methylguanine-DNA Methyltransferase Promoter Research Articles

Identification and validation of a novel robust glioblastoma prognosis model based on bioinformatics

BackgroundGlioblastoma (GBM) is a very common primary malignant tumor of the central nervous system (CNS). Aging, macrophage, autophagy, and methylation related genes are hypothesized to be crucial to its pathogenesis. In this study, we aimed to explore the role of these genes in the prognosis of GBM. MethodsThe RNA sequence (RNA-seq) and clinical information were downloaded from The Cancer Genome Atlas database (TCGA) and the Chinese Glioma Genome Atlas database (CGGA). We performed univariate and least absolute shrinkage and selection operator (LASSO) multivariate Cox regression analysis to identify risk signatures related to overall survival (OS). We further developed a nomogram to predict individual outcomes. In addition, the immune microenvironment was analyzed by CIBERSORT. Results256 differentially expressed genes (DEGs) were obtained based on aging, macrophage, autophagy, and methylation related genes between GBM samples and normal tissues in TCGA-GBM cohort. We identified five optimal risk signatures with prognostic values in TCGA-GBM cohort and established a prognostic risk score model. The validity of the model was verified in the CGGA cohort and Huanhu cohort. Finally, we constructed a nomogram for clinical application by combining age, O(6)-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, and risk score. Activated NK cells and resting mast cells were highly expressed and memory B cells, plasma cells, resting NK cells, M1 macrophages, and neutrophils exhibited low expression in the high-risk score group. GBM patients with a low-risk score had a higher Tumor Immune Dysfunction and Exclusion (TIDE) score. The risk score of hot tumors was higher than that of the cold tumors. Additionally, 29 genes involved in glucose and lipid metabolism were highly expressed with a high-risk score. 31 metabolism-related pathways were significantly different between high-risk and low-risk groups. ConclusionsWe constructed and validated a novel prognostic model for GBM. Aging, macrophage, autophagy, and methylation related genes may serve as prognostic and therapeutic biomarkers. The model developed may assist in guiding treatment for GBM patients. Our research had great significance in accurately predicting the prognosis of GBM and may offer reference for immunotherapy decision for GBM patients.

Achieving multi-modal brain disease diagnosis performance using only single-modal images through generative AI

Brain disease diagnosis using multiple imaging modalities has shown superior performance compared to using single modality, yet multi-modal data is not easily available in clinical routine due to cost or radiation risk. Here we propose a synthesis-empowered uncertainty-aware classification framework for brain disease diagnosis. To synthesize disease-relevant features effectively, a two-stage framework is proposed including multi-modal feature representation learning and representation transfer based on hierarchical similarity matching. Besides, the synthesized and acquired modality features are integrated based on evidential learning, which provides diagnosis decision and also diagnosis uncertainty. Our framework is extensively evaluated on five datasets containing 3758 subjects for three brain diseases including Alzheimer’s disease (AD), subcortical vascular mild cognitive impairment (MCI), and O[6]-methylguanine-DNA methyltransferase promoter methylation status for glioblastoma, achieving 0.950 and 0.806 in area under the ROC curve on ADNI dataset for discriminating AD patients from normal controls and progressive MCI from static MCI, respectively. Our framework not only achieves quasi-multimodal performance although using single-modal input, but also provides reliable diagnosis uncertainty.

Prediction of O(6)-methylguanine-DNA methyltransferase promoter methylation status in IDH-wildtype glioblastoma using MRI histogram analysis.

To evaluate the utility of magnetic resonance imaging (MRI) histogram parameters in predicting O(6)-methylguanine-DNA methyltransferase promoter (pMGMT) methylation status in IDH-wildtype glioblastoma (GBM). From November 2021 to July 2023, forty-six IDH-wildtype GBM patients with known pMGMT methylation status (25 unmethylated and 21 methylated) were enrolled in this retrospective study. Conventional MRI signs (including location, across the midline, margin, necrosis/cystic changes, hemorrhage, and enhancement pattern) were assessed and recorded. Histogram parameters were extracted and calculated by Firevoxel software based on contrast-enhanced T1-weighted images (CET1). Differences and diagnostic performance of conventional MRI signs and histogram parameters between the pMGMT-unmethylated and pMGMT-methylated groups were analyzed and compared. No differences were observed in the conventional MRI signs between pMGMT-unmethylated and pMGMT-methylated groups (all p > 0.05). Compared with the pMGMT-methylated group, pMGMT-unmethylated showed a higher minimum, mean, Perc.01, Perc.05, Perc.10, Perc.25, Perc.50, and coefficient of variation (CV) (all p < 0.05). Among all significant CET1 histogram parameters, minimum achieved the best distinguishing performance, with an area under the curve of 0.836. CET1 histogram parameters could provide additional value in predicting pMGMT methylation status in patients with IDH-wildtype GBM, with minimum being the most promising parameter.

