Adult type diffuse gliomas in the new 2021 WHO Classification

Surgical outcomes in high-grade adult type diffuse gliomas (ATDG) with a previous diagnosis of anaplastic astrocytoma without adjuvant therapy

INTRODUCTION

Gliomas pose a significant treatment challenge due to their varied genetic makeup and clinical presentations. This study examines a unique cohort of high-grade adult type diffuse gliomas (ATDG) previously diagnosed as anaplastic astrocytoma prior to the WHO 2021 tumor classification changes. This cohort chose to undergo only surgical resection without adjuvant therapies. We provide a rare dataset of patients allowing for new insight into the natural progression of this disease with surgical treatment alone.

METHODS

A retrospective review was conducted of patients who were operated on by a single surgeon from the years 2002-2022 and who were diagnosed as having a Grade III Anaplastic Astrocytoma before the WHO 2021 guidelines were published. Correcting for the criteria in the 2021 Guidelines resulted in a mixture of adult-type diffuse malignant gliomas (ATDG), including IDH-Mutant astrocytomas (Grade 3 and 4) and IDH-WT Glioblastoma. All patients included underwent surgical resection alone after declining any adjuvant therapy for various reasons.

RESULTS

A total of 20 patients met the inclusion criteria with an average age of 38 years. Among them, 15 had IDH-mutant (IDH-mt) Grade 3 astrocytomas (75 %), 1 had an IDH-mt Grade 4 astrocytoma (5 %), and 4 had IDH-wildtype (IDH-WT) glioblastomas (20 %). The 5-year survival rate for the entire cohort was 74.0 %. Grade 3 astrocytomas had a 5-year survival of 86.7 %, while Grade 4 astrocytomas and IDH-WT GBM patients exhibited a 5-year survival rate of 40 %. 5-year progression-free survival (PFS) rates were derived from the surgery date up until the recurrence or censorship. The collective cohort had a PFS rate of 34.3 %. Grade 3 astrocytomas achieved a 5-year PFS of 32.0 %, whereas Grade 4 astrocytomas and IDH-WT GBM reached a PFS of 40.0 %.

CONCLUSION

In our cohort study, we demonstrate that patients with ATDG can potentially achieve relative long-term survival through surgical resection alone. This unique cohort highlights the natural progression of this disease with surgery alone and provides the foundation for future more rigorous studies to evaluate the additive benefit of different adjuvant therapies. With evolving tumor classifications and variable responses to standard therapeutics, it becomes imperative to revisit and understand the additive benefits of different chemotherapeutic protocols in addition to surgical resection.

Mediation Mendelian randomization analysis of immune cell phenotypes and glioma risk: unveiling the regulation of cerebrospinal fluid metabolites

BACKGROUND AND OBJECTIVE

Gliomas, particularly glioblastoma multiforme (GBM), are the most common primary central nervous system tumors in adults and are notoriously difficult to treat due to their high heterogeneity and invasiveness. Despite advances in molecular diagnostics and personalized therapies, prognosis remains poor. The immune system plays a critical role in glioma progression. This study employed mediation Mendelian randomization analysis to explore the relationships between immune cell phenotypes, cerebrospinal fluid metabolites, and glioma, aiming to uncover potential mechanisms of tumor progression and immune evasion.

METHOD

In this study, we employed several analytical methods including IVW, MR Egger, Simple mode, Weighted median, and Weighted mode, with IVW results being considered the primary basis. We assessed heterogeneity and pleiotropy, and used leave-one-out analysis to determine sensitivity, ensuring the stability and reliability of the results. The potential mediating effects of cerebrospinal fluid metabolites were investigated to explore the underlying mechanisms linking immune cell function and glioma. The GWAS data for immune cells, cerebrospinal fluid metabolites, and glioma used in this study were sourced from public databases.

RESULT

We identified nine risk immune cell phenotypes for glioma (such as CD19 on IgD( +) CD24(-)), and ten protective immune cell phenotypes (such as CD11c on monocytes). Mediation analysis revealed that levels of 7-alpha-hydroxy-3-oxo-4-cholestenoate (7-hoca) (MP = - 14.6%) and Palmitoyl dihydrosphingomyelin (d18:0/16:0) (MP = 7.9%) partially mediated the relationship between CD3 on CD39( +) resting Treg cells and glioma. Additionally, 7-hoca levels (MP = - 12.3%) and Phenyllactate (pla) levels (MP = 4.12%) partially mediated the association between FSC-A on NKT cells and glioma. Furthermore, Glycerophosphoinositol levels (MP = - 12.1%) and Orotate levels (MP = - 11.4%) partially mediated the relationship between Granulocyte adenylyl cyclase (Granulocyte AC) and glioma.

