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

Overexpression of LSR suppresses glioma proliferation and invasion via regulating FOXO3a

PURPOSE

Gliomas, the most prevalent type of central nervous system tumors, currently lack effective therapeutic options. Lipolysis-stimulated lipoprotein receptors (LSR) have been implicated in tumor development and progression. This study aims to investigate the influence of LSR on gliomas and elucidate the underlying mechanisms.

METHODS

We analyze LSR expression in gliomas and its association with patient prognosis using bioinformatics tools. Western blotting and immunohistochemistry revealed differential expression of LSR across different grades of glioma. The effects of LSR on glioma cell proliferation and invasion are evaluated through a series of cellular assays. Subcutaneous xenografts in nude mice are utilized to assess the impact of LSR on gliomas in vivo. Additionally, western blotting is employed to detect changes in protein levels related to the FOXO3a signaling pathway following LSR overexpression.

RESULTS

LSR expression is higher in tissues from low-grade gliomas compared to those from glioblastomas. Patients with low LSR expression exhibit poorer prognoses. Overexpression of LSR inhibit glioma cell proliferation and invasion. The protein levels of PCNA, Cyclin D1, MMP2, and MMP9 are significantly decreased in the OE-LSR group. Tumor volume is reduced in nude mice injected subcutaneously with LSR-overexpressing glioma cells. Overexpression of LSR increases nuclear FOXO3a level while reduces p-FOXO3a and p-14-3-3 levels. Knockdown of FOXO3a reverse the inhibitory effects of LSR overexpression on glioma cell proliferation and invasion.

CONCLUSION

Low LSR expression is associated with adverse prognosis in glioma patients. By modulating FOXO3a, LSR overexpression suppresses glioma cell proliferation and invasion.

MRI-based habitat imaging predicts high-risk molecular subtypes and early risk assessment of lower-grade gliomas

BACKGROUND

In lower-grade gliomas (LrGGs, histological grades 2-3), there exist a minority of high-risk molecular subtypes with malignant transformation potential, associated with unfavorable clinical outcomes and shorter survival prognosis. Identifying high-risk molecular subtypes early in LrGGs and conducting preoperative prognostic evaluations are crucial for precise clinical diagnosis and treatment.

MATERIALS AND METHODS

We retrospectively collected data from 345 patients with LrGGs and comprehensively screened key high-risk molecular markers. Based on preoperative MRI sequences (CE-T1WI/T2-FLAIR), we employed seven classifiers to construct models based on habitat, radiomics, and combined. Eventually, we identified Extra Trees based on habitat features as the optimal predictive model for identifying high-risk molecular subtypes of LrGGs. Moreover, we developed a prognostic prediction model based on radiomics score (Radscore) to assess the survival outlook of patients with LrGGs. We utilized Kaplan-Meier (KM) survival analysis alongside the log-rank test to discern variations in survival probabilities among high-risk and low-risk cohorts. The concordance index was employed to gauge the efficacy of habitat, clinical, and amalgamated prognosis models. Calibration curves were utilized to appraise the congruence between the anticipated survival probability and the actual survival probability projected by the models.

RESULTS

The habitat model for predicting high-risk molecular subtypes of LrGGs, achieved AUCs of 0.802, 0.771, and 0.768 in the training set, internal test set, and external test set, respectively. Comparison among habitat, clinical, combined prognostic models revealed that the combined prognostic model exhibited the highest performance (C-index = 0.781 in the training set, C-index = 0.778 in the internal test set, C-index = 0.743 in the external test set), followed by the habitat prognostic model (C-index = 0.749 in the training set, C-index = 0.716 in the internal test set, C-index = 0.707 in the external test set), while the clinical prognostic model performed the worst (C-index = 0.717 in the training set, C-index = 0.687 in the internal test set, C-index = 0.649 in the external test set). Furthermore, the calibration curves of the combined model exhibited satisfactory alignment when forecasting the 1-year, 2-year, and 3-year survival probabilities of patients with LrGGs.

CONCLUSION

The MRI-based habitat model simultaneously achieves the objectives of non-invasive prediction of high-risk molecular subtypes of LrGGs and assessment of survival prognosis. This has incremental value for early non-invasive warning of malignant transformation in LrGGs and risk-stratified management.

Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma

BACKGROUND

Glioblastoma (GBM) is a highly aggressive brain tumor associated with a poor patient prognosis. The survival rate remains low despite standard therapies, highlighting the urgent need for novel treatment strategies. Advanced imaging techniques, particularly magnetic resonance imaging (MRI), are crucial in assessing GBM. Disruptions in various oncogenic signaling pathways, such as Receptor Tyrosine Kinase (RTK)-Ras-Extracellular signal-regulated kinase (ERK) signaling, Phosphoinositide 3- Kinases (PI3Ks), tumor protein p53 (TP53), and Neurogenic locus notch homolog protein (NOTCH), contribute to the development of different tumor types, each exhibiting distinct morphological and phenotypic features that can be observed at a microscopic level. However, identifying genetic abnormalities for targeted therapy often requires invasive procedures, prompting exploration into non-invasive approaches like radiogenomics. This study explores the utility of radiogenomics and machine learning (ML) in predicting these oncogenic signaling pathways in GBM patients.

METHODS

We collected post-operative MRI scans (T1w, T1c, FLAIR, T2w) from the BRATS-19 dataset, including scans from patients with both GBM and LGG, linked to genetic and clinical data via TCGA and CPTAC. Signaling pathway data was manually extracted from cBioPortal. Radiomic features were extracted from four MRI modalities using PyRadiomics. Dimensionality reduction and feature selection were applied and Data imbalance was addressed with SMOTE. Five ML models were trained to predict signaling pathways, with Grid Search optimizing hyperparameters and 5-fold cross-validation ensuring unbiased performance. Each model's performance was evaluated using various metrics on test data.

RESULTS

Our results showed a positive association between most signaling pathways and the radiomic features derived from MRI scans. The best models achieved high AUC scores, namely 0.7 for RTK-RAS, 0.8 for PI3K, 0.75 for TP53, and 0.4 for NOTCH, and therefore, demonstrated the potential of ML models in accurately predicting oncogenic signaling pathways from radiomic features, thereby informing personalized therapeutic approaches and improving patient outcomes.

CONCLUSION

We present a novel approach for the non-invasive prediction of deregulation in oncogenic signaling pathways in glioblastoma (GBM) by integrating radiogenomic data with machine learning models. This research contributes to advancing precision medicine in GBM management, highlighting the importance of integrating radiomics with genomic data to understand tumor behavior and treatment response better.