MGMT promoter methylation prediction based on multiparametric MRI via vision graph neural network.

Glioblastoma (GBM) is aggressive and malignant. The methylation status of the -methylguanine-DNA methyltransferase (MGMT) promoter in GBM tissue is considered an important biomarker for developing the most effective treatment plan. Although the standard method for assessing the MGMT promoter methylation status is via bisulfite modification and deoxyribonucleic acid (DNA) sequencing of biopsy or surgical specimens, a secondary automated method based on medical imaging may improve the efficiency and accuracy of those tests. We propose a deep vision graph neural network (ViG) using multiparametric magnetic resonance imaging (MRI) to predict the MGMT promoter methylation status noninvasively. Our model was compared to the RSNA radiogenomic classification winners. The dataset includes 583 usable patient cases. Combinations of MRI sequences were compared. Our multi-sequence fusion strategy was compared with those using single MR sequences. Our best model [Fluid Attenuated Inversion Recovery (FLAIR), T1-weighted pre-contrast (T1w), T2-weighted (T2)] outperformed the winning models with a test area under the curve (AUC) of 0.628, an accuracy of 0.632, a precision of 0.646, a recall of 0.677, a specificity of 0.581, and an F1 score of 0.661. Compared to the winning models with single MR sequences, our ViG utilizing fused-MRI showed a significant improvement statistically in AUC scores, which are FLAIR (), T1w (), T1wCE (), and T2 (). Our model is superior to challenge champions. A graph representation of the medical images enabled good handling of complexity and irregularity. Our work provides an automatic secondary check pipeline to ensure the correctness of MGMT methylation status prediction.

EPID-15. SEX-SPECIFIC DIFFERENCES IN GLIOBLASTOMA IN THE RESPOND CONSORTIUM

Abstract AIM The goal of this study was to understand sex-specific differences in the molecular, clinical and radiological tumor parameters and survival outcomes of Glioblastoma (GBM) patients within the international GBM dataset, known as the ReSPOND (Radiomic Signatures for PrecisiON Diagnostics) consortium. METHODS Sex-based differences were retrospectively studied in 1922 GBM patients from the ReSPOND consortium which includes information from over 14 institutions across 3 continents. The parameters include age, Methylguanine-DNA Methyltransferase (MGMT) promoter methylation status, isocitrate dehydrogenase 1 (IDH1) mutation status, Karnofsky performance status (KPS), extent of resection (EOR), tumor epicenter, volumes, laterality and spatial extent. Non-parametric tests, log-rank test and cox-proportional hazard analysis were performed to understand sex-based differences in tumor parameters, survival rates and hazard ratios. Spatial atlases were generated to understand radiological parameters such as tumor spatial extent. RESULTS GBM in was diagnosed at a median age of 62.6 years in females compared to 61 years in males (p = 0.001). Additionally, 44% females compared to 37% males (p = 0.04) had methylated MGMT and 79% females compared to 73% males (p = 0.004) had IDH1 wildtype. The tumor volumes were smaller in females (necrotic core, edema, and enhancing tumor) compared to males. Females exhibited a higher prevalence of right hemisphere (39.6%) and right temporal lobe tumors (19.7%), while males showed a higher prevalence of left hemisphere (40.3%) left temporal lobe tumors (23.7%). No significant sex-based differences in OS and PFS was observed in overall sample, although longer PFS was observed in elderly (above 60 years) female patients. CONCLUSION This is a first international large cohort study looking at sex-based differences in GBM patients using the ReSPOND consortium data. Several sex-specific differences in the distribution of various tumor phenotypes were noted, however sex was not a contributing factor in OS and PFS.

Proteomic and metabolomic analyses illustrate the mechanisms of expression of the O6 -methylguanine-DNA methyltransferase gene in glioblastoma.