CONCLUSION

This study identified that specific immune cell phenotypes directly influence glioma risk and indirectly modulate this risk through cerebrospinal fluid metabolites. CD19 on IgD( +) CD24(-) B cells were identified as risk factors, while CD11c on monocytes were protective. Metabolites like 7-hoca and glycerophosphoinositol play key mediating roles. These findings enhance our understanding of glioma pathophysiology and suggest that immune modulation and metabolic intervention may be promising therapeutic strategies.

Integrating Multiplex Immunohistochemistry and Machine Learning for Glioma Subtyping and Prognosis Prediction

Glioma subtyping is crucial for treatment decisions, but traditional approaches often fail to capture tumor heterogeneity. This study proposes a novel framework integrating multiplex immunohistochemistry (mIHC) and machine learning for glioma subtyping and prognosis prediction. 185 patient samples from the Huashan hospital cohort were stained using a multi-label mIHC panel and analyzed with an AI-based auto-scanning system to calculate cell ratios and determine the proportion of positive tumor cells for various markers. Patients were divided into two cohorts (training: N = 111, testing: N = 74), and a machine learning model was then developed and validated for subtype classification and prognosis prediction. The framework identified two distinct glioma subtypes with significant differences in prognosis, clinical characteristics, and molecular profiles. The high-risk subtype, associated with older age, poorer outcomes, astrocytoma/glioblastoma, higher tumor grades, elevated mesenchymal scores, and an inhibitory immune microenvironment, exhibited IDH wild-type, 1p19q non-codeletion, and MGMT promoter unmethylation, suggesting chemotherapy resistance. Conversely, the low-risk subtype, characterized by younger age, better prognosis, astrocytoma/oligodendroglioma, lower tumor grades, and favorable molecular profiles (IDH mutation, 1p19q codeletion, MGMT promoter methylation), indicated chemotherapy sensitivity. The mIHC-based framework enables rapid glioma classification, facilitating tailored treatment strategies and accurate prognosis prediction, potentially improving patient management and outcomes.

Gene regulatory networks analysis for the discovery of prognostic genes in gliomas

Gliomas are the most common and aggressive primary tumors of the central nervous system. Dysregulated transcription factors (TFs) and genes have been implicated in glioma progression, yet these tumors' overall structure of gene regulatory networks (GRNs) remains undefined. We analyzed transcriptional data from 989 primary gliomas in The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) to address this. GRNs were reconstructed using the RTN package which identifies regulons-sets of genes regulated by a common TF based on co-expression and mutual information. Regulon activity was evaluated through Gene Set Enrichment Analysis. Elastic net regularization and Cox regression identified 31 and 32 prognostic genes in the TCGA and CGGA datasets, respectively, with 11 genes overlapping, many of which are associated with neural development and synaptic processes. GAS2L3, HOXD13, and OTP demonstrated the strongest correlations with survival outcomes among these. Single-cell RNA-seq analysis of 201,986 cells revealed distinct expression patterns for these genes in glioma subpopulations, particularly oligoprogenitor cells. This study uncovers key GRNs and prognostic genes in gliomas, offering new insights into tumor biology and potential therapeutic targets.

NID2 Affects Prognosis of Glioma via Activating the Akt Signaling Pathway

Nidogen-2 (NID2) is a critical component of the extracellular matrix (ECM), which plays a regulatory role in cell adhesion, migration, differentiation, and survival. Previous studies have shown that NID2 is deregulated in several types of cancer, but its role in glioma is unknown. The present study investigated the prognostic value of NID2 in glioma and its associated molecular pathways and functional roles in malignant progression. The performed analyses included investigating the NID2 expression profile using the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and tumor tissue microarray. The findings demonstrated that NID2 high expression predicts worse patient survival by both univariable and multivariable analyses. There is a strong correlation between NID2 upregulation and tumor grade. In stably NID2-overexpressed glioma cells, RNA-Seq analysis revealed coactivation of oncogenic functional pathways, including cell proliferation, survival, epithelial-mesenchymal transition, ECM organization, and migration. Overexpression of NID2 in U87MG and T98G cells promoted cell proliferation, migration, and invasion. TUNEL assay showed NID2 overexpression protected cells from apoptosis. Western blotting analysis showed activation of Akt and Bcl-xL in NID2-overexpressed cells. Our results show that NID2 is a promising prognostic marker in glioma.

