IDH/MGMT-driven molecular classification of low-grade glioma is a strong predictor for long-term survival

Leu, S. and von Felten, S. and Frank, S. and Vassella, E. and Vajtai, I. and Taylor, E. and Schulz, M. and Hutter, G. and Hench, J. and Schucht, P. and Boulay, J. L. and Mariani, L.. (2013) IDH/MGMT-driven molecular classification of low-grade glioma is a strong predictor for long-term survival. Neuro-oncology, Vol. 15, H. 4. pp. 469-479.

Full text not available from this repository.

Official URL: http://edoc.unibas.ch/dok/A6338322

Downloads: Statistics Overview


BACKGROUND: Low-grade gliomas (LGGs) are rare brain neoplasms, with survival spanning up to a few decades. Thus, accurate evaluations on how biomarkers impact survival among patients with LGG require long-term studies on samples prospectively collected over a long period. METHODS: The 210 adult LGGs collected in our databank were screened for IDH1 and IDH2 mutations (IDHmut), MGMT gene promoter methylation (MGMTmet), 1p/19q loss of heterozygosity (1p19qloh), and nuclear TP53 immunopositivity (TP53pos). Multivariate survival analyses with multiple imputation of missing data were performed using either histopathology or molecular markers. Both models were compared using Akaike's information criterion (AIC). The molecular model was reduced by stepwise model selection to filter out the most critical predictors. A third model was generated to assess for various marker combinations. RESULTS: Molecular parameters were better survival predictors than histology (DeltaAIC = 12.5, P> .001). Forty-five percent of studied patients died. MGMTmet was positively associated with IDHmut (P> .001). In the molecular model with marker combinations, IDHmut/MGMTmet combined status had a favorable impact on overall survival, compared with IDHwt (hazard ratio [HR] = 0.33, P> .01), and even more so the triple combination, IDHmut/MGMTmet/1p19qloh (HR = 0.18, P> .001). Furthermore, IDHmut/MGMTmet/TP53pos triple combination was a significant risk factor for malignant transformation (HR = 2.75, P> .05). CONCLUSION: By integrating networks of activated molecular glioma pathways, the model based on genotype better predicts prognosis than histology and, therefore, provides a more reliable tool for standardizing future treatment strategies.
Faculties and Departments:03 Faculty of Medicine > Departement Biomedizin > Department of Biomedicine, University Hospital Basel > Brain Tumor Biology (Mariani)
UniBasel Contributors:Mariani, Luigi
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Oxford University Press
Note:Publication type according to Uni Basel Research Database: Journal article
Related URLs:
Identification Number:
Last Modified:10 Apr 2015 09:12
Deposited On:10 Apr 2015 09:12

Repository Staff Only: item control page