Glioblastoma (GBM) has been reported to be the most common high-grade primary malignant brain tumor in clinical practice and has a poor prognosis. O6 -methylguanine-DNA methyltransferase (MGMT) promoter methylation has been related to prolonged overall survival (OS) in GBM patients after temozolomide treatment. Proteomics and metabolomics were combined to explore the dysregulated metabolites and possible protein expression alterations in white matter (control group), MGMT promoter unmethylated GBM (GBM group) or MGMT promoter methylation positive GBM (MGMT group). In total, 2745 upregulated and 969 downregulated proteins were identified in the GBM group compared to the control group, and 131 upregulated and 299 downregulated proteins were identified in the MGMT group compared to the GBM group. Furthermore, 131 upregulated and 299 downregulated metabolites were identified in the GBM group compared to the control group, and 187 upregulated and 147 downregulated metabolites were identified in the MGMT group compared to the GBM group. The results showed that 94 upregulated and 19 downregulated proteins and 20 upregulated and 16 downregulated metabolites in the MGMT group were associated with DNA repair. KEGG pathway enrichment analysis illustrated that the dysregulated proteins and metabolites were involved in multiple metabolic pathways, including the synthesis and degradation of ketone bodies, amino sugar and nucleotide sugar metabolism. Moreover, integrated metabolomics and proteomics analysis was performed, and six key proteins were identified in the MGMT group and GBM group. Three key pathways were recognized as potential biomarkers for recognizing MGMT promoter unmethylated GBM and MGMT promoter methylation positive GBM from GBM patient samples, with areas under the curve of 0.7895, 0.7326 and 0.7026, respectively. This study provides novel mechanisms to understand methylation in GBM and identifies some biomarkers for the prognosis of two different GBM types, MGMT promoter unmethylated or methylated GBM, by using metabolomics and proteomics analyses.

Molecular imaging of gliomas.

Molecular characterization has become a key diagnostic tool for the classification and grading of primary brain tumors. Molecular markers, such as isocitrate dehydrogenase (IDH) mutation status, 1p/19q codeletion, methylation of the O(6)-methylguanine-DNA methyltransferase (MGMT) promoter, or CDKN2A/B homozygous deletion discriminate different tumor entities and grades, and play a crucial role for treatment response and prognosis. In recent years, magnetic resonance imaging (MRI), whose main functions has been to detect a tumor, to provide spatial information for neurosurgical and radiotherapy planning, and to monitor treatment response, has shown potential in assessing molecular features of gliomas from image-based biomarkers. As an outstanding example, numerous studies have proven that the T2/FLAIR mismatch sign can identify IDH-mutant, 1p/19q non-codeleted astrocytomas with a specificity of up to 100%. For other purposes, multiparametric MRI, often coupled with machine learning methods, seems to achieve the highest accuracy in predicting molecular markers. Relevant future applications might be anticipating changes in the molecular composition of gliomas and providing useful information about the cellular and genetic heterogeneity of gliomas, especially in the non-resected tumor parts.

BORTEM-17: A phase IB/II single arm, multicentre study investigating the efficacy of sequential bortezomib and temozolomide in recurrent GBM with unmethylated MGMT promoter—The results of an interim analysis.

2019 Background: Glioblastoma (GBM) is the most frequent, lethal primary brain tumor in adults. Patients harboring tumors with functional O6 methylguanine DNA methyltransferase ( MGMT) DNA enzyme gain limited benefit from temozolomide therapy. As shown in preclinical studies, bortezomib depletes the MGMT enzyme, restoring the tumor ́s susceptibility to temozolomide, if administered in a precise schedule when the MGMT enzyme is depleted. We hypothesized that recurrent GBM patients with unmethylated MGMT promoter may obtain clinical benefit from sequential BTZ and TMZ treatment. Methods: BORTEM-17 is a multicenter, open label, single arm, non-randomized phase IB/II trial to investigate potential survival benefit for recurrent glioblastoma patients administered bortezomib 48hrs prior to temozolomide. Sample size is calculated to 63 patients, of whom 10 included in phase IB. The doses of bortezomib is 1.3mg/m2 intravenously days 1, 4, and 7 during 4-week cycle, and temozolomide 200mg/m2 days 5-7. The control group is a retrospective cohort of 467 patients treated at 2 referral hospitals in Norway from January 2015 to December 2017. For the survival analysis, the patients included in BORTEM-17 (n=44) were compared with MGMT unmethylated control, age matched patients (n=116). The pre-defined interim analysis was performed after inclusion of 15 patients and as more than 2 of 15 patients had clinical benefit, the study was continued. Results: Until January 2023, 44 patients with median age 55 years (range 25-69), 30 males and 14 females were treated. Median KPS was 90 (70-100) and median NANO score 1 (0-7). The interim analysis was performed after inclusion of 15 patients in the phase IB and II and clinical benefit was observed in 5 patients. Two of them had tumor volume reduction and three experienced stable disease. The study proceeded to the next step. No treatment related deaths were observed. The most common adverse effects included hematological toxicity, gastrointestinal symptoms, muscle weakness, lower back pain and fatigue. Patients that progressed during the BORTEM-17 trial and were fit for further therapy received treatment at their physician’s discretion. The preliminary data analysis after 44 of planned 63 patients were treated indicate prolonged median survival for BORTEM-17 patients 19.0 months vs 12.2 months of control MGMT unmethylated age matched patients. Median survival after recruitment is 5.5 months (1.0-23.8). Conclusions: The sequential BTZ+TMZ therapy is safe, feasible and effective as indicated by preliminary data when 70% of planned MGMT unmethylated patients have been included. Preliminary data indicate that the combination of BTZ and TMZ may offer an additional line of treatment with limited toxicity to the group of patients with particularly dismal prognosis. Clinical trial information: NCT03643549 .

