Polly Taylor, Gabrielle Cruickshank, Jack Wildman, George Doyle, Ed Whittaker, Sara Walker, Claire McKeeve, Claire Faulkner, Laura Yarram-Smith, Paul White, Kathreena M Kurian
{"title":"定义o6 -甲基鸟嘌呤- dna甲基转移酶启动子甲基化焦磷酸测序报告中的推荐灰色地带:一种实施新的EANO指南的稳健、可翻译的方法。","authors":"Polly Taylor, Gabrielle Cruickshank, Jack Wildman, George Doyle, Ed Whittaker, Sara Walker, Claire McKeeve, Claire Faulkner, Laura Yarram-Smith, Paul White, Kathreena M Kurian","doi":"10.1093/noajnl/vdaf061","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The DNA repair protein O6-methylguanine-DNA methyltransferase (MGMT) may cause resistance of tumor cells to alkylating agents and is a predictive biomarker in high-grade gliomas treated with temozolomide. Recent European Association of Neuro-Oncology (EANO) guidelines recommend internal validation of MGMT methylation cutoffs and reporting of gray zone values. This study aimed to develop a method to derive a gray zone from pyrosequencing MGMT methylation data.</p><p><strong>Methods: </strong>We developed a method to find the optimal gray zone using pyrosequencing MGMT methylation values (CpG sites 72-83) from 308 glioblastoma cases with overall survival data. Each integer below the methylated threshold defined a new possible gray zone and categorization which was used as a variable in a multivariate Cox proportional hazards regression model. The optimal gray zone was selected as the option that had a statistically different survival function from the methylated and unmethylated groups, with the largest log-likelihood ratio test statistic. We applied the method to a validation cohort of 115 glioblastoma cases.</p><p><strong>Results: </strong>Our method successfully identified a gray zone in our development cohort. The following categorization gave 3 distinct survival functions: methylated ≥12% (<i>n</i> = 152 cases), gray zone 5%-12% (<i>n</i> = 43), and unmethylated <5% (<i>n</i> = 113). This categorization was better at predicting survival than the existing categorization (methylated ≥12%, unmethylated <12%). Validating our method showed a sufficient sample size and time to follow up is recommended to apply our method.</p><p><strong>Conclusions: </strong>We have developed a translatable method to identify the optimal MGMT gray zone from pyrosequencing data in line with recent EANO guidelines, to enhance clinical decision-making.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"7 1","pages":"vdaf061"},"PeriodicalIF":3.7000,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12121717/pdf/","citationCount":"0","resultStr":"{\"title\":\"Defining the recommended gray zone in O6-methylguanine-DNA methyltransferase promoter methylation pyrosequencing reporting: A robust, translatable method to implement new EANO guidelines.\",\"authors\":\"Polly Taylor, Gabrielle Cruickshank, Jack Wildman, George Doyle, Ed Whittaker, Sara Walker, Claire McKeeve, Claire Faulkner, Laura Yarram-Smith, Paul White, Kathreena M Kurian\",\"doi\":\"10.1093/noajnl/vdaf061\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The DNA repair protein O6-methylguanine-DNA methyltransferase (MGMT) may cause resistance of tumor cells to alkylating agents and is a predictive biomarker in high-grade gliomas treated with temozolomide. Recent European Association of Neuro-Oncology (EANO) guidelines recommend internal validation of MGMT methylation cutoffs and reporting of gray zone values. This study aimed to develop a method to derive a gray zone from pyrosequencing MGMT methylation data.</p><p><strong>Methods: </strong>We developed a method to find the optimal gray zone using pyrosequencing MGMT methylation values (CpG sites 72-83) from 308 glioblastoma cases with overall survival data. Each integer below the methylated threshold defined a new possible gray zone and categorization which was used as a variable in a multivariate Cox proportional hazards regression model. The optimal gray zone was selected as the option that had a statistically different survival function from the methylated and unmethylated groups, with the largest log-likelihood ratio test statistic. We applied the method to a validation cohort of 115 glioblastoma cases.</p><p><strong>Results: </strong>Our method successfully identified a gray zone in our development cohort. The following categorization gave 3 distinct survival functions: methylated ≥12% (<i>n</i> = 152 cases), gray zone 5%-12% (<i>n</i> = 43), and unmethylated <5% (<i>n</i> = 113). This categorization was better at predicting survival than the existing categorization (methylated ≥12%, unmethylated <12%). Validating our method showed a sufficient sample size and time to follow up is recommended to apply our method.</p><p><strong>Conclusions: </strong>We have developed a translatable method to identify the optimal MGMT gray zone from pyrosequencing data in line with recent EANO guidelines, to enhance clinical decision-making.</p>\",\"PeriodicalId\":94157,\"journal\":{\"name\":\"Neuro-oncology advances\",\"volume\":\"7 1\",\"pages\":\"vdaf061\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2025-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12121717/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neuro-oncology advances\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/noajnl/vdaf061\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuro-oncology advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/noajnl/vdaf061","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Defining the recommended gray zone in O6-methylguanine-DNA methyltransferase promoter methylation pyrosequencing reporting: A robust, translatable method to implement new EANO guidelines.
Background: The DNA repair protein O6-methylguanine-DNA methyltransferase (MGMT) may cause resistance of tumor cells to alkylating agents and is a predictive biomarker in high-grade gliomas treated with temozolomide. Recent European Association of Neuro-Oncology (EANO) guidelines recommend internal validation of MGMT methylation cutoffs and reporting of gray zone values. This study aimed to develop a method to derive a gray zone from pyrosequencing MGMT methylation data.
Methods: We developed a method to find the optimal gray zone using pyrosequencing MGMT methylation values (CpG sites 72-83) from 308 glioblastoma cases with overall survival data. Each integer below the methylated threshold defined a new possible gray zone and categorization which was used as a variable in a multivariate Cox proportional hazards regression model. The optimal gray zone was selected as the option that had a statistically different survival function from the methylated and unmethylated groups, with the largest log-likelihood ratio test statistic. We applied the method to a validation cohort of 115 glioblastoma cases.
Results: Our method successfully identified a gray zone in our development cohort. The following categorization gave 3 distinct survival functions: methylated ≥12% (n = 152 cases), gray zone 5%-12% (n = 43), and unmethylated <5% (n = 113). This categorization was better at predicting survival than the existing categorization (methylated ≥12%, unmethylated <12%). Validating our method showed a sufficient sample size and time to follow up is recommended to apply our method.
Conclusions: We have developed a translatable method to identify the optimal MGMT gray zone from pyrosequencing data in line with recent EANO guidelines, to enhance clinical decision-making.