Molecular Pathology of Gliomas

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

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

WZ-3146 acts as a novel small molecule inhibitor of KIF4A to inhibit glioma progression by inducing apoptosis

BACKGROUND

Glioma is considered the most common primary malignant tumor of the central nervous system. Although traditional treatments have not achieved satisfactory outcomes, recently, targeted therapies for glioma have shown promising efficacy. However, due to the single-target nature of targeted therapy, traditional targeted therapies are ineffective; thus, novel therapeutic targets are urgently needed.

METHODS

The gene expression data for glioma patients were derived from the GEO (GSE4290, GSE50161), TCGA and CGGA databases. Next, the upregulated genes obtained from the above databases were cross-analyzed, finally, 10 overlapping genes (BIRC5, FOXM1, EZH2, CDK1, KIF11, KIF4A, NDC80, PBK, RRM2, and TOP2A) were ultimately screened and only KIF4A expression has the strongest correlation with clinical characteristics in glioma patients. Futher, the TCGA and CGGA database were utilized to explore the correlation of KIF4A expression with glioma prognosis. Then, qRT-PCR and Western blot was used to detect the KIF4A mRNA and protein expression level in glioma cells, respectively. And WZ-3146, the small molecule inhibitor targeting KIF4A, were screened by Cmap analysis. Subsequently, the effect of KIF4A knockdown or WZ-3146 treatment on glioma was measured by the MTT, EdU, Colony formation assay and Transwell assay. Ultimately, GSEA enrichment analysis was performed to find that the apoptotic pathway could be regulated by KIF4A in glioma, in addition, the effect of WZ-3146 on glioma apoptosis was detected by flow cytometry and Western blot.

RESULTS

In the present study, we confirmed that KIF4A is abnormally overexpressed in glioma. In addition, KIF4A overexpression is a key indicator of glioma prognosis; moreover, suppressing KIF4A expression can inhibit glioma progression. We also discovered that WZ-3146, a small molecule inhibitor of KIF4A, can induce apoptosis in glioma cells and exhibit antiglioma effects.

CONCLUSION

In conclusion, these observations demonstrated that targeting KIF4A can inhibit glioma progression. With further research, WZ-3146, a small molecule inhibitor of KIF4A, could be combined with other molecular targeted drugs to cooperatively inhibit glioma progression.

A cuproptosis-related gene DLAT as a novel prognostic marker and its relevance to immune infiltration in low-grade gliomas

DLAT has been recognized as a cuproptosis-related gene that is crucial for cuproptosis in earlier research. The study is to look at how DLAT affects individuals with low-grade glioma's prognosis and immune infiltration. The Genotype-Tissue Expression (GTEx) database and the TCGA database were used in this work to download RNAseq data in TPM format. DLAT was found to be overexpressed in LGG by comparing DLAT expression levels between LGG and normal brain tissue, and the expression of DLAT was verified by immunohistochemistry and semi-quantitative analysis. Then, the functional enrichment analysis revealed that the biological functional pathways and possible signal transduction pathways involved were primarily focused on extracellular matrix organization, transmembrane transporter complex, ion channel complex, channel activity, neuroactive ligand-receptor interaction, complement and coagulation cascades, and channel activity. The level of immune cell infiltration by plasmacytoid dendritic cells and CD8 T cells was subsequently evaluated using single-sample gene set enrichment analysis, which showed that high DLAT expression was inversely connected with that level of infiltration. The link between the methylation and mRNA transcription of DLAT was then further investigated via the MethSurv database, and the results showed that DLAT's hypomethylation status was linked to a poor outcome. Finally, by evaluating the prognostic value of DLAT using the Cox regression analysis and Kaplan-Meier technique, a column line graph was created to forecast the overall survival (OS) rate at 1, 3, and 5 years after LGG identification. The aforementioned results demonstrated that high DLAT expression significantly decreased OS and DSS, and that overexpression of DLAT in LGG was significantly linked with WHO grade, IDH status, primary therapy outcome, overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) events. DLAT was discovered as a separate predictive sign of OS in the end. DLAT might thus represent a brand-new predictive biomarker.