Identification of MGMT Downregulation Induced by miRNA in Glioblastoma and Possible Effect on Temozolomide Sensitivity.

Glioblastoma multiforme (GBM) remains one of the tumors with the worst prognosis. In recent years, a better overall survival (OS) has been described in cases subjected to Gross Total Resection (GTR) that were presenting hypermethylation of Methylguanine-DNA methyltransferase (MGMT) promoter. Recently, also the expression of specific miRNAs involved in MGMT silencing has been related to survival. In this study, we evaluate MGMT expression by immunohistochemistry (IHC), MGMT promoter methylation and miRNA expression in 112 GBMs and correlate the data to patients' clinical outcomes. Statistical analyses demonstrate a significant association between positive MGMT IHC and the expression of miR-181c, miR-195, miR-648 and miR-767.3p between unmethylated cases and the low expression of miR-181d and miR-648 and between methylated cases and the low expression of miR-196b. Addressing the concerns of clinical associations, a better OS has been described in presence of negative MGMT IHC, in methylated patients and in the cases with miR-21, miR-196b overexpression or miR-767.3 downregulation. In addition, a better progression-free survival (PFS) is associated with MGMT methylation and GTR but not with MGMT IHC and miRNA expression. In conclusion, our data reinforce the clinical relevance of miRNA expression as an additional marker to predict efficacy of chemoradiation in GBM.

Givinostat Inhibition of Sp1-dependent MGMT Expression Sensitizes Glioma Stem Cells to Temozolomide.

Givinostat is a pan-histone deacetylase (HDAC) inhibitor that has demonstrated excellent tolerability as well as efficacy in patients with polycythemia vera. Accumulating in vitro and in vivo evidence suggests givinostat is also promising as a therapeutic agent targeting glioma stem cells (GSCs), the cancer stem cells of glioblastoma (GBM) considered responsible for its intractable nature. However, it remains to be shown how givinostat impacts the therapeutic effects of temozolomide, a DNA-alkylating agent and the key component of GBM treatment given not only during postoperative radiotherapy but also thereafter as maintenance chemotherapy. The effects of givinostat and knockdown of O6-methylguanine-DNA methyltransferase (MGMT) or Sp1 on the mRNA and protein expression of relevant genes in human GSC lines were examined by RT-PCR and western blot analyses. The dye exclusion method was used to evaluate cell viability. Givinostat enhanced the cytotoxic activity of temozolomide in GSC lines expressing MGMT, in which the MGMT expression was shown to contribute to their temozolomide resistance. Givinostat inhibited MGMT expression in GSCs and, in parallel, the expression of Sp1, a transcription factor involved in the control of MGMT promoter activity. Knockdown experiments demonstrated Sp1 expression was indeed required for MGMT expression in GSCs. Givinostat, in addition to its own cytotoxic activity, sensitizes GSCs to temozolomide by inhibiting Sp1-dependent MGMT expression in GSCs. Combining givinostat with temozolomide could therefore be a rational therapeutic strategy to effectively eliminate GSCs and thus help overcome the therapy resistance of GBM.

Brain Tumor Radiogenomic Classification of O6-Methylguanine-DNA Methyltransferase Promoter Methylation in Malignant Gliomas-Based Transfer Learning.

Artificial Intelligence (AI) is the subject of a challenge and attention in the field of oncology and raises many promises for preventive diagnosis, but also fears, some of which are based on highly speculative visions for the classification and detection of tumors. A brain tumor that is malignant is a life-threatening disorder. Glioblastoma is the most prevalent kind of adult brain cancer and the 1 with the poorest prognosis, with a median survival time of less than a year. The presence of O6 -methylguanine-DNA methyltransferase (MGMT) promoter methylation, a particular genetic sequence seen in tumors, has been proven to be a positive prognostic indicator and a significant predictor of recurrence.This strong revival of interest in AI is modeled in particular to major technological advances which have significantly increased the performance of the predicted model for medical decision support. Establishing reliable forecasts remains a significant challenge for electronic health records (EHRs). By enhancing clinical practice, precision medicine promises to improve healthcare delivery. The goal is to produce improved prognosis, diagnosis, and therapy through evidence-based sub stratification of patients, transforming established clinical pathways to optimize care for each patient's individual requirements. The abundance of today's healthcare data, dubbed "big data," provides great resources for new knowledge discovery, potentially advancing precision treatment. The latter necessitates multidisciplinary initiatives that will use the knowledge, skills, and medical data of newly established organizations with diverse backgrounds and expertise.The aim of this paper is to use magnetic resonance imaging (MRI) images to train and evaluate your model to detect the presence of MGMT promoter methylation in this competition to predict the genetic subtype of glioblastoma based transfer learning. Our objective is to emphasize the basic problems in the developing disciplines of radiomics and radiogenomics, as well as to illustrate the computational challenges from the perspective of big data analytics.

