Epigenetic profiling for the molecular classification of metastatic brain tumors

Javier Orozco, Dr. Theo Knijnenburg, Ayla Manughian-Peter, Matthew Salomon, Garni Barkhoudarian, John Jalas, Dr. James Wilmott, Parvinder Hothi, Xiaowen Wang, Yuki Takasumi, Prof. Michael Buckland, Prof. John F. Thompson, Prof. Georgina Long, Charles Cobbs, Prof. Ilya Shmulevich, Dr. Daniel Kelly, Richard Scolyer, Prof. Dave hoon, and Dr. Diego Marzese

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Received: 5th February 18

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.

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This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.

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