| Abstract |
Neuroblastoma is the most common extracranial childhood tumor, comprising 15% of all childhood cancer deaths. In an initial study, we used Affymetrix oligonucleotide microarrays to analyse gene expression in 68 primary neuroblastomas and compared different data mining approaches for prediction of early relapse. Here, we performed re-analyses of the data including prolonged follow-up and applied support vector machine (SVM) algorithms and outer cross-validation strategies to improve reliability of expression profiling based predictors. Accuracy of outcome prediction was significantly improved by the use of innovative SVM algorithms on the updated data. In addition, CASPAR, a hierarchical Bayesian approach, was used to predict survival times for the individual patient based on expression profiling data. CASPAR reliably predicted event-free survival, given a cut-off time of three years. Differential expression of genes used by CASPAR to predict patient outcome was validated in an independent cohort of 117 neuroblastomas. In conclusion, we show here for the first time that reanalysis of microarray data using improved methodology, state-of-the-art performance tests and updated follow-up data improves prognosis prediction, and may further improve risk stratification of individual patients.
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