Methyltransferase Promoter Methylation Research Articles

Correlations of O6‑methylguanine DNA methyltransferase (MGMT) promoter methylation status with magnetic resonance imaging texture features and prognosis of glioblastomas

Correlations of <i>O⁶</i>‑methylguanine DNA methyltransferase (<i>MGMT</i>) promoter methylation status with magnetic resonance imaging texture features and prognosis of glioblastomas

CTNI-31. AI-GUIDED PERSONALIZED PRECISION RADIATION THERAPY WITH TARGETED DOSE ESCALATION FOR NEWLY DIAGNOSED GLIOBLASTOMA: A MATCHED-CONTROL STUDY

Abstract BACKGROUND The purpose of this study was to assess the feasibility and effectiveness of AI-guided personalized precision radiation therapy (PPRT) with targeted dose escalation in enhancing outcomes for patients with newly diagnosed glioblastoma. METHODS An open-label trial was conducted at the University of Pennsylvania (NCT03477513) from August 2018 to April 2023, enrolling 20 patients with IDH-wildtype glioblastoma who underwent maximal safe resection. They were matched with a contemporaneous cohort of glioblastoma patients based on age, sex, extent of resection (EOR), O6-methylguanine-DNA methyltransferase promoter (MGMTp) methylation status, and IDH status. Propensity score matching (PSM) was employed to select the control group, utilizing one-to-four nearest neighbor matching. One patient was excluded due to concurrent treatment with tumor treating fields (TTFields). The PPRT group received personalized radiation dose escalation to 75 Gy in 30 fractions guided by AI-based predictive modeling of recurrence, along with temozolomide chemotherapy. The control group received standard-of-care chemoradiotherapy, 60 Gy in 30 fractions. A previously published and evaluated (retrospectively and prospectively) predictive AI model, which has demonstrated high predictive value for neoplastic cell infiltration and future tumor recurrence using preoperative, multi-parametric MRIs, was utilized. RESULTS Median overall survival was 24.3 months in the PPRT-temozolomide group compared to 17.5 months in the standard-of-care treatment group (hazard ratio [HR]=0.30; 95% CI: 0.16-0.57; p&amp;lt;0.001). Excluding two patients with leptomeningeal disease and bone marrow metastasis, the median survival was 35.4 months (HR=0.25; 95% CI: 0.12-0.51; p&amp;lt;0.001). CONCLUSION AI-guided PPRT for newly diagnosed glioblastoma patients demonstrated feasibility in routine clinical practice and significantly improved overall survival compared to matched controls receiving standard-of-care treatment. These findings underscore the potential of personalized precision radiation therapy, with focused dose escalation, in improving outcomes for glioblastoma patients and emphasize the need for prospective validation in a randomized controlled clinical trial.

A Comparative Study of Methyl-BEAMing and Droplet Digital PCR for MGMT Gene Promoter Hypermethylation Detection

Background: O-6-methylguanine-DNA methyltransferase is responsible for the direct repair of O6-methylguanine lesions induced by alkylating agents, including temozolomide. O-6-methylguanine-DNA methyltransferase promoter hypermethylation is a well-established biomarker for temozolomide response in glioblastoma patients, also correlated with therapeutic response in colorectal cancer. Objectives: The ARETHUSA clinical trial aims to stratify colorectal cancer patients based on their mismatch repair status. Mismatch repair-deficient patients are eligible for treatment with immune checkpoint inhibitors (anti-PDL-1), whereas mismatch repair-proficient samples are screened for O-6-methylguanine-DNA methyltransferase promoter methylation to identify those suitable for temozolomide treatment. Methods: In this context, a subset of ARETHUSA metastatic colorectal cancer samples was used to compare two different techniques for assessing O-6-methylguanine-DNA methyltransferase hypermethylation: Methyl-BEAMing, a highly sensitive digital PCR approach that combines emulsion PCR and flow cytometry, and droplet digital PCR, a more automated procedure that enables the rapid, operator-independent analysis of a large number of samples. Results: Our study clearly demonstrates that the results obtained using Methyl-BEAMing and droplet digital PCR are comparable, with both techniques showing similar accuracy, sensitivity, and reproducibility. Conclusions: Digital droplet PCR proved to be an efficient method for detecting gene promoter methylation. However, the Methyl-BEAMing method has proved more sensitive for detecting low quantities of DNA.