MiRNAs as major players in brain health and disease: current knowledge and future perspectives

MicroRNAs are regulators of gene expression and their dysregulation can lead to various diseases. MicroRNA-135 (MiR-135) exhibits brain-specific expression, and performs various functions such as neuronal morphology, neural induction, and synaptic function in the human brain. Dysfunction of miR-135 has been reported in brain tumors, and neurodegenerative and neurodevelopmental disorders. Several reports show downregulation of miR-135 in glioblastoma, indicating its tumor suppressor role in the pathogenesis of brain tumors. In this review, by performing in silico analysis of molecular targets of miR-135, we reveal the significant pathways and processes modulated by miR-135. We summarize the biological significance, roles, and signaling pathways of miRNAs in general, with a focus on miR-135 in different neurological diseases including brain tumors, and neurodegenerative and neurodevelopmental disorders. We also discuss methods, limitations, and potential of glioblastoma organoids in recapitulating disease initiation and progression. We highlight the promising therapeutic potential of miRNAs as antitumor agents for aggressive human brain tumors including glioblastoma.

PRMT1 promotes radiotherapy resistance in glioma stem cells by inhibiting ferroptosis

PURPOSE

The existence of glioma stem cells (GSCs) in cancer is related to glioma radiotherapy resistance. In this research, the effect of protein arginine methyltransferase 1 (PRMT1) on the radiosensitivity of glioma stem cell (GSC)-like cells, as well as its underlying mechanism, was investigated.

METHODS

GSCs-like cells were analyzed and identified by flow cytometry. The self-renewal capability was evaluated by sphere-forming assay. The PRMT1 expression level in glioblastoma were analyzed using the Gene Expression Profiling Interactive Analysis database. The mRNA and protein were scrutinized by RT-qPCR and western blot, respectively. The radiosensitivity was evaluated by clonogenic survival assay. Ferroptosis was evaluated by detecting the levels of reactive oxygen species, malondialdehyde, Fe2+, glutathione, and 4-hydroxynonenal.

RESULTS

U87 and SHG44 cells with GSC-like phenotype (GSC-U87 and GSC-SHG44) displayed strong expression of CD133 and nestin versus the glioma cells. GSC-U87 and GSC-SHG44 possess the self-renewal capability. The level of PRMT1 was higher in glioblastoma tumor tissues than in the normal paracancer tissues. Knockdown of PRMT1 enhanced the radiotherapy sensitivity of GSCs-like cells, which was evidenced by reduced survival fraction in GSC-U87 and GSC-SHG44 underwent sh-PRMT1 transfection. But, this effect was attenuated by Fer-1 (a ferroptosis inhibitor) treatment, accompanied by the abatement of ferroptosis.

CONCLUSION

PRMT1 promoted radiotherapy resistance in GSCs-like cells by inhibiting ferroptosis.

Heterogeneity of intracellular calcium signaling of glioblastoma cells depends on intratumoral location and migration state

Background

Glioblastoma (GB), is an incurable brain tumor characterized by extreme malignancy and invasiveness, and the cellular mechanisms underlying such a severe phenotype are not completely understood. Although calcium (Ca2+) plays an important part in tumor proliferation and infiltration, it remains unclear whether Ca2+ signaling in GB cells is related to its location within the tumor and on the infiltrative potential of the cells.

Methods

In this study, we developed a stably transfected GL261 cell line that coexpresses a red fluorescent protein for actin cytoskeleton staining and the intracellular Ca2+ sensor, GCaMP6s. By means of intravital 2-photon imaging, we have characterized the morphological and functional properties of cells at different locations within the tumor.

Results

Our results showed that cells located at the tumor core are densely packed and rounded in shape, contrasting sharply with the polarized morphology observed in the peripheral cells. This anatomical heterogeneity corresponded to notable variations of the physiological phenotype: cells at the tumor core displayed low Ca2+ activity and very limited motility, while peripheral cells displayed intense Ca2+ activity and increased migration rates. Moreover, peripheral cells formed a cellular ensemble characterized by synchronized Ca2+ activity accompanied by a directionally biased collective motility.

Conclusions

These findings suggest that GB cells manifest activity patterns depending upon their spatial location within the tumor and that these correlate with their migration.

Regulation of a novel circATP8B4/miR-31-5p/nestin ceRNA crosstalk in proliferation, motility, invasion and radiosensitivity of human glioma cells

Deregulation of circular RNAs (circRNAs) is frequent in human glioma. Although circRNA ATPase phospholipid transporting 8B4 (circATP8B4) is highly expressed in glioma, its precise action in glioma development is still not fully understood. The relationship of microRNA (miR)-31-5p and circATP8B4 or nestin (NES) was predicted by bioinformatic analysis and confirmed by RNA pull-down and Dual-luciferase reporter assays. CircATP8B4, miR-31-5p and NES were quantified by qRT-PCR or western blot. Cell functional behaviors were assessed by EdU, wound-healing and transwell invasion assays. Xenograft model experiments were performed to define circATP8B4's activity in vivo. CircATP8B4, a true circular transcript, was upregulated in human glioma. CircATP8B4 downregulation weakened glioma cell growth, motility, and invasion and facilitated radiosensitivity. Mechanistically, circATP8B4 and NES 3'UTR harbored a shared miR-31-5p pairing site, and circATP8B4 involved the post-transcriptional NES regulation by functioning as a competing endogenous RNA (ceRNA). Furthermore, the miR-31-5p/NES axis participated in circATP8B4's activity in glioma cell proliferation, motility, invasion and radiosensitivity. Additionally, circATP8B4 loss diminished tumor growth and enhanced the anticancer effect of radiotherapy in vivo. We have uncovered an uncharacterized ceRNA cascade, circATP8B4/miR-31-5p/NES axis, underlying glioma development and radiosensitivity. Targeting the ceRNA crosstalk may have potential to improve the outcome of glioma patients.

Integrating HRMAS-NMR Data and Machine Learning-Assisted Profiling of Metabolite Fluxes to Classify Low- and High-Grade Gliomas

Diagnosing and classifying central nervous system tumors such as gliomas or glioblastomas pose a significant challenge due to their aggressive and infiltrative nature. However, recent advancements in metabolomics and magnetic resonance spectroscopy (MRS) offer promising avenues for differentiating tumor grades both in vivo and ex vivo. This study aimed to explore tissue-based metabolic signatures to classify/distinguish between low- and high-grade gliomas. Forty-six histologically confirmed, intact solid tumor samples from glioma patients were analyzed using high-resolution magic angle spinning nuclear magnetic resonance (HRMAS-NMR) spectroscopy. By integrating machine learning (ML) algorithms, spectral regions with the most discriminative potential were identified. Validation was performed through univariate and multivariate statistical analyses, along with HRMAS-NMR analyses of 46 paired plasma samples. Amongst the various ML models applied, the logistics regression identified 46 spectral regions capable of sub-classifying gliomas with accuracy 87% (F1-measure 0.87, Precision 0.82, Recall 0.93), whereas the extra-tree classifier identified three spectral regions with predictive accuracy of 91% (F1-measure 0.91, Precision 0.85, Recall 0.97). Wilcoxon test presented 51 spectral regions significantly differentiating low- and high-grade glioma groups (p < 0.05). Based on sensitivity and area under the curve values, 40 spectral regions corresponding to 18 metabolites were considered as potential biomarkers for tissue-based glioma classification and amongst these N-acetyl aspartate, glutamate, and glutamine emerged as the most important markers. These markers were validated in paired plasma samples, and their absolute concentrations were computed. Our results demonstrate that the metabolic markers identified through the HRMAS-NMR-ML analysis framework, and their associated metabolic networks, hold promise for targeted treatment planning and clinical interventions in the future.

