MGMT Promoter Methylation Status Research Articles

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.

CDKN2A Homozygous Deletion Is a Stronger Predictor of Outcome than IDH1/2-Mutation in CNS WHO Grade 4 Gliomas.

Background: We primarily investigated the prognostic role of CDKN2A homozygous deletion in CNS WHO grade 4 gliomas. Additionally, we plan to examine traditional prognostic factors for grade 4 gliomas and validate the findings. Materials: We conducted a retrospective analysis of the glioma cohorts at our institute. We reviewed medical records spanning a 15-year period and examined pathological slides for an updated diagnosis according to the 2021 WHO classification of CNS tumors. We examined the IDH1/2 mutation and CDKN2A deletion using NGS analysis with ONCOaccuPanel®. Further, we examined traditional prognostic factors, including age, WHO performance status, extent of resection, and MGMT promoter methylation status. Results: The mean follow-up duration was 27.5 months (range: 4.1-43.5 months) and mean overall survival (OS) was 20.7 months (SD, ±1.759). After the exclusion of six patients with a poor status of pathologic samples, a total of 136 glioblastoma cases diagnosed by previous WHO classification criteria were newly classified into 29 (21.3%) astrocytoma, IDH-mutant, and CNS WHO grade 4 cases, and 107 (78.7%) glioblastoma, IDH-wildtype, and CNS WHO grade 4 cases. Among them, 61 (56.0%) had CDKN2A deletions. The high-risk group with CDKN2A deletion regardless of IDH1/2 mutation had a mean OS of 16.65 months (SD, ±1.554), the intermediate-risk group without CDKN2A deletion and with IDH1/2 mutation had a mean OS of 21.85 months (SD, ±2.082), and the low-risk group without CDKN2A deletion and with IDH1/2 mutation had a mean OS of 33.38 months (SD, ±2.946). Multifactor analysis showed that age (≥50 years vs. <50 years; HR 4.645), WHO performance (0, 1 vs. 2; HR 5.002), extent of resection (gross total resection vs. others; HR 5.528), MGMT promoter methylation, (methylated vs. unmethylated; HR 5.078), IDH1/2 mutation (mutant vs. wildtype; HR 6.352), and CDKN2A deletion (absence vs. presence; HR 13.454) were associated with OS independently. Conclusions: The present study suggests that CDKN2A deletion plays a powerful prognostic role in CNS WHO grade 4 gliomas. Even if CNS WHO grade 4 gliomas have mutant IDH1/2, they may have poor clinical outcomes because of CDKN2A deletion.

Analysis of gliomas DNA methylation: Assessment of pre-analytical variables.

Precision oncology is driven by biomarkers. For glioblastoma multiforme (GBM), the most common malignant adult primary brain tumor, O6-methylguanine-DNA methyltransferase (MGMT) gene promoter methylation is an important prognostic and treatment clinical biomarker. Time consuming pre-analytical steps such as biospecimen storage, fixation, sampling, and processing are sources of data irreproducibility, and all these pre-analytical variables are confounded by intratumor heterogeneity of MGMT promoter methylation. To assess the effect of pre-analytical variables on GBM DNA methylation, tissue storage/sampling (CryoGrid), sample preparation multi-sonicator (PIXUL), and 5-methylcytosine (5mC) DNA immunoprecipitation (Matrix MeDIP-qPCR/seq) platforms were used. MGMT promoter methylation status assayed by MeDIP-qPCR was validated with methylation specific PCR (MS-PCR). MGMT promoter methylation levels in frozen and formalin fixed paraffin embedded (FFPE) sample pairs were not statistically different, confirming reliability of FFPEs for MGMT promoter methylation analysis. Warm ex-vivo ischemia (up to 4hrs at 37oC) and 3 cycles of repeated sample thawing and freezing did not statistically impact 5mC at MGMT promoter, exon, and enhancer regions, indicating the resistance of DNA methylation to common variations in sample processing conditions that might be encountered in research and clinical settings. 26-34% of specimens exhibited intratumor heterogeneity in the MGMT DNA promoter methylation. These data demonstrate that variations in sample fixation, ischemia duration and temperature, and DNA methylation assay technique do not have a statistically significant impact on MGMT promoter methylation assessment. However, intratumor methylation heterogeneity underscores the value of multiple biopsies at different GBM geographic tumor sites in the evaluation of MGMT promoter methylation status. Matrix-MeDIP-seq analysis revealed that MGMT promoter methylation status clustered with other differentially methylated genomic loci (e.g. HOXA and lncRNAs) that are resilient to variation in the above pre-analytical conditions. These observations offer new opportunities to develop more granular data-based epigenetic GBM biomarkers. In this regard, the high throughput CryoGrid-PIXUL-Matrix toolbox could be useful.

