Grading of adult diffuse gliomas according to the 2021 WHO Classification of Tumors of the Central Nervous System

Imply on diagnosis and early prognosis of preoperative [68Ga]Ga-PSMA-11 PET/CT in patients with suspected brain tumours of glial origin

PET/CT targeting prostate-specific membrane antigen (PSMA) is commonly used in patients with prostate cancer. PSMA has been found in other solid tumours, including primary brain tumours. The aim of this study was to evaluate the usefulness of [68Ga]Ga-PSMA-11 PET/CT for preoperative diagnosis and 2-year prognosis. We prospectively screened patients with suspected glioma tumour. The PET/CT qualitative and quantitative results were compared to the histopathological examination. We compared glioblastoma (GBM) diagnostic data or between high-grade (HGG) and low-grade (LGG) gliomas. Overall (OS) and progression free survival (PFS) were analysed. Forty-four patients met the inclusion criteria. Twenty of them had positive and twenty-four negative scans. The sensitivity, specificity, positive predictive value, and negative predictive value for HGG diagnosis were 71.4 (95% confidence interval - 51.3-86.8), 100.0 (79.4-100.0), 100.0 (83.1-100.0), and 66.7 (44.7-84.4), respectively. For differentiation between GBM vs non-GBM tumours, the best parameter was the tumour-to-background ratio, with the area under the receiver operating characteristic curve 0.81 (0.66-0.96; 42.2) (95% CI; cut-off). Patients with positive PET/CT scans had similar PFS and OS to patients with HGG. PSMA accumulation negatively affected the PFS and OS of patients with diagnosed GBM. [68Ga]Ga-PSMA-11 PET/CT showed optimistic diagnostic results and may be prognostic a factor.Registration at www.clinicaltrials.gov 09/06/2023 with number NCT05896449.

Altering fractionation during radiation overcomes radio-resistance in patient-derived glioblastoma cells assessed using a novel longitudinal radiation cytotoxicity assay

PURPOSE

Current radiotherapy (RT) in glioblastoma (GBM) is delivered as constant dose fractions (CDF), which do not account for intratumoral-heterogeneity and radio-selection in GBM. These factors contribute to differential treatment response complicating the therapeutic efficacy of this principle. Our study aims to investigate an alternative dosing strategy to overcome radio-resistance using a novel longitudinal radiation cytotoxicity assay.

METHODS

Theoretical In-silico mathematical assumptions were combined with an in-vitro experimental strategy to investigate alternative radiation regimens. Patient-derived xenograft (PDX) brain tumor-initiating cells (BTICs) with differential radiation-sensitivities were tested individually with sham control and three regimens of the same nominal and average dose of 16 Gy (over four fractions), but with altered doses per fraction. Fractions were delivered conventionally (CDF: 4, 4, 4, 4 Gy), or as dynamic dose fractions (DDF) "ramped down" (RD: 7, 5, 3, 1 Gy), or DDF "ramped up" (RU: 1, 3, 5, 7 Gy), every 4 days. Interfraction-longitudinal data were collected by imaging cells every 5 days, and endpoint viability was taken on day 20.

RESULTS

The proposed method of radiosensitivity assessment allows for longitudinal-interfraction investigation in addition to endpoint analysis. Delivering four-fraction doses in an RD manner proves to be most effective at overcoming acquired radiation resistance in BTICs (Relative cell viability: CDF vs. RD: P < 0.0001; Surviving fraction: CDF: vs. RD: P < 0.0001).

CONCLUSIONS

Using in-silico cytotoxicity prediction modeling and an altered radiosensitivity assessment, we show DDF-RD is effective at inducing cytotoxicity in three BTIC lines with differential radiosensitivity.

Grade 4 astrocytoma vs. grade 4 glioblastoma: is there any clue in H&E?

Objective: Gliomas are the most common primary tumors of the central nervous system. The fifth edition of the World Health Organization (WHO) Classification of Tumors of the CNS identifies IDH mutant astrocytomas grade 4 and IDH wild type glioblastomas grade 4 as distinct entities. This study aimed to identify morphological indicators that could predict IDH mutation status in grade 4 diffuse astrocytomas and grade 4 glioblastomas among fifty patients from two groups.

Methods: Hematoxylin and eosin (H&E)-stained tumor slides were scanned using a digital scanner and further histopathological examinations were performed on digital images, with additional calculations and measurements.

Results: The study showed that, IDH-wildtype glioblastomas and IDH-mutant grade 4 astrocytomas exhibit unique morphological features, particularly in relation to levels of necrosis, microvessel density, and the presence of "C" or "Ring" shape giant cells.

Conclusion: Despite advancements in genomic biomarker technology, histology remains an essential tool for predicting patient outcomes. Therefore, pathologists must continue to investigate and document the morphological implications of molecular changes in CNS tumors.

COL8A1 overexpression promotes glioma cell growth by activating focal adhesion kinase signaling cascade

We explored expression and biological roles of collagen type VIII alpha-1 chain (COL8A1) in glioma. Bioinformatics analyses unveiled COL8A1 overexpression within glioma tissues correlates with adverse clinical outcomes of patients. COL8A1 overexpression was also detected in local glioma tissues and various glioma cells. In primary and immortalized glioma cells, COL8A1 shRNA or knockout (KO) reduced cell viability, proliferation and mobility, disrupted cell cycle, and prompted apoptosis. While COL8A1 overexpression augmented the malignant behaviors in glioma cells. COL8A1 shRNA or KO in primary glioma cells decreased phosphorylation of FAK and downstream targets Akt and Erk1/2. Conversely, elevating COL8A1 expression increased their phosphorylations. In vivo experiments confirmed growth inhibition of patient-derived glioma xenografts within the mouse brain following COL8A1 KO. Hindered proliferation, lowered phosphorylation levels of FAK, Akt, and Erk1/2, as well as increased apoptosis were observed within the COL8A1 KO intracranial glioma xenografts. Thus, COL8A1 overexpression promotes glioma cell growth.

GelMA microneedle-loaded bio-derived nanovaccine shows therapeutic potential for gliomas

Glioma is the most common primary malignant tumor of the central nervous system in adults. Although immunotherapy, especially tumor vaccines, has made some progress in the treatment of gliomas compared with surgery and radiotherapy. However, the lack of specific or relevant tumor antigens severely limits the further development of tumor vaccines. Here, we report a bio-derived vaccine (TMV@CpG) derived from glioma cell membrane vesicles and carrying TLR9 agonist CpG as adjuvant, which was loaded onto the GelMA microneedle to obtain the microneedle vaccine (MN-TMV@CpG). Microneedle vaccine fully utilize the innate immune cells rich in the skin, inducing stronger cellular immune responses. In subcutaneous tumor models, MN-TMV@CpG reversed the immune-suppressing microenvironment of tumor, and effectively inhibited tumor progression. In an intracranial tumor model, MN-TMV@CpG significantly prolonged the survival duration and induced stronger immune memory responses in tumor bearing mice when combined with anti-PD1 mAb. These results suggest that bio-derived nanovaccines can be used as a potential antitumor immunotherapy strategy.

When do I ask for a DNA methylation array for primary brain tumor diagnosis?

PURPOSE OF REVIEW

Despite remarkable advances in molecular characterization, the diagnosis of brain tumors remains challenging, particularly in cases with ambiguous histology or contradictory molecular features. In this context, DNA methylation profiling plays an important role in improving diagnostic and prognostic accuracy. This review aims to provide diagnostic guidance regarding when DNA methylation arrays represent a useful tool for the diagnosis of primary brain tumors.

RECENT FINDINGS

Large-scale profiling has revealed that DNA methylation profiles of brain tumors are highly reproducible and stable. Therefore, DNA methylation profiling has been successfully used to classify brain tumors and identify new entities. This approach seems to be particularly promising for heterogeneous groups of tumors, such as IDH -wildtype gliomas, and glioneuronal and embryonal tumors, which include a variety of entities that are still under characterization.

SUMMARY

As underlined in the fifth edition of the WHO classification of central nervous system tumors, the diagnosis of brain tumors requires the integration of histological, molecular, clinical, and radiological features. Although advanced imaging and histological examination remain the standard diagnostic tools, DNA methylation analysis can significantly improve diagnostic accuracy, with a substantial impact on patient management.

SEOM-GEINO clinical guidelines for grade 2 gliomas (2023)

The 2021 World Health Organization (WHO) classification has updated the definition of grade 2 gliomas and the presence of isocitrate dehydrogenase (IDH) mutation has been deemed the cornerstone of diagnosis. Though slow-growing and having a low proliferative index, grade 2 gliomas are incurable by surgery and complementary treatments are vital to improving prognosis. This guideline provides recommendations on the multidisciplinary treatment of grade 2 astrocytomas and oligodendrogliomas based on the best evidence available.

Quantifying H&E staining results, grading and predicting IDH mutation status of gliomas using hybrid multi-dimensional MRI

OBJECTIVE

To assess the performance of hybrid multi-dimensional magnetic resonance imaging (HM-MRI) in quantifying hematoxylin and eosin (H&E) staining results, grading and predicting isocitrate dehydrogenase (IDH) mutation status of gliomas.