Overcoming Resistance to Temozolomide in Glioblastoma: A Scoping Review of Preclinical and Clinical Data

Glioblastoma (GB) is the most common and most aggressive primary brain tumor in adults, with an overall survival almost 14.6 months. Optimal resection followed by combined temozolomide chemotherapy and radiotherapy, also known as Stupp protocol, remains the standard of treatment; nevertheless, resistance to temozolomide, which can be obtained throughout many molecular pathways, is still an unsurpassed obstacle. Several factors influence the efficacy of temozolomide, including the involvement of other DNA repair systems, aberrant signaling pathways, autophagy, epigenetic modifications, microRNAs, and extracellular vesicle production. The blood-brain barrier, which serves as both a physical and biochemical obstacle, the tumor microenvironment's pro-cancerogenic and immunosuppressive nature, and tumor-specific characteristics such as volume and antigen expression, are the subject of ongoing investigation. In this review, preclinical and clinical data about temozolomide resistance acquisition and possible ways to overcome chemoresistance, or to treat gliomas without restoration of chemosensitinity, are evaluated and presented. The objective is to offer a thorough examination of the clinically significant molecular mechanisms and their intricate interrelationships, with the aim of enhancing understanding to combat resistance to TMZ more effectively.

Survival Analysis of Patients Undergoing Intraoperative Contrast-enhanced Ultrasound in the Surgical Treatment of Malignant Glioma

OBJECTIVE

Complete resection of malignant gliomas is often challenging. Our previous study indicated that intraoperative contrast-enhanced ultrasound (ICEUS) could aid in the detection of residual tumor remnants and the total removal of brain lesions. This study aimed to investigate the survival rates of patients undergoing resection with or without the use of ICEUS and to assess the impact of ICEUS on the prognosis of patients with malignant glioma.

METHODS

A total of 64 patients diagnosed with malignant glioma (WHO grade HI and IV) who underwent surgery between 2012 and 2018 were included. Among them, 29 patients received ICEUS. The effects of ICEUS on overall survival (OS) and progression-free survival (PFS) of patients were evaluated. A quantitative analysis was performed to compare ICEUS parameters between gliomas and the surrounding tissues.

RESULTS

The ICEUS group showed better survival rates both in OS and PFS than the control group. The univariate analysis revealed that age, pathology and ICEUS were significant prognostic factors for PFS, with only age being a significant prognostic factor for OS. In multivariate analysis, age and ICEUS were significant prognostic factors for both OS and PFS. The quantitative analysis showed that the intensity and transit time of microbubbles reaching the tumors were significantly different from those of microbubbles reaching the surrounding tissue.

CONCLUSION

ICEUS facilitates the identification of residual tumors. Age and ICEUS are prognostic factors for malignant glioma surgery, and use of ICEUS offers a better prognosis for patients with malignant glioma.

Metal ion stimulation-related gene signatures correlate with clinical and immunologic characteristics of glioma

Background

Environmental factors serve as one of the important pathogenic factors for gliomas. Yet people focus only on the effect of electromagnetic radiation on its pathogenicity, while metals in the environment are neglected. This study aimed to investigate the relationship between metal ion stimulation and the clinical characteristics and immune status of GM patients.

Methods

Firstly, mRNA expression profiles of GM patients and normal subjects were obtained from Chinese GM Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) to identify differentially expressed metal ion stimulation-related genes(DEMISGs). Secondly, two molecular subtypes were identified and validated based on these DEMISGs using consensus clustering. Diagnostic and prognostic models for GM were constructed after screening these features based on machine learning. Finally, supervised classification and unsupervised clustering were combined to classify and predict the grade of GM based on SHAP values.

Results

GM patients are divided into two different response states to metal ion stimulation, M1 and M2, which are related to the grade and IDH status of the GM. Six genes with diagnostic value were obtained: SLC30A3, CRHBP, SYT13, DLG2, CDK1, and WNT5A. The AUC in the external validation set was higher than 0.90. The SHAP value improves the performance of classification prediction.

Conclusion

The gene features associated with metal ion stimulation are related to the clinical and immune characteristics of transgenic patients. XGboost/LightGBM Kmeans has a higher classification prediction accuracy in predicting glioma grades compared to using purely supervised classification techniques.

