Epigenetic profiling for the molecular classification of metastatic brain tumors
| Autor: | 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, Parvinder Hothi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
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 |
| Zdroj: | Nature Communications, Vol 9, Iss 1, Pp 1-14 (2018) Nature Communications |
| ISSN: | 2041-1723 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|