Photodynamic therapy with curcumin and near-infrared radiation as an antitumor strategy to glioblastoma cells

Glioblastoma is a malignant neoplasm that develops in the central nervous system and is characterized by high rates of cell proliferation and invasion, presenting resistance to treatments and a poor prognosis. Photodynamic therapy (PDT) is a therapeutic modality that can be applied in oncological cases and stands out for being less invasive. Photosensitizers (PS) of natural origin gained prominence in PDT. Curcumin (CUR) is a natural compound that has been used in PDT, considered a promising PS. In this work, we evaluated the effects of PDT-mediated CUR and near-infrared radiation (NIR) in glioblastoma cells. Through trypan blue exclusion analysis, we chose the concentration of 5 μM of CUR and the dose of 2 J/cm2 of NIR that showed better responses in reducing the viable cell number in the C6 cell line and did not show cytotoxic/cytostatic effects in the HaCat cell line. Our results show that there is a positive interaction between CUR and NIR as a PDT model since there was an increase in ROS levels, a decrease in cell proliferation, increase in cytotoxicity with cell death by autophagy and necrosis, in addition to the presence of oxidative damage to proteins. These results suggest that the use of CUR and NIR is a promising strategy for the antitumor application of PDT.

Glioblastoma Vaccines as Promising Immune-Therapeutics: Challenges and Current Status

Glioblastoma (GBM) is the most common and aggressive malignant brain tumor. Standard treatments including surgical resection, radiotherapy, and chemotherapy, have failed to significantly improve the prognosis of glioblastoma patients. Currently, immunotherapeutic approaches based on vaccines, chimeric antigen-receptor T-cells, checkpoint inhibitors, and oncolytic virotherapy are showing promising results in clinical trials. The combination of different immunotherapeutic approaches is proving satisfactory and promising. In view of the challenges of immunotherapy and the resistance of glioblastomas, the treatment of these tumors requires further efforts. In this review, we explore the obstacles that potentially influence the efficacy of the response to immunotherapy and that should be taken into account in clinical trials. This article provides a comprehensive review of vaccine therapy for glioblastoma. In addition, we identify the main biomarkers, including isocitrate dehydrogenase, epidermal growth factor receptor, and telomerase reverse transcriptase, known as potential immunotherapeutic targets in glioblastoma, as well as the current status of clinical trials. This paper also lists proposed solutions to overcome the obstacles facing immunotherapy in glioblastomas.

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.

THOC6 is a novel biomarker of glioma and a target of anti-glioma drugs: An analysis based on bioinformatics and molecular docking

Glioma is a typical malignant tumor of the nervous system. It is of great significance to identify new biomarkers for accurate diagnosis of glioma. In this context, THOC6 has been studied as a highly diagnostic prognostic biomarker, which contributes to improve the dilemma in diagnosing gliomas. We used online databases and a variety of statistical methods, such as Wilcoxon rank sum test, Dunn test and t test. We analyzed the mutation, location and expression profile of THOC6, revealing the network of THOC6 interaction with disease. Wilcoxon rank sum test showed that THOC6 is highly expressed in gliomas (P < 0.001). Dunn test, Wilcoxon rank sum test and t test showed that THOC6 expression was correlated with multiple clinical features. Logistic regression analysis further confirmed that THOC6 gene expression was a categorical dependent variable related to clinical features of poor prognosis. Kaplan-Meier survival analysis showed that the overall survival (OS) of glioma patients with high expression of THOC6 was poor (P < 0.001). Both univariate (P < 0.001) and multivariate (P = 0.04) Cox analysis confirmed that THOC6 gene expression was an independent risk factor for OS in patients with glioma. ROC curve analysis showed that THOC6 had a high diagnostic value in glioma (AUC = 0.915). Based on this, we constructed a nomogram to predict patient survival. Enrichment analysis showed that THOC6 expression was associated with multiple signal pathways. Immuno-infiltration analysis showed that the expression of THOC6 in glioma was closely related to the infiltration level of multiple immune cells. Molecular docking results showed that THOC6 might be the target of anti-glioma drugs. THOC6 is a novel diagnostic factor and prognostic biomarker of glioma.

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.

Disco interacting protein 2 homolog A (DIP2A): A key component in the regulation of brain disorders

Disco Interacting Protein 2 Homolog A (DIP2A) is expressed throughout the body and abundantly expressed in the brain tissue. It is activated by Follistatin-like 1 (FSTL1). Activated DIP2A interacts with several pathways, such as AMPK/mTOR and AKT pathways, to contribute to many biological processes, such as oxidative stress, transcriptional regulation, and apoptosis. Dysregulated DIP2A activation has been implicated in numerous processes in the brain. If the upstream pathways of DIP2A remain globally unexplored, many proteins, including cortactin, AMPK, and AKT, have been identified as its downstream targets in the literature. Recent studies have linked DIP2A to a variety of mechanisms in many types of brain disorders, suggesting that regulation of DIP2A could provide novel diagnostic and therapeutic approaches for brain disorders. In this review, we comprehensively summarized and discussed the current research on DIP2A in various brain disorders, such as stroke, autism spectrum disorders (ASD), Alzheimer's disease (AD), dyslexia, and glioma.

Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas

Objective

This study aims to assess the value of biomarker based radiomics to predict IDH mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of primary glioma (WHO grades 2-4) from 3 different centers.

Methods

To quantify the DSC perfusion signal two different mathematical modeling methods were used (Gamma fitting, leakage correction algorithms) considering the assumptions about the compartments contributing in the blood flow between the extra- and intra vascular space.

Results

The Mean slope of increase (MSI) and the K1 parameter of the bidirectional exchange model exhibited the highest performance with (ACC 74.3% AUROC 74.2%) and (ACC 75% AUROC 70.5%) respectively.

Conclusion

The proposed framework on DSC-MRI radiogenomics in gliomas has the potential of becoming a reliable diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status through a more reproducible and standardized signal analysis scheme for facilitating clinical translation.