Case report: Primary sarcoma of the mandible with a novel SLMAP-BRAF fusion

Primary sarcomas of the jaw are very rare tumor with unclear mechanism of tumorigenesis. Identification of genetic alterations contributes to better understanding of tumorigenesis and extension of tumor spectrum, as well as potential therapeutic targets application. Herein, we firstly report a case of primary sarcoma in the mandible with novel SLMAP-BRAF fusion. Morphologically, the tumor was composed of histiocyte-like cells, larger epithelioid cells, spindle cells and osteoclast-like giant cells with moderate atypia. Focally, it mimicked tenosynovial giant cell tumor or biphasic synovial sarcoma, and even giant cell tumor of bone. SATB2 was diffusely expressed, while p63 and p16 were locally positive with loss expression of p16 in histiocyte-like and larger epithelioid cells. SLMAP-BRAF (S11:B10) fusion was detected by both DNA and RNA NGS, and further verified by sanger sequencing, DNA electrophoresis and FISH. Then a descriptive diagnosis of BRAF rearrangement sarcoma with moderate-grade malignancy (non-specific type) was given according to the biological behavior, morphological features and gene alteration. The patient finished six cycles of chemotherapy after hemimaxillectomy. Within 7 months of follow-up, no tumor recurrence or metastasis was observed. Our case has enriched the spectrum of jaw bone tumor and BRAF rearrangement tumor.

High expression of RTEL1 predicates worse progression in gliomas and promotes tumorigenesis through JNK/ELK1 cascade

Gliomas are the most common primary intracranial tumor worldwide. The maintenance of telomeres serves as an important biomarker of some subtypes of glioma. In order to investigate the biological role of RTEL1 in glioma. Relative telomere length (RTL) and RTEL1 mRNA was explored and regression analysis was performed to further examine the relationship of the RTL and the expression of RTEL1 with clinicopathological characteristics of glioma patients. We observed that high expression of RTEL1 is positively correlated with telomere length in glioma tissue, and serve as a poor prognostic factor in TERT wild-type patients. Further in vitro studies demonstrate that RTEL1 promoted proliferation, formation, migration and invasion ability of glioma cells. In addition, in vivo studies also revealed the oncogene role of RTEL1 in glioma. Further study using RNA sequence and phospho-specific antibody microarray assays identified JNK/ELK1 signaling was up-regulated by RTEL1 in glioma cells through ROS. In conclusion, our results suggested that RTEL1 promotes glioma tumorigenesis through JNK/ELK1 cascade and indicate that RTEL1 may be a prognostic biomarker in gliomas.

Intraoperative Touch Imprint Cytology of Brain Neoplasms: A Useful High-Diagnostic Tool in 93 Consecutive Cases; Differential Diagnoses, Pitfalls, and Traps

Introduction

Intraoperative cytological examination of central nervous system (CNS) lesions was first introduced in 1920 by Eisenhardt and Cushing for rapid evaluation of neurosurgical specimens and to guide surgical treatment. It is recognized that this method not only confirms the adequacy of biopsy in CNS samples but also indicates the presence and preliminary diagnosis of lesional tissue.

Methods

A total of 93 patients who underwent touch imprint cytology (TIC) for CNS tumors or lesions between 2018 and 2023 were included in the study. All cases were correlated with the final histopathological diagnosis, and pitfalls and difficulties encountered with discrepancies were noted.

Result

The most common primary CNS tumors were gliomas and meningiomas, while secondary (metastatic) tumors were predominantly lung, breast, and gastrointestinal system carcinomas. Sensitivity, specificity, positive predictive value, and negative predictive value for diagnosis with TIC were 94.1%, 100%, and 61.5%, respectively. Final histopathological diagnosis by TIC was made in 88 cases (94.6%) and the discrepancy was found in 5 cases (5.37%). Three of the five discrepancies (3.2%) were haematolymphoid malignancies (two lymphomas and one plasma cell neoplasia), one glioblastoma, and one hemangioblastoma case.

Conclusion

TIC is a fast, safe, and inexpensive diagnostic tool used during intraoperative neuropathology consultation. Awareness of the pitfalls of using this method during intraoperative consultation will enable high-diagnostic accuracy.

Pyroptosis in glioma: Current management and future application

Glioma, the predominant form of central nervous system (CNS) malignancies, presents a significant challenge due to its high prevalence and low 5-year survival rate. The efficacy of current treatment methods is limited by the presence of the blood-brain barrier, the immunosuppressive microenvironment, and other factors. Immunotherapy has emerged as a promising approach, as it can overcome the blood-brain barrier. A tumor's immune privilege, which is induced by an immunosuppressive environment, constricts immunotherapy's clinical impact in glioma. Pyroptosis, a programmed cell death mechanism facilitated by gasdermins, plays a significant role in the management of glioma. Its ability to initiate and regulate tumor occurrence, progression, and metastasis is well-established. However, it is crucial to note that uncontrolled or excessive cell death can result in tissue damage, acute inflammation, and cytokine release syndrome, thereby potentially promoting tumor advancement or recurrence. This paper aims to elucidate the molecular pathways involved in pyroptosis and subsequently discuss its induction in cancer therapy. In addition, the current treatment methods of glioma and the use of pyroptosis in these treatments are introduced. It is hoped to provide more ideas for the treatment of glioma.

