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. 2016 May;18(3):350-361.
doi: 10.1016/j.jmoldx.2015.11.009. Epub 2016 Feb 27.

Sensitivity Analysis of the MGMT-STP27 Model and Impact of Genetic and Epigenetic Context to Predict the MGMT Methylation Status in Gliomas and Other Tumors

Pierre Bady  1 , Mauro Delorenzi  2 , Monika E Hegi  3
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Free article

Sensitivity Analysis of the MGMT-STP27 Model and Impact of Genetic and Epigenetic Context to Predict the MGMT Methylation Status in Gliomas and Other Tumors

Pierre Bady et al. J Mol Diagn. 2016 May.
Free article

Abstract

The methylation status of the O(6)-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Our model MGMT-STP27 allows prediction of the methylation status of the MGMT promoter using data from the Illumina's Human Methylation BeadChips (HM-27K and HM-450K) that is publically available for many cancer data sets. Here, we investigate the impact of the context of genetic and epigenetic alterations and tumor type on the classification and report on technical aspects, such as robustness of cutoff definition and preprocessing of the data. The association between gene copy number variation, predicted MGMT methylation, and MGMT expression revealed a gene dosage effect on MGMT expression in lower grade glioma (World Health Organization grade II/III) that in contrast to glioblastoma usually carry two copies of chromosome 10 on which MGMT resides (10q26.3). This implies some MGMT expression, potentially conferring residual repair function blunting the therapeutic effect of alkylating agents. A sensitivity analyses corroborated the performance of the original cutoff for various optimization criteria and for most data preprocessing methods. Finally, we propose an R package mgmtstp27 that allows prediction of the methylation status of the MGMT promoter and calculation of appropriate confidence and/or prediction intervals. Overall, MGMT-STP27 is a robust model for MGMT classification that is independent of tumor type and is adapted for single sample prediction.

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