
Validation of a prognostic multi‐gene signature in high‐risk neuroblastoma using the high throughput digital NanoString nCounter™ system
Author(s) -
Stricker Thomas P.,
Morales La Madrid Andres,
Chlenski Alexandre,
Guerrero Lisa,
Salwen Helen R.,
Gosiengfiao Yasmin,
Perlman Elizabeth J.,
Furman Wayne,
Bahrami Armita,
Shohet Jason M.,
Zage Peter E.,
Hicks M. John,
Shimada Hiroyuki,
Suganuma Rie,
Park Julie R.,
So Sara,
London Wendy B.,
Pytel Peter,
Maclean Kirsteen H.,
Cohn Susan L.
Publication year - 2014
Publication title -
molecular oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.332
H-Index - 88
eISSN - 1878-0261
pISSN - 1574-7891
DOI - 10.1016/j.molonc.2014.01.010
Subject(s) - neuroblastoma , gene signature , oncology , gene expression profiling , biology , microarray , microarray analysis techniques , dna microarray , cancer research , gene expression , bioinformatics , gene , computational biology , medicine , genetics , cell culture
Microarray‐based molecular signatures have not been widely integrated into neuroblastoma diagnostic classification systems due to the complexities of the assay and requirement for high‐quality RNA. New digital technologies that accurately quantify gene expression using RNA isolated from formalin‐fixed paraffin embedded (FFPE) tissues are now available. In this study, we describe the first use of a high‐throughput digital system to assay the expression of genes in an “ultra‐high risk” microarray classifier in FFPE high‐risk neuroblastoma tumors. Customized probes corresponding to the 42 genes in a published multi‐gene neuroblastoma signature were hybridized to RNA isolated from 107 FFPE high‐risk neuroblastoma samples using the NanoString nCounter™ Analysis System. For classification of each patient, the Pearson's correlation coefficient was calculated between the standardized nCounter™ data and the molecular signature from the microarray data. We demonstrate that the nCounter™ 42‐gene panel sub‐stratified the high‐risk cohort into two subsets with statistically significantly different overall survival ( p = 0.0027) and event‐free survival ( p = 0.028). In contrast, none of the established prognostic risk markers (age, stage, tumor histology, MYCN status, and ploidy) were significantly associated with survival. We conclude that the nCounter™ System can reproducibly quantify expression levels of signature genes in FFPE tumor samples. Validation of this microarray signature in our high‐risk patient cohort using a completely different technology emphasizes the prognostic relevance of this classifier. Prospective studies testing the prognostic value of molecular signatures in high‐risk neuroblastoma patients using FFPE tumor samples and the nCounter™ System are warranted.