Establishment of a Prediction Model Based on Preoperative MRI Radiomics for Diffuse Astrocytic Glioma, IDH-Wildtype, with Molecular Features of Glioblastoma

PURPOSE

In 2021, the WHO central nervous system (CNS) tumor classification criteria added the diagnosis of diffuse astrocytic glioma, IDH wild-type, with molecular features of glioblastoma, WHO grade 4 (DAG-G). DAG-G may exhibit the aggressiveness and malignancy of glioblastoma (GBM) despite the lower histological grade, and thus a precise preoperative diagnosis can help neurosurgeons develop more refined individualized treatment plans. This study aimed to establish a predictive model for the non-invasive identification of DAG-G based on preoperative MRI radiomics.

PATIENTS AND METHODS

Patients with pathologically confirmed glioma in Huashan Hospital, Fudan University, between September 2019 and July 2021 were retrospectively analyzed. Furthermore, two external validation datasets from Wuhan Union Hospital and Xuzhou Cancer Hospital were also utilized to verify the reliability and accuracy of the prediction model. Two regions of interest (ROI) were delineated on the preoperative MRI images of the patients using the semi-automatic tool ITK-SNAP (version 4.0.0), which were named the maximum anomaly region (ROI1) and the tumor region (ROI2), and Pyradiomics 3.0 was applied for feature extraction. Feature selection was performed using a least absolute shrinkage and selection operator (LASSO) filter and a Spearman correlation coefficient. Six classifiers, including Gauss naive Bayes (GNB), K-nearest neighbors (KNN), Random forest (RF), Adaptive boosting (AB), and Support vector machine (SVM) with linear kernel and multilayer perceptron (MLP), were used to build the prediction models, and the prediction performance of the six classifiers was evaluated by fivefold cross-validation. Moreover, the performance of prediction models was evaluated using area under the curve (AUC), precision (PRE), and other metrics.

RESULTS

According to the inclusion and exclusion criteria, 172 patients with grade 2-3 astrocytoma were finally included in the study, and a total of 44 patients met the diagnosis of DAG-G. In the prediction task of DAG-G, the average AUC of GNB classifier was 0.74 ± 0.07, that of KNN classifier was 0.89 ± 0.04, that of RF classifier was 0.96 ± 0.03, that of AB classifier was 0.97 ± 0.02, that of SVM classifier was 0.88 ± 0.05, and that of MLP classifier was 0.91 ± 0.03, among which, AB classifier achieved the best prediction performance. In addition, the AB classifier achieved AUCs of 0.91 and 0.89 in two external validation datasets obtained from Wuhan Union Hospital and Xuzhou Cancer Hospital, respectively.

CONCLUSIONS

The prediction model constructed based on preoperative MRI radiomics established in this study can basically realize the prospective, non-invasive, and accurate diagnosis of DAG-G, which is of great significance to help further optimize treatment plans for such patients, including expanding the extent of surgery and actively administering radiotherapy, targeted therapy, or other treatments after surgery, to fundamentally maximize the prognosis of patients.

Integration analysis of single-cell and spatial transcriptomics reveal the cellular heterogeneity landscape in glioblastoma and establish a polygenic risk model

Background

Glioblastoma (GBM) is adults' most common and fatally malignant brain tumor. The heterogeneity is the leading cause of treatment failure. However, the relationship between cellular heterogeneity, tumor microenvironment, and GBM progression is still elusive.

Methods

Integrated analysis of single-cell RNA sequencing (scRNA-seq) and spatial transcriptome sequencing (stRNA-seq) of GBM were conducted to analyze the spatial tumor microenvironment. We investigated the subpopulation heterogeneity of malignant cells through gene set enrichment analyses, cell communications analyses, and pseudotime analyses. Significantly changed genes of the pseudotime analysis were screened to create a tumor progress-related gene risk score (TPRGRS) using Cox regression algorithms in the bulkRNA-sequencing(bulkRNA-seq) dataset. We combined the TPRGRS and clinical characteristics to predict the prognosis of patients with GBM. Furthermore, functional analysis was applied to uncover the underlying mechanisms of the TPRGRS.

