Awake Surgery for Left Posterior Insular Low-Grade Glioma Through the Parietorolandic Operculum: The Need to Preserve the Functional Connectivity. A Case Series

Objective: Surgical approach to low-grade glioma (LGG) involving the posterior insula is challenging, especially in the left hemisphere, with a high risk of sensorimotor, language, or visual deterioration. In this study, a case series of 5 right-handed patients harboring a left posterior insular LGG is reported, by detailing a transcorticosubcortical approach.Method: The five surgeries were achieved in awake patients using cortical and axonal electrostimulation mapping. The glioma was removed through the left rolandic and/or parietal opercula, with preservation of the subcortical connectivity.Results: The cortical mapping was positive in the five patients, enabling the selection of an optimal transcortical approach, via the anterolateral supramarginal gyrus in four patients and/or via the lateral retrocentral gyrus in three cases (plus through the left superior temporal gyrus in one case). Moreover, the white matter tracts were identified in all cases, i.e., the lateral part of the superior longitudinal fasciculus (five cases), the arcuate fasciculus (four cases), the thalamocortical somatosensory pathways (four cases), the motor pathway (one case), the semantic pathway (three cases), and the optic tract (one case). Complete resection of the LGG was achieved in two patients and near-total resection in three patients. There were no postoperative permanent sensorimotor, language, or visual deficits.Conclusion: A transcortical approach through the parietorolandic operculum in awake patients represents safe and effective access to the left posterior insular LGG. Detection and preservation of the functional connectivity using direct electrostimulation of the white matter bundles are needed in this cross-road brain region to prevent otherwise predictable postsurgical impairments.

Clinical and economic impact of molecular testing for BRAF fusion in pediatric low-grade Glioma

Background Treatment personalization via tumor molecular testing holds promise for improving outcomes for patients with pediatric low-grade glioma (PLGG). We evaluate the health economic impact of employing tumor molecular testing to guide treatment for patients diagnosed with PLGG, particularly the avoidance of radiation therapy (RT) for patients with BRAF-fusion. Methods We performed a model-based cost-utility analysis comparing two strategies: molecular testing to determine BRAF fusion status at diagnosis against no molecular testing. We developed a microsimulation to model the lifetime health and cost outcomes (in quality-adjusted life years (QALYs) and 2018 CAD, respectively) for a simulated cohort of 100,000 patients newly diagnosed with PLGG after their initial surgery. Results The life expectancy after diagnosis for individuals who did not receive molecular testing was 39.01 (95% Confidence Intervals (CI): 32.94;44.38) years and 40.08 (95% CI: 33.19;45.76) years for those who received testing. Our findings indicate that patients who received molecular testing at diagnosis experienced a 0.38 (95% CI: 0.08;0.77) gain in QALYs and $1384 (95% CI: $-3486; $1204) reduction in costs over their lifetime. Cost and QALY benefits were driven primarily by the avoidance of long-term adverse events (stroke, secondary neoplasms) associated with unnecessary use of radiation. Conclusions We demonstrate the clinical benefit and cost-effectiveness of molecular testing in guiding the decision to provide RT in PLGG. While our results do not consider the impact of targeted therapies, this work is an example of the value of simulation modeling in assessing the long-term costs and benefits of precision oncology interventions for childhood cancer, which can aid decision-making about health system reimbursement.

Brain Tumor Assortment into High Level and Low Level Gliomas

Brain is recognized as one of the complex organ of the human body. Abnormal formation of cells may affect the normal functioning of the brain. These abnormal cells may belong to category of benign cells resulting in low grade glioma or malignant cells resulting in high grade glioma. The treatment plans vary according to grade of glioma detected. This results in need of precise glioma grading. As per World Health Organization, biopsy is considered to be gold standard in glioma grading. Biopsy is an invasive procedure which may contains sampling errors. Biopsy may also contain subjectivity errors. This motivated the clinician to look for other methods which may overcome the limitations of biopsy reports. Machine learning and deep learning approaches using MRI is considered to be most promising alternative approach reported by scientist in literature. The presented work were based on the concept of AdaBoost approach which is an ensemble learning approach. The developed model was optimized w.r.t to two hyper parameters i.e. no. of estimators and learning rate keeping the base model fixed. The decision tree was used as a base model. The proposed developed model was trained and validated on BraTS 2018 dataset. The developed optimized model achieves reasonable accuracy in carrying out classification task i.e. high grade glioma vs. low grade glioma. Keywords: High grade glioma, low grade glioma, AdaBoost, Texture Features,

Comprehensive Analysis to Identify the Vasohibin1 (VASH1) Emerges as a Novel Prognosis Biomarker in High-Risk Low-Grade Glioma

