groups, this review explores their roles in cell cycle regulation, microenvironmental dynamics, interphase processes, and therapy resistance reduction. The complexity of CRISPR-Cas9 applications in GBM research is highlighted, providing unique insights into apoptosis, cell proliferation, and immune responses within the tumor microenvironment. The studies challenge conventional perspectives on specific genes, emphasizing the potential therapeutic implications of manipulating key molecular players in cell cycle dynamics. Exploring CRISPR/Cas9 gene therapy in GBMs yields significant insights into the regulation of cellular processes, spanning cell interphase, renewal, and migration. Researchers, by precisely targeting specific genes, uncover the molecular orchestration governing cell proliferation, growth, and differentiation during critical phases of the cell cycle. The findings underscore the potential of CRISPR/Cas9 technology in unraveling the complex dynamics of the GBM microenvironment, offering promising avenues for targeted therapies to curb GBM growth. This review also outlines studies addressing therapy resistance in GBM, employing CRISPR/Cas9 to target genes associated with chemotherapy resistance, showcasing its transformative potential in effective GBM treatments.

Evaluation of Immunohistochemical Expression of ALK-1 in Gliomas, WHO Grade 4 and Its Correlation with IDH1-R132H Mutation Status

BACKGROUND

Glioblastoma (GB), a grade 4 glioma is the most common primary malignant brain tumor in adults. Recently, the mutation status of isocitrate dehydrogenase (IDH) has been crucial in the treatment of GB. IDH mutant cases display a more favorable prognosis than IDH-wild type ones. The anaplastic lymphoma kinase (ALK) is expressed as a receptor tyrosine kinase in both the developing central and peripheral nervous systems. Increasing lines of evidence suggest that ALK is over-expressed in GB and represents a potential therapeutic target.

OBJECTIVES

The goal of the current study was to investigate ALK-1 immunohistochemical expression in gliomas, grade 4, besides its correlation with IDH1-R132H mutation status and the clinicopathological parameters of the tumors.

MATERIAL AND METHODS

Seventy cases of gliomas, grade 4 were tested for immunohistochemical expression of ALK-1 & IDH1-R132H in the tumor cells.

RESULTS

ALK-1 immunoexpression was detected in 22.9% of our cases and IDH1-R132H mutation was detected in 12.9% of them. ALK-1 expression (100%) was only detected in the more aggressive IDH R132H-negative GBs. ALK-1 expression was also noted in the larger-sized tumors, more in males and patients older than the mean age.  Conclusion: Our results suggest that mutations in ALK-1 may predict a more dismal prognosis since ALK expression was only noted in IDH-R132H negative GBs known to have a considerably poorer outcome compared to IDH-R132H mutant cases. GBs with detectable ALK-protein expression could potentially experience substantial clinical advantages through the utilization of newly introduced ALK inhibitors allowing personalized treatment to a subset of patients. Hence, future studies targeting ALK in IDH wildtype Glioblastomas including clinical trials on larger scales are recommended.

Identification of IDH and TERTp mutations using dynamic susceptibility contrast MRI with deep learning in 162 gliomas

PURPOSE

Isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutations play crucial roles in glioma biology. Such genetic information is typically obtained invasively from excised tumor tissue; however, these mutations need to be identified preoperatively for better treatment planning. The relative cerebral blood volume (rCBV) information derived from dynamic susceptibility contrast MRI (DSC-MRI) has been demonstrated to correlate with tumor vascularity, functionality, and biology, and might provide some information about the genetic alterations in gliomas before surgery. Therefore, this study aims to predict IDH and TERTp mutational subgroups in gliomas using deep learning applied to rCBV images.

METHOD

After the generation of rCBV images from DSC-MRI data, classical machine learning algorithms were applied to the features obtained from the segmented tumor volumes to classify IDH and TERTp mutation subgroups. Furthermore, pre-trained convolutional neural networks (CNNs) and CNNs enhanced with attention gates were trained using rCBV images or a combination of rCBV and anatomical images to classify the mutational subgroups.

RESULTS

The best accuracies obtained with classical machine learning algorithms were 83 %, 68 %, and 76 % for the identification of IDH mutational, TERTp mutational, and TERTp-only subgroups, respectively. On the other hand, the best-performing CNN model achieved 88 % accuracy (86 % sensitivity, 91 % specificity) for the IDH-mutational subgroups, 70 % accuracy (73 % sensitivity and 67 % specificity) for the TERTp-mutational subgroups, and 84 % accuracy (86 % sensitivity, 81 % specificity) for the TERTp-only subgroup using attention gates.

CONCLUSIONS

DSC-MRI can be utilized to noninvasively classify IDH- and TERTp-based molecular subgroups of gliomas, facilitating preoperative identification of these genetic alterations.

Synergistic inhibition of glioblastoma multiforme through an in-silico analysis of luteolin and ferulic acid derived from Angelica sinensis and Cannabis sativa: Advancements in computational therapeutics

The primary objective of this study is to uncover novel therapeutic agents for the treatment of Glioblastoma Multiforme (GBM), a highly aggressive form of brain cancer, and Alzheimer's Disease (AD). Given the complexity and resistance associated with both conditions, the study underscores the imperative need for therapeutic alternatives that can traverse the biological intricacies inherent in both neuro-oncological and neurodegenerative disorders. To achieve this, a meticulous, target-based virtual screening was employed on an ensemble of 50 flavonoids and polyphenol derivatives primarily derived from plant sources. The screening focused predominantly on molecular targets pertinent to GBM but also evaluated the potential overlap with neural pathways involved in AD. The study utilized molecular docking and Molecular Dynamic (MD) simulation techniques to analyze the interaction of these compounds with a key biological target, protein tyrosine phosphatase receptor-type Z (PTPRZ). Out of the 50 compounds examined, 10 met our stringent criteria for binding affinity and specificity. Subsequently, the highest value of binding energy was observed for the synergistic binding of luteolin and ferulic acid with the value of -10.5 kcal/mol. Both compounds exhibited inherent neuroprotective properties and demonstrated significant potential as pathway inhibitors in GBM as well as molecular modulators in AD. Drawing upon advanced in-silico cytotoxicity predictions and sophisticated molecular modeling techniques, this study casts a spotlight on the therapeutic capabilities of polyphenols against GBM. Furthermore, our findings suggest that leveraging these compounds could catalyze a much-needed paradigm shift towards more integrative therapeutic approaches that span the breadth of both neuro-oncology and neurodegenerative diseases. The identification of cross-therapeutic potential in flavonoids and polyphenols could drastically broaden the scope of treatment modalities against both fatal diseases.

Liquid biopsy: creating opportunities in brain space

In recent years, liquid biopsy has emerged as an alternative method to diagnose and monitor tumors. Compared to classical tissue biopsy procedures, liquid biopsy facilitates the repetitive collection of diverse cellular and acellular analytes from various biofluids in a non/minimally invasive manner. This strategy is of greater significance for high-grade brain malignancies such as glioblastoma as the quantity and accessibility of tumors are limited, and there are collateral risks of compromised life quality coupled with surgical interventions. Currently, blood and cerebrospinal fluid (CSF) are the most common biofluids used to collect circulating cells and biomolecules of tumor origin. These liquid biopsy analytes have created opportunities for real-time investigations of distinct genetic, epigenetic, transcriptomics, proteomics, and metabolomics alterations associated with brain tumors. This review describes different classes of liquid biopsy biomarkers present in the biofluids of brain tumor patients. Moreover, an overview of the liquid biopsy applications, challenges, recent technological advances, and clinical trials in the brain have also been provided.