Results

GBM cells were accurately charted to their spatial locations and uncovered their spatial colocalization. The malignant cells were divided into five clusters with transcriptional and functional heterogeneity, including unclassified malignant cells and astrocyte-like, mesenchymal-like, oligodendrocytes-progenitor-like, and neural-progenitor-like malignant cells. Cell-cell communications analysis in scRNA-seq and stRNA-seq identified ligand-receptor pairs of the CXCL, EGF, FGF, and MIF signaling pathways as bridges implying that tumor microenvironment may cause malignant cells' transcriptomic adaptability and disease progression. Pseudotime analysis showed the differentiation trajectory of GBM cells from proneural to mesenchymal transition and identified genes or pathways that affect cell differentiation. TPRGRS could successfully divide patients with GBM in three datasets into high- and low-risk groups, which was proved to be a prognostic factor independent of routine clinicopathological characteristics. Functional analysis revealed the TPRGRS associated with growth factor binding, cytokine activity, signaling receptor activator activity functions, and oncogenic pathways. Further analysis revealed the association of the TPRGRS with gene mutations and immunity in GBM. Finally, the external datasets and qRT-PCR verified high expressions of the TPRGRS mRNAs in GBM cells.

Conclusion

Our study provides novel insights into heterogeneity in GBM based on scRNA-seq and stRNA-seq data. Moreover, our study proposed a malignant cell transition-based TPRGRS through integrated analysis of bulkRNA-seq and scRNA-seq data, combined with the routine clinicopathological evaluation of tumors, which may provide more personalized drug regimens for GBM patients.

Predicting Histopathological Grading of Adult Gliomas Based On Preoperative Conventional Multimodal MRI Radiomics: A Machine Learning Model

PURPOSE

The accurate preoperative histopathological grade diagnosis of adult gliomas is of great significance for the formulation of a surgical plan and the implementation of a subsequent treatment. The aim of this study is to establish a predictive model for classifying adult gliomas into grades 2-4 based on preoperative conventional multimodal MRI radiomics.

PATIENTS AND METHODS

Patients with pathologically confirmed gliomas at Huashan Hospital, Fudan University, between February 2017 and July 2019 were retrospectively analyzed. Two regions of interest (ROIs), called the maximum anomaly region (ROI1) and the tumor region (ROI2), were delineated on the patients' preoperative MRIs utilizing the tool ITK-SNAP, and Pyradiomics 3.0 was applied to execute feature extraction. Feature selection was performed utilizing a least absolute shrinkage and selection operator (LASSO) filter. Six classifiers, including Gaussian naive Bayes (GNB), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) with a linear kernel, adaptive boosting (AB), and multilayer perceptron (MLP) were used to establish predictive models, and the predictive performance of the six classifiers was evaluated through five-fold cross-validation. The performance of the predictive models was evaluated using the AUC and other metrics. After that, the model with the best predictive performance was tested using the external data from The Cancer Imaging Archive (TCIA).

RESULTS

According to the inclusion and exclusion criteria, 240 patients with gliomas were identified for inclusion in the study, including 106 grade 2, 68 grade 3, and 66 grade 4 gliomas. A total of 150 features was selected, and the MLP classifier had the best predictive performance among the six classifiers based on T2-FLAIR (mean AUC of 0.80 ± 0.07). The SVM classifier had the best predictive performance among the six classifiers based on DWI (mean AUC of 0.84 ± 0.05); the SVM classifier had the best predictive performance among the six classifiers based on CE-T1WI (mean AUC of 0.85 ± 0.06). Among the six classifiers, based on ROI1, the MLP classifier had the best prediction performance (mean AUC of 0.78 ± 0.07); among the six classifiers, based on ROI2, the SVM classifier had the best prediction performance (mean AUC of 0.82 ± 0.07). Among the six classifiers, based on the multimodal MRI of all the ROIs, the SVM classifier had the best prediction performance (average AUC of 0.85 ± 0.04). The SVM classifier, based on the multimodal MRI of all the ROIs, achieved an AUC of 0.81 using the external data from TCIA.

CONCLUSIONS

The prediction model, based on preoperative conventional multimodal MRI radiomics, established in this study can conveniently, accurately, and noninvasively classify adult gliomas into grades 2-4, providing certain assistance for the precise diagnosis and treatment of patients and optimizing their clinical management.

