Identifying tumorigenic non-coding mutations through altered -regulation.

Identification of non-coding mutations driving tumorigenesis requires alternative approaches to coding mutations. Enriched associations between mutated regulatory elements and altered cis-regulation in tumors are a promising approach to stratify candidate non-coding driver mutations. Here we provide a bioinformatics pipeline to mine data from the Cancer Genomic Commons (GDC) for such associations.

regulatory mutations with driver hallmarks in major cancers.

Despite the recent availability of complete genome sequences of tumors from thousands of patients, isolating disease-causing (driver) non-coding mutations from the plethora of somatic variants remains challenging, and only a handful of validated examples exist. By integrating whole-genome sequencing, genetic data, and allele-specific gene expression from TCGA, we identified 320 somatic non-coding mutations that affect gene expression in (FDR<0.25).