Strategies increasing the effectiveness of temozolomide at various levels of anti-GBL therapy

Glioblastoma (GBL) is the most common (60-70% of primary brain tumours) and the most malignant of the glial tumours. Although current therapies remain palliative, they have been proven to prolong overall survival. Within an optimal treatment regimen (incl. surgical resection, radiation therapy, and chemotherapy) temozolomide as the current anti-GBL first-line chemotherapeutic has increased the median overall survival to 14-15 months, and the percentage of patients alive at two years has been reported to rise from 10.4% to 26.5%. Though, the effectiveness of temozolomide chemotherapy is limited by the serious systemic, dose-related side effects. Therefore, the ponderation regarding novel treatment methods along with innovative formulations is crucial to emerging the therapeutic potential of the widely used drug simultaneously reducing the drawbacks of its use. Herein the complex temozolomide application restrictions present at different levels of therapy as well as, the currently proposed strategies aimed at reducing those limitations are demonstrated. Approaches increasing the efficacy of anti-GBL treatment are addressed. Our paper is focused on the most recent developments in the field of nano/biomaterials-based systems for temozolomide delivery and their functionalization towards more effective blood-brain-barrier crossing and/or tumour targeting. Appropriate designing accounting for the physical and chemical features of formulations along with distinct routes of administration is also discussed. In addition, considering the multiple resistance mechanisms, the molecular heterogeneity and the evolution of tumour the purposely selected delivery methods, the combined therapeutic approaches and specifically focused on GBL cells therapies are reviewed.

Insights into the Role of LncRNAs and miRNAs in Glioma Progression and Their Potential as Novel Therapeutic Targets

Accumulating evidence supports that both long non-coding and micro RNAs (lncRNAs and miRNAs) are implicated in glioma tumorigenesis and progression. Poor outcome of gliomas has been linked to late-stage diagnosis and mostly ineffectiveness of conventional treatment due to low knowledge about the early stage of gliomas, which are not possible to observe with conventional diagnostic approaches. The past few years witnessed a revolutionary advance in biotechnology and neuroscience with the understanding of tumor-related molecules, including non-coding RNAs that are involved in the angiogenesis and progression of glioma cells and thus are used as prognostic biomarkers as well as novel therapeutic targets. The emerging research on lncRNAs and miRNAs highlights their crucial role in glioma progression, offering new insights into the disease. These non-coding RNAs hold significant potential as novel therapeutic targets, paving the way for innovative treatment approaches against glioma. This review encompasses a comprehensive discussion about the role of lncRNAs and miRNAs in gene regulation that is responsible for the promotion or the inhibition of glioma progression and collects the existing links between these key cancer-related molecules.

Association of dynamic susceptibility contrast- and dynamic contrast-enhanced magnetic resonance imaging parameters with molecular marker status in lower-grade gliomas: A retrospective study

PURPOSE

Molecular marker status is clinically relevant for treatment planning and predicting the prognosis of gliomas. This study aimed to assess whether quantitative imaging parameters from dynamic susceptibility contrast- (DSC-) and dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) can predict the molecular marker status of lower-grade gliomas (LGGs).

MATERIALS AND METHODS

Overall, 132 patients with LGGs who underwent DSC- and DCE-MRI were retrospectively enrolled. Statuses of relevant molecular markers including isocitrate dehydrogenase isoenzyme (IDH), 1p19q codeletion, epidermal growth factor receptor (EGFR), O6-methylguanine-DNA methyltransferase (MGMT), and telomerase reverse transcriptase (TERT) were collected. For each molecular marker, age, tumor diameter and location, and DSC- and DCE-MRI parameters, including the normalized cerebral blood volume (nCBV), volume transfer constant (Ktrans), rate transfer coefficient (Kep), extravascular extracellular volume fraction (Ve), and plasma volume fraction (Vp), were compared. Multivariable logistic regression analyses were performed.

RESULTS

The nCBV was significantly lower in LGGs with IDH mutation (p = .001) and TERT mutation (p = .027) than those without these mutations. Ktrans (p = .034), Ve (p = .023), and Vp (p = .044) values were significantly lower in MGMT methylated LGGs than in MGMT unmethylated LGGs. Perfusion parameters were not significantly associated with EGFR amplification and 1p19q codeletion. Young age (p < .001) and small diameter (p = .001) were significantly associated with IDH mutation. The nCBV was independently associated with IDH status (AUC, 0.817; 95% CI: 0.739-0.894).

CONCLUSIONS

DSC- and DCE-MRI parameters demonstrated correlations with molecular markers of LGGs. Especially, the nCBV can be helpful in predicting the IDH mutation status.

Circular RNA circFAT1(e2) Facilitates Cell Progression through the miR-30e-5P/MYBL2 Pathway in Glioma

Objective

To explore the mechanism of glioma from MYB family genes from the perspective of the circRNA-miRNA-mRNA regulatory network.

Methods

First, the MYB family genes were analyzed by multiple bioinformatics analyses to identify one gene most associated with glioma. Then, the prognostic value and clinical characteristics of the gene were evaluated by bioinformatics analysis and experiments in glioma cells. Next, the target miRNA and circRNA were predicted and verified by dual-luciferase reporter assays. Besides, the functions of target circRNA in glioma were investigated by CCK-8 and Transwell assays. At last, the relation between the screened MYB gene, miRNA, and circRNA in glioma was identified by rescue experiments.

Results

After expression and Cox and survival analysis of six MYB family genes, MYBL2 was identified as the gene most associated with glioma. Then, we found that MYBL2 expression in primary gliomas was higher than those in other histologies, and it had variable expression according to clinical features. Furthermore, MYBL2 knockdown in glioma cells impairs cell growth, invasion, and migration in functional studies. Then, miR-30e-5p and circFAT1(e2) were identified as targets of MYBL2 by bioinformatics prediction and experimental verification. Finally, the relationship between circFAT1(e2), MYBL2, and miR-30e-5p was elucidated by rescue experiments.

Conclusion

circFAT1(e2) could promote glioma development by regulating MYBL2 and miR-30e-5p, and MYBL2 has diagnostic and prognostic values in glioma.

Qualitative and Quantitative Magnetic Resonance Imaging Phenotypes May Predict CDKN2A/B Homozygous Deletion Status in Isocitrate Dehydrogenase-Mutant Astrocytomas: A Multicenter Study

OBJECTIVE

Cyclin-dependent kinase inhibitor (CDKN)2A/B homozygous deletion is a key molecular marker of isocitrate dehydrogenase (IDH)-mutant astrocytomas in the 2021 World Health Organization. We aimed to investigate whether qualitative and quantitative MRI parameters can predict CDKN2A/B homozygous deletion status in IDH-mutant astrocytomas.

MATERIALS AND METHODS

Preoperative MRI data of 88 patients (mean age ± standard deviation, 42.0 ± 11.9 years; 40 females and 48 males) with IDH-mutant astrocytomas (76 without and 12 with CDKN2A/B homozygous deletion) from two institutions were included. A qualitative imaging assessment was performed. Mean apparent diffusion coefficient (ADC), 5th percentile of ADC, mean normalized cerebral blood volume (nCBV), and 95th percentile of nCBV were assessed via automatic tumor segmentation. Logistic regression was performed to determine the factors associated with CDKN2A/B homozygous deletion in all 88 patients and a subgroup of 47 patients with histological grades 3 and 4. The discrimination performance of the logistic regression models was evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

In multivariable analysis of all patients, infiltrative pattern (odds ratio [OR] = 4.25, p = 0.034), maximal diameter (OR = 1.07, p = 0.013), and 95th percentile of nCBV (OR = 1.34, p = 0.049) were independent predictors of CDKN2A/B homozygous deletion. The AUC, accuracy, sensitivity, and specificity of the corresponding model were 0.83 (95% confidence interval [CI], 0.72-0.91), 90.4%, 83.3%, and 75.0%, respectively. On multivariable analysis of the subgroup with histological grades 3 and 4, infiltrative pattern (OR = 10.39, p = 0.012) and 95th percentile of nCBV (OR = 1.24, p = 0.047) were independent predictors of CDKN2A/B homozygous deletion, with an AUC accuracy, sensitivity, and specificity of the corresponding model of 0.76 (95% CI, 0.60-0.88), 87.8%, 80.0%, and 58.1%, respectively.