Personalized Medicine in Brain Gliomas: Targeted Therapy, Patient-Derived Tumor Models (Review)

Gliomas are the most common type of primary malignant brain tumors. The choice of treatments for these tumors was quite limited for many years, and therapy results generally remain still unsatisfactory. Recently, a significant breakthrough in the treatment of many forms of cancer occurred when personalized targeted therapies were introduced which inhibit tumor growth by affecting a specific molecular target. Another trend gaining popularity in oncology is the creation of patient-derived tumor models which can be used for drug screening to select the optimal therapy regimen. Molecular and genetic mechanisms of brain gliomas growth are considered, consisting of individual components which could potentially be exposed to targeted drugs. The results of the literature review show a higher efficacy of the personalized approach to the treatment of individual patients compared to the use of standard therapies. However, many unresolved issues remain in the area of predicting the effectiveness of a particular drug therapy regimen. The main hopes in solving this issue are set on the use of patient-derived tumor models, which can be used in one-stage testing of a wide range of antitumor drugs.

From Theory to Practice: Implementing the WHO 2021 Classification of Adult Diffuse Gliomas in Neuropathology Diagnosis

Diffuse gliomas are the most common type of primary central nervous system (CNS) neoplasm to affect the adult population. The diagnosis of adult diffuse gliomas is dependent upon the integration of morphological features of the tumour with its underlying molecular alterations, and the integrative diagnosis has become of increased importance in the fifth edition of the WHO classification of CNS neoplasms (WHO CNS5). The three major diagnostic entities of adult diffuse gliomas are as follows: (1) astrocytoma, IDH-mutant; (2) oligodendroglioma, IDH-mutant and 1p/19q-codeleted; and (3) glioblastoma, IDH-wildtype. The aim of this review is to summarize the pathophysiology, pathology, molecular characteristics, and major diagnostic updates encountered in WHO CNS5 of adult diffuse gliomas. Finally, the application of implementing the necessary molecular tests for diagnostic workup of these entities in the pathology laboratory setting is discussed.

Gene Therapy for High Grade Glioma: The Clinical Experience

INTRODUCTION

High-grade gliomas (HGG) are the most common malignant primary brain tumors in adults, with a median survival of ~18 months. The standard of care (SOC) is maximal safe surgical resection, and radiation therapy with concurrent and adjuvant temozolomide. This protocol remains unchanged since 2005, even though HGG median survival has marginally improved.

AREAS COVERED

Gene therapy was developed as a promising approach to treat HGG. Here, we review completed and ongoing clinical trials employing viral and non-viral vectors for adult and pediatric HGG, as well as the key supporting preclinical data.

EXPERT OPINION

These therapies have proven safe, and pre- and post-treatment tissue analyses demonstrated tumor cell lysis, increased immune cell infiltration, and increased systemic immune function. Although viral therapy in clinical trials has not yet significantly extended the survival of HGG, promising strategies are being tested. Oncolytic HSV vectors have shown promising results for both adult and pediatric HGG. A recently published study demonstrated that HG47Δ improved survival in recurrent HGG. Likewise, PVSRIPO has shown survival improvement compared to historical controls. It is likely that further analysis of these trials will stimulate the development of new administration protocols, and new therapeutic combinations that will improve HGG prognosis.

Nomogram Model for Predicting the Prognosis of High-Grade Glioma in Adults Receiving Standard Treatment: A Retrospective Cohort Study

OBJECTIVES

To identify the critical factors associated with the progression-free survival (PFS) and overall survival (OS) of high-grade glioma (HGG) in adults who have received standard treatment and establish a novel graphical nomogram and an online dynamic nomogram.

PATIENTS AND METHODS

This is a retrospective study of adult HGG patients receiving standard treatment (surgery, postoperative radiotherapy, and temozolomide (TMZ) chemotherapy) at Huashan Hospital, Fudan University between January 2017 and December 2019. We used uni- and multi-variable COX models to identify the significant prognostic factors for PFS and OS. Based on the significant predictors, graphical and online nomograms were established.

RESULTS

A total of 246 patients were enrolled in the study based on the inclusion criteria. The average PFS and OS were 22.99 ± 11.43 and 30.51 ± 13.73 months, respectively. According to the multi-variable COX model, age, extent of resection (EOR), and IDH mutation were associated with PFS and OS, while edema index (EI) was relevant to PFS. In addition, patients with IDH and TERT promoter co-mutations had longer PFSs and OSs, and no apparent survival benefit was found in the long-cycle TMZ adjuvant chemotherapy compared with the standard Stupp protocol. Based on these critical factors, a graphical nomogram and online nomogram were developed for predicting PFS and OS, respectively. The calibration curve showed favorable consistency between the predicted and actual survival rates. C-index and time-dependent AUC showed good discrimination abilities.