MATERIALS AND METHODS

Included were 71 glioma patients (mean age, 50.17 ± 13.38 years; 35 men). HM-MRI images were collected at five different echo times (80-200 ms) with seven b-values (0-3000 s/mm2). A modified three-compartment model with very-slow, slow and fast diffusion components was applied to calculate HM-MRI metrics, including fractions, diffusion coefficients and T2 values of each component. Pearson correlation analysis was performed between HM-MRI derived fractions and H&E staining derived percentages. HM-MRI metrics were compared between high-grade and low-grade gliomas, and between IDH-wild and IDH-mutant gliomas. Using receiver operational characteristic (ROC) analysis, the diagnostic performance of HM-MRI in grading and genotyping was compared with mono-exponential models.

RESULTS

HM-MRI metrics FDvery-slow and FDslow demonstrated a significant correlation with the H&E staining results (p < .05). Besides, FDvery-slow showed the highest area under ROC curve (AUC = 0.854) for grading, while Dslow showed the highest AUC (0.845) for genotyping. Furthermore, a combination of HM-MRI metrics FDvery-slow and T2Dslow improved the diagnostic performance for grading (AUC = 0.876).

DISCUSSION

HM-MRI can aid in non-invasive diagnosis of gliomas.

Unveiling the Molecular Mechanisms of Glioblastoma through an Integrated Network-Based Approach

Background/Objectives: Despite current treatments extending the lifespan of Glioblastoma (GBM) patients, the average survival time is around 15-18 months, underscoring the fatality of GBM. This study aims to investigate the impact of sample heterogeneity on gene expression in GBM, identify key metabolic pathways and gene modules, and explore potential therapeutic targets. Methods: In this study, we analysed GBM transcriptome data derived from The Cancer Genome Atlas (TCGA) using genome-scale metabolic models (GEMs) and co-expression networks. We examine transcriptome data incorporating tumour purity scores (TPSs), allowing us to assess the impact of sample heterogeneity on gene expression profiles. We analysed the metabolic profile of GBM by generating condition-specific GEMs based on the TPS group. Results: Our findings revealed that over 90% of genes showing brain and glioma specificity in RNA expression demonstrate a high positive correlation, underscoring their expression is dominated by glioma cells. Conversely, negatively correlated genes are strongly associated with immune responses, indicating a complex interaction between glioma and immune pathways and non-tumorigenic cell dominance on gene expression. TPS-based metabolic profile analysis was supported by reporter metabolite analysis, highlighting several metabolic pathways, including arachidonic acid, kynurenine and NAD pathway. Through co-expression network analysis, we identified modules that significantly overlap with TPS-correlated genes. Notably, SOX11 and GSX1 are upregulated in High TPS, show a high correlation with TPS, and emerged as promising therapeutic targets. Additionally, NCAM1 exhibits a high centrality score within the co-expression module, which shows a positive correlation with TPS. Moreover, LILRB4, an immune-related gene expressed in the brain, showed a negative correlation and upregulated in Low TPS, highlighting the importance of modulating immune responses in the GBM mechanism. Conclusions: Our study uncovers sample heterogeneity's impact on gene expression and the molecular mechanisms driving GBM, and it identifies potential therapeutic targets for developing effective treatments for GBM patients.

A data-driven intravoxel mean diffusivities distribution approach for molecular classifications and MIB-1 prediction of gliomas

BACKGROUND

Measuring non-parametric intravoxel mean diffusivity distributions (MDDs) using magnetic resonance imaging (MRI) is a sensitive method for detecting intracellular diffusivity changes during physiological alterations. Histological and molecular glioma classifications are essential for prognosis and treatment, with distinct water diffusion dynamics among subtypes.

PURPOSE

We developed a data-driven approach using a fully connected network (FCN) to enhance the speed and stability of calculating MDDs across varying SNRs, enable tumor microstructural mapping, and test its reliability in identifying MIB-1 labeling index (LI) levels and molecular status of gliomas.

METHODS

An FCN was trained to learn the mapping between the simulated diffusion decay curves and the ground truth MDDs. We performed 5 000 000 simulation curves with various diffusivity components and random SNR∈ [ 30 , 300 ] $ \in [ {30,\ 300} ]$ . Eighty percent of simulation curves were used for the FCN training, 10% for validation, and the others were external tests for the FCN performance evaluation. In vivo data were collected to evaluate its clinical reliability. One hundred one patients (44 years ± $ \pm $ 14, 67 men) with gliomas and six healthy controls underwent a 3.0 T MRI examination with a spin echo-echo planar imaging (SE-EPI) diffusion-weighted imaging (DWI) sequence. The trained FCN was employed to calculate MDDs of each brain voxel by voxel. We used the Fuzzy C-means algorithm to cluster the MDDs of tumor voxels, facilitating the characterization of distinct glioma tissues. Quantitative assessments were conducted through sectional integrals of the MDDs, demarcated by six bands to derive signal fractions (f n , n = 1 - 6 ${{f}_n},\ n = 1 -6$ ) and diffusivities of the maximum peaks (D p e a k ${{D}_{peak}}$ ). Cosine similarity scores (CSS) were used for MDD similarity. ANOVA and Mann-Whitney U test were used for difference analysis. Logistic regression and area under the receiver operator characteristic curve (AUC) were used for classification evaluation.

RESULTS

The simulation results showed that the FCN-based MDD approach (FCN-MDD) achieved higher CSS than non-negative least squares-based MDD (NNLS-MDD). For in vivo data, the spectra of ET and NET obtained by FCN-MDD are more distinguishable than NNLS-MDD. Fraction maps delineate the characteristics of different tumor tissues (enhancing and non-enhancing tumor, edema, and necrosis).f 3 , f 4 , D p e a k ${{f}_3},\ {{f}_4},{{D}_{peak}}$ showed a positive and negative correlation with MIB-1 respectively (r = 0.568 , r = - 0.521 , r = - 0.654 $r = 0.568,\ r = - 0.521,\ r = - 0.654$ , allp < 0.001 $p < 0.001$ ). The AUC ofD p e a k ${{D}_{peak}}$ for predicting MIB-1 LI levels was 0.900 (95% CI, 0.826-0.974), versus 0.781 (0.677-0.886) of ADC. The highest AUC of isocitrate dehydrogenase (IDH) mutation status, assessed by a logistic regression model (f 1 + f 3 ${{f}_1} + {{f}_3}$ ) was 0.873 (95% CI, 0.802-0.944).

CONCLUSION

The proposed FCN-MDD method was more robust to variations in SNR and less reliant on empirically set regularization values than the NNLS-MDD method. FCN-MDD also enabled qualitative and quantitative evaluation of the composition of gliomas.

Sequential Patching Lattice for Image Classification and Enquiry: Streamlining Digital Pathology Image Processing

Digital pathology and the integration of artificial intelligence (AI) models have revolutionized histopathology, opening new opportunities. With the increasing availability of whole-slide images (WSIs), demand is growing for efficient retrieval, processing, and analysis of relevant images from vast biomedical archives. However, processing WSIs presents challenges due to their large size and content complexity. Full computer digestion of WSIs is impractical, and processing all patches individually is prohibitively expensive. In this article, we propose an unsupervised patching algorithm, Sequential Patching Lattice for Image Classification and Enquiry (SPLICE). This novel approach condenses a histopathology WSI into a compact set of representative patches, forming a collage of WSI while minimizing redundancy. SPLICE prioritizes patch quality and uniqueness by sequentially analyzing a WSI and selecting nonredundant representative features. In search and match applications, SPLICE showed improved accuracy, reduced computation time, and storage requirements compared with existing state-of-the-art methods. As an unsupervised method, SPLICE effectively reduced storage requirements for representing tissue images by 50%. This reduction can enable numerous algorithms in computational pathology to operate much more efficiently, paving the way for accelerated adoption of digital pathology.

PROTON THERAPY FOR ADULT-TYPE DIFFUSE GLIOMA: A SYSTEMATIC REVIEW

BACKGROUND

We conducted a systematic review to evaluate outcomes and toxicities associated with proton therapy in the treatment of adult-type diffuse glioma.

METHODS

Following PRISMA guidelines, we searched PubMed for both prospective and retrospective studies on proton therapy for adult diffuse gliomas, including low-grade gliomas and glioblastomas. Survival and toxicity outcomes were reported separately for these glioma types.

RESULTS

Twelve studies from 2013 to 2023 were selected, comprising 3 prospective and 9 retrospective studies. The analysis covered 570 patients with low-grade gliomas and 240 patients with glioblastoma or WHO grade 4 gliomas. Proton therapy was found to be comparable to conventional radiotherapy in terms of survival outcomes. Its main advantage is the ability to minimize radiation exposure to healthy tissues.

DISCUSSION

Proton therapy offers comparable survival outcomes to conventional radiotherapy for adult diffuse gliomas and may enhance treatment tolerance, especially regarding neurocognitive function. A major limitation of this review is the predominance of retrospective studies. Future research should ensure rigorous patient selection and adhere to the latest WHO 2021 classification.

