Prognostic value of three different methods of MGMT promoter methylation analysis in a prospective trial on newly diagnosed glioblastoma

• Published in: PloS one Vol. 7; no. 3; p. e33449
• Main Authors: Christians, Arne, Hartmann, Christian, Benner, Axel, Meyer, Jochen, von Deimling, Andreas, Weller, Michael, Wick, Wolfgang, Weiler, Markus
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.03.2012; Public Library of Science (PLoS)
• Subjects: Alkylating agents; Alkylation; Brain cancer; Brain research; Cancer; Care and treatment; Chemoradiotherapy; Clinical trials; Cooperation; Cost analysis; Cost effectiveness; CpG islands; Dacarbazine - analogs & derivatives; Deoxyribonucleic acid; Diagnostic systems; Disease-Free Survival; DNA; DNA methylation; DNA Methylation - genetics; DNA methyltransferase; DNA Modification Methylases - genetics; DNA Modification Methylases - metabolism; DNA Primers - genetics; DNA repair; DNA Repair Enzymes - genetics; DNA Repair Enzymes - metabolism; Feasibility studies; Gene expression; Genes; Genomics; Glioblastoma; Glioblastoma - diagnosis; Glioblastoma - drug therapy; Glioblastoma - genetics; Glioblastomas; Humans; Kaplan-Meier Estimate; Likelihood Functions; Likelihood ratio; Medical diagnosis; Medical research; Medicine; Methods; Methylation; Methylguanine; Molecular Probe Techniques; Multiplexing; Neurology; Neuropathology; Nucleic Acid Amplification Techniques - methods; O6-methylguanine-DNA methyltransferase; Paraffin; Patients; Polymerase chain reaction; Polymerase Chain Reaction - methods; Predictions; Prognosis; Promoter Regions, Genetic - genetics; Prospective Studies; Sequence Analysis, DNA - methods; Studies; Survival; Survival analysis; Temozolomide; Tissues; Tumor Suppressor Proteins - genetics; Tumor Suppressor Proteins - metabolism; Tumors; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0033449

Hypermethylation in the promoter region of the MGMT gene encoding the DNA repair protein O(6)-methylguanine-DNA methyltransferase is among the most important prognostic factors for patients with glioblastoma and predicts response to treatment with alkylating agents like temozolomide. Hence, the MGMT status is widely determined in most clinical trials and frequently requested in routine diagnostics of glioblastoma. Since various different techniques are available for MGMT promoter methylation analysis, a generally accepted consensus as to the most suitable diagnostic method remains an unmet need. Here, we assessed methylation-specific polymerase chain reaction (MSP) as a qualitative and semi-quantitative method, pyrosequencing (PSQ) as a quantitative method, and methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) as a semi-quantitative method in a series of 35 formalin-fixed, paraffin-embedded glioblastoma tissues derived from patients treated in a prospective clinical phase II trial that tested up-front chemoradiotherapy with dose-intensified temozolomide (UKT-05). Our goal was to determine which of these three diagnostic methods provides the most accurate prediction of progression-free survival (PFS). The MGMT promoter methylation status was assessable by each method in almost all cases (n = 33/35 for MSP; n = 35/35 for PSQ; n = 34/35 for MS-MLPA). We were able to calculate significant cut-points for the continuous methylation signals at each CpG site analysed by PSQ (range, 11.5 to 44.9%) and at one CpG site assessed by MS-MLPA (3.6%) indicating that a dichotomisation of continuous methylation data as a prerequisite for comparative survival analyses is feasible. Our results show that, unlike MS-MLPA, MSP and PSQ provide a significant improvement of predicting PFS compared with established clinical prognostic factors alone (likelihood ratio tests: p<0.001). Conclusively, taking into consideration prognostic value, cost effectiveness and ease of use, we recommend pyrosequencing for analyses of MGMT promoter methylation in high-throughput settings and MSP for clinical routine diagnostics with low sample numbers.