CONCLUSIONS

We identified the significant predictors for the PFS and OS of HGG adults receiving standard treatment and established user-friendly nomogram models to assist neurosurgeons in optimizing clinical management and treatment strategies.

Predicting IDH Mutation Status in Low-Grade Gliomas Based on Optimal Radiomic Features Combined with Multi-Sequence Magnetic Resonance Imaging

The IDH somatic mutation status is an important basis for the diagnosis and classification of gliomas. We proposed a "6-Step" general radiomics model to noninvasively predict the IDH mutation status by simultaneously tuning combined multi-sequence MRI and optimizing the full radiomics processing pipeline. Radiomic features (n = 3776) were extracted from multi-sequence MRI (T1, T2, FLAIR, and T1Gd) in low-grade gliomas (LGGs), and a total of 45,360 radiomics pipeline were investigated according to different settings. The predictive ability of the general radiomics model was evaluated with regards to accuracy, stability, and efficiency. Based on numerous experiments, we finally reached an optimal pipeline for classifying IDH mutation status, namely the T2+FLAIR combined multi-sequence with the wavelet image filter, mean data normalization, PCC dimension reduction, RFE feature selection, and SVM classifier. The mean and standard deviation of AUC, accuracy, sensitivity, and specificity were 0.873 ± 0.05, 0.876 ± 0.09, 0.875 ± 0.11, and 0.877 ± 0.15, respectively. Furthermore, 14 radiomic features that best distinguished the IDH mutation status of the T2+FLAIR multi-sequence were analyzed, and the gray level co-occurrence matrix (GLCM) features were shown to be of high importance. Apart from the promising prediction of the molecular subtypes, this study also provided a general tool for radiomics investigation.

Emerging Roles of TRIM Family Proteins in Gliomas Pathogenesis

Simple Summary

Gliomas remain challenging tumors due to their increased heterogeneity, complex molecular profile, and infiltrative phenotype that are often associated with a dismal prognosis. In a constant search for molecular changes and associated mechanisms, the TRIM protein family has emerged as an important area of investigation because of the regulation of vital cellular processes involved in brain pathophysiology that may possibly lead to brain tumor development. Herein, we discuss the diverse role of TRIM proteins in glioma progression, aiming to detect potential targets for future intervention.

Abstract

Gliomas encompass a vast category of CNS tumors affecting both adults and children. Treatment and diagnosis are often impeded due to intratumor heterogeneity and the aggressive nature of the more malignant forms. It is therefore essential to elucidate the molecular mechanisms and explore the intracellular signaling pathways underlying tumor pathology to provide more promising diagnostic, prognostic, and therapeutic tools for gliomas. The tripartite motif-containing (TRIM) superfamily of proteins plays a key role in many physiological cellular processes, including brain development and function. Emerging evidence supports the association of TRIMs with a wide variety of cancers, exhibiting both an oncogenic as well as a tumor suppressive role depending on cancer type. In this review, we provide evidence of the pivotal role of TRIM proteins in gliomagenesis and exploit their potential as prognostic biomarkers and therapeutic targets.

Clinical and Prognostic Implications of 1p/19q, IDH, BRAF, MGMT Promoter, and TERT Promoter Alterations, and Expression of Ki-67 and p53 in Human Gliomas

Background and Objective

Genetic alterations, including IDH, BRAF, and TERT promoter mutations (IDH-mu, BRAF-mu, TERTp-mu, respectively), 1p/19q co-deletion (1p/19q-codel), and MGMT promoter methylation (MGMTp-M), are correlated with glioma tumor development. Therefore, these genetic alterations could serve as biomarkers for the diagnosis, prognosis, and classification of gliomas, combined with the immunohistochemical markers Ki-67 and p53. However, the correlation between these alterations and the expression of Ki-67 and p53 is poorly understood.

Methods

We analyzed the prevalence and prognosis of these five alterations, as well as Ki-67 and p53 expression, in 103 primary grade II–IV gliomas via fluorescence qPCR, Sanger sequencing, fluorescence in situ hybridization, and immunohistochemistry.