Geriatric grade 2 and 3 gliomas: A national cancer database analysis of demographics, treatment utilization, and survival

With increasing life expectancies and population aging, the incidence of elderly patients with grade 2 and 3 gliomas is increasing. However, there is a paucity of knowledge on factors affecting their treatment selection and overall survival (OS). Geriatric patients aged between 60 and 89 years with histologically proven grade 2 and 3 intracranial gliomas were identified from the National Cancer Database between 2010 and 2017. We analyzed patients' demographic data, tumor characteristics, treatment modality, and outcomes. The Kaplan-Meier method was used to analyze OS. Univariate and multivariate analyses were performed to assess the predictive factors of mortality and treatment selection. A total of 6257 patients were identified: 3533 (56.3 %) hexagenerians, 2063 (32.9 %) septuagenarians, and 679 (10.8 %) octogenarians. We identified predictors of lower OS in patients, including demographic factors (older age, non-zero Charlson-Deyo score, non-Hispanic ethnicity), socioeconomic factors (low income, treatment at non-academic centers, government insurance), and tumor-specific factors (higher grade, astrocytoma histology, multifocality). Receiving surgery and chemotherapy were associated with a lower risk of mortality, whereas receiving radiotherapy was not associated with better OS. Our findings provide valuable insights into the complex interplay of demographic, socioeconomic, and tumor-specific factors that influence treatment selection and OS in geriatric grade 2 and 3 gliomas. We found that advancing age correlates with a decrease in OS and a reduced likelihood of undergoing surgery, chemotherapy, or radiotherapy. While receiving surgery and chemotherapy were associated with improved OS, radiotherapy did not exhibit a similar association.

NKG2C/KLRC2 tumor cell expression enhances immunotherapeutic efficacy against glioblastoma

BACKGROUND

Activating and inhibitory receptors of natural killer (NK) cells such as NKp, NKG2, or CLEC are highly relevant to cold tumors including glioblastoma (GBM). Here, we aimed to characterize the expression of these receptors in GBM to gain insight into their potential role as modulators of the intratumoral microenvironment.

METHODS

We performed a transcriptomic analysis of several NK receptors with a focus on the activating receptor encoded by KLRC2, NKG2C, among bulk and single-cell RNA sequencing GBM data sets. We also evaluated the effects of KLRC2-overexpressing GL261 cells in mice treated with or without programmed cell death protein-1 (PD-1) monoclonal antibody (mAb). Finally, we analyzed samples from two clinical trials evaluating PD-1 mAb effects in patients with GBM to determine the potential of NKG2C to serve as a biomarker of response.

RESULTS

We observed significant expression of several inhibitory NK receptors on GBM-infiltrating NK and T cells, which contrasts with the strong expression of KLRC2 on tumor cells, mainly at the infiltrative margin. Neoplastic KLRC2 expression was associated with a reduction in the number of myeloid-derived suppressor cells and with a higher level of tumor-resident lymphocytes. A stronger antitumor activity after PD-1 mAb treatment was observed in NKG2Chigh-expressing tumors both in mouse models and patients with GBM whereas the expression of inhibitory NK receptors showed an inverse association.

CONCLUSIONS

This study explored the role of neoplastic NKG2C/KLRC2 expression in shaping the immune profile of GBM and suggests that it is a predictive biomarker for positive responses to immune checkpoint inhibitor treatment in patients with GBM. Future studies could further validate this finding in prospective trials.

Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for Comprehensive Learning, Discovering, and Quantifying Microenvironmental Features

Gliomas are primary brain tumors that arise from neural stem cells, or glial precursors. Diagnosis of glioma is based on histological evaluation of pathological cell features and molecular markers. Gliomas are infiltrated by myeloid cells that accumulate preferentially in malignant tumors, and their abundance inversely correlates with survival, which is of interest for cancer immunotherapies. To avoid time-consuming and laborious manual examination of images, a deep learning approach for automatic multiclass classification of tumor grades was proposed. As an alternative way of investigating characteristics of brain tumor grades, we implemented a protocol for learning, discovering, and quantifying tumor microenvironment elements on our glioma dataset. Using only single-stained biopsies we derived characteristic differentiating tumor microenvironment phenotypic neighborhoods. The study was complicated by the small size of the available human leukocyte antigen stained on glioma tissue microarray dataset - 206 images of 5 classes - as well as imbalanced data distribution. This challenge was addressed by image augmentation for underrepresented classes. In practice, we considered two scenarios, a whole slide supervised learning classification, and an unsupervised cell-to-cell analysis looking for patterns of the microenvironment. In the supervised learning investigation, we evaluated 6 distinct model architectures. Experiments revealed that a DenseNet121 architecture surpasses the baseline's accuracy by a significant margin of 9% for the test set, achieving a score of 69%, increasing accuracy in discerning challenging WHO grade 2 and 3 cases. All experiments have been carried out in a cross-validation manner. The tumor microenvironment analysis suggested an important role for myeloid cells and their accumulation in the context of characterizing glioma grades. Those promising approaches can be used as an additional diagnostic tool to improve assessment during intraoperative examination or subtyping tissues for treatment selection, potentially easing the workflow of pathologists and oncologists.

Long non-coding RNAs; potential contributors in cancer chemoresistance through modulating diverse molecular mechanisms and signaling pathways

One of the mainstays of cancer treatment is chemotherapy. Drug resistance, however, continues to be the primary factor behind clinical treatment failure. Gene expression is regulated by long non-coding RNAs (lncRNAs) in several ways, including chromatin remodeling, translation, epigenetic, and transcriptional levels. Cancer hallmarks such as DNA damage, metastasis, immunological evasion, cell stemness, drug resistance, metabolic reprogramming, and angiogenesis are all influenced by LncRNAs. Numerous studies have been conducted on LncRNA-driven mechanisms of resistance to different antineoplastic drugs. Diverse medication kinds elicit diverse resistance mechanisms, and each mechanism may have multiple contributing factors. As a result, several lncRNAs have been identified as new biomarkers and therapeutic targets for identifying and managing cancers. This compels us to thoroughly outline the crucial roles that lncRNAs play in drug resistance. In this regard, this article provides an in-depth analysis of the recently discovered functions of lncRNAs in the pathogenesis and chemoresistance of cancer. As a result, the current research might offer a substantial foundation for future drug resistance-conquering strategies that target lncRNAs in cancer therapies.

Prognostic factors to predict postoperative survival in patients with recurrent glioblastoma

Background

There are no generally accepted criteria for selecting patients with recurrent glioblastoma for surgery. This retrospective study in a Danish population-based cohort aimed to identify prognostic factors affecting postoperative survival after repeated surgery for recurrent glioblastoma and to test if the preoperative New Scale for Recurrent Glioblastoma Surgery (NSGS) developed by Park CK et al could assist in the selection of patients for repeat glioblastoma surgery.

Methods

Clinical data from 66 patients with recurrent glioblastoma and repeated surgery were analyzed. Kaplan-Meier plots were produced to illustrate survival in each of the three NSGS prognostic groups, and Cox proportional hazard regression was used to identify prognostic variables. Multivariable analysis was used to identify differences in survival in the three prognostic groups.

Results

Six variables significantly affected postoperative survival: preoperative Karnofsky Performance Status (KPS) < 70 (p = 0.002), decreased KPS after second surgery (p = 0.012), ependymal involvement (p = 0.002), tumor volume ≧ 50 cm3 (p = 0.021), age (p = 0.033) and Ki-67 (p = 0.005). Retrospective application of the criteria previously published by Park CK et al showed that median postoperative survival for the three prognostic groups was 390 days (0 points), 279 days (1 point), and 80 days (2 points), respectively.

Conclusion

Several prognostic variables to predict postoperative survival in patients with recurrent glioblastoma were identified and should be considered when selecting patient for repeat surgery. The NSGS scoring system was useful as there were significant differences in postoperative survival between its three prognostic groups.

Diterpenoid honatisine overcomes temozolomide resistance in glioblastoma by inducing mitonuclear protein imbalance through disruption of TFAM-mediated mtDNA transcription

BACKGROUND

Glioblastoma (GBM) represents as the most formidable intracranial malignancy. The systematic exploration of natural compounds for their potential applications in GBM therapy has emerged as a pivotal and fruitful avenue of research.

PURPOSE

In the present study, a panel of 96 diterpenoids was systematically evaluated as a repository of potential antitumour agents. The primary objective was to discern their potency in overcoming resistance to temozolomide (TMZ). Through an extensive screening process, honatisine, a heptacyclic diterpenoid alkaloid, emerged as the most robust candidate. Notably, honatisine exhibited remarkable efficacy in patient-derived primary and recurrent GBM strains. Subsequently, we subjected this compound to comprehensive scrutiny, encompassing GBM cultured spheres, GBM organoids (GBOs), TMZ-resistant GBM cell lines, and orthotopic xenograft mouse models of GBM cells.

RESULTS

Our investigative efforts delved into the mechanistic underpinnings of honatisine's impact. It was discerned that honatisine prompted mitonuclear protein imbalance and elicited the mitochondrial unfolded protein response (UPRmt). This effect was mediated through the selective depletion of mitochondrial DNA (mtDNA)-encoded subunits, with a particular emphasis on the diminution of mitochondrial transcription factor A (TFAM). The ultimate outcome was the instigation of deleterious mitochondrial dysfunction, culminating in apoptosis. Molecular docking and surface plasmon resonance (SPR) experiments validated honatisine's binding affinity to TFAM within its HMG-box B domain. This binding may promote phosphorylation of TFAM and obstruct the interaction of TFAM bound to heavy strand promoter 1 (HSP1), thereby enhancing Lon-mediated TFAM degradation. Finally, in vivo experiments confirmed honatisine's antiglioma properties. Our comprehensive toxicological assessments underscored its mild toxicity profile, emphasizing the necessity for a thorough evaluation of honatisine as a novel antiglioma agent.

