Identification of Single Nucleotide Non-coding Driver Mutations in Cancer

Abstract

Recent whole-genome sequencing studies have identified millions of somatic variants present in tumor samples. Most of these variants reside in non-coding regions of the genome potentially affecting transcriptional and post-transcriptional gene regulation. Although a few hallmark examples of driver mutations in non-coding regions have been reported, the functional role of the vast majority of somatic non-coding variants remains to be determined. This is because the few driver variants in each sample must be distinguished from the thousands of passenger variants and because the logic of regulatory element function has not yet been fully elucidated. Thus, variants prioritized based on mutational burden and location within regulatory elements need to be validated experimentally. This is generally achieved by combining assays that measure physical binding, such as chromatin immunoprecipitation, with those that determine regulatory activity, such as luciferase reporter assays. Here, we present an overview of in silico approaches used to prioritize somatic non-coding variants and the experimental methods used for functional validation and characterization.

Somatic mutations in miRNA genes in lung cancer – potential functional consequences of non-coding sequence variants

“…So far, most of the attention has been on the analysis of protein-coding genes; therefore, the majority of experimental approaches and bioinformatics algorithms used for somatic mutation detection and identification of potential driver signals are not well suited for non-protein-coding parts of the genome. The limitations of tools dedicated to the analysis of genetic variation in non-coding sections of the genome (~99%) and thus the limited number of studies focusing on these regions were recently described and discussed (34,65). It should also be noted that a recent increase in interest in non-coding genomic variation, most likely inspired by the identification of highly recurrent mutations in the promoter of the TERT gene in melanoma and other cancers (66), is focused mostly on regions playing a role in DNA:protein interactions (e.g., promoters and enhancers) (65).…”

“…The challenge in identifying driver mutations in non-coding regions stems mostly from the lack of a simple code (such as the protein code) that would allow one to predict the function of mutations and to distinguish deleterious from benign or neutral mutations. Approaches such as MutSigNC and LARVA, which utilize analysis of background mutation distribution, were recently modified for the identification of non-coding drivers, but they remain mostly limited to regions such as promoters, enhancers, and transcription factor binding sites (65). To the best of our knowledge, there is currently no tool dedicated to the automated and statistically supported identification of driver mutations in miRNA genes.…”

Cis-regulatory mutations associate with transcriptional and post-transcriptional deregulation of the gene regulatory program in cancers

“…Molecular alterations at these regions can alter the regulatory network of the cells, conferring oncogenic behaviours, which has been associated with clinical and histopathological features in cancer [3]. However, identification of noncoding cancer driver events at cis-regulatory regions has been limited to a few examples with high recurrence or high functional impact [3][4][5][6][7]. In recent work based on mutation recurrence along the human genome, the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, claimed that patients harbour ~4.6 driver mutations.…”

Discovering human transcription factor interactions with genetic variants, novel DNA motifs, and repetitive elements using enhanced yeast one-hybrid assays

“…Experimentally determining whether TF binding is altered by genomic variants associated with genetic diseases or cancer has been challenging as this cannot be performed using ChIPseq without a priori TF candidates. This is because all ~1,500 human TFs would have to be evaluated individually, and because samples from the appropriate tissues and conditions from healthy and sick individuals need to be obtained and compared (Fuxman Bass et al 2015;Gan et al 2018). Thus, the most widely used approach to prioritize TFs consists of using known DNA binding specificities (available for ~50% of human TFs (Weirauch et al 2014)) and motif search algorithms such as FIMO, BEEML-PWM or TFM-pvalue to compare predicted TF binding between the different noncoding alleles (Touzet and Varre 2007;Grant et al 2011;Zhao and Stormo 2011;Weirauch et al 2014;Rheinbay et al 2017).…”