MUC1 and MUC4 expression are inversely correlated and trigger immunological response and transport pathways in adult-type diffuse gliomas

Adult-type diffuse gliomas arise from glial or progenitor cells. These tumors are currently classified as astrocytoma isocitrate dehydrogenase (IDH)-mutant or IDH-mutant oligodendroglioma with co-deletion of chromosomal arms 1p and 19q, both of which could be either slow-growing tumors, or glioblastoma (GBM), which is a more aggressive tumor. Despite advances in diagnosis and treatment, the median survival time after GBM diagnosis remains low at approximately 15 months, with a 5-year overall survival (OS) rate of 6.8 %. Therefore, new biomarker and therapeutic target discoveries are required to improve prognosis. Mucin 1 (MUC1) and MUC4 are membrane-bound mucins and potential biomarkers of several tumors. However, the role of these mucins in adult gliomas has not been well explored. In this retrospective study, in silico analysis of data from patients with adult-type diffuse glioma revealed differential methylation and expression patterns of MUC1 and MUC4 between GBM and non-GBM groups. In the GBM group, decreased methylation and elevated expression of MUC1 were observed (r = -0.25, p < 0.0001), whereas increased methylation and decreased expression of MUC4 were observed (r = -0.13, p = 0.1344). Conversely, in the non-GBM group, MUC1 exhibited higher methylation and lower expression (r = -0.27, p < 0.0001), whereas MUC4 showed lower methylation and higher expression (r = -0.32, p < 0.0001). The expression of these genes influenced OS in adult patients with glioma (p = 0.0344), with high MUC1 and low MUC4 expression associated with worse OS. MUC1 and MUC4 expression correlated with that of MUC20 in both GBM (r = 0.54) and non-GBM (r = 0.53) groups (p < 0.0001). Functional enrichment analysis identified the biological roles of MUC1-co-expressed genes as involvement in innate immunity, antigen processing, and proinflammatory responses in both the non-GBM and GBM groups, and integrin-based signaling pathways in the GBM group. MUC4-co-expressed genes are involved in ion transport in GBM patients. Using molecular docking, we observed that MUC1 domains physically interact with immune response-related proteins, such as receptors for advanced glycation end products (RAGE), major histocompatibility complex II (MHC-II), and extracellular matrix receptor integrin alpha 2 (ITGA2). To our knowledge, this is the first retrospective study and in silico analysis demonstrating the relevance and correlation of MUC1 and MUC4 in adult gliomas. These findings elucidate the molecular mechanisms underlying adult-type diffuse glioma progression and highlight MUC1 and MUC4 as potential prognostic markers and therapeutic targets for glioma management.

Deficits of Attention and Working Memory in Patients with Gliomas of Supplementary Motor Area

Objectives: The effects of brain tumors located in the supplementary motor area (SMA) have so far been described mainly in the context of motor and speech disorders. There are few studies that have considered other cognitive domains, so this study aimed to fill this gap by focusing on examining attention and working memory in a population of patients with gliomas in the SMA region. Methods: This study included 50 patients diagnosed with gliomas located in the SMA who have not yet had any treatment and 57 demographically matched healthy individuals. A set of neuropsychological tests was conducted to assess attention and working memory: Digit Span from WAIS-R, Visual Elevator from TEA, Verbal Fluency Test (switching condition), and Color Trails Test (CTT). Results: The analyses showed that patients scored lower in most of the evaluated tests and indicators, namely in Digit Span-forward (t = -2.05; p = 0.022), Digit Span-backward (t = -2.63; p = 0.005), CTT-2 (t = 4.24; p = 0.001), CTT-interference (t = 2.31; p = 0.012), Visual Elevator-time (t = 1.83; p = 0.035), Visual Elevator-accuracy (t = -2.42, p = 0.010), and Verbal Fluency-switching (t = -3.41; p = 0.001). A significant relationship was also demonstrated between the grade of tumor malignancy and the results achieved in some of the neuropsychological tests. The lateralization of the tumor, the size of the lesion, and the presence of epilepsy did not prove to be particularly significant. Conclusions: Due to the significant decline in cognitive performance in terms of attention and working memory, we believe that every patient with a tumor in the SMA should undergo a detailed neuropsychological examination, which will profile their functioning and help tailor the best possible psychological care.

The role of lncRNAs in the interplay of signaling pathways and epigenetic mechanisms in glioma

Gliomas, highly aggressive tumors of the central nervous system, present overwhelming challenges due to their heterogeneity and therapeutic resistance. Glioblastoma multiforme (GBM), the most malignant form, underscores this clinical urgency due to dismal prognosis despite aggressive treatment regimens. Recent advances in cancer research revealed signaling pathways and epigenetic mechanisms that intricately govern glioma progression, offering multifaceted targets for therapeutic intervention. This review explores the dynamic interplay between signaling events and epigenetic regulation in the context of glioma, with a particular focus on the crucial roles played by non-coding RNAs (ncRNAs). Through direct and indirect epigenetic targeting, ncRNAs emerge as key regulators shaping the molecular landscape of glioblastoma across its various stages. By dissecting these intricate regulatory networks, novel and patient-tailored therapeutic strategies could be devised to improve patient outcomes with this devastating disease.