Dynamic susceptibility contrast‑enhanced perfusion magnetic resonance imaging parameters for predicting MGMT promoter methylation and prognostic value in newly diagnosed patients with glioblastoma.

O6-methylguanine DNA methyltransferase (MGMT) promoter methylation is an important clinical biomarker of newly diagnosed glioblastoma. Previous radiological studies using dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) perfusion have aimed to predict MGMT methylation status non-invasively in gliomas with radiological characteristics. The possibility of predicting MGMT methylation status using DSC-MRI perfusion with a radiological approach remains controversial. The present study aimed to evaluate the usefulness of MRI perfusion parameters as non-invasive markers to predict MGMT methylation status and prognosis in newly diagnosed glioblastoma patients. Thus, 50 patients with histologically confirmed primary glioblastoma, IDH-wildtype who underwent tumor resection at Osaka University Hospital (Suita, Japan) between January 2017 and January 2023 were included in this study. The mean cerebral blood volume (CBV) ratio (rCBV) and cerebral blood flow (CBF) ratio (rCBF) for tumors with MGMT methylation (mean rCBV:2.09 and mean rCBF:3.08) were significantly higher compared with those for tumors without MGMT methylation (mean rCBV:1.33 and mean rCBF:1.85; P<0.05). While patients with MGMT methylation had longer progression-free survival (PFS) compared with those without MGMT methylation (P<0.05), there was no significant difference in OS with or without MGMT methylation (P=0.06). By contrast, there was no association between MRI perfusion parameters and OS or PFS in patients with glioblastoma. Furthermore, the association between CBV, CBF, MGMT promotor methylation status, OS, and PFS were explored. There was no significant prognostic difference between low vascularity tumors (rCBV <1.3 or rCBF <1.8) with or without MGMT methylation. On the other hand, high vascularity tumors (rCBF ≥1.8) with MGMT promotor methylation were associated to longer OS and PFS compared with those without. However, there was no association between MGMT methylation status and OS or PFS in patients with high rCBV (rCBV ≥1.3). The present study indicated that CBV and CBF could be used to predict the MGMT methylation status in glioblastomas. However, the prognostic value of tumor vascularity and MGMT methylation status may be limited.

The Role of Systemic Therapies in the Treatment of Grades 1-4 Gliomas.

The primary treatment for gliomas typically involves tumor resection followed by adjuvant radiotherapy, with increasing emphasis on chemotherapy and molecularly targeted drugs. This study aimed to review and summarize the literature on the systemic therapy of malignant gliomas.Chemotherapy may be considered in grades 2 and 3 gliomas, especially when mutations in 1p19q-codeletion are detected. The beneficial impact of adding chemotherapy to radiotherapy (PCV: procarbazine, lomustine, vincristine) has also been demonstrated. In grade 4 glioblastoma multiforme (GBM), wild-type isocitrate dehydrogenase (IDH) status showed the best treatment outcomes with temozolomide (TMZ) in patients with O-6-methylguanine-DNA methyltransferase (MGMT)promoter methylation. Prolonging adjuvant TMZ therapy improves treatment outcomes compared to the standard 6-cycle adjuvant therapy. Bevacizumab (BEV) monotherapy can improve progression-free survival and maintain the initial quality of life. Despite advancements in GBM treatment, outcomes remain unsatisfactory, with a median survival of 14-16 months. Further research is still needed regarding the systemic treatment of central nervous system gliomas.

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.

