Why the study?
Isolating disease-causing driver non-coding mutations from somatic variants in tumor genomes is notoriously challenging, with only a handful of validated examples.
This computational genomics study identifies 301 non-coding somatic mutations in tumors that act as drivers by affecting gene expression and contributing to disease progression.
May expand non-coding driver catalog in cancer; leaves open validation as prognostic markers.
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 is notoriously challenging, and only a handful of validated examples exist. By integrating whole-genome sequencing, gene expression, chromatin accessibility, and genetic data from TCGA, we identified 301 non-coding somatic mutations that affect gene expression in cis. These mutations cluster into 36 hotspot regions with diverse molecular mechanisms of gene expression regulation. We further show that these mutations have hallmark features of noncoding drivers; namely, that they confer a positive selection on growth, functionally disrupt transcription factor binding sites, and contribute to disease progression reflected in decreased overall patient survival.
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Hudson et al. (2020) studied this question.
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