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

• Published in: Journal of clinical oncology Vol. 24; no. 31; pp. 5070 - 5078
• Main Authors: OBERTHUER, André, BERTHOLD, Frank, SCHWAB, Manfred, BRORS, Benedikt, WESTERMANN, Frank, FISCHER, Matthias, WARNAT, Patrick, HERO, Barbara, KAHLERT, Yvonne, SPITZ, Rudiger, ERNESTUS, Karen, KONIG, Rainer, HAAS, Stefan, EILS, Roland
• Format: Journal Article
• Language: English
• Published: Baltimore, MD American Society of Clinical Oncology 01.11.2006; Lippincott Williams & Wilkins
• Subjects: Biological and medical sciences; Biomarkers, Tumor - analysis; Biomarkers, Tumor - genetics; Disease-Free Survival; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Germany - epidemiology; Humans; Japan - epidemiology; Medical sciences; Multivariate Analysis; Neuroblastoma - chemistry; Neurology; Odds Ratio; Oligonucleotide Array Sequence Analysis; Predictive Value of Tests; Proportional Hazards Models; Reproducibility of Results; Risk Assessment; Survival Analysis; Tumors; Tumors of the nervous system. Phacomatoses; United States - epidemiology; United States; Japan; Germany; Human; Nervous system diseases; Cancerology; Classification; Risk factor; DNA chip; Malignant tumor; Neuroblastoma; Autonomic neuropathy; Gene expression; Epidemiology
• ISSN: 0732-183X; 1527-7755
• DOI: 10.1200/JCO.2006.06.1879

To develop a gene expression-based classifier for neuroblastoma patients that reliably predicts courses of the disease. 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. 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]). Integration of gene expression-based class prediction of neuroblastoma patients may improve risk estimation of current neuroblastoma trials.