Accurate outcome prediction in neuroblastoma across independent data sets using a multigene signature.
OBJECTIVE
Reliable prognostic stratification remains a challenge for cancer patients, especially for diseases with variable clinical course such as neuroblastoma. Although numerous studies have shown that outcome might be predicted using gene expression signatures, independent cross-platform validation is often lacking.


METHODS
Using eight independent studies comprising 933 neuroblastoma patients, a prognostic gene expression classifier was developed, trained, tested, and validated. The classifier was established based on reanalysis of four published studies with updated clinical information, reannotation of the probe sequences, common risk definition for training cases, and a single method for gene selection (prediction analysis of microarray) and classification (correlation analysis).


RESULTS
Based on 250 training samples from four published microarray data sets, a correlation signature was built using 42 robust prognostic genes. The resulting classifier was validated on 351 patients from four independent and unpublished data sets and on 129 remaining test samples from the published studies. Patients with divergent outcome in the total cohort, as well as in the different risk groups, were accurately classified (log-rank P < 0.001 for overall and progression-free survival in the four independent data sets). Moreover, the 42-gene classifier was shown to be an independent predictor for survival (odds ratio, >5).


CONCLUSIONS
The strength of this 42-gene classifier is its small number of genes and its cross-platform validity in which it outperforms other published prognostic signatures. The robustness and accuracy of the classifier enables prospective assessment of neuroblastoma patient outcome. Most importantly, this gene selection procedure might be an example for development and validation of robust gene expression signatures in other cancer entities.
De Preter
Katleen
K
Center for Medical Genetics, Ghent University, Ghent University Hospital, Ghent, Belgium.
Vermeulen
Joëlle
J
Brors
Benedikt
B
Delattre
Olivier
O
Eggert
Angelika
A
Fischer
Matthias
M
Janoueix-Lerosey
Isabelle
I
Lavarino
Cinzia
C
Maris
John M
JM
Mora
Jaume
J
Nakagawara
Akira
A
Oberthuer
André
A
Ohira
Miki
M
Schleiermacher
Gudrun
G
Schramm
Alexander
A
Schulte
Johannes H
JH
Wang
Qun
Q
Westermann
Frank
F
Speleman
Frank
F
Vandesompele
Jo
J
D014408:Biomarkers, Tumor / Q000235:genetics*; D002648:Child; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans; D053208:Kaplan-Meier Estimate; D009447:Neuroblastoma / Q000235:genetics* / Q000401:mortality*; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis
D014408:Biomarkers, Tumor