The Role of ATRX in Glioma Biology

MRI-derived radiomics and end-to-end deep learning models for predicting glioma ATRX status: a systematic review and meta-analysis of diagnostic test accuracy studies

We aimed to systematically review and meta-analyze the predictive value of magnetic resonance imaging (MRI)-derived radiomics/end-to-end deep learning (DL) models in predicting glioma alpha thalassemia/mental retardation syndrome X-linked (ATRX) status. We conducted a comprehensive search across four major databases-Web of Science, PubMed, Scopus, and Embase. All the studies that assessed the performance of radiomics and/or end-to-end DL models for predicting glioma ATRX status were included. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) criteria and the METhodological RadiomICs Score (METRICS). Pooled estimates for performance metrics were calculated. I-squared was used to assess heterogeneity, while subgroup and sensitivity analyses were performed to find its potential sources. Publication bias was assessed using Deeks' funnel plots. Seventeen and eleven studies were included in the systematic review and meta-analysis, respectively. Most of the studies had a low risk of bias and low concern for applicability according to the QUADAS-2. Also, most of them had good quality according to the METRICS. Meta-analysis showed a pooled sensitivity of 0.80 (95%CI: 0.71-0.96), a specificity of 0.82 (95%CI: 0.67-0.93), a positive diagnostic likelihood ratio (DLR) of 6.77 (95%CI: 4.67-9.82), a negative DLR of 0.15 (95%CI: 0.06-0.38), a diagnostic odds ratio of 30.36 (95%CI: 15.87-58.05), and an area under the curve (AUC) of 0.92 (95%CI: 0.89-0.94). Subgroup analysis revealed significant intergroup differences based on several factors. Radiomics models can accurately predict ATRX status in gliomas, enhancing non-invasive tumor characterization and guiding treatment strategies.

Intra-tumoral susceptibility signals in brain gliomas: where do we stand?

Nowadays, the genetic and biomolecular profile of neoplasms-related with their biological behaviour-have become a key issue in oncology, as they influence many aspects of both diagnosis and treatment. In the neuro-oncology field, neuroradiological research has recently explored the potential of non-invasively predicting the molecular phenotype of primary brain neoplasms, particularly gliomas, based on magnetic resonance imaging (MRI), using both conventional and advanced imaging techniques. Among these, diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS) and susceptibility-weighted imaging (SWI) and have been used to explore various aspects of glioma biology, including predicting treatment response and understanding treatment-related changes during follow-up imaging. Recently, intratumoral susceptibility signals (ITSSs)-visible on SWI-have been recognised as an important new imaging tool in the evaluation of brain gliomas, as they offer a fast and simple non-invasive window into their microenvironment. These intratumoral hypointensities reflect critical pathological features such as microhemorrhages, calcifications, necrosis and vascularization. Therefore, ITSSs can provide neuroradiologists with more biological information for glioma differential diagnosis, grading and subtype differentiation, providing significant clinical support in prognosis assessment, therapeutic management and treatment response evaluation. This review summarizes recent advances in ITSS applications in glioma assessment, emphasizing both its potential and limitations while referencing key studies in the field.

Unlocking the code: The role of molecular and genetic profiling in revolutionizing glioblastoma treatment

Glioblastoma (GBM) is the most aggressive primary brain cancer, characterized by profound molecular and cellular heterogeneity, which contributes to its resistance to conventional therapies and poor prognosis. Despite multimodal treatments including surgical resection, radiation, and chemotherapy, median survival remains approximately 15 months. Recent advances in molecular and genetic profiling have elucidated key genetic alterations and molecular subtypes of GBM, such as EGFR amplification, PTEN and ATRX loss, and TP53 alterations, which have significant prognostic and therapeutic implications. These discoveries have spurred the development of targeted therapies aimed at disrupting aberrant signaling pathways like RTK/RAS/PI3K and TP53. However, treatment resistance remains a formidable challenge, driven by tumor heterogeneity, the complex tumor microenvironment (TME), and intrinsic adaptive mechanisms. Emerging therapeutic approaches aim to address these challenges, including the use of immunotherapies such as immune checkpoint inhibitors and CAR T-cell therapies, which target specific tumor antigens but face hurdles due to the immunosuppressive TME. Additionally, novel strategies like biopolymer-based interstitial therapies, focused ultrasound for blood-brain barrier disruption, and nanoparticle-based drug delivery systems show promise in enhancing the efficacy and precision of GBM treatments. This review explores the evolving landscape of GBM therapy, emphasizing the importance of personalized medicine through molecular profiling, the potential of combination therapies, and the need for innovative approaches to overcome therapeutic resistance. Continued research into GBM's biology and treatment modalities offers hope for improving patient outcomes.

Context aware machine learning techniques for brain tumor classification and detection - A review

Background

Machine learning has tremendous potential in acute medical care, particularly in the field of precise medical diagnosis, prediction, and classification of brain tumors. Malignant gliomas, due to their aggressive growth and dismal prognosis, stand out among various brain tumor types. Recent advancements in understanding the genetic abnormalities that underlie these tumors have shed light on their histo-pathological and biological characteristics, which support in better classification and prognosis.

Objectives

This review aims to predict gene alterations and establish structured correlations among various tumor types, extending the prediction of genetic mutations and structures using the latest machine learning techniques. Specifically, it focuses on multi-modalities of Magnetic Resonance Imaging (MRI) and histopathology, utilizing Convolutional Neural Networks (CNN) for image processing and analysis.

Methods

The review encompasses the most recent developments in MRI, and histology image processing methods across multiple tumor classes, including Glioma, Meningioma, Pituitary, Oligodendroglioma, and Astrocytoma. It identifies challenges in tumor classification, segmentation, datasets, and modalities, employing various neural network architectures. A competitive analysis assesses the performance of CNN. Furthermore it also implies K-MEANS clustering to predict Genetic structure, Genes Clusters prediction and Molecular Alteration of various types and grades of tumors e.g. Glioma, Meningioma, Pituitary, Oligodendroglioma, and Astrocytoma.

Results

CNN and KNN structures, with their ability to extract highlights in image-based information, prove effective in tumor classification and segmentation, surmounting challenges in image analysis. Competitive analysis reveals that CNN and outperform others algorithms on publicly available datasets, suggesting their potential for precise tumor diagnosis and treatment planning.

Conclusion

Machine learning, especially through CNN and SVM algorithms, demonstrates significant potential in the accurate diagnosis and classification of brain tumors based on imaging and histo-pathological data. Further advancements in this area hold promise for improving the accuracy and efficiency of intra-operative tumor diagnosis and treatment.

Prognostic Role of Pyruvate Kinase M2 in High-Grade Gliomas: A Quantitative Immunohistochemistry Study

BACKGROUND

Glioblastoma (GBM) and grade 4 astrocytoma (ASTROG4) are aggressive primary brain tumors characterized by rapid growth, invasiveness, and poor prognosis, differentiated by the presence or absence of isocitrate dehydrogenase (IDH) mutation according to the World Health Organization (WHO) 2021 classification. Essential molecular markers, in addition to IDH mutations, include alpha-thalassemia/mental retardation syndrome X-linked (ATRX) loss and p53 expression, which significantly influence their classification and prognosis. Pyruvate kinase M2 (PKM2), a critical enzyme in tumor metabolism, has been implicated in glioma progression, but its prognostic significance remains unclear.

METHODS

This prospective study aimed to quantitatively measure PKM2 immunohistochemistry (IHC) expression in GBM (IDH wildtype) versus ASTROG4 (IDH R132H mutant), to assess the correlation between PKM2 expression and prognosis in these two patient groups, and to investigate the prognostic significance of ATRX and p53 expression in relation to PKM2 levels. A total of 67 patients with high-grade gliomas (43 GBM, 24 ASTROG4) were analyzed using IHC for IDH1, ATRX, p53, and PKM2. PKM2 expression was quantified using 3DHISTECH (Budapest, Hungary) image analysis software, and correlations with clinical parameters, survival, and other molecular markers were evaluated. Kaplan-Meier survival analysis and Cox regression models assessed the impact of PKM2 expression and clinical factors on prognosis.

