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Customized Oligonucleotide Microarray Gene Expression–Based Classification of Neuroblastoma Patients Outperforms Current Clinical Risk Stratification

André Oberthuer, Frank Berthold, Patrick Warnat, Barbara Hero, Yvonne Kahlert, Rüdiger Spitz, Karen Ernestus, Rainer König, Stefan Haas, Roland Eils, Manfred Schwab, Benedikt Brors, Frank Westermann, Matthias Fischer

From the Department of Pediatric Oncology and Hematology, Children's Hospital; the Center for Molecular Medicine; Department of Pathology, University of Cologne, Cologne; Departments of Tumor Genetics (B030) and Theoretical Bioinformatics (B080), German Cancer Research Center, Heidelberg; Max-Planck-Institute for Molecular Genetics, Berlin, Germany

Address reprint requests to André Oberthuer, MD, Children's Hospital, Department of Pediatric Oncology and Hematology, University of Cologne, Kerpener Strasse 62, D-50924 Cologne, Germany; e-mail: andre.oberthuer{at}uk-koeln.de

Purpose To develop a gene expression–based classifier for neuroblastoma patients that reliably predicts courses of the disease.

Patients and Methods Two hundred fifty-one neuroblastoma specimens were analyzed using a customized oligonucleotide microarray comprising 10,163 probes for transcripts with differential expression in clinical subgroups of the disease. Subsequently, the prediction analysis for microarrays (PAM) was applied to a first set of patients with maximally divergent clinical courses (n = 77). The classification accuracy was estimated by a complete 10-times-repeated 10-fold cross validation, and a 144-gene predictor was constructed from this set. This classifier's predictive power was evaluated in an independent second set (n = 174) by comparing results of the gene expression–based classification with those of risk stratification systems of current trials from Germany, Japan, and the United States.

Results The first set of patients was accurately predicted by PAM (cross-validated accuracy, 99%). Within the second set, the PAM classifier significantly separated cohorts with distinct courses (3-year event-free survival [EFS] 0.86 ± 0.03 [favorable; n = 115] v 0.52 ± 0.07 [unfavorable; n = 59] and 3-year overall survival 0.99 ± 0.01 v 0.84 ± 0.05; both P < .0001) and separated risk groups of current neuroblastoma trials into subgroups with divergent outcome (NB2004: low-risk 3-year EFS 0.86 ± 0.04 v 0.25 ± 0.15, P < .0001; intermediate-risk 1.00 v 0.57 ± 0.19, P = .018; high-risk 0.81 ± 0.10 v 0.56 ± 0.08, P = .06). In a multivariate Cox regression model, the PAM predictor classified patients of the second set more accurately than risk stratification of current trials from Germany, Japan, and the United States (P < .001; hazard ratio, 4.756 [95% CI, 2.544 to 8.893]).

Conclusion Integration of gene expression–based class prediction of neuroblastoma patients may improve risk estimation of current neuroblastoma trials.

Supported by the Deutsche Krebshilfe (Grant No. 50-2719), the Bundesministerium für Bildung und Forschung through the National Genome Research Network 2 (NGFN2 Grants No. 01GS0456 and 01GR0450), the Competence Network Pediatric Oncology and Hematology, and the Fördergesellschaft Kinderkrebs-Neuroblastom-Forschung e.V.

B.B., F.W., and M.F. contributed equally to this work.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

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