Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology.

PURPOSE: Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensional (3D) convolutional U-Net based deformable image registration (ConvUNet-DIR) method using unsupervised learning to establish correspondence between baseline pre-operative and follow-up MRI scans of patients with brain glioma. METHODS: This study involved multi-parametric brain MRI scans (T1, T1-contrast enhanced, T2, FLAIR) acquired at pre-operative and follow-up time for 160 patients diagnosed with glioma, representing the BraTS-Reg 2022 challenge dataset. ConvUNet-DIR, a deep learning-based deformable registration workflow using 3D U-Net style architecture as a core, was developed to establish correspondence between the MRI scans. The workflow consists of three components: (1) the U-Net learns features from pairs of MRI scans and estimates a mapping between them, (2) the grid generator computes the sampling grid based on the derived transformation parameters, and (3) the spatial transformation layer generates a warped image by applying the sampling operation using interpolation. A similarity measure was used as a loss function for the network with a regularization parameter limiting the deformation. The model was trained via unsupervised learning using pairs of MRI scans on a training data set (n = 102) and validated on a validation data set (n = 26) to assess its generalizability. Its performance was evaluated on a test set (n = 32) by computing the Dice score and structural similarity index (SSIM) quantitative metrics. The model's performance also was compared with the baseline state-of-the-art VoxelMorph (VM1 and VM2) learning-based algorithms. RESULTS: The ConvUNet-DIR model showed promising competency in performing accurate 3D deformable registration. It achieved a mean Dice score of 0.975 ± 0.003 and SSIM of 0.908 ± 0.011 on the test set (n = 32). Experimental results also demonstrated that ConvUNet-DIR outperformed the VoxelMorph algorithms concerning Dice (VM1: 0.969 ± 0.006 and VM2: 0.957 ± 0.008) and SSIM (VM1: 0.893 ± 0.012 and VM2: 0.857 ± 0.017) metrics. The time required to perform a registration for a pair of MRI scans is about 1 s on the CPU. CONCLUSIONS: The developed deep learning-based model can perform an end-to-end deformable registration of a pair of 3D MRI scans for glioma patients without human intervention. The model could provide accurate, efficient, and robust deformable registration without needing pre-alignment and labeling. It outperformed the state-of-the-art VoxelMorph learning-based deformable registration algorithms and other supervised/unsupervised deep learning-based methods reported in the literature.

Expression of brain-derived neurotrophic factor and formation of migrasome increases in the glioma cells induced by the adipokinetic hormone.

OBJECTIVE: It has been previously shown that brain-derived neurotrophic factor is linked with various types of cancer. Brain-derived neurotrophic factor is found to be highly expressed in multiple human cancers and associated with tumor growth, invasion, and metastasis. Adipokinetic hormones are functionally related to the vertebrate glucagon, as they have similar functionalities that manage the nutrient-dependent secretion of these two hormones. Migrasomes are new organelles that contain numerous small vesicles, which aid in transmitting signals between the migrating cells. Therefore, the aim of this study was to investigate the effects of Anax imperator adipokinetic hormone on brain-derived neurotrophic factor expression and ultrastructure of cells in the C6 glioma cell line. METHODS: The rat C6 glioma cells were treated with concentrations of 5 and 10 Anax imperator adipokinetic hormone for 24 h. The effects of the Anax imperator adipokinetic hormone on the migrasome formation and brain-derived neurotrophic factor expression were analyzed using immunocytochemistry and transmission electron microscope. RESULTS: The rat C6 glioma cells of the 5 and 10 µM Anax imperator adipokinetic hormone groups showed significantly high expressions of brain-derived neurotrophic factor and migrasomes numbers, compared with the control group. CONCLUSION: A positive correlation was found between the brain-derived neurotrophic factor expression level and the formation of migrasome, which indicates that the increased expression of brain-derived neurotrophic factor and the number of migrasomes may be involved to metastasis of the rat C6 glioma cell line induced by the Anax imperator adipokinetic hormone. Therefore, the expression of brain-derived neurotrophic factor and migrasome formation may be promising targets for preventing tumor proliferation, invasion, and metastasis in glioma.

