DNA methylation, isocitrate dehydrogenase mutation, and survival in glioma

Abstract

Background: Although much is known about molecular and chromosomal characteristics that distinguish glioma histological subtypes, DNA methylation patterns of gliomas and their association with other tumor features such as mutation of isocitrate dehydrogenase (IDH) genes have only recently begun to be investigated.

Methods: DNA methylation of glioblastomas, astrocytomas, oligodendrogliomas, oligoastrocytomas, ependymomas, and pilocytic astrocytomas (n = 131) from the Brain Tumor Research Center at the University of California San Francisco, as well as nontumor brain tissues (n = 7), was assessed with the Illumina GoldenGate methylation array. Methylation data were subjected to recursively partitioned mixture modeling (RPMM) to derive methylation classes. Differential DNA methylation between tumor and nontumor was also assessed. The association between methylation class and IDH mutation (IDH1 and IDH2) was tested using univariate and multivariable analysis for tumors (n = 95) with available substrate for sequencing. Survival of glioma patients carrying mutant IDH (n = 57) was compared with patients carrying wild-type IDH (n = 38) using a multivariable Cox proportional hazards model and Kaplan-Meier analysis. All statistical tests were two-sided.

Results: We observed a statistically significant association between RPMM methylation class and glioma histological subtype (P < 2.2 × 10(-16)). Compared with nontumor brain tissues, across glioma tumor histological subtypes, the differential methylation ratios of CpG loci were statistically significantly different (permutation P < .0001). Methylation class was strongly associated with IDH mutation in gliomas (P = 3.0 × 10(-16)). Compared with glioma patients whose tumors harbored wild-type IDH, patients whose tumors harbored mutant IDH showed statistically significantly improved survival (hazard ratio of death = 0.27, 95% confidence interval = 0.10 to 0.72).

Conclusion: The homogeneity of methylation classes for gliomas with IDH mutation, despite their histological diversity, suggests that IDH mutation is associated with a distinct DNA methylation phenotype and an altered metabolic profile in glioma.

Figure 1

Association between glioma histological subtypes and DNA methylation pattern. A) The average methylation beta (β) values of both gliomas (n = 131) and nontumor tissue samples (n = 7) were subjected to unsupervised hierarchical clustering based on Euclidean distance metric and Ward linkage and are shown in the heatmap. Each row represents a sample and each column represents a CpG locus (all 1413 autosomal loci). The scale bar at the bottom shows the range of β values (0–1). Tissue histology and grade are defined in color keys next to the heatmap, on the left. GBM2 = secondary glioblastoma multiforme; GBM = primary glioblastoma multiforme; AS3 = grade 3 astrocytoma; AS2 = grade 2 astrocytoma; OA3 = grade 3 oligoastrocytoma; OA2 = grade 2 oligoastrocytoma; OD2 = grade 2 oligodendroglioma; EP = ependymoma; PA = pilocytic astrocytoma. B) Recursively partitioned mixture model (RPMM) of glioma and nontumor brain tissue samples (n = 138). Methylation profile classes are stacked in rows separated by red lines and class height corresponds to the number of samples in each class. Class methylation at each CpG locus (columns) is the mean methylation for all samples in a class. To the left of the RPMM is the clustering dendrogram. In the heatmap and RPMM, blue designates methylated CpG loci (average β = 1), and yellow designates unmethylated CpG loci (average β = 0).

Figure 2

Differential methylation and the ratio of hyper- to hypomethylated loci in gliomas. Differential methylation values (Δβ) were calculated by subtracting tumor average β value from the mean β value of the nontumor brain samples (n = 7) for each CpG locus. A) The number of statistically significantly differentially hyper- and hypomethylated loci (Q < .05 and |Δβ| > 0.2) are plotted by grade-specific glioma histology. GBM2 = secondary glioblastoma multiforme; GBM = primary glioblastoma multiforme; AS3 = grade 3 astrocytoma; AS2 = grade 2 astrocytoma; OA3 = grade 3 oligoastrocytoma; OA2 = grade 2 oligoastrocytoma; OD2 = grade 2 oligodendroglioma; EP = ependymoma; PA = pilocytic astrocytoma. B) Δβ values for all tumors (n = 131) were subjected to unsupervised hierarchical clustering based on Euclidean distance metric and Ward linkage. Each row represents a sample and each column represents a CpG locus (all 1413 autosomal loci). The scale bar at the top shows the range of Δβ values (−1 to 1). Tissue histology and grade are defined in color keys next to the heatmap on the left. In the heatmap, blue designates differentially hypermethylated CpG loci in tumors (Δβ = 1), and yellow designates differentially hypomethylated CpG loci in tumors (Δβ = −1).

Figure 3

Association between IDH mutation and methylation phenotype in gliomas. A) The number of statistically significantly differentially hyper- and hypomethylated loci (Q < .05 and |Δβ| > 0.2), are plotted by tumor IDH mutation status. B) Recursively partitioned mixture model (RPMM) of glioma samples with both methylation and mutation data (n = 95). Methylation profile classes are stacked in rows separated by red lines, class height corresponds to the number of samples in each class. Class methylation at each CpG locus (columns) is the mean methylation for all samples in a class where blue designates methylated CpG loci (average β = 1), and yellow designates unmethylated CpG loci (average β = 0). To the right of the RPMM is the clustering dendrogram. C) Methylation-class-specific IDH mutation status (Fisher P = 3.0 × 10⁻¹⁶). D) Kaplan–Meier survival probability strata for IDH mutant (red, n = 57) and IDH wild-type (black, n = 38) tumors, tick marks are censored observations and banding patterns represent 95% confidence intervals.