Title |
An 18 gene expression-based score classifier predicts the clinical outcome in stage 4 neuroblastoma
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Published in |
Journal of Translational Medicine, May 2016
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DOI | 10.1186/s12967-016-0896-7 |
Pubmed ID | |
Authors |
Daniela Formicola, Giuseppe Petrosino, Vito Alessandro Lasorsa, Piero Pignataro, Flora Cimmino, Simona Vetrella, Luca Longo, Gian Paolo Tonini, André Oberthuer, Achille Iolascon, Matthias Fischer, Mario Capasso |
Abstract |
The prognosis of children with metastatic stage 4 neuroblastoma (NB) has remained poor in the past decade. Using microarray analyses of 342 primary tumors, we here developed and validated an easy to use gene expression-based risk score including 18 genes, which can robustly predict the outcome of stage 4 patients. This classifier was a significant predictor of overall survival in two independent validation cohorts [cohort 1 (n = 214): P = 6.3 × 10(-5); cohort 2 (n = 27): P = 3.1 × 10(-2)]. The prognostic value of the risk score was validated by multivariate analysis including the established markers age and MYCN status (P = 0.027). In the pooled validation cohorts (n = 241), integration of the risk score with the age and/or MYCN status identified subgroups with significantly differing overall survival (ranging from 35 to 100 %). Together, the 18-gene risk score classifier can identify patients with stage 4 NB with favorable outcome and may therefore improve risk assessment and treatment stratification of NB patients with disseminated disease. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Belgium | 1 | 4% |
Unknown | 27 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 7 | 25% |
Student > Bachelor | 3 | 11% |
Student > Master | 3 | 11% |
Lecturer | 2 | 7% |
Student > Postgraduate | 2 | 7% |
Other | 5 | 18% |
Unknown | 6 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 8 | 29% |
Medicine and Dentistry | 6 | 21% |
Agricultural and Biological Sciences | 3 | 11% |
Unspecified | 1 | 4% |
Computer Science | 1 | 4% |
Other | 1 | 4% |
Unknown | 8 | 29% |