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Mod Pathol. 2019 Jan;32(1):4-15. doi: 10.1038/s41379-018-0143-2. Epub 2018 Oct 5.

A novel analytical model of MGMT methylation pyrosequencing offers improved predictive performance in patients with gliomas.

Chai RC1,2, Liu YQ1,2, Zhang KN1,2,3, Wu F1,2, Zhao Z1,2, Wang KY1,2,3, Jiang T1,2,3, Wang YZ4,5,6.

Author information

1
Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
2
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
3
Chinese Glioma Genome Atlas, Beijing, China.
4
Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China. yongzhiwang_bni@163.com.
5
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. yongzhiwang_bni@163.com.
6
Chinese Glioma Genome Atlas, Beijing, China. yongzhiwang_bni@163.com.

Abstract

The methylation status of the promoter of MGMT gene is a crucial factor influencing clinical decision-making in patients with gliomas. MGMT pyrosequencing results are often dichotomized by a cut-off value based on an average of several tested CpGs. However, this method frequently results in a "gray zone", representing a dilemma for physicians. We therefore propose a novel analytical model for MGMT methylation pyrosequencing. MGMT CpG heterogeneity was investigated in 213 glioma patients in two tested cohorts: cohort A in which CpGs 75-82 were tested and cohort B in which CpGs 72-78 were tested. The predictive performances of the novel and traditional averaging models were compared in 135 patients who received temozolomide using receiver operating characteristic curves and Kaplan-Meier curves, and in patients stratified according to isocitrate dehydrogenase gene mutation status. The results were validated in an independent cohort of 65 consecutive patients with high-grade gliomas from the Chinese Glioma Genome Atlas database. Heterogeneity of MGMT promoter CpG methylation level was observed in most gliomas. The optimal cut-off value for each individual CpG varied from 4-16%. The current analysis defined MGMT promoter methylation as occurring when at least three CpGs exceeded their respective cut-off values. This novel analysis could accurately predict the prognosis of patients in the methylation "gray zone" according to the standard averaging method, and improved the area under the curves from 0.67, 0.76, and 0.67 to 0.70, 0.84, and 0.72 in cohorts A, B, and the validation cohort, respectively, demonstrating superiority of this analytical method in all three cohorts. Furthermore, the advantages of the novel analysis were retained regardless of WHO grade and isocitrate dehydrogenase gene mutation status. In conclusion, this novel analytical model offers an improved clinical predictive performance for MGMT pyrosequencing results and is suitable for clinical use in patients with gliomas.

PMID:
30291347
DOI:
10.1038/s41379-018-0143-2

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