Risk Factors of Distant Recurrence and Dissemination of IDH Wild-Type Glioblastoma: A Single-Center Study and Meta-Analysis.

Isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) is a highly aggressive brain tumor with a high recurrence rate despite adjuvant treatment. This study aimed to evaluate the risk factors for non-local recurrence of GBM. In the present study, we analyzed 104 GBMs with a single lesion (non-multifocal or multicentric). Univariate analysis revealed that subventricular zone (SVZ) involvement was significantly associated with non-local recurrence (hazard ratio [HR]: 2.09 [1.08-4.05]). Tumors in contact with the trigone of the lateral ventricle tended to develop subependymal dissemination (p = 0.008). Ventricular opening via surgery did not increase the risk of non-local recurrence in patients with SVZ involvement (p = 0.190). A systematic review was performed to investigate the risk of non-local recurrence, and 21 studies were identified. A meta-analysis of previous studies confirmed SVZ involvement (odds ratio [OR]: 1.30 [1.01-1.67]) and O-6-methylguanine DNA methyltransferase promoter methylation (OR: 1.55 [1.09-2.20]) as significant risk factors for local recurrence. A time-dependent meta-analysis revealed a significant association between SVZ involvement and dissemination (HR: 1.69 [1.09-2.63]), while no significant association was found for distant recurrence (HR: 1.29 [0.74-2.27]). Understanding SVZ involvement and specific tumor locations associated with non-local recurrence provides critical insights for the management of GBM.

Unveiling hierarchy and spatial distribution of O6-methylguanine-DNA methyltransferase promoter methylation in World Health Organization grade 2-3 gliomas.

This study investigated the role of O6-methylguanine-DNA methyltransferase promoter (MGMTp) methylation hierarchy and heterogeneity in grade 2-3 gliomas, focusing on variations in chemotherapy benefits and resection dependency. A cohort of 668 newly diagnosed grade 2-3 gliomas, with comprehensive clinical, radiological, and molecular data, formed the basis of this analysis. The extent of resection was categorized into gross total resection (GTR ≥100%), subtotal resection (STR >90%), and partial resection (PR ≤90%). MGMTp methylation levels were examined using quantitative pyrosequencing. Our findings highlighted the critical role of GTR in improving the prognosis for astrocytomas (IDH1/2-mutant and 1p/19q non-codeleted), contrasting with its lesser significance for oligodendrogliomas (IDH1/2 mutation and 1p/19q codeletion). Oligodendrogliomas demonstrated the highest average MGMTp methylation levels (median: 28%), with a predominant percentage of methylated cases (average methylation levels >20%). Astrocytomas were more common in the low-methylated group (10%-20%), while IDH wild-type gliomas were mostly unmethylated (<10%). Spatial distribution analysis revealed a decrement in frontal lobe involvement from methylated, low-methylated to unmethylated cases (72.8%, 59.3%, and 47.8%, respectively). In contrast, low-methylated and unmethylated cases were more likely to invade the temporal-insular region (19.7%, 34.3%, and 40.4%, respectively). Astrocytomas with intermediate MGMTp methylation were notably associated with temporal-insular involvement, potentially indicating a moderate response to temozolomide and underscoring the importance of aggressive resection strategies. In conclusion, our study elucidates the complex interplay of MGMTp methylation hierarchy and heterogeneity among grade 2-3 gliomas, providing insights into why astrocytomas and IDH wild-type lower-grade glioma might derive less benefit from chemotherapy.

AB003. Clinical analysis of central nervous system (CNS) World Health Organization (WHO) grade 4 gliomas with IDH-mutant versus IDH-wildtype focused on CDKN2A homozygous deletion.

