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

Author

Listed:
  • Jie Yang

    (NYU Grossman School of Medicine
    NYU Langone Health
    MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences)

  • Qianghu Wang

    (Nanjing Medical University)

  • Ze-Yan Zhang

    (NYU Grossman School of Medicine
    NYU Langone Health)

  • Lihong Long

    (The University of Texas MD Anderson Cancer Center)

  • Ravesanker Ezhilarasan

    (NYU Grossman School of Medicine
    NYU Langone Health)

  • Jerome M. Karp

    (NYU Grossman School of Medicine
    NYU Langone Health)

  • Aristotelis Tsirigos

    (NYU Grossman School of Medicine
    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
    The University of Texas MD Anderson Cancer Center)

  • Erik P. Sulman

    (NYU Grossman School of Medicine
    NYU Langone Health)

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.

Suggested Citation

  • 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, 2022. "DNA methylation-based epigenetic signatures predict somatic genomic alterations in gliomas," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31827-x
    DOI: 10.1038/s41467-022-31827-x
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    References listed on IDEAS

    as
    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. David Capper & David T. W. Jones & Martin Sill & Volker Hovestadt & Daniel Schrimpf & Dominik Sturm & Christian Koelsche & Felix Sahm & Lukas Chavez & David E. Reuss & Annekathrin Kratz & Annika K. We, 2018. "DNA methylation-based classification of central nervous system tumours," Nature, Nature, vol. 555(7697), pages 469-474, March.
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