Customized Oligonucleotide Microarray Gene Expression–Based Classification of Neuroblastoma Patients Outperforms Current Clinical Risk Stratification
Open accessAbstract
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.
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A number of microarray-based gene expression models have been proposed to predict neuroblastoma patient outcome.
“In addition, a number of microarray-based gene expression models have been proposed to predict neuroblastoma patient outcome [16, 17].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [16]
The best performing models significantly discriminated patients with favorable and unfavorable outcome in the entire cohort and in the class labeled cohort.
“The models significantly discriminated patients with favorable and unfavorable outcome in the entire cohort and in the class labeled cohort (Additional file 1: Figure S9a-d, Table S3 and S4), which is consistent with previous observations made by us and others [16–18, 20].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [16]
Both gene expression-based models derived from RNA-seq and microarray analyses were capable of predicting patient outcome in the entire neuroblastoma cohort accurately.
“Both gene expression-based models derived from RNA-seq and microarray analyses were capable of predicting patient outcome in the entire neuroblastoma cohort accurately, thereby validating results from previous studies and underscoring their potential clinical utility for risk estimation in neuroblastoma [16–18, 20].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [16]
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- Authors
- Oberthuer, A., Berthold, F., Warnat, P., Hero, B., Kahlert, Y., Spitz, R., Ernestus, K., König, R., Haas, S., Eils, R., Schwab, M., Brors, B., Westermann, F., & Fischer, M.
- Affiliations
- German Cancer Research Center (DE)
- ORCID
- Venue
- Journal of Clinical Oncology
- Published
- 2006-11-01
- Volume
- 24
- Issue
- 31
- Pages
- 5070-5078
- ISSN
- 0732-183X (ISSN-L)
- 1527-7755
- OpenAlex
- W2106713259
- Cited by
- 291
- References
- 37
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 5 · 2023: 20 · 2022: 6 · 2021: 7 · 2020: 4 · 2019: 7 · 2018: 12 · 2017: 13 · 2016: 17 · 2015: 13 · 2014: 21 · 2013: 20 · 2012: 27
- FWCI
- 9.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Clinical Oncology (publishedVersion)
- PubMed (publishedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- Funders
- Deutsche Krebshilfe
- Topics
- Health Sciences › Medicine › Neurology › Neuroblastoma Research and Treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- blastoma, gene expression classifier, oligonucleotide microarrays, risk stratification, event-free survival, overall survival, prognostic factors, childhood cancer, gene expression signatures
- MeSH terms
- Germany, Humans, Japan, Neuroblastoma, Predictive Value of Tests, Biomarkers, Tumor, United States, Reproducibility of Results, Gene Expression Regulation, Neoplastic, Multivariate Analysis, Proportional Hazards Models, Odds Ratio, Survival Analysis, Risk Assessment, Disease-Free Survival, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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