We are primarily investigating the prognostic role of cell-cycle-dependent kinase inhibitor (CDKN)-2A homozygous deletion in central nervous system (CNS) World Health Organization (WHO) grade 4 gliomas. Additionally, traditional prognostic factors for grade 4 gliomas will be examined, and our results will be validated. We conducted a retrospective analysis of glioma cohorts in our institute. Medical records were reviewed for 142 glioblastoma patients for 15 years, and pathological slides were examined again for the updated diagnosis according to the 2021 WHO classification of CNS tumors. The isocitrate dehydrase (IDH) mutation and CDKN2A deletion were examined by next generation sequencing (NGS) analysis using ONCO accuPanel®. Traditional prognostic factors including age, WHO performance status, extent of resection, and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation were examined. After the exclusion of 6 patients with poor status of pathologic samples, 136 glioblastoma that were diagnosed by previous WHO criteria were changed into 29 (21.3%) astrocytoma, IDH-mutant, CNS WHO grade 4 and 107 (78.7%) glioblastoma, IDH-wildtype, CNS WHO grade 4. Among them, 61 patients (56.0%) had CDKN2A deletion. Group A with IDH-wildtype and CDKN2A deletion had a mean overall survival (OS) of 15.70 months [95% confident interval (CI): 13.86-17.54], group B with IDH-mutant and CDKN2A deletion had a mean OS of 19.37 months (95% CI: 13.43-25.30), group C with IDH-wildtype and intact CDKN2A had a mean OS of 22.63 months (95% CI: 20.10-25.17), and group D with IDH-mutant and intact CDKN2A had a mean OS of 33.38 months (95% CI: 29.35-37.40). Multifactor analysis showed following factors were independently associated with OS: age [≥50 vs. <50 years; hazard ratio (HR) 4.642], extent of resection (gross total resection vs. others; HR 5.523), WHO performance (0, 1 vs. 2; HR 5.007), MGMT promoter methylation, (methylated vs. unmethylated; HR 5.075), IDH mutation (mutant vs. wildtype; HR 6.358), and CDKN2A deletion (absence vs. presence; HR 13.452). The presenting study suggests that CDKN2A deletion should play a powerful prognostic role in CNS WHO grade 4 gliomas as well as low-grade glioma. Even if CNS WHO grade 4 gliomas had mutant IDH, they can have poor clinical outcomes due to CDKN2A deletion.

Adaptive fine-tuning based transfer learning for the identification of MGMT promoter methylation status.

Background.Glioblastoma Multiforme (GBM) is an aggressive form of malignant brain tumor with a generally poor prognosis.O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation has been shown to be a predictive bio-marker for resistance to treatment of GBM, but it is invasive and time-consuming to determine methylation status. There has been effort to predict the MGMT methylation status through analyzing MRI scans using machine learning, which only requires pre-operative scans that are already part of standard-of-care for GBM patients.Purpose.To improve the performance of conventional transfer learning in the identification of MGMT promoter methylation status, we developed a 3D SpotTune network with adaptive fine-tuning capability. Using the pretrained weights of MedicalNet with the SpotTune network, we compared its performance with a randomly initialized network for different combinations of MR modalities.Methods.Using a ResNet50 as the base network, three categories of networks are created: (1) A 3D SpotTune network to process volumetric MR images, (2) a network with randomly initialized weights, and (3) a network pre-trained on MedicalNet. These three networks are trained and evaluated using a public GBM dataset provided by the University of Pennsylvania. The MRI scans from 240 patients are used, with 11 different modalities corresponding to a set of perfusion, diffusion, and structural scans. The performance is evaluated using 5-fold cross validation with a hold-out testing dataset.Results.The SpotTune network showed better performance than the randomly initialized network. The best performing SpotTune model achieved an area under the Receiver Operating Characteristic curve (AUC), average precision of the precision-recall curve (AP), sensitivity, and specificity values of 0.6604, 0.6179, 0.6667, and 0.6061 respectively.Conclusions.SpotTune enables transfer learning to be adaptive to individual patients, resulting in improved performance in predicting MGMT promoter methylation status in GBM using equivalent MRI modalities as compared to a randomly initialized network.

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.

