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