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

Jie Yang, Qianghu Wang, Ze-Yan Zhang, Lihong Long, Ravesanker Ezhilarasan, Jerome M. Karp, Aristotelis Tsirigos, Matija Snuderl, Benedikt Wiestler, Wolfgang Wick, Yinsen Miao, Jason T. Huse and Erik P. Sulman ()
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Jie Yang: NYU Grossman School of Medicine
Qianghu Wang: Nanjing Medical University
Ze-Yan Zhang: NYU Grossman School of Medicine
Lihong Long: The University of Texas MD Anderson Cancer Center
Ravesanker Ezhilarasan: NYU Grossman School of Medicine
Jerome M. Karp: NYU Grossman School of Medicine
Aristotelis Tsirigos: NYU Grossman School of Medicine
Matija Snuderl: NYU Grossman School of Medicine
Benedikt Wiestler: Technical University of Munich
Wolfgang Wick: University of Heidelberg
Yinsen Miao: Rice University
Jason T. Huse: The University of Texas MD Anderson Cancer Center
Erik P. Sulman: NYU Grossman School of Medicine

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-31827-x

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