RESULTS

PKM2 expression was observed in both GBM and ASTROG4, with no significant differences in positivity rates. However, high PKM2 intensity scores significantly correlated with increased mortality risk (p=0.041). ATRX-negative tumors showed elevated PKM2 levels, suggesting compensatory metabolic adaptations. ASTROG4 cases had better survival outcomes than GBM. Severe preoperative motor deficits were associated with a threefold increase in mortality risk, highlighting the critical role of clinical factors in determining prognosis.

CONCLUSIONS

PKM2 plays an important role in glioma metabolism and can serve as a potential therapeutic target. Its association with ATRX highlights its involvement in tumor progression and genomic instability. Combining molecular markers with clinical parameters can improve prognostic accuracy and inform personalized treatment strategies for astrocytic tumors.

Oligoastrocytoma: The Vanishing Entity With True Dual Genotype, a Report, its Molecular Profiles and Review of Literature

Isocitrate dehydrogenase (IDH) mutant gliomas are classified as astrocytoma or oligodendroglioma based on the recent application of ATRX mutation, TP53 mutation, and 1p/19q co-deletion. Astrocytomas classically show ATRX and TP53 mutations, whereas oligodendrogliomas are defined by 1p/19q co-deletion. However, there are reports of gliomas that harbor both astrocytoma and oligodendroglioma morphologically and molecularly. Here we present a patient of a 29-year-old woman who presented with a headache and underwent gross total excision. Magnetic resonance imaging showed a right frontal space-occupying lesion with T2 fluid-attenuated inversion recovery mismatch. Histology showed 2 distinct areas of morphology compatible with oligodendroglioma and astrocytoma. Immunohistochemistry showed both components being positive for IDH R132H. Alpha thalassemia/mental retardation syndrome X-linked (ATRX) showed loss of nuclear expression and p53 was strongly positive in the morphologic astrocytoma component, whereas ATRX was retained and p53 was negative in the morphologic oligodendroglioma component. Fluorescence in situ hybridization showed 1p/19q co-deletion in the oligodendroglioma component while co-deletion was absent in the astrocytoma component. TERT mutation was present in the oligodendroglioma component, whereas it was absent in the astrocytoma component. Although rare, gliomas harboring both oligodendroglioma and astrocytoma components in a single tumor exist and show genetically distinct areas.

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

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

Mutant ATRX: pathogenesis of ATRX syndrome and cancer

The transcriptional regulator ATRX, a genetic factor, is associated with a range of disabilities, including intellectual, hematopoietic, skeletal, facial, and urogenital disabilities. ATRX mutations substantially contribute to the pathogenesis of ATRX syndrome and are frequently detected in gliomas and many other cancers. These mutations disrupt the organization, subcellular localization, and transcriptional activity of ATRX, leading to chromosomal instability and affecting interactions with key regulatory proteins such as DAXX, EZH2, and TERRA. ATRX also functions as a transcriptional regulator involved in the pathogenesis of neuronal disorders and various diseases. In conclusion, ATRX is a central protein whose abnormalities lead to multiple diseases.

Primary spinal cord glioblastoma multiforme: a single-center experience

INTRODUCTION

Primary spinal glioblastoma (GBM) are very rare tumors of the spinal cord, with dismal prognosis and their exact management is controversial. We attempt to formulate treatment guidelines for these extremely rare tumors based on our institutional experience and a comprehensive review of the literature.

MATERIALS AND METHODS

In this retrospective study from 2008 to 2020, all the patients diagnosed with primary spinal GBM who underwent surgery at our institution were included. Clinical data were retrieved from case files, outpatient records and telephonic follow-up. Data on postoperative chemoradiation was noted in all the patients. The final diagnosis of spinal GBM was confirmed as per the histopathology reports. Patients who could not be followed up and those with prior history of cranial GBM were excluded from the study.

RESULTS

Nine patients were followed up and a median survival of 11 months was noted. Chemotherapy with TMZ and radiotherapy to the whole craniospinal axis significantly improved survival in these patients. The extent of surgical resection was not shown to be significant. Intracranial metastasis was the leading cause of mortality in such patients. Three patients developed documented intracranial metastasis during the course of the disease.

CONCLUSIONS

Low threshold must be kept in mind in diagnosing patients with high-grade spinal cord intramedullary tumors in view of the rapidly progressing nature of the disease. In case of positive histopathological diagnosis of spinal GBM, the whole craniospinal axis should be imaged and any cranial metastasis which was originally missed during initial workup could be given appropriate radiotherapy.

Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients

This study aimed to construct an imaging genomics nomogram based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to predict the status of the alpha thalassemia/mental retardation syndrome X-linked (ATRX) gene in patients with brain gliomas. We retrospectively analyzed routine MR and IVIM-DWI data from 85 patients with pathologically confirmed brain gliomas from January 2017 to May 2023. The data were divided into a training set (N=61) and a test set (N=24) in a 7:3 ratio. Regions of interest (ROIs) of brain gliomas, including the solid tumor region (rCET), edema region (rE), and necrotic region (rNec), were delineated using 3D-Slicer software and projected onto the D, D*, and f sequences. A total of 1037 features were extracted from each ROI, resulting in 3111 features per patient. Age was incorporated in the calculation of the Radscore, and a clinical-imaging genomics combined model was constructed, from which a nomogram graph was generated. Separate models were built for the D, D*, and f parameters. The AUC value of the D parameter model was 0.97 (95% CI: 0.93-1.00) in the training set and 0.91 (95% CI: 0.79-1.00) in the validation set, which was significantly higher than that of the D* parameter model (0.90, 0.82) and the f parameter model (0.89, 0.91). The imaging genomics nomogram based on IVIM-DWI can effectively predict the ATRX gene status of patients with brain gliomas, with the D parameter showing the highest efficacy.

Circulating Liquid Biopsy Biomarkers in Glioblastoma: Advances and Challenges

Gliomas, particularly glioblastoma (GBM), represent the most prevalent and aggressive tumors of the central nervous system (CNS). Despite recent treatment advancements, patient survival rates remain low. The diagnosis of GBM traditionally relies on neuroimaging methods such as magnetic resonance imaging (MRI) or computed tomography (CT) scans and postoperative confirmation via histopathological and molecular analysis. Imaging techniques struggle to differentiate between tumor progression and treatment-related changes, leading to potential misinterpretation and treatment delays. Similarly, tissue biopsies, while informative, are invasive and not suitable for monitoring ongoing treatments. These challenges have led to the emergence of liquid biopsy, particularly through blood samples, as a promising alternative for GBM diagnosis and monitoring. Presently, blood and cerebrospinal fluid (CSF) sampling offers a minimally invasive means of obtaining tumor-related information to guide therapy. The idea that blood or any biofluid tests can be used to screen many cancer types has huge potential. Tumors release various components into the bloodstream or other biofluids, including cell-free nucleic acids such as microRNAs (miRNAs), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), proteins, extracellular vesicles (EVs) or exosomes, metabolites, and other factors. These factors have been shown to cross the blood-brain barrier (BBB), presenting an opportunity for the minimally invasive monitoring of GBM as well as for the real-time assessment of distinct genetic, epigenetic, transcriptomic, proteomic, and metabolomic changes associated with brain tumors. Despite their potential, the clinical utility of liquid biopsy-based circulating biomarkers is somewhat constrained by limitations such as the absence of standardized methodologies for blood or CSF collection, analyte extraction, analysis methods, and small cohort sizes. Additionally, tissue biopsies offer more precise insights into tumor morphology and the microenvironment. Therefore, the objective of a liquid biopsy should be to complement and enhance the diagnostic accuracy and monitoring of GBM patients by providing additional information alongside traditional tissue biopsies. Moreover, utilizing a combination of diverse biomarker types may enhance clinical effectiveness compared to solely relying on one biomarker category, potentially improving diagnostic sensitivity and specificity and addressing some of the existing limitations associated with liquid biomarkers for GBM. This review presents an overview of the latest research on circulating biomarkers found in GBM blood or CSF samples, discusses their potential as diagnostic, predictive, and prognostic indicators, and discusses associated challenges and future perspectives.

