1. ¹Department of Pediatric Oncology and Hematology, University Hospital of Essen, Hufelandstr 55, Essen 45122, Germany
2. ²Institute for Cell Biology, University Hospital of Essen, Essen, Germany
3. ³Department of Pediatric Oncology, University Children's Hospital, Heidelberg, Germany
4. ⁴Department of Hematology, University Children's Hospital, Heidelberg, Germany
5. ⁵University Children's Hospital, Marburg, Germany
6. ⁶Division of Intelligent Bioinformatics Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany

Correspondence: A Eggert, E-mail: angelika.eggert@uni-essen.de

Received 11 February 2005; Revised 31 May 2005; Accepted 7 June 2005; Published online 15 August 2005.

Abstract

Neuroblastoma is a common childhood tumor comprising cases with rapid disease progression as well as spontaneous regression. Although numerous prognostic factors have been identified, risk evaluation in individual patients remains difficult. To define a reliable prognostic predictor and gene signatures characteristic of biological subgroups, we performed mRNA expression profiling of 68 neuroblastomas of all stages. Expression data were analysed using support vector machines (SVM-rbf), prediction analysis of microarrays (PAM), k-nearest neighbors (k-NN) algorithms and multiple decision trees. SVM-rbf performed best of all methods, and predicted recurrence of neuroblastoma with an accuracy of 85% (sensitivity 77%, specificity 94%). PAM identified a classifier of 39 genes reliably predicting outcome with an accuracy of 80%. In comparison, conventional risk stratification based on stage, age and MYCN-status only reached a predictive accuracy of 64%. Kaplan–Meier analysis using the PAM classifier indicated a 5-year survival of 20 versus 78% for patients with unfavorably versus favorably predicted neuroblastomas, respectively (P=0.0001). Significance analysis of microarrays (SAM) identified additional genes differentially expressed among subgroups. MYCN-amplification and high expression of NTRK1/TrkA demonstrated a strong association with specific gene expression patterns. Our data suggest that microarray-derived data in addition to traditional clinical factors will be useful for risk assessment and defining biological properties of neuroblastoma.