CONCLUSION

In summary, our data provide new insights into the therapeutic mechanisms underlying honatisine's selective inducetion of apoptosis and its ability to overcome chemotherapy resistance in GBM. These actions are mediated through the disruption of mitochondrial proteostasis and function, achieved by the inhibition of TFAM-mediated mtDNA transcription. This study highlights honatisine's potential as a promising agent for glioblastoma therapy, underscoring the need for further exploration and investigation.

Harnessing the potential of nanoengineered siRNAs carriers for target responsive glioma therapy: Recent progress and future opportunities

Past scientific testimonials in the field of glioma research, the deadliest tumor among all brain cancer types with the life span of 10-15 months after diagnosis is considered as glioblastoma multiforme (GBM). Even though the availability of treatment options such as chemotherapy, radiotherapy, and surgery, are unable to completely cure GBM due to tumor microenvironment complexity, intrinsic cellular signalling, and genetic mutations which are involved in chemoresistance. The blood-brain barrier is accountable for restricting drugs entry at the tumor location and related biological challenges like endocytic degradation, short systemic circulation, and insufficient cellular penetration lead to tumor aggression and progression. The above stated challenges can be better mitigated by small interfering RNAs (siRNA) by knockdown genes responsible for tumor progression and resistance. However, siRNA encounters with challenges like inefficient cellular transfection, short circulation time, endogenous degradation, and off-target effects. The novel functionalized nanocarrier approach in conjunction with biological and chemical modification offers an intriguing potential to address challenges associated with the naked siRNA and efficiently silence STAT3, coffilin-1, EGFR, VEGF, SMO, MGMT, HAO-1, GPX-4, TfR, LDLR and galectin-1 genes in GBM tumor. This review highlights the nanoengineered siRNA carriers, their recent advancements, future perspectives, and strategies to overcome the systemic siRNA delivery challenges for glioma treatment.

Survival Outcome Prediction in Glioblastoma: Insights from MRI Radiomics

Background: Extracting multiregional radiomic features from multiparametric MRI for predicting pretreatment survival in isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) patients is a promising approach. Methods: MRI data from 49 IDH wild-type glioblastoma patients pre-treatment were utilized. Diffusion and perfusion maps were generated, and tumor subregions segmented. Radiomic features were extracted for each tissue type and map. Feature selection on 1862 radiomic features identified 25 significant features. The Cox proportional-hazards model with LASSO regularization was used to perform survival analysis. Internal and external validation used a 38-patient training cohort and an 11-patient validation cohort. Statistical significance was set at p < 0.05. Results: Age and six radiomic features (shape and first and second order) from T1W, diffusion, and perfusion maps contributed to the final model. Findings suggest that a small necrotic subregion, inhomogeneous vascularization in the solid non-enhancing subregion, and edema-related tissue damage in the enhancing and edema subregions are linked to poor survival. The model's C-Index was 0.66 (95% C.I. 0.54-0.80). External validation demonstrated good accuracy (AUC > 0.65) at all time points. Conclusions: Radiomics analysis, utilizing segmented perfusion and diffusion maps, provide predictive indicators of survival in IDH wild-type glioblastoma patients, revealing associations with microstructural and vascular heterogeneity in the tumor.

Unraveling the Landscape of Pediatric Glioblastoma Biomarkers: A Comprehensive Review of Enhancing Diagnostics and Therapeutic Insights

Glioblastoma, the most common and aggressive form of primary brain tumor, poses significant challenges to patients, caregivers, and clinicians alike. Pediatric glioblastoma is a rare and aggressive brain tumor that presents unique challenges in treatment. It differs from its adult counterpart in terms of genetic and molecular characteristics. Its incidence is relatively low, but the prognosis remains grim due to its aggressive behavior. Diagnosis relies on imaging techniques and histopathological analysis. The rarity of the disease underscores the need for effective treatment strategies. In recent years, the quest to understand and manage pediatric glioblastoma has seen a significant shift towards unraveling the intricate landscape of biomarkers. Surgery remains a cornerstone of glioblastoma management, aiming to resect as much of the tumor as possible. Glioblastoma's infiltrative nature presents challenges in achieving a complete surgical resection. This comprehensive review delves into the realm of pediatric glioblastoma biomarkers, shedding light on their potential to not only revolutionize diagnostics but also shape therapeutic strategies. From personalized treatment selection to the development of targeted therapies, the potential impact of these biomarkers on clinical outcomes is undeniable. Moreover, this review underscores the substantial implications of biomarker-driven approaches for therapeutic interventions. All advancements in targeted therapies and immunotherapy hold promise for the treatment of pediatric glioblastoma. The genetic profiling of tumors allows for personalized approaches, potentially improving treatment efficacy. The ethical dilemmas surrounding pediatric cancer treatment, particularly balancing potential benefits with risks, are complex. Ongoing clinical trials and preclinical research suggest exciting avenues for future interventions.

Development of preoperative and postoperative models to predict recurrence in postoperative glioma patients: a longitudinal cohort study

BACKGROUND

Glioma recurrence, subsequent to maximal safe resection, remains a pivotal challenge. This study aimed to identify key clinical predictors influencing recurrence and develop predictive models to enhance neurological diagnostics and therapeutic strategies.

METHODS

This longitudinal cohort study with a substantial sample size (n = 2825) included patients with non-recurrent glioma who were pathologically diagnosed and had undergone initial surgical resection between 2010 and 2018. Logistic regression models and stratified Cox proportional hazards models were established with the top 15 clinical variables significantly influencing outcomes screened by the least absolute shrinkage and selection operator (LASSO) method. Preoperative and postoperative models predicting short-term (within 6 months) postoperative recurrence in glioma patients were developed to explore the risk factors associated with short- and long-term recurrence in glioma patients.

RESULTS

Preoperative and postoperative logistic models predicting short-term recurrence had accuracies of 0.78 and 0.87, respectively. A range of biological and early symptomatic characteristics linked to short- and long-term recurrence have been pinpointed. Age, headache, muscle weakness, tumor location and Karnofsky score represented significant odd ratios (t > 2.65, p < 0.01) in the preoperative model, while age, WHO grade 4 and chemotherapy or radiotherapy treatments (t > 4.12, p < 0.0001) were most significant in the postoperative period. Postoperative predictive models specifically targeting the glioblastoma and IDH wildtype subgroups were also performed, with an AUC of 0.76 and 0.80, respectively. The 50 combinations of distinct risk factors accommodate diverse recurrence risks among glioma patients, and the nomograms visualizes the results for clinical practice. A stratified Cox model identified many prognostic factors for long-term recurrence, thereby facilitating the enhanced formulation of perioperative care plans for patients, and glioblastoma patients displayed a median progression-free survival (PFS) of only 11 months.

CONCLUSION

The constructed preoperative and postoperative models reliably predicted short-term postoperative glioma recurrence in a substantial patient cohort. The combinations risk factors and nomograms enhance the operability of personalized therapeutic strategies and care regimens. Particular emphasis should be placed on patients with recurrence within six months post-surgery, and the corresponding treatment strategies require comprehensive clinical investigation.

Molecular mechanisms of tumour development in glioblastoma: an emerging role for the circadian clock

Glioblastoma is one of the most lethal cancers with current therapeutic options lacking major successes. This underlines the necessity to understand glioblastoma biology on other levels and use these learnings for the development of new therapeutic concepts. Mounting evidence in the field of circadian medicine points to a tight interplay between disturbances of the circadian system and glioblastoma progression. The circadian clock, an internal biological mechanism governing numerous physiological processes across a 24-h cycle, also plays a pivotal role in regulationg key cellular functions, including DNA repair, cell cycle progression, and apoptosis. These processes are integral to tumour development and response to therapy. Disruptions in circadian rhythms can influence tumour growth, invasion, and response to treatment in glioblastoma patients. In this review, we explore the robust association between the circadian clock, and cancer hallmarks within the context of glioblastoma. We further discuss the impact of the circadian clock on eight cancer hallmarks shown previously to link the molecular clock to different cancers, and summarize the putative role of clock proteins in circadian rhythm disturbances and chronotherapy in glioblastoma. By unravelling the molecular mechanisms behind the intricate connections between the circadian clock and glioblastoma progression, researchers can pave the way for the identification of potential therapeutic targets, the development of innovative treatment strategies and personalized medicine approaches. In conclusion, this review underscores the significant influence of the circadian clock on the advancement and understanding of future therapies in glioblastoma, ultimately leading to enhanced outcomes for glioblastoma patients.

Evaluation of Key Molecular Markers in Adult Diffuse Gliomas Based on a Novel Combination of Diffusion and Perfusion MRI and MR Spectroscopy

BACKGROUND

Preoperative identification of isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status could help clinicians select the optimal therapy in patients with diffuse glioma. Although, the value of multimodal intersection was underutilized.

PURPOSE

To evaluate the value of quantitative MRI biomarkers for the identification of IDH mutation and 1p/19q codeletion in adult patients with diffuse glioma.

STUDY TYPE

Retrospective.

POPULATION

Two hundred sixteen adult diffuse gliomas with known genetic test results, divided into training (N = 130), test (N = 43), and validation (N = 43) groups.

SEQUENCE/FIELD STRENGTH

Diffusion/perfusion-weighted-imaging sequences and multivoxel MR spectroscopy (MRS), all 3.0 T using three different scanners.

ASSESSMENT

The apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) of the core tumor were calculated to identify IDH-mutant and 1p/19q-codeleted statuses and to determine cut-off values. ADC models were built based on the 30th percentile and lower, CBV models were built based on the 75th centile and higher (both in five centile steps). The optimal tumor region was defined and the metabolite concentrations of MRS voxels that overlapped with the ADC/CBV optimal region were calculated and added to the best-performing diagnostic models.