Adult-type and Pediatric-type Diffuse Gliomas : What the Neuroradiologist Should Know

The classification of diffuse gliomas into the adult type and the pediatric type is the new basis for the diagnosis and clinical evaluation. The knowledge for the neuroradiologist should not remain limited to radiological aspects but should be based additionally on the current edition of the World Health Organization (WHO) classification of tumors of the central nervous system (CNS). This classification defines the 11 entities of diffuse gliomas, which are included in the 3 large groups of adult-type diffuse gliomas, pediatric-type diffuse low-grade gliomas, and pediatric-type diffuse high-grade gliomas. This article provides a detailed overview of important molecular, morphological, and clinical aspects for all 11 entities, such as typical genetic alterations, age distribution, variability of the tumor localization, variability of histopathological and radiological findings within each entity, as well as currently available statistical information on prognosis and outcome. Important differential diagnoses are also discussed.

Comparison of MRI Sequences to Predict ATRX Status Using Radiomics-Based Machine Learning

ATRX is an important molecular marker according to the 2021 WHO classification of adult-type diffuse glioma. We aim to predict the ATRX mutation status non-invasively using radiomics-based machine learning models on MRI and to determine which MRI sequence is best suited for this purpose. In this retrospective study, we used MRI images of patients with histologically confirmed glioma, including the sequences T1w without and with the administration of contrast agent, T2w, and the FLAIR. Radiomics features were extracted from the corresponding MRI images by hand-delineated regions of interest. Data partitioning into training data and independent test data was repeated 100 times to avoid random effects. Feature preselection and subsequent model development were performed using Lasso regression. The T2w sequence was found to be the most suitable and the FLAIR sequence the least suitable for predicting ATRX mutations using radiomics-based machine learning models. For the T2w sequence, our seven-feature model developed with Lasso regression achieved a mean AUC of 0.831, a mean accuracy of 0.746, a mean sensitivity of 0.772, and a mean specificity of 0.697. In conclusion, for the prediction of ATRX mutation using radiomics-based machine learning models, the T2w sequence is the most suitable among the commonly used MRI sequences.

Applications of chemical exchange saturation transfer magnetic resonance imaging in identifying genetic markers in gliomas

Chemical exchange saturation transfer (CEST) imaging is an important molecular magnetic resonance imaging technique that can image numerous low-concentration biomolecules with water-exchangeable protons (such as cellular proteins) and tissue pH. CEST, or more specially amide proton transfer-weighted imaging, has been widely used for the detection, diagnosis, and response assessment of brain tumors, and its feasibility in identifying molecular markers in gliomas has also been explored in recent years. In this paper, after briefing on the basic principles and quantification methods of CEST imaging, we review its early applications in identifying isocitrate dehydrogenase mutation status, MGMT methylation status, 1p/19q deletion status, and H3K27M mutation status in gliomas. Finally, we discuss the limitations or weaknesses in these studies.

Role of B7 family members in glioma: Promising new targets for tumor immunotherapy

Glioma, is a representative type of intracranial tumor among adults, usually has a weak prognosis and limited treatment options. Traditional therapies, including surgery, chemotherapy, and radiotherapy, have had little impact on patient survival time. Immunotherapies designed to target the programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) signaling pathway have successfully treated various human cancers, informing the development of similar therapies for glioma. However, anti-PD-L1 response rates remain limited in glioma patients. Thus, exploring novel checkpoints targeting additional immunomodulatory pathways for activating durable antitumor immune responses and improving glioma outcomes is needed. Researchers have identified other B7 family checkpoint molecules, including PD-L2, B7-H2, B7-H3, B7-H4, and B7-H6. The current review article evaluates the expression of all 10 reported members of the B7 family in human glioma using The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) data, as well as summarizes studies evaluating the clinical meanings and functions of B7 family molecules in gliomas. B7 family checkpoints may contribute to different immunotherapeutic management options for glioma patients.

Noninvasive Classification of Glioma Subtypes Using Multiparametric MRI to Improve Deep Learning

Background: Deep learning (DL) methods can noninvasively predict glioma subtypes; however, there is no set paradigm for the selection of network structures and input data, including the image combination method, image processing strategy, type of numeric data, and others. Purpose: To compare different combinations of DL frameworks (ResNet, ConvNext, and vision transformer (VIT)), image preprocessing strategies, magnetic resonance imaging (MRI) sequences, and numerical data for increasing the accuracy of DL models for differentiating glioma subtypes prior to surgery. Methods: Our dataset consisted of 211 patients with newly diagnosed gliomas who underwent preoperative MRI with standard and diffusion-weighted imaging methods. Different data combinations were used as input for the three different DL classifiers. Results: The accuracy of the image preprocessing strategies, including skull stripping, segment addition, and individual treatment of slices, was 5%, 10%, and 12.5% higher, respectively, than that of the other strategies. The accuracy increased by 7.5% and 10% following the addition of ADC and numeric data, respectively. ResNet34 exhibited the best performance, which was 5% and 17.5% higher than that of ConvNext tiny and VIT-base, respectively. Data Conclusions: The findings demonstrated that the addition of quantitatively numeric data, ADC images, and effective image preprocessing strategies improved model accuracy for datasets of similar size. The performance of ResNet was superior for small or medium datasets.

Down-regulation of CX43 expression by miR-1 inhibits the proliferation and invasion of glioma cells

BACKGROUND

Connexin (CX) 43 makes glioblastoma resistant to temozolomide, the first-line chemotherapy drug. However, targeting CX43 is very difficult because the mechanisms underlying CX43-mediated resistance remain unclear. CX43 is highly expressed in glioblastoma, which is closely associated with poor prognosis and chemotherapy resistance. The present study was to analyze the mechanism of microRNA (miR)-1 in regulating the proliferation and invasion of glioma cells.

METHODS

The effects of knockdown of miR-1 on the growth of glioma cell lines were observed by establishing blank, miR-1 inhibitor, and miR-1 mimic groups. Cell proliferation was detected using a Cell Counting Kit-8 (CCK-8) assay, cell apoptosis was detected by flow cytometry, and protein expression was detected by western blot. We used the Student's t-test to assess continuous data between the two groups and the Kruskal-Wallis test was adopted for multiple group comparisons.

RESULTS

Compared with the mimics normal control (NC) group, the apoptosis rate of the miR-1-3p mimics group was decreased, while that of the miR-1-3p inhibitor group was increased compared to the inhibitor NC group. In addition, the miR-1-3p mimics model of U251 cells exerted an inhibitory effect on the invasion ability of cells, whereas the miR-1-3p inhibitor model of U251 cells showed an invasion-promoting effect. The dual-luciferase assay showed that miR-1-3p had a targeted relationship with the CX43 gene.

CONCLUSIONS

Down-regulation of CX43 expression by miR-1 inhibited the infiltration and growth of glioma cells and further promoted the apoptosis of glioma cells by regulating CX43 expression.

