A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.

Although cancer genomes are replete with noncoding mutations, the effects of these mutations remain poorly characterized. Here we perform an integrative analysis of 930 tumor whole genomes and matched transcriptomes, identifying a network of 193 noncoding loci in which mutations disrupt target gene expression. These 'somatic eQTLs' (expression quantitative trait loci) are frequently mutated in specific cancer tissues, and the majority can be validated in an independent cohort of 3,382 tumors. Among these, we find that the effects of noncoding mutations on DAAM1, MTG2 and HYI transcription are recapitulated in multiple cancer cell lines and that increasing DAAM1 expression leads to invasive cell migration. Collectively, the noncoding loci converge on a set of core pathways, permitting a classification of tumors into pathway-based subtypes. The somatic eQTL network is disrupted in 88% of tumors, suggesting widespread impact of noncoding mutations in cancer.

Identifying DNase I hypersensitive sites as driver distal regulatory elements in breast cancer.

Efforts to identify driver mutations in cancer have largely focused on genes, whereas non-coding sequences remain relatively unexplored. Here we develop a statistical method based on characteristics known to influence local mutation rate and a series of enrichment filters in order to identify distal regulatory elements harboring putative driver mutations in breast cancer. We identify ten DNase I hypersensitive sites that are significantly mutated in breast cancers and associated with the aberrant expression of neighboring genes. A pan-cancer analysis shows that three of these elements are significantly mutated across multiple cancer types and have mutation densities similar to protein-coding driver genes. Functional characterization of the most highly mutated DNase I hypersensitive sites in breast cancer (using in silico and experimental approaches) confirms that they are regulatory elements and affect the expression of cancer genes. Our study suggests that mutations of regulatory elements in tumors likely play an important role in cancer development.Cancer driver mutations can occur within noncoding genomic sequences. Here, the authors develop a statistical approach to identify candidate noncoding driver mutations in DNase I hypersensitive sites in breast cancer and experimentally demonstrate they are regulatory elements of known cancer genes.

Large-Scale Profiling Reveals the Influence of Genetic Variation on Gene Expression in Human Induced Pluripotent Stem Cells.

In this study, we used whole-genome sequencing and gene expression profiling of 215 human induced pluripotent stem cell (iPSC) lines from different donors to identify genetic variants associated with RNA expression for 5,746 genes. We were able to predict causal variants for these expression quantitative trait loci (eQTLs) that disrupt transcription factor binding and validated a subset of them experimentally. We also identified copy-number variant (CNV) eQTLs, including some that appear to affect gene expression by altering the copy number of intergenic regulatory regions. In addition, we were able to identify effects on gene expression of rare genic CNVs and regulatory single-nucleotide variants and found that reactivation of gene expression on the X chromosome depends on gene chromosomal position. Our work highlights the value of iPSCs for genetic association analyses and provides a unique resource for investigating the genetic regulation of gene expression in pluripotent cells.

Syntax compensates for poor binding sites to encode tissue specificity of developmental enhancers.

Transcriptional enhancers are short segments of DNA that switch genes on and off in response to a variety of intrinsic and extrinsic signals. Despite the discovery of the first enhancer more than 30 y ago, the relationship between primary DNA sequence and enhancer activity remains obscure. In particular, the importance of "syntax" (the order, orientation, and spacing of binding sites) is unclear. A high-throughput screen identified synthetic notochord enhancers that are activated by the combination of ZicL and ETS transcription factors in Ciona embryos. Manipulation of these enhancers elucidated a "regulatory code" of sequence and syntax features for notochord-specific expression. This code enabled in silico discovery of bona fide notochord enhancers, including those containing low-affinity binding sites that would be excluded by standard motif identification methods. One of the newly identified enhancers maps upstream of the known enhancer that regulates Brachyury (Ci-Bra), a key determinant of notochord specification. This newly identified Ci-Bra shadow enhancer contains binding sites with very low affinity, but optimal syntax, and therefore mediates surprisingly strong expression in the notochord. Weak binding sites are compensated by optimal syntax, whereas enhancers containing high-affinity binding affinities possess suboptimal syntax. We suggest this balance has obscured the importance of regulatory syntax, as noncanonical binding motifs are typically disregarded by enhancer detection methods. As a result, enhancers with low binding affinities but optimal syntax may be a vastly underappreciated feature of the regulatory genome.

Suboptimization of developmental enhancers.

Transcriptional enhancers direct precise on-off patterns of gene expression during development. To explore the basis for this precision, we conducted a high-throughput analysis of the Otx-a enhancer, which mediates expression in the neural plate of Ciona embryos in response to fibroblast growth factor (FGF) signaling and a localized GATA determinant. We provide evidence that enhancer specificity depends on submaximal recognition motifs having reduced binding affinities ("suboptimization"). Native GATA and ETS (FGF) binding sites contain imperfect matches to consensus motifs. Perfect matches mediate robust but ectopic patterns of gene expression. The native sites are not arranged at optimal intervals, and subtle changes in their spacing alter enhancer activity. Multiple tiers of enhancer suboptimization produce specific, but weak, patterns of expression, and we suggest that clusters of weak enhancers, including certain "superenhancers," circumvent this trade-off in specificity and activity.

Regulatory Principles Governing Tissue Specificity of Developmental Enhancers.

Transcriptional enhancers are short segments of genomic DNA (50 bp to 1 kb in length) that can work over long distances (≥1 Mb) to regulate gene expression in specific cells and tissues. Genomic assays have identified on the order of 400,000 to one million putative enhancers in the human genome (e.g., ENCODE Consortium). This suggests that a typical gene is regulated by tens of enhancers, ensuring stringent regulation of gene expression in response to a variety of intrinsic and external signals. Despite the discovery of the first transcriptional enhancer more than 30 years ago, we know surprisingly little about how enhancers regulate gene expression. In particular, the relationship between primary DNA sequence and enhancer specificity remains obscure. Here we summarize recent high-throughput studies in whole embryos aimed at the systematic identification of the sequence and organizational constraints underlying enhancer function and specificity.