0000000000143268
AUTHOR
André Oberthuer
Comparison of RNA-seq and microarray-based models for clinical endpoint prediction
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 …
Predicting outcomes for children with neuroblastoma using a multigene-expression signature: a retrospective SIOPEN/COG/GPOH study
Summary Background More accurate prognostic assessment of patients with neuroblastoma is required to better inform the choice of risk-related therapy. The aim of this study is to develop and validate a gene-expression signature to improve outcome prediction. Methods 59 genes were selected using an innovative data-mining strategy, and were profiled in the largest neuroblastoma patient series (n=579) to date using real-time quantitative PCR starting from only 20 ng of RNA. A multigene-expression signature was built using 30 training samples, tested on 313 test samples, and subsequently validated in a blind study on an independent set of 236 tumours. Findings The signature has a performance, s…
Revised risk estimation and treatment stratification of low- and intermediate-risk neuroblastoma patients by integrating clinical and molecular prognostic markers.
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…