A Synopsis of Biomarkers in Glioblastoma: Past and Present

Accounting for 48% of malignant brain tumors in adults, glioblastoma has been of great interest in the last decades, especially in the biomolecular and neurosurgical fields, due to its incurable nature and notable neurological morbidity. The major advancements in neurosurgical technologies have positively influenced the extent of safe tumoral resection, while the latest progress in the biomolecular field of GBM has uncovered new potential therapeutical targets. Although GBM currently has no curative therapy, recent progress has been made in the management of this disease, both from surgical and molecular perspectives. The main current therapeutic approach is multimodal and consists of neurosurgical intervention, radiotherapy, and chemotherapy, mostly with temozolomide. Although most patients will develop treatment resistance and tumor recurrence after surgical removal, biomolecular advancements regarding GBM have contributed to a better understanding of this pathology and its therapeutic management. Over the past few decades, specific biomarkers have been discovered that have helped predict prognosis and treatment responses and contributed to improvements in survival rates.

Biomarker identification by interpretable maximum mean discrepancy

MOTIVATION

In many biomedical applications, we are confronted with paired groups of samples, such as treated versus control. The aim is to detect discriminating features, i.e. biomarkers, based on high-dimensional (omics-) data. This problem can be phrased more generally as a two-sample problem requiring statistical significance testing to establish differences, and interpretations to identify distinguishing features. The multivariate maximum mean discrepancy (MMD) test quantifies group-level differences, whereas statistically significantly associated features are usually found by univariate feature selection. Currently, few general-purpose methods simultaneously perform multivariate feature selection and two-sample testing.

RESULTS

We introduce a sparse, interpretable, and optimized MMD test (SpInOpt-MMD) that enables two-sample testing and feature selection in the same experiment. SpInOpt-MMD is a versatile method and we demonstrate its application to a variety of synthetic and real-world data types including images, gene expression measurements, and text data. SpInOpt-MMD is effective in identifying relevant features in small sample sizes and outperforms other feature selection methods such as SHapley Additive exPlanations and univariate association analysis in several experiments.

AVAILABILITY AND IMPLEMENTATION

The code and links to our public data are available at https://github.com/BorgwardtLab/spinoptmmd.

Knockdown of ATRX enhances radiosensitivity in glioblastoma

BACKGROUND

Glioblastoma are highly malignant type of primary brain tumors. Treatment for glioblastoma multiforme (GBM) generally involves surgery combined with chemotherapy and radiotherapy. However, the development of tumoral chemo- and radioresistance induces complexities in clinical practice. Multiple signaling pathways are known to be involved in radiation-induced cell survival. However, the role of alpha-thalassemia X-linked mutant retardation syndrome (ATRX), a chromatin remodeling protein, in GBM radioresistance remains unclear.

METHODS

In the present study, the ATRX mutation rate in patients with glioma was obtained from The Cancer Genome Atlas, while its expression analyzed using bioinformatics. Datasets were also obtained from the Gene Expression Omnibus, and ATRX expression levels following irradiation of GBM were determined. The effects of ATRX on radiosensitivity were investigated using a knockdown assays.

RESULTS

The present study demonstrated that the ATRX mutation rate in patients with GBM was significantly lower than that in patients with low-grade glioma, and that patients harboring an ATRX mutation exhibited a prolonged survival, compared with to those harboring the wild-type gene. Single-cell RNA sequencing demonstrated that ATRX counts increased 2 days after irradiation, with ATRX expression levels also increasing in U-251MG radioresistant cells. Moreover, the results of in vitro irradiation assays revealed that ATRX expression was increased in U-251MG cells, while ATRX knockdown was associated with increased levels of radiosensitivity.

CONCLUSIONS

High ATRX expression levels in primary GBM may contribute to high levels of radioresistance. Thus ATRX is a potential target for overcoming the radioresistance in GBM.

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

OBJECTIVES

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

MATERIAL AND METHODS

In our retrospective IRB-approved study, we used the MRI images of 106 patients with histologically confirmed gliomas. The MRI images were acquired using the T1 sequence with and without administration of a contrast agent, the T2 sequence, and the Fluid-Attenuated Inversion Recovery (FLAIR) sequence. To objectively compare performance in predicting the IDH mutation status as a function of the MRI sequence used, we included only patients in our study cohort for whom MRI images of all four sequences were available. Seventy-one of the patients had an IDH mutation, and the remaining 35 patients did not have an IDH mutation (IDH wild-type). For each of the four MRI sequences used, 107 radiomic features were extracted from the corresponding MRI images by hand-delineated regions of interest. Data partitioning into training data and independent test data was repeated 100 times to avoid random effects associated with the data partitioning. Feature preselection and subsequent model development were performed using Random Forest, Lasso regression, LDA, and Naïve Bayes. The performance of all models was determined with independent test data.

RESULTS

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

CONCLUSIONS

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

Liquid biopsy: creating opportunities in brain space

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

Amyloids and brain cancer: molecular linkages and crossovers

Amyloids are high-order proteinaceous formations deposited in both intra- and extracellular spaces. These aggregates have tendencies to deregulate cellular physiology in multiple ways; for example, altered metabolism, mitochondrial dysfunctions, immune modulation, etc. When amyloids are formed in brain tissues, the endpoint often is death of neurons. However, interesting but least understood is a close connection of amyloids with another set of conditions in which brain cells proliferate at an extraordinary rate and form tumor inside brain. Glioblastoma is one such condition. Increasing number of evidence indicate a possible link between amyloid formation and depositions in brain tumors. Several proteins associated with cell cycle regulation and apoptotic pathways themselves have shown to possess high tendencies to form amyloids. Tumor suppressor protein p53 is one prominent example that mutate, oligomerize and form amyloids leading to loss- or gain-of-functions and cause increased cell proliferation and malignancies. In this review article, we present available examples, genetic links and common pathways that indicate that possibly the two distantly placed pathways: amyloid formation and developing cancers in the brain have similarities and are mechanistically intertwined together.

General Clinico-Pathological Characteristics in Glioblastomas in Correlation with p53 and Ki67

Introduction: A glioblastoma is an intra-axial brain tumour of glial origin that belongs to the category of diffuse gliomas and is the most common malignant neoplasia of the central nervous system. The rate of survival at 5 years, from the moment of diagnosis, is not higher than 10%. Materials and methods: In this retrospective study, fifty-four patients diagnosed with glioblastoma, from the Pathology Department of the County Emergency Clinical Hospital of Târgu Mureș, between 2014 and 2017 were included. We studied the clinico-pathological data (age, gender, location, and laterality) and, respectively, the immunoexpression of p53, Ki67, ATRX, and IDH-1 proteins. Results: We observed a statistically significant association between the laterality of the tumour according to the age groups, with the localization on the right side being more frequent in the age group below 65 years of age, while the involvement of the left hemisphere was more prevalent in those over 65 years. Out of the total 54 cases, 87.04% were found to be primary glioblastomas; more than 70% of the cases were ATRX immunopositive; almost 80% of the glioblastomas studied had wild-type p53 profile; and 35% of the cases were found to have a Ki67 index greater than 20%. A statistically significant association between gender and ATRX mutation was found; female cases were ATRX immunopositive in 92% of the cases. Almost 70% of the cases were both IDH-1 and p53 wild-type, and we observed the presence of both mutations in only 3.7% of the cases. Approximately 83% of primary glioblastomas were ATRX positive, respectively, and all IDH-1 mutant cases were ATRX negative. Conclusions: Glioblastomas still represent a multidisciplinary challenge considering their reserved prognosis. In this study, we described the most common clinico-pathological characteristics and IHC marker expression profiles, highlighting a variety of percentage ranges in primary and secondary glioblastomas. Given the small number of studied cases, further prospective studies on larger cohorts are needed in the future to evaluate the role of these immunohistochemical markers as prognostic factors for survival or recurrence.

