Cancer Letters

Cancer Letters

Volume 282, Issue 1, 8 September 2009, Pages 55-62
Cancer Letters

Reanalysis of neuroblastoma expression profiling data using improved methodology and extended follow-up increases validity of outcome prediction

https://doi.org/10.1016/j.canlet.2009.02.052Get rights and content

Abstract

Neuroblastoma is the most common extracranial childhood tumor, comprising 15% of all childhood cancer deaths. In an initial study, we used Affymetrix oligonucleotide microarrays to analyse gene expression in 68 primary neuroblastomas and compared different data mining approaches for prediction of early relapse. Here, we performed re-analyses of the data including prolonged follow-up and applied support vector machine (SVM) algorithms and outer cross-validation strategies to improve reliability of expression profiling based predictors. Accuracy of outcome prediction was significantly improved by the use of innovative SVM algorithms on the updated data. In addition, CASPAR, a hierarchical Bayesian approach, was used to predict survival times for the individual patient based on expression profiling data. CASPAR reliably predicted event-free survival, given a cut-off time of three years. Differential expression of genes used by CASPAR to predict patient outcome was validated in an independent cohort of 117 neuroblastomas. In conclusion, we show here for the first time that reanalysis of microarray data using improved methodology, state-of-the-art performance tests and updated follow-up data improves prognosis prediction, and may further improve risk stratification of individual patients.

Section snippets

Background

Neuroblastoma is the most common extracranial tumor of childhood and accounts for approximately 15% of all childhood cancer deaths [1], [2], [3]. Although numerous prognostic factors have been identified, risk evaluation of individual patients remains difficult due to the clinical heterogeneity of neuroblastoma. This malignancy is unique in its wide spectrum of clinical behaviour, which ranges from spontaneous regression and differentiation to highly aggressive disease that often results in

Affymetrix microarray data

Microarray data was obtained from our initial neuroblastoma expression profiling study. Data were normalized using the MAS5.0 algorithm implemented in R statistical language as previously described [13].

Patient cohort

Updated follow-up information was obtained from the study center of the German neuroblastoma study group. Of 68 patients in our initial study [14], one was lost to follow-up and was therefore excluded from further analysis.

SVM

For SVM analysis, RapidMiner 4.1 (Rapid-I, Dortmund, Germany) was used

Sample annotation and follow-up

We first obtained updates of annotations and follow-up data of our initial patient cohort [14]. Updated follow-up data was available from the German Neuroblastoma Study Data Centre for 67 of 68 patients. One patient was lost to follow-up, and was therefore excluded from further analysis. The median follow-up time for patients in this study was 1.717 days (IQR: 640 days), compared to 1.108 days (IQR: 810 days) at the time of our initial analysis. Since our initial analysis, six additional

Discussion

On the route to clinical application of high-throughput expression profiling, different strategies have been applied to increase validity and practicability. For example, we applied high-throughput real-time PCR to verify our predictive signature using a technique feasible for routine clinical use [9]. Nevertheless, a prerequisite for any array-based diagnostic tool is the validity of predictive patterns obtained from microarray based expression profiling studies. This can be achieved by

Conflicts of interest statement

None declared.

Acknowledgements

We thank Kathy Astrahantseff for the critical reading of the manuscript, Shahab Asgharzadeh for helpful discussions and Barbara Hero and the German Neuroblastoma Study Group for providing clinical data. A.E. and A.S. were supported by Grants from the National Genome Research Network (NGFN) and the EU (Framework 6, EET Pipeline, Grant no. 037260). L.K. received funding from the BMBF through ViroQuant (Grant nr. 0313923).

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