Prognostic Role of Platelet-to-Lymphocyte and Neutrophil-to-Lymphocyte Ratios in Patients Irradiated for Glioblastoma Multiforme.

Previous studies suggested pre-operative platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) to be predictive factors in patients with glioblastoma multiforme (GBM). This study investigated the prognostic role of PLR and NLR prior to or at the beginning of radiotherapy. In 80 patients with GBM receiving conventionally fractionated radiotherapy plus concurrent temozolomide following resection or biopsy, 12 factors including PLR and NLR were retrospectively evaluated regarding progression-free survival (PFS) and overall survival (OS). On multivariable analyses, PLR ≤150, Karnofsky performance score (KPS) 90-100, and O6-methylguanine-DNA methyltransferase promoter methylation were significantly associated with improved PFS. Single lesion, KPS 90-100, and adjuvant chemotherapy were significantly associated with OS; PLR ≤150 showed a trend. NLR ≤3 showed a trend for associations with PFS and OS on univariable analyses. PLR prior to or at the beginning of radiotherapy was associated with treatment outcomes in patients irradiated for GBM and should be considered in future clinical trials.

MGMT promoter methylation in prognosis of patients with newly diagnosed IDH1 wildtype Glioblastoma

The objective of this study was to analyse the prognosis values of O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation on the overall survival in patients with newly diagnosed isocitrate dehydrogenase 1 (IDH1) wild-type glioblastoma multiforme (GBM). A retrospective observational study was conducted on 108 IDH1 wild-type glioblastoma patients who underwent tumour resection at neurosurgical centres, including Hanoi Medical University Hospital and Viet Duc University Hospital, from October 2017 to December 2021. The MGMT status was assessed using methylation-specific Polymerase Chain Reaction (MSP). Multivariable Cox proportional hazards models and Kaplan-Meier survival analysis were performed. Of 108 patients, 79.6% had methylated MGMT. The median overall survival of patients with IDH1 wild-type GBM was 8.47 months (95%Confidence Interval (CI) =6.93-11.07). Patients with Methylated MGMT had a lower mortality risk (Hazard Ratio (HR) =0.63, 95%CI=0.41-0.98) than those without Methylated MGMT. Patients aged &lt;55 HR=0.55, CI=0.35-0.85)&gt;=55 years old. Having tumours on both sides of the hemisphere was associated with a higher risk of mortality (HR=3.18, 95%CI=1.68-6.00) compared to patients with tumours on the right side of the hemisphere. To conclude, our data underline the impact of the MGMT promoter methylation status in the treatment response of IDH1-wildtype GBM. In addition, age, tumour size and hemisphere were also significant prognostic factors in the overall survival of patients.

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.

Evaluation of VAL-083 in GBM AGILE, a phase 3 registration platform trial for newly diagnosed and recurrent glioblastoma.