Previously reported CCDC26 risk variant and novel germline variants in GALNT13, AR, and MYO10 associated with familial glioma in Finland.

Predisposing factors underlying familial aggregation of non-syndromic gliomas are still to be uncovered. Whole-exome sequencing was performed in four Finnish families with brain tumors to identify rare predisposing variants. A total of 417 detected exome variants and 102 previously reported glioma-related variants were further genotyped in 19 Finnish families with brain tumors using targeted sequencing. Rare damaging variants in GALNT13, MYO10 and AR were identified. Two families carried either c.553C>T (R185C) or c.1214T>A (L405Q) on GALNT13. Variant c.553C>T is located on the substrate-binding site of GALNT13. AR c.2180G>T (R727L), which is located on a ligand-binding domain of AR, was detected in two families, one of which also carried a GALNT13 variant. MYO10 c.4448A>G (N1483S) was detected in two families and c.1511C>T (A504V) variant was detected in one family. Both variants are located on functional domains related to MYO10 activity in filopodia formation. In addition, affected cases in six families carried a known glioma risk variant rs55705857 in CCDC26 and low-risk glioma variants. These novel findings indicate polygenic inheritance of familial glioma in Finland and increase our understanding of the genetic contribution to familial glioma susceptibility.

Clinical characteristics and prognostic factors of adult brainstem gliomas: A retrospective analysis of histologically-proven 40 cases.

To illustrate the clinical characteristics and prognostic factors of adult patients pathologically confirmed with brainstem gliomas (BSGs). Clinical data of 40 adult patients pathologically diagnosed with BSGs admitted to Beijing Shijitan Hospital from 2009 to 2022 were recorded and retrospectively analyzed. The primary parameters included relevant symptoms, duration of symptoms, Karnofsky performance status (KPS), tumor location, type of surgical resection, diagnosis, treatment, and survival. Univariate and multivariate analyses were evaluated by Cox regression models. The gliomas were located in the midbrain of 9 patients, in the pons of 14 cases, in the medulla of 5 cases, in the midbrain and pons of 6 cases and invading the medulla and pons of 6 cases, respectively. The proportion of patients with low-grade BSGs was 42.5%. Relevant symptoms consisted of visual disturbance, facial paralysis, dizziness, extremity weakness, ataxia, paresthesia, headache, bucking, dysphagia, dysacousia, nausea, dysphasia, dysosmia, hypomnesia and nystagmus. 23 (57.5%) patients accepted stereotactic biopsy, 17 (42.5%) patients underwent surgical resection. 39 patients received radiotherapy and 34 cases were treated with temozolomide. The median overall survival (OS) of all patients was 26.2 months and 21.5 months for the median progression-free survival (PFS). Both duration of symptoms (P = .007) and tumor grading (P = .002) were the influencing factors for OS, and tumor grading was significantly associated with PFS (P = .001). Duration of symptoms for more than 2 months and low-grade are favorable prognostic factors for adult patients with BSGs.

State of the Art in Low-Grade Glioma Management: Insights From Isocitrate Dehydrogenase and Beyond.

Low-grade gliomas present a formidable challenge in neuro-oncology because of the challenges imposed by the blood-brain barrier, predilection for the young adult population, and propensity for recurrence. In the past two decades, the systematic examination of genomic alterations in adults and children with primary brain tumors has uncovered profound new insights into the pathogenesis of these tumors, resulting in more accurate tumor classification and prognostication. It also identified several common recurrent genomic alterations that now define specific brain tumor subtypes and have provided a new opportunity for molecularly targeted therapeutic intervention. Adult-type diffuse low-grade gliomas are frequently associated with mutations in isocitrate dehydrogenase 1 and 2 (IDH1/2), resulting in production of 2-hydroxyglutarate, an oncometabolite important for tumorigenesis. Recent studies of IDH inhibitors have yielded promising results in patients at early stages of disease with prolonged progression-free survival (PFS) and delayed time to radiation and chemotherapy. Pediatric-type gliomas have high rates of alterations in BRAF, including BRAF V600E point mutations or BRAF-KIAA1549 rearrangements. BRAF inhibitors, often combined with MEK inhibitors, have resulted in radiographic response and improved PFS in these patients. This article reviews emerging approaches to the treatment of low-grade gliomas, including a discussion of targeted therapies and how they integrate with the current treatment modalities of surgical resection, chemotherapy, and radiation.

