RNA-Seq and microarray-based models perform similarly in clinical endpoint prediction

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, a team led by researchers from BGI-Shenzhen 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.

The team generated gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays.

Characteristics of the neuroblastoma transcriptome according to RNA-seq data using the Magic-AceView pipeline. A Percentage of reads mapped to distinct targets. B Number of genes, transcripts and exon-junctions expressed in the entire neuroblastoma cohort according to their annotation by AceView. C Absolute numbers and overlap of differentially expressed genes (DEGs) identified by RNA-seq (red) and microarrays (blue) in four disease subgroups.

Characterization of the neuroblastoma transcriptome by RNA-seq revealed that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. They also found that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays.

To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, the researchers divided the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances revealed that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs.microarrays), RNA-seq data analysis pipelines and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models.