CONCLUSION

The presence of an infiltrative pattern, larger maximal diameter, and higher 95th percentile of the nCBV may be useful MRI biomarkers for CDKN2A/B homozygous deletion in IDH-mutant astrocytomas.

Biological implications and clinical potential of invasion and migration related miRNAs in glioma

Gliomas are the most common primary malignant brain tumors and are highly aggressive. Invasion and migration are the main causes of poor prognosis and treatment resistance in gliomas. As migration and invasion occur, patient survival and prognosis decline dramatically. MicroRNAs (miRNAs) are small, non-coding 21–23 nucleotides involved in regulating the malignant phenotype of gliomas, including migration and invasion. Numerous studies have demonstrated the mechanism and function of some miRNAs in glioma migration and invasion. However, the biological and clinical significance (including diagnosis, prognosis, and targeted therapy) of glioma migration and invasion-related miRNAs have not been systematically discussed. This paper reviews the progress of miRNAs-mediated migration and invasion studies in glioma and discusses the clinical value of migration and invasion-related miRNAs as potential biomarkers or targeted therapies for glioma. In addition, these findings are expected to translate into future directions and challenges for clinical applications. Although many biomarkers and their biological roles in glioma invasion and migration have been identified, none have been specific so far, and further exploration of clinical treatment is still in progress; therefore, we aimed to further identify specific markers that may guide clinical treatment and improve the quality of patient survival.

The diagnostic and prognostic potential of the EGFR/MUC4/MMP9 axis in glioma patients

Glioblastoma is the most aggressive form of brain cancer, presenting poor prognosis despite current advances in treatment. There is therefore an urgent need for novel biomarkers and therapeutic targets. Interactions between mucin 4 (MUC4) and the epidermal growth factor receptor (EGFR) are involved in carcinogenesis, and may lead to matrix metalloproteinase-9 (MMP9) overexpression, exacerbating cancer cell invasiveness. In this study, the role of MUC4, MMP9, and EGFR in the progression and clinical outcome of glioma patients was investigated. Immunohistochemistry (IHC) and immunofluorescence (IF) in fixed tissue samples of glioma patients were used to evaluate the expression and localization of EGFR, MMP9, and MUC4. Kaplan-Meier survival analysis was also performed to test the prognostic utility of the proteins for glioma patients. The protein levels were assessed with enzyme-linked immunosorbent assay (ELISA) in serum of glioma patients, to further investigate their potential as non-invasive serum biomarkers. We demonstrated that MUC4 and MMP9 are both significantly upregulated during glioma progression. Moreover, MUC4 is co-expressed with MMP9 and EGFR in the proliferative microvasculature of glioblastoma, suggesting a potential role for MUC4 in microvascular proliferation and angiogenesis. The combined high expression of MUC4/MMP9, and MUC4/MMP9/EGFR was associated with poor overall survival (OS). Finally, MMP9 mean protein level was significantly higher in the serum of glioblastoma compared with grade III glioma patients, whereas MUC4 mean protein level was minimally elevated in higher glioma grades (III and IV) compared with control. Our results suggest that MUC4, along with MMP9, might account for glioblastoma progression, representing potential therapeutic targets, and suggesting the 'MUC4/MMP9/EGFR axis' may play a vital role in glioblastoma diagnostics.

Fully automated radiomics-based machine learning models for multiclass classification of single brain tumors: Glioblastoma, lymphoma, and metastasis

BACKGROUND AND PURPOSE

To investigate the diagnostic performance of fully automated radiomics-based models for multiclass classification of a single enhancing brain tumor among glioblastoma, central nervous system lymphoma, and metastasis.

MATERIALS AND METHODS

The training and test sets were comprised of 538 cases (300 glioblastomas, 73 lymphomas, and 165 metastases) and 169 cases (101 glioblastomas, 29 lymphomas, and 39 metastases), respectively. After fully automated segmentation, radiomic features were extracted. Three conventional machine learning classifiers, including least absolute shrinkage and selection operator (LASSO), adaptive boosting (Adaboost), and support vector machine with the linear kernel (SVC), combined with one of four feature selection methods, including forward sequential feature selection, F score, mutual information, and LASSO, were trained. Additionally, one ensemble classifier based on the three classifiers was used. The diagnostic performance of the optimized models was tested in the test set using the accuracy, F1-macro score, and the area under the receiver operating characteristic curve (AUCROC).

RESULTS

The best performance was achieved when the LASSO was used as a feature selection method. In the test set, the best performance was achieved by the ensemble classifier, showing an accuracy of 76.3% (95% CI, 70.0-82.7), a F1-macro score of 0.704, and an AUCROC of 0.878.

CONCLUSION

Our fully automated radiomics-based models for multiclass classification might be useful for differential diagnosis of a single enhancing brain tumor with a good diagnostic performance and generalizability.

The complex interactions between the cellular and non-cellular components of the brain tumor microenvironmental landscape and their therapeutic implications

Glioblastoma (GBM), an aggressive high-grade glial tumor, is resistant to therapy and has a poor prognosis due to its universal recurrence rate. GBM cells interact with the non-cellular components in the tumor microenvironment (TME), facilitating their rapid growth, evolution, and invasion into the normal brain. Herein we discuss the complexity of the interactions between the cellular and non-cellular components of the TME and advances in the field as a whole. While the stroma of non-central nervous system (CNS) tissues is abundant in fibrillary collagens, laminins, and fibronectin, the normal brain extracellular matrix (ECM) predominantly includes proteoglycans, glycoproteins, and glycosaminoglycans, with fibrillary components typically found only in association with the vasculature. However, recent studies have found that in GBMs, the microenvironment evolves into a more complex array of components, with upregulated collagen gene expression and aligned fibrillary ECM networks. The interactions of glioma cells with the ECM and the degradation of matrix barriers are crucial for both single-cell and collective invasion into neighboring brain tissue. ECM-regulated mechanisms also contribute to immune exclusion, resulting in a major challenge to immunotherapy delivery and efficacy. Glioma cells chemically and physically control the function of their environment, co-opting complex signaling networks for their own benefit, resulting in radio- and chemo-resistance, tumor recurrence, and cancer progression. Targeting these interactions is an attractive strategy for overcoming therapy resistance, and we will discuss recent advances in preclinical studies, current clinical trials, and potential future clinical applications. In this review, we also provide a comprehensive discussion of the complexities of the interconnected cellular and non-cellular components of the microenvironmental landscape of brain tumors to guide the development of safe and effective therapeutic strategies against brain cancer.

DRAXIN as a Novel Diagnostic Marker to Predict the Poor Prognosis of Glioma Patients

An increasing number of evidences have shown that the carcinogenic effect of DRAXIN plays an important role in the malignant process of tumors, but the mechanism of its involvement in glioma has not yet been revealed. The main aim of this study is to explore the relationship between DRAXIN and the prognosis and pathogenesis of glioma through a large quality of data analysis. Firstly, thousands of tissue samples with clinical information were collected based on various public databases. Then, a series of bioinformatics analyses were performed to mine data from information of glioma samples extracted from several reputable databases to reveal the key role of DRAXIN in glioma development and progression, with the confirmation of basic experiments. Our results showed that high expression of the oncogene DRAXIN in tumor tissue and cells could be used as an independent risk factor for poor prognosis in glioma patients and was strongly associated with clinical risk features. The reverse transcription-quantitative PCR technique was then utilized to validate the DRAXIN expression results we obtained. In addition, co-expression analysis identified, respectively, top 10 genes that were closely associated with DRAXIN positively or negatively. Finally, in vitro experiments demonstrated that knockdown of DRAXIN significantly inhibited proliferation and invasion of glioma cell. To sum up, this is the first report of DRAXIN being highly expressed in gliomas and leading to poor prognosis of glioma patients. DRAXIN may not only benefit to explore the pathogenesis of gliomas, but also serve as a novel biological target for the treatment of glioma.