Results

In the 103 cases, MGMTp-M was the most common alteration (70.9%), followed by TERTp-mu (58.3%), IDH-mu (46.6%), 1p/19q-codel (34.0%), and BRAF-mu (5.8%). No cases showed quintuple-positive alterations, but 26 cases (25.2%) showed quadruple-positive alterations (IDH-mu/TERTp-mu/MGMTp-M/1p/19q-codel). The percentage of TERTp-mu and 1p/19q-codel cases decreased with p53 expression, and the percentage of IDH-mu and 1p/19q-codel cases decreased with Ki-67 expression. IDH-mu, MGMTp-M, and 1p/19q-codel were positive factors for survival rates in glioma patients, while TERTp-mu, p53, and Ki-67 positivity were negative factors. Old age, histological grade IV, IDH-mu, 1p/19q-codel, Ki-67+, and p53+/Ki-67+ were significantly correlated with overall survival (OS). However, only p53+/Ki-67+ was an independent prognostic factor for OS in the multivariate Cox-model analysis.

Conclusion

IDH-mu only and quadruple-positivity were associated with good OS in glioma patients, while TERTp-mu only, TERTp-mu/MGMTp-M and p53+/Ki-67+ were associated with poor prognosis. Combining these genomic alterations and Ki-67/p53 expression should have clinical value in gliomas.

Parental Pesticide Exposure and Childhood Brain Cancer: A Systematic Review and Meta-Analysis Confirming the IARC/WHO Monographs on Some Organophosphate Insecticides and Herbicides

Background: Brain tumors are the second most common neoplasm in the pediatric age. Pesticides may play an etiologic role, but literature results are conflicting. This review provides a systematic overview, meta-analysis, and IARC/WHO consideration of data on parental exposure to pesticides and childhood brain tumors. Methods: We searched PubMed, SCOPUS, and Google Scholar for literature (1 January 1966–31 December 2020) that assessed childhood brain tumors and parental exposure to pesticides. We undertook a meta-analysis addressing prenatal exposure, exposure after birth, occupational exposure, and residential exposure. A total of 130 case-control investigations involving 43,598 individuals (18,198 cases and 25,400 controls) were included. Results: Prenatal exposure is associated with childhood brain tumors (odds ratio, OR = 1.32; 95% CI: 1.17–1.49; I² = 41.1%). The same occurs after birth exposure (OR = 1.22; 95% CI: 1.03–1.45, I² = 72.3%) and residential exposure to pesticides (OR = 1.31; 95% CI: 1.11–1.54, I² = 67.2%). Parental occupational exposure is only marginally associated with CBT (OR = 1.17, 95% CI: 0.99–1.38, I² = 67.0%). Conclusions: There is an association between CBT and parental pesticides exposure before childbirth, after birth, and residential exposure. It is in line with the IARC Monograph evaluating the carcinogenicity of diazinon, glyphosate, malathion, parathion, and tetrachlorvinphos.

A Computational Framework to Identify Biomarkers for Glioma Recurrence and Potential Drugs Targeting Them

Background: Recurrence is still a major obstacle to the successful treatment of gliomas. Understanding the underlying mechanisms of recurrence may help for developing new drugs to combat gliomas recurrence. This study provides a strategy to discover new drugs for recurrent gliomas based on drug perturbation induced gene expression changes. Methods: The RNA-seq data of 511 low grade gliomas primary tumor samples (LGG-P), 18 low grade gliomas recurrent tumor samples (LGG-R), 155 glioblastoma multiforme primary tumor samples (GBM-P), and 13 glioblastoma multiforme recurrent tumor samples (GBM-R) were downloaded from TCGA database. DESeq2, key driver analysis and weighted gene correlation network analysis (WGCNA) were conducted to identify differentially expressed genes (DEGs), key driver genes and coexpression networks between LGG-P vs LGG-R, GBM-P vs GBM-R pairs. Then, the CREEDS database was used to find potential drugs that could reverse the DEGs and key drivers. Results: We identified 75 upregulated and 130 downregulated genes between LGG-P and LGG-R samples, which were mainly enriched in human papillomavirus (HPV) infection, PI3K-Akt signaling pathway, Wnt signaling pathway, and ECM-receptor interaction. A total of 262 key driver genes were obtained with frizzled class receptor 8 (FZD8), guanine nucleotide-binding protein subunit gamma-12 (GNG12), and G protein subunit β2 (GNB2) as the top hub genes. By screening the CREEDS database, we got 4 drugs (Paclitaxel, 6-benzyladenine, Erlotinib, Cidofovir) that could downregulate the expression of up-regulated genes and 5 drugs (Fenofibrate, Oxaliplatin, Bilirubin, Nutlins, Valproic acid) that could upregulate the expression of down-regulated genes. These drugs may have a potential in combating recurrence of gliomas. Conclusion: We proposed a time-saving strategy based on drug perturbation induced gene expression changes to find new drugs that may have a potential to treat recurrent gliomas.