Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks

Abstract

A comprehensive catalogue of the mutations that drive tumorigenesis and progression is essential to understanding tumor biology and developing therapies. Protein-coding driver mutations have been well-characterized by large exome-sequencing studies, however many tumors have no mutations in protein-coding driver genes. Non-coding mutations are thought to explain many of these cases, however few non-coding drivers besides TERT promoter are known. To fill this gap, we analyzed 150,000 cis-regulatory regions in 1,844 whole cancer genomes from the ICGC-TCGA PCAWG project. Using our new method, ActiveDriverWGS, we found 41 frequently mutated regulatory elements (FMREs) enriched in non-coding SNVs and indels (FDR<0.05) characterized by aging-associated mutation signatures and frequent structural variants. Most FMREs are distal from genes, reported here for the first time and also recovered by additional driver discovery methods. FMREs were enriched in super-enhancers, H3K27ac enhancer marks of primary tumors and long-range chromatin interactions, suggesting that the mutations drive cancer by distally controlling gene expression through threedimensional genome organization. In support of this hypothesis, the chromatin interaction network of FMREs and target genes revealed associations of mutations and differential gene expression of known and novel cancer genes (e.g., CNNB1IP1, RCC1), activation of immune response pathways and altered enhancer marks. Thus distal genomic regions may include additional, infrequently mutated drivers that act on target genes via chromatin loops. Our study is an important step towards finding such regulatory regions and deciphering the somatic mutation landscape of the non-coding genome..

Non-coding somatic mutations converge on the PAX8 pathway in ovarian cancer

“…6). Enhancer knockout induced downregulation of both ZSCAN16 and ZSCAN12, and ZKSCAN3 and HIST1H2AI, which are previously predicted targets of this enhancer 32 (Fig. 3h, i and Supplementary Fig.…”

Functional and genetic determinants of mutation rate variability in regulatory elements of cancer genomes

“…To find potential genetic mechanisms of localised mutagenesis, we asked whether any recurrent mutations in tumor genomes could explain the observed mutation rate increases in the three classes of genomic elements. We considered 14 cancer types with 15 driver genes with frequent SNVs and indels detected using the ActiveDriverWGS method 34 , and 60 recurrent copy-number amplifications detected in the PCAWG project using the GISTIC2 method 49 ( Supplementary Figure 5 ). We found 27 driver alterations that positively interacted with local mutation rates, including 26 recurrent copy-number amplifications and one driver gene with SNVs and indels (RM2 FDR < 0.05, interaction P < 0.05) ( Figure 4A ) ( Supplementary Table 1E-F ).…”

“…For example, the mutation hotspot in the TERT promoter creates a TFBS of the ETS TF family that leads to constitutive activation of TERT and enables replicative immortality of cancer cells 31-33 . Recent studies have catalogued candidate non-coding driver elements in gene regulatory and chromatin architectural regions of the cancer genome with functional validations of novel elements 34-36 and highlighted the convergence of non-coding mutations on molecular pathways and regulatory networks 37,38 . Thus we need to characterise localised mutational processes to understand the evolution of the somatic genome and the effects of carcinogens and endogenous mutational processes, but also to evaluate the effects of positive selection in the non-coding genome.…”

MutSpot: detection of non-coding mutation hotspots in cancer genomes

“…Current tools designed to identify non-coding drivers are based on mutation recurrence within regulatory elements [4][5][6] , predicted functional impact of somatic mutations 7 , or a combination of these approaches 8,9 . However, existing methods are designed to explore mutations within defined regulatory regions, such as promoters, enhancers or UTRs, therefore ignoring the rest of the non-coding genome.…”