Molecular Profile as an Outcome Predictor in Glioblastoma along with MRI Features and Surgical Resection: A Scoping Review.

Glioblastoma (GBM) is one of the most aggressive malignant tumors of the brain. We queried PubMed for articles about molecular predictor markers in GBM. This scoping review aims to analyze the most important outcome predictors in patients with GBM and to compare these factors in terms of absolute months of survival benefit and percentages. Performing a gross total resection for patients with GBM undergoing optimal chemo- and radiotherapy provides a significant benefit in overall survival compared to those patients who received a subtotal or partial resection. However, compared to IDH-Wildtype GBMs, patients with IDH-Mutant 1/2 GBMs have an increased survival. MGMT promoter methylation status is another strong outcome predictor for patients with GBM. In the reviewed literature, patients with methylated MGMT promoter lived approximately 50% to 90% longer than those with an unmethylated MGMT gene promoter. Moreover, KPS is an important predictor of survival and quality of life, demonstrating that we should refrain from aggressive surgery in important brain areas. As new therapies (such as TTFs) emerge, we are optimistic that the overall median survival will increase, even for IDH-Wildtype GBMs. In conclusion, molecular profiles are stronger outcome predictors than the extent of neurosurgical resection for GBM.

The Role of Amide Proton Transfer (APT)-Weighted Imaging in Glioma: Assessment of Tumor Grading, Molecular Profile and Survival in Different Tumor Components.

Amide Proton Transfer-weighted (APTw) imaging is a molecular MRI technique used to quantify protein concentrations in gliomas, which have heterogeneous components with varying cellularity and metabolic activity. This study aimed to assess the correlation between the component-specific APT signal of the neoplasm and WHO grade, molecular profile and survival status. Sixty-one patients with adult-type diffuse gliomas were retrospectively analyzed. APT values were semi-automatically extracted from tumor solid and, whenever present, necrotic components. APT values were compared between groups stratified by WHO grade, IDH-mutation, MGMT promoter methylation and 1- and 2-year survival status using Wilcoxon rank-sum test, adjusting for multiple comparisons. Overall survival (OS) was analyzed in the subgroup of 48 patients with grade 4 tumors using Cox proportional-hazards models. Random-effects models were used to assess inter-subject heterogeneity of the mean APT values in each tumor component. APT values of the solid component significantly differed between patients with grades 2-3 and 4 tumors (mean 1.58 ± 0.50 vs. 2.04 ± 0.56, p = 0.028) and correlated with OS after 1 year (1.81 ± 0.58 in survivors vs. 2.17 ± 0.51 in deceased patients, p = 0.030). APT values did not differ by IDH-mutation, MGMT methylation, and 2-year survival status. Within grade 4 glioma patients, higher APT kurtosis of the solid component was a negative prognostic factor (hazard ratio = 1.60, p = 0.040). Mean APT values of the necrosis showed high inter-subject variability, although most necrotic tumors were grade 4 and IDH wildtype. In conclusion, APTw imaging in the solid component provided metrics associated with glioma grade and survival status but showed weak correlation with IDH-mutation and MGMT promoter methylation status, in contrast to previous works. Further research is needed to understand APT signal variability within the necrotic component of high-grade gliomas.

