Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis

Abstract

Background: There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression.

Main body: We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data.

Conclusion: We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.

Keywords: Cancer; Chromosome conformation; GWAS; Germline mutation; Hi-C; Non-coding mutation; Somatic mutation; eQTL.

Fig. 1

An overview of cis-regulatory elements and the experimental methods to identify them

Fig. 2

Methods to link enhancers to their target genes

Fig. 3

Expression quantitative trait locus. A An expression QTL arises due to DNA variation (single nucleotide polymorphism; SNP) modifying the transcription level of target gene X in an allele-specific manner. B Association between a SNP’s genotype and the expression level of target gene X in hundreds of individuals

Fig. 4

An overview of TWAS pipeline (PrediXcan [116]). The general method of TWAS is composed of three steps. First, using individual-level genotype and matching gene expression data from a reference eQTL dataset, predictive models are trained to estimate the expression level of each gene based on local genotype. Second, the models are used to predict (or “impute”) the expression level of genes (normally not captured in GWAS) for each individual-level genotype in a GWAS dataset. Third, an association test is conducted for each predicted expression with the trait to elucidate gene–trait associations. The first step has been improved by subsequent methods, which allow summary-level GWAS data as input (e.g., FUSION [117], S-PrediXcan [118], MOSTWAS [119], and UTMOST [120]). For example, UTMOST takes summary-level data and simultaneously trains models across multiple tissues to increase power [120]

Fig. 5

The non-random packaging of chromatin inside the nucleus. A On the nuclear scale, each chromosome occupies individual regions, termed chromosomal territories [135]. B Within these chromosomes, chromatin clusters into transcriptionally active (“A”) and inactive (“B”) compartments [136]. C Within these compartments, further organization occurs in the form of megabase-long loop structures called “topologically associating domains” (TADs) [137]. TADs are highly conserved between cell types and tend to insulate enhancers and genes contained within it from elements outside of the TAD, thereby preventing inappropriate enhancer–promoter contacts [138]. D Finally, TADs are further compartmentalized into smaller sub-TAD loops that frequently facilitate enhancer–promoter interactions. Unlike TADs, these smaller loops are more cell-type specific [139]. Generally, it is thought that TAD and sub-TAD loops are formed by the interaction of CTCF DNA binding proteins and cohesin ring-shaped complexes that bring distant chromatin regions into physical proximity [140]. However, even this is a simplistic model as further evidence suggest the involvement of many other factors. For example, recent evidence suggest that sub-TAD loops are more commonly stabilized by YY1 proteins in a manner analogous to CTCF [141]. Another evidence shows the involvement of ZNF143 as a chromatin-looping factor that bind to promoter and establish loops through interaction with enhancer-bound CTCF and cohesin [142]. Overall, the mechanisms behind chromatin looping are still an active area of research

Fig. 6

Illustration of two different recurrence model. A Site-level recurrence model: three point mutations from three individual tumor samples are clustered on the same site, making it a hotspot of somatic mutations. B Gene-level recurrence model: three point mutations from three individual tumor samples are scattered on three different regulatory elements, but when spatial conformation is taken into account, those mutations converge on the same target gene. Figure is adapted from [175]

Fig. 7

Many trans-eQTLs are also cis-eQTLs. A SNP may first alter the expression level of a cis diffusible mediator, such as a transcription factor, which then alters the expression of the trans genes through altering the binding of the transcription factor to its binding sites near the downstream trans genes