A Multimodal Knowledge-Based Deep Learning Approach for MGMT Promoter Methylation Identification.

Glioblastoma Multiforme (GBM) is considered one of the most aggressive malignant tumors, characterized by a tremendously low survival rate. Despite alkylating chemotherapy being typically adopted to fight this tumor, it is known that O(6)-methylguanine-DNA methyltransferase (MGMT) enzyme repair abilities can antagonize the cytotoxic effects of alkylating agents, strongly limiting tumor cell destruction. However, it has been observed that MGMT promoter regions may be subject to methylation, a biological process preventing MGMT enzymes from removing the alkyl agents. As a consequence, the presence of the methylation process in GBM patients can be considered a predictive biomarker of response to therapy and a prognosis factor. Unfortunately, identifying signs of methylation is a non-trivial matter, often requiring expensive, time-consuming, and invasive procedures. In this work, we propose to face MGMT promoter methylation identification analyzing Magnetic Resonance Imaging (MRI) data using a Deep Learning (DL) based approach. In particular, we propose a Convolutional Neural Network (CNN) operating on suspicious regions on the FLAIR series, pre-selected through an unsupervised Knowledge-Based filter leveraging both FLAIR and T1-weighted series. The experiments, run on two different publicly available datasets, show that the proposed approach can obtain results comparable to (and in some cases better than) the considered competitor approach while consisting of less than 0.29% of its parameters. Finally, we perform an eXplainable AI (XAI) analysis to take a little step further toward the clinical usability of a DL-based approach for MGMT promoter detection in brain MRI.

Immunohistochemical Expression of PD-L1 and IDH1 with Detection of MGMT Promoter Methylation in Astrocytoma

Programmed death ligand 1 (PD-L1) expression was suggested as a poor prognostic predictor for glioblastoma. While isocitrate dehydrogenase (IDH) has been linked to enhanced overall survival in glioma cells. In glioblastoma patients receiving treatment with alkylating drugs, the methylguanine-DNA methyltransferase (MGMT) promoter's methylation status has been discovered as a potent and distinct predictor of good survival. In this study, we aimed to investigate the expression rate of PD-L1, IDH1, and MGMT methylation in patients with different grades of astrocytoma. The present retrospective study retrieved the data and archived paraffin blocks of 60 cases of astrocytoma. Immunohistochemical evaluation was done to assess the expressions of PD-L1 and IDH1, Methylation-specific-PCR was used to investigate the MGMT promoter. This study included astrocytoma grade II 18% (11/60), grade III 22% (13/60), grade IV 60% (36 cases). PD-L1 expression was detected in 82% of all studied cases (49/60) while IDH1 mutant astrocytoma were 73% (44/60) & methylation was reported in 58.3% (35 cases). High grade astrocytoma showed highrer expression of PD-L1 & IDH1 but with insignificant correlation (p=0.989). There is a relatively high expression of PD-L1 and IDH1 in patients with astrocytoma. More than half of the patients presented with MGMT promoter methylation. Further studies with larger sample size are required to investigate the association between these biomarkers and characteristics of patients with astrocytoma.

RTID-05. TRIAL IN PROGRESS: DOSE-FINDING STUDY AND EVALUATION OF [177LU]LU-DOTA-TATE IN COMBINATION WITH STANDARD OF CARE IN NEWLY DIAGNOSED GLIOBLASTOMA AND AS A SINGLE AGENT IN RECURRENT GLIOBLASTOMA