2005 Background: GBM AGILE (NCT03970447) is a phase 2/3 Bayesian adaptive registration platform trial testing multiple therapies efficiently against a common control (C) with a primary endpoint of overall survival (OS). VAL-083 (VAL) is a DNA targeting agent that, independent of O6-methylguanine DNA methyltransferase promoter methylation status, targets the N7 position of guanine residues and facilitates inter-strand DNA crosslinks, leading to DNA double-strand breaks and cell death. It entered the trial in January 2021, and it is the 2nd arm (of 6) to complete its evaluation. Methods: Patient subtypes considered in GBM AGILE are newly diagnosed methylated (NDM), ND unmethylated (NDU), &amp; recurrent disease (RD). C is temozolomide (in ND) &amp; lomustine (in RD). Arms open to all 3 subtypes are evaluated in 5 prospectively defined signatures (sig): NDU, NDM, RD, all ND and All. Randomization to C is 20% in each subtype. Exp arms in GBM AGILE have 1 or 2 stages. Efficacy is based on OS hazard ratio (HR) of arm/C. Efficacy goal is a final Bayesian probability ≥ 98% for HR &lt;1.00 in combined Stages 1 &amp; 2. Arms stop accruing in Stage 1 if they reach max sample size (N) or drop for futility or safety. Exp arms in Stage 1 are adaptively randomized with allocation being proportional to an arm’s current probability of having ≥ 30% benefit in OS, P(HR &lt;0.70). In stage 1, exp arms are evaluated monthly, and arms showing Bayesian predictive power (PP) ≥ 0.8, graduate into Stage 2 with fixed randomization in one sig. For all exp arms, follow up continues for 12 mos after accrual stops (clinical cutoff). Arms are declared futile at any monthly analysis when PP is &lt;0.25 for all sigs. Open to all 3 subtypes, VAL entered as the 1st arm in NDM and was randomized 1:1 to C in this subtype until additional arms entered. The target max N for VAL in its Stages 1 &amp; 2 were 150 and 50, resp. Results: At the interim after VAL reached max sample size in Stage 1, the PP for all signatures was &lt;0.8 and &gt;0.25 for at least one sig. Thus, VAL did not graduate nor drop for futility, but accrual stopped for maximum N in Stage 1 (see table). Final results will be presented at the meeting. Columns 2-5 show results at the interim after which VAL stopped for max N. Columns 6-8 show near final results. Conclusions: GBM AGILE is an efficient &amp; effective model for phase 3 drug development. VAL did not increase OS compared to C in any glioblastoma subtype. GBM AGILE evaluated this agent in less time, at lower cost, &amp; with fewer patients than typical registration trials &amp; is currently evaluating several other arms. Clinical trial information: NCT03970447 . [Table: see text]

Accurate and comprehensive evaluation of O6-methylguanine-DNA methyltransferase promoter methylation by nanopore sequencing.

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. 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. 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. 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 for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences.

Using radiomics to predict O6-methylguanine-DNA methyltransferase promoter methylation status in patients with newly diagnosed glioblastoma and compare the performances of different MRI sequences. Preoperative MRI scans from 215 patients were included in this retrospective study. After image preprocessing and feature extraction, two kinds of machine-learning models were established and compared for their performances. One kind was established using all MRI sequences (T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient), and the other kind was based on single MRI sequence as listed above. For the machine-learning model based on all sequences, a total of seven radiomic features were selected with the Maximum Relevance and Minimum Redundancy algorithm. The predictive accuracy was 0.993 and 0.750 in the training and validation sets, respectively, and the area under curves were 1.000 and 0.754 in the two sets, respectively. For the machine-learning model based on single sequence, the numbers of selected features were 8, 10, 10, 13, 9, 7 and 6 for T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient, respectively, with predictive accuracies of 0.797-1.000 and 0.583-0.694 in the training and validation sets, respectively, and the area under curves of 0.874-1.000 and 0.538-0.697 in the two sets, respectively. Specifically, T1-weighted image-based model performed best, while contrast enhancement-based model performed worst in the independent validation set. The machine-learning models based on seven different single MRI sequences performed differently in predicting O6-methylguanine-DNA methyltransferase status in glioblastoma, while the machine-learning model based on the combination of all sequences performed best.

Metabolomic Differentiation of Tumor Core vs. Edge in Glioma Via Machine Learning

INTRODUCTION: Gliomas exhibit high intra-tumor and inter-patient heterogeneity. Recently, it has been shown that the microenvironment and phenotype differ significantly between glioma core (inner) and edge (invasive) regions. METHODS: Paired glioma core and infiltrating edge samples were obtained from 27 human patients after craniotomy. Liquid-liquid metabolite extraction was performed on the samples, and metabolomic data was obtained via 2DLC-MS/MS. A boosted generalized linear model was employed to predict metabolomic profiles associated with O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation. RESULTS: A panel of 52 of 160 metabolites were found to significantly differ between the glioma core and edge regions (p = 0.05). Top metabolites with significantly different relative abundances included nicotinamide, creatine, cystathione, D-pantothenic acid, and maleic acid. The machine learning model predicted MGMT promotor methylation status using 5 key metabolic features within the core and edge tissue specimens with AUROCEdge = 0.935 and AUROCCore = 0.959. Top metabolites associated with MGMT status in core included Y-L-glutamyl-L-glutamic acid, D-pantothenic acid, uric acid, and in edge included itaconic acid, N,N-dimethylarginine, cytosine. CONCLUSIONS: Key metabolic differences are identified between core and edge tissue in glioma and can predict MGMT status.