Understanding the immunosuppressive microenvironment of glioma: mechanistic insights and clinical perspectives.

Glioblastoma (GBM), the predominant and primary malignant intracranial tumor, poses a formidable challenge due to its immunosuppressive microenvironment, thereby confounding conventional therapeutic interventions. Despite the established treatment regimen comprising surgical intervention, radiotherapy, temozolomide administration, and the exploration of emerging modalities such as immunotherapy and integration of medicine and engineering technology therapy, the efficacy of these approaches remains constrained, resulting in suboptimal prognostic outcomes. In recent years, intensive scrutiny of the inhibitory and immunosuppressive milieu within GBM has underscored the significance of cellular constituents of the GBM microenvironment and their interactions with malignant cells and neurons. Novel immune and targeted therapy strategies have emerged, offering promising avenues for advancing GBM treatment. One pivotal mechanism orchestrating immunosuppression in GBM involves the aggregation of myeloid-derived suppressor cells (MDSCs), glioma-associated macrophage/microglia (GAM), and regulatory T cells (Tregs). Among these, MDSCs, though constituting a minority (4-8%) of CD45+ cells in GBM, play a central component in fostering immune evasion and propelling tumor progression, angiogenesis, invasion, and metastasis. MDSCs deploy intricate immunosuppressive mechanisms that adapt to the dynamic tumor microenvironment (TME). Understanding the interplay between GBM and MDSCs provides a compelling basis for therapeutic interventions. This review seeks to elucidate the immune regulatory mechanisms inherent in the GBM microenvironment, explore existing therapeutic targets, and consolidate recent insights into MDSC induction and their contribution to GBM immunosuppression. Additionally, the review comprehensively surveys ongoing clinical trials and potential treatment strategies, envisioning a future where targeting MDSCs could reshape the immune landscape of GBM. Through the synergistic integration of immunotherapy with other therapeutic modalities, this approach can establish a multidisciplinary, multi-target paradigm, ultimately improving the prognosis and quality of life in patients with GBM.

Comprehensive analysis of the REST transcription factor regulatory networks in IDH mutant and IDH wild-type glioma cell lines and tumors.

The RE1-silencing transcription factor (REST) acts either as a repressor or activator of transcription depending on the genomic and cellular context. REST is a key player in brain cell differentiation by inducing chromatin modifications, including DNA methylation, in a proximity of its binding sites. Its dysfunction may contribute to oncogenesis. Mutations in IDH1/2 significantly change the epigenome contributing to blockade of cell differentiation and glioma development. We aimed at defining how REST modulates gene activation and repression in the context of the IDH mutation-related phenotype in gliomas. We studied the effects of REST knockdown, genome wide occurrence of REST binding sites, and DNA methylation of REST motifs in IDH wild type and IDH mutant gliomas. We found that REST target genes, REST binding patterns, and TF motif occurrence proximal to REST binding sites differed in IDH wild-type and mutant gliomas. Among differentially expressed REST targets were genes involved in glial cell differentiation and extracellular matrix organization, some of which were differentially methylated at promoters or gene bodies. REST knockdown differently impacted invasion of the parental or IDH1 mutant glioma cells. The canonical REST-repressed gene targets showed significant correlation with the GBM NPC-like cellular state. Interestingly, results of REST or KAISO silencing suggested the interplay between these TFs in regulation of REST-activated and repressed targets. The identified gene regulatory networks and putative REST cooperativity with other TFs, such as KAISO, show distinct REST target regulatory networks in IDH-WT and IDH-MUT gliomas, without concomitant DNA methylation changes. We conclude that REST could be an important therapeutic target in gliomas.

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

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

The diagnostic challenge of lack of choline level elevation on 1H-MR spectroscopy in grade II-III gliomas.