Glioma Single-Cell Biomechanical Analysis by Cyclic Conical Constricted Microfluidics

Determining the grade of glioma is a critical step in choosing patients' treatment plans in clinical practices. The pathological diagnosis of patient's glioma samples requires extensive staining and imaging procedures, which are expensive and time-consuming. Current advanced uniform-width-constriction-channel-based microfluidics have proven to be effective in distinguishing cancer cells from normal tissues, such as breast cancer, ovarian cancer, prostate cancer, etc. However, the uniform-width-constriction channels can result in low yields on glioma cells with irregular morphologies and high heterogeneity. In this research, we presented an innovative cyclic conical constricted (CCC) microfluidic device to better differentiate glioma cells from normal glial cells. Compared with the widely used uniform-width-constriction microchannels, the new CCC configuration forces single cells to deform gradually and obtains the biophysical attributes from each deformation. The human-derived glioma cell lines U-87 and U-251, as well as the human-derived normal glial astrocyte cell line HA-1800 were selected as the proof of concept. The results showed that CCC channels can effectively obtain the biomechanical characteristics of different 12-25 μm glial cell lines. The patient glioma samples with WHO grades II, III, and IV were tested by CCC channels and compared between Elastic Net (ENet) and Lasso analysis. The results demonstrated that CCC channels and the ENet can successfully select critical biomechanical parameters to differentiate the grades of single-glioma cells. This CCC device can be potentially further applied to the extensive family of brain tumors at the single-cell level.

GradWise: A Novel Application of a Rank-Based Weighted Hybrid Filter and Embedded Feature Selection Method for Glioma Grading with Clinical and Molecular Characteristics

Glioma grading plays a pivotal role in guiding treatment decisions, predicting patient outcomes, facilitating clinical trial participation and research, and tailoring treatment strategies. Current glioma grading in the clinic is based on tissue acquired at the time of resection, with tumor aggressiveness assessed from tumor morphology and molecular features. The increased emphasis on molecular characteristics as a guide for management and prognosis estimation underscores is driven by the need for accurate and standardized grading systems that integrate molecular and clinical information in the grading process and carry the expectation of the exposure of molecular markers that go beyond prognosis to increase understanding of tumor biology as a means of identifying druggable targets. In this study, we introduce a novel application (GradWise) that combines rank-based weighted hybrid filter (i.e., mRMR) and embedded (i.e., LASSO) feature selection methods to enhance the performance of feature selection and machine learning models for glioma grading using both clinical and molecular predictors. We utilized publicly available TCGA from the UCI ML Repository and CGGA datasets to identify the most effective scheme that allows for the selection of the minimum number of features with their names. Two popular feature selection methods with a rank-based weighting procedure were employed to conduct comprehensive experiments with the five supervised models. The computational results demonstrate that our proposed method achieves an accuracy rate of 87.007% with 13 features and an accuracy rate of 80.412% with five features on the TCGA and CGGA datasets, respectively. We also obtained four shared biomarkers for the glioma grading that emerged in both datasets and can be employed with transferable value to other datasets and data-based outcome analyses. These findings are a significant step toward highlighting the effectiveness of our approach by offering pioneering results with novel markers with prospects for understanding and targeting the biologic mechanisms of glioma progression to improve patient outcomes.

Somatic mutation effects diffused over microRNA dysregulation

MOTIVATION

As an important player in transcriptome regulation, microRNAs may effectively diffuse somatic mutation impacts to broad cellular processes and ultimately manifest disease and dictate prognosis. Previous studies that tried to correlate mutation with gene expression dysregulation neglected to adjust for the disparate multitudes of false positives associated with unequal sample sizes and uneven class balancing scenarios.

RESULTS

To properly address this issue, we developed a statistical framework to rigorously assess the extent of mutation impact on microRNAs in relation to a permutation-based null distribution of a matching sample structure. Carrying out the framework in a pan-cancer study, we ascertained 9008 protein-coding genes with statistically significant mutation impacts on miRNAs. Of these, the collective miRNA expression for 83 genes showed significant prognostic power in nine cancer types. For example, in lower-grade glioma, 10 genes' mutations broadly impacted miRNAs, all of which showed prognostic value with the corresponding miRNA expression. Our framework was further validated with functional analysis and augmented with rich features including the ability to analyze miRNA isoforms; aggregative prognostic analysis; advanced annotations such as mutation type, regulator alteration, somatic motif, and disease association; and instructive visualization such as mutation OncoPrint, Ideogram, and interactive mRNA-miRNA network.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available in MutMix, at http://innovebioinfo.com/Database/TmiEx/MutMix.php.

Understanding the molecular profiling of diffuse gliomas classification: A brief overview

Background

Gliomas represent almost 30% of all primary brain tumors and account for 80% of malignant primary ones. In the last two decades, significant progress has been made in understanding gliomas' molecular origin and development. These advancements have demonstrated a remarkable improvement in classification systems based on mutational markers, which contribute paramount information in addition to traditional histology-based classification.

Methods

We performed a narrative review of the literature including each molecular marker described for adult diffuse gliomas used in the World Health Organization (WHO) central nervous system 5.

Results

The 2021 WHO classification of diffuse gliomas encompasses many molecular aspects considered in the latest proposed hallmarks of cancer. The outcome of patients with diffuse gliomas relies on their molecular behavior and consequently, to determine clinical outcomes for these patients, molecular profiling should be mandatory. At least, the following molecular markers are necessary for the current most accurate classification of these tumors: (1) isocitrate dehydrogenase (IDH) IDH-1 mutation, (2) 1p/19q codeletion, (3) cyclin-dependent kinase inhibitor 2A/B deletion, (4) telomerase reverse transcriptase promoter mutation, (5) α-thalassemia/ mental retardation syndrome X-linked loss, (6) epidermal growth factor receptor amplification, and (7) tumor protein P53 mutation. These molecular markers have allowed the differentiation of multiple variations of the same disease, including the differentiation of distinct molecular Grade 4 gliomas. This could imply different clinical outcomes and possibly impact targeted therapies in the years to come.

Conclusion

Physicians face different challenging scenarios according to the clinical features of patients with gliomas. In addition to the current advances in clinical decision-making, including radiological and surgical techniques, understanding the disease's molecular pathogenesis is paramount to improving the benefits of its clinical treatments. This review aims to describe straightforwardly the most remarkable aspects of the molecular pathogenesis of diffuse gliomas.

Immunohistochemical Expression of IDH1, ATRX, Ki67, GFAP, and Prognosis in Indonesian Glioma Patients

Background

The current World Health Organization (WHO) 2021 classification of human glioma is based on key molecular biomarkers to define neoplastic entities. This review further delineates mutant IDH (isocitrate dehydrogenase) from wild-type IDH disease, a necessity given the large survival gap between mutant IDH and wild-type IDH tumors. In Indonesia, there are currently few reports on the distribution and significance of these mutations. Therefore, this research aims to determine the relationship between IDH mutations, as well as clinicopathological and prognostic factors in patients with gliomas. Other immunohistochemical markers including ATRX (alpha-thalassemia/mental retardation, X-linked), Ki67 and GFAP (glial fibrillary acidic protein) expression were also evaluated.

