暂无摘要。

BACKGROUND: High-grade gliomas (HGGs) have a rapid relapse and short survival. Studies have identified many clinical characteristics and biomarkers associated with progression-free survival (PFS) and over-survival (OS). However, there has not yet a comprehensive study on survival after the first progression (SAP).
METHODS: From CGGA and TCGA, 319 and 308 HGGs were confirmed as the first progression. The data on clinical characteristics and biomarkers were analyzed in accordance with OS, PFS, and SAP.
RESULTS: Analysis of 319 patients from CGGA, significant predictors of improved OS/PFS/SAP were WHO grade, MGMT promoter methylation, and Ki-67 expression in univariate analysis. Further multivariate analysis showed MGMT promoter methylation and Ki-67 expression were independent predictors. However, an analysis of 308 patients from TCGA found MGMT promoter methylation is the only prognostic marker. A longer SAP was observed in patients with methylated MGMT promoter after standard chemoradiotherapy. In our data, HGGs could be divided into low, intermediate, and high-risk groups for SAP by MGMT methylation and Ki-67 expression.
CONCLUSIONS: Patients with MGMT promoter methylation have a prolonger SAP after standard chemoradiotherapy. HGGs could be divided into low, intermediate, and high-risk groups for SAP according to MGMT status and Ki-67 expression.

OBJECTIVE: The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter region is essential in evaluating the prognosis and predicting the drug response in patients with glioblastoma. In this study, we evaluated the utility of using nanopore long-read sequencing as a method for assessing methylation levels throughout the MGMT CpG-island, compared its performance to established techniques and demonstrated its clinical applicability.
METHODS: We analysed 165 samples from CNS tumours, focusing on the MGMT CpG-island using nanopore sequencing. Oxford Nanopore Technologies (ONT) MinION and PromethION flow cells were employed for single sample or barcoded assays, guided by a CRISPR/Cas9 protocol, adaptive sampling or as part of a whole genome sequencing assay. Methylation data obtained through nanopore sequencing were compared to results obtained via pyrosequencing and methylation bead arrays. Hierarchical clustering was applied to nanopore sequencing data for patient stratification.
RESULTS: Nanopore sequencing displayed a strong correlation (R2 = 0.91) with pyrosequencing results for the four CpGs of MGMT analysed by both methods. The MGMT-STP27 algorithm\'s classification was effectively reproduced using nanopore data. Unsupervised hierarchical clustering revealed distinct patterns in methylated and unmethylated samples, providing comparable survival prediction capabilities. Nanopore sequencing yielded high-confidence results in a rapid timeframe, typically within hours of sequencing, and extended the analysis to all 98 CpGs of the MGMT CpG-island.
CONCLUSIONS: This study presents nanopore sequencing as a valid and efficient method for determining MGMT promotor methylation status. It offers a comprehensive view of the MGMT promoter methylation landscape, which enables the identification of potentially clinically relevant subgroups of patients. Further exploration of the clinical implications of patient stratification using nanopore sequencing of MGMT is warranted.

Radiomics has been widely recognized for its effectiveness in decoding tumor phenotypes through the extraction of quantitative imaging features. However, the robustness of radiomic methods to estimate clinically relevant biomarkers non-invasively remains largely untested. In this study, we propose Cascaded Data Processing Network (CDPNet), a radiomic feature learning method to predict tumor molecular status from medical images. We apply CDPNet to an epigenetic case, specifically targeting the estimation of O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation from Magnetic Resonance Imaging (MRI) scans of glioblastoma patients. CDPNet has three components: 1) Principal Component Analysis (PCA), 2) Fisher Linear Discriminant (FLD), and 3) a combination of hashing and blockwise histograms. The outlined architectural framework capitalizes on PCA to reconstruct input image patches, followed by FLD to extract discriminative filter banks, and finally using binary hashing and blockwise histogram module for indexing, pooling, and feature generation. To validate the effectiveness of CDPNet, we conducted an exhaustive evaluation on a comprehensive retrospective cohort comprising 484 IDH-wildtype glioblastoma patients with pre-operative multi-parametric MRI scans (T1, T1-Gd, T2, and T2-FLAIR). The prediction of MGMT promoter methylation status was cast as a binary classification problem. The developed model underwent rigorous training via 10-fold cross-validation on a discovery cohort of 446 patients. Subsequently, the model\'s performance was evaluated on a distinct and previously unseen replication cohort of 38 patients. Our method achieved an accuracy of 70.11% and an area under the curve of 0.71 (95% CI: 0.65 - 0.74).