The accurate diagnosis of brain tumour is very important in modern neuro-oncology medicine. Magnetic resonance spectroscopy (MRS) is supposed to be a promising tool for detecting cancerous lesions. However, the interpretation of MRS data is complicated by the fact that not all cancerous lesions exhibit elevated choline (Cho) levels. The main goal of our study was to investigate the lack of Cho lesion /Cho ref elevation in the population of grade II-III gliomas. 89 cases of gliomas grade II and III were used for the retrospective analysis - glioma (astrocytoma or oligodendroglioma) grade II (74 out of 89 cases [83%]) and III (15 out of 89 cases [17%]) underwent conventional MRI extended by MRS before treatment. Histopathological diagnosis was obtained either by biopsy or surgical resection. Gliomas were classified to the group of no-choline elevation when the ratio of choline measured within the tumour (Cho lesion ) to choline from NABT (Cho ref ) were equal to or lower than 1. Significant differences were observed between ratios of Cho lesion /Cr lesion calculated for no-choline elevation and glial tumour groups as well as in the NAA lesion /Cr lesion ratio between the no-choline elevation group and glial tumour group. With consistent data concerning choline level elevation and slightly lower NAA value, the Cho lesion /NAA lesion ratio is significantly higher in the WHO II glial tumour group compared to the no-choline elevation cases ( p < 0.000). In the current study the results demonstrated possibility of lack of choline elevation in patients with grade II-III gliomas, so it is important to remember that the lack of elevated choline levels does not exclude neoplastic lesion.

The relationship between imaging features, therapeutic response, and overall survival in pediatric diffuse intrinsic pontine glioma.

We aimed to evaluate the relationship between imaging features, therapeutic responses (comparative cross-product and volumetric measurements), and overall survival (OS) in pediatric diffuse intrinsic pontine glioma (DIPG). A total of 134 patients (≤ 18 years) diagnosed with DIPG were included. Univariate and multivariate analyses were performed to evaluate correlations of clinical and imaging features and therapeutic responses with OS. The correlation between cross-product (CP) and volume thresholds in partial response (PR) was evaluated by linear regression. The log-rank test was used to compare OS patients with discordant therapeutic response classifications and those with concordant classifications. In univariate analysis, characteristics related to worse OS included lower Karnofsky, larger extrapontine extension, ring-enhancement, necrosis, non-PR, and increased ring enhancement post-radiotherapy. In the multivariate analysis, Karnofsky, necrosis, extrapontine extension, and therapeutic response can predict OS. A 25% CP reduction (PR) correlated with a 32% volume reduction (R2 = 0.888). Eight patients had discordant therapeutic response classifications according to CP (25%) and volume (32%). This eight patients' median survival time was 13.0 months, significantly higher than that in the non-PR group (8.9 months), in which responses were consistently classified as non-PR based on CP (25%) and volume (32%). We identified correlations between imaging features, therapeutic responses, and OS; this information is crucial for future clinical trials. Tumor volume may represent the DIPG growth pattern more accurately than CP measurement and can be used to evaluate therapeutic response.

Quantification of attenuation and speckle features from endoscopic OCT images for the diagnosis of human brain glioma.

Application of optical coherence tomography (OCT) in neurosurgery mostly includes the discrimination between intact and malignant tissues aimed at the detection of brain tumor margins. For particular tissue types, the existing approaches demonstrate low performance, which stimulates the further research for their improvement. The analysis of speckle patterns of brain OCT images is proposed to be taken into account for the discrimination between human brain glioma tissue and intact cortex and white matter. The speckle properties provide additional information of tissue structure, which could help to increase the efficiency of tissue differentiation. The wavelet analysis of OCT speckle patterns was applied to extract the power of local brightness fluctuations in speckle and its standard deviation. The speckle properties are analysed together with attenuation ones using a set of ex vivo brain tissue samples, including glioma of different grades. Various combinations of these features are considered to perform linear discriminant analysis for tissue differentiation. The results reveal that it is reasonable to include the local brightness fluctuations at first two wavelet decomposition levels in the analysis of OCT brain images aimed at neurosurgical diagnosis.

