Prognostic Impact of Gene Expression–Based Classification for Neuroblastoma
Abstract
Purpose: To evaluate the impact of a predefined gene expression–based classifier for clinical risk estimation and cytotoxic treatment decision making in neuroblastoma patients. Patients and Methods: Gene expression profiles of 440 internationally collected neuroblastoma specimens were investigated by microarray analysis, 125 of which were examined prospectively. Patients were classified as either favorable or unfavorable by a 144-gene prediction analysis for microarrays (PAM) classifier established previously on a separate set of 77 patients. PAM classification results were compared with those of current prognostic markers and risk estimation strategies. Results: The PAM classifier reliably distinguished patients with contrasting clinical courses (favorable [n = 249] and unfavorable [n = 191]; 5-year event free survival [EFS] 0.84 ± 0.03 v 0.38 ± 0.04; 5-year overall survival [OS] 0.98 ± 0.01 v 0.56 ± 0.05, respectively; both P < .001). Moreover, patients with divergent outcome were robustly discriminated in both German and international cohorts and in prospectively analyzed samples (P ≤ .001 for both EFS and OS for each). In subgroups with clinical low-, intermediate-, and high-risk of death from disease, the PAM predictor significantly separated patients with divergent outcome (low-risk 5-year OS: 1.0 v 0.75 ± 0.10, P < .001; intermediate-risk: 1.0 v 0.82 ± 0.08, P = .042; and high-risk: 0.81 ± 0.08 v 0.43 ± 0.05, P = .001). In multivariate Cox regression models based on both EFS and OS, PAM was a significant independent prognostic marker (EFS: hazard ratio [HR], 3.375; 95% CI, 2.075 to 5.492; P < .001; OS: HR, 11.119, 95% CI, 2.487 to 49.701; P < .001). The highest potential clinical impact of the classifier was observed in patients currently considered as non–high-risk (n = 289; 5-year EFS: 0.87 ± 0.02 v 0.44 ± 0.07; 5-year OS: 1.0 v 0.80 ± 0.06; both P < .001). Conclusion: Gene expression–based classification using the 144-gene PAM predictor can contribute to improved treatment stratification of neuroblastoma patients.
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Predictive models were highly accurate in risk assessment of current low- and intermediate-risk neuroblastoma patients.
“However, while predictive models were highly accurate in risk assessment of current low- and intermediate-risk patients [18], the prediction of high-risk patient outcome has remained challenging [18–20].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [18]
Prediction of high-risk neuroblastoma patient outcome has remained challenging.
“However, while predictive models were highly accurate in risk assessment of current low- and intermediate-risk patients [18], the prediction of high-risk patient outcome has remained challenging [18–20].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [18]
The best performing models were able to predict outcome of high-risk patients with a similar precision as previously published multigene signatures, and independently from current prognostic markers.
“In addition, we found that the best performing models were able to predict outcome of high-risk patients with a similar precision as previously published multigene signatures [18, 20, 33], and independently from current prognostic markers.”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [18]
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 [18]
Endpoints were included that cover a broad range of predictive difficulties.
“Considering the fact that the nature of the clinical endpoint strongly affects a classifier’s performance [7, 18], we included endpoints that are known to cover a broad range of predictive difficulties (low difficulty, SEX, and CLASS LABEL; intermediate difficulty, EFS ALL, and OS ALL; high difficulty, EFS HR, and OS HR).”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [18]
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 [18]
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Comparison of RNA-seq and microarray-based models for clinical endpoint prediction
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- Authors
- Oberthuer, A., Hero, B., Berthold, F., Juraeva, D., Faldum, A., Kahlert, Y., Asgharzadeh, S., Seeger, R., Scaruffi, P., Tonini, G. P., Janoueix-Lerosey, I., Delattre, O., Schleiermacher, G., Vandesompele, J., Vermeulen, J., Speleman, F., Noguera, R., Piqueras, M., Bénard, J., Valent, A., Avigad, S., Yaniv, I., Weber, A., Christiansen, H., Grundy, R. G., Schardt, K., Schwab, M., Eils, R., Warnat, P., Kaderali, L., Simon, T., DeCarolis, B., Theissen, J., Westermann, F., Brors, B., & Fischer, M.
- Affiliations
- Universitat de València (ES)
- From the University of Cologne, Cologne; University Clinic Mainz, Mainz; University of Leipzig, Leipzig; German Cancer Research Center, Heidelberg, Germany; Children's Hospital Los Angeles, Los Angeles, CA; National Institute for Cancer Research, Genoa, Italy; Institut Curie, Paris; Institut Gustave Roussy, Villejuif, France; Ghent University Hospital, Ghent, Belgium; University of Valencia, Valencia, Spain; Schneider Children's Medical Center of Israel, Petah Tikva; Tel Aviv University, Tel Aviv,...
- ORCID
- Oberthuer, André
- Hero, Barbara
- Berthold, Frank
- Juraeva, Dilafruz
- Faldum, Andreas
- Kahlert, Yvonne
- Asgharzadeh, Shahab
- Seeger, Robert
- Scaruffi, Paola
- Tonini, Gian Paolo
- Delattre, Olivier
- Schleiermacher, Gudrun
- Vandesompele, Jo
- Speleman, Frank
- Noguera, Rosa
- Piqueras, Marta
- Avigad, Smadar
- Yaniv, Isaac
- Weber, Axel
- Christiansen, Holger
- Grundy, Richard G.
- Eils, Roland
- Kaderali, Lars
- Simon, Thorsten
- DeCarolis, Boris
- Theissen, Jessica
- Westermann, Frank
- Brors, Benedikt
- Fischer, Matthias
- Venue
- Journal of Clinical Oncology
- Published
- 2010-07-20
- Volume
- 28
- Issue
- 21
- Pages
- 3506-3515
- ISSN
- 0732-183X (ISSN-L)
- 1527-7755
- OpenAlex
- W2161707860
- Cited by
- 135
- References
- 33
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 4 · 2022: 4 · 2021: 7 · 2020: 11 · 2019: 6 · 2018: 5 · 2017: 8 · 2016: 9 · 2015: 10 · 2014: 12 · 2013: 14 · 2012: 21
- FWCI
- 9.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Clinical Oncology (publishedVersion)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- Funders
- Deutsche Krebshilfe (RD06/0020)
- Topics
- Health Sciences › Medicine › Neurology › Neuroblastoma Research and Treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Health Sciences › Medicine › Neurology › Neurofibromatosis and Schwannoma Cases
- Keywords
- neuroblastoma, gene expression classifier, prognostic factors, risk stratification, event-free survival, overall survival, prognostic validation, gene expression microarrays, treatment response prediction, recurrence prediction, prognostic signature
- MeSH terms
- Adolescent, Adult, Child, Child, Preschool, Humans, Infant, Infant, Newborn, Neuroblastoma, Prognosis, Proportional Hazards Models, Gene Expression Profiling
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