4.7 Article

miRNA Expression Profiling Enables Risk Stratification in Archived and Fresh Neuroblastoma Tumor Samples

Journal

CLINICAL CANCER RESEARCH
Volume 17, Issue 24, Pages 7684-7692

Publisher

AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1078-0432.CCR-11-0610

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Funding

  1. Ghent University Research Fund (BOF) [01D31406]
  2. Fund for Scientific Research Flanders
  3. Belgian Kid's Fund and the Fondation Nuovo-Soldati
  4. FOD [NKP_29_014]
  5. Fondation Fournier Majoie pour l'Innovation
  6. Belgian Federal Public Health Service
  7. Association Hubert Gouin Enfance et Cancer
  8. Flemish League against Cancer
  9. Children Cancer Fund Ghent
  10. Belgian Society of Paediatric Haematology and Oncology
  11. Fund for Scientific Research Flanders [G.0198.08)]
  12. Institute for the Promotion of Innovation by Science and Technology in Flanders, Strategisch basisonderzoek [IWT-SBO 60848]
  13. Children's Oncology Group [U10 CA98413, U10 CA98543]
  14. Instituto Carlos III Spain [RD 06/0020/0102]
  15. European Community [037260]
  16. Science Foundation Ireland [07/IN.1/B1776]
  17. Children's Medical and Research Foundation
  18. NIH [5R01CA127496]

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Purpose: More accurate assessment of prognosis is important to further improve the choice of risk-related therapy in neuroblastoma (NB) patients. In this study, we aimed to establish and validate a prognostic miRNA signature for children with NB and tested it in both fresh frozen and archived formalin-fixed paraffin-embedded (FFPE) samples. Experimental Design: Four hundred-thirty human mature miRNAs were profiled in two patient subgroups with maximally divergent clinical courses. Univariate logistic regression analysis was used to select miRNAs correlating with NB patient survival. A 25-miRNA gene signature was built using 51 training samples, tested on 179 test samples, and validated on an independent set of 304 fresh frozen tumor samples and 75 archived FFPE samples. Results: The 25-miRNA signature significantly discriminates the test patients with respect to progression-free and overall survival (P < 0.0001), both in the overall population and in the cohort of high-risk patients. Multivariate analysis indicates that the miRNA signature is an independent predictor of patient survival after controlling for current risk factors. The results were confirmed in an external validation set. In contrast to a previously published mRNA classifier, the 25-miRNA signature was found to be predictive for patient survival in a set of 75 FFPE neuroblastoma samples. Conclusions: In this study, we present the largest NB miRNA expression study so far, including more than 500 NB patients. We established and validated a robust miRNA classifier, able to identify a cohort of high-risk NB patients at greater risk for adverse outcome using both fresh frozen and archived material. Clin Cancer Res; 17(24); 7684-92. (C)2011 AACR.

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