Design of a multi-signature ensemble classifier predicting neuroblastoma patients' outcome

Background: Neuroblastoma is the most common pediatric solid tumor of the sympathetic nervous system.Development of improved predictive tools for patients stratification is a crucial requirement for neuroblastomatherapy. Several studies utilized gene expression-based signatures to stratify neuroblastoma patients anddemonstrated a clear advantage of adding genomic analysis to risk assessment. There is little overlapping amongsignatures and merging their prognostic potential would be advantageous. Here, we describe a new strategy tomerge published neuroblastoma related gene signatures into a single, highly accurate, Multi-Signature Ensemble(MuSE)-classifier of neuroblastoma (NB) patients outcome.

Methods: Gene expression profiles of 182 neuroblastoma tumors, subdivided into three independent datasets,were used in the various phases of development and validation of neuroblastoma NB-MuSE-classifier. Thirty threesignatures were evaluated for patients’outcome prediction using 22 classification algorithms each and generating726 classifiers and prediction results. The best-performing algorithm for each signature was selected, validated onan independent dataset and the 20 signatures performing with an accuracy > = 80% were retained.

Results: We combined the 20 predictions associated to the corresponding signatures through the selection of thebest performing algorithm into a single outcome predictor. The best performance was obtained by the DecisionTable algorithm that produced the NB-MuSE-classifier characterized by an external validation accuracy of 94%.Kaplan-Meier curves and log-rank test demonstrated that patients with good and poor outcome prediction by theNB-MuSE-classifier have a significantly different survival (p < 0.0001). Survival curves constructed on subgroups ofpatients divided on the bases of known prognostic marker suggested an excellent stratification of localized andstage 4s tumors but more data are needed to prove this point.

Conclusions: The NB-MuSE-classifier is based on an ensemble approach that merges twenty heterogeneous,neuroblastoma-related gene signatures to blend their discriminating power, rather than numeric values, into asingle, highly accurate patients’outcome predictor. The novelty of our approach derives from the way to integratethe gene expression signatures, by optimally associating them with a single paradigm ultimately integrated into asingle classifier. This model can be exported to other types of cancer and to diseases for which dedicateddatabases exist.


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