STATISTICAL TESTS

DeLong's test, diagnostic test, and decision curve analysis were performed. A P value <0.05 was considered to be statistically significant.

RESULTS

Almost all ADC models achieved good performance in identifying IDH mutation status, among which ADC_15th was the most valuable parameter (threshold = 1.186; Youden index = 0.734; AUC_train = 0.896). The differential power of CBV histogram metrics for predicting 1p/19q codeletion outperformed ADC histogram metrics, and the CBV_80th-related model performed best (threshold = 1.435; Youden index = 0.458; AUC_train = 0.724). The AUCs of ADC_15th and CBV_80th models in the validation set were 0.857 and 0.733. These models tended to improve after incorporation of N-acetylaspartate/total_creatine and glutamate-plus-glutamine/total_creatine, respectively.

DATA CONCLUSION

The intersection of ADC-, CBV-based histogram and MRS provide a reliable paradigm for identifying the key molecular markers in adult diffuse gliomas.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 3.

Glioma: bridging the tumor microenvironment, patient immune profiles and novel personalized immunotherapy

Glioma is the most common primary brain tumor, characterized by a consistently high patient mortality rate and a dismal prognosis affecting both survival and quality of life. Substantial evidence underscores the vital role of the immune system in eradicating tumors effectively and preventing metastasis, underscoring the importance of cancer immunotherapy which could potentially address the challenges in glioma therapy. Although glioma immunotherapies have shown promise in preclinical and early-phase clinical trials, they face specific limitations and challenges that have hindered their success in further phase III trials. Resistance to therapy has been a major challenge across many experimental approaches, and as of now, no immunotherapies have been approved. In addition, there are several other limitations facing glioma immunotherapy in clinical trials, such as high intra- and inter-tumoral heterogeneity, an inherently immunosuppressive microenvironment, the unique tissue-specific interactions between the central nervous system and the peripheral immune system, the existence of the blood-brain barrier, which is a physical barrier to drug delivery, and the immunosuppressive effects of standard therapy. Therefore, in this review, we delve into several challenges that need to be addressed to achieve boosted immunotherapy against gliomas. First, we discuss the hurdles posed by the glioma microenvironment, particularly its primary cellular inhabitants, in particular tumor-associated microglia and macrophages (TAMs), and myeloid cells, which represent a significant barrier to effective immunotherapy. Here we emphasize the impact of inducing immunogenic cell death (ICD) on the migration of Th17 cells into the tumor microenvironment, converting it into an immunologically "hot" environment and enhancing the effectiveness of ongoing immunotherapy. Next, we address the challenge associated with the accurate identification and characterization of the primary immune profiles of gliomas, and their implications for patient prognosis, which can facilitate the selection of personalized treatment regimens and predict the patient's response to immunotherapy. Finally, we explore a prospective approach to developing highly personalized vaccination strategies against gliomas, based on the search for patient-specific neoantigens. All the pertinent challenges discussed in this review will serve as a compass for future developments in immunotherapeutic strategies against gliomas, paving the way for upcoming preclinical and clinical research endeavors.

Advancing noninvasive glioma classification with diffusion radiomics: Exploring the impact of signal intensity normalization

Background

This study investigates the influence of diffusion-weighted Magnetic Resonance Imaging (DWI-MRI) on radiomic-based prediction of glioma types according to molecular status and assesses the impact of DWI intensity normalization on model generalizability.

Methods

Radiomic features, compliant with image biomarker standardization initiative standards, were extracted from preoperative MRI of 549 patients with diffuse glioma, known IDH, and 1p19q-status. Anatomical sequences (T1, T1c, T2, FLAIR) underwent N4-Bias Field Correction (N4) and WhiteStripe normalization (N4/WS). Apparent diffusion coefficient (ADC) maps were normalized using N4 or N4/z-score. Nine machine-learning algorithms were trained for multiclass prediction of glioma types (IDH-mutant 1p/19q codeleted, IDH-mutant 1p/19q non-codeleted, IDH-wild type). Four approaches were compared: Anatomical, anatomical + ADC naive, anatomical + ADC N4, and anatomical + ADC N4/z-score. The University of California San Francisco (UCSF)-glioma dataset (n = 409) was used for external validation.

Results

Naïve-Bayes algorithms yielded overall the best performance on the internal test set. Adding ADC radiomics significantly improved AUC from 0.79 to 0.86 (P = .011) for the IDH-wild-type subgroup, but not for the other 2 glioma subgroups (P > .05). In the external UCSF dataset, the addition of ADC radiomics yielded a significantly higher AUC for the IDH-wild-type subgroup (P ≤ .001): 0.80 (N4/WS anatomical alone), 0.81 (anatomical + ADC naive), 0.81 (anatomical + ADC N4), and 0.88 (anatomical + ADC N4/z-score) as well as for the IDH-mutant 1p/19q non-codeleted subgroup (P < .012 each).

Conclusions

ADC radiomics can enhance the performance of conventional MRI-based radiomic models, particularly for IDH-wild-type glioma. The benefit of intensity normalization of ADC maps depends on the type and context of the used data.

Multimodal MRI-based radiomic nomogram for predicting telomerase reverse transcriptase promoter mutation in IDH-wildtype histological lower-grade gliomas

The presence of TERTp mutation in isocitrate dehydrogenase-wildtype (IDHwt) histologically lower-grade glioma (LGA) has been linked to a poor prognosis. In this study, we aimed to develop and validate a radiomic nomogram based on multimodal MRI for predicting TERTp mutations in IDHwt LGA. One hundred and nine IDH wildtype glioma patients (TERTp-mutant, 78; TERTp-wildtype, 31) with clinical, radiomic, and molecular information were collected and randomly divided into training and validation set. Clinical model, fusion radiomic model, and combined radiomic nomogram were constructed for the discrimination. Radiomic features were screened with 3 algorithms (Wilcoxon rank sum test, elastic net, and the recursive feature elimination) and the clinical characteristics of combined radiomic nomogram were screened by the Akaike information criterion. Finally, receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis were utilized to assess these models. Fusion radiomic model with 4 radiomic features achieved an area under the curve value of 0.876 and 0.845 in the training and validation set. And, the combined radiomic nomogram achieved area under the curve value of 0.897 (training set) and 0.882 (validation set). Above that, calibration curve and Hosmer-Lemeshow test showed that the radiomic model and combined radiomic nomogram had good agreement between observations and predictions in the training set and the validation set. Finally, the decision curve analysis revealed that the 2 models had good clinical usefulness for the prediction of TERTp mutation status in IDHwt LGA. The combined radiomics nomogram performed great performance and high sensitivity in prediction of TERTp mutation status in IDHwt LGA, and has good clinical application.

Low-Grade Gliomas: Histological Subtypes, Molecular Mechanisms, and Treatment Strategies

Low-Grade Gliomas (LGGs) represent a diverse group of brain tumors originating from glial cells, characterized by their unique histopathological and molecular features. This article offers a comprehensive exploration of LGGs, shedding light on their subtypes, histological and molecular aspects. By delving into the World Health Organization's grading system, 5th edition, various specificities were added due to an in-depth understanding of emerging laboratory techniques, especially genomic analysis. Moreover, treatment modalities are extensively discussed. The degree of surgical resection should always be considered according to postoperative quality of life and cognitive status. Adjuvant therapies focused on chemotherapy and radiotherapy depend on tumor grading and invasiveness. In the current literature, emerging targeted molecular therapies are well discussed due to their succinctly therapeutic effect; in our article, those therapies are summarized based on posttreatment results and possible adverse effects. This review serves as a valuable resource for clinicians, researchers, and medical professionals aiming to deepen their knowledge on LGGs and enhance patient care.

Diagnostic Accuracy of PET with 18F-Fluciclovine ([18F]FACBC) in Detecting High-Grade Gliomas: A Systematic Review and Meta-Analysis

BACKGROUND

18F-Fluciclovine ([18F]FACBC) has been recently proposed as a synthetic radiolabeled amino acid for positron emission tomography (PET) imaging in patients with brain neoplasms. Our aim is to evaluate the diagnostic performance of [18F]FACBC PET in high-grade glioma (HGG) patients, taking into account the literature data.

METHODS

A comprehensive literature search was performed. We included original articles evaluating [18F]FACBC PET in the detection of HGG before therapy and for the suspicion of tumor recurrence. Pooled sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR-), and diagnostic odds ratios (DOR), including 95% confidence intervals (95% CI), were measured. Statistical heterogeneity and publication bias were also assessed.

RESULTS

ten studies were included in the review and eight in the meta-analysis (113 patients). Regarding the identification of HGG, the sensitivity of [18F]FACBC PET ranged between 85.7% and 100%, with a pooled estimate of 92.9% (95% CI: 84.4-96.9%), while the specificity ranged from 50% to 100%, with a pooled estimate of 70.7% (95% CI: 47.5-86.5%). The pooled LR+, LR-, and DOR of [18F]FACBC PET were 2.5, 0.14, and 37, respectively. No significant statistical heterogeneity or publication bias were found.

CONCLUSIONS

evidence-based data demonstrate the good diagnostic accuracy of [18F]FACBC PET for HGG detection. Due to the still limited data, further studies are warranted to confirm the promising role of [18F]FACBC PET in this context.

