BACKGROUND: There is a strong need to determine the best technique for O(6) -methylguanine-DNA-methyltranferase (MGMT) analysis, because MGMT status is currently used in clinical trials and occasionally in routine clinical practice for glioblastoma patients. METHODS: The authors compared analytical performances and predictive values of 5 techniques in a series of 100 glioblastoma patients who received standard of care treatment (Stupp protocol). RESULTS: MGMT promoter was considered methylated in 33%, 33%, 42%, and 60% of patients by methylation-sensitive high-resolution melting, MethyLight, pyrosequencing (with an optimal risk cutoff at 8% for the average percentage of the 5 CpGs tested), and methylation-specific polymerase chain reaction (MS-PCR), respectively. Fifty-nine percent of the samples had <23% (the optimal risk cutoff) of MGMT-positive tumor cells. The best predictive values for overall survival (OS), after adjustment for age and performance status, were obtained by pyrosequencing (hazard ratio [HR], 0.32; P < .0001), MS-PCR (HR, 0.37; P < .0001), and immunohistochemistry (HR, 0.43; P = .0005) as compared with methylation-sensitive high-resolution melting (HR, 0.52 P = .02) and MethyLight (HR, 0.6; P = .05). For progression-free survival (PFS), the best predictive values were obtained with pyrosequencing (HR, 0.35; P < .0001), methylation-sensitive high-resolution melting (HR, 0.46; P = .002), and MS-PCR (HR, 0.49; P = .002). Combining pyrosequencing and immunohistochemistry slightly improved predictive power for OS, but not for PFS. Poor reproducibility and interobserver variability were, however, observed for immunohistochemistry. CONCLUSIONS: Good prediction of survival in addition to high reproducibility and sensitivity made pyrosequencing the best among the 5 techniques tested in this study.
BACKGROUND: There is a strong need to determine the best technique for O(6) -methylguanine-DNA-methyltranferase (MGMT) analysis, because MGMT status is currently used in clinical trials and occasionally in routine clinical practice for glioblastomapatients. METHODS: The authors compared analytical performances and predictive values of 5 techniques in a series of 100 glioblastomapatients who received standard of care treatment (Stupp protocol). RESULTS:MGMT promoter was considered methylated in 33%, 33%, 42%, and 60% of patients by methylation-sensitive high-resolution melting, MethyLight, pyrosequencing (with an optimal risk cutoff at 8% for the average percentage of the 5 CpGs tested), and methylation-specific polymerase chain reaction (MS-PCR), respectively. Fifty-nine percent of the samples had <23% (the optimal risk cutoff) of MGMT-positive tumor cells. The best predictive values for overall survival (OS), after adjustment for age and performance status, were obtained by pyrosequencing (hazard ratio [HR], 0.32; P < .0001), MS-PCR (HR, 0.37; P < .0001), and immunohistochemistry (HR, 0.43; P = .0005) as compared with methylation-sensitive high-resolution melting (HR, 0.52 P = .02) and MethyLight (HR, 0.6; P = .05). For progression-free survival (PFS), the best predictive values were obtained with pyrosequencing (HR, 0.35; P < .0001), methylation-sensitive high-resolution melting (HR, 0.46; P = .002), and MS-PCR (HR, 0.49; P = .002). Combining pyrosequencing and immunohistochemistry slightly improved predictive power for OS, but not for PFS. Poor reproducibility and interobserver variability were, however, observed for immunohistochemistry. CONCLUSIONS: Good prediction of survival in addition to high reproducibility and sensitivity made pyrosequencing the best among the 5 techniques tested in this study.
Authors: Patrick Y Wen; Michael Weller; Eudocia Quant Lee; Brian M Alexander; Jill S Barnholtz-Sloan; Floris P Barthel; Tracy T Batchelor; Ranjit S Bindra; Susan M Chang; E Antonio Chiocca; Timothy F Cloughesy; John F DeGroot; Evanthia Galanis; Mark R Gilbert; Monika E Hegi; Craig Horbinski; Raymond Y Huang; Andrew B Lassman; Emilie Le Rhun; Michael Lim; Minesh P Mehta; Ingo K Mellinghoff; Giuseppe Minniti; David Nathanson; Michael Platten; Matthias Preusser; Patrick Roth; Marc Sanson; David Schiff; Susan C Short; Martin J B Taphoorn; Joerg-Christian Tonn; Jonathan Tsang; Roel G W Verhaak; Andreas von Deimling; Wolfgang Wick; Gelareh Zadeh; David A Reardon; Kenneth D Aldape; Martin J van den Bent Journal: Neuro Oncol Date: 2020-08-17 Impact factor: 12.300
Authors: Benedikt Wiestler; David Capper; Volker Hovestadt; Martin Sill; David T W Jones; Christian Hartmann; Joerg Felsberg; Michael Platten; Wolfgang Feiden; Kathy Keyvani; Stefan M Pfister; Otmar D Wiestler; Richard Meyermann; Guido Reifenberger; Thorsten Pietsch; Andreas von Deimling; Michael Weller; Wolfgang Wick Journal: Neuro Oncol Date: 2014-07-15 Impact factor: 12.300
Authors: H Pinson; G Hallaert; J Van der Meulen; F Dedeurwaerdere; D Vanhauwaert; C Van den Broecke; J Van Dorpe; D Van Roost; J P Kalala; T Boterberg Journal: J Neurooncol Date: 2019-11-07 Impact factor: 4.130