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. 2017 Mar;133(3):431-444.
doi: 10.1007/s00401-017-1678-x. Epub 2017 Jan 27.

Global epigenetic profiling identifies methylation subgroups associated with recurrence-free survival in meningioma

Adriana Olar  1 , Khalida M Wani  2 , Charmaine D Wilson  3 , Gelareh Zadeh  4 , Franco DeMonte  5 , David T W Jones  6 , Stefan M Pfister  6   7 , Erik P Sulman  2   8 , Kenneth D Aldape  4
Affiliations

Global epigenetic profiling identifies methylation subgroups associated with recurrence-free survival in meningioma

Adriana Olar et al. Acta Neuropathol. 2017 Mar.

Abstract

Meningioma is the most common primary brain tumor and carries a substantial risk of local recurrence. Methylation profiles of meningioma and their clinical implications are not well understood. We hypothesized that aggressive meningiomas have unique DNA methylation patterns that could be used to better stratify patient management. Samples (n = 140) were profiled using the Illumina HumanMethylation450BeadChip. Unsupervised modeling on a training set (n = 89) identified 2 molecular methylation subgroups of meningioma (MM) with significantly different recurrence-free survival (RFS) times between the groups: a prognostically unfavorable subgroup (MM-UNFAV) and a prognostically favorable subgroup (MM-FAV). This finding was validated in the remaining 51 samples and led to a baseline meningioma methylation classifier (bMMC) defined by 283 CpG loci (283-bMMC). To further optimize a recurrence predictor, probes subsumed within the baseline classifier were subject to additional modeling using a similar training/validation approach, leading to a 64-CpG loci meningioma methylation predictor (64-MMP). After adjustment for relevant clinical variables [WHO grade, mitotic index, Simpson grade, sex, location, and copy number aberrations (CNAs)] multivariable analyses for RFS showed that the baseline methylation classifier was not significant (p = 0.0793). The methylation predictor, however, was significantly associated with tumor recurrence (p < 0.0001). CNAs were extracted from the 450k intensity profiles. Tumor samples in the MM-UNFAV subgroup showed an overall higher proportion of CNAs compared to the MM-FAV subgroup tumors and the CNAs were complex in nature. CNAs in the MM-UNFAV subgroup included recurrent losses of 1p, 6q, 14q and 18q, and gain of 1q, all of which were previously identified as indicators of poor outcome. In conclusion, our analyses demonstrate robust DNA methylation signatures in meningioma that correlate with CNAs and stratify patients by recurrence risk.

Keywords: Copy number aberrations; DNA methylation; Epigenetics; Meningioma; Molecular subgroups; Recurrence risk.

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Figures

Figure 1
Figure 1
Supervised hierarchical clustering with the 283 probes in the training dataset (n=86) separates 2 differentially methylated groups of tumors. The copy number information is represented at the bottom. Each row represents a probe and each column represents a sample. Methylation values are shown after batch normalization. Kaplan-Meier survival analysis illustrates significantly decreased tumor recurrence time for patients with meningioma from the MM-UNFAV group.
Figure 2
Figure 2
Supervised hierarchical clustering with the 283 probes in the validation dataset (n=51) separates 2 differentially methylated groups of tumors. The copy number information is represented at the bottom. Each row represents a probe and each column represents a sample. Methylation values are shown after BMIQ normalization. Kaplan-Meier survival analysis illustrates significantly decreased tumor recurrence time for patients with meningioma from the MM-UNFAV group.
Figure 3
Figure 3
The 64-MMP model developed on the training dataset (a) and then applied to the validation dataset (b) significantly separates risk groups for meningioma recurrence.
See this image and copyright information in PMC

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