Genomic profiling of lower-grade gliomas uncovers cohesive disease groups: implications for diagnosis and treatment

• Type of Material: Article
• Authors: Chang-Ming Zhang, Emory University; Daniel Brat, Emory University
• Language: English
• Date: 2016-01-12
• Publisher: BioMed Central
• Publication Version: Final Published Version
• Copyright Statement: © 2016 Zhang and Brat.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s40880-015-0071-1
• Title of Journal or Parent Work: Chinese Journal of Cancer
• ISSN: 1000-467X
• Volume: 35
• Issue: 1
• Start Page: 12
• End Page: 12
• Keywords: IDH; MUTATIONS; Molecular class; CENTRAL-NERVOUS-SYSTEM; The Cancer Genome Atlas; Science & Technology; Lower-grade glioma; TUMORS; Isocitrate dehydrogenase (IDH) mutation; CLASSIFICATION; Oncology; 1P/19Q; Life Sciences & Biomedicine; Histologic class
• Research Categories: Biology, Genetics; Health Sciences, Pathology; Health Sciences, Oncology

Abstract

Lower-grade gliomas (including low- and intermediate-grade gliomas, World Health Organization grades II and III) are diffusely infiltrative neoplasms that arise most often in the cerebral hemispheres of adults and have traditionally been classified based on their presumed histogenesis as astrocytomas, oligodendrogliomas, or oligoastrocytomas. Although the histopathologic classification of lower-grade glioma has been the accepted standard for nearly a century, it suffers from high intra- and inter-observer variability and does not adequately predict clinical outcomes. Based on integrated analysis of multiplatform genomic data from The Cancer Genome Atlas, lower-grade gliomas have been found to segregate into three cohesive, clinically relevant molecular classes. Molecular classes were closely aligned with the status of isocitrate dehydrogenase (IDH) mutations, tumor protein 53 mutations and the co-deletion of chromosome arms 1p and 19q, but were not closely aligned with histologic classes. These findings emphasize the potential for improved definition of clinically relevant disease subsets using integrated molecular approaches and highlight the importance of biomarkers for brain tumor classification.