MPI für molekulare Genetik / Department of Computational Molecular Biology |
|Gene-expression based classification of neuroblastoma patients using a customized oligonucleotide-microarray outperforms current clinical risk stratification|
|Authors:||Oberthuer, André; Berthold, Frank; Warnat, Patrick; Hero, Barbara; Kahlert, Yvonne; Spitz, Rüdiger; Ernestus, Karen; König, Rainer; Haas, Stefan; Eils, Roland; Schwab, Manfred; Brors, Benedikt; Westermann, Frank; Fischer, Matthias|
|Research Context:||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.|
|Date of Publication (YYYY-MM-DD):||2006-11-01|
|Title of Journal:||Journal of Clinical Oncology : Jco ; Official Journal of the American Society of Clinical Oncology|
|Journal Abbrev.:||J. Clin. Oncol.|
|Issue / Number:||31|
|Copyright:||© 2006 American Society of Clinical Oncology.|
|Review Status:||not specified|
|Abstract / Description:||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.
|External Publication Status:||published|
|Version Comment:||Automatic journal name synchronization|
|Communicated by:||Martin Vingron|
|Affiliations:||MPI für molekulare Genetik|
|External Affiliations:||1.Department of Pediatric Oncology and Hematology, Children's Hospital; the Center for Molecular Medicine; Department of Pathology, University of Cologne, Cologne, Germany
2.Departments of Tumor Genetics (B030) and Theoretical Bioinformatics (B080), German Cancer Research Center, Heidelberg, Germany.
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