1. Epigenetic profiling for the molecular classification of metastatic brain tumors
- Author
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James S. Wilmott, Ayla O. Manughian-Peter, Diego M. Marzese, Yuki Takasumi, Daniel F. Kelly, Ilya Shmulevich, Matthew P. Salomon, John R. Jalas, Javier I. J. Orozco, Garni Barkhoudarian, Dave S.B. Hoon, Xiaowen Wang, John F. Thompson, Charles Cobbs, Georgina V. Long, Theo A. Knijnenburg, Richard A. Scolyer, Michael E. Buckland, and Parvinder Hothi
- Subjects
0301 basic medicine ,Epigenomics ,Lung Neoplasms ,Skin Neoplasms ,General Physics and Astronomy ,Epigenesis, Genetic ,chemistry.chemical_compound ,0302 clinical medicine ,Molecular classification ,Neoplasm ,Medicine ,Neoplasm Metastasis ,lcsh:Science ,Melanoma ,Epigenesis ,Regulation of gene expression ,0303 health sciences ,Multidisciplinary ,Brain Neoplasms ,DNA, Neoplasm ,3. Good health ,Gene Expression Regulation, Neoplastic ,Metastatic brain tumor ,030220 oncology & carcinogenesis ,DNA methylation ,Female ,Supervised Machine Learning ,Algorithms ,Science ,Metastatic tumor ,General Biochemistry, Genetics and Molecular Biology ,Article ,03 medical and health sciences ,Text mining ,Humans ,Epigenetics ,030304 developmental biology ,business.industry ,Optimal treatment ,General Chemistry ,DNA Methylation ,medicine.disease ,030104 developmental biology ,chemistry ,Cancer research ,lcsh:Q ,business ,DNA - 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.
- Published
- 2018