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Title:
MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMP-status | Acta Neuropathologica
Description:
The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Recent studies in anaplastic glioma suggest a prognostic value for MGMT methylation. Investigation of pathogenetic and epigenetic features of this intriguingly distinct behavior requires accurate MGMT classification to assess high throughput molecular databases. Promoter methylation-mediated gene silencing is strongly dependent on the location of the methylated CpGs, complicating classification. Using the HumanMethylation450 (HM-450K) BeadChip interrogating 176 CpGs annotated for the MGMT gene, with 14 located in the promoter, two distinct regions in the CpG island of the promoter were identified with high importance for gene silencing and outcome prediction. A logistic regression model (MGMT-STP27) comprising probes cg1243587 and cg12981137 provided good classification properties and prognostic value (kappa = 0.85; log-rank p < 0.001) using a training-set of 63 glioblastomas from homogenously treated patients, for whom MGMT methylation was previously shown to be predictive for outcome based on classification by methylation-specific PCR. MGMT-STP27 was successfully validated in an independent cohort of chemo-radiotherapy-treated glioblastoma patients (n = 50; kappa = 0.88; outcome, log-rank p < 0.001). Lower prevalence of MGMT methylation among CpG island methylator phenotype (CIMP) positive tumors was found in glioblastomas from The Cancer Genome Atlas than in low grade and anaplastic glioma cohorts, while in CIMP-negative gliomas MGMT was classified as methylated in approximately 50 % regardless of tumor grade. The proposed MGMT-STP27 prediction model allows mining of datasets derived on the HM-450K or HM-27K BeadChip to explore effects of distinct epigenetic context of MGMT methylation suspected to modulate treatment resistance in different tumor types.
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Keywords {🔍}
methylation, mgmt, glioblastoma, model, probes, article, promoter, google, scholar, hmk, cpg, gene, classification, status, methylated, fig, prediction, supplementary, pubmed, cimp, mgmtstp, cas, glioma, patients, data, cancer, outcome, datasets, expression, samples, dna, analysis, based, grade, msp, cpgs, clinical, anaplastic, unmethylated, survival, island, iii, temozolomide, information, treated, dataset, values, predictive, cohort, tumors,
Topics {✒️}
edema/cellular invasion mri-phenotypes o6-methylguanine-dna methyltransferase t-glioma-ii/iii datasets basically real-time methylation-specific pcr dna damage o6-methylguanine t-glioma-ii/iii datasets o6-methylguanine methyltransferase classic methylation-specific pcr external data-sets suggests dna-repair gene mgmt methylation-specific pyrosequencing-based vb-glioma-iii samples analyzed jocelyne bloch chemo-radiotherapy-treated glioblastoma patients van den bent hm-27k dna methylation t-glioma-ii/iii t-glioma ii/iii parametric smirnov–kolmogorov test log-likelihood ratio test mgmt-stp27-based outcome prediction vb-glioma-iii data pairwise quantile-quantile representation expression-based glioblastoma subtypes hm-450k beadarray platform logit-transformed response fitted wald-based confidence intervals intriguingly distinct behavior article download pdf location-specific cpg methylation vb-glioma-iii dataset vb-glioma-iii datasets grade ii/iii glioma msp-based methylation classification hm-450k methylation data standard chemo-radiotherapy comprised hm-27k data dna methylation analysis hm-27k information consisted pierre-yves dietrich hm-27k data revealed cimp-negative patient subpopulation cimp-negative tumors exhibited roger stupp & monika dna methylome data ms-mlpa methylation calls methylation-specific pcr respective glioblastoma-derived spheres ms-pseq-based prediction methylation-specific pyrosequencing
Questions {❓}
- Weller M, Stupp R, Reifenberger G et al (2010) MGMT promoter methylation in malignant gliomas: ready for personalized medicine?
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headline:MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMP-status
description:The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Recent studies in anaplastic glioma suggest a prognostic value for MGMT methylation. Investigation of pathogenetic and epigenetic features of this intriguingly distinct behavior requires accurate MGMT classification to assess high throughput molecular databases. Promoter methylation-mediated gene silencing is strongly dependent on the location of the methylated CpGs, complicating classification. Using the HumanMethylation450 (HM-450K) BeadChip interrogating 176 CpGs annotated for the MGMT gene, with 14 located in the promoter, two distinct regions in the CpG island of the promoter were identified with high importance for gene silencing and outcome prediction. A logistic regression model (MGMT-STP27) comprising probes cg1243587 and cg12981137 provided good classification properties and prognostic value (kappa = 0.85; log-rank p < 0.001) using a training-set of 63 glioblastomas from homogenously treated patients, for whom MGMT methylation was previously shown to be predictive for outcome based on classification by methylation-specific PCR. MGMT-STP27 was successfully validated in an independent cohort of chemo-radiotherapy-treated glioblastoma patients (n = 50; kappa = 0.88; outcome, log-rank p < 0.001). Lower prevalence of MGMT methylation among CpG island methylator phenotype (CIMP) positive tumors was found in glioblastomas from The Cancer Genome Atlas than in low grade and anaplastic glioma cohorts, while in CIMP-negative gliomas MGMT was classified as methylated in approximately 50 % regardless of tumor grade. The proposed MGMT-STP27 prediction model allows mining of datasets derived on the HM-450K or HM-27K BeadChip to explore effects of distinct epigenetic context of MGMT methylation suspected to modulate treatment resistance in different tumor types.
