Prevalence and clinicopathologic characteristics of the molecular subtypes in malignant glioma: a multi-institutional analysis of 941 cases

PLoS One. 2014 Apr 22;9(4):e94871. doi: 10.1371/journal.pone.0094871. eCollection 2014.

Abstract

Background: Glioblastoma can be classified into four distinct molecular subtypes (Proneural, Neural, Classical and Mesenchymal), based on gene expression profiling. This study aimed to investigate the prevalence, clinicopathologic features and overall survival (OS) of the four molecular subtypes among all malignant gliomas.

Methods: A total of 941 gene expression arrays with clinical data were obtained from the Rembrandt, GSE16011 and CGGA datasets. Molecular subtypes were predicted with a prediction analysis of microarray.

Results: Among 941 malignant gliomas, 32.73% were Proneural, 15.09% Neural, 19.77% Classical and 32.41% Mesenchymal. The Proneural and Neural subtypes occurred largely in low-grade gliomas, while the Classical and Mesenchymal subtypes were more frequent in high-grade gliomas. A survival analysis showed that the Proneural subtype displayed a good prognosis, Neural had an intermediate correlation with overall survival, Mesenchymal had a worse prognosis than Neural, and Classical had the worst clinical outcome. Furthermore, oligodendrocytomas were preferentially assigned to the Proneural subtype, while the Mesenchymal subtype included a higher percentage of astrocytomas, compared with oligodendrocytomas. Additionally, nearly all classical gliomas harbored EGFR amplifications. Classical anaplastic gliomas have similar clinical outcomes as their glioblastoma counterparts and should be treated more aggressively.

Conclusions: Molecular subtypes exist stably in all histological malignant gliomas subtypes. This could be an important improvement to histological diagnoses for both prognosis evaluations and clinical outcome predictions.

Publication types

  • Multicenter Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers, Tumor / metabolism
  • Brain Neoplasms / classification*
  • Brain Neoplasms / epidemiology
  • Brain Neoplasms / genetics
  • Brain Neoplasms / pathology*
  • ErbB Receptors / metabolism
  • Gene Expression Regulation, Neoplastic
  • Glioma / classification*
  • Glioma / epidemiology
  • Glioma / genetics
  • Glioma / pathology*
  • Humans
  • Neoplasm Grading
  • Prevalence
  • Prognosis
  • ROC Curve
  • Survival Analysis
  • World Health Organization

Substances

  • Biomarkers, Tumor
  • EGFR protein, human
  • ErbB Receptors

Grants and funding

This work was supported by grants from the National High Technology Research and Development Program of China (863) (2012AA02A508), International Cooperation Program (2012DFA30470), National Natural Science Foundation of China (91229121, 81272792, 81172389, 81372709, 81302185, 81101901, 81302184), Jiangsu Province's Natural Science Foundation (BK2011847 and 20131019), Jiangsu Province's Key Provincial Talents Program (RC2011051), Jiangsu Province's Key Discipline of Medicine (XK201117), Jiangsu Provincial Special Program of Medical Science (BL2012028), and Program for Development of Innovative Research Team in the First Affiliated Hospital of NJMU, and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.