Dissecting the functional significance of HSP90AB1 and other heat shock proteins in countering glioblastomas and ependymomas using omics analysis and drug prediction using virtual screening

Heat shock proteins (HSPs) are the evolutionary family of proteins that are highly conserved and present widely in various organisms and play an array of important roles and cellular functions. Currently, very few or no studies are based on the systematic analysis of the HSPs in Glioblastoma (GBMs) and ependymomas. We performed an integrated omics analysis to predict the mutual regulatory differential HSP signatures that were associated with both glioblastoma and ependymomas. Further, we explored the various common dysregulated biological processes operating in both the tumors, and were analyzed using functional enrichment, gene ontology along with the pathway analysis of the predicted HSPs. We established an interactome network of protein-protein interaction (PPIN) to identify the hub HSPs that were commonly associated with GBMs and ependymoma. To understand the mutual molecular mechanism of the HSPs in both malignancies, transcription factors, and miRNAs overlapping with both diseases were explored. Moreover, a transcription factor-miRNAs-HSPs coregulatory network was constructed along with the prediction of potential candidate drugs that were based on perturbation-induced gene expression analysis. Based on the RNA-sequencing data, HSP90AB1 was identified as the most promising target among other predicted HSPs. Finally, the ranking of the drugs was arranged based on various drug scores. In conclusion, this study gave a spotlight on the mutual targetable HSPs, biological pathways, and regulatory signatures associated with GBMs and ependymoma with an improved understanding of crosstalk involved. Additionally, the role of therapeutics was also explored against HSP90AB1. These findings could potentially be able to explain the interplay of HSP90AB1 and other HSPs within these two malignancies.

Successes and challenges in modeling heterogeneous BRAFV600E mutated central nervous system neoplasms

Central nervous system (CNS) neoplasms are difficult to treat due to their sensitive location. Over the past two decades, the availability of patient tumor materials facilitated large scale genomic and epigenomic profiling studies, which have resulted in detailed insights into the molecular underpinnings of CNS tumorigenesis. Based on results from these studies, CNS tumors have high molecular and cellular intra-tumoral and inter-tumoral heterogeneity. CNS cancer models have yet to reflect the broad diversity of CNS tumors and patients and the lack of such faithful cancer models represents a major bottleneck to urgently needed innovations in CNS cancer treatment. Pediatric cancer model development is lagging behind adult tumor model development, which is why we focus this review on CNS tumors mutated for BRAFV600E which are more prevalent in the pediatric patient population. BRAFV600E-mutated CNS tumors exhibit high inter-tumoral heterogeneity, encompassing clinically and histopathological diverse tumor types. Moreover, BRAFV600E is the second most common alteration in pediatric low-grade CNS tumors, and low-grade tumors are notoriously difficult to recapitulate in vitro and in vivo. Although the mutation predominates in low-grade CNS tumors, when combined with other mutations, most commonly CDKN2A deletion, BRAFV600E-mutated CNS tumors are prone to develop high-grade features, and therefore BRAFV600E-mutated CNS are a paradigm for tumor progression. Here, we describe existing in vitro and in vivo models of BRAFV600E-mutated CNS tumors, including patient-derived cell lines, patient-derived xenografts, syngeneic models, and genetically engineered mouse models, along with their advantages and shortcomings. We discuss which research gaps each model might be best suited to answer, and identify those areas in model development that need to be strengthened further. We highlight areas of potential research focus that will lead to the heightened predictive capacity of preclinical studies, allow for appropriate validation, and ultimately improve the success of "bench to bedside" translational research.

Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images

Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.

Development and validation of cuproptosis molecular subtype-related signature for predicting immune prognostic characterization in gliomas

PURPOSE

Cuproptosis, a novel programmed cell death, plays an important role in glioma growth, angiogenesis, and immune response. Nonetheless, the role of cuproptosis-related genes (CRGs) in the prognosis and tumor microenvironment (TME) of gliomas remains unknown.

METHODS

By non-negative matrix factorization consensus clustering, 1286 glioma patients were classified based on the mRNA expression levels of 27 CRGs and investigated the association of immune infiltration and clinical characteristics with cuproptosis subtypes. A CRG-score system was constructed using LASSO and multivariate Cox regression methods and validated in independent cohorts to predict the prognosis of glioma patients.

RESULTS

Glioma patients were divided into two cuproptosis subtypes. Cluster C2 was enriched in immune-related pathways, had higher macrophage M2, neutrophils, and CD8 + T cells, and poorer prognosis compared with cluster C1 which was enriched in metabolism-related pathways. We further constructed and validated the ten-gene CRG risk scores. Glioma patients in the high CRG-score group had higher tumor mutation burden, higher TME scores, and poorer prognoses compared with the low CRG-score group. Additionally, the AUC value of the CRG-score was 0.778 in predicting the prognosis of gliomas. WHO grading, IDH mutation, 1p/19q codeletion, and MGMT methylation were significant differences between high and low CRG-score groups.

CONCLUSION

This study demonstrated that CRG-score was related to immune cell infiltration and could accurately predict gliomas' prognosis. Our findings may provide a novel understanding of the potential role of cuproptosis molecular pattern and TME in the immune response and prognosis of glioma patients.

Improving Noninvasive Classification of Molecular Subtypes of Adult Gliomas With Diffusion-Weighted MR Imaging: An Externally Validated Machine Learning Algorithm

BACKGROUND

Genetic testing for molecular markers of gliomas sometimes is unavailable because of time-consuming and expensive, even limited tumor specimens or nonsurgery cases.

PURPOSE

To train a three-class radiomic model classifying three molecular subtypes including isocitrate dehydrogenase (IDH) mutations and 1p/19q-noncodeleted (IDHmut-noncodel), IDH wild-type (IDHwt), IDH-mutant and 1p/19q-codeleted (IDHmut-codel) of adult gliomas and investigate whether radiomic features from diffusion-weighted imaging (DWI) could bring additive value.

STUDY TYPE

Retrospective.

POPULATION

A total of 755 patients including 111 IDHmut-noncodel, 571 IDHwt, and 73 IDHmut-codel cases were divided into training (n = 480) and internal validation set (n = 275); 139 patients including 21 IDHmut-noncodel, 104 IDHwt, and 14 IDHmut-codel cases were utilized as external validation set.

FIELD STRENGTH/SEQUENCE

A 1.5 T or 3.0 T/multiparametric MRI, including T1-weighted (T1), T1-weighted gadolinium contrast-enhanced (T1c), T2-weighted (T2), fluid attenuated inversion recovery (FLAIR), and DWI.

ASSESSMENT

The performance of multiparametric radiomic model (random-forest model) using 22 selected features from T1, T2, FLAIR, T1c images and apparent diffusion coefficient (ADC) maps, and conventional radiomic model using 20 selected features from T1, T2, FLAIR, and T1c images was assessed in internal and external validation sets by comparing probability values and actual incidence.

STATISTICAL TESTS

Mann-Whitney U test, Chi-Squared test, Wilcoxon test, receiver operating curve (ROC), and area under the curve (AUC); DeLong analysis. P < 0.05 was statistically significant.

RESULTS

The multiparametric radiomic model achieved AUC values for IDHmut-noncodel, IDHwt, and IDHmut-codel of 0.8181, 0.8524, and 0.8502 in internal validation set and 0.7571, 0.7779, and 0.7491 in external validation set, respectively. Multiparametric radiomic model showed significantly better diagnostic performance after DeLong analysis, especially in classifying IDHwt and IDHmut-noncodel subtypes.

DATA CONCLUSION

Radiomic features from DWI could bring additive value and improve the performance of conventional MRI-based radiomic model for classifying the molecular subtypes especially IDHmut-noncodel and IDHwt of adult gliomas.

LEVEL OF EVIDENCE: 3

TECHNICAL EFFICACY

Stage 2.

Repurposing FDA-Approved Drugs for Temozolomide-Resistant IDH1 Mutant Glioma Using High-Throughput Miniaturized Screening on Droplet Microarray Chip

To address the challenge of drug resistance and limited treatment options for recurrent gliomas with IDH1 mutations, a highly miniaturized screening of 2208 FDA-approved drugs is conducted using a high-throughput droplet microarray (DMA) platform. Two patient-derived temozolomide-resistant tumorspheres harboring endogenous IDH1 mutations (IDH1mut ) are utilized. Screening identifies over 20 drugs, including verteporfin (VP), that significantly affected tumorsphere formation and viability. Proteomics analysis reveals that nuclear pore complex may be a potential VP target, suggesting a new mechanism of action independent of its known effects on YAP1. Knockdown experiments exclude YAP1 as a drug target in tumorspheres. Pathway analysis shows that NUP107 is a potential upstream regulator associated with VP response. Analysis of publicly available genomic datasets shows a significant correlation between high NUP107 expression and decreased survival in IDH1mut astrocytoma, suggesting NUP107 may be a potential biomarker for VP response. This study demonstrates a miniaturized approach for cost-effective drug repurposing using 3D glioma models and identifies nuclear pore complex as a potential target for drug development. The findings provide preclinical evidence to support in vivo and clinical studies of VP and other identified compounds to treat IDH1mut gliomas, which may ultimately improve clinical outcomes for patients with this challenging disease.

