Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma

Abstract

Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease. Whole-genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co-deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH-wild-type diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes.

Copyright © 2016 Elsevier Inc. All rights reserved.

Figures

Figure 1. Telomere length associations in glioma

A. Heatmap of relative tumor/normal telomere lengths of 119 gliomas, grouped by TERTp and ATRX mutation status. B. Telomere length decreases with increasing age (measured in years at diagnosis) in blood normal control samples (n=137). C. Quantitative telomere length estimates of tumors and blood normal, grouped by TERTp mutant (n=67, 56%), ATRX mutant (n=40, 33%) and double negative (n=13, 11%) status. *** = P<0.0001; ** = P<0.001.

Figure 2. Pan-glioma DNA methylation and transcriptome subtypes

A. Heatmap of DNA methylation data. Columns represent 932 TCGA glioma samples grouped according to unsupervised cluster analysis, rows represent DNA methylation probes sorted by hierarchical clustering. Non-neoplastic samples are represented on the left of the heatmap (n = 77) (Guintivano et al, 2013). B. Heatmap of RNA sequencing data. Unsupervised clustering analysis for 667 TCGA glioma samples profiled using RNA sequencing are plotted in the heatmap using 2,275 most variant genes. Previously published subtypes were derived from Brennan et al. Cell, 2013 and TCGA Research Network, NEJM, 2015. C. Tumor Map based on mRNA expression and DNA methylation data. Each data point is a TCGA sample colored coded according to their identified status. A live interactive version of this map is available at http://tumormap.ucsc.edu/?p=ynewton.gliomas-paper.

Figure 3. Identification of a distinct G-CIMP subtype defined by epigenomics

A. Heatmap of probes differentially methylated between the two IDH-mutant-non-codel DNA methylation clusters allowed the identification of a low-methylation subgroup named G-CIMP-low. Non-tumor brain samples (n=12) are represented on the left of the heatmap. B. Heatmap of genes differentially expressed between the two IDH-mutant-non-codel DNA methylation clusters. C. Kaplan-Meier survival curves of IDH-mutant methylation subtypes. Ticks represent censored values. D. Distribution of genomic alterations in genes frequently altered in IDH-mutant glioma. E. Genomic distribution of 633 CpG probes differentially demethylated between co-clustered G-CIMP-low and G-CIMP-high. CpG probes are grouped by UCSC genome browser defined CpG Islands, shores flanking CpG island +/− 2kb and open seas (regions not in CpG islands or shores). F. DNA methylation heatmap of TCGA glioma samples ordered per Figure 2A, and the epigenetically regulated (EReg) gene signatures defined for G-CIMP-low, G-CIMP-high and Codel subtypes. NThe mean RNA sequencing counts for each gene matched to the promoter of the identified cgID across each cluster are plotted to the right. G. Heatmap of the validation set classified using the random forest method1,300 probes defined in Figure 2A. H. Heatmap of probes differentially methylated between G-CIMP-low and G-CIMP-high in longitudinally matched tumor samples.

Figure 4. A distinct subgroup of IDH-wildtype diffuse glioma with molecular features of pilocytic astrocytoma

A. Kaplan-Meier survival curves for the IDH-wildtype glioma subtypes. Ticks represent censorship. B. Distribution of previous published DNA methylation subtypes in the validation set, across the TCGA IDH-wildtype specific DNA methylation clusters. C. Distribution of genomic alterations in genes frequently altered in IDH-wildtype glioma. D. Heatmap of TCGA glioma samples ordered according to Figure 2A and two EReg gene signatures defined for the IDH-wildtype DNA methylation clusters. Mean RNA sequencing counts for each gene matched to the promoter of the identified cgID across each cluster are plotted to the right. E. Heatmap of the validation set classified using the random forest method using the 1,300 probes defined in Figure 2A.

Figure 5. Overview of major subtypes of adult diffuse glioma

Integrative analysis of 1,122 adult glioma resulted in seven different subtypes with distinct biological and clinical characteristics. The groups extend across six DNA methylation subtypes of which the LGm6 cluster was further separated by tumor grade, into PA-like and LGm6-GBM. The size of the circles is proportional to the percentages of samples within each group. DNA methylation plot is a cartoon representation of overall genome-wide epigenetic pattern within glioma subtypes. Survival information is represented as a set of Kaplan-Meier curves, counts of grade, histology and LGG/GBM subtypes within the groups are represented as bar-plots, whereas age is represented as density. Labeling of telomere length and maintenance status is based on the enrichment of samples within each column, similarly for the biomarkers and the validation datasets.