Ensemble learning prediction framework for EGFR amplification status of glioma based on terahertz spectral features.

Epidermal growth factor receptor (EGFR) plays a pivotal role in the initiation and progression of gliomas. In particular, in glioblastoma, EGFR amplification emerges as a catalyst for invasion, proliferation, and resistance to radiotherapy and chemotherapy. Current approaches are not capable of providing rapid diagnostic results of molecular pathology. In this study, we propose a terahertz spectroscopic approach for predicting the EGFR amplification status of gliomas for the first time. A machine learning model was constructed using the terahertz response of the measured glioma tissues, including the absorption coefficient, refractive index, and dielectric loss tangent. The novelty of our model is the integration of three classical base classifiers, i.e., support vector machine, random forest, and extreme gradient boosting. The ensemble learning method combines the advantages of various base classifiers, this model has more generalization ability. The effectiveness of the proposed method was validated by applying an individual test set. The optimal performance of the integrated algorithm was verified with an area under the curve (AUC) maximum of 85.8 %. This signifies a significant stride toward more effective and rapid diagnostic tools for guiding postoperative therapy in gliomas.

Crosstalk of different cell-death patterns predicts prognosis and drug sensitivity in glioma.

BACKGROUND: Glioma is a malignant brain tumor originating from glial cells, and there still a challenge to accurately predict the prognosis. Programmed cell death (PCD) plays a key role in tumorigenesis and immune response. However, the crosstalk and potential role of various PCDs in prognosis and tumor microenvironment remains unknown. Therefore, we comprehensively discussed the relationship between different models of PCD and the prognosis of glioma and provided new ideas for the optimal targeted therapy of glioma. MATERIALS AND METHODS: We compared and analyzed the role of 14 PCD patterns on the prognosis from different levels. We constructed the cell death risk score (CDRS) index and conducted a comprehensive analysis of CDRS and TME characteristics, clinical characteristics, and drug response. RESULTS: Effects of different PCDs at the genomic, functional, and immune microenvironment levels were discussed. CDRS index containing 6 gene signatures and a nomogram were established. High CDRS is associated with a worse prognosis. Through transcriptome and single-cell data, we found that patients with high CDRS showed stronger immunosuppressive characteristics. Moreover, the high-CDRS group was resistant to the traditional glioma chemotherapy drug Vincristine, but more sensitive to the Temozolomide and the clinical experimental drug Bortezomib. In addition, we identified 19 key potential therapeutic targets during malignant differentiation of tumor cells. CONCLUSION: Overall, we provide the first systematic description of the role of 14 PCDs in glioma. A new CDRS model was built to predict the prognosis and to provide a new idea for the targeted therapy of glioma.

Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.

BACKGROUND: The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas. METHODS: This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data. RESULTS: We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617]. CONCLUSIONS: Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma.

Accurate intraoperative differentiation of primary central nervous system lymphoma (PCNSL) remains pivotal in guiding neurosurgical decisions. However, distinguishing PCNSL from other lesions, notably glioma, through frozen sections challenges pathologists. Here we sought to develop and validate a deep learning model capable of precisely distinguishing PCNSL from non-PCNSL lesions, especially glioma, using hematoxylin and eosin (H&E)-stained frozen whole-slide images. Also, we compared its performance against pathologists of varying expertise. Additionally, a human-machine fusion approach integrated both model and pathologic diagnostics. In external cohorts, LGNet achieved AUROCs of 0.965 and 0.972 in distinguishing PCNSL from glioma and AUROCs of 0.981 and 0.993 in differentiating PCNSL from non-PCNSL lesions. Outperforming several pathologists, LGNet significantly improved diagnostic performance, further augmented to some extent by fusion approach. LGNet's proficiency in frozen section analysis and its synergy with pathologists indicate its valuable role in intraoperative diagnosis, particularly in discriminating PCNSL from glioma, alongside other lesions.

Laminin-associated integrins mediate Diffuse Intrinsic Pontine Glioma infiltration and therapy response within a neural assembloid model.