NeuroD4 converts glioblastoma cells into neuron-like cells through the SLC7A11-GSH-GPX4 antioxidant axis

Cell fate and proliferation ability can be transformed through reprogramming technology. Reprogramming glioblastoma cells into neuron-like cells holds great promise for glioblastoma treatment, as it induces their terminal differentiation. NeuroD4 (Neuronal Differentiation 4) is a crucial transcription factor in neuronal development and has the potential to convert astrocytes into functional neurons. In this study, we exclusively employed NeuroD4 to reprogram glioblastoma cells into neuron-like cells. In vivo, the reprogrammed glioblastoma cells demonstrated terminal differentiation, inhibited proliferation, and exited the cell cycle. Additionally, NeuroD4 virus-infected xenografts exhibited smaller sizes compared to the GFP group, and tumor-bearing mice in the GFP+NeuroD4 group experienced prolonged survival. Mechanistically, NeuroD4 overexpression significantly reduced the expression of SLC7A11 and Glutathione peroxidase 4 (GPX4). The ferroptosis inhibitor ferrostatin-1 effectively blocked the NeuroD4-mediated process of neuron reprogramming in glioblastoma. To summarize, our study demonstrates that NeuroD4 overexpression can reprogram glioblastoma cells into neuron-like cells through the SLC7A11-GSH-GPX4 signaling pathway, thus offering a potential novel therapeutic approach for glioblastoma.

CDYL knockdown reduces glioma development through an antitumor immune response in the tumor microenvironment

Gliomas are highly prevalent and aggressive brain tumors. Growing evidence shows that epigenetic changes are closely related to cancer development. Here we report the roles of Chromodomain Y-like (CDYL), an important epigenetic transcriptional corepressor in the central nervous system in glioma progression. We found that CDYL was highly expressed in glioma tissues and cell lines. CDYL knockdown decreased cell mobility in vitro and significantly reduced tumor burden in the xenograft mouse in vivo. RNA sequencing analysis revealed the upregulation of immune pathways after CDYL knockdown, as well as chemokine (C-C motif) ligand 2 (CCL2) and chemokine (C-X-C motif) ligand 12. The immunohistochemistry staining and macrophage polarization assays showed increased infiltration of M1-like tumor-associated macrophages/microglia (TAMs) while decreased infiltration of M2-like TAMs after CDYL knockdown in vivo and in vitro. Following the in situ TAMs depletion or CCL2 antibody neutralization, the tumor-suppressive role of CDYL knockdown was abolished. Collectively, our results show that CDYL knockdown suppresses glioma progression, which is associated with CCL2-recruited monocytes/macrophages and the polarization of M1-like TAMs in the tumor microenvironment, indicating CDYL as a promising target for glioma treatment.

Prediction of Glioma Grade by Tumor Heterogeneity Radiomic Analysis Based on Multiparametric MRI

Predicting glioma grade plays a pivotal role in treatment and prognosis. However, several current methods for grading depend on the characteristics of the whole tumor. Predicting grade by analyzing tumor subregions has not been thoroughly investigated, which aims to improve the prediction performance. To predict glioma grade via analysis of tumor heterogeneity with features extracted from tumor subregions, it is mainly divided into four magnetic resonance imaging (MRI) sequences, including T2-weighted (T2), fluid-attenuated inversion recovery (FLAIR), pre-gadolinium T1-weighted (T1), and post-gadolinium T1-weighted methods. This study included the data of 97 patients with glioblastomas and 42 patients with low-grade gliomas before surgery. Three subregions, including enhanced tumor (ET), non-enhanced tumor, and peritumoral edema, were obtained based on segmentation labels generated by the GLISTRBoost algorithm. One hundred radiomic features were extracted from each subregion. Feature selection was performed using the cross-validated recursive feature elimination with a support vector machine (SVM) algorithm. SVM classifiers with grid search were established to predict glioma grade based on unparametric and multiparametric MRI. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the classifiers, and the performance of the subregions was compared with the results of the whole tumor. In uniparametric analysis, the features from the ET subregion yielded a higher AUC value of 0.8697, 0.8474, and 0.8474 than those of the whole tumor of FLAIR, T1, and T2. In multiparametric analysis, the ET subregion achieved the best performance (AUC = 0.8755), which was higher than the uniparametric results. Radiomic features from the tumor subregion can potentially be used as clinical markers to improve the predictive accuracy of glioma grades.

Hypermethylation of HIC2 is a potential prognostic biomarker and tumor suppressor of glioma based on bioinformatics analysis and experiments

INTRODUCTION

Glioma is the most common primary tumor in the central nervous system, and prognostic biomarkers are still lacking. HIC ZBTB transcriptional repressor 2 (HIC2) is a hypermethylated gene that plays an important functional role in cardiac development. However, the actual role of HIC2 in glioma progression remains unclear. This study aimed to investigate the function of HIC2 and whether it could be a prognostic biomarker in glioma.

METHODS

The DNA methylation and mRNA expression profiles of HIC2 were downloaded from public databases. The prognostic prediction ability and mechanism research of HIC2 were evaluated.

RESULTS

We found that HIC2 was hypermethylated and expressed at low levels in glioma samples. Hypermethylation and low expression of HIC2 predicted poor prognosis. Multivariate Cox regression analysis suggested that HIC2 was an independent prognostic factor for gliomas. Co-IP assays demonstrated that HIC2 interacts with RNF44, and dual-luciferase reporter assays and ChIP assays revealed that HIC2 transcriptionally inhibits PTPRN2 expression.

CONCLUSIONS

Our findings suggest that HIC2 represents a tumor suppressor gene and prognostic biomarker for glioma progression and that overexpression of HIC2 inhibits the proliferation of glioma in vitro and in vivo by interacting with RNF44 and PTPRN2.

Application of Intraoperative Rapid Molecular Diagnosis in Precision Surgery for Glioma: Mimic the World Health Organization CNS5 Integrated Diagnosis

BACKGROUND

With the advent of the molecular era, the diagnosis and treatment systems of glioma have also changed. A single histological type cannot be used for prognosis grade. Only by combining molecular diagnosis can precision medicine be realized.

OBJECTIVE

To develop an automatic integrated gene detection system (AIGS) for intraoperative detection in glioma and to explore its positive role in intraoperative diagnosis and treatment.

METHODS

We analyzed the isocitrate dehydrogenase 1 (IDH1) mutation status of 105 glioma samples and evaluated the product's potential value for diagnosis; 37 glioma samples were detected intraoperatively to evaluate the feasibility of using the product in an actual situation. A blinding method was used to evaluate the effect of the detection technology on the accuracy of intraoperative histopathological diagnosis by pathologists. We also reviewed the current research status in the field of intraoperative molecular diagnosis.

RESULTS

Compared with next-generation sequencing, the accuracy of AIGS in detecting IDH1 was 100% for 105 samples and 37 intraoperative samples. The blind diagnostic results were compared between the 2 groups, and the molecular information provided by AIGS increased the intraoperative diagnostic accuracy of glioma by 16.2%. Using the technical advantages of multipoint synchronous detection, we determined the tumor molecular margins for 5 IDH-positive patients and achieved accurate resection at the molecular level.

CONCLUSION

AIGS can quickly and accurately provide molecular information during surgery. This methodology not only improves the accuracy of intraoperative pathological diagnosis but also provides an important molecular basis for determining tumor margins to facilitate precision surgery.

Long non-coding RNA in glioma: novel genetic players in temozolomide resistance

Glioma is the most common primary malignant brain tumor in adults and accounts for approximately 80% of brain and central nervous system tumors. In 2021, the World Health Organization (WHO) published a new taxonomy for glioma based on its histological features and molecular alterations. Isocitrate dehydrogenase (IDH) catalyzes the decarboxylation of isocitrate, a critical metabolic reaction in energy generation in cells. Mutations in the IDH genes interrupt cell differentiation and serve as molecular biomarkers that can be used to classify gliomas. For example, the mutant IDH is widely detected in low-grade gliomas, whereas the wild type is in high-grade ones, including glioblastomas. Long non-coding RNAs (lncRNAs) are epigenetically involved in gene expression and contribute to glioma development. To investigate the potential use of lncRNAs as biomarkers, we examined lncRNA dysregulation dependent on the IDH mutation status. We found that several lncRNAs, namely, AL606760.2, H19, MALAT1, PVT1 and SBF2-AS1 may function as glioma risk factors, whereas AC068643.1, AC079228.1, DGCR5, FAM13A-AS1, HAR1A and WDFY3-AS2 may have protective effects. Notably, H19, MALAT1, PVT1, and SBF2-AS1 have been associated with temozolomide resistance in glioma patients. This review study suggests that targeting glioma-associated lncRNAs might aid the treatment of glioma.

Nanomechanical Signatures in Glioma Cells Depend on CD44 Distribution in IDH1 Wild-Type but Not in IDH1R132H Mutant Early-Passage Cultures

Atomic force microscopy (AFM) recently burst into biomedicine, providing morphological and functional characteristics of cancer cells and their microenvironment responsible for tumor invasion and progression, although the novelty of this assay needs to coordinate the malignant profiles of patients' specimens to diagnostically valuable criteria. Applying high-resolution semi-contact AFM mapping on an extended number of cells, we analyzed the nanomechanical properties of glioma early-passage cell cultures with a different IDH1 R132H mutation status. Each cell culture was additionally clustered on CD44+/- cells to find possible nanomechanical signatures that differentiate cell phenotypes varying in proliferative activity and the characteristic surface marker. IDH1 R132H mutant cells compared to IDH1 wild-type ones (IDH1wt) characterized by two-fold increased stiffness and 1.5-fold elasticity modulus. CD44+/IDH1wt cells were two-fold more rigid and much stiffer than CD44-/IDH1wt ones. In contrast to IDH1 wild-type cells, CD44+/IDH1 R132H and CD44-/IDH1 R132H did not exhibit nanomechanical signatures providing statistically valuable differentiation of these subpopulations. The median stiffness depends on glioma cell types and decreases according to the following manner: IDH1 R132H mt (4.7 mN/m), CD44+/IDH1wt (3.7 mN/m), CD44-/IDH1wt (2.5 mN/m). This indicates that the quantitative nanomechanical mapping would be a promising assay for the quick cell population analysis suitable for detailed diagnostics and personalized treatment of glioma forms.