Methods

Forty-two glioma samples were collected from patients who underwent surgery at Dr. Kariadi General Hospital in Semarang, Central Java, Indonesia. Fresh and paraffin-embedded, formalin-fixed tissue samples were removed and sectioned for hematoxylin and eosin staining, immunohistochemistry, and IDH analysis of mutation. Medical records were used to collect clinicopathological and survival data.

Results

IDH1 mutations were discovered in 32 (76,1%) patients, and those with IDH1 mutation had longer overall survival when corresponded to patients with IDH1-wild-type. Lower expression of Ki67 was discovered to be very associated with a better prognosis.

Conclusion

IDH1 mutations status showed a significant relationship with prognosis in patients with glioma. Meanwhile, other markers (ATRX, Ki67, and GFAP) did not correlate with the prognosis.

Proteomics separates adult-type diffuse high-grade gliomas in metabolic subgroups independent of 1p/19q codeletion and across IDH mutational status

High-grade adult-type diffuse gliomas are malignant neuroepithelial tumors with poor survival rates in combined chemoradiotherapy. The current WHO classification is based on IDH1/2 mutational and 1p/19q codeletion status. Glioma proteome alterations remain undercharacterized despite their promise for a better molecular patient stratification and therapeutic target identification. Here, we use mass spectrometry to characterize 42 formalin-fixed, paraffin-embedded (FFPE) samples from IDH-wild-type (IDHwt) gliomas, IDH-mutant (IDHmut) gliomas with and without 1p/19q codeletion, and non-neoplastic controls. Based on more than 5,500 quantified proteins and 5,000 phosphosites, gliomas separate by IDH1/2 mutational status but not by 1p/19q status. Instead, IDHmut gliomas split into two proteomic subtypes with widespread perturbations, including aerobic/anaerobic energy metabolism. Validations with three independent glioma proteome datasets confirm these subgroups and link the IDHmut subtypes to the established proneural and classic/mesenchymal subtypes in IDHwt glioma. This demonstrates common phenotypic subtypes across the IDH status with potential therapeutic implications for patients with IDHmut gliomas.

Siglec10-An immunosuppressor and negative predictor of survival prognosis in gliomas

Glioma is a type of tumor occurring in the central nervous system. In recent decades, specific gene mutations and molecular aberrations have been used to conduct the glioma classification and clinical decisions. Siglec10 is a member of the sialic acid-binding immunoglobulin superfamily. In this study, we investigated the expression and functions of siglec10 in gliomas. We analyzed the siglec10 expression in glioma patients with immunohistochemical (IHC) staining and evaluated the survival prognosis. The high siglec10 expression had a shorter survival prognosis than the low siglec10 expression in patients, especially in malignant gliomas. Bioinformatic datasets, including TCGA and CGGA, validated the IHC results and discovered the expression of siglec10 was higher in the malignant subtype than a benign subtype of gliomas. So, siglec10 is associated with the poor prognosis of gliomas. Furthermore, the related mechanisms of siglec10 in gliomas were investigated by functional enrichment analysis, including GSEA, GO, and KEGG analysis. Siglec10 was correlated with inflammatory mediators, inflammatory cells, and inflammatory pathways in gliomas. Siglec10 might take part in the immune response in the tumor microenvironment to induce glioma's progression and metastasis. This study showed siglec10 was a biomarker in glioma, and it might be the potential target of glioma immunotherapy in the future.

Changing paradigms in oncology: Toward noncytotoxic treatments for advanced gliomas

Glial-lineage malignancies (gliomas) recurrently mutate and/or delete the master regulators of apoptosis p53 and/or p16/CDKN2A, undermining apoptosis-intending (cytotoxic) treatments. By contrast to disrupted p53/p16, glioma cells are live-wired with the master transcription factor circuits that specify and drive glial lineage fates: these transcription factors activate early-glial and replication programs as expected, but fail in their other usual function of forcing onward glial lineage-maturation-late-glial genes have constitutively "closed" chromatin requiring chromatin-remodeling for activation-glioma-genesis disrupts several epigenetic components needed to perform this work, and simultaneously amplifies repressing epigenetic machinery instead. Pharmacologic inhibition of repressing epigenetic enzymes thus allows activation of late-glial genes and terminates glioma self-replication (self-replication = replication without lineage-maturation), independent of p53/p16/apoptosis. Lineage-specifying master transcription factors therefore contrast with p53/p16 in being enriched in self-replicating glioma cells, reveal a cause-effect relationship between aberrant epigenetic repression of late-lineage programs and malignant self-replication, and point to specific epigenetic targets for noncytotoxic glioma-therapy.

U-Net Based Segmentation and Characterization of Gliomas

(1) Background: Gliomas are the most common primary brain neoplasms accounting for roughly 40−50% of all malignant primary central nervous system tumors. We aim to develop a deep learning-based framework for automated segmentation and prediction of biomarkers and prognosis in patients with gliomas. (2) Methods: In this retrospective two center study, patients were included if they (1) had a diagnosis of glioma with known surgical histopathology and (2) had preoperative MRI with FLAIR sequence. The entire tumor volume including FLAIR hyperintense infiltrative component and necrotic and cystic components was segmented. Deep learning-based U-Net framework was developed based on symmetric architecture from the 512 × 512 segmented maps from FLAIR as the ground truth mask. (3) Results: The final cohort consisted of 208 patients with mean ± standard deviation of age (years) of 56 ± 15 with M/F of 130/78. DSC of the generated mask was 0.93. Prediction for IDH-1 and MGMT status had a performance of AUC 0.88 and 0.62, respectively. Survival prediction of <18 months demonstrated AUC of 0.75. (4) Conclusions: Our deep learning-based framework can detect and segment gliomas with excellent performance for the prediction of IDH-1 biomarker status and survival.

Surgical approaches to intramedullary spinal cord astrocytomas in the age of genomics

Intramedullary astrocytomas represent approximately 30%–40% of all intramedullary tumors and are the most common intramedullary tumor in children. Surgical resection is considered the mainstay of treatment in symptomatic patients with neurological deficits. Gross total resection (GTR) can be difficult to achieve as astrocytomas frequently present as diffuse lesions that infiltrate the cord. Therefore, GTR carries a substantial risk of new post-operative deficits. Consequently, subtotal resection and biopsy are often the only surgical options attempted. A midline or paramedian sulcal myelotomy is frequently used for surgical resection, although a dorsal root entry zone myelotomy can be used for lateral tumors. Intra-operative neuromonitoring using D-wave integrity, somatosensory, and motor evoked potentials is critical to facilitating a safe resection. Adjuvant radiation and chemotherapy, such as temozolomide, are often administered for high-grade recurrent or progressive lesions; however, consensus is lacking on their efficacy. Biopsied tumors can be analyzed for molecular markers that inform clinicians about the tumor’s prognosis and response to conventional as well as targeted therapeutic treatments. Stratification of intramedullary tumors is increasingly based on molecular features and mutational status. The landscape of genetic and epigenetic mutations in intramedullary astrocytomas is not equivalent to their intracranial counterparts, with important difference in frequency and type of mutations. Therefore, dedicated attention is needed to cohorts of patients with intramedullary tumors. Targeted therapeutic agents can be designed and administered to patients based on their mutational status, which may be used in coordination with traditional surgical resection to improve overall survival and functional status.

