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Nat Commun. 2018 Nov 6;9(1):4627. doi: 10.1038/s41467-018-06715-y.

Epigenetic profiling for the molecular classification of metastatic brain tumors.

Author information

1
Department of Translational Molecular Medicine, John Wayne Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, 90404, USA.
2
Institute for Systems Biology, Seattle, WA, 98109, USA.
3
Pacific Neuroscience Institute, John Wayne Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, 90404, USA.
4
Department of Pathology, Providence Saint John's Health Center, Santa Monica, CA, 90404, USA.
5
Melanoma Institute Australia, The University of Sydney, Sydney, NSW, 2065, Australia.
6
Ben & Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA, 98122, USA.
7
Department of Neuropathology, Royal Prince Alfred Hospital, the Brain and Mind Centre, The University of Sydney, Camperdown, NSW, 2050, Australia.
8
Sydney Medical School, The University of Sydney, Camperdown, NSW, 2006, Australia.
9
Royal North Shore Hospital, Sydney, NSW, 2065, Australia.
10
Royal Prince Alfred Hospital, Sydney, NSW, 2050, Australia.
11
Sequencing Center, John Wayne Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, 90404, USA.
12
Department of Translational Molecular Medicine, John Wayne Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, 90404, USA. MarzeseD@jwci.org.

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.

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