Abstract Patients with glioblastoma have a poor prognosis despite extensive efforts to develop new treatments. Standard of care for newly diagnosed patients is surgery followed by radiotherapy plus temozolomide. However, temozolomide only provides a clinical benefit to patients who have glioblastomas with a methylated O(6)-methylguanine-DNA methyltransferase (MGMT) gene promotor. Disease recurrence is inevitable and subsequent treatments have poor outcomes due to limited efficacy and a lack of established therapeutic regimens. [68Ga]Ga-DOTA-TATE is a radioligand imaging agent that selectively binds to somatostatin receptors (SSTRs) and is used with positron emission tomography (PET) for the molecular imaging of SSTR-positive neuroendocrine tumors. Glioblastoma tumoral PET scans have shown moderate uptake of [68Ga]Ga-DOTA-TATE. The radioligand therapy [177Lu]Lu-DOTA-TATE has the same SSTR-targeted ligand and demonstrated promising clinical activity in a pilot study in patients with Grade III/IV gliomas. This Phase 1b open-label dose-finding study (NCT05109728) will determine the recommended [177Lu]Lu-DOTA-TATE dose in three different groups of glioblastoma (newly diagnosed with methylated MGMT promoter [group 1], newly diagnosed with unmethylated MGMT promoter [group 2], and recurrent disease [group 3]). All participants will be scanned with [68Ga]Ga-DOTA-TATE imaging during screening and, for each group, approximately 15 participants will be enrolled in consecutive cohorts of 3–6 participants. All participants will receive up to 6 administrations of [177Lu]Lu-DOTA-TATE (initial dose 150 mCi with 3 provisional dose levels up to 250 mCi) using the Bayesian Optimal Interval design. Group 1 will receive [177Lu]Lu-DOTA-TATE every 4 weeks with concomitant radiotherapy and temozolomide, followed by temozolomide maintenance. Group 2 will receive [177Lu]Lu-DOTA-TATE every 4 weeks for 3 doses with radiotherapy then every 3 weeks as a single agent. Group 3 will receive [177Lu]Lu-DOTA-TATE only, every 3 weeks. The primary endpoint is incidence of dose limiting toxicity. Secondary endpoints include overall objective status (modified RANO criteria), progression free survival, overall survival and safety.

140P Retrospective review of actionable IDH mutations and MGMT promoter methylation among CNS tumors

In the revised World Health Organization (WHO) Classification of Tumors of the Central Nervous System, molecular genetic alterations have been incorporated to the classic histology of primary brain tumors (PBT), including isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) mutations and methylation of the O6 -methylguanine-DNA methyltransferase promoter (MGMT). The IDH mutation is associated with a better prognosis in patients with glioma, independently of histological parameters and tumor grade. MGMT methylation silences the MGMT gene and reduces the ability of tumor cells to repair damage caused by temozolomide and other alkylating agents.

P12.06.A Relationship between ascorbate and DNA methylation markers in clinical glioma tumours

Abstract Background Members of the 2-oxoglutarate-dependent dioxygenase (OGDD) enzyme family play an important role in gliomas as they regulate epigenetic modifications and response to hypoxia. The OGDDs require 2-OG and O2 as substrates, and ferrous iron and ascorbate as cofactors. Both hypoxia and aberrant DNA methylation are prognostic indicators for gliomas. The ten-eleven translocase (TET) DNA demethylases are OGDDs that convert 5-methyl cytosine (5mC) to 5-hydroxymethylcytosine (5hmC), with 5hmC levels related to better prognosis. Despite this, there is limited data on the OGDD enzymes and their substrates/cofactors in glioma tissues. Our previous study showed an association between ascorbate content and markers of the hypoxic response in glioblastoma tissue. Here we determine whether there is an association between ascorbate and DNA methylation in glioma. In addition, we assess whether methylation of the methylguanine-DNA methyltransferase (DNA repair enzyme MGMT) promoter is associated with ascorbate content. Materials and methods Frozen clinical glioma samples from 37 patients (n=11 WHO grade I-III, n=26 glioblastoma) were obtained from the Cancer Society Tissue Bank (Ethics approval H19/163). Isocitrate dehydrogenase 1 (IDH1) mutation status was determined by sequencing. Samples were processed on dry ice in liquid nitrogen and analysed for ascorbate (high-performance liquid chromatography), global DNA methylation (mass spectrometry) and MGMT promoter analyses (methylation specific PCR). Results Many grade I-III tumours were IDH1 R132H mutant (6/11), and most glioblastomas were not (2/26). Glioblastoma had significantly lower ascorbate content than grade I-III tumours (p=0.026). Glioblastoma also had lower global 5hmC levels (p=0.0013). IDH1 R132H tumours tended to have a lower ascorbate content (p=0.09). Ascorbate and 5hmC levels were directly correlated (Spearman r= 0.466, p=0.004). However, cytosine and 5mC showed no association with grade or ascorbate. MGMT promoter methylation status was not associated with global methylation or ascorbate content (p=0.97, p=0.96, respectively). Conclusion Our data suggests that ascorbate supports TET activity in clinical glioma. It also appears that site-specific (promoter) methylation was not affected by ascorbate availability. These findings may have clinical implications, as higher 5hmC levels are associated with improved outcome, whilst continued MGMT suppression suggests chemotherapy responsiveness. However, evidence that raising tumour ascorbate leads to increased 5hmC levels, or an associated improvement in survival, requires intervention trials.

