P12.11.B MGMT METHYLATION STATUS IS STABLE AT PROGRESSION OF GLIOBLASTOMAS

Abstract BACKGROUND Promoter methylation of O6-methylguanine-DNA methyltransferase (MGMT) is associated with longer survival and with response to chemotherapy in newly diagnosed glioblastoma patients. As demethylation may form a means to treatment resistance, we tested the stability of MGMT-promoter methylation at tumour recurrence. MATERIAL AND METHODS We generated whole genome DNA methylation data of 64 matched primary-recurrent samples from IDHwt glioblastoma patients1. Data were combined with three publicly available datasets Malta et al2 (n=90), Drexler et al3 (n=82), and Lucas et al4 (n=182). MGMT promoter methylation was determined using the MGMTSTP27 algorithm, genome wide methylation data (M-values) and genotyping SNPs were extracted using the Minfi package, differential methylation analysis was performed using Limma package, copynumber profiles were calculated using the CoNuMee package to determine tumour purity, survival analysis was conducted using the survival package. Samples with predicted low tumour purity were subject to visual inspection for confirmation. RESULTS Our combined dataset consisted of 418 samples from 209 patients. We excluded 7/209 cases (3%) as the primary and recurrent samples were not derived from the same patient, and 26/418 samples (6%) which had too low purity for analysis. The final dataset therefore consisted of 358 samples from 179 patients. In 66 (37%) of patients, a methylated MGMT promoter was found at initial diagnosis, and in 113 (63%) of patients, it was unmethylated. MGMT promoter methylation was stable in the majority of samples (92% to 98%, depending on set threshold). In n=8/66 (12%)samples, we found MGMT promoter methylation decreased > 0.4 where none 0/113 (0%) of the samples showed such an increase methylation (Fisher’s exact test, p<0.05). Patients harboring tumours with a stable methylated MGMT promoter had longer survival compared to those with stable unmethylated cases. Those with change in MGMT promoter methylation showed a trend towards intermediate survival, though the numbers were too low for statistical analysis. We did not identify significantly differentially methylated CpGs following MGMT promoter methylation change. CONCLUSION MGMT promoter methylation status remains stable at progression in most IDHwt glioblastoma patients.

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Journal
Neuro-Oncology
Published
2025-10-01
DOI
https://doi.org/10.1093/neuonc/noaf193.390
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
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article

P12.11.B MGMT METHYLATION STATUS IS STABLE AT PROGRESSION OF GLIOBLASTOMAS

T. Gorlia, Monique C.J. Hanse, Martin J. van den Bent, P A Robe et al.
Neuro-Oncology
Radiomics and Machine Learning in Medical Imaging
article

P12.11.B MGMT METHYLATION STATUS IS STABLE AT PROGRESSION OF GLIOBLASTOMAS

T. Gorlia, Monique C.J. Hanse, Martin J. van den Bent, P A Robe, Nicos Angelopoulos, Caroline G. Watts, Pim J. French, Giuseppe Lombardi, Slávka Lukacova, K Draaisma, Ann Hoeben, Sieger Leenstra, Youri Hoogstrate, Vincent Bours, Ruth Fleischeuer, Bin Deng, V Golfonopoulos
article en

Abstract

Abstract BACKGROUND Promoter methylation of O6-methylguanine-DNA methyltransferase (MGMT) is associated with longer survival and with response to chemotherapy in newly diagnosed glioblastoma patients. As demethylation may form a means to treatment resistance, we tested the stability of MGMT-promoter methylation at tumour recurrence. MATERIAL AND METHODS We generated whole genome DNA methylation data of 64 matched primary-recurrent samples from IDHwt glioblastoma patients1. Data were combined with three publicly available datasets Malta et al2 (n=90), Drexler et al3 (n=82), and Lucas et al4 (n=182). MGMT promoter methylation was determined using the MGMTSTP27 algorithm, genome wide methylation data (M-values) and genotyping SNPs were extracted using the Minfi package, differential methylation analysis was performed using Limma package, copynumber profiles were calculated using the CoNuMee package to determine tumour purity, survival analysis was conducted using the survival package. Samples with predicted low tumour purity were subject to visual inspection for confirmation. RESULTS Our combined dataset consisted of 418 samples from 209 patients. We excluded 7/209 cases (3%) as the primary and recurrent samples were not derived from the same patient, and 26/418 samples (6%) which had too low purity for analysis. The final dataset therefore consisted of 358 samples from 179 patients. In 66 (37%) of patients, a methylated MGMT promoter was found at initial diagnosis, and in 113 (63%) of patients, it was unmethylated. MGMT promoter methylation was stable in the majority of samples (92% to 98%, depending on set threshold). In n=8/66 (12%)samples, we found MGMT promoter methylation decreased > 0.4 where none 0/113 (0%) of the samples showed such an increase methylation (Fisher’s exact test, p<0.05). Patients harboring tumours with a stable methylated MGMT promoter had longer survival compared to those with stable unmethylated cases. Those with change in MGMT promoter methylation showed a trend towards intermediate survival, though the numbers were too low for statistical analysis. We did not identify significantly differentially methylated CpGs following MGMT promoter methylation change. CONCLUSION MGMT promoter methylation status remains stable at progression in most IDHwt glioblastoma patients.

Neuro-OncologyVol. 27(Supplement_3)
Radboud University Nijmegen (NL), University of Liège (BE), University of Applied Sciences Utrecht (NL), Utrecht University (NL), Maastricht University Medical Centre (NL), Catharina Ziekenhuis (NL), Erasmus MC (NL), Aarhus University Hospital (DK), University Medical Center Utrecht (NL), Maastricht University (NL), Istituto Oncologico Veneto (IT), European Organisation for Research and Treatment of Cancer (BE), Elisabeth-TweeSteden Ziekenhuis (NL), Erasmus MC Cancer Institute (NL), Cardiff University (GB), University of Birmingham (GB), Erasmus University Rotterdam (NL)
Openalex Percentile: Top 59%
Radiomics and Machine Learning in Medical Imaging
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