Purpose: To optimize neuroblastoma treatment stratification, we aimed at developing a novel risk estimation system by integrating gene expression-based classification and established prognostic markers. Material and Methods: Gene expression profiles were generated from 709 neuroblastoma specimens using customized 4x44K microarrays. Classification models were built using 75 tumors with contrasting courses of disease. Validation was performed in an independent test set (n=634) by Kaplan-Meier estimates and Cox regression analyses. Results: The best-performing classifier predicted patient outcome with an accuracy of 0.95 (sensitivity 0.93, specificity 0.97) in the validation cohort. The highest potential clinical value of this predictor was observed for current low-risk patients (LR: 5-year EFS 0.84+/-0.02 vs 0.29+/-0.10; 5-year OS 0.99+/-0.01vs 0.76+/-0.11; both p〈0.001) and intermediate-risk patients (IR: 5-year EFS 0.88+/-0.06 vs 0.41+/-0.10; 5-year OS 1.0 vs 0.70+/-0.09; both p〈0.001). In multivariate Cox regression models for LR/IR patients the classifier outperformed risk assessment of the current German trial NB2004 (EFS: HR 5.07, 95%-CI 3.20-8.02, OS: HR 25.54, 95%-CI 8.40-77.66; both p〈0.001). Based on these findings, we propose to integrate the classifier into a revised risk stratification system for LR/IR patients. According to this system, we identified novel subgroups with poor outcome (5-year EFS 0.19+/-0.08; 5-year OS 0.59+/-0.1), for whom we propose intensified treatment, and with beneficial outcome (5-year EFS 0.87+/-0.05; 5-year OS 1.0), who may benefit from treatment de-escalation. Conclusion: Combination of gene expression-based classification and established prognostic markers improves risk estimation of LR/IR neuroblastoma patients. We propose to implement our revised treatment stratification system in a prospective clinical trial.
Type of Publication:
Journal article published