Epigenetic profiling for the molecular classification of metastatic brain tumors

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, Michael E. Buckland, John F. Thompson, Georgina V. Long, Charles S. Cobbs, Ilya Shmulevich, Daniel F. Kelly, Richard A. Scolyer, Dave S. B. Hoon and Diego M. Marzese ()
Additional contact information
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
Georgina V. Long: The University of Sydney
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
Dave S. B. Hoon: 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

Nature Communications, 2018, vol. 9, issue 1, 1-14

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-018-06715-y

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