Classification and Diagnosis of Adult Glioma: A Scoping Review

Gliomas are primary central nervous system tumors that arise from glial progenitor cells. Gliomas have been classically classified morphologically based on their histopathological characteristics. However, with recent advances in cancer genomics, molecular profiles have now been integrated into the classification and diagnosis of gliomas. In this review article, we discuss the clinical features, imaging findings, and molecular profiles of adult-type diffuse gliomas based on the new 2021 World Health Organization Classifications of Tumors of the central nervous system.

Identification of a Signature for Predicting Prognosis and Immunotherapy Response in Patients with Glioma

Glioma is a deadly tumor that accounts for the vast majority of brain tumors. Thus, it is important to elucidate the molecular pathogenesis and potential diagnostic and prognostic biomarkers of glioma. In the present study, gene expression profiles of GSE2223 were obtained from the Gene Expression Omnibus (GEO) database. Core modules and hub genes related to glioma were identified using weighted gene coexpression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis of differentially expressed genes (DEGs). After a series of database screening tests, we identified 11 modules during glioma progression, followed by six hub genes (RAB3A, TYROBP, SYP, CAMK2A, VSIG4, and GABRA1) that can predict the prognosis of glioma and were validated in glioma tissues by qRT-PCR. The CIBERSORT algorithm was used to analyze the difference of immune cell infiltration between the glioma and control groups. Finally, Identification VSIG4 for immunotherapy response in patients with glioma demonstrating utility for immunotherapy research.

Characterization and comparison of human glioblastoma models

Glioblastoma (GBM) is one of the deadliest cancers. Treatment options are limited, and median patient survival is only several months. Translation of new therapies is hindered by a lack of GBM models that fully recapitulate disease heterogeneity. Here, we characterize two human GBM models (U87-luc2, U251-RedFLuc). In vitro, both cell lines express similar levels of luciferase and show comparable sensitivity to temozolomide and lapatinib exposure. In vivo, however, the two GBM models recapitulate different aspects of the disease. U87-luc2 cells quickly grow into large, well-demarcated tumors; U251-RedFLuc cells form small, highly invasive tumors. Using a new method to assess GBM invasiveness based on detecting tumor-specific anti-luciferase staining in brain slices, we found that U251-RedFLuc cells are more invasive than U87-luc2 cells. Lastly, we determined expression levels of ABC transporters in both models. Our findings indicate that U87-luc2 and U251-RedFLuc GBM models recapitulate different aspects of GBM heterogeneity that need to be considered in preclinical research.

miR-133a-5p Inhibits Glioma Cell Proliferation by Regulating IGFBP3

Objective

This research aims to investigate the expression of miR-133a-5p in glioma tissues and its impact on glioma cell proliferation.

Methods

Fluorescence-quantitative PCR was used to detect the expression of miR-133a-5p in 25 cases of glioma and adjuncent tissues. CCK-8 and colony formation analyses were used to evaluate the impact of transfection with miR-133a-5p inhibitors or mimics on glioma cell growth and colony formation. The IGFBP3 (insulin-like growth factor-binding protein-3) and miR-133a-5p binding sites were predicted using Starbase, and the miR-133a-5p binding capacity with 3'UTR of IGFBP3 gene was determined using a luciferase gene reporter system. Following transfection with miR-133a-5p mimics or inhibitors, the IGFBP3 protein expression in glioma cells was determined by western blotting. The colony formation assay was applied to evaluate the influence of IGFBP3 overexpression on the miR-133a-5p in glioma cell proliferation. For assessment of the IGFBP3 expression in glioma tissues and prognosis, TCGA database was employed.

Results

The expression of miR-133a-5p was considerably reduced in glioma tissue compared to adjuncent control tissue. In addition, miR-133a-5p expression decreased with increasing glioma malignancy. Glioma cell growth and colony formation were reduced after miR-133a-5p mimics were transfected, while transfection of miR-133a-5p inhibitors had a reverse impact. The expression of IGFBP3 was affected by miR-133a-5p by binding to its 3'UTR region. Additional study demonstrated that the overall survival (OS) of subjects with increased IGFBP3 expression was considerably lower compared to patients with decreased IGFBP3 expression. The IGFBP3 overexpression effectively counteracts the glioma cell proliferation-inhibiting impact of miR-133a-5p.

Conclusion

miR-133a-5p acts as a glioma tumor suppressor gene. It reduces glioma cell proliferation by modulating IGFBP3 and could be a target for glioma therapy.

Modern surgical management of incidental gliomas

Purpose

Gliomas are the most common primary tumors of the central nervous system and are categorized by the World Health Organization into either low-grade (grades 1 and 2) or high-grade (grades 3 and 4) gliomas. A subset of patients with glioma may experience no tumor-related symptoms and be incidentally diagnosed. These incidental low-grade gliomas (iLGG) maintain controversial treatment course despite scientific advancements. Here we highlight the recent advancements in classification, neuroimaging, and surgical management of these tumors.

Methods

A review of the literature was performed. The authors created five subtopics of focus: histological criteria, diagnostic imaging, surgical advancements, correlation of surgical resection and survival outcomes, and clinical implications.

Conclusions

Alternating studies suggest that these tumors may experience higher mutational rates than their counterparts. Significant progress in management of gliomas, regardless of the grade, has been made through modern neurosurgical treatment modalities, diagnostic neuroimaging, and a better understanding of the genetic composition of these tumors. An optimal treatment approach for patients with newly diagnosed iLGG remains ill-defined despite multiple studies arguing in favor of safe maximal resection. Our review emphasizes the not so benign nature of incidental low grade glioma and further supports the need for future studies to evaluate survival outcomes following surgical resection.

Dialogue among Lymphocytes and Microglia in Glioblastoma Microenvironment

Simple Summary

In this review, we summarize in vitro and in vivo studies related to glioblastoma models and human patients to outline the symbiotic bidirectional interaction between microglia, lymphocytes, and tumor cells that develops during tumor progression. Particularly, we highlight the current experimental therapeutic approaches that aim to shape these interplays, such as adeno-associated virus (AAV) delivery and CAR-T and -NK cell infusion, and to modulate the tumor microenvironment in an anti-tumoral way, thus counteracting glioblastoma growth.

Abstract

Microglia and lymphocytes are fundamental constituents of the glioblastoma microenvironment. In this review, we summarize the current state-of-the-art knowledge of the microglial role played in promoting the development and aggressive hallmarks of this deadly brain tumor. Particularly, we report in vitro and in vivo studies related to glioblastoma models and human patients to outline the symbiotic bidirectional interaction between microglia, lymphocytes, and tumor cells that develops during tumor progression. Furthermore, we highlight the current experimental therapeutic approaches that aim to shape these interplays, such as adeno-associated virus (AAV) delivery and CAR-T and -NK cell infusion, and to modulate the tumor microenvironment in an anti-tumoral way, thus counteracting glioblastoma growth.