TElomeric repeat-containing RNA (TERRA): Physiological functions and relevance in cancer

Telomeres are complex protective structures located at the ends of linear eukaryotic chromosomes. Their purpose is to prevent genomic instability. Research progress in telomere biology during the past decades has identified a network of telomeric transcripts of which the best-studied is TElomeric Repeat-containing RNA (TERRA). TERRA was shown to be important not only for the preservation of telomere homeostasis and genomic stability but also for the expression of hundreds of genes across the human genome. These findings added a new level of complexity to telomere biology. Herein we provide insights on the telomere transcriptome, its relevance for proper telomere function, and its implications in human pathology. We also discuss possible clinical opportunities of exosomal telomere transcripts detection as a biomarker in cancer precision medicine.

Molecular and Circulating Biomarkers in Patients with Glioblastoma

Glioblastoma is the most aggressive malignant tumor of the central nervous system with a low survival rate. The difficulty of obtaining this tumor material represents a major limitation, making the real-time monitoring of tumor progression difficult, especially in the events of recurrence or resistance to treatment. The identification of characteristic biomarkers is indispensable for an accurate diagnosis, the rigorous follow-up of patients, and the development of new personalized treatments. Liquid biopsy, as a minimally invasive procedure, holds promise in this regard. The purpose of this paper is to summarize the current literature regarding the identification of molecular and circulating glioblastoma biomarkers and the importance of their integration as a valuable tool to improve patient care.

Magnetic Resonance Spectroscopy in Diagnosis and Follow-Up of Gliomas: State-of-the-Art

Preoperative grade prediction is important in diagnostics of glioma. Even more important can be follow-up after chemotherapy and radiotherapy of high grade gliomas. In this review we provide an overview of MR-spectroscopy (MRS), technical aspects, and different clinical scenarios in the diagnostics and follow-up of gliomas in pediatric and adult populations. Furthermore, we provide a recap of the current research utility and possible future strategies regarding proton- and phosphorous-MRS in glioma research.

A Targeted Next-Generation Sequencing Panel to Genotype Gliomas

Gliomas account for the majority of primary brain tumors. Glioblastoma is the most common and malignant type. Based on their extreme molecular heterogeneity, molecular markers can be used to classify gliomas and stratify patients into diagnostic, prognostic, and therapeutic clusters. In this work, we developed and validated a targeted next-generation sequencing (NGS) approach to analyze variants or chromosomal aberrations correlated with tumorigenesis and response to treatment in gliomas. Our targeted NGS analysis covered 13 glioma-related genes (ACVR1, ATRX, BRAF, CDKN2A, EGFR, H3F3A, HIST1H3B, HIST1H3C, IDH1, IDH2, P53, PDGFRA, PTEN), a 125 bp region of the TERT promoter, and 54 single nucleotide polymorphisms (SNPs) along chromosomes 1 and 19 for reliable assessment of their copy number alterations (CNAs). Our targeted NGS approach provided a portrait of gliomas’ molecular heterogeneity with high accuracy, specificity, and sensitivity in a single workflow, enabling the detection of variants associated with unfavorable outcomes, disease progression, and drug resistance. These preliminary results support its use in routine diagnostic neuropathology.

Advances in nanoparticle mediated targeting of RNA binding protein for cancer

RNA binding proteins (RBPs) enact a very crucial part in the RNA directive processes. Atypical expression of these RBPs affects many steps of RNA metabolism, majorly altering its expression. Altered expression and dysfunction of RNA binding proteins lead to cancer progression and other diseases. We enumerate various available interventions, and recent findings focused on targeting RBPs for cancer therapy and diagnosis. The treatment, sensitization, chemoprevention, gene-mediated, and virus mediated interventions were studied to treat and diagnose cancer. The application of passively and actively targeted lipidic nanoparticles, polymeric nanoparticles, virus-based particles, and vaccine-based immunotherapy for the delivery of therapeutic agent/s against cancer are discussed. We also discuss the formulation aspect of nanoparticles for achieving delivery at the site of action and ongoing clinical trials targeting RBPs.

Systematic review and meta-analysis of genomic alterations in acral melanoma

Acral melanoma (AM) tumors arise on the palms, soles, fingers, toes, and nailbeds. A comprehensive systematic meta-analysis of AM genomic aberrations has not been conducted to date. A literature review was carried out to identify studies sequencing AM. Whole-genome/exome data from 181 samples were identified. Targeted panel sequencing data from MSK-IMPACT were included as a validation cohort (n = 92), and studies using targeted hot spot sequencing were also collated for BRAF (n = 26 studies), NRAS (n = 21), and KIT (n = 32). Statistical analysis indicated BRAF, NRAS, PTEN, TYRP1, and KIT as significantly mutated genes. Frequent copy-number aberrations were also found for important cancer genes, such as CDKN2A, KIT, MDM2, CCND1, CDK4, and PAK1, among others. Mapping genomic alterations within the context of the hallmarks of cancer identified four components frequently altered, including (i) sustained proliferative signaling and (ii) evading growth suppression, (iii) genome instability and mutation, and (iv) enabling replicative immortality. This analysis provides the largest analysis of genomic aberrations in AM in the literature to date and highlights pathways that may be therapeutically targetable.

ATRX-dependent SVCT2 mediates macrophage infiltration in the glioblastoma xenograft model

Alpha thalassemia/mental retardation syndrome X-linked (ATRX) mutation impairs DNA damage repair in glioblastoma (GBM), making these cells more susceptible to treatment, which may contribute to the survival advantage in GBM patients containing ATRX mutations. To better understand the role of ATRX in GBM, genes correlated with ATRX expression were screened in the Cancer Genome Atlas (702 cases) and Chinese Glioma Genome Atlas (325 cases) databases. Sodium-vitamin C cotransporter 2 (SVCT2) was the most positively correlated gene with ATRX expression. ATRX (about 1.99-fold) and SVCT2 (about 2.25-fold) were upregulated in GBM tissues from 40 patients compared to normal brain tissues from 23 subjects. ShSVCT2 transfection did not alter the in vitro viability of GL261 cells. At the same time, it could inhibit the proliferation of GL261 cells in the orthotopic transplantation model with diminished infiltrating macrophages (CD45^highCD11b⁺), down-regulated chemokine (C-C motif) ligand 2 (Ccl2), Ccl4, C-X-C motif chemokine ligand 1 (Cxcl1), and Cxcl15 expression, and decreased p-IκBα and p-c-Jun expression. Effect of ShSVCT2 transfection could be reversed by overexpression of SVCT2. siRNA interference of ATRX-dependent SVCT2 signal with shSVCT2 could inhibit tumor cell proliferation in Glu261-LuNeo xenograft tumor model with more survival advantage, probably by the inhibited macrophage chemotaxis. These results indicate that ATRX-dependent SVCT2-mediated chemokine-induced macrophage infiltration is regulated by the NF-κB pathway, which could be considered as treatment targets.

Perspective on the Use of DNA Repair Inhibitors as a Tool for Imaging and Radionuclide Therapy of Glioblastoma

Simple Summary

The current routine treatment for glioblastoma (GB), the most lethal high-grade brain tumor in adults, aims to induce DNA damage in the tumor. However, the tumor cells might be able to repair that damage, which leads to therapy resistance. Fortunately, DNA repair defects are common in GB cells, and their survival is often based on a sole backup repair pathway. Hence, targeted drugs inhibiting essential proteins of the DNA damage response have gained momentum and are being introduced in the clinic. This review gives a perspective on the use of radiopharmaceuticals targeting DDR kinases for imaging in order to determine the DNA repair phenotype of GB, as well as for effective radionuclide therapy. Finally, four new promising radiopharmaceuticals are suggested with the potential to lead to a more personalized GB therapy.