Optimizing Glioblastoma, IDH-wildtype Treatment Outcomes : A Radiomics and Support Vector Machine -Based Approach to Overall Survival Estimation.

Glioblastoma multiforme (GBM), particularly the IDH-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. This study utilizes a Support Vector Machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (< 12 Months) and long (>=12 Months) survivors. A dataset comprising multi-parametric MRI (mpMRI) scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, FLAIR, and T1-Gd sequences. Low variance features were removed, and Recursive Feature Elimination (RFE) was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT promoter methylation status were integrated to enhance prediction accuracy. The model showed reasonable results in terms of cross-validated AUC of 0.84 (95% CI: 0.80-0.90) with (p-value < 0.001) effectively categorizing patients into short and long survivors. Log-rank test (Chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen's d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value<0.0001. The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

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.

Preoperative prediction of MGMT promoter methylation in glioblastoma based on multiregional and multi-sequence MRI radiomics analysis

O6-methylguanine-DNA methyltransferase (MGMT) has been demonstrated to be an important prognostic and predictive marker in glioblastoma (GBM). To establish a reliable radiomics model based on MRI data to predict the MGMT promoter methylation status of GBM. A total of 183 patients with glioblastoma were included in this retrospective study. The visually accessible Rembrandt images (VASARI) features were extracted for each patient, and a total of 14676 multi-region features were extracted from enhanced, necrotic, “non-enhanced, and edematous” areas on their multiparametric MRI. Twelve individual radiomics models were constructed based on the radiomics features from different subregions and different sequences. Four single-sequence models, three single-region models and the combined radiomics model combining all individual models were constructed. Finally, the predictive performance of adding clinical factors and VASARI characteristics was evaluated. The ComRad model combining all individual radiomics models exhibited the best performance in test set 1 and test set 2, with the area under the receiver operating characteristic curve (AUC) of 0.839 (0.709–0.963) and 0.739 (0.581–0.897), respectively. The results indicated that the radiomics model combining multi-region and multi-parametric MRI features has exhibited promising performance in predicting MGMT methylation status in GBM. The Modeling scheme that combining all individual radiomics models showed best performance among all constructed moels.

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.

A prognostic matrix gene expression signature defines functional glioblastoma phenotypes and niches.

Interactions among tumor, immune, and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, through computational genomics and proteomics approaches, we analyzed the functional and clinical relevance of CMP expression in GBM at bulk, single cell, and spatial anatomical resolution. We identified genes encoding CMPs whose expression levels categorize GBM tumors into CMP expression-high (M-H) and CMP expression-low (M-L) groups. CMP enrichment is associated with worse patient survival, specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells, and immune checkpoint gene expression. Anatomical and single-cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative niches that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene CMP expression signature, termed Matrisome 17 (M17) signature that further refines the prognostic value of CMP genes. The M17 signature is a significantly stronger prognostic factor compared to MGMT promoter methylation status as well as canonical subtypes, and importantly, potentially predicts responses to PD1 blockade. The matrisome gene expression signature provides a robust stratification of GBM patients by survival and potential biomarkers of functionally relevant GBM niches that can mediate mesenchymal-immune cross talk. Patient stratification based on matrisome profiles can contribute to selection and optimization of treatment strategies.

