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Genome-wide methylomic and transcriptomic analyses identify subtype-specific epigenetic signatures commonly dysregulated in glioma stem cells and glioblastoma

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posted on 2018-06-21, 18:13 authored by Rajendra P. Pangeni, Zhou Zhang, Angel A. Alvarez, Xuechao Wan, Namratha Sastry, Songjian Lu, Taiping Shi, Tianzhi Huang, Charles X. Lei, C. David James, John A. Kessler, Cameron W. Brennan, Ichiro Nakano, Xinghua Lu, Bo Hu, Wei Zhang, Shi-Yuan Cheng

Glioma stem cells (GSCs), a subpopulation of tumor cells, contribute to tumor heterogeneity and therapy resistance. Gene expression profiling classified glioblastoma (GBM) and GSCs into four transcriptomically-defined subtypes. Here, we determined the DNA methylation signatures in transcriptomically pre-classified GSC and GBM bulk tumors subtypes. We hypothesized that these DNA methylation signatures correlate with gene expression and are uniquely associated either with only GSCs or only GBM bulk tumors. Additional methylation signatures may be commonly associated with both GSCs and GBM bulk tumors, i.e., common to non-stem-like and stem-like tumor cell populations and correlating with the clinical prognosis of glioma patients. We analyzed Illumina 450K methylation array and expression data from a panel of 23 patient-derived GSCs. We referenced these results with The Cancer Genome Atlas (TCGA) GBM datasets to generate methylomic and transcriptomic signatures for GSCs and GBM bulk tumors of each transcriptomically pre-defined tumor subtype. Survival analyses were carried out for these signature genes using publicly available datasets, including from TCGA. We report that DNA methylation signatures in proneural and mesenchymal tumor subtypes are either unique to GSCs, unique to GBM bulk tumors, or common to both. Further, dysregulated DNA methylation correlates with gene expression and clinical prognoses. Additionally, many previously identified transcriptionally-regulated markers are also dysregulated due to DNA methylation. The subtype-specific DNA methylation signatures described in this study could be useful for refining GBM sub-classification, improving prognostic accuracy, and making therapeutic decisions.

Funding

This work was carried out with support in part from NIH grants CA158911, and CA158911-S, (S.Y. Cheng); a Brain Cancer Research Award from James S. McDonnell Foundation (B. Hu); Fishel Fellowship Award from the Robert H. Lurie Comprehensive Cancer Center at Northwestern University (N. Sastry); NIH/NCI training grant T32 CA070085 and NIH LRP award L32 MD010147 (A.A. Alvarez), NS095642 (C.D. James), NS081774, AR066539 (J. Kessler), CA155764 (C. Horbinski), NS083767, (I. Nakano), CA209345 (W. Zhang and S.Y. Cheng), LM012011, (X. Lu), LM011673 (S. Lu), and support from Northwestern Brain Tumor Institute at Northwestern University (S.Y. Cheng, B. Hu). S.Y. Cheng is a Zell Scholar at Northwestern University; National Cancer Institute [CA159811];National Cancer Institute [CA209345].

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