New RNA-Seq mapping pipeline reveals the landscape of coding RNA editing events in pediatric cancer

RNA editing leads to post-transcriptional variation in protein sequences and has important biological implications. Researchers from the St. Jude Children’s Research Hospital sought to elucidate the landscape of RNA editing events across pediatric cancers.

Using RNA-Seq data mapped by a pipeline designed to minimize mapping ambiguity, the researchers investigated RNA editing in 711 pediatric cancers from the St. Jude/Washington University Pediatric Cancer Genome Project focusing on coding variants which can potentially increase protein sequence diversity. They combined de novo detection using paired tumor DNA-RNA data with analysis of known RNA editing sites.

The researchers identified 722 unique RNA editing sites in coding regions across pediatric cancers, 70% of which were nonsynonymous recoding variants. Nearly all editing sites represented the canonical A-to-I (n = 706) or C-to-U sites (n = 14). RNA editing was enriched in brain tumors compared to other cancers, including editing of glutamate receptors and ion channels involved in neurotransmitter signaling. RNA editing profiles of each pediatric cancer subtype resembled those of the corresponding normal tissue profiled by the Genotype-Tissue Expression (GTEx) project.

In this first comprehensive analysis of RNA editing events in pediatric cancer, researchers found that the RNA editing profile of each cancer subtype is similar to its normal tissue of origin. Tumor-specific RNA editing events were not identified indicating that successful immunotherapeutic targeting of RNA-edited peptides in pediatric cancer should rely on increased antigen presentation on tumor cells compared to normal but not on tumor-specific RNA editing per se.

The StrongArm RNA-Seq mapping and RNA editing detection pipelines

A Schematic workflow of StrongArm RNA-seq mapping pipeline. The pipeline starts with competitive mapping of 5 different combinations of mapper and database, followed by further local refinement. B RNA editing identification pipeline. RNA-Seq BAM files are aligned with StrongArm as shown in (A), and germline and somatic DNA variants are also called from the same patient using WGS or WES of matched tumor and germline DNA. The pipeline searches for RNA-specific (RNA editing) variants in coding (CDS) regions by comparing RNA-Seq reads to DNA-Seq. A series of false editing filters is then employed to remove RNA editing artifacts, followed by manual review of the BAM alignment. The RNA editing candidates are then used to evaluate the editing levels cross the whole cohort