Abstract

Despite numerous innovative treatment strategies, the treatment of glioblastoma (GB) remains challenging. With the current state-of-the-art therapy, most GB patients succumb after about a year. In the evolution of personalized medicine, targeted radionuclide therapy (TRT) is gaining momentum, for example, to stratify patients based on specific biomarkers. One of these biomarkers is deficiencies in DNA damage repair (DDR), which give rise to genomic instability and cancer initiation. However, these deficiencies also provide targets to specifically kill cancer cells following the synthetic lethality principle. This led to the increased interest in targeted drugs that inhibit essential DDR kinases (DDRi), of which multiple are undergoing clinical validation. In this review, the current status of DDRi for the treatment of GB is given for selected targets: ATM/ATR, CHK1/2, DNA-PK, and PARP. Furthermore, this review provides a perspective on the use of radiopharmaceuticals targeting these DDR kinases to (1) evaluate the DNA repair phenotype of GB before treatment decisions are made and (2) induce DNA damage via TRT. Finally, by applying in-house selection criteria and analyzing the structural characteristics of the DDRi, four drugs with the potential to become new therapeutic GB radiopharmaceuticals are suggested.

The epigenetic-metabolic interplay in gliomagenesis

Although tumourigenesis occurs due to genetic mutations, the role of epigenetic dysregulations in cancer is also well established. Epigenetic dysregulations in cancer may occur as a result of mutations in genes encoding histone/DNA-modifying enzymes and chromatin remodellers or mutations in histone protein itself. It is also true that misregulated gene expression without genetic mutations in these factors could also support tumour initiation and progression. Interestingly, metabolic rewiring has emerged as a hallmark of cancer due to gene mutations in specific metabolic enzymes or dietary/environmental factors. Recent studies report an intricate cross-talk between epigenetic and metabolic reprogramming in cancer. This review discusses the role of epigenetic and metabolic dysregulations and their cross-talk in tumourigenesis with a special focus on gliomagenesis. We also discuss the role of recently developed human embryonic stem cells/induced pluripotent stem cells-derived organoid models of gliomas and how these models are proving instrumental in uncovering human-specific cellular and molecular complexities of gliomagenesis.

Glioma: molecular signature and crossroads with tumor microenvironment

In patients with glioblastoma, the average survival time with current treatments is short, mainly due to recurrences and resistance to therapy. This insufficient treatment success is, in large parts, due to the tremendous molecular heterogeneity of gliomas, which affects the overall prognosis and response to therapies and plays a vital role in gliomas’ grading. In addition, the tumor microenvironment is a major player for glioma development and resistance to therapy. Active communication between glioma cells and local or neighboring healthy cells and the immune environment promotes the cancerogenic processes and contributes to establishing glioma stem cells, which drives therapy resistance. Besides genetic alterations in the primary tumor, tumor-released factors, cytokines, proteins, extracellular vesicles, and environmental influences like hypoxia provide tumor cells the ability to evade host tumor surveillance machinery and promote disease progression. Moreover, there is increasing evidence that these players affect the molecular biological properties of gliomas and enable inter-cell communication that supports pro-cancerogenic cell properties. Identifying and characterizing these complex mechanisms are inevitably necessary to adapt therapeutic strategies and to develop novel measures. Here we provide an update about these junctions where constant traffic of biomolecules adds complexity in the management of glioblastoma.

Potential Diagnostic Value of the Differential Expression of Histone H3 Variants between Low- and High-Grade Gliomas

Glioblastoma (GB) is the most aggressive form of glioma and is characterized by poor prognosis and high recurrence despite intensive clinical interventions. To retrieve the key factors underlying the high malignancy of GB with potential diagnosis utility, we combined the analysis of The Cancer Gene Atlas and the REMBRANDT datasets plus a molecular examination of our own collection of surgical tumor resections. We determined a net reduction in the levels of the non-canonical histone H3 variant H3.3 in GB compared to lower-grade astrocytomas and oligodendrogliomas with a concomitant increase in the levels of the canonical histone H3 variants H3.1/H3.2. This increase can be potentially useful in the clinical diagnosis of high-grade gliomas, as evidenced by an immunohistochemistry screening of our cohort and can be at least partially explained by the induction of multiple histone genes encoding these canonical forms. Moreover, GBs showing low bulk levels of the H3.1/H3.2 proteins were more transcriptionally similar to low-grade gliomas than GBs showing high levels of H3.1/H3.2. In conclusion, this study identifies an imbalanced ratio between the H3 variants associated with glioma malignancy and molecular patterns relevant to the biology of gliomas, and proposes the examination of the H3.3 and H3.1/H3.2 levels to further refine diagnosis of low- and high-grade gliomas in future studies.

A systematic analysis of genetic interactions and their underlying biology in childhood cancer

Childhood cancer is a major cause of child death in developed countries. Genetic interactions between mutated genes play an important role in cancer development. They can be detected by searching for pairs of mutated genes that co-occur more (or less) often than expected. Co-occurrence suggests a cooperative role in cancer development, while mutual exclusivity points to synthetic lethality, a phenomenon of interest in cancer treatment research. Little is known about genetic interactions in childhood cancer. We apply a statistical pipeline to detect genetic interactions in a combined dataset comprising over 2,500 tumors from 23 cancer types. The resulting genetic interaction map of childhood cancers comprises 15 co-occurring and 27 mutually exclusive candidates. The biological explanation of most candidates points to either tumor subtype, pathway epistasis or cooperation while synthetic lethality plays a much smaller role. Thus, other explanations beyond synthetic lethality should be considered when interpreting genetic interaction test results.

Glioma-Targeted Therapeutics: Computer-Aided Drug Design Prospective

Amongst the several types of brain cancers known to humankind, glioma is one of the most severe and life-threatening types of cancer, comprising 40% of all primary brain tumors. Recent reports have shown the incident rate of gliomas to be 6 per 100,000 individuals per year globally. Despite the various therapeutics used in the treatment of glioma, patient survival rate remains at a median of 15 months after undergoing first-line treatment including surgery, radiation, and chemotherapy with Temozolomide. As such, the discovery of newer and more effective therapeutic agents is imperative for patient survival rate. The advent of computer-aided drug design in the development of drug discovery has emerged as a powerful means to ascertain potential hit compounds with distinctively high therapeutic effectiveness against glioma. This review encompasses the recent advances of bio-computational in-silico modeling that have elicited the discovery of small molecule inhibitors and/or drugs against various therapeutic targets in glioma. The relevant information provided in this report will assist researchers, especially in the drug design domains, to develop more effective therapeutics against this global disease.

Age is associated with unfavorable neuropathological and radiological features and poor outcome in patients with WHO grade 2 and 3 gliomas

With the rising life expectancy and availability of neuroimaging, increased number of older patients will present with diffuse and anaplastic gliomas. The aim of our study was therefore to investigate age-related prognostic clinical, neuropathological and radiological features of lower-grade gliomas. All consecutive patients with diffuse or anaplastic glioma WHO grade 2 and 3 who underwent first tumor resection between 2010 and 2018, were selected from the institutional neuro-oncological database and evaluated. The mean age of 55 males and 44 females was 46 years (SD ± 16). Wild-type IDH1 (p = 0.012), persistent nuclear ATRX expression (p = 0.012) and anaplasia (p < 0.001) were significantly associated with higher age. The CE volume before resection was found to be increased in older patients (r = 0.42, p < 0.0001), and CE rate was higher in the IDH wild-type population only (p = 0.02). The extent of resection did not differ with age. Overall, one year of life resulted in a PFS reduction of 9 days (p = 0.047); in IDH sub-group analysis, this dependency was confirmed only in wild-type tumors (p = 0.05). OS was significantly reduced in older patients (p = 0.033). In conclusion, behavior and prognosis of WHO grade 2 and 3 glioma were unfavorable in correlation to patient’s age, even if the extent of resection was comparable. Older age imparted a poorer PFS and higher CE rate only in the IDH wild-type population.

