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Epigenetic Classifiers for Precision Diagnosis of Brain Tumors.

Authors :
Orozco JI
Manughian-Peter AO
Salomon MP
Marzese DM
Source :
Epigenetics insights [Epigenet Insights] 2019 Mar 31; Vol. 12, pp. 2516865719840284. Date of Electronic Publication: 2019 Mar 31 (Print Publication: 2019).
Publication Year :
2019

Abstract

DNA methylation profiling has proven to be a powerful analytical tool, which can accurately identify the tissue of origin of a wide range of benign and malignant neoplasms. Using microarray-based profiling and supervised machine learning algorithms, we and other groups have recently unraveled DNA methylation signatures capable of aiding the histomolecular diagnosis of different tumor types. We have explored the methylomes of metastatic brain tumors from patients with lung cancer, breast cancer, and cutaneous melanoma and primary brain neoplasms to build epigenetic classifiers. Our brain metastasis methylation (BrainMETH) classifier has the ability to determine the type of brain tumor, the origin of the metastases, and the clinical-therapeutic subtype for patients with breast cancer brain metastases. To facilitate the translation of these epigenetic classifiers into clinical practice, we selected and validated the most informative genomic regions utilizing quantitative methylation-specific polymerase chain reaction (qMSP). We believe that the refinement, expansion, integration, and clinical validation of BrainMETH and other recently developed epigenetic classifiers will significantly contribute to the development of more comprehensive and accurate systems for the personalized management of patients with brain metastases.<br />Competing Interests: Declaration of conflicting interests:The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Details

Language :
English
ISSN :
2516-8657
Volume :
12
Database :
MEDLINE
Journal :
Epigenetics insights
Publication Type :
Academic Journal
Accession number :
30968063
Full Text :
https://doi.org/10.1177/2516865719840284