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Epigenetic profiling for the molecular classification of metastatic brain tumors

Author

Listed:
  • Javier I. J. Orozco

    (John Wayne Cancer Institute at Providence Saint John’s Health Center)

  • Theo A. Knijnenburg

    (Institute for Systems Biology)

  • Ayla O. Manughian-Peter

    (John Wayne Cancer Institute at Providence Saint John’s Health Center)

  • Matthew P. Salomon

    (John Wayne Cancer Institute at Providence Saint John’s Health Center)

  • Garni Barkhoudarian

    (John Wayne Cancer Institute at Providence Saint John’s Health Center)

  • John R. Jalas

    (Providence Saint John’s Health Center)

  • James S. Wilmott

    (The University of Sydney)

  • Parvinder Hothi

    (Swedish Neuroscience Institute)

  • Xiaowen Wang

    (John Wayne Cancer Institute at Providence Saint John’s Health Center)

  • Yuki Takasumi

    (Providence Saint John’s Health Center)

  • Michael E. Buckland

    (The University of Sydney)

  • John F. Thompson

    (The University of Sydney
    The University of Sydney)

  • Georgina V. Long

    (The University of Sydney
    Royal North Shore Hospital)

  • Charles S. Cobbs

    (Swedish Neuroscience Institute)

  • Ilya Shmulevich

    (Institute for Systems Biology)

  • Daniel F. Kelly

    (John Wayne Cancer Institute at Providence Saint John’s Health Center)

  • Richard A. Scolyer

    (The University of Sydney
    The University of Sydney
    Royal Prince Alfred Hospital)

  • Dave S. B. Hoon

    (John Wayne Cancer Institute at Providence Saint John’s Health Center
    Sequencing Center, John Wayne Cancer Institute at Providence Saint John’s Health Center)

  • Diego M. Marzese

    (John Wayne Cancer Institute at Providence Saint John’s Health Center)

Abstract

Optimal treatment of brain metastases is often hindered by limitations in diagnostic capabilities. To meet this challenge, here we profile DNA methylomes of the three most frequent types of brain metastases: melanoma, breast, and lung cancers (n = 96). Using supervised machine learning and integration of DNA methylomes from normal, primary, and metastatic tumor specimens (n = 1860), we unravel epigenetic signatures specific to each type of metastatic brain tumor and constructed a three-step DNA methylation-based classifier (BrainMETH) that categorizes brain metastases according to the tissue of origin and therapeutically relevant subtypes. BrainMETH predictions are supported by routine histopathologic evaluation. We further characterize and validate the most predictive genomic regions in a large cohort of brain tumors (n = 165) using quantitative-methylation-specific PCR. Our study highlights the importance of brain tumor-defining epigenetic alterations, which can be utilized to further develop DNA methylation profiling as a critical tool in the histomolecular stratification of patients with brain metastases.

Suggested Citation

  • Javier I. J. Orozco & Theo A. Knijnenburg & Ayla O. Manughian-Peter & Matthew P. Salomon & Garni Barkhoudarian & John R. Jalas & James S. Wilmott & Parvinder Hothi & Xiaowen Wang & Yuki Takasumi & Mic, 2018. "Epigenetic profiling for the molecular classification of metastatic brain tumors," Nature Communications, Nature, vol. 9(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06715-y
    DOI: 10.1038/s41467-018-06715-y
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