Coexistence of TERT C228T mutation and MALAT1 dysregulation in primary glioblastoma: new prognostic and therapeutic targets

Objective: This study was designed to conduct molecular classification based on IDH1/2, TERT, ATRX, and DAXX changes in pediatric and adult primary glioblastoma (GB) and to analyze the potential interaction of LncRNA MALAT1 in the determined homogeneous subgroups.Methods: We analyzed the expression profiles of ATRX/DAXX and MALAT1 using the qRT-PCR method and IDH and TERT mutation status using DNA sequencing analysis in 85 primary pediatric and adult GB patients.Results: IDH1 mutation was observed in 5 (5.88%) and TERT mutation in 65 (76.47%) primary pediatric and adult GB patients. ATRX and DAXX were detected in 18 (21.18%) and 7 (8.24%) patients. TERT mutation and loss of ATRX/DAXX were associated with short overall survival (p < 0.001, p < 0.001, respectively). Patients carrying especially TERT C228T mutation had worse prognosis (p < 0.001). Six subgroups were obtained from the genetic analysis. Among the subgroups, MALAT1 was highly expressed in group A that had a single TERT mutation as compared to that in groups D and E (p = 0.001 and p < 0.001, respectively); further, high MALAT1 expression was associated with worse prognosis in patients with C228T mutation (p < 0.001).Conclusions: Our findings highlight that the presence of TERT C228T mutation and expression of MALAT1 can be used as primary targets during the follow-up of primary GB patients and in the development of new treatment strategies.

Deep Neural Network Analysis of Pathology Images With Integrated Molecular Data for Enhanced Glioma Classification and Grading

Gliomas are primary brain tumors that originate from glial cells. Classification and grading of these tumors is critical to prognosis and treatment planning. The current criteria for glioma classification in central nervous system (CNS) was introduced by World Health Organization (WHO) in 2016. This criteria for glioma classification requires the integration of histology with genomics. In 2017, the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) was established to provide up-to-date recommendations for CNS tumor classification, which in turn the WHO is expected to adopt in its upcoming edition. In this work, we propose a novel glioma analytical method that, for the first time in the literature, integrates a cellularity feature derived from the digital analysis of brain histopathology images integrated with molecular features following the latest WHO criteria. We first propose a novel over-segmentation strategy for region-of-interest (ROI) selection in large histopathology whole slide images (WSIs). A Deep Neural Network (DNN)-based classification method then fuses molecular features with cellularity features to improve tumor classification performance. We evaluate the proposed method with 549 patient cases from The Cancer Genome Atlas (TCGA) dataset for evaluation. The cross validated classification accuracies are 93.81% for lower-grade glioma (LGG) and high-grade glioma (HGG) using a regular DNN, and 73.95% for LGG II and LGG III using a residual neural network (ResNet) DNN, respectively. Our experiments suggest that the type of deep learning has a significant impact on tumor subtype discrimination between LGG II vs. LGG III. These results outperform state-of-the-art methods in classifying LGG II vs. LGG III and offer competitive performance in distinguishing LGG vs. HGG in the literature. In addition, we also investigate molecular subtype classification using pathology images and cellularity information. Finally, for the first time in literature this work shows promise for cellularity quantification to predict brain tumor grading for LGGs with IDH mutations.

The advanced development of Cx43 and GAP-43 mediated intercellular networking in IDH1 wildtype diffuse and anaplastic gliomas with lower mitotic rate

PURPOSE

The biologic behavior and the therapeutic resistance of diffuse and anaplastic gliomas varies greatly. This may be explained by differences in cell-to-cell communication, determined by the Cx43-associated junctional activity and the microtubules-defined network, in which GAP-43 is the dominant structural component. We assessed the expression of these crucial communication proteins in samples of patients harboring WHO°II and III gliomas, graded according to the current 4th revised WHO classification.

METHODS

Tissue of adult patients with WHO°II and III gliomas, who underwent surgery between 2014 and 2018, were selected from our institutional biobank. GAP-43 and Cx43 expression was analyzed using IHC. Routine clinical and neuropathological findings were additionally retrieved from our institutional prospective database.

RESULTS

43 (57%) males and 33 (43%) females with a median age of 47 (IqR: 35-61) years were selected. IDH1 wildtype tumors showed a significantly higher expression of Cx43 (p = 0.014) and a tendency for increased GAP-43 production. Advanced Cx43 expression significantly correlated with lower mitosis rate (p = 0.014): more in IDH1 wildtype (r = - 0.57, p = 0.003) than in mutated gliomas (r = - 0.37, p = 0.019). There was no difference in Cx43 or GAP-43 expression in relation to anaplastic phenotype, Gadolinum-contrasted enhancement (CE) on MRI and advanced EGFR or p53 expression.

CONCLUSIONS

Intercellular communication tends to be more relevant in slower proliferating, e.g. lower malignant tumors. They could have more time to establish this network, providing longitudinally acquired resistance against specific oncological therapy. This feature matches the unfavorable IDH1 wildtype status of glioma and supports the noted malignant behavior of these tumors in the upcoming 5th WHO classification of gliomas.

Mutation-based clustering and classification analysis reveals distinctive age groups and age-related biomarkers for glioma

BACKGROUND

Malignant brain tumor diseases exhibit differences within molecular features depending on the patient's age.

METHODS

In this work, we use gene mutation data from public resources to explore age specifics about glioma. We use both an explainable clustering as well as classification approach to find and interpret age-based differences in brain tumor diseases. We estimate age clusters and correlate age specific biomarkers.

RESULTS

Age group classification shows known age specifics but also points out several genes which, so far, have not been associated with glioma classification.

CONCLUSIONS

We highlight mutated genes to be characteristic for certain age groups and suggest novel age-based biomarkers and targets.

Advances in Research of Adult Gliomas

Diffuse gliomas are the most frequent brain tumours, representing 75% of all primary malignant brain tumours in adults. Because of their locally aggressive behaviour and the fact that they cannot be cured by current therapies, they represent one of the most devastating cancers. The present review summarises recent advances in our understanding of glioma development and progression by use of various in vitro and in vivo models, as well as more complex techniques including cultures of 3D organoids and organotypic slices. We discuss the progress that has been made in understanding glioma heterogeneity, alteration in gene expression and DNA methylation, as well as advances in various in silico models. Lastly current treatment options and future clinical trials, which aim to improve early diagnosis and disease monitoring, are also discussed.

MN1 overexpression with varying tumor grade is a promising predictor of survival of glioma patients

Gliomas have substantial mortality to incidence rate ratio and a dismal clinical course. Newer molecular insights, therefore, are imperative to refine glioma diagnosis, prognosis and therapy. Meningioma 1 (MN1) gene is a transcriptional co-regulator implicated in other malignancies, albeit its significance in glioma pathology remains to be explored. IGFBP5 is regulated transcriptionally by MN1 and IGF1 and is associated with higher glioma grade and shorter survival time, prompting us to ascertain their correlation in these tumors. We quantified the expression of MN1, IGFBP5 and IGF1 in 40 glioma samples and examined their interrelatedness. MN1 mRNA-protein inter-correlation and the gene's copy number were evaluated in these tumors. Publicly available TCGA datasets were used to examine the association of MN1 expression levels with patient survival and for validating our findings. We observed MN1 overexpression correlated with low-grade (LGGs) and not high-grade gliomas and is not determined by the copy number alteration of the gene. Notably, gliomas with upregulated MN1 have better overall survival (OS) and progression-free survival (PFS). IGFBP5 expression associated inversely with MN1 expression levels in gliomas but correlated positively with IGF1 expression in only LGGs. This suggests a potential grade-specific interplay between repressive and activating roles of MN1 and IGF1, respectively, in the regulation of IGFBP5. Thus, MN1 overexpression, a promising predictor of OS and PFS in gliomas, may serve as a prognostic biomarker in clinical practice to categorize patients with survival advantage.