Natural Source of Drugs Targeting Central Nervous System Tumors-Focus on NAD(P)H Oxidoreductase 1 (NQO1) Activity

Central nervous system (CNS) tumors involve a large and diverse group of malignancies that arise from various cell types within the brain tissue. Although there are advances in treatments, CNS tumors still remain challenging, due to their complex biology and the delicate nature of the surrounding tissue. NAD(P)H O=oxidoreductase 1 (NQO1) is an enzyme that plays a critical role in the detoxification of quinones, protecting cells from oxidative stress. In CNS tumors this enzyme is often overexpressed, which contributes to the resistance of tumor cells to chemotherapy by enhancing their antioxidant defenses. NQO1 influences the progression of CNS tumors by affecting downstream signaling pathways, such as those involving the transcription factor SNAIL, as well as others that are associated with tumor behavior. Plants represent a valuable source of numerous constituents with different chemical structures known to affect different molecular signaling pathways associated with different pathologies.

Concurrent RB1 and P53 pathway disruption predisposes to the development of a primitive neuronal component in high-grade gliomas depending on MYC-driven EBF3 transcription

The foremost feature of glioblastoma (GBM), the most frequent malignant brain tumours in adults, is a remarkable degree of intra- and inter-tumour heterogeneity reflecting the coexistence within the tumour bulk of different cell populations displaying distinctive genetic and transcriptomic profiles. GBM with primitive neuronal component (PNC), recently identified by DNA methylation-based classification as a peculiar GBM subtype (GBM-PNC), is a poorly recognized and aggressive GBM variant characterised by nodules containing cells with primitive neuronal differentiation along with conventional GBM areas. In addition, the presence of a PNC component has been also reported in IDH-mutant high-grade gliomas (HGGs), and to a lesser extent to other HGGs, suggesting that regardless from being IDH-mutant or IDH-wildtype, peculiar genetic and/or epigenetic events may contribute to the phenotypic skewing with the emergence of the PNC phenotype. However, a clear hypothesis on the mechanisms responsible for this phenotypic skewing is still lacking. We assumed that the biphasic nature of these entities represents a unique model to investigate the relationships between genetic alterations and their phenotypic manifestations. In this study we show that in HGGs with PNC features both components are highly enriched in genetic alterations directly causing cell cycle deregulation (RB inactivation or CDK4 amplification) and p53 pathway inactivation (TP53 mutations or MDM2/4 amplification). However, the PNC component displays further upregulation of transcriptional pathways associated with proliferative activity, including overexpression of MYC target genes. Notably, the PNC phenotype relies on the expression of EBF3, an early neurogenic transcription factor, which is directly controlled by MYC transcription factors in accessible chromatin sites. Overall our findings indicate that the concomitant presence of genetic alterations, impinging on both cell cycle and p53 pathway control, strongly predisposes GBM to develop a concomitant poorly differentiated primitive phenotype depending on MYC-driven EBF3 transcription in a subset of glioma stem-like progenitor cells.

Completely non-invasive prediction of IDH mutation status based on preoperative native CT images

The isocitrate dehydrogenase (IDH) mutation status is one of the most important markers according to the 2021 WHO classification of CNS tumors. Preoperatively, this information is usually obtained based on invasive biopsies, contrast-enhanced MR images or PET images generated using radioactive tracers. However, the completely non-invasive determination of IDH mutation status using routinely acquired preoperative native CT images has hardly been investigated to date. In our study, we show that radiomics-based machine learning allows to determine IDH mutation status based on preoperative native CT images both with very high accuracy and completely non-invasively. Based on independent test data, we are able to correctly identify 91.1% of cases with an IDH mutation. Our final model, containing only six features, exhibits a high area under the curve of 0.847 and an excellent area under the precision-recall curve of 0.945. In the future, such models may be used for a completely non-invasive prediction of important genetic markers, potentially allowing treating physicians to reduce the number of biopsies and speed up further treatment planning.

Highly Multiplexed Tissue Imaging in Precision Oncology and Translational Cancer Research

Precision oncology tailors treatment strategies to a patient's molecular and health data. Despite the essential clinical value of current diagnostic methods, hematoxylin and eosin morphology, immunohistochemistry, and gene panel sequencing offer an incomplete characterization. In contrast, highly multiplexed tissue imaging allows spatial analysis of dozens of markers at single-cell resolution enabling analysis of complex tumor ecosystems; thereby it has the potential to advance our understanding of cancer biology and supports drug development, biomarker discovery, and patient stratification. We describe available highly multiplexed imaging modalities, discuss their advantages and disadvantages for clinical use, and potential paths to implement these into clinical practice. Significance: This review provides guidance on how high-resolution, multiplexed tissue imaging of patient samples can be integrated into clinical workflows. It systematically compares existing and emerging technologies and outlines potential applications in the field of precision oncology, thereby bridging the ever-evolving landscape of cancer research with practical implementation possibilities of highly multiplexed tissue imaging into routine clinical practice.