Glioblastoma, IDH-wild type, the most common malignant primary central nervous system tumor, represents a formidable challenge in clinical management due to its poor prognosis and limited therapeutic responses. With an evolving understanding of its underlying biology, there is an urgent need to identify prognostic molecular groups that can be subject to targeted therapy. This study established a cohort of 124 sequential glioblastomas from a tertiary hospital and aimed to find correlations between molecular features and survival outcomes. Comprehensive molecular characterization of the cohort revealed prevalent alterations as previously described, such as TERT promoter mutations and involvement of the PI3K-Akt-mTOR, CK4/6-CDKN2A/B-RB1, and p14ARF-MDM2-MDM4-p53 pathways. MGMT promoter methylation is a significant predictor of improved overall survival, aligned with previous data. Conversely, age showed a marginal association with higher mortality. Multivariate analysis to account for the effect of MGMT promoter methylation and age showed that, in contrast to other published series, this cohort demonstrated improved survival for tumors harboring PTEN mutations, and that there was no observed difference for most other molecular alterations, including EGFR amplification, RB1 loss, or the coexistence of EGFR amplification and deletion/exon skipping (EGFRvIII). Despite limitations in sample size, this study contributes data to the molecular landscape of glioblastomas, prompting further investigations to examine these findings more closely in larger cohorts.

UNASSIGNED: The study aims to explore MRI phenotypes that predict glioblastoma\'s (GBM) methylation status of the promoter region of MGMT gene (pMGMT) by qualitatively assessing contrast-enhanced T1-weighted intensity images.
UNASSIGNED: A total of 193 histologically and molecularly confirmed GBMs at the Kansai Network for Molecular Diagnosis of Central Nervous Tumors (KANSAI) were used as an exploratory cohort. From the Cancer Imaging Archive/Cancer Genome Atlas (TCGA) 93 patients were used as validation cohorts. \"Thickened structure\" was defined as the solid tumor component presenting circumferential extension or occupying >50% of the tumor volume. \"Methylated contrast phenotype\" was defined as indistinct enhancing circumferential border, heterogenous enhancement, or nodular enhancement. Inter-rater agreement was assessed, followed by an investigation of the relationship between radiological findings and pMGMT methylation status.
UNASSIGNED: Fleiss\'s Kappa coefficient for \"Thickened structure\" was 0.68 for the exploratory and 0.55 for the validation cohort, and for \"Methylated contrast phenotype,\" 0.30 and 0.39, respectively. The imaging feature, the presence of \"Thickened structure\" and absence of \"Methylated contrast phenotype,\" was significantly predictive of pMGMT unmethylation both for the exploratory (p = .015, odds ratio = 2.44) and for the validation cohort (p = .006, odds ratio = 7.83). The sensitivities and specificities of the imaging feature, the presence of \"Thickened structure,\" and the absence of \"Methylated contrast phenotype\" for predicting pMGMT unmethylation were 0.29 and 0.86 for the exploratory and 0.25 and 0.96 for the validation cohort.
UNASSIGNED: The present study showed that qualitative assessment of contrast-enhanced T1-weighted intensity images helps predict GBM\'s pMGMT methylation status.

BACKGROUND: Glioblastoma is one of the deadliest tumors, and limited improvement in managing glioblastoma has been achieved in the past decades. The unmethylated promoter area of 6-O-Methylguanine-DNA Methyltransferase (MGMT) is a significant biomarker for recognizing a subset of glioblastoma that is resistant to chemotherapy. Here we identified MGMT methylation can also work as a specific biomarker to classify the lipid metabolism patterns between methylated and unmethylated glioblastoma and verify the potential novel therapeutic strategy for unmethylated MGMT glioblastoma.
METHODS: Liquid Chromatograph Mass Spectrometer has been applied for non-targeted metabolome and targeted lipidomic profiling to explore the metabolism pattern correlated with MGMT promoter methylation. Transcriptome has been performed to explore the biological differences and the potential mechanism of lipid metabolism in glioblastoma samples. In vivo and ex vivo assays were performed to verify the anti-tumor activity of atorvastatin in the administration of glioblastoma.
RESULTS: Multi-omics assay has described a significant difference in lipid metabolism between MGMT methylated and unmethylated glioblastoma. Longer and unsaturated fatty acyls were found enriched in MGMT-UM tumors. Lipid droplets have been revealed remarkably decreased in MGMT unmethylated glioblastoma. In vivo and ex vivo assays revealed that atorvastatin and also together with temozolomide showed significant anti-tumor activity, and atorvastatin alone was able to achieve better survival and living conditions for tumor-hosting mice.
CONCLUSIONS: MGMT promoter methylation status might be a well-performed biomarker of lipid metabolism in glioblastoma. The current study can be the basis of further mechanism studies and implementation of clinical trials, and the results provide preclinical evidence of atorvastatin administration in glioblastoma, especially for MGMT unmethylated tumors.

The methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a molecular marker associated with a better response to chemotherapy in patients with glioblastoma (GB). Standard pre-operative magnetic resonance imaging (MRI) analysis is not adequate to detect MGMT promoter methylation. This study aims to evaluate whether the radiomic features extracted from multiple tumor subregions using multiparametric MRI can predict MGMT promoter methylation status in GB patients. This retrospective single-institution study included a cohort of 277 GB patients whose 3D post-contrast T1-weighted images and 3D fluid-attenuated inversion recovery (FLAIR) images were acquired using two MRI scanners. Three separate regions of interest (ROIs) showing tumor enhancement, necrosis, and FLAIR hyperintensities were manually segmented for each patient. Two machine learning algorithms (support vector machine (SVM) and random forest) were built for MGMT promoter methylation prediction from a training cohort (196 patients) and tested on a separate validation cohort (81 patients), based on a set of automatically selected radiomic features, with and without demographic variables (i.e., patients\' age and sex). In the training set, SVM based on the selected radiomic features of the three separate ROIs achieved the best performances, with an average of 83.0% (standard deviation: 5.7%) for accuracy and 0.894 (0.056) for the area under the curve (AUC) computed through cross-validation. In the test set, all classification performances dropped: the best was obtained by SVM based on the selected features extracted from the whole tumor lesion constructed by merging the three ROIs, with 64.2% (95% confidence interval: 52.8-74.6%) accuracy and 0.572 (0.439-0.705) for AUC. The performances did not change when the patients\' age and sex were included with the radiomic features into the models. Our study confirms the presence of a subtle association between imaging characteristics and MGMT promoter methylation status. However, further verification of the strength of this association is needed, as the low diagnostic performance obtained in this validation cohort is not sufficiently robust to allow clinically meaningful predictions.

OBJECTIVE: This study aims to explore the relationship between the methylation levels of the O-6-methylguanine-DNA methyltransferase (MGMT) promoter and the structural connectivity in insular gliomas across hemispheres.
METHODS: We analyzed 32 left and 29 right insular glioma cases and 50 healthy controls, using differential tractography, correlational tractography, and graph theoretical analysis to investigate the correlation between structural connectivity and the methylation level.
RESULTS: The differential tractography results revealed that in left insular glioma, the volume of affected inferior fronto-occipital fasciculus (IFOF, p = 0.019) significantly correlated with methylation levels. Correlational tractography results showed that the quantitative anisotropy (QA) value of peritumoral fiber tracts also exhibited a significant correlation with methylation levels (FDR < 0.05). On the other hand, in right insular glioma, anterior internal part of the reticular tract, IFOF, and thalamic radiation showed a significant correlation with methylation levels but at a different correlation direction from the left side (FDR < 0.05). The graph theoretical analysis showed that in the left insular gliomas, only the radius of graph was significantly lower in methylated MGMT group than unmethylated group (p = 0.047). No significant correlations between global properties and methylation levels were observed in insular gliomas on both sides.
CONCLUSIONS: Our findings highlight a significant, hemisphere-specific correlation between MGMT promoter methylation and structural connectivity in insular gliomas. This study provides new insights into the genetic influence on glioma pathology, which could inform targeted therapeutic strategies.

OBJECTIVE: A molecular signature based on 10 mRNA abundances that characterizes the mesenchymal-to-proneural phenotype of glioblastoma stem(like) cells (GSCs) enriched in primary culture has been previously established. As this phenotype has been proposed to be prognostic for disease outcome the present study aims to identify features of the preoperative MR imaging that may predict the GSC phenotype of individual tumors.
METHODS: Molecular mesenchymal-to-proneural mRNA signatures and intrinsic radioresistance (SF4, survival fraction at 4 Gy) of primary GSC-enriched cultures were associated with survival data and pre-operative MR imaging of the corresponding glioblastoma patients of a prospective cohort (n = 24). The analyzed imaging parameters comprised linear vectors derived from tumor volume, necrotic volume and edema as contoured manually.
RESULTS: A necrosis/tumor vector ratio and to a weaker extent the product of this ratio and the edema vector were identified to correlate with the mesenchymal-to-proneural mRNA signature and the SF4 of the patient-derived GSC cultures. Importantly, both parameter combinations were predictive for overall survival of the whole patient cohort. Moreover, the combination of necrosis/tumor vector ratio and edema vector differed significantly between uni- and multifocally recurring tumors.
CONCLUSIONS: Features of the preoperative MR images may reflect the molecular signature of the GSC population and might be used in the future as a prognostic factor and for treatment stratification especially in the MGMT promotor-unmethylated sub-cohort of glioblastoma patients.