MS4A6A is a new prognostic biomarker produced by macrophages in glioma patients

MS4A6A has been recognized as being associated with aging and the onset of neurodegenerative disease. However, the mechanisms of MS4A6A in glioma biology and prognosis are ill-defined. Here, we show that MS4A6A is upregulated in glioma tissues, resulting in unfavorable clinical outcomes and poor responses to adjuvant chemotherapy. Multivariate Cox regression analysis suggested that MS4A6A expression can act as a strong and independent predictor for glioma outcomes (CGGA1: HR: 1.765, p < 0.001; CGGA2: HR: 2.626, p < 0.001; TCGA: HR: 1.415, p < 0.001; Rembrandt: HR: 1.809, p < 0.001; Gravendeel: HR: 1.613, p < 0.001). A protein–protein interaction (PPI) network revealed that MS4A6A might be coexpressed with CD68, CD163, and macrophage-specific signatures. Enrichment analysis showed the innate immune response and inflammatory response to be markedly enriched in the high MS4A6A expression group. Additionally, single-cell RNA sequencing (scRNA-seq) analysis revealed distinctive expression features for MS4A6A in macrophages in the glioma immune microenvironment (GIME). Immunofluorescence staining confirmed colocalization of CD68/MS4A6A and CD163/MS4A6A in macrophages. Correlation analysis revealed that MS4A6A expression is positively related to the tumor mutation burden (TMB) of glioma, displaying the high potential of applying MS4A6A to evaluate responsiveness to immunotherapy. Altogether, our research indicates that MS4A6A upregulation may be used as a promising and effective indicator for adjuvant therapy and prognosis assessment.

Clinical outcomes and a therapeutic indication of intramedullary spinal cord astrocytoma

STUDY DESIGN

Retrospective cohort study.

OBJECTIVES

Although intramedullary astrocytoma is associated with a high mortality rate, the optimal treatment has not reached a consensus. This study aimed at evaluating neurologic function and overall survival rate (OSR) in the treatment of this tumor.

SETTING

The single institution in Japan.

METHODS

This study enrolled 67 subjects who underwent surgical treatment for intramedullary astrocytoma. Demographic, imaging, and surgical information were collected from each participant. Tumors were histologically categorized using the World Health Organization classification, and subjects were divided into low-grade (I and II; n = 40) and high-grade (III and IV; n = 27) groups. Neurologic status was evaluated using the modified McCormick scale (MMS). OSR was assessed using Kaplan-Meier methods.

RESULTS

The OSR decreased when the pathological grade increased (p < 0.01). Regarding the therapeutic efficacy for low-grade astrocytomas, subjects who underwent gross total resection (GTR) showed a higher OSR than those who did not (p = 0.02). GTR prevented worsening of MMS score, while non-GTR increased the MMS score (p < 0.01). In the high-grade group, 19 and 10 underwent radiation therapy and chemotherapy, respectively. However, both treatments did not improve OSR. Cordotomy was performed for subjects whose lesional area was at the thoracic level, but the OSR did not significantly increase.

CONCLUSIONS

The most beneficial therapeutic strategy for low-grade astrocytomas was GTR, whereas that for the high-grade tumors was unclear. Further studies with a larger sample size are warranted to validate the effective treatment for malignant astrocytomas.

ITGB3BP is a potential biomarker associated with poor prognosis of glioma

Despite the growing recognition of ITGB3BP as an essential feature of various cancers, the relationship between ITGB3BP and glioma remains unclear. The main aim of this study was to determine the prognostic and diagnostic value of ITGB3BP in glioma. RNA-Seq and microarray data from 2222 glioma patients were included, and we found that the expression level of ITGB3BP in glioma tissues was significantly higher than that in normal brain tissues. Moreover, ITGB3BP can be considered an independent risk factor for poor prognosis and has great predictive value for the prognosis of glioma. Gene Set Enrichment Analysis results showed that ITGB3BP contributes to the poor prognosis of glioma by activating tumour-related signalling pathways. Some small-molecule drugs were identified, such as hexestrol, which may specifically inhibit ITGB3BP and be useful in the treatment of glioma. The TIMER database analysis results revealed a correlation between the expression of ITGB3BP and the infiltration of various immune cells in glioma. Our findings provide the first evidence that the up-regulation of ITGB3BP correlates with poor prognosis in human glioma. Thus, ITGB3BP is a potential new biomarker that can be used for the clinical diagnosis and treatment of glioma.

Whole-Tumor Histogram Analysis of Multiple Diffusion Metrics for Glioma Genotyping

Background The isocitrate dehydrogenase (IDH) genotype and 1p/19q codeletion status are key molecular markers included in glioma pathologic diagnosis. Advanced diffusion models provide additional microstructural information. Purpose To compare the diagnostic performance of histogram features of multiple diffusion metrics in predicting glioma IDH and 1p/19q genotyping. Materials and Methods In this prospective study, participants were enrolled from December 2018 to December 2020. Diffusion-weighted imaging was performed by using a spin-echo echo-planar imaging sequence with five b values (500, 1000, 1500, 2000, and 2500 sec/mm²) in 30 directions for every b value and one b value of 0. Diffusion metrics of diffusion-tensor imaging (DTI), diffusion-kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) were calculated, and their histogram features were analyzed in regions that included the entire tumor and peritumoral edema. Comparisons between groups were performed according to IDH genotype and 1p/19q codeletion status. Logistic regression analysis was used to predict the IDH and 1p/19q genotypes. Results A total of 215 participants (115 men, 100 women; mean age, 48 years ± 13 [standard deviation]) with grade II (n = 68), grade III (n = 35), and grade IV (n = 112) glioma were included. Among the DTI, DKI, NODDI, MAP, and total diffusion models, there were no significant differences in the areas under the receiver operating characteristic curve (AUCs) for predicting IDH mutations (AUC, 0.76, 0.82, 0.78, 0.81, and 0.82, respectively; P > .05) and 1p/19q codeletion in gliomas with IDH mutations (AUC, 0.83, 0.81, 0.82, 0.83, and 0.88, respectively; P > .05). A regression model with an R² value of 0.84 was used for the Ki-67 labeling index and histogram features of the diffusion metrics. Conclusion Whole-tumor histogram analysis of multiple diffusion metrics is a promising approach for glioma isocitrate dehydrogenase and 1p/19q genotyping, and the performance of diffusion-tensor imaging is similar to that of advanced diffusion models. Clinical trial registration no. ChiCTR2100048119 © RSNA, 2021 Online supplemental material is available for this article.

Intramolecular charge transfer enhancing strategy based MAO-A specific two-photon fluorescent probes for glioma cell/tissue imaging

MAO-A promotes the proliferation of human glioma cells. Herein, we report a series of MAO-A specific two-photon small molecular fluorescent probes (A1-5) based on an intramolecular charge transfer enhancing strategy. The activity of endogenous MAO-A can be selectively imaged using A3 as a representative probe in different biological samples including human glioma cells/tissues via two-photon fluorescence microscopy. The study provides new tools for the visual detection of glioma.

Overexpression of GINS4 is associated with poor prognosis and survival in glioma patients

Background

GINS4, an indispensable component of the GINS complex, is vital for a variety of cancer. However, no known empirical research has focused on exploring relationships between GINS4 and glioma. Thus, this study aims to understand and explain the role of GINS4 in glioma.

Method

First, we used the data in the CGGA, TCGA, GEO, GEPIA, and HPA databases to explore the expression level of GINS4 in glioma, the correlation between GINS4 expression and the clinical features of glioma, its impact on the survival of glioma patients, and verified the analysis results through RT-qPCR, IHC, and meta-analysis. Subsequently, GSEA enrichment analysis is used to find the potential molecular mechanism of GINS4 to promote the malignant process of glioma and the anti-glioma drugs that may target GINS4 screened by CMap analysis. Moreover, we further explored the influence of the GINS4 expression on the immune microenvironment of glioma patients through the TIMER database.