Prediction of early recurrence of adult-type diffuse gliomas following radiotherapy using multi-modal magnetic resonance images

BACKGROUND

Adult-type diffuse gliomas are among the central nervous system's most aggressive malignant primary neoplasms. Despite advancements in systemic therapies and technological improvements in radiation oncology treatment delivery, the survival outcome for these patients remains poor. Fast and accurate assessment of tumor response to oncologic treatments is crucial, as it can enable the early detection of recurrent or refractory gliomas, thereby allowing timely intervention with life-prolonging salvage therapies.

PURPOSE

Radiomics is a developing field with great potential to improve medical image interpretation. This study aims to apply a radiomics-based predictive model for classifying response to radiotherapy within the first 3 months post-treatment.

METHODS

Ninety-five patients were selected from the Burdenko Glioblastoma Progression Dataset. Tumor regions were delineated in the axial plane on contrast-enhanced T1(CE T1W) and T2 fluid-attenuated inversion recovery (T2_FLAIR) magnetic resonance imaging (MRI). Hand-crafted radiomic (HCR) features, including first- and second-order features, were extracted using PyRadiomics (3.7.6) in Python (3.10). Then, recursive feature elimination with a random forest (RF) classifier was applied for feature dimensionality reduction. RF and support vector machine (SVM) classifiers were built to predict treatment outcomes using the selected features. Leave-one-out cross-validation was employed to tune hyperparameters and evaluate the models.

RESULTS

For each segmented target, 186 HCR features were extracted from the MRI sequence. Using the top-ranked radiomic features from a combination of CE T1W and T2_FLAIR, an optimized classifier achieved the highest averaged area under the curve (AUC) of 0.829 ± 0.075 using the RF classifier. The HCR features of CE T1W produced the worst outcomes among all models (0.603 ± 0.024 and 0.615 ± 0.075 for RF and SVM classifiers, respectively).

CONCLUSIONS

We developed and evaluated a radiomics-based predictive model for early tumor response to radiotherapy, demonstrating excellent performance supported by high AUC values. This model, harnessing radiomic features from multi-modal MRI, showed superior predictive performance compared to single-modal MRI approaches. These results underscore the potential of radiomics in clinical decision support for this disease process.

A Comprehensive Review on the Role of MRI in the Assessment of Supratentorial Neoplasms: Comparative Insights Into Adult and Pediatric Cases

Magnetic resonance imaging (MRI) is a critical diagnostic tool in assessing supratentorial neoplasms, offering unparalleled detail and specificity in brain imaging. Supratentorial neoplasms in the cerebral hemispheres, basal ganglia, thalamus, and other structures above the tentorium cerebelli present significant diagnostic and therapeutic challenges. These challenges vary notably between adult and pediatric populations due to differences in tumor types, biological behavior, and patient management strategies. This comprehensive review explores the role of MRI in diagnosing, planning treatment, monitoring response, and detecting recurrence in supratentorial neoplasms, providing comparative insights into adult and pediatric cases. The review begins with an overview of the epidemiology and pathophysiology of these tumors in different age groups, followed by a detailed examination of standard and advanced MRI techniques, including diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), and magnetic resonance spectroscopy (MRS). We discuss the specific imaging characteristics of various neoplasms and the importance of tailored approaches to optimize diagnostic accuracy and therapeutic efficacy. The review also addresses the technical and interpretative challenges unique to pediatric imaging and the implications for long-term patient outcomes. By highlighting the comparative utility of MRI in adult and pediatric cases, this review aims to enhance the understanding of its pivotal role in managing supratentorial neoplasms. It underscores the necessity of age-specific diagnostic and therapeutic strategies. Emerging MRI technologies and future research directions are also discussed, emphasizing the potential for advancements in personalized imaging approaches and improved patient care across all age groups.

Connectomic insights into the impact of 1p/19q co-deletion in dominant hemisphere insular glioma patients

Objectives

This study aimed to elucidate the influences of 1p/19q co-deletion on structural connectivity alterations in patients with dominant hemisphere insular diffuse gliomas.

Methods

We incorporated 32 cases of left insular gliomas and 20 healthy controls for this study. Using diffusion MRI, we applied correlational tractography, differential tractography, and graph theoretical analysis to explore the potential connectivity associated with 1p/19q co-deletion.

Results

The study revealed that the quantitative anisotropy (QA) of key deep medial fiber tracts, including the anterior thalamic radiation, superior thalamic radiation, fornix, and cingulum, had significant negative associations with 1p/19q co-deletion (FDR = 4.72 × 10-5). These tracts are crucial in maintaining the integrity of brain networks. Differential analysis further supported these findings (FWER-corrected p < 0.05). The 1p/19q non-co-deletion group exhibited significantly higher clustering coefficients (FDR-corrected p < 0.05) and reduced betweenness centrality (FDR-corrected p < 0.05) in regions around the tumor compared to HC group. Graph theoretical analysis indicated that non-co-deletion patients had increased local clustering and decreased betweenness centrality in peritumoral brain regions compared to co-deletion patients and healthy controls (FDR-corrected p < 0.05). Additionally, despite not being significant through correction, patients with 1p/19q co-deletion exhibited lower trends in weighted average clustering coefficient, transitivity, small worldness, and global efficiency, while showing higher tendencies in weighted path length compared to patients without the co-deletion.