HGG-08. DIFFUSE HEMISPHERIC GLIOMA, H3 G34-MUTANT: A MULTI-INSTITUTIONAL EXPERIENCE

Abstract BACKGROUND Diffuse hemispheric glioma (DHG), H3 G34-mutant, is a rare, aggressive brain tumor molecularly defined by mutations in the H3-3A gene. Our study explored the association between patient outcomes and key molecular findings, resection extent, and temozolomide (TMZ) use. METHODS Multi-institutional chart and molecular analysis of 32 patients with H3 G34-mutant DHG. RESULTS The median patient age was 14 (10-28 years), with a M/F ratio of 1.4. The 1-year, 2-year, and 5-year PFS were 40.4% (23.7%, 57.1%), 22.1% (9.0%, 38.7%), and 11.0% (1.9%, 33.7%), respectively. The 1-year, 2-year, and 5-year OS were 82.5% (63.2%, 92.3%), 49.3% (28.7%, 66.3%), and 29.6% (9.1%, 50.6%), respectively. Patients who underwent a gross total resection (GTR) had improved PFS (p=0.0105) and OS (p=0.0184) compared to non-GTR patients. 22 patients (68.8%) received concurrent and/or adjuvant TMZ and had improved PFS (p=0.0278) compared to patients who did not receive front-line TMZ. Disease recurrence in relation to the radiation therapy (RT) field was analyzed in 17 patients; 5 recurred locally within the high-dose RT field, 5 recurred distantly, and 7 had both local and distant recurrences. There was no significant association between MGMT promoter methylation status (18 evaluable cases), PDGFRA amplification (N=6), and CDKN2A homozygous deletion (N=10) and survival outcomes. MGMT promoter methylation was not associated with increased TMZ efficacy (N=8 with MGMT silencing, N=4 without). CONCLUSIONS In our cohort, MGMT promoter methylation was not a favorable prognostic factor, in distinction from prior reports in adult and pediatric high-grade gliomas. Resection extent and the use of TMZ were associated with improved survival outcomes. As most treatment failures occurred within the high-dose RT field, extended fields do not appear warranted. Our findings may guide prognostic and therapeutic decisions in patients with H3 G34-mutant DHG and support the need for prospective efforts to improve outcomes in this patient population.

Racial/ethnic disparities in healthcare utilization, post-operative outcomes, and overall survival for IDH-wildtype glioblastoma stratifying by MGMT promoter methylation status.

2081 Background: Glioblastoma is the most common malignant primary brain tumor and molecular profiles such as IDH1/2 and MGMT promoter methylation status have significant influence on survival outcomes. There is no "real-world" study has investigated the racial/ethnic disparities in healthcare utilization and outcomes for IDH-wildtype glioblastoma patients with MGMT promoter methylation status. Methods: A total of 21971 IDH-wildtype glioblastoma patients ( MGMT promoter-methylated: 41.5%, 9110/21971) were derived from the National Cancer Database (NCDB, 2018-2021). Race/Ethnicity was grouped into Non-Hispanic White (NHW), Non-Hispanic Black (NHB), Asian/Pacific Islander (API), American Indian/Alaska Native (AIAN), Hispanic, and Others. Overall survival (OS) was set as primary outcome. Healthcare utilization and post-operative outcomes are secondary endpoints. Multivariable logistic regression models, Kaplan-Meier methods, and Cox proportional hazards models were performed for post-operative outcomes, healthcare utilization, and overall survival. Results: Compared to NHW, AIAN were 78% more likely to have MGMT promoter methylation after adjusting age and gender (aOR=1.78, p=0.030). API and Hispanic were significantly less likely to undergo GTR over STR comparing to NHW. The odds of receiving chemotherapy and RT for race/ethnicity minorities patients were significantly lower than NHW (Chemotherapy: NHB: aOR=0.79, AIAN: aOR=0.57, Hispanic: aOR=0.83; RT: NHB: aOR=0.88, Hispanic: aOR=0.80). The median OSs by race/ethnicity (NHW, NHB, API, AIAN, Hispanic, and Others, respectively) were: 11.5, 12.3, 14.5, 9.2, 13.6, and 15.6, months ( MGMT promoter-unmethylated, p&lt;0.001), 15.9, 18.8, 23.7, 14.8, 19.6, and 15.5, months ( MGMT promoter-methylated, p&lt;0.001). After adjusting the covariates, NHB, API, and Hispanic experienced significantly lower risk of death comparing to NHW (aHR=0.82, 0.66, and 0.75, all p&lt;0.001). Similar results were validated in stratification analysis by MGMT promoter methylation status, except MGMT promoter-unmethylated AIAN experienced 70% higher risk of death than NHW (aHR=1.70, p=0.021). Conclusions: Our findings suggests that racial/ethnic disparities exist in healthcare utilization, postoperative outcomes, and overall survival in IDH-wildtype glioblastoma population.