Decoding the Role of MDM2 as a Potential Ubiquitin E3 Ligase and Identifying the Therapeutic Efficiency of Alkaloids against MDM2 in Combating Glioblastoma

Glioblastomas (GBMs) represent the most aggressive form of brain tumor arising from the malignant transformation of astrocytes. Despite various advancements, treatment options remain limited to chemotherapy and radiotherapy followed by surgery giving an overall survival of 14-15 months. These therapies are somewhere restricted in giving a better survival and cure. There is a need for new therapeutics that could potentially target GBM based on molecular pathways and pathology. Here, ubiquitin E3 ligases can be used as targets as they bind a wide array of substrates and therefore can be attractive targets for new inhibitors. Through this study, we have tried to sort various ubiquitin E3 ligases based on their expression, pathways to which these ligases are associated, and mutational frequencies, and then we tried to screen potent inhibitors against the most favorable E3 ligase as very few studies are available concerning inhibition of E3 ligase in GBM. Our study found MDM2 to be the most ideal E3 ligase and further we tried to target MDM2 against various compounds under the alkaloid class. Molecular Docking and MD simulations combined with ADMET properties and BBB scores revealed that only evodiamine and sanguinarine were effective in inhibiting MDM2. We also tried to give a proposed mechanism of how these inhibitors mediate the p53 signaling in GBM. Therefore, the new scaffolds predicted by the computational approach could help in designing promising therapeutic agents targeting MDM2 in glioblastoma.

Characterization of an IDH1 R132H Rabbit Monoclonal Antibody, MRQ-67, and Its Applications in the Identification of Diffuse Gliomas

The current diagnosis of diffuse glioma involves isocitrate dehydrogenase (IDH) mutation testing. Most IDH mutant gliomas carry a G-to-A mutation at IDH1 position 395, resulting in the R132H mutant. R132H immunohistochemistry (IHC), therefore, is used to screen for the IDH1 mutation. In this study, the performance of MRQ-67, a recently generated IDH1 R132H antibody, was characterized in comparison with H09, a frequently used clone. Selective binding was demonstrated by an enzyme-linked immunosorbent assay for MRQ-67 to the R132H mutant, with an affinity higher than that for H09. By Western and dot immunoassays, MRQ-67 was found to bind specifically to the IDH1 R1322H, with a higher capacity than H09. IHC testing with MRQ-67 demonstrated a positive signal in most diffuse astrocytomas (16/22), oligodendrogliomas (9/15), and secondary glioblastomas tested (3/3), but not in primary glioblastomas (0/24). While both clones demonstrated a positive signal with similar patterns and equivalent intensities, H09 exhibited a background stain more frequently. DNA sequencing on 18 samples showed the R132H mutation in all IHC positive cases (5/5), but not in negative cases (0/13). These results demonstrate that MRQ-67 is a high-affinity antibody suitable for specific detection of the IDH1 R132H mutant by IHC and with less background as compared with H09.

Preclinical Models of Low-Grade Gliomas

Diffuse infiltrating low-grade glioma (LGG) is classified as WHO grade 2 astrocytoma with isocitrate dehydrogenase (IDH) mutation and oligodendroglioma with IDH1 mutation and 1p/19q codeletion. Despite their better prognosis compared with glioblastoma, LGGs invariably recur, leading to disability and premature death. There is an unmet need to discover new therapeutics for LGG, which necessitates preclinical models that closely resemble the human disease. Basic scientific efforts in the field of neuro-oncology are mostly focused on high-grade glioma, due to the ease of maintaining rapidly growing cell cultures and highly reproducible murine tumors. Development of preclinical models of LGG, on the other hand, has been difficult due to the slow-growing nature of these tumors as well as challenges involved in recapitulating the widespread genomic and epigenomic effects of IDH mutation. The most recent WHO classification of CNS tumors emphasizes the importance of the role of IDH mutation in the classification of gliomas, yet there are relatively few IDH-mutant preclinical models available. Here, we review the in vitro and in vivo preclinical models of LGG and discuss the mechanistic challenges involved in generating such models and potential strategies to overcome these hurdles.

Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI

In the context of brain tumour response assessment, deep learning-based three-dimensional (3D) tumour segmentation has shown potential to enter the routine radiological workflow. The purpose of the present study was to perform an external evaluation of a state-of-the-art deep learning 3D brain tumour segmentation algorithm (HD-GLIO) on an independent cohort of consecutive, post-operative patients. For 66 consecutive magnetic resonance imaging examinations, we compared delineations of contrast-enhancing (CE) tumour lesions and non-enhancing T2/FLAIR hyperintense abnormality (NE) lesions by the HD-GLIO algorithm and radiologists using Dice similarity coefficients (Dice). Volume agreement was assessed using concordance correlation coefficients (CCCs) and Bland-Altman plots. The algorithm performed very well regarding the segmentation of NE volumes (median Dice = 0.79) and CE tumour volumes larger than 1.0 cm³ (median Dice = 0.86). If considering all cases with CE tumour lesions, the performance dropped significantly (median Dice = 0.40). Volume agreement was excellent with CCCs of 0.997 (CE tumour volumes) and 0.922 (NE volumes). The findings have implications for the application of the HD-GLIO algorithm in the routine radiological workflow where small contrast-enhancing tumours will constitute a considerable share of the follow-up cases. Our study underlines that independent validations on clinical datasets are key to asserting the robustness of deep learning algorithms.

Impact of epigenetic reprogramming on antitumor immune responses in glioma

Epigenetic remodeling is a molecular hallmark of gliomas, and it has been identified as a key mediator of glioma progression. Epigenetic dysregulation contributes to gliomagenesis, tumor progression, and responses to immunotherapies, as well as determining clinical features. This epigenetic remodeling includes changes in histone modifications, chromatin structure, and DNA methylation, all of which are driven by mutations in genes such as histone 3 genes (H3C1 and H3F3A), isocitrate dehydrogenase 1/2 (IDH1/2), α-thalassemia/mental retardation, X-linked (ATRX), and additional chromatin remodelers. Although much of the initial research primarily identified how the epigenetic aberrations impacted glioma progression by solely examining the glioma cells, recent studies have aimed at establishing the role of epigenetic alterations in shaping the tumor microenvironment (TME). In this review, we discuss the mechanisms by which these epigenetic phenomena in glioma remodel the TME and how current therapies targeting epigenetic dysregulation affect the glioma immune response and therapeutic outcomes. Understanding the link between epigenetic remodeling and the glioma TME provides insights into the implementation of epigenetic-targeting therapies to improve the antitumor immune response.

The Role of Cellular Prion Protein in Glioma Tumorigenesis Could Be through the Autophagic Mechanisms: A Narrative Review

The carcinogenesis of glial tumors appears complex because of the many genetic and epigenetic phenomena involved. Among these, cellular prion protein (PrPC) is considered a key factor in cell-death resistance and important aspect implicated in tumorigenesis. Autophagy also plays an important role in cell death in various pathological conditions. These two cellular phenomena are related and share the same activation by specific alterations in the cellular microenvironment. Furthermore, there is an interdependence between autophagy and prion activity in glioma tumorigenesis. Glioma is one of the most aggressive known cancers, and the fact that such poorly studied processes as autophagy and PrPC activity are so strongly involved in its carcinogenesis suggests that by better understanding their interaction, more can be understood about its origin and treatment. Few studies in the literature relate these two cellular phenomena, much less try to explain their combined activity and role in glioma carcinogenesis. In this study, we explored the recent findings on the molecular mechanism and regulation pathways of autophagy, examining the role of PrPC in autophagy processes and how they may play a central role in glioma tumorigenesis. Among the many molecular interactions that PrP physiologically performs, it appears that processes shared with autophagy activity are those most implicated in glial tumor carcinogeneses such as activity on MAP kinases, PI3K, and mTOR. This work can be supportive and valuable as a basis for further future studies on this topic.

Three-dimensional visualization of human brain tumors using the CUBIC technique

Application of tissue clearing techniques on human brain tumors is still limited. This study was to investigate the application of CUBIC on 3D pathological studies of human brain tumors. Brain tumor specimens derived from 21 patients were cleared with CUBIC. Immunostaining was conducted on cleared specimens to label astrocytes, microglia and microvessels, respectively. All tumor specimens achieved transparency after clearing. Immunostaining and CUBIC are well compatible in a variety of human brain tumors. Spatial morphologies of microvessels, astrocytes and microglia of tumors were clearly visualized in 3D, and their 3D morphological parameters were easily quantified. By comparing the quantitative morphological parameters of microvessels among brain tumors of different malignancy, we found that mean vascular diameter was positively correlated with tumor malignancy. Our study demonstrates that CUBIC can be successfully applied to 3D pathological studies of various human brain tumors, and 3D studies of human brain tumors hold great promise in helping us better understand brain tumor pathology in the future.