Conclusion

The findings of this study underline the significant role of 1p/19q co-deletion in altering structural connectivity in insular glioma patients. These alterations in brain networks could have profound implications for the neural functionality in patients with dominant hemisphere insular gliomas.

The combination therapy using tyrosine kinase receptors inhibitors and repurposed drugs to target patient-derived glioblastoma stem cells

The lesson from many studies investigating the efficacy of targeted therapy in glioblastoma (GBM) showed that a future perspective should be focused on combining multiple target treatments. Our research aimed to assess the efficacy of drug combinations against glioblastoma stem cells (GSCs). Patient-derived cells U3042, U3009, and U3039 were obtained from the Human Glioblastoma Cell Culture resource. Additionally, the study was conducted on a GBM commercial U251 cell line. Gene expression analysis related to receptor tyrosine kinases (RTKs), stem cell markers and genes associated with significant molecular targets was performed, and selected proteins encoded by these genes were assessed using the immunofluorescence and flow cytometry methods. The cytotoxicity studies were preceded by analyzing the expression of specific proteins that serve as targets for selected drugs. The cytotoxicity study using the MTS assay was conducted to evaluate the effects of selected drugs/candidates in monotherapy and combinations. The most cytotoxic compounds for U3042 cells were Disulfiram combined with Copper gluconate (DSF/Cu), Dacomitinib, and Foretinib with IC50 values of 52.37 nM, 4.38 µM, and 4.54 µM after 24 h incubation, respectively. Interactions were assessed using SynergyFinder Plus software. The analysis enabled the identification of the most effective drug combinations against patient-derived GSCs. Our findings indicate that the most promising drug combinations are Dacomitinib and Foretinib, Dacomitinib and DSF/Cu, and Foretinib and AZD3759. Since most tested combinations have not been previously examined against glioblastoma stem-like cells, these results can shed new light on designing the therapeutic approach to target the GSC population.

High-Grade Glioma Recurrence Is Delayed in Hispanic Patients despite Severe Social Vulnerability: A Retrospective Cohort Study

High-grade gliomas (HGGs; WHO grade III or IV) are the most common and lethal brain malignancy. Patients of Hispanic ethnicity are diagnosed with HGGs earlier than non-Hispanic patients, but they exhibit improved HGG survival following diagnosis. Either environmental or biological factors could explain this survival benefit. We aimed to determine if post-diagnosis advantages would still be present in Hispanic patients with high social vulnerability, an environmental condition predisposing patients to poor oncologic outcomes. HGG outcomes were retrospectively assessed in a cohort of 22 Hispanic patients and 33 non-Hispanic patients treated for HGGs from 2015 to 2020 at a single institution that serves a highly vulnerable region. Compared to non-Hispanic patients, Hispanic patients demonstrated higher social vulnerability index scores (96.8 + 0.7 vs. 76.3 + 4.6; *** p = 0.0002) and a 14-month longer interval between diagnosis and recurrence (19.7 + 5.9 (n = 13) vs. 5.5 + 0.6 months (n = 19); ** p = 0.001). In only those patients with more aggressive IDH-1 wildtype tumors (glioblastoma), Hispanic ethnicity still related to a longer time before recurrence (15.8 + 5.9 months (n = 9); 5.5 + 0.6 months (n = 18); * p = 0.034), and in a multivariate analysis, Hispanic ethnicity predicted time-to-recurrence (* p = 0.027) independent of patient age, functional status, MGMT gene methylation, or treatments received. Therefore, environmental factors, specifically social vulnerability, did not obscure the post-diagnosis benefits associated with Hispanic ethnicity. In future experiments, basic studies should be prioritized which investigate the cellular or genetic mechanisms underlying this ethnicity effect on HGG progression in the hopes of improving care for these devastating malignancies.

Comparison of MRI Sequences to Predict IDH Mutation Status in Gliomas Using Radiomics-Based Machine Learning

OBJECTIVES

Regarding the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, the isocitrate dehydrogenase (IDH) mutation status is one of the most important factors for CNS tumor classification. The aim of our study is to analyze which of the commonly used magnetic resonance imaging (MRI) sequences is best suited to obtain this information non-invasively using radiomics-based machine learning models. We developed machine learning models based on different MRI sequences and determined which of the MRI sequences analyzed yields the highest discriminatory power in predicting the IDH mutation status.