Can targeting TSP-1 with gabapentin enhance survival in glioblastoma? A 20-year retrospective cohort study.

2048 Background: Molecularly-defined cellular subpopulations of glioblastoma secrete high levels of the synaptogenic protein thrombospondin-1 (TSP-1) which promotes functional integration of tumor into neural circuity. A greater extent of glioma-neuronal crosstalk portends worse survival for patients. In preclinical studies, gabapentin was shown to inhibit TSP-1, in turn disrupting neuronal synaptogenesis and neuronal activity-dependent glioblastoma proliferation; however, clinical survival data is lacking. We aim to determine whether treatment with gabapentin is associated with improved survival and reduced serum TSP-1 in a retrospective cohort of patients with IDH-wildtype glioblastoma. Methods: Newly-diagnosed, IDH-wildtype glioblastoma patients who received care at the UCSF Brain Tumor Center between 1997-2017 were included in this study. Kaplan-Meier curves and multivariate Cox proportional-hazards models, controlled for age, MGMT promoter methylation status, preoperative tumor volume, and extent of resection, were used for survival analyses. Differences in serum TSP-1 measured by ELISA for gabapentin treated patients and matched non-gabapentin treated controls were assessed using unpaired two-tailed student’s t-test. Control samples were matched 2:1 by age, tumor volume, and extent of resection. After 2005, patients were treated with chemoradiation with concurrent and adjuvant temodar. Results: Among 379 adult patients with glioblastoma, 36 (9.5%) were treated with gabapentin. The median daily dose of gabapentin was 600 mg (IQR: 300-900 mg). There were no significant differences between gabapentin treated and non-gabapentin treated patients by age (0.06), sex (p=0.14), or race (p=0.52). Median overall survival for gabapentin treated and non-gabapentin treated patients was 20.8 months (IQR: 11.7 - 32.1) and 14.7 months (IQR: 8.9-23.5), respectively (p= 0.02). On Kaplan-Meier survival analysis, overall survival was longer for gabapentin treated patients compared with non-gabapentin treated patients (p=0.005). In the multivariate Cox proportional-hazards model, gabapentin use was associated with decreased hazard of death (HR 0.67, p=0.030). Mean serum TSP-1 in gabapentin-treated patients was significantly lower than in the matched control group (10181 ng/ml vs 17015 ng/ml, p=0.017). Conclusions: Gabapentin use is associated with a survival benefit for patients with glioblastoma, possibly mediated through a reduction in TSP-1. This promising result lays the foundation for future prospective studies to further evaluate TSP-1 as a circulating prognostic biomarker as well as to explore the therapeutic benefits of gabapentin as a life-prolonging treatment for patients with newly diagnosed glioblastoma.

Development and validation of a clinical risk score for postoperative outcome in newly diagnosed glioblastoma: A report of the RANO Resect group.

