0000000000808593
AUTHOR
Smadar Avigad
showing 3 related works from this author
Comparison of RNA-seq and microarray-based models for clinical endpoint prediction
2015
Background Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. Results We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being …
High frequency of subclonal ALK mutations in high risk neuroblastoma patients. A SIOPEN study
2015
Introduction: In neuroblastoma (NB), activating ALK receptor tyrosine kinase point mutations are detected in 8–10% at diagnosis using conventional sequencing. To determine the potential occurrence and the prognostic impact of ALK mutations in a series of high risk NB patients we studied ALK variation frequencies using targeted deep sequencing in samples of patients enrolled in the SIOPEN HR-NBL01 study
Revised risk estimation and treatment stratification of low- and intermediate-risk neuroblastoma patients by integrating clinical and molecular progn…
2014
Abstract Purpose: To optimize neuroblastoma treatment stratification, we aimed at developing a novel risk estimation system by integrating gene expression–based classification and established prognostic markers. Experimental Design: Gene expression profiles were generated from 709 neuroblastoma specimens using customized 4 × 44 K microarrays. Classification models were built using 75 tumors with contrasting courses of disease. Validation was performed in an independent test set (n = 634) by Kaplan–Meier estimates and Cox regression analyses. Results: The best-performing classifier predicted patient outcome with an accuracy of 0.95 (sensitivity, 0.93; specificity, 0.97) in the validation coh…