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Combining MGMT promoter pyrosequencing and protein expression to optimize prognosis stratification in glioblastoma

Pyrosequencing (PSQ) represents the golden standard for MGMT promoter status determination. Binary interpretation of results based on the threshold from the average of several CpGs tested would neglect the existence of the “gray zone”. How to define the gray zone and reclassify patients in this subg...

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Autores principales: Li, Mingxiao, Dong, Gehong, Zhang, Weiwei, Ren, Xiaohui, Jiang, Haihui, Yang, Chuanwei, Zhao, Xuzhe, Zhu, Qinghui, Li, Ming, Chen, Hongyan, Yu, Kefu, Cui, Yong, Song, Lin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409410/
https://www.ncbi.nlm.nih.gov/pubmed/34115910
http://dx.doi.org/10.1111/cas.15024
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author Li, Mingxiao
Dong, Gehong
Zhang, Weiwei
Ren, Xiaohui
Jiang, Haihui
Yang, Chuanwei
Zhao, Xuzhe
Zhu, Qinghui
Li, Ming
Chen, Hongyan
Yu, Kefu
Cui, Yong
Song, Lin
author_facet Li, Mingxiao
Dong, Gehong
Zhang, Weiwei
Ren, Xiaohui
Jiang, Haihui
Yang, Chuanwei
Zhao, Xuzhe
Zhu, Qinghui
Li, Ming
Chen, Hongyan
Yu, Kefu
Cui, Yong
Song, Lin
author_sort Li, Mingxiao
collection PubMed
description Pyrosequencing (PSQ) represents the golden standard for MGMT promoter status determination. Binary interpretation of results based on the threshold from the average of several CpGs tested would neglect the existence of the “gray zone”. How to define the gray zone and reclassify patients in this subgroup remains to be elucidated. A consecutive cohort of 312 primary glioblastoma patients were enrolled. CpGs 74‐81 in the promoter region of MGMT were tested by PSQ and the protein expression was assessed by immunohistochemistry (IHC). Receiver operating characteristic curves were constructed to calculate the area under the curves (AUC). Kaplan‐Meier plots were used to estimate the survival rate of patients compared by the log‐rank test. The optimal threshold of each individual CpG differed from 5% to 11%. Patients could be separated into the hypomethylated subgroup (all CpGs tested below the corresponding optimal thresholds, n = 126, 40.4%), hypermethylated subgroup (all CpGs tested above the corresponding optimal thresholds, n = 108, 34.6%), and the gray zone subgroup (remaining patients, n = 78, 25.0%). Patients in the gray zone harbored an intermediate prognosis. The IHC score instead of the average methylation levels could successfully predict the prognosis for the gray zone (AUC for overall survival, 0.653 and 0.519, respectively). Combining PSQ and IHC significantly improved the efficiency of survival prediction (AUC: 0.662, 0.648, and 0.720 for PSQ, IHC, and combined, respectively). Immunohistochemistry is a robust method to predict prognosis for patients in the gray zone defined by PSQ. Combining PSQ and IHC could significantly improve the predictive ability for clinical outcomes.
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spelling pubmed-84094102021-09-03 Combining MGMT promoter pyrosequencing and protein expression to optimize prognosis stratification in glioblastoma Li, Mingxiao Dong, Gehong Zhang, Weiwei Ren, Xiaohui Jiang, Haihui Yang, Chuanwei Zhao, Xuzhe Zhu, Qinghui Li, Ming Chen, Hongyan Yu, Kefu Cui, Yong Song, Lin Cancer Sci Original Articles Pyrosequencing (PSQ) represents the golden standard for MGMT promoter status determination. Binary interpretation of results based on the threshold from the average of several CpGs tested would neglect the existence of the “gray zone”. How to define the gray zone and reclassify patients in this subgroup remains to be elucidated. A consecutive cohort of 312 primary glioblastoma patients were enrolled. CpGs 74‐81 in the promoter region of MGMT were tested by PSQ and the protein expression was assessed by immunohistochemistry (IHC). Receiver operating characteristic curves were constructed to calculate the area under the curves (AUC). Kaplan‐Meier plots were used to estimate the survival rate of patients compared by the log‐rank test. The optimal threshold of each individual CpG differed from 5% to 11%. Patients could be separated into the hypomethylated subgroup (all CpGs tested below the corresponding optimal thresholds, n = 126, 40.4%), hypermethylated subgroup (all CpGs tested above the corresponding optimal thresholds, n = 108, 34.6%), and the gray zone subgroup (remaining patients, n = 78, 25.0%). Patients in the gray zone harbored an intermediate prognosis. The IHC score instead of the average methylation levels could successfully predict the prognosis for the gray zone (AUC for overall survival, 0.653 and 0.519, respectively). Combining PSQ and IHC significantly improved the efficiency of survival prediction (AUC: 0.662, 0.648, and 0.720 for PSQ, IHC, and combined, respectively). Immunohistochemistry is a robust method to predict prognosis for patients in the gray zone defined by PSQ. Combining PSQ and IHC could significantly improve the predictive ability for clinical outcomes. John Wiley and Sons Inc. 2021-07-02 2021-09 /pmc/articles/PMC8409410/ /pubmed/34115910 http://dx.doi.org/10.1111/cas.15024 Text en © 2021 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Original Articles
Li, Mingxiao
Dong, Gehong
Zhang, Weiwei
Ren, Xiaohui
Jiang, Haihui
Yang, Chuanwei
Zhao, Xuzhe
Zhu, Qinghui
Li, Ming
Chen, Hongyan
Yu, Kefu
Cui, Yong
Song, Lin
Combining MGMT promoter pyrosequencing and protein expression to optimize prognosis stratification in glioblastoma
title Combining MGMT promoter pyrosequencing and protein expression to optimize prognosis stratification in glioblastoma
title_full Combining MGMT promoter pyrosequencing and protein expression to optimize prognosis stratification in glioblastoma
title_fullStr Combining MGMT promoter pyrosequencing and protein expression to optimize prognosis stratification in glioblastoma
title_full_unstemmed Combining MGMT promoter pyrosequencing and protein expression to optimize prognosis stratification in glioblastoma
title_short Combining MGMT promoter pyrosequencing and protein expression to optimize prognosis stratification in glioblastoma
title_sort combining mgmt promoter pyrosequencing and protein expression to optimize prognosis stratification in glioblastoma
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409410/
https://www.ncbi.nlm.nih.gov/pubmed/34115910
http://dx.doi.org/10.1111/cas.15024
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