Results

Our results suggested that GINS4 was elevated in glioma, and the overexpression of GINS4 was connected with a vast number of clinical features. The next, GINS4 as an independent prognostic factor, which can result in an unfavorable prognosis of glioma. Once more, GINS4 may be participating in the oncogenesis of glioma through JAK-STAT signaling pathways, etc. 6-thioguanine, Doxazosin, and Emetine had potential value in the clinical application of drugs targeting GINS4. Finally, the expression exhibited a close relationship with some immune cells, especially Dendritic cells.

Conclusion

GINS4 is an independent prognostic factor that led to a poor prognosis of glioma. The present study revealed the probable underlying molecular mechanisms of GINS4 in glioma and provided a potential target for improving the prognosis of glioma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-021-00378-0.

Deep cross-view co-regularized representation learning for glioma subtype identification

The new subtypes of diffuse gliomas are recognized by the World Health Organization (WHO) on the basis of genotypes, e.g., isocitrate dehydrogenase and chromosome arms 1p/19q, in addition to the histologic phenotype. Glioma subtype identification can provide valid guidances for both risk-benefit assessment and clinical decision. The feature representations of gliomas in magnetic resonance imaging (MRI) have been prevalent for revealing underlying subtype status. However, since gliomas are highly heterogeneous tumors with quite variable imaging phenotypes, learning discriminative feature representations in MRI for gliomas remains challenging. In this paper, we propose a deep cross-view co-regularized representation learning framework for glioma subtype identification, in which view representation learning and multiple constraints are integrated into a unified paradigm. Specifically, we first learn latent view-specific representations based on cross-view images generated from MRI via a bi-directional mapping connecting original imaging space and latent space, and view-correlated regularizer and output-consistent regularizer in the latent space are employed to explore view correlation and derive view consistency, respectively. We further learn view-sharable representations which can explore complementary information of multiple views by projecting the view-specific representations into a holistically shared space and enhancing via adversary learning strategy. Finally, the view-specific and view-sharable representations are incorporated for identifying glioma subtype. Experimental results on multi-site datasets demonstrate the proposed method outperforms several state-of-the-art methods in detection of glioma subtype status.

Immunohistochemical characterisation and histopathology of astrocytic neoplasms at a tertiary Nigerian hospital

AIM

To describe histological features and pattern of expression of selected markers including epithelial growth factor receptor (EGFR), mutant p53 and mutant isocitrate dehydrogenase-1 (IDH-1R132H) among astrocytic neoplasms at the University College Hospital, Ibadan, Nigeria.

DESIGN

A retrospective cross-sectional study involving histologically diagnosed Central Nervous System (CNS) neoplasms between January 2004 and December 2015. Haematoxylin and Eosin Slides of 81 cases of astrocytomas were retrieved, re-cut and reviewed. Ethical clearance was obtained from the ethical board of the hospital. Immunohistochemistry using the Biotin-Streptavidin system was performed with IDH-1 R132H, p53 and EGFR mouse monoclonal antibodies (MOABs) specific against all the cases of astrocytomas under review. All cases were graded and classified using the World Health Organisation (WHO) Classification of Central Nervous System tumours (2016). Membranous and cytoplasmic staining of EGFR and IDH-1R132H mouse monoclonal antibodies, respectively, were regarded as positive while nuclear staining of p53 mouse monoclonal antibody was regarded as positive. The data obtained were analysed with the level of statistical significance set at P < .05.

RESULTS

Males constituted a majority of cases, 50 (61.7%). Male-Female ratio was 1.6:1. Mean age was 30.6 years. Tumours were of a higher WHO grade with increasing age, albeit glioblastoma cases tended to present at younger ages. The higher WHO grades were more likely to be located supratentorially. Glioblastomas accounted for most of the diagnosis 39 (48.1%), followed by pilocytic astrocytomas at 23 (28.4%). There was a low positive cytoplasmic expression of IDH-1 with only three (3.7%) being positive, eight (9.9%) showed a positive nuclear expression for mutant p53 while 17 (21%) showed membranous positivity for EGFR expression.

CONCLUSION

There are similar epidemiological trends between our cohort of patients and as described in most instances worldwide. Optimal stratification for astrocytomas can be achieved using a combination of IDH-1/EGFR immunohistochemistry.

Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis

Simple Summary

Glioma prognosis and treatment are based on histopathological characteristics and molecular profile. Following the World Health Organization (WHO) guidelines (2016), the most important molecular diagnostic markers include IDH1/2-genotype and 1p/19q codeletion status, although more recent publications also include ARTX genotype and TERT- and MGMT promoter methylation. Machine learning algorithms (MLAs), however, were described to successfully determine these molecular characteristics non-invasively by using magnetic resonance imaging (MRI) data. The aim of this review and meta-analysis was to define the diagnostic accuracy of MLAs with regard to these different molecular markers. We found high accuracies of MLAs to predict each individual molecular marker, with IDH1/2-genotype being the most investigated and the most accurate. Radiogenomics could therefore be a promising tool for discriminating genetically determined gliomas in a non-invasive fashion. Although encouraging results are presented here, large-scale, prospective trials with external validation groups are warranted.

Abstract

Treatment planning and prognosis in glioma treatment are based on the classification into low- and high-grade oligodendroglioma or astrocytoma, which is mainly based on molecular characteristics (IDH1/2- and 1p/19q codeletion status). It would be of great value if this classification could be made reliably before surgery, without biopsy. Machine learning algorithms (MLAs) could play a role in achieving this by enabling glioma characterization on magnetic resonance imaging (MRI) data without invasive tissue sampling. The aim of this study is to provide a performance evaluation and meta-analysis of various MLAs for glioma characterization. Systematic literature search and meta-analysis were performed on the aggregated data, after which subgroup analyses for several target conditions were conducted. This study is registered with PROSPERO, CRD42020191033. We identified 724 studies; 60 and 17 studies were eligible to be included in the systematic review and meta-analysis, respectively. Meta-analysis showed excellent accuracy for all subgroups, with the classification of 1p/19q codeletion status scoring significantly poorer than other subgroups (AUC: 0.748, p = 0.132). There was considerable heterogeneity among some of the included studies. Although promising results were found with regard to the ability of MLA-tools to be used for the non-invasive classification of gliomas, large-scale, prospective trials with external validation are warranted in the future.

Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics

BACKGROUND

Glioma prognosis depends on isocitrate dehydrogenase (IDH) mutation status. We aimed to predict the IDH status of gliomas from preoperative MR images using a fully automated hybrid approach with convolutional neural networks (CNNs) and radiomics.

METHODS

We reviewed 1166 preoperative MR images of gliomas (grades II-IV) from Severance Hospital (n = 856), Seoul National University Hospital (SNUH; n = 107), and The Cancer Imaging Archive (TCIA; n = 203). The Severance set was subdivided into the development (n = 727) and internal test (n = 129) sets. Based on T1 postcontrast, T2, and fluid-attenuated inversion recovery images, a fully automated model was developed that comprised a CNN for tumor segmentation (Model 1) and CNN-based classifier for IDH status prediction (Model 2) that uses a hybrid approach based on 2D tumor images and radiomic features from 3D tumor shape and loci guided by Model 1. The trained model was tested on internal (a subset of the Severance set) and external (SNUH and TCIA) test sets.

RESULTS

The CNN for tumor segmentation (Model 1) achieved a dice coefficient of 0.86-0.92 across datasets. Our hybrid model achieved accuracies of 93.8%, 87.9%, and 78.8%, with areas under the receiver operating characteristic curves of 0.96, 0.94, and 0.86 and areas under the precision-recall curves of 0.88, 0.82, and 0.81 in the internal test, SNUH, and TCIA sets, respectively.

