Epigenetic profiling for the molecular classification of metastatic brain tumors. Issue 1 (December 2018) - Orozco, Javier; Knijnenburg, Theo; Manughian-Peter, Ayla; Salomon, Matthew; Barkhoudarian, Garni; Jalas, John; Wilmott, James; Hothi, Parvinder; Wang, Xiaowen; Takasumi, Yuki; Buckland, Michael; Thompson, John; et al.
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. The treatment of brain metastases is often limited by the ability to diagnose their origins. Here the authors generate DNA methylomes from the three most frequent types of brain metastases, identify epigenetic signatures specific to each type of metastasis and construct a DNA methylation-based classifier (BrainMETH) to advance brain metastasis diagnosis.