2060 Background: Following resection or biopsy, patients with newly diagnosed glioblastoma frequently enter clinical trials. To standardize terminology for extent of resection, we proposed the RANO classification acknowledging four prognostic resection classes reflecting different amounts of residual tumor. Here, we aim to make use of our classification to (I) analyze the interactive effects of residual tumor with clinical and molecular factors on outcome (II) to define a prognostic postoperative risk score. Methods: The RANO resect group retrospectively compiled an international, seven-center training cohort of newly diagnosed glioblastoma. Predictors of outcome were identified by uni- and multivariate Cox’s proportional hazard regression. The combined effects of residual tumor with prognostic molecular and clinical factors on survival were assessed by recursive partitioning analysis (significance level: p≤ 0.05). Terminal regression tree nodes were combined into risk classes guided by Kaplan-Meier survival analyses and log-rank tests. The risk classes were converted into a numerical score which was prognostically verified in an external validation cohort. Results: Our training cohort compromised 1003 patients with newly diagnosed IDH-wildtype glioblastoma, including 744 patients who underwent adjuvant radiochemotherapy per EORTC-NCIC (TMZ/RT→TMZ). Residual tumor per RANO classification, MGMT promotor methylation status, age (as continuous variable; optimal cutoff: ≤65 years), and KPS (as continuous variable; optimal cutoff: ≥80) were prognostic of overall survival and forwarded into regression tree analysis. By combining eleven terminal nodes and individually weighting the prognostic factors, an additive scale (range, 0-9 points) integrating these four variables distinguished patients with low (0-2 points), intermediate (3-5 points), and high risk (6-9 points) for poor postoperative outcome (median overall survival: 24 vs. 16 vs. 6 months; p &lt; 0.01). Adjustment of the regression tree for adjuvant therapy was applied, and the presence of the three risk groups was confirmed in the subgroups of patients treated with or without RT/TMZ→TMZ. The prognostic value of the risk score was verified in a external single-center validation cohort of newly diagnosed glioblastoma ( n = 231, p &lt; 0.01). When we compared our risk score to previously postulated risk models, our score was superior in concordance between predicted with observed survival (c-index; development cohort: 0.7, validation cohorts: 0.6). Conclusions: The novel RANO risk score integrates molecular and clinical factors to serve as an easy-to-use, yet highly prognostic tool to stratify patients after resection or biopsy of newly diagnosed glioblastoma. The score may serve to guide patient management and reduce imbalances between study arms in prospective interventional trials.

MGMT inhibition regulates radioresponse in GBM, GSC, and melanoma

Radiotherapy is the standard treatment for glioblastoma (GBM), but the overall survival rate for radiotherapy treated GBM patients is poor. The use of adjuvant and concomitant temozolomide (TMZ) improves the outcome; however, the effectiveness of this treatment varies according to MGMT levels. Herein, we evaluated whether MGMT expression affected the radioresponse of human GBM, GBM stem-like cells (GSCs), and melanoma. Our results indicated a correlation between MGMT promoter methylation status and MGMT expression. MGMT-producing cell lines ACPK1, GBMJ1, A375, and MM415 displayed enhanced radiosensitivity when MGMT was silenced using siRNA or when inhibited by lomeguatrib, whereas the OSU61, NSC11, WM852, and WM266-4 cell lines, which do not normally produce MGMT, displayed reduced radiosensitivity when MGMT was overexpressed. Mechanistically lomeguatrib prolonged radiation-induced γH2AX retention in MGMT-producing cells without specific cell cycle changes, suggesting that lomeguatrib-induced radiosensitization in these cells is due to radiation-induced DNA double-stranded break (DSB) repair inhibition. The DNA-DSB repair inhibition resulted in cell death via mitotic catastrophe in MGMT-producing cells. Overall, our results demonstrate that MGMT expression regulates radioresponse in GBM, GSC, and melanoma, implying a role for MGMT as a target for radiosensitization.

MGMT ProFWise: Unlocking a New Application for Combined Feature Selection and the Rank-Based Weighting Method to Link MGMT Methylation Status to Serum Protein Expression in Patients with Glioblastoma.