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MGMT
DNA methylation
MSP
Infinium methylation platform
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Pathology
Neurosciences
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headline:MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMP-status
description:The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Recent studies in anaplastic glioma suggest a prognostic value for MGMT methylation. Investigation of pathogenetic and epigenetic features of this intriguingly distinct behavior requires accurate MGMT classification to assess high throughput molecular databases. Promoter methylation-mediated gene silencing is strongly dependent on the location of the methylated CpGs, complicating classification. Using the HumanMethylation450 (HM-450K) BeadChip interrogating 176 CpGs annotated for the MGMT gene, with 14 located in the promoter, two distinct regions in the CpG island of the promoter were identified with high importance for gene silencing and outcome prediction. A logistic regression model (MGMT-STP27) comprising probes cg1243587 and cg12981137 provided good classification properties and prognostic value (kappa = 0.85; log-rank p < 0.001) using a training-set of 63 glioblastomas from homogenously treated patients, for whom MGMT methylation was previously shown to be predictive for outcome based on classification by methylation-specific PCR. MGMT-STP27 was successfully validated in an independent cohort of chemo-radiotherapy-treated glioblastoma patients (n = 50; kappa = 0.88; outcome, log-rank p < 0.001). Lower prevalence of MGMT methylation among CpG island methylator phenotype (CIMP) positive tumors was found in glioblastomas from The Cancer Genome Atlas than in low grade and anaplastic glioma cohorts, while in CIMP-negative gliomas MGMT was classified as methylated in approximately 50 % regardless of tumor grade. The proposed MGMT-STP27 prediction model allows mining of datasets derived on the HM-450K or HM-27K BeadChip to explore effects of distinct epigenetic context of MGMT methylation suspected to modulate treatment resistance in different tumor types.
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DNA methylation
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type:PostalAddress
type:Organization
name:Roger Stupp
affiliation:
name:Lausanne University Hospital
address:
name:Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
type:PostalAddress
type:Organization
name:Mauro Delorenzi
affiliation:
name:Swiss Institute for Bioinformatics
address:
name:Bioinformatics Core Facility, Swiss Institute for Bioinformatics, Lausanne, Switzerland
type:PostalAddress
type:Organization
name:Lausanne University Hospital
address:
name:Département de Formation et de recherche, Lausanne University Hospital, Lausanne, Switzerland
type:PostalAddress
type:Organization
name:ISREC-SV-EPFL
address:
name:National Center of Competence in Research Molecular Oncology, ISREC-SV-EPFL, Lausanne, Switzerland
type:PostalAddress
type:Organization
name:Monika E. Hegi
affiliation:
name:Lausanne University Hospital
address:
name:Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
type:PostalAddress
type:Organization
name:ISREC-SV-EPFL
address:
name:National Center of Competence in Research Molecular Oncology, ISREC-SV-EPFL, Lausanne, Switzerland
type:PostalAddress
type:Organization
name:Centre Hospitalier Universitaire Vaudois (CHUV BH19-110)
address:
name:Department of Neurosurgery, Laboratory of Brain Tumor Biology and Genetics, Centre Hospitalier Universitaire Vaudois (CHUV BH19-110), Lausanne, Switzerland
type:PostalAddress
type:Organization
email:[email protected]
PostalAddress:
name:Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
name:Bioinformatics Core Facility, Swiss Institute for Bioinformatics, Lausanne, Switzerland
name:Département de Formation et de recherche, Lausanne University Hospital, Lausanne, Switzerland
name:Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
name:Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
name:Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
name:Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
name:Medical University of Vienna, Vienna, Austria
name:University Hospital Geneva, Geneva, Switzerland
name:Department of Neurology, University of Tübingen, Tübingen, Germany
name:Department of Neurology, University Hospital Zurich, Zurich, Switzerland
name:Department of Neurosurgery, Inselspital Berne, Berne, Switzerland
name:Department of Neuropathology, University Hospital Zurich, Zurich, Switzerland
name:Neurology and Neuro-Oncology, London Regional Cancer Program London Health Sciences Centre, University of Western Ontario, London, Canada
name:EORTC Headquaters, Brussels, Belgium
name:Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
name:Bioinformatics Core Facility, Swiss Institute for Bioinformatics, Lausanne, Switzerland
name:Département de Formation et de recherche, Lausanne University Hospital, Lausanne, Switzerland
name:National Center of Competence in Research Molecular Oncology, ISREC-SV-EPFL, Lausanne, Switzerland
name:Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
name:National Center of Competence in Research Molecular Oncology, ISREC-SV-EPFL, Lausanne, Switzerland
name:Department of Neurosurgery, Laboratory of Brain Tumor Biology and Genetics, Centre Hospitalier Universitaire Vaudois (CHUV BH19-110), Lausanne, Switzerland
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