Promoter capture Hi-C-based identification of recurrent noncoding mutations in colorectal cancer

Efforts are being directed to systematically analyze the non-coding regions of the genome for cancer-driving mutations1-6. cis-regulatory elements (CREs) represent a highly enriched subset of the non-coding regions of the genome in which to search for such mutations. Here we use high-throughput chromosome conformation capture techniques (Hi-C) for 19,023 promoter fragments to catalog the regulatory landscape of colorectal cancer in cell lines, mapping CREs and integrating these with whole-genome sequence and expression data from The Cancer Genome Atlas7,8. We identify a recurrently mutated CRE interacting with the ETV1 promoter affecting gene expression. ETV1 expression influences cell viability and is associated with patient survival. We further refine our understanding of the regulatory effects of copy-number variations, showing that RASL11A is targeted by a previously identified enhancer amplification1. This study reveals new insights into the complex genetic alterations driving tumor development, providing a paradigm for employing chromosome conformation capture to decipher non-coding CREs relevant to cancer biology

Mutational heterogeneity in cancer and the search for new cancer-associated genes

Major international projects are underway that are aimed at creating a comprehensive catalogue of all the genes responsible for the initiation and progression of cancer. These studies involve the sequencing of matched tumour-normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significant genes produced by current analytical methods burgeons into the hundreds. The list includes many implausible genes (such as those encoding olfactory receptors and the muscle protein titin), suggesting extensive false-positive findings that overshadow true driver events. We show that this problem stems largely from mutational heterogeneity and provide a novel analytical methodology, MutSigCV, for resolving the problem. We apply MutSigCV to exome sequences from 3,083 tumour-normal pairs and discover extraordinary variation in mutation frequency and spectrum within cancer types, which sheds light on mutational processes and disease aetiology, and in mutation frequency across the genome, which is strongly correlated with DNA replication timing and also with transcriptional activity. By incorporating mutational heterogeneity into the analyses, MutSigCV is able to eliminate most of the apparent artefactual findings and enable the identification of genes truly associated with cancer