Glioblastoma (GBM) is a fatal brain tumor with limited treatment options. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is the central molecular biomarker linked to both the response to temozolomide, the standard chemotherapy drug employed for GBM, and to patient survival. However, MGMT status is captured on tumor tissue which, given the difficulty in acquisition, limits the use of this molecular feature for treatment monitoring. MGMT protein expression levels may offer additional insights into the mechanistic understanding of MGMT but, currently, they correlate poorly to promoter methylation. The difficulty of acquiring tumor tissue for MGMT testing drives the need for non-invasive methods to predict MGMT status. Feature selection aims to identify the most informative features to build accurate and interpretable prediction models. This study explores the new application of a combined feature selection (i.e., LASSO and mRMR) and the rank-based weighting method (i.e., MGMT ProFWise) to non-invasively link MGMT promoter methylation status and serum protein expression in patients with GBM. Our method provides promising results, reducing dimensionality (by more than 95%) when employed on two large-scale proteomic datasets (7k SomaScan® panel and CPTAC) for all our analyses. The computational results indicate that the proposed approach provides 14 shared serum biomarkers that may be helpful for diagnostic, prognostic, and/or predictive operations for GBM-related processes, given further validation.

CDPNet: a radiomic feature learning method with epigenetic application to estimating MGMT promoter methylation status in glioblastoma.

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).

MGMT Methylation and Differential Survival Impact by Sex in Glioblastoma.

Introduction: Sex differences in glioblastoma (GBM) have been observed in incidence, genetic and epigenetic alterations, and immune response. These differences have extended to the methylation of the MGMT promoter, which critically impacts temozolomide resistance. However, the association between sex, MGMT methylation, and survival is poorly understood, which this study sought to evaluate. Methods: A retrospective cohort study was conducted and reported following STROBE guidelines, based on adults with newly diagnosed GBM who received their first surgical intervention at Cleveland Clinic (Ohio, USA) between 2012 and 2018. Kaplan-Meier and multivariable Cox proportional hazards models were used to analyze the association between sex and MGMT promoter methylation status on overall survival (OS). MGMT was defined as methylated if the mean of CpG 1-5 ≥ 12. Propensity score matching was performed on a subset of patients to evaluate the effect of individual CpG site methylation. Results: A total of 464 patients had documented MGMT methylation status with a mean age of 63.4 (range 19-93) years. A total of 170 (36.6%) were female, and 133 (28.7%) received gross total resection as a first intervention. A total of 42.5% were MGMT methylated, with females more often having MGMT methylation than males (52.1% vs. 37.4%, p = 0.004). In univariable analysis, OS was significantly longer for MGMT promoter methylated than un-methylated groups for females (2 yr: 36.8% vs. 11.1%; median: 18.7 vs. 9.5 months; p = 0.001) but not for males (2 yr: 24.3% vs. 12.2%; median: 12.4 vs. 11.3 months; p = 0.22, p for MGMT-sex interaction = 0.02). In multivariable analysis, MGMT un-methylated versus methylated promoter females (2.07; 95% CI, 1.45-2.95; p < 0.0001) and males (1.51; 95% CI, 1.14-2.00; p = 0.004) had worse OS. Within the MGMT promoter methylated group, males had significantly worse OS than females (1.42; 95% CI: 1.01-1.99; p = 0.04). Amongst patients with data on MGMT CpG promoter site methylation values (n = 304), the median (IQR) of CpG mean methylation was 3.0% (2.0, 30.5). Females had greater mean CpG methylation than males (11.0 vs. 3.0, p < 0.002) and higher per-site CpG methylation with a significant difference at CPG 1, 2, and 4 (p < 0.008). After propensity score matching, females maintained a significant survival benefit (18.7 vs. 10.0 months, p = 0.004) compared to males (13.0 vs. 13.6 months, p = 0.76), and the pattern of difference was significant (P for CpG-sex interaction = 0.03). Conclusions: In this study, females had higher mean and individual CpG site methylation and received a greater PFS and OS benefit by MGMT methylation that was not seen in males despite equal degrees of CpG methylation.

Hypofractionated re-irradiation with bevacizumab for relapsed chemorefractory glioblastoma after prior high dose radiotherapy: a feasible option for patients with large-volume relapse.

