Validation of a prognostic multi‐gene signature in high‐risk neuroblastoma using the high throughput digital NanoString nCounter™ system. Issue 3 (31st January 2014)

• Record Type: Journal Article
• Authors: 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.
• Is Part Of: Molecular oncology. Volume 8:Issue 3(2014:May)
• Page Start: 669
• Page End: 678
• Publication Date: 2014-01-31
• Subjects: High‐risk neuroblastoma -- Gene signature -- Molecular classifier -- NanoString -- nCounter; Cancer -- Molecular aspects -- Periodicals; 616.994005
• DOI: 10.1016/j.molonc.2014.01.010
• Languages: English
• ISSNs: 1574-7891

Abstract

Abstract : 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. Highlights: Improved neuroblastoma risk classification is needed to refine treatment. Microarray signatures provide prognostic information for neuroblastoma patients. Complex technology has limited clinical integration of microarray signatures. Expression signatures can be obtained using nCounter™ system's simple technology.