CONCLUSIONS

Our fully automated hybrid model demonstrated the potential to be a highly reproducible and generalizable tool across different datasets for the noninvasive prediction of the IDH status of gliomas.

Satellitosis, a Crosstalk between Neurons, Vascular Structures and Neoplastic Cells in Brain Tumours; Early Manifestation of Invasive Behaviour

The secondary structures of Scherer commonly known as perineuronal and perivascular satellitosis have been identified as a histopathological hallmark of diffuse, invasive, high-grade gliomas. They are recognised as perineuronal satellitosis when clusters of neoplastic glial cells surround neurons cell bodies and perivascular satellitosis when such tumour cells surround blood vessels infiltrating Virchow-Robin spaces. In this review, we provide an overview of emerging knowledge regarding how interactions between neurons and glioma cells can modulate tumour evolution and how neurons play a key role in glioma growth and progression, as well as the role of perivascular satellitosis into mechanisms of glioma cells spread. At the same time, we review the current knowledge about the role of perineuronal satellitosis and perivascular satellitosis within the tumour microenvironment (TME), in order to highlight critical knowledge gaps in research space.

Evaluation of the prognostic potential of EGFL7 in pilocytic astrocytomas

Pilocytic astrocytoma (PA) is the most frequent solid neoplasm in childhood. It has a good 5-year overall survival (90% in childhood and 52% in adults). However, up to 20% of patients experience residual tumor growth, recurrence, and death. Although the main genetic alteration of PAs, including KIAA1549:BRAF fusion, involves chromosome 7q34, we previously found frequent loss in chr9q34.3 locus in a small subset of these tumors. Among the genes present in this locus, EGFL7 is related to poor prognosis in several tumor types. In this study, we aimed to assess EGFL7 expression through immunohistochemistry, and to evaluate its prognostic value in a series of 64 clinically and molecularly well-characterized pilocytic astrocytomas. We found high expression of EGFL7 in 71.9% of patients. Low EGFL7 expression was associated with older patients, the mean age mainly older than 11 years (P = 0.027). EGFL7 expression was not associated with presence of KIAA1549:BRAF fusion, BRAF mutation, FGFR1 mutation, nor FGFR1 duplication. Moreover, high EGFL7 expression was associated with high FGFR1 (P = 0.037) and 5'-deoxy-5'-methyltioadenosine phosphorylase (MTAP) (P = 0.005) expression, and with unfavorable outcome of patients (P = 0.047). Multivariate analysis revealed low EGFL7 expression related to older patients and high EGFL7 expression related to retained expression of MTAP. In addition, we found a borderline significance of unfavorable outcome and high EGFL7 expression. Finally, EGFL7 expression was not associated with overall or event-free survival of PA patients. Our findings point to EGFL7 expression as a novel candidate prognostic marker in PA, which should be further investigated.

The Role of Translocator Protein TSPO in Hallmarks of Glioblastoma

Simple Summary

The translocator protein (TSPO) has been under extensive investigation as a specific marker in positron emission tomography (PET) to visualize brain lesions following injury or disease. In recent years, TSPO is increasingly appreciated as a potential novel therapeutic target in cancer. In Glioblastoma (GBM), the most malignant primary brain tumor, TSPO expression levels are strongly elevated and scientific evidence accumulates, hinting at a pivotal role of TSPO in tumorigenesis and glioma progression. The aim of this review is to summarize the current literature on TSPO with respect to its role both in diagnostics and especially with regard to the critical hallmarks of cancer postulated by Hanahan and Weinberg. Overall, our review contributes to a better understanding of the functional significance of TSPO in Glioblastoma and draws attention to TSPO as a potential modulator of treatment response and thus an important factor that may influence the clinical outcome of GBM.

Abstract

Glioblastoma (GBM) is the most fatal primary brain cancer in adults. Despite extensive treatment, tumors inevitably recur, leading to an average survival time shorter than 1.5 years. The 18 kDa translocator protein (TSPO) is abundantly expressed throughout the body including the central nervous system. The expression of TSPO increases in states of inflammation and brain injury due to microglia activation. Not least due to its location in the outer mitochondrial membrane, TSPO has been implicated with a broad spectrum of functions. These include the regulation of proliferation, apoptosis, migration, as well as mitochondrial functions such as mitochondrial respiration and oxidative stress regulation. TSPO is frequently overexpressed in GBM. Its expression level has been positively correlated to WHO grade, glioma cell proliferation, and poor prognosis of patients. Several lines of evidence indicate that TSPO plays a functional part in glioma hallmark features such as resistance to apoptosis, invasiveness, and proliferation. This review provides a critical overview of how TSPO could regulate several aspects of tumorigenesis in GBM, particularly in the context of the hallmarks of cancer proposed by Hanahan and Weinberg in 2011.

Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign

PURPOSE

The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG.

METHODS

Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10^-3mm²/s (V_ADC>1.5), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach.

RESULTS

V_ADC>1.5 classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with V_ADC>1.5 ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign-all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited V_ADC>1.5 ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign.

CONCLUSION

Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with V_ADC>1.5 achieving > 90% classification specificity in both internal and validation cohorts. V_ADC>1.5 exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.

Isocitrate Dehydrogenase Mutations in Glioma: Genetics, Biochemistry, and Clinical Indications

Mutations in isocitrate dehydrogenase (IDH) are commonly observed in lower-grade glioma and secondary glioblastomas. IDH mutants confer a neomorphic enzyme activity that converts α-ketoglutarate to an oncometabolite D-2-hydroxyglutarate, which impacts cellular epigenetics and metabolism. IDH mutation establishes distinctive patterns in metabolism, cancer biology, and the therapeutic sensitivity of glioma. Thus, a deeper understanding of the roles of IDH mutations is of great value to improve the therapeutic efficacy of glioma and other malignancies that share similar genetic characteristics. In this review, we focused on the genetics, biochemistry, and clinical impacts of IDH mutations in glioma.

Glioblastoma: Is There Any Blood Biomarker with True Clinical Relevance?

Glioblastoma (GBM) is the most frequent malignant primary brain tumor in adults, characterized by a highly aggressive, inflammatory and angiogenic phenotype. It is a remarkably heterogeneous tumor at several levels, including histopathologically, radiographically and genetically. The 2016 update of the WHO Classification of Tumours of the Central Nervous System highlighted molecular parameters as paramount features for the diagnosis, namely IDH1/2 mutations that distinguish primary and secondary GBM. An ideal biomarker is a molecule that can be detected/quantified through simple non- or minimally invasive methods with the potential to assess cancer risk; promote early diagnosis; increase grading accuracy; and monitor disease evolution and treatment response, as well as fundamentally being restricted to one aspect. Blood-based biomarkers are particularly attractive due to their easy access and have been widely used for various cancer types. A number of serum biomarkers with multiple utilities for glioma have been reported that could classify glioma grades more precisely and provide prognostic value among these patients. At present, screening for gliomas has no clinical relevance. This is because of the low incidence, the lack of sensitive biomarkers in plasma, and the observation that gliomas may develop apparently de novo within few weeks or months. To the best of our knowledge, there is no routine use of a serum biomarker for clinical follow-up. The purpose of this paper is to review the serum biomarkers described in the literature related to glioblastoma and their possible relationship with clinical features.

Investigation of Human Cytomegalovirus and Human Papillomavirus in Glioma

The study investigated the human cytomegalovirus (HCMV) and human papillomavirus (HPV) in gliomas. A retrospective study was conducted on 112 samples. HCMV was investigated by PCR, in situ hybridization (ISH) and immunohistochemistry. HPV was tested by PCR and DNA ISH. HCMV was identified in 60 gliomas, including 55 GBM. However, RNA ISH and immunohistochemistry failed to detect HCMV positivity. HPV was identified in 44 GBM. No significant relationship was identified between HCMV and HPV and tumour characteristics (p > 0.05). Our findings support the HCMV and HPV presence in gliomas. Further assays are required to more explore the potential efficient antiviral management.