Diffuse Intrinsic Pontine Glioma (DIPG) is a highly aggressive and fatal pediatric brain cancer. One pre-requisite for tumor cells to infiltrate is adhesion to extracellular matrix (ECM) components. However, it remains largely unknown which ECM proteins are critical in enabling DIPG adhesion and migration and which integrin receptors mediate these processes. Here, we identify laminin as a key ECM protein that supports robust DIPG cell adhesion and migration. To study DIPG infiltration, we developed a DIPG-neural assembloid model, which is composed of a DIPG spheroid fused to a human induced pluripotent stem cell-derived neural organoid. Using this assembloid model, we demonstrate that knockdown of laminin-associated integrins significantly impedes DIPG infiltration. Moreover, laminin-associated integrin knockdown improves DIPG response to radiation and HDAC inhibitor treatment within the DIPG-neural assembloids. These findings reveal the critical role of laminin-associated integrins in mediating DIPG progression and drug response. The results also provide evidence that disrupting integrin receptors may offer a novel therapeutic strategy to enhance DIPG treatment outcomes. Finally, these results establish DIPG-neural assembloid models as a powerful tool to study DIPG disease progression and enable drug discovery.

Metabolic models predict fotemustine and the combination of eflornithine/rifamycin and adapalene/cannabidiol for the treatment of gliomas.

Gliomas are the most common type of malignant brain tumors, with glioblastoma multiforme (GBM) having a median survival of 15 months due to drug resistance and relapse. The treatment of gliomas relies on surgery, radiotherapy and chemotherapy. Only 12 anti-brain tumor chemotherapies (AntiBCs), mostly alkylating agents, have been approved so far. Glioma subtype-specific metabolic models were reconstructed to simulate metabolite exchanges, in silico knockouts and the prediction of drug and drug combinations for all three subtypes. The simulations were confronted with literature, high-throughput screenings (HTSs), xenograft and clinical trial data to validate the workflow and further prioritize the drug candidates. The three subtype models accurately displayed different degrees of dependencies toward glutamine and glutamate. Furthermore, 33 single drugs, mainly antimetabolites and TXNRD1-inhibitors, as well as 17 drug combinations were predicted as potential candidates for gliomas. Half of these drug candidates have been previously tested in HTSs. Half of the tested drug candidates reduce proliferation in cell lines and two-thirds in xenografts. Most combinations were predicted to be efficient for all three glioma types. However, eflornithine/rifamycin and cannabidiol/adapalene were predicted specifically for GBM and low-grade glioma, respectively. Most drug candidates had comparable efficiency in preclinical tests, cerebrospinal fluid bioavailability and mode-of-action to AntiBCs. However, fotemustine and valganciclovir alone and eflornithine and celecoxib in combination with AntiBCs improved the survival compared to AntiBCs in two-arms, phase I/II and higher glioma clinical trials. Our work highlights the potential of metabolic modeling in advancing glioma drug discovery, which accurately predicted metabolic vulnerabilities, repurposable drugs and combinations for the glioma subtypes.

Contemporary Management of Pediatric Brainstem Tumors.

Brain tumors are the second most common malignancy in childhood. Around 15-20% of pediatric brain tumors occur in the brainstem. The most common type of brainstem tumor are diffuse tumors in the ventral pons, whereas focal tumors tend to arise from the midbrain, medulla, and dorsal pons. Glioma is the most common pathological entity. Contemporary management consists of surgery, radiotherapy, chemotherapy, and other adjuvant treatment. Surgical options range from biopsy to radical excision. Biopsy can be performed for diagnostic and prognostic purposes, or in the setting of clinical trials, mainly for diffuse intrinsic pontine gliomas. For focal tumors, surgeons need to carefully balance clinical outcomes against possible neurological sequelae in order to achieve maximal safe resection. Radiotherapy is essential for control of high-grade tumors and may be applied to residual or recurrent low-grade tumors. Proton therapy may provide similar efficacy and less neurotoxicity in comparison to conventional photon therapy. Oncological treatment continues to evolve from conventional chemotherapy to targeted therapy, immunotherapy, and other novel treatment methods and holds great potential as adjuvant therapy for pediatric brainstem tumors.

AS1411 binds to nucleolin via its parallel structure and disrupts the exos-miRNA-27a-mediated reciprocal activation loop between glioma and astrocytes.

The interaction between glioma cells and astrocytes promotes the proliferation of gliomas. Micro-RNAs (miRNAs) carried by astrocyte exosomes (exos) may be involved in this process, but the mechanism remains unclear. The oligonucleotide AS1411, which consists of 26 bases and has a G-quadruplex structure, is an aptamer that targets nucleolin. In this study, we demonstrate exosome-miRNA-27a-mediated cross-activation between astrocytes and glioblastoma and show that AS1411 reduces astrocytes' pro-glioma activity. The enhanced affinity of AS1411 toward nucleolin is attributed to its G-quadruplex structure. After binding to nucleolin, AS1411 inhibits the entry of the NF-κB pathway transcription factor P65 into the nucleus, then downregulates the expression of miRNA-27a in astrocytes surrounding gliomas. Then, AS1411 downregulates astrocyte exosome-miRNA-27a and upregulates the expression of INPP4B, the target gene of miRNA-27a in gliomas, thereby inhibiting the PI3K/AKT pathway and inhibiting glioma proliferation. These results were verified in mouse orthotopic glioma xenografts and human glioma samples. In conclusion, the parallel structure of AS1411 allows it to bind to nucleolin and disrupt the exosome-miRNA-27a-mediated reciprocal activation loop between glioma cells and astrocytes. Our results may help in the development of a novel approach to therapeutic modulation of the glioma microenvironment.

Comparison of two region-of-interest placement methods for histogram analysis of apparent diffusion coefficient maps for glioma grading.

Objectives: We assessed the value of histogram analysis (HA) of apparent diffusion coefficient (ADC) maps for grading low-grade (LGG) and high-grade (HGG) gliomas. Methods: We compared the diagnostic performance of two region-of-interest (ROI) placement methods (ROI 1: the entire tumor; ROI 2: the tumor excluding cystic and necrotic portions). We retrospectively evaluated 54 patients with supratentorial gliomas (18 LGG and 36 HGG). All subjects underwent standard 3T contrast-enhanced magnetic resonance imaging. Histogram parameters of ADC maps calculated with the two segmentation methods comprised mean, median, maxi-mum, minimum, kurtosis, skewness, entropy, standard deviation (sd), mean of positive pixels (mpp), uniformity of positive pixels, and their ratios (r) between lesion and normal white matter. They were compared using the independent t-test, chi-square test, or Mann-Whitney U test. For statistically significant results, receiver operating characteristic curves were constructed, and the optimal cutoff value, sensitivity, and specificity were determined by maximizing Youden's index. Results: The ROI 1 method resulted in significantly higher rADC mean, rADC median, and rADC mpp for LGG than for HGG; these parameters had value for predicting the histological glioma grade with a cutoff (sensitivity, specificity) of 1.88 (77.8%, 61.1%), 2.25 (44.4%, 97.2%), and 1.88 (77.8%, 63.9%), respectively. The ROI 2 method resulted in significantly higher ADC mean, ADC median, ADC mpp, ADC sd, ADC max, rADC median, rADC mpp, rADC mean, rADC sd, and rADC max for LGG than for HGG, while skewness was lower for LGG than for HGG (0.27 [0.98] vs 0.91 [0.81], p = 0.014). In ROI 2, ADC median, ADC mpp, ADC mean, rADC median, rADC mpp, and rADC mean performed well in differentiating glioma grade with cutoffs (sensitivity, specificity) of 1.28 (77.8%, 88.9%), 1.28 (77.8%, 88.9%), 1.25 (77.8%, 91.7%), 1.81 (83.3%, 91.7%), 1.74 (83.3%, 91.7%), and 1.81 (83.3%, 91.7%), respectively. Conclusions: HA parameters had value for grading gliomas. Ex-cluding cystic and necrotic portions of the tumor for measuring HA parameters was preferable to using the entire tumor as the ROI. In this segmentation, rADC median showed the highest performance in predicting histological glioma grade, followed by rADC mpp, rADC mean, ADC median, ADC mpp, and ADC mean.