MATERIAL AND METHODS

In our retrospective IRB-approved study, we used the MRI images of 106 patients with histologically confirmed gliomas. The MRI images were acquired using the T1 sequence with and without administration of a contrast agent, the T2 sequence, and the Fluid-Attenuated Inversion Recovery (FLAIR) sequence. To objectively compare performance in predicting the IDH mutation status as a function of the MRI sequence used, we included only patients in our study cohort for whom MRI images of all four sequences were available. Seventy-one of the patients had an IDH mutation, and the remaining 35 patients did not have an IDH mutation (IDH wild-type). For each of the four MRI sequences used, 107 radiomic 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 associated with the data partitioning. Feature preselection and subsequent model development were performed using Random Forest, Lasso regression, LDA, and Naïve Bayes. The performance of all models was determined with independent test data.

RESULTS

Among the different approaches we examined, the T1-weighted contrast-enhanced sequence was found to be the most suitable for predicting IDH mutations status using radiomics-based machine learning models. Using contrast-enhanced T1-weighted MRI images, our seven-feature model developed with Lasso regression achieved a mean area under the curve (AUC) of 0.846, a mean accuracy of 0.792, a mean sensitivity of 0.847, and a mean specificity of 0.681. The administration of contrast agents resulted in a significant increase in the achieved discriminatory power.

CONCLUSIONS

Our analyses show that for the prediction of the IDH mutation status using radiomics-based machine learning models, among the MRI images acquired with the commonly used MRI sequences, the contrast-enhanced T1-weighted images are the most suitable.

Pediatric-Like Brain Tumors in Adults

Pediatric brain tumors are different to those found in adults in pathological type, anatomical site, molecular signature, and probable tumor drivers. Although these tumors usually occur in childhood, they also rarely present in adult patients, either as a de novo diagnosis or as a delayed recurrence of a pediatric tumor in the setting of a patient that has transitioned into adult services.Due to the rarity of pediatric-like tumors in adults, the literature on these tumor types in adults is often limited to small case series, and treatment decisions are often based on the management plans taken from pediatric studies. However, the biology of these tumors is often different from the same tumors found in children. Likewise, adult patients are often unable to tolerate the side effects of the aggressive treatments used in children-for which there is little or no evidence of efficacy in adults. In this chapter, we review the literature and summarize the clinical, pathological, molecular profile, and response to treatment for the following pediatric tumor types-medulloblastoma, ependymoma, craniopharyngioma, pilocytic astrocytoma, subependymal giant cell astrocytoma, germ cell tumors, choroid plexus tumors, midline glioma, and pleomorphic xanthoastrocytoma-with emphasis on the differences to the adult population.

Decoding Diffuse Midline Gliomas: A Comprehensive Review of Pathogenesis, Diagnosis and Treatment

Diffuse midline gliomas (DMGs) are a group of aggressive CNS tumors, primarily affecting children and young adults, which have historically been associated with dismal outcomes. As the name implies, they arise in midline structures in the CNS, primarily in the thalamus, brainstem, and spinal cord. In more recent years, significant advances have been made in our understanding of DMGs, including molecular features, with the identification of potential therapeutic targets. We aim to provide an overview of the most recent updates in the field of DMGs, including classification, molecular subtypes, diagnostic techniques, and emerging therapeutic strategies including a review of the ongoing clinical trials, thus providing the treating multidisciplinary team with a comprehensive understanding of the current landscape and potential therapeutic strategies for this devastating group of tumors.

High LYRM4-AS1 predicts poor prognosis in patients with glioma and correlates with immune infiltration

Background

Many researches proved that non-coding RNAs are important in glioma development. We screened the differentially expressed genes through The Cancer Genome Atlas (TCGA) database and identified the molecule LYRM4-AS1 associated with prognosis. As a lncRNA, the expression level and role of LYRM4-AS1 in glioma are inconclusive. Therefore, we attempted to assess the clinical significance, expression and related mechanisms of LYRM4-AS1 in glioma by employing cell experiments and an integrative in silico methodology.

Methods

RNA-seq data were obtained from UCSC XENA and TCGA datasets. The Gene Expression Omnibus (GEO) database was used to download glioma-related expression profile data. The LYRM4-AS1 expression level was evaluated. Survival curves were constructed by the Kaplan-Meier method. Cox regression analysis was used to analyze independent variables. Patients were divided into high and low expression group base on the median LYRM4-AS1 expression value in glioma tissues. The DESeq2 R package was used to identify differentially expressed genes (DEGs) between two different expression LYRM4-AS1 groups. Gene set enrichment analysis (GSEA) was conducted. Next, the single-sample Gene Set Enrichment Analysis (ssGSEA) was done to quantify the immune infiltration of immune cells in glioma tissues. Gene expression profiles for glioma tumor tissues were used to quantify the relative enrichment score for each immune cell. Spearman correlation analysis was used to analyze the correlation between LYRM4-AS1 and biomarkers of immune cells as well as immune checkpoints in glioma. Finally, assays for cell apoptosis, cell viability and wound healing were conducted to evaluate the function on U87 MG and U251 cells after knocking down LYRM4-AS1.