Comprehensive analysis of potential prognostic biomarker in gliomas

Purpose

Gliomas are common intracranial tumors with the characteristic of diffuse and invasive growth. The prognosis is poor, and the recurrence rate and mortality are higher. With the development of big data technology, many methods such as natural language processing, computer vision and image processing have been deeply applied in the medical field. This can help clinicians to provide personalized and precise diagnosis and therapeutic schedule for patients with different type of gliomas to achieve the best therapeutic effect. The purpose of this paper is to summarize and extract useful information from published research results by conducting a secondary analysis of the literature.

Design/methodology/approach

The PubMed and China National Knowledge Infrastructure (CNKI) literature database were used to retrieve published Chinese and English research papers about human gliomas. Comprehensive analysis was applied to conduct this research. The factors affecting survival and prognosis were screened and analyzed respectively in this paper, and different methods for multidimensional data of patients were discussed.

Findings

This paper identified biomarkers and therapeutic modalities associated with prognosis for different grade of gliomas. This paper investigated the relationship among these clinical prognostic factors and different histopathologic tying and grade of gliomas by comprehensive analysis. This paper summarizes the research progress of biomarker in medical imaging and genomics of gliomas to improve prognosis and the current status of treatment in China.

Originality/value

Combined with multimodal data such as genomics data, medical image data and clinical information data, this paper comprehensively analyzed the prognostic factors of glioma and provided guidance and evidence for rational treatment planning and improvement of clinical treatment prognosis.

Abnormally high expression of HOXA2 as an independent factor for poor prognosis in glioma patients

In recent years, studies have revealed HOXA2 as a new oncogene, but its function is unknown in gliomas. We aimed to reveal the relationship between HOXA2 and glioma based on the Chinese Glioma Genome Atlas(CGGA) and the cancer genome atlas (TCGA). HOXA2 expression data and clinically relevant information of glioma patients were obtained from the CGGA and TCGA containing 1447 glioma tissues and five non-tumor brain tissues. The Wilcox or Kruskal tests were used to detect the correlation between the HOXA2 expression level and clinical data of glioma patients. the Kaplan-Meier method were used to examine the relationship between HOXA2 and overall patient survival. Gene set enrichment analysis (GSEA) was conducted to indirectly reveal the signaling pathways involved in HOXA2, and RT-PCR was used to detect HOXA2 expression in gliomas and non-tumor brain tissues. High HOXA2 expression was found to be positively correlated with clinical grade, histological type, age, and tumor recurrence, but negatively correlated with 1p19 codeletion and isocitrate dehydrogenase mutation status.RT-PCR results showed that HOXA2 expression levels were significantly higher in tumor tissues than in non-tumor brain tissues. GSEA showed that HOXA2 promoted the activation of the activation of the JAK-STAT-signaling pathway, focal adhesion, cell-adhesion-molecules-CAMS pathway, cytosolic DNA sensing pathway, and natural killer cell-mediated cytotoxicity. This study revealed for the first time that the novel oncogene,HOXA2, leads to poor prognosis in gliomas, and can be used as a biomarker for the diagnosis and treatment of gliomas.

Adult Glioma WHO Classification Update, Genomics, and Imaging: What the Radiologists Need to Know

Recent advances in the understanding of the genetic makeup of gliomas have led to a paradigm shift in the diagnosis and classification of these tumors. Driven by these changes, the World Health Organization (WHO) introduced an update to its classification system of central nervous system (CNS) tumors in 2016. The updated glioma classification system incorporates molecular markers into tumor subgrouping, which has been shown to better correlate with tumor biology and behavior as well as patient prognosis than the previous purely histology-based classification system. Familiarity with this new classification scheme, the individual molecular markers, and corresponding imaging findings is critical for the radiologists who play an important role in diagnostic and surveillance imaging of patients with CNS tumors. The goals of this article are to review these updates to the WHO classification of CNS tumors with a focus on adult gliomas, provide an overview of key genomic markers of gliomas, and review imaging features pertaining to various genomic subgroups of adult gliomas.

Glioma tumor proteomics: clinically useful protein biomarkers and future perspectives

Introduction: Despite being rare cancers, gliomas account for a significant number of cancer-related deaths. Identification and treatment of these tumors at an early stage would greatly improve the therapeutic outcomes. There is an urgent need for diagnostic and prognostic markers, which can identify disease early and discriminate the subtypes of these tumors thereby improving the existing treatment modalities.Areas covered: In this article, we have reviewed published literature on proteomics biomarkers for gliomas and their importance in diagnosis or prognosis. Proteomic studies for the discovery of protein, autoantibody biomarkers, and biological pathway alterations in serum, CSF and tumor biopsies have been discussed in this review.Expert opinion: The rapid development in the field of mass spectrometry and increased sensitivity and reproducibility in assays has led to the identification and quantification of large number of proteins very precisely. Though genomic markers are the prime focus in the classification of gliomas, incorporating protein markers would further improve the existing classification. In this regard, data mining and studies on large cohorts of glioma patients would help in the identification of diagnostic and prognostic markers ultimately translating to the clinics.

MR image phenotypes may add prognostic value to clinical features in IDH wild-type lower-grade gliomas

PURPOSE

To identify significant prognostic magnetic resonance imaging (MRI) features and their prognostic value when added to clinical features in patients with isocitrate dehydrogenase wild-type (IDHwt) lower-grade gliomas.

MATERIALS AND METHODS

Preoperative MR images of 158 patients (discovery set = 112, external validation set = 46) with IDHwt lower-grade gliomas (WHO grade II or III) were retrospectively analyzed using the Visually Accessible Rembrandt Images feature set. Radiologic risk scores (RRSs) for overall survival were derived from the least absolute shrinkage and selection operator and elastic net. Multivariable Cox regression analysis, including age, Karnofsky Performance score, extent of resection, WHO grade, and RRS, was performed. The added prognostic value of RRS was calculated by comparing the integrated area under the receiver operating characteristic curve (iAUC) between models with and without RRS.

RESULTS

The presence of cysts, pial invasion, and cortical involvement were favorable prognostic factors, while ependymal extension, multifocal or multicentric distribution, nonlobar location, proportion of necrosis > 33%, satellites, and eloquent cortex involvement were significantly associated with worse prognosis. RRS independently predicted survival and significantly enhanced model performance for survival prediction when integrated to clinical features (iAUC increased to 0.773-0.777 from 0.737), which was successfully validated on the validation set (iAUC increased to 0.805-0.830 from 0.735).

CONCLUSION

MRI features associated with prognosis in patients with IDHwt lower-grade gliomas were identified. RRSs derived from MRI features independently predicted survival and significantly improved performance of survival prediction models when integrated into clinical features.

KEY POINTS

• Comprehensive analysis of MRI features conveys prognostic information in patients with isocitrate dehydrogenase wild-type lower-grade gliomas. • Presence of cysts, pial invasion, and cortical involvement of the tumor were favorable prognostic factors. • Radiological phenotypes derived from MRI independently predict survival and have the potential to improve survival prediction when added to clinical features.