Deep Convolutional Neural Network-Based Brain Magnetic Resonance Imaging Applied in Glioma Diagnosis and Tumor Region Identification

The aim of this study was to explore the application value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on a convolutional neural network (CNN) algorithm in glioma diagnosis and tumor segmentation. 66 patients with gliomas who were diagnosed and treated in the hospital were selected as the research objects. The patients were rolled into the high-grade glioma group (HGG, 46 cases) and the low-grade glioma group (LGG, 20 cases) according to the World Health Organization glioma grading standard. All patients received a conventional plain scan and a DCE-MRI. Parameters such as volume transfer constant (K trans), rate constant (K ep ), extracellular volume (V e ), and mean plasma volume (V p ) were calculated, and the parameters of patients of each grade were analyzed. The efficacy of each parameter in diagnosing glioma was analyzed through a receiver operating characteristic curve. All images were segmented by the CNN algorithm. The CNN algorithm showed good performance in DCE-MRI image segmentation. The mean, standard deviation, kurtosis, and skewness of K trans and V e , the standard deviation and skewness of K ep , and the mean and standard deviation of V p were statistically considerable in differentiating HGG and LGG (P < 0.05). ROC analysis showed that the standard deviation of K trans (0.885) had the highest diagnostic accuracy in distinguishing HGG and LGG. The values of K trans, V e , and V p were positively correlated with Ki-67 (r = 0.346, P = 0.014; r = 0.335, P = 0.017; r = 0.323, P = 0.022). In summary, the CNN-based DCE-MRI technology had high application value in glioma diagnosis and tumor segmentation.

Adult diffuse midline gliomas H3 K27-altered: review of a redefined entity

INTRODUCTION

Diffuse midline glioma (DMG) H3 K27-altered is a type of high-grade gliomas first recognized as a new entity in the 2016 World Health Organization Classification of Central Nervous System (CNS) Tumors as DMG H3 K27M-mutant, recently renamed in the new 2021 WHO classification. The aim of this review is to describe the characteristics of diffuse midline gliomas H3 K27-altered in the adult population.

METHODS

We performed a review of the current literature regarding the genetic, clinical, imaging characteristics and management of diffuse midline gliomas H3 K27-altered in adult patients.

RESULTS

The 2021 WHO classification now designates the previously recognized DMG H3K27M-mutant as DMG H3 K27-altered, recognizing the alternative mechanisms by which the pathogenic pathway can be altered. Thus, the diagnostic criteria for this entity consist of diffuse growth pattern, midline anatomic location, and H3 K27-specific neuroglial mutations. DMGs' characteristic midline location makes them difficult to surgically resect and biopsy, carrying high mortality and morbidity rates, with median survival ranging from 9 to 12 months in adult patients.

CONCLUSION

The diagnosis of DMGs H3 K27-altered in adult patients should be considered upon neurological symptoms associated with an infiltrative midline brain tumor detected on imaging. Future studies are necessary to continue refining their characteristics in this age group.

Comprehensive analysis of histone deacetylases genes in the prognosis and immune infiltration of glioma patients

The occurrence and development of tumors are closely related to histone deacetylases (HDACs). However, their relationship with the overall biology and prognosis of glioma is still unknown. In the present study, we developed and validated a prognostic model for glioma based on HDAC genes. Glioma patients can be divided into two subclasses based on eleven HDAC genes, and patients from the two subclasses had markedly different survival outcomes. Then, using six HDAC genes (HDAC1, HDAC3, HDAC4, HDAC5, HDAC7, and HDAC9), we established a prognostic model for glioma patients, and this prognostic model was validated in an independent cohort. Furthermore, the calculated risk score from six HDACA genes expression was found to be an independent prognostic factor that could predict the five-year overall survival of glioma patients well. High-risk patients have changes in multiple complex functions and molecular signaling pathways, and the gene alterations of high- and low-risk patients were significantly different. We also found that the different survival outcomes of high- and low-risk patients could be related to the differences in immune filtration levels and the tumor microenvironment. Subsequently, we identified several small molecular compounds that could be favorable for glioma patient treatment. Finally, the expression levels of HDAC genes from the prognostic model were validated in glioma and nontumor tissue samples. Our results revealed the clinical utility and potential molecular mechanisms of HDAC genes in glioma. A model based on six HDAC genes can predict the overall survival of glioma patients well, and these genes are potential therapeutic targets.

Exploration of CT Images Based on the BN-U-net-W Network Segmentation Algorithm in Glioma Surgery

This study aimed to explore the application value of computed tomography (CT) imaging features based on the deep learning batch normalization (batch normalization, BN) U-net-W network image segmentation algorithm in evaluating and diagnosing glioma surgery. 72 patients with glioma who were admitted to hospital were selected as the research subjects. They were divided into a low-grade group (grades I-II, N = 27 cases) and high-grade group (grades III-IV, N = 45 cases) according to postoperative pathological examination results. The CT perfusion imaging (CTPI) images of patients were processed by using the deep learning-based BN-U-net-W network image segmentation algorithm. The application value of the algorithm was comprehensively evaluated by comparing the average Dice coefficient, average recall rate, and average precision of the BN-U-net-W network image segmentation algorithm with the U-net and BN-U-net network algorithms. The results showed that the Dice coefficient, recall, and precision of the BN-U-net-W network were 86.31%, 88.43%, and 87.63% respectively, which were higher than those of the U-net and BN-U-net networks, and the differences were statistically significant (P < 0.05). Cerebral blood flow (CBF), cerebral blood volume (CBV), and capillary permeability (PMB) in the glioma area were 56.85 mL/(min·100 g), 18.03 mL/(min·100 g), and 8.57 mL/100 g, respectively, which were significantly higher than those of normal brain tissue, showing statistically significant differences (P < 0.05). The mean transit time (MTT) difference between the two was not statistically significant (P > 0.05). The receiver operating characteristic (ROC) curves of CBF, CBV, and PMB in CTPI parameters of glioma had area under the curve (AUC) of 0.685, 0.724, and 0.921, respectively. PMB parameters were significantly higher than those of CBF and CVB, and the differences were statistically obvious (P < 0.05). It showed that the BN-U-net-W network model had a better image segmentation effect, and CBF, CBV, and PMB showed better sensitivity in diagnosing glioma tissue and normal brain tissue and high-grade and low-grade gliomas, among which PBM showed the highest predictability.

