Protein-coding mutations in clear cell renal cell carcinoma (ccRCC) have been extensively characterized, frequently involving inactivation of the von Hippel-Lindau ( VHL ) tumor suppressor. Roles for noncoding cis -regulatory aberrations in ccRCC tumorigenesis, however, remain unclear. Analyzing 10 primary tumor/normal pairs and 9 cell lines across 79 chromatin profi les, we observed pervasive enhancer malfunction in ccRCC, with cognate enhancer-target genes associated with tissue-specifi c aspects of malignancy. Superenhancer profi ling identifi ed ZNF395 as a ccRCCspecifi c and VHL-regulated master regulator whose depletion causes near-complete tumor elimination in vitro and in vivo . VHL loss predominantly drives enhancer/superenhancer deregulation more so than promoters, with acquisition of active enhancer marks (H3K27ac, H3K4me1) near ccRCC hallmark genes. Mechanistically, VHL loss stabilizes HIF2α-HIF1β heterodimer binding at enhancers, subsequently recruiting histone acetyltransferase p300 without overtly affecting preexisting promoter-enhancer interactions. Subtype-specifi c driver mutations such as VHL may thus propagate unique pathogenic dependencies in ccRCC by modulating epigenomic landscapes and cancer gene expression. SIGnIFICAnCE:Comprehensive epigenomic profi ling of ccRCC establishes a compendium of somatically altered cis -regulatory elements, uncovering new potential targets including ZNF395, a ccRCC master regulator. Loss of VHL , a ccRCC signature event, causes pervasive enhancer malfunction, with binding of enhancer-centric HIF2α and recruitment of histone acetyltransferase p300 at preexisting lineage-specifi c promoter-enhancer complexes. Cancer Discov; 7(11); 1284-305.
Regulatory enhancer elements in solid tumours remain poorly characterized. Here we apply micro-scale chromatin profiling to survey the distal enhancer landscape of primary gastric adenocarcinoma (GC), a leading cause of global cancer mortality. Integrating 110 epigenomic profiles from primary GCs, normal gastric tissues and cell lines, we highlight 36,973 predicted enhancers and 3,759 predicted super-enhancers respectively. Cell-line-defined super-enhancers can be subclassified by their somatic alteration status into somatic gain, loss and unaltered categories, each displaying distinct epigenetic, transcriptional and pathway enrichments. Somatic gain super-enhancers are associated with complex chromatin interaction profiles, expression patterns correlated with patient outcome and dense co-occupancy of the transcription factors CDX2 and HNF4α. Somatic super-enhancers are also enriched in genetic risk SNPs associated with cancer predisposition. Our results reveal a genome-wide reprogramming of the GC enhancer and super-enhancer landscape during tumorigenesis, contributing to dysregulated local and regional cancer gene expression.
BackgroundCarcinoma of the oral tongue (OTSCC) is the most common malignancy of the oral cavity, characterized by frequent recurrence and poor survival. The last three decades has witnessed a change in the OTSCC epidemiological profile, with increasing incidence in younger patients, females and never-smokers. Here, we sought to characterize the OTSCC genomic landscape and to determine factors that may delineate the genetic basis of this disease, inform prognosis and identify targets for therapeutic intervention.MethodsSeventy-eight cases were subjected to whole-exome (n = 18) and targeted deep sequencing (n = 60).ResultsWhile the most common mutation was in TP53, the OTSCC genetic landscape differed from previously described cohorts of patients with head and neck tumors: OTSCCs demonstrated frequent mutations in DST and RNF213, while alterations in CDKN2A and NOTCH1 were significantly less frequent. Despite a lack of previously reported NOTCH1 mutations, integrated analysis showed enrichments of alterations affecting Notch signaling in OTSCC. Importantly, these Notch pathway alterations were prognostic on multivariate analyses. A high proportion of OTSCCs also presented with alterations in drug targetable and chromatin remodeling genes. Patients harboring mutations in actionable pathways were more likely to succumb from recurrent disease compared with those who did not, suggesting that the former should be considered for treatment with targeted compounds in future trials.ConclusionsOur study defines the Asian OTSCC mutational landscape, highlighting the key role of Notch signaling in oral tongue tumorigenesis. We also observed somatic mutations in multiple therapeutically relevant genes, which may represent candidate drug targets in this highly lethal tumor type.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0219-2) contains supplementary material, which is available to authorized users.
Transcript-level quantification is often measured across two groups of patients to aid the discovery of biomarkers and detection of biