Massive computational identification of somatic variants in exonic splicing enhancers using The Cancer Genome Atlas

Owing to the development of next‐generation sequencing (NGS) technologies, a large number of somatic variants have been identified in various types of cancer. However, the functional significance of most somatic variants remains unknown. Somatic variants that occur in exonic splicing enhancer (ESE) regions are thought to prevent serine and arginine‐rich (SR) proteins from binding to ESE sequence motifs, which leads to exon skipping. We computationally identified somatic variants in ESEs by compiling numerous open‐access datasets from The Cancer Genome Atlas (TCGA). Using somatic variants and RNA‐seq data from 9635 patients across 32 TCGA projects, we identified 646 ESE‐disrupting variants. The false positive rate of our method, estimated using a permutation test, was approximately 1%. Of these ESE‐disrupting variants, approximately 71% were located in the binding motifs of four classical SR proteins. ESE‐disrupting variants occurred in proportion to the number of somatic variants, but not necessarily in the specific genes associated with the biological processes of cancer. Existing bioinformatics tools could not predict the pathogenicity of ESE‐disrupting variants identified in this study, although these variants could cause exon skipping. We demonstrated that ESE‐disrupting nonsense variants tended to escape nonsense‐mediated decay surveillance. Using integrated analyses of open access data, we could specifically identify ESE‐disrupting variants. We have generated a powerful tool, which can handle datasets without normal samples or raw data, and thus contribute to reducing variants of uncertain significance because our statistical approach only uses the exon‐junction read counts from the tumor samples.