Artificial Intelligence Algorithm-Based Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) in the Treatment of Glioma Biopsy

This study was aimed at exploring the application value of positron emission tomography (PET) + magnetic resonance imaging (MRI) technology based on convolutional neural network (CNN) in the biopsy and treatment of intracranial glioma. 35 patients with preoperatively suspicious gliomas were selected as the research objects. Their imaging images were processed using CNN. They were performed with the preoperative head MRI, fluorodeoxyglucose (FDG) PET, and ethylcholine (FECH) PET scans to construct the cancer tissue contours. In addition, the performance of CNN was evaluated, and the postoperative pathology of patients was analyzed. The results suggested that the CNN-based PET + MRI technology showed a recognition accuracy of 97% for images. Semiquantitative analysis was adopted to analyze the standard uptake value (SUV). It was found that the SUV_FDG and SUV_FECH of grade II/III glioma were 9.77 ± 4.87 and 1.82 ± 0.50, respectively, and the SUV_FDG and SUV_FECH of grade IV glioma were 13.91 ± 1.83 and 3.65 ± 0.34, respectively. According to FDG PET, the mean value of SUV on the lesion side of grade IV glioma was greater than that of grade II-III glioma, and the difference was significant (P < 0.05), and similar results were obtained on FECH PET. It showed that CNN-based PET + MRI fusion technology can effectively improve the recognition effect of glioma, can more accurately determine the scope of glioma lesions, and can predict the degree of malignant glioma to a certain extent.

Characterization of the Immune Cell Infiltration Landscape and a New Prognostic Score in Glioblastoma

Glioblastoma (GBM) is the most aggressive, malignant primary brain tumor, which has abundant tumor-infiltrating immune cells and stroma in the tumor microenvironment (TME). So far, the TME landscape of GBM has not been elucidated. GBM samples were retrieved from TCGA and GEO databases. We used ESTIMATE and CIBERSORT algorithms to calculate risk score associated with TME, and immune cell infiltration (ICI) score of each patient is calculated by PCA. GSEA analysis is explored for each subgroup. Finally, the patient prognosis in different ICI score subgroup is determined. Two ICI clusters are determined in 208 GBM patients, and 207 differentially expressed genes (DGEs) are found between ICI clusters. And then, two gene clusters were determined. Finally, we obtained ICI score for each patient using principal component analysis (PCA). Patients were divided into high and low ICI score subgroups by setting the median as cutoff. Through GSEA, we found ECM-receptor interaction, mTOR signaling pathway, pathways in cancer, TGF-beta signaling pathway, and other immunosuppressive pathway related genes in the low ICI score group. Furthermore, patients with high ICI score group have more better prognosis. Targeting the stroma in GBM may be an effective new therapeutic approach, and the ICI score is an effective potential prognostic classifier of GBM.

Surgical Neuro-Oncology

Gliomas are the most common intrinsic brain tumor in adults. Although maximal tumor resection improves survival, this must be balanced with preservation of neurologic function. Technological advancements have greatly expanded our ability to safely maximize tumor resection and design innovative therapeutic trials that take advantage of intracavitary delivery of therapeutic agents after resection. In this article, we review the role of surgical intervention for both low-grade and high-grade gliomas and the innovations that are driving and expanding the role of surgery in this therapeutically challenging group of malignancies.

Matrix Remodeling-Associated Protein 8 as a Novel Indicator Contributing to Glioma Immune Response by Regulating Ferroptosis

Glioma is a highly malignant brain tumor with a poor survival rate. Novel biomarkers that act as prompt indicators of glioma are urgently needed. In this study, we identified and validated prognosis-related differentially expressed genes by datasets of glioma in the GEO and TCGA databases. Ferroptosis is a newly recognized process of cell death playing a vital role in cancer biology. Pearson correlation coefficient were used to discovery the prognosis-related genes which have the highest correlation with ferroptosis. Matrix remodeling-associated protein 8 (MXRA8) was identified as a novel prognosis indicator which may be involved in ferroptosis. The expression of MXRA8 was significantly higher in glioma compared with normal brain tissue, and increased expression of MXRA8 was associated with unfavorable survivals. Furthermore, in vitro analysis showed that knockdown of MXRA8 inhibited the cell viability in T98G and U251 cells and increased the sensitivity of glioma cells to temozolomide. We further observed that downregulation of MXRA8 elevated the levels of intracellular ferrous iron and lipid peroxidation, accompanied by upregulation of NCOA4 and suppression of FTH1. Moreover, co-expression analyses showed that GO term and KEGG pathways were mainly enriched in immunity-related pathways, such as neutrophil-related immunity, adaptive immune response, and cytokine binding. Through ssGSEA algorithm and TISIDB database, immunological analyses showed that MXRA8 was significantly correlated with various immune infiltration cells including NK cells, macrophages, and neutrophils. Meanwhile, MXRA8 was also associated with chemokines and multiple immunoinhibitory molecules, such as TGF-β1, IL-10, PD-L1, and CTLA4. We also found that MXRA8 was positively associated with immune infiltration score, and patients with higher immune score underwent worse overall survivals. Moreover, IHC staining indicated a highly positive correlation of MXRA8 with a macrophage marker CSF1R. The co-cultured models of glioma cells and M2 macrophages showed MXRA8 knockdown glioma cells alleviated the infiltration of M2 macrophage, while the reduced M2 macrophage infiltration generated by MXRA8 could be rescued by Fer-1 treatment. These results suggest that MXRA8 promotes glioma progression and highlight the pivotal role of MXRA8 in ferroptosis and immune microenvironment of glioma. Therefore, MXRA8 may serve as a novel prognostic marker and therapeutic target for glioma.

Neurosurgical Advances for Malignant Gliomas: Intersection of Biology and Technology

ABSTRACT

The intersection of biology and technology has led to many advancements for the field of neurosurgery. Molecular developments have led to the identification of specific mutations, allowing for more accurate discussions in regard to prognosis and treatment effect. Even amid the progress from basic science benchwork, malignant gliomas continue to have a bleak natural history in lieu of the resistance to chemotherapy and the diffuse nature of the disease, leaving room for further research to discover more effective treatment modalities. Novel imaging methods, including the emerging field of radiogenomics, involve the merging of molecular and radiographic data, enabling earlier, detailed molecular diagnoses and improved surveillance of this pathology. Furthermore, surgical advancements have led to safer and more extensive resections. This review aims to delineate the various advancements in the many facets that are used daily in the care of our glioma population, specifically pertaining to its biology, imaging modalities, and perioperative adjuncts used in the operating room.

