Chromatin structure–based prediction of recurrent noncoding mutations in cancer

Abstract

Recurrence is a hallmark of cancer-driving mutations. Recurrent mutations can arise at the same site or affect the same gene at different sites. Here we identified a set of mutations arising in individual samples and altering different cis-regulatory elements that converge on a common gene via chromatin interactions. The mutations and genes identified in this fashion showed strong relevance to cancer, in contrast to noncoding mutations with site-specific recurrence only. We developed a prediction method that identifies potentially recurrent mutations on the basis of the features shared by mutations whose recurrence is observed in a given cohort. Our method was capable of accurately predicting recurrent mutations at the level of target genes but not mutations recurring at the same site. We experimentally validated predicted mutations in distal regulatory regions of the TERT gene. In conclusion, we propose a novel approach to discovering potential cancer-driving mutations in noncoding regions.

Enhancer Remodeling and MicroRNA Alterations Are Associated with Acquired Resistance to ALK Inhibitors

“…Epigenetic adaptations may provide initial resistance and allow tumor cells to survive until they acquire secondary mutations that further drive progression (16,45,46). Furthermore, mutations in noncoding regions, such as enhancers and promoters, influence gene expression and can be new drivers for cancer progression and evolution (47)(48)(49).…”

Most regulatory interactions are not in linkage disequilibrium

“…Additionally, diseaseassociated variants are enriched in regulatory regions [4], especially those from tissues relevant to the phenotype [5]. Functionally annotating non-coding variants and correctly mapping causal variants to the genes and pathways they affect is critical for understanding disease mechanisms and using genetics in precision medicine [6][7][8][9].…”

“…Additionally, diseaseassociated variants are enriched in regulatory regions [4], especially those from tissues relevant to the phenotype [5]. Functionally annotating non-coding variants and correctly mapping causal variants to the genes and pathways they affect is critical for understanding disease mechanisms and using genetics in precision medicine [6][7][8][9].Common practice associates non-coding variants with the closest gene promoter or promoters within the same LD block. However, regulatory variants can affect phenotypes by changing the expression of target genes up to several megabases (mb) away [10][11][12][13], well beyond their LD block (median length ≈ 1-2kb, Supplementary Table 1b).…”

How to interpret and integrate multi-omics data at systems level

“…In our previous study, we developed a prediction model to identify candidates for cancer driver genes by leveraging a variety of genomic and epigenome data in the context of transcriptional regulation (Kim et al 2016). The epigenome data for chromatin long-range interactions was critical in improving sensitivity to identify driver mutations.…”