Background: Gliomas are complex and heterogeneous central nervous system tumors, with Low-grade Glioma (LGG)as the most common pathological type. But studies on the predictive effect of a single gene on LGG are limited. VASH1 is an epigenetic regulator with various tumors. However, the role of VASH1 in LGG remains confused. This is the first research focusing on the prognostic value and underlying mechanism of VASH1 in LGG.Methods: In this research, three independent datasets were used for mRNA-related analysis: two datasets from the TCGA and CGGA (CGGA-mRNA seq 693 and CGGA-mRNA seq 325). We analyzed and screened the clinical significance of VASH1 in overall survival and DSS of various cancers. TIMER and CIBERSORT algorithms were employed to investigate the effect of VASH1 on the tumor microenvironment. GSEA along with GO and KEGG enrichment analyses were conducted to uncover the biological functions of VASH1. In addition, a LGG patient cohort (The First Affiliated Hospital of Xinjiang Medical University) was utilized for analysis of cell infiltration by immunohistochemical, Western-blot, and qPCR; then to verify its function in regulating LGG progression in vitro.Result: In this study, the results of generalized cancer survival analysis showed that abnormal VASH1 expression was associated with poor prognosis (overall survival (OS) and disease-specific survival (DSS) in patients with adrenal cortical carcinoma (ACC), low-grade glioma (LGG), pancreatic adenocarcinoma (PAAD) and hepatocellular carcinoma (LIHC) (P<0.05). Meanwhile, VASH1 was correlated with the immune invasion, immune score, immune checkpoint, and TBM of the above four tumors, and the correlation between VASH1 expression and LGG was the strongest. In addition, we found that VASH1-mediated changes in gene expression are closely related to cell cycle, P53, Notch, and TGF-β signaling pathways. In addition, immunostaining and RT-PCR were performed on our cohort, and the results showed that VASH1 expression was significantly higher than that of para-cancer tissues (P<0.05). Kaplan-Meier survival analysis results showed that VASH1 was associated with shorter survival (OS) and shorter DFS in high-risk LGG patients (P<0.05). Multivariate Cox analysis showed that high VASH1 expression was an independent risk factor for the prognosis of LGG patients (HR=1.65, P=0.02). Finally, a high level of VASH1 was found in U-251 cell lines by in vitro cell experiments, and the migration and invasion ability of U-251 cells were significantly improved after knockdown of VASH1 (P<0.01), which further confirmed the function of VASH1.Conclusion: In conclusion, this study preliminarily indicates that VASH1 can be used as a prognostic biomarker and potential therapeutic target for LGG, and has important clinical application value.

CCNB2 is a novel prognostic factor and a potential therapeutic target in Low-grade glioma (LGG)

Background: Cyclin B2 (CCNB2) is an important component of the cyclin pathway and plays a key role in the occurrence and development of cancer. However, the correlation between prognosis of low-grade glioma (LGG), CCNB2, and tumor infiltrating lymphocytes is not clear. Methods: The expression of CCNB2 in LGG was queried in Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and TIMER databases. The relationships between CCNB2 and the clinicopathological features of LGG were analyzed using the Chinese Glioma Genome Atlas (CGGA) database. The relationship between CCNB2 expression and overall survival (OS) was evaluated by GEPIA2. The correlation between CCNB2 and LGG immune infiltration was analyzed by the TIMER database. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect CCNB2 expression. Results: The expression of CCNB2 differed across different tumor tissues, but was higher in LGG than in normal tissues. LGG patients with high expression of CCNB2 have poorer prognosis. The expression of CCNB2 was correlated with age, WHO grade, IDH mutational status, 1p/19q codeletion status, and other clinicopathological features. The expression of CCNB2 in LGG was positively correlated with the infiltration level of B cells, dendritic cells, and macrophages. qRT-PCR results revealed that the expression of CCNB2 in LGG tissues was higher than normal tissues and higher expression of CCNB2 was associated with worse prognosis. Conclusion: CCNB2 may be used as a potential biomarker to determine the prognosis of LGG and is also related to immune infiltration.

The Effect of Necroptosis-related Gene Signature on the Progression, Prognosis, and Immune Microenvironment of Low-grade Glioma

BackgroundDespite the incorporation of various clinical and molecular criteria in the diagnosis and prognosis prediction of low-grade glioma, individual variation and risk stratification have not been completely explored. Necroptosis is considered closely related to different types of cancers, including low-grade gliomas. In this study, we obtained the necroptosis genes from the Kyoto Encyclopedia of Genes and Genomes website, extracted necroptosis genes from The Cancer Genome Atlas, and established a necroptosis-related gene signature (NECSig) through hazard analyses. Then we established a prognostic risk model consisting of four NECSig (BID, H2AFY2, MAPK9, and TNFRSF10B).ResultBased on the model, the high-risk group is significantly associated with poorer overall survival. The accuracy of this model is further supported by the receiver operating characteristic curve. Then, we constructed a prognostic nomogram combining NECSig and clinical features, which shows good predictive power for stratification of survival risk. We discovered variations in the kind of immune infiltration, immune cells, and functions between the high-risk and low-risk groups using this risk model. We also showed that drug therapy is more sensitive in high-risk populations.ConclusionThe results revealed a prognostic indicator of NECSig, which may provide information for immunological research and treatment of low-grade gliomas.

Resting-State Electroencephalography Functional Connectivity Networks Relate to Pre- and Postoperative Language Functioning in Low-Grade Glioma and Meningioma Patients

Introduction: Preservation of language functioning in patients undergoing brain tumor surgery is essential because language impairments negatively impact the quality of life. Brain tumor patients have alterations in functional connectivity (FC), the extent to which brain areas functionally interact. We studied FC networks in relation to language functioning in glioma and meningioma patients.Method: Patients with a low-grade glioma (N = 15) or meningioma (N = 10) infiltrating into/pressing on the language-dominant hemisphere underwent extensive language testing before and 1 year after surgery. Resting-state EEG was registered preoperatively, postoperatively (glioma patients only), and once in healthy individuals. After analyzing FC in theta and alpha frequency bands, weighted networks and Minimum Spanning Trees were quantified by various network measures.Results: Pre-operative FC network characteristics did not differ between glioma patients and healthy individuals. However, hub presence and higher local and global FC are associated with poorer language functioning before surgery in glioma patients and predict worse language performance at 1 year after surgery. For meningioma patients, a greater small worldness was related to worse language performance and hub presence; better average clustering and global integration were predictive of worse outcome on language function 1 year after surgery. The average eccentricity, diameter and tree hierarchy seem to be the network metrics with the more pronounced relation to language performance.Discussion: In this exploratory study, we demonstrated that preoperative FC networks are informative for pre- and postoperative language functioning in glioma patients and to a lesser extent in meningioma patients.