DNA methylation-based epigenetic signatures predict somatic genomic alterations in gliomas

Abstract

Molecular classification has improved diagnosis and treatment for patients with malignant gliomas. However, classification has relied on individual assays that are both costly and slow, leading to frequent delays in treatment. Here, we propose the use of DNA methylation, as an emerging clinical diagnostic platform, to classify gliomas based on major genomic alterations and provide insight into subtype characteristics. We show that using machine learning models, DNA methylation signatures can accurately predict somatic alterations and show improvement over existing classifiers. The established Unified Diagnostic Pipeline (UniD) we develop is rapid and cost-effective for genomic alterations and gene expression subtypes diagnostic at early clinical phase and improves over individual assays currently in clinical use. The significant relationship between genetic alteration and epigenetic signature indicates broad applicability of our approach to other malignancies.

Methylation Array Signals are Predictive of Chronological Age Without Bisulfite Conversion

“…The relationship between somatic mutations and age have been explored in the cancer field[34], and the relationship is also dependent on TP53 mutations [35]. The idea that these mutations affect methylation readouts is also discussed [36, 37], but the direct evidence that the mutation itself is read out as loss of methylation has not been tested. This has potentially interesting ramifications in epigenetic clock studies, as the potential for mutations to influence methylation data means that clocks may contain information from both mutations and methylation which are currently undetermined.…”

Evaluation of DNA Methylation Array for Glioma Tumor Profiling and Description of a Novel Epi-Signature to Distinguish IDH1/IDH2 Mutant and Wild-Type Tumors

“…Thus, our in-house protocol for EPIC DNA methylation analysis of IDH1/IDH2 status and 1p19q co-deletion showed a better diagnostic yield than the other classifier used. Additional DNA methylation signatures for classification and/or prognostication of gliomas as well as other tumors have been developed in the last year and have been shown to be cost-effective and improve diagnostics over current clinical assays [ 25 , 26 , 27 , 28 ]. Further assessment of the utility of these classifiers for the detection of CIMP versus the CIMP epi-signature reported herein is beyond the scope of this project but represents an important area for future study.…”