Quantitative Analysis of the MGMT Methylation Status of Glioblastomas in Light of the 2021 WHO Classification.

Simple SummaryThe status of a DNA repair protein called MGMT is a prognostic marker in patients with glioblastomas, the most frequent malignant brain tumor. Epigenetic silencing of this gene predicts increased sensitivity to chemotherapy. Silencing is assessed by analyzing modifications, so-called methylation, of a certain gene region. Whereas most studies report such information only in a qualitative manner, quantitative testing might provide additional information. The aim of our study was to determine a quantitative threshold for better survival among patients with glioblastomas. Among 321 patients suffering from glioblastoma, we found better survival in patients with glioblastomas that have ≥16% methylation of a particular region of the MGMT gene. Above 16% methylation, we found no additional benefit with increasing degree of methylation. We suggest using this threshold for selection in clinical trials and for patient counseling.Background: Glioblastomas with methylation of the promoter region of the O(6)-methylguanine-DNA methyltransferase (MGMT) gene exhibit increased sensitivity to alkylating chemotherapy. Quantitative assessment of the MGMT promoter methylation status might provide additional prognostic information. The aim of our study was to determine a quantitative methylation threshold for better survival among patients with glioblastomas. Methods: We included consecutive patients ≥18 years treated at our department between 11/2010 and 08/2018 for a glioblastoma, IDH wildtype, undergoing quantitative MGMT promoter methylation analysis. The primary endpoint was overall survival. Results: A total of 321 patients were included. Median overall survival was 12.6 months. Kaplan–Meier and adjusted Cox regression analysis showed better survival for the groups with 16–30%, 31–60%, and 61–100% methylation. In contrast, survival in the group with 1–15% methylation was similar to those with unmethylated promoter regions. A secondary analysis confirmed this threshold. Conclusions: Better survival is observed in patients with glioblastomas with ≥16% methylation of the MGMT promoter region than with <16% methylation. Survival with tumors with 1–15% methylation is similar to with unmethylated tumors. Above 16% methylation, we found no additional benefit with increasing methylation.

Bortezomib sensitization of recurrent glioblastoma with unmethylated MGMT promoter to temozolomide, a phase II study (NCT03643549).

TPS2081 Background: Patients with glioblastoma with functional O6 methylguanine DNA methyltransferase ( MGMT) DNA repair enzyme gain limited benefit from temozolomide (TMZ). Bortezomib depletes the MGMT enzyme, restoring the tumor ́s susceptibility to TMZ, if the chemotherapy is administered in the precise schedule. Additionally, bortezomib inhibits the tumor growth by blocking autophagy flux. Thus, pre-treatment with bortezomib prior to temozolomide leads to DNA repair enzyme depletion and blockade of autophagy-induced survival signals. Methods: The academic, industry independent, multicenter, open label, single arm, non-randomized phase II trial is designed to investigate the survival benefits for patients treated with bortezomib 48hrs prior to TMZ. Efficacy of this therapy will be compared to historical cohorts receiving standard management. The study will include 53 patients with recurrent or progressive, MGMT unmethylated glioblastoma. The additional 10 patients were treated in the phase IB. All patients will receive a combination of bortezomib 1.3mg/m2 administered IV on days 1, 4, 7, during each 4-week chemotherapy cycle with PO TMZ at 200mg/m2 5 days/week q4w starting on day 3. Dose reduction of TMZ is allowed if reduced bone marrow tolerance is present. Major eligibility criteria include histologically confirmed glioblastoma with unmethylated MGMT promoter, MRI evidence of recurrence within 14 days prior to enrolment, age ≥ 18 years with life expectancy &gt; 8 weeks, KPS ≥ 70, radiologically (MRI) confirmed tumor relapse/progression ≥ 12 weeks since completed radiotherapy, tumor not available for radiosurgery, adequate bone marrow, renal and liver function, no contraindications for bortezomib and/or TMZ. Endpoints: Estimation of the median progression free survival (PFS) and overall survival (OS) of patients with recurrent or progressed glioblastoma as well as progression free rate at 6 months. Secondary objectives: Therapy response assessed by contrast enhanced MRI (RANO criteria) and neurological examination (NANO criteria) as well as identification of novel biomarkers that correlate with treatment response. Twenty-four of the planned 53 patients are enrolled. Five of them receive treatment. The prespecified activity goal for the first stage of the study was met. Discussion: Patients with glioblastoma harboring active MGMT enzyme have median survival of 12.7 months, compared to 21.7 months for patients with the MGMT gene promoter silenced by methylation. This dismal prognosis for the approx. 55% of GBM patients underscores the unmet need for novel combination therapies that sensitize the tumors to chemotherapy. Results from this trial will serve as preliminary evidence of the role of bortezomib administered in a precise manner to abolish the temozolomide resistance of GBM with unmethylated MGMT promotor. The study is currently active and recruiting. Clinical trial information: NCT03643549.

