Detection of circular RNAs and their potential as biomarkers predictive of drug response

The introduction of high-throughput sequencing technologies has allowed for comprehensive RNA species detection, both coding and non-coding, which opened new avenues for the discovery of predictive and prognostic biomarkers. However the consistency of the detection of different RNA species depends on the RNA selection protocol used for RNA-sequencing. While preliminary reports indicated that non-coding RNAs, in particular circular RNAs, constitute a rich source of biomarkers predictive of drug response, the reproducibility of this novel class of biomarkers has not been rigorously investigated. To address this issue, we assessed the inter- lab consistency of circular RNA expression in cell lines profiled in large pharmacogenomic datasets. We found that circular RNA expression quantified from rRNA-depleted RNA-seq data is stable and yields robust prognostic markers in cancer. On the other hand, quantification of the expression of circular RNA from poly(A)-selected RNA-seq data yields highly inconsistent results, calling into question results from previous studies reporting their potential as predictive biomarkers in cancer. We have also identified median expression of transcripts and transcript length as potential factors influencing the consistency of RNA detection. Our study provides a framework to quantitatively assess the stability of coding and non-coding RNA expression through the analysis of biological replicates within and across independent studies.