Results

We found that LYRM4-AS1 was upregulated and related to the grade and malignancy of glioma. Survival analyses showed that high expression LYRM4-AS1 patients had poor clinical outcomes (P < 0.01). Cox regression analyses demonstrated that LYRM4-AS1 was an independent risk factor for overall survival (OS) in glioma (HR: 274 1.836; CI [1.278-2.639]; P = 0.001). Enrichment and immune infiltration analysis showed interferon signaling and cytokine-cytokine receptor interaction enriched in the LYRM4-AS1 high-expression phenotype, and LYRM4-AS1 showed significantly positively related to immune infiltration as well as immune checkpoints (P < 0.01). The knockdown of LYRM4-AS1 in U87 MG and U251 cells can inhibit migration and proliferation of cells (P < 0.05).

Conclusions

These findings indicated that the increased LYRM4-AS1 may be useful for the diagnosis and prognosis of glioma and might participate in the immune infiltration.

Radiotherapy of high-grade gliomas: dealing with a stalemate

This article discusses the studies on radiotherapy of high-grade gliomas published between January 1, 2022, and June 30, 2022, with special reference to their molecular biology basis. The focus was on advances in radioresistance, radiosensitization and the toxicity of radiotherapy treatments. In the first half of 2022, several important advances have been made in understanding resistance mechanisms in high-grade gliomas. Furthermore, the development of several radiosensitization procedures for these deadly tumors, including studies with small molecule radiosensitizers, new fractionation protocols, and new immunostimulatory agents, has progressed in both the preclinical and clinical settings, reflecting the frantic research effort in the field. However, since 2005 our research efforts fail to produce significant improvements to treatment guidelines for high-grade gliomas. Possible reasons for this stalemate and measures to overcome it are discussed.

Stereotactic Radiosurgery for Contrast-Enhancing Satellite Nodules in Recurrent Glioblastoma: A Rare Case Series From a Single Institution

Introduction Glioblastoma (GBM) is the most common malignant adult brain tumor and is invariably fatal. The standard treatment for GBM involves resection where possible, followed by chemoradiation per Stupp's protocol. We frequently use stereotactic radiosurgery (SRS) as a single-fraction treatment for small (volume ≤ 1cc) nodular recurrent GBM to the contrast-enhancing target on T1 MRI scan. In this paper, we aimed to evaluate the safety and efficacy of SRS for patients with contrast-enhancing satellite nodules in recurrent GBM. Methods This retrospective study analyzed the clinical and radiological outcomes of five patients who underwent CyberKnife (Accuray Inc., Sunnyvale, California) SRS at the institute between 2013 and 2022. Results From 96 patients receiving SRS for GBM, five (four males, one female; median age 53) had nine distinct new satellite lesions on MRI, separate from their primary tumor beds. Those nine lesions were treated with a median margin dose of 20 Gy in a single fraction. The three-, six, and 12-month local tumor control rates were 77.8%, 66.7%, and 26.7%, respectively. Median progression-free survival (PFS) was seven months, median overall survival following SRS was 10 months, and median overall survival (OS) was 35 months. Interestingly, the only lesion that did not show radiological progression was separate from the T2-fluid attenuated inversion recovery (FLAIR) signal of the main tumor. Conclusion Our SRS treatment outcomes for recurrent GBM satellite lesions are consistent with existing findings. However, in a unique case, a satellite nodule distinct from the primary tumor's T2-FLAIR signal and treated with an enlarged target volume showed promising control until the patient's demise. This observation suggests potential research avenues, given the limited strategies for 'multicentric' GBM lesions.

Recapitulating the Key Advances in the Diagnosis and Prognosis of High-Grade Gliomas: Second Half of 2021 Update

High-grade gliomas (World Health Organization grades III and IV) are the most frequent and fatal brain tumors, with median overall survivals of 24–72 and 14–16 months, respectively. We reviewed the progress in the diagnosis and prognosis of high-grade gliomas published in the second half of 2021. A literature search was performed in PubMed using the general terms “radio* and gliom*” and a time limit from 1 July 2021 to 31 December 2021. Important advances were provided in both imaging and non-imaging diagnoses of these hard-to-treat cancers. Our prognostic capacity also increased during the second half of 2021. This review article demonstrates slow, but steady improvements, both scientifically and technically, which express an increased chance that patients with high-grade gliomas may be correctly diagnosed without invasive procedures. The prognosis of those patients strictly depends on the final results of that complex diagnostic process, with widely varying survival rates.