Monocentric evaluation of Ki-67 labeling index in combination with a modified RPA score as a prognostic factor for survival in IDH-wildtype glioblastoma patients treated with radiochemotherapy

PurposeThe prognosis for glioblastoma patients remains dismal despite intensive research on better treatment options. Molecular and immunohistochemical markers are increasingly being investigated as understanding of their role in disease progression grows. O(6)-methylguanine-DNA methyltransferase (MGMT) promoter methylation has been shown to have prognostic and therapeutic relevance for glioblastoma patients. Other markers implicated in tumor formation and/or malignancy are p53, Alpha thalassemia/mental retardation syndrome X-linked (ATRX), Epidermal Growth Factor Receptor splice variant III (EGFRvIII), and Ki-67, with loss of nuclear ATRX expression and lower Ki-67 index being associated with prolonged survival. For p53 and EGFRvIII the data are contradictory. Our aim was to investigate the markers mentioned above regarding progression-free (PFS) and overall survival (OS) to evaluate their viability as independent prognostic markers for our patient collective.MethodsIn this retrospective study, we collected data on patients undergoing radiotherapy due to isocitrate dehydrogenase (IDH) wildtype glioblastoma at a single university hospital between 2014 and 2020.ResultsOur findings confirm Ki-67 labeling index ≤ 20% as an independent prognostic factor for prolonged PFS as well as MGMT promoter methylation for both prolonged PFS and OS, in consideration of age and Eastern Cooperative Oncology Group (ECOG) status, chemotherapy treatment, and total radiation dose for PFS as well as additionally sex, resection status, and receipt of treatment for progression or recurrence for OS. Additionally, Ki-67 labeling index ≤ 20% showed a significant correlation with prolonged OS in univariate analysis. Modification of the recursive partitioning analysis (RPA) score to include Ki-67 labeling index resulted in a classification with the possible ability to distinguish long-term-survivors from patients with unfavorable prognosis.ConclusionMGMT promoter methylation and Ki-67 labeling index were independent predictors of survival in our collective. We see further studies pooling patient collectives to reach larger patient numbers concerning Ki-67 labeling index as being warranted.

Associations among smoking, IDH mutations, MGMT promoter methylation, and grading in glioma: a cross-sectional study

Background Several molecular markers have important roles in glioma management. Mutations in the isocitrate dehydrogenase (IDH) gene are associated with the grading and prognosis of glioma. Methylation in the promoter region of the O (6)-methylguanine-DNA methyltransferase (MGMT) gene is an important determinant of glioma sensitivity to alkylating agents. Studies in various cancers indicated that IDH1 mutations and MGMT promoter methylations were associated with smoking habits. However, these associations in gliomas are still unclear. Accordingly, this study aimed to examine the association among smoking, IDH1 mutations, MGMT promoter methylation, and grading in glioma patients. Methods Patients were recruited from Dr. Sardjito General Hospital (a referral hospital in Yogyakarta and Central Java region) and its network hospitals. Genomic DNA was extracted from formalin-fixed paraffin-embedded samples or fresh glioma tissues. Identification of IDH1 mutation was performed using Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP) or DNA sequencing. Methylation-specific real-time PCR was performed to identify MGMT promoter methylation status. Smoking status was obtained by history taken from the patient or family members. Results In total, 122 patients were included in this study. As many as 35 patients (28.7%) had a smoking history. Most smokers (57.1%) smoke less than ten cigarettes per day. However, most of them (68.8%) have been smoking for more than 20 years. Smoking patients have a significantly higher proportion of high-grade glioma than non-smokers (80% vs. 55.2%, p=0.01). Among 122 patients, 24 (19.7%) of them carried IDH1 mutation. Smoking patients have a significantly higher proportion of IDH1 mutation compared with non-smokers (31.4% vs. 14.9%, p&lt;0.001). No significant association was found between intensity and duration of smoking with IDH1 mutations and glioma grading. No significant association was found between smoking and MGMT promoter methylation. Conclusions In glioma patients, smoking is associated with IDH1 mutations and grading but not with MGMT promoter methylation.