Multiple myeloma (MM) is a biologically heterogeneous malignancy, however, the mechanisms underlying this complexity are incompletely understood. We report an analysis of the whole-genome sequencing of 765 MM patients from CoMMpass. By employing promoter capture Hi-C in naïve B-cells, we identify cis-regulatory elements (CREs) that represent a highly enriched subset of the non-coding genome in which to search for driver mutations. We identify regulatory regions whose mutation significantly alters the expression of genes as candidate non-coding drivers, including copy number variation (CNV) at CREs of MYC and single-nucleotide variants (SNVs) in a PAX5 enhancer. To better inform the interplay between non-coding driver mutations with other driver mechanisms, and their respective roles in oncogenic pathways, we extended our analysis identifying coding drivers in 40 genes, including 11 novel candidates. We demonstrate the same pathways can be targeted by coding and non-coding mutations; exemplified by IRF4 and PRDM1, along with BCL6 and PAX5, genes that are central to plasma cell differentiation. This study reveals new insights into the complex genetic alterations driving MM development and an enhanced understanding of oncogenic pathways.

Background Epidemiological studies have linked lifestyle, cardiometabolic, reproductive, developmental, and inflammatory factors to the risk of colorectal cancer. However, which specific factors affect risk and the strength of these effects are unknown. We aimed to examine the relationship between potentially modifiable risk factors and colorectal cancer. MethodsWe used a random-effects model to examine the relationship between 39 potentially modifiable risk factors and colorectal cancer in 26 397 patients with colorectal cancer and 41 481 controls (ie, people without colorectal cancer). These population data came from a genome-wide association study of people of European ancestry, which was amended to exclude UK BioBank data. In the model, we used genetic variants as instruments via two-sample mendelian randomisation to limit bias from confounding and reverse causation. We calculated odds ratios per genetically predicted SD unit increase in each putative risk factor (OR SD ) for colorectal cancer risk. We did mendelian randomisation Egger regressions to identify evidence of potential violations of mendelian randomisation assumptions. A Bonferroni-corrected threshold of p=1•3 × 10 -³ was considered significant, and p values less than 0•05 were considered to be suggestive of an association. FindingsNo putative risk factors were significantly associated with colorectal cancer risk after correction for multiple testing. However, suggestive associations with increased risk were noted for genetically predicted body fat percentage (OR SD 1•14 [95% CI 1•03-1•25]; p=0•0086), body-mass index (1•09 [1•01-1•17]; p=0•023), waist circumference (1•13 [1•02-1•26]; p=0•018), basal metabolic rate (1•10 [1•03-1•18]; p=0•0079), and concentrations of LDL cholesterol (1•14 [1•04-1•25]; p=0•0056), total cholesterol (1•09 [1•01-1•18]; p=0•025), circulating serum iron (1•17 [1•00-1•36]; p=0•049), and serum vitamin B12 (1•21 [1•04-1•42]; p=0•016), although potential pleiotropy among genetic variants used as instruments for vitamin B12 constrains the finding. A suggestive association was also noted between adult height and increased risk of colorectal cancer (OR SD 1•04 [95% CI 1•00-1•08]; p=0•032). Low blood selenium concentration had a suggestive association with decreased risk of colorectal cancer (OR SD 0•85 [95% CI 0•75-0•96]; p=0•0078) based on a single variant, as did plasma concentrations of interleukin-6 receptor subunit α (also based on a single variant; 0•98 [0•96-1•00]; p=0•035). Risk of colorectal cancer was not associated with any sex hormone or reproductive factor, serum calcium, or circulating 25-hydroxyvitamin D concentrations.Interpretation This analysis identified several modifiable targets for primary prevention of colorectal cancer, including lifestyle, obesity, and cardiometabolic factors, that should inform public health policy.

While most testicular germ cell tumours (TGCTs) exhibit exquisite sensitivity to platinum chemotherapy,~10% are platinum resistant. To gain insight into the underlying mechanisms, we undertake whole exome sequencing and copy number analysis in 40 tumours from 26 cases with platinum-resistant TGCT, and combine this with published genomic data on an additional 624 TGCTs. We integrate analyses for driver mutations, mutational burden, global, arm-level and focal copy number (CN) events, and SNV and CN signatures. Albeit preliminary and observational in nature, these analyses provide support for a possible mechanistic link between early driver mutations in RAS and KIT and the widespread copy number events by which TGCT is characterised.