There remains no standard of care for patients with recurrent and chemorefractory glioblastoma. Re-irradiation (reRT) provides an additional management option. However, published series predominantly focus on small reRT volumes utilizing stereotactic hypofractionated regimens. Concerns regarding toxicity have limited utilisation of reRT for larger recurrences, however this may be mitigated with use of bevacizumab (BEV). A prospective database of patients managed with the EORTC-NCIC (Stupp) protocol 60Gy chemoradiotherapy protocol for glioblastoma between 2007 and 2021 was reviewed for those patients receiving reRT for chemorefractory relapse. Serial MRI and PET were used to establish true progression and exclude patients with pseudoprogression or radionecrosis from reRT. The primary endpoint was overall survival (OS) from date of reRT. Prognostic factors were also assessed. 447 patients managed for glioblastoma under the Stupp protocol were identified, of which 372 had relapsed and were thus eligible for reRT. 71 patients underwent reRT. Median relapse-free survival from diagnosis for the reRT and overall cohorts were similar at 11.6 months (95%CI:9.4-14.2) and 11.8 months (95%CI:9.4-14.2) respectively. 60/71 (85%) reRT patients had received BEV prior to reRT and continued concurrent BEV during reRT. Of the 11 patients not managed with BEV during reRT, 10 required subsequent salvage BEV. ReRT patients were younger (median 53 vs. 59 years, p < 0.001), had better performance status (86% vs. 69% ECOG 0-1, p = 0.002) and more commonly had MGMT promoter-methylated tumours (54% vs. 40%, p = 0.083) compared to non-reRT patients. Median reRT PTV volume was 135cm3 (IQR: 69-207cm3). Median OS from reRT to death was 7.1 months (95%CI:6.3-7.9). Patients aged < 50, 50-70 and > 70 years had post-reRT median OS of 7.7, 6.4 and 6.0 months respectively (p = 0.021). Median post-reRT survival was longer for patients with ECOG performance status 0-1 compared to 2-3 (8.1 vs. 6.3 months, p = 0.039). PTV volume, site of relapse, MGMT promoter-methylation status and extent of initial surgical resection were not associated with post-reRT survival. ReRT was well-tolerated. Out of the 6 patients (8%) admitted to hospital after reRT, only one was for reRT toxicity. This was a CTCAE grade 3 radiation necrosis event in a patient managed without prior BEV. Patients with recurrent glioblastoma who have been previously treated with 60Gy radiotherapy have a meaningful survival benefit from large volume re-irradiation which is well tolerated. ReRT should not be ignored as a salvage treatment option in patients with chemorefractory progressive disease.

Adjuvant re-irradiation vs. no early re-irradiation of resected recurrent glioblastoma: pooled comparative cohort analysis from two tertiary centers

BackgroundThe optimal management strategy for recurrent glioblastoma (rGBM) remains uncertain, and the impact of re-irradiation (Re-RT) on overall survival (OS) is still a matter of debate. This study included patients who achieved gross total resection (GTR) after a second surgery after recurrence, following the GlioCave criteria.MethodsInclusion criteria include being 18 years or older, having histologically confirmed locally recurrent IDHwt or IDH unknown GBM, achieving MRI-proven GTR after the second surgery, having a Karnofsky performance status of at least 60% after the second surgery, having a minimum interval of 6 months between the first radiotherapy and the second surgery, and a maximum of 8 weeks from second surgery to the start of Re-RT.ResultsA total of 44 patients have met the inclusion criteria. The median OS after the second surgery was 14 months. All patients underwent standard treatment after initial diagnosis, including maximum safe resection, adjuvant radiochemotherapy and adjuvant chemotherapy. Re-RT did not significantly impact OS. However, MGMT promoter methylation status and a longer interval (> 12 months) between treatments were associated with better OS. Multivariate analysis revealed the MGMT status as the only significant predictor of OS.ConclusionFactors such as MGMT promoter methylation status and treatment interval play crucial roles in determining patient outcomes after second surgery. Personalized treatment strategies should consider these factors to optimize the management of rGBM. Prospective research is needed to define the value of re-RT after second surgery and to inform decision making in this situation.