Dissecting Molecular Features of Gliomas: Genetic Loci and Validated Biomarkers

Recently, several studies focused on the genetics of gliomas. This allowed identifying several germline loci that contribute to individual risk for tumor development, as well as various somatic mutations that are key for disease classification. Unfortunately, none of the germline loci clearly confers increased risk per se. Contrariwise, somatic mutations identified within the glioma tissue define tumor genotype, thus representing valid diagnostic and prognostic markers. Thus, genetic features can be used in glioma classification and guided therapy. Such copious genomic variabilities are screened routinely in glioma diagnosis. In detail, Sanger sequencing or pyrosequencing, fluorescence in-situ hybridization, and microsatellite analyses were added to immunohistochemistry as diagnostic markers. Recently, Next Generation Sequencing was set-up as an all-in-one diagnostic tool aimed at detecting both DNA copy number variations and mutations in gliomas. This approach is widely used also to detect circulating tumor DNA within cerebrospinal fluid from patients affected by primary brain tumors. Such an approach is providing an alternative cost-effective strategy to genotype all gliomas, which allows avoiding surgical tissue collection and repeated tumor biopsies. This review summarizes available molecular features that represent solid tools for the genetic diagnosis of gliomas at present or in the next future.

Multivariable non-invasive association of isocitrate dehydrogenase mutational status in World Health Organization grade II and III gliomas with advanced magnetic resonance imaging T2 mapping techniques

AIM

To investigate multivariable analyses for noninvasive association of the isocitrate dehydrogenase (IDH) mutational status in grade II and III gliomas including evaluation of T2 mapping-sequences.

METHODS

Magnetic resonance imaging (MRI) examinations with histopathologically proven World Health Organization grade II and III gliomas were retrospectively enrolled. Multivariate receiver operating characteristics (ROC) analyses to associate IDH mutational status were performed containing quantitative T2 mapping analyses and qualitative characteristics (sex, age, localization, heterogeneity, oedema, necrosis and diameter). Relaxation times were calculated pixelwise by means of standardized ROI analyses. Interobserver variability also was tested.

RESULTS

Out of 32 patients (mean age: 50.7 years; range: 32-83), nine had grade II gliomas and 24 grade III, while 59.5% showed a positive IDH mutated state (IDHm) and 40.5% were wildtype (IDHw). Multivariable ROC analyses were calculated for relaxation time and range, localization and age with a cumulative 0.955 area under the curve (AUC) (p < 0.001), while central T2-relaxation time had by far the highest single variable sensitivity (AUC: 0.873; range: 0.762; age: 0.809; localization: 0.713). Age (cut off: 49 years; p = 0.031) and localization (p = 0.014) were the only qualitative parameters found to be significant as IDHw gliomas were older and IDHm gliomas were preferentially located fronto-temporal.

CONCLUSIONS

This is the first study evaluating quantitative T2 mapping sequences for association of the IDH mutational status in grade II and III gliomas demonstrating an association between relaxation time and mutational status. Analyses of T2 mapping relaxation times may even be suitable for predicting the correct IDH mutational state. Prognostic accuracy increases significantly in predicting the correct mutational state when combing T2 relaxation time characteristics and the qualitative MRI features age and localization.

Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status

PURPOSE

To evaluate whether diffusion tensor imaging (DTI) radiomics with machine learning improves the prediction of isocitrate dehydrogenase (IDH) mutation status of lower-grade gliomas beyond radiomic features from conventional MRI and DTI histogram parameters.

METHODS

A total of 168 patients with pathologically confirmed lower-grade gliomas were retrospectively enrolled. A total of 158 and 253 radiomic features were extracted from DTI (DTI radiomics) and conventional MRI (T1-weighted image with contrast enhancement, T2-weighted image, and FLAIR [conventional radiomics]), respectively. The random forest models for predicting IDH status were trained with variable combinations as follows: (1) DTI radiomics, (2) conventional radiomics, (3) conventional radiomics + DTI radiomics, and (4) conventional radiomics + DTI histogram. The models were validated with nested cross-validation. The predictive performances of those models were compared by using area under the curve (AUC) from receiver operating characteristic analysis, and 95% confidence interval (CI) was calculated.

RESULTS

Adding DTI radiomics to conventional radiomics significantly improved the accuracy of IDH status subtyping (AUC, 0.900 [95% CI, 0.855-0.945], p = 0.006), whereas adding DTI histogram parameters yielded nonsignificant trend toward improvement (0.869 [95% CI, 0.816-0.922], p = 0.150) compared with the model with conventional radiomics alone (0.835 [95% CI, 0.773-0.896]). The performance of the model consisting of both DTI and conventional radiomics was significantly superior than that of model consisting of both DTI histogram parameters and conventional radiomics (0.900 vs 0.869, p = 0.040).

CONCLUSION

DTI radiomics with machine learning can help improve the subtyping of IDH status beyond conventional radiomics and DTI histogram parameters in patients with lower-grade gliomas.

Genomic analysis of primary and recurrent gliomas reveals clinical outcome related molecular features

Tremendous efforts have been made to explore biomarkers for classification and grading on gliomas. The goal of this study was to identify more molecular features that are associated with clinical outcomes by comparing the genomic profiles of primary and recurrent gliomas and determine potential recurrence leading factors that are significantly enriched in relapse tumors. Hybrid capture based next generation sequencing (NGS) analysis was performed on 64 primary and 17 recurrent glioma biopsies. Copy number variation (CNV) was more frequent in recurrent tumors and CDKN2A/B loss was significantly enriched. In addition, overall mutations in cell cycle pathway are more common in relapse tumors. The patterns of gene sets, including IDH1/TERT and IDH1/TP53 exhibited significant difference between the groups. Survival analysis uncovered the worse disease-free survival (DFS) and overall survival (OS) associated with altered copy number and excessive activation of CELL CYCLE pathway. High Tumor Mutation Burden (TMB) was also a biomarker with great potential for poor prognosis. The assessment of genomic characteristics in primary versus recurrent gliomas aids the discovery of potential predictive biomarkers. The prognostic value of TMB in gliomas was raised for the first time.

The FDA NIH Biomarkers, EndpointS, and other Tools (BEST) resource in neuro-oncology

In early 2016, the FDA and the National Institutes of Health (NIH) published the first version of the glossary included in the Biomarkers, EndpointS, and other Tools (BEST) resource.1 The BEST glossary was constructed to harmonize and clarify terms used in translational science and medical product development and to provide a common language used for communication by those agencies. It is considered a "living" document that will be updated in the future. This review will discuss the main biomarker and clinical outcome categories contained in the BEST glossary as they apply to neuro-oncology, as well as the overlapping and hierarchical relationships among them.

Clinical prognostic value of isocitrate dehydrogenase mutation, O-6-methylguanine-DNA methyltransferase promoter methylation, and 1p19q co-deletion in glioma patients

Background

Gliomas are the most frequently occurring malignant brain cancers. Recently, isocitrate dehydrogenase (IDH) mutations, O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation, and 1p/19q co-deletion have been suggested to indicate a favorable prognosis in gliomas. However, the clinical prognostic value of these genetic tests in human gliomas is not fully understood.

Methods

We included glioma patients who accepted genetic testing including IDH, MGMT and 1p/19q at Xiangya Hospital, Central South University in China (Jan 2015 to Jun 2017) and further analyzed the effect of the above gene states in high-grade gliomas.

Results

In 103 high-grade glioma patients, IDH mutation, MGMT promoter methylation, and 1p/19q co-deletion had better progression-free survival (PFS) than IDH wild-type (P=0.005), MGMT unmethylated promoter (P=0.002), and without 1p19q co-deletion (P=0.008), respectively. Additionally, we classified the above gliomas into 5 molecular groups, triple-positive, IDH mutation and MGMT methylation, methylation in MGMT only, mutation in IDH only, and triple-negative, according to characteristics of recruited patients. We found that triple-positive gliomas had better PFS than triple-negative cases in high-grade patients (P=0.016). Moreover, the IDH mutation and MGMT methylation groups had prolonged PFS compared to triple-negative (P=0.029).

Conclusions

Our study reinforced the clinical value of biomarkers, including 1p/19q co-deletion, IDH mutation, and the most prominent MGMT methylation, as previously described in glioma prognosis. Further, triple-negative patients have poorer PFS, indicating that the states of these genes can be divided into subgroups as a potential prognostic marker for clinical treatment, which requires a larger, multicenter study to testify.