Deep learning convolutional neural network ResNet101 and radiomic features accurately analyzes mpMRI imaging to predict MGMT promoter methylation status with transfer learning approach

AbstractAccurate brain tumor classification is crucial for enhancing the diagnosis, prognosis, and treatment of glioblastoma patients. We employed the ResNet101 deep learning method with transfer learning to analyze the 2021 Radiological Society of North America (RSNA) Brain Tumor challenge dataset. This dataset comprises four structural magnetic resonance imaging (MRI) sequences: fluid‐attenuated inversion‐recovery (FLAIR), T1‐weighted pre‐contrast (T1w), T1‐weighted post‐contrast (T1Gd), and T2‐weighted (T2). We assessed the model's performance using standard evaluation metrics. The highest performance to detect MGMT methylation status for patients suffering glioblastoma was an accuracy (85.48%), sensitivity (80.64%), specificity (90.32%). Whereas classification performance with no tumor was yielded with accuracy (85.48%), sensitivity (90.32%), specificity (80.64%). The radiomic features (74) computed with ensembled Bagged Tree and relief feature selection method (30/74) improved the validation accuracy of 84.3% and AUC of 0.9038 to detect. O6‐methylguanine‐DNA methyltransferase (MGMT) promoter methylation status in glioblastoma patients holds promise for optimizing treatment planning and prognosis. By understanding MGMT methylation status, clinicians can make informed decisions about treatment strategies, potentially leading to improved clinical outcomes.

Imaging Role in Diagnosis, Prognosis, and Treatment Response Prediction Associated with High-grade Glioma.

Glioma is one of the most drug and radiation-resistant tumors. Gliomas suffer from inter- and intratumor heterogeneity which makes the outcome of similar treatment protocols vary from patient to patient. This article is aimed to overview the potential imaging markers for individual diagnosis, prognosis, and treatment response prediction in malignant glioma. Furthermore, the correlation between imaging findings and biological and clinical information of glioma patients is reviewed. The search strategy in this study is to select related studies from scientific websites such as PubMed, Scopus, Google Scholar, and Web of Science published until 2022. It comprised a combination of keywords such as Biomarkers, Diagnosis, Prognosis, Imaging techniques, and malignant glioma, according to Medical Subject Headings. Some imaging parameters that are effective in glioma management include: ADC, FA, Ktrans, regional cerebral blood volume (rCBV), cerebral blood flow (CBF), ve, Cho/NAA and lactate/lipid ratios, intratumoral uptake of 18F-FET (for diagnostic application), RD, ADC, ve, vp, Ktrans, CBFT1, rCBV, tumor blood flow, Cho/NAA, lactate/lipid, MI/Cho, uptakes of 18F-FET, 11C-MET, and 18F-FLT (for prognostic and predictive application). Cerebral blood volume and Ktrans are related to molecular markers such as vascular endothelial growth factor (VEGF). Preoperative ADCmin value of GBM tumors is associated with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. 2-hydroxyglutarate metabolite and dynamic 18F-FDOPA positron emission tomography uptake are related to isocitrate dehydrogenase (IDH) mutations. Parameters including ADC, RD, FA, rCBV, Ktrans, vp, and uptake of 18F-FET are useful for diagnosis, prognosis, and treatment response prediction in glioma. A significant correlation between molecular markers such as VEGF, MGMT, and IDH mutations with some diffusion and perfusion imaging parameters has been identified.