Linking networks of molecular interactions to cellular functions and phenotypes is a key goal in systems biology. Here, we adapt concepts of spatial statistics to assess the functional content of molecular networks. Based on the guilt-by-association principle, our approach (called SANTA) quantifies the strength of association between a gene set and a network, and functionally annotates molecular networks like other enrichment methods annotate lists of genes. As a general association measure, SANTA can (i) functionally annotate experimentally derived networks using a collection of curated gene sets and (ii) annotate experimentally derived gene sets using a collection of curated networks, as well as (iii) prioritize genes for follow-up analyses. We exemplify the efficacy of SANTA in several case studies using the S. cerevisiae genetic interaction network and genome-wide RNAi screens in cancer cell lines. Our theory, simulations, and applications show that SANTA provides a principled statistical way to quantify the association between molecular networks and cellular functions and phenotypes. SANTA is available from http://bioconductor.org/packages/release/bioc/html/SANTA.html.

Efforts are being directed to systematically analyze the non-coding regions of the genome for cancer-driving mutations. 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 Atlas. 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 amplification. 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.

To gain insight into multiple myeloma (MM) tumorigenesis, we analyzed the mutational signatures in 874 whole-exome and 850 whole-genome data from the CoMMpass Study. We identified that coding and non-coding regions are differentially dominated by distinct single-nucleotide variant (SNV) mutational signatures, as well as five de novo structural rearrangement signatures. Mutational signatures reflective of different principle mutational processes—aging, defective DNA repair, and apolipoprotein B editing complex (APOBEC)/activation-induced deaminase activity—characterize MM. These mutational signatures show evidence of subgroup specificity—APOBEC-attributed signatures associated with MAF translocation t(14;16) and t(14;20) MM; potentially DNA repair deficiency with t(11;14) and t(4;14); and aging with hyperdiploidy. Mutational signatures beyond that associated with APOBEC are independent of established prognostic markers and appear to have relevance to predicting high-risk MM.

Supplementary data are available at Bioinformatics online.

BackgroundAn inverse relationship between allergies with glioma risk has been reported in several but not all epidemiological observational studies. We performed an analysis of genetic variants associated with atopy to assess the relationship with glioma risk using Mendelian randomisation (MR), an approach unaffected by biases from temporal variability and reverse causation that might have affected earlier investigations.MethodsTwo-sample MR was undertaken using genome-wide association study data. We used single nucleotide polymorphisms (SNPs) associated with atopic dermatitis, asthma and hay fever, IgE levels, and self-reported allergy as instrumental variables. We calculated MR estimates for the odds ratio (OR) for each risk factor with glioma using SNP-glioma estimates from 12,488 cases and 18,169 controls, using inverse-variance weighting (IVW), maximum likelihood estimation (MLE), weighted median estimate (WME) and mode-based estimate (MBE) methods. Violation of MR assumptions due to directional pleiotropy were sought using MR-Egger regression and HEIDI-outlier analysis.ResultsUnder IVW, MLE, WME and MBE methods, associations between glioma risk with asthma and hay fever, self-reported allergy and IgE levels were non-significant. An inverse relationship between atopic dermatitis and glioma risk was found by IVW (OR 0.96, 95% confidence interval (CI) 0.93–1.00, P = 0.041) and MLE (OR 0.96, 95% CI 0.94–0.99, P = 0.003), but not by WME (OR 0.96, 95% CI 0.91–1.01, P = 0.114) or MBE (OR 0.97, 95% CI 0.92–1.02, P = 0.194).ConclusionsOur investigation does not provide strong evidence for relationship between atopy and the risk of developing glioma, but findings do not preclude a small effect in relation to atopic dermatitis. Our analysis also serves to illustrate the value of using several MR methods to derive robust conclusions.Electronic supplementary materialThe online version of this article (10.1186/s12916-018-1027-5) contains supplementary material, which is available to authorized users.