The Mathematics of Quasi-Diffusion Magnetic Resonance Imaging

Quasi-diffusion imaging (QDI) is a novel quantitative diffusion magnetic resonance imaging (dMRI) technique that enables high quality tissue microstructural imaging in a clinically feasible acquisition time. QDI is derived from a special case of the continuous time random walk (CTRW) model of diffusion dynamics and assumes water diffusion is locally Gaussian within tissue microstructure. By assuming a Gaussian scaling relationship between temporal (α) and spatial (β) fractional exponents, the dMRI signal attenuation is expressed according to a diffusion coefficient, D (in mm2 s−1), and a fractional exponent, α. Here we investigate the mathematical properties of the QDI signal and its interpretation within the quasi-diffusion model. Firstly, the QDI equation is derived and its power law behaviour described. Secondly, we derive a probability distribution of underlying Fickian diffusion coefficients via the inverse Laplace transform. We then describe the functional form of the quasi-diffusion propagator, and apply this to dMRI of the human brain to perform mean apparent propagator imaging. QDI is currently unique in tissue microstructural imaging as it provides a simple form for the inverse Laplace transform and diffusion propagator directly from its representation of the dMRI signal. This study shows the potential of QDI as a promising new model-based dMRI technique with significant scope for further development.

GASC1 promotes glioma progression by enhancing NOTCH1 signaling

Recent studies have reported that gene amplified in squamous cell carcinoma 1 (GASC1) is involved in the progression of several types of cancer. However, whether GASC1 promotes glioma progression remains unknown. Therefore, the present study aimed to investigate the effect of GASC1 exposure on glioma tumorigenesis. The western blot demonstrated that grade III and IV glioma tissues exhibited a higher mRNA and protein expression of GASC1. Moreover, CD133+ U87 or U251 cells from magnetic cell separation exhibited a higher GASC1 expression. Invasion Transwell assay, clonogenic assay and wound healing assay have shown that GASC1 inhibition using a pharmacological inhibitor and specific short hairpin (sh)RNA suppressed the invasive, migratory and tumorsphere forming abilities of primary culture human glioma cells. Furthermore, GASC1‑knockdown decreased notch receptor (Notch) responsive protein hes family bHLH transcription factor 1 (Hes1) signaling. GASC1 inhibition reduced notch receptor 1 (NOTCH1) expression, and a NOTCH1 inhibitor enhanced the effects of GASC1 inhibition on the CD133+ U87 or U251 cell tumorsphere forming ability, while NOTCH1 overexpression abrogated these effects. In addition, the GASC1 inhibitor caffeic acid and/or the NOTCH1 inhibitor DAPT (a γ‑Secretase Inhibitor), efficiently suppressed the human glioma xenograft tumors. Thus, the present results demonstrated the importance of GASC1 in the progression of glioma and identified that GASC1 promotes glioma progression, at least in part, by enhancing NOTCH signaling, suggesting that GASC1/NOTCH1 signaling may be a potential therapeutic target for glioma treatment.

Magnetic Resonance Imaging Parameters for Noninvasive Prediction of Epidermal Growth Factor Receptor Amplification in Isocitrate Dehydrogenase-Wild-Type Lower-Grade Gliomas: A Multicenter Study

BACKGROUND

The epidermal growth factor receptor (EGFR) amplification status of isocitrate dehydrogenase-wild-type (IDHwt) lower-grade gliomas (LGGs; grade II/III) is one of the key markers for diagnosing molecular glioblastoma. However, the association between EGFR status and imaging parameters is unclear.

OBJECTIVE

To identify noninvasive imaging parameters from diffusion-weighted and dynamic susceptibility contrast imaging for predicting the EGFR amplification status of IDHwt LGGs.

METHODS

A total of 86 IDHwt LGG patients with known EGFR amplification status (62 nonamplified and 24 amplified) from 3 tertiary institutions were included. Qualitative and quantitative imaging features, including histogram parameters from apparent diffusion coefficient (ADC), normalized cerebral blood volume (nCBV), and normalized cerebral blood flow (nCBF), were assessed. Univariable and multivariable logistic regression models were constructed.

RESULTS

On multivariable analysis, multifocal/multicentric distribution (odds ratio [OR] = 11.77, P = .006), mean ADC (OR = 0.01, P = .044), 5th percentile of ADC (OR = 0.01, P = .046), and 95th percentile of nCBF (OR = 1.24, P = .031) were independent predictors of EGFR amplification. The diagnostic performance of the model with qualitative imaging parameters increased significantly when quantitative imaging parameters were added, with areas under the curves of 0.81 and 0.93, respectively (P = .004).

CONCLUSION

The presence of multifocal/multicentric distribution patterns, lower mean ADC, lower 5th percentile of ADC, and higher 95th percentile of nCBF may be useful imaging biomarkers for EGFR amplification in IDHwt LGGs. Moreover, quantitative imaging biomarkers may add value to qualitative imaging parameters.

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Simple Summary

Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored.

Abstract

In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.

Diffusion and perfusion MRI may predict EGFR amplification and the TERT promoter mutation status of IDH-wildtype lower-grade gliomas

OBJECTIVES

Epidermal growth factor receptor (EGFR) amplification and telomerase reverse transcriptase promoter (TERTp) mutation status of isocitrate dehydrogenase-wildtype (IDHwt) lower-grade gliomas (LGGs; grade II/III) are crucial for identifying IDHwt LGG with an aggressive clinical course. The purpose of this study was to assess whether parameters from diffusion tensor imaging, dynamic susceptibility contrast (DSC), and diffusion tensor imaging, dynamic contrast-enhanced imaging can predict the EGFR amplification and TERTp mutation status of IDHwt LGGs.

METHODS

A total of 49 patients with IDHwt LGGs with either known EGFR amplification (39 non-amplified, 10 amplified) or TERTp mutation (19 wildtype, 21 mutant) statuses underwent MRI. The mean ADC, fractional anisotropy (FA), normalized cerebral blood volume (nCBV), normalized cerebral blood flow (nCBF), volume transfer constant (K^trans), rate transfer coefficient (K_ep), extravascular extracellular volume fraction (V_e), and plasma volume fraction (V_p) values were assessed. Univariate and multivariate logistic regression models were constructed.

RESULTS

EGFR-amplified tumors showed lower mean ADC values than EGFR-non-amplified tumors (p = 0.019). Mean ADC was an independent predictor of EGFR amplification, with an AUC of 0.75. TERTp mutant tumors showed higher mean nCBV (p = 0.020), higher mean nCBF (p = 0.017), and higher mean V_p (p = 0.002) than TERTp wildtype tumors. With multivariate logistic regression, mean V_p was the independent predictor of TERTp mutation status, with an AUC of 0.85.

CONCLUSION

This exploratory pilot study shows that lower ADC values may be useful for prediction of EGFR amplification, whereas higher V_p values may be useful for prediction of the TERTp mutation status of IDHwt LGGs.

KEY POINTS

• EGFR amplification and TERTp mutation are key molecular markers that predict an aggressive clinical course of IDHwt LGGs. • EGFR-amplified tumors showed lower ADC values than EGFR-non-amplified tumors, suggesting higher cellularity. • TERTp mutant tumors showed a higher plasma volume fraction than TERTp wildtype tumors, suggesting higher vascular proliferation and tumor angiogenesis.