Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Mod Pathol. 2019 Jan;32(1):4-15. doi: 10.1038/s41379-018-0143-2. Epub 2018 Oct 5.
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.
MGMT 基因启动子的甲基化状态是影响胶质瘤患者临床决策的关键因素。MGMT 焦磷酸测序结果通常通过基于几个测试 CpG 的平均值的截止值进行二分法。然而，这种方法经常导致“灰色地带”，这对医生来说是一个困境。因此，我们提出了一种新的 MGMT 甲基化焦磷酸测序分析模型。在两个测试队列的 213 名胶质瘤患者中研究了 MGMT CpG 异质性：队列 A 中测试了 CpG 75-82，队列 B 中测试了 CpG 72-78。通过接受替莫唑胺治疗的 135 名患者的接收者操作特性曲线和 Kaplan-Meier 曲线以及根据异柠檬酸脱氢酶基因突变状态分层的患者，比较了新模型和传统平均模型的预测性能。在来自中国胶质瘤基因组图谱数据库的 65 例连续高级别胶质瘤患者的独立队列中验证了结果。大多数胶质瘤中观察到 MGMT 启动子 CpG 甲基化水平的异质性。每个单独 CpG 的最佳截止值从 4-16%不等。目前的分析将 MGMT 启动子甲基化定义为至少三个 CpG 超过其各自截止值时发生。这种新的分析可以根据标准平均方法准确预测甲基化“灰色地带”患者的预后，并将曲线下面积从 0.67、0.76 和 0.67 分别提高到 0.70、0.84 和 0.72，在队列 A、B 和验证队列中，表明该分析方法在所有三个队列中均具有优越性。此外，无论 WHO 分级和异柠檬酸脱氢酶基因突变状态如何，该新分析的优势均得以保留。总之，该新分析模型为 MGMT 焦磷酸测序结果提供了更好的临床预测性能，适合胶质瘤患者的临床应用。
