Customized oligonucleotide microarray gene expression-based classification of neuroblastoma patients outperforms current clinical risk stratification.

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.

[1]  M. Ivimey.  Annual report , 1958, IRE Transactions on Engineering Writing and Speech.

[2]  A. Evans,et al.  A proposed staging for children with neuroblastoma. Children's cancer study group A , 1971, Cancer.

[3]  H. Varmus,et al.  Amplification of N-myc in untreated human neuroblastomas correlates with advanced disease stage. , 1984, Science.

[4]  A. Look,et al.  Cellular DNA content as a predictor of response to chemotherapy in infants with unresectable neuroblastoma. , 1984, The New England journal of medicine.

[5]  R. Seeger,et al.  Molecular analysis and clinical significance of N-myc amplification and chromosome 1p monosomy in human neuroblastomas. , 1988, Progress in clinical and biological research.

[6]  M. Papotti,et al.  Expression of Members of the Chromogranin Family in Primary Neuroblastomas , 1992, Diagnostic molecular pathology : the American journal of surgical pathology, part B.

[7]  J Pritchard,et al.  Revisions of the international criteria for neuroblastoma diagnosis, staging, and response to treatment. , 1993, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[8]  A. Cantor,et al.  Association between high levels of expression of the TRK gene and favorable outcome in human neuroblastoma. , 1993, The New England journal of medicine.

[9]  K K Matthay,et al.  The International Neuroblastoma Pathology Classification (the Shimada system) , 1999, Cancer.

[10]  F. Berthold,et al.  Neuroblastoma: current drug therapy recommendations as part of the total treatment approach. , 2000, Drugs.

[11]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[12]  P. Ambros,et al.  Pathology and biology guidelines for resectable and unresectable neuroblastic tumors and bone marrow examination guidelines. , 2001, Medical and pediatric oncology.

[13]  Frank Schacherer,et al.  The TRANSPATH signal transduction database: a knowledge base on signal transduction networks , 2001, Bioinform..

[14]  I. Lewis,et al.  17q gain in neuroblastoma predicts adverse clinical outcome. U.K. Cancer Cytogenetics Group and the U.K. Children's Cancer Study Group. , 2001, Medical and pediatric oncology.

[15]  Holger Christiansen,et al.  Loss of a FYN-regulated differentiation and growth arrest pathway in advanced stage neuroblastoma. , 2002, Cancer cell.

[16]  Martin Vingron,et al.  Variance stabilization applied to microarray data calibration and to the quantification of differential expression , 2002, ISMB.

[17]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Van,et al.  A gene-expression signature as a predictor of survival in breast cancer. , 2002, The New England journal of medicine.

[19]  J. Ioannidis,et al.  Predictive ability of DNA microarrays for cancer outcomes and correlates: an empirical assessment , 2003, The Lancet.

[20]  F. Berthold,et al.  Deletions in chromosome arms 3p and 11q are new prognostic markers in localized and 4s neuroblastoma. , 2003, Clinical cancer research : an official journal of the American Association for Cancer Research.

[21]  B. Ozenberger,et al.  Expression of NALP1 in cerebellar granule neurons stimulates apoptosis. , 2004, Cellular signalling.

[22]  Xia Li,et al.  Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling. , 2004, Nucleic acids research.

[23]  D. Ransohoff.  Rules of evidence for cancer molecular-marker discovery and validation , 2004, Nature Reviews Cancer.

[24]  Qing-Rong Chen,et al.  Prediction of Clinical Outcome Using Gene Expression Profiling and Artificial Neural Networks for Patients with Neuroblastoma , 2004, Cancer Research.

[25]  Wolfgang Huber,et al.  A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks , 2004, Statistical applications in genetics and molecular biology.

[26]  R. Tibshirani,et al.  Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. , 2004, The New England journal of medicine.

[27]  Roland Eils,et al.  Prediction of clinical outcome and biological characterization of neuroblastoma by expression profiling , 2004 .

[28]  Stefan Michiels,et al.  Prediction of cancer outcome with microarrays: a multiple random validation strategy , 2005, The Lancet.

[29]  Wolfgang Huber,et al.  arrayMagic: two-colour cDNA microarray quality control, preprocessing , 2005, Bioinform..

[30]  J. Khan,et al.  Altered expression of cell cycle genes distinguishes aggressive neuroblastoma , 2005, Oncogene.

[31]  John M Maris,et al.  The biologic basis for neuroblastoma heterogeneity and risk stratification , 2005, Current opinion in pediatrics.

[32]  Frank Berthold,et al.  Erratum: Prediction of clinical outcome using gene expression profiling and artificial neural networks for patients with neuroblastoma (Cancer Research (October 1, 2004) 64 (6883-6891)) , 2005 .

[33]  Y. Oda,et al.  Prognostic significance of expressions of cell-cycle regulatory proteins in gastrointestinal stromal tumor and the relevance of the risk grade. , 2005, Human pathology.

[34]  Hiroyuki Shimada,et al.  Chromosome 1p and 11q deletions and outcome in neuroblastoma. , 2005, The New England journal of medicine.

[35]  S. Ishii,et al.  Expression profiling using a tumor-specific cDNA microarray predicts the prognosis of intermediate risk neuroblastomas. , 2005, Cancer cell.

[36]  G. Getz,et al.  Outcome signature genes in breast cancer: is there a unique set? , 2005, Breast Cancer Research.