ArticleMolecular oncology2014
Validation of a prognostic multi-gene signature in high-risk neuroblastoma using the high throughput digital NanoString nCounter™ system.
Article in Molecular oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Implementation of a High-Accuracy Targeted Gene Expression Panel for Clinical Care.The Journal of molecular diagnostics : JMD · 2024Article
- Development and validation of a 21-gene prognostic signature in neuroblastoma.Scientific reports · 2023Article
- Cellular and gene signatures of tumor-infiltrating dendritic cells and natural-killer cells predict prognosis of neuroblastoma.Nature communications · 2020Article
- Molecular profiling of radical prostatectomy tissue from patients with no sign of progression identifiesOncotarget · 2019Article
- Pathway activity profiling of growth factor receptor network and stemness pathways differentiates metaplastic breast cancer histological subtypes.BMC cancer · 2019Article
- Too many targets, not enough patients: rethinking neuroblastoma clinical trials.Nature reviews. Cancer · 2018Review
- Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma.PloS one · 2018Article
- Incorporating genomic, transcriptomic and clinical data: a prognostic and stem cell-like MYC and PRC imbalance in high-risk neuroblastoma.BMC systems biology · 2017Article
- Cell-of-origin of diffuse large B-cell lymphomas determined by the Lymph2Cx assay: better prognostic indicator than Hans algorithm.Oncotarget · 2017Article
- Artificial neural network classifier predicts neuroblastoma patients' outcome.BMC bioinformatics · 2016Article
- Transcript signatures that predict outcome and identify targetable pathways in MYCN-amplified neuroblastoma.Molecular oncology · 2016Article
- Genetic discoveries and treatment advances in neuroblastoma.Current opinion in pediatrics · 2016Review
- Kinase Gene Expression Profiling of Metastatic Clear Cell Renal Cell Carcinoma Tissue Identifies Potential New Therapeutic Targets.PloS one · 2016Article
- NanoString expression profiling identifies candidate biomarkers of RAD001 response in metastatic gastric cancer.ESMO open · 2016Article
- Leveraging global gene expression patterns to predict expression of unmeasured genes.BMC genomics · 2015Article
- An Update on the Role of Immunotherapy and Vaccine Strategies for Primary Brain Tumors.Current treatment options in oncology · 2015Review
- Clinical Application of Prognostic Gene Expression Signature in Fusion Gene-Negative Rhabdomyosarcoma: A Report from the Children's Oncology Group.Clinical cancer research : an official journal of the American Association for Cancer Research · 2015Article
- MYCN concurrence with SAHA-induced cell death in human neuroblastoma cells.Cellular oncology (Dordrecht, Netherlands) · 2015Article
- Biomarkers for glioma immunotherapy: the next generation.Journal of neuro-oncology · 2015Review
- Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.Genome biology · 2015Article
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20 authors.
Funding
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.
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