PT  - JOURNAL ARTICLE
AU  - Javier I. J. Orozco
AU  - Theo A. Knijnenburg
AU  - Ayla O. Manughian-Peter
AU  - Matthew P. Salomon
AU  - Garni Barkhoudarian
AU  - John R. Jalas
AU  - James S. Wilmott
AU  - Parvinder Hothi
AU  - Xiaowen Wang
AU  - Yuki Takasumi
AU  - Michael E. Buckland
AU  - John F. Thompson
AU  - Georgina V. Long
AU  - Charles S. Cobbs
AU  - Ilya Shmulevich
AU  - Daniel F. Kelly
AU  - Richard A. Scolyer
AU  - Dave S. B. Hoon
AU  - Diego M. Marzese
TI  - Epigenetic Profiling for the Molecular Classification of Metastatic Brain Tumors
AID  - 10.1101/268193
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 268193
4099  - http://biorxiv.org/content/early/2018/02/22/268193.short
4100  - http://biorxiv.org/content/early/2018/02/22/268193.full
AB  - Optimal treatment of brain metastases is often hindered by limitations in diagnostic capabilities. To meet these challenges, we generated genome-scale 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 multiple DNA methylomes from normal, primary, and metastatic tumor specimens (n=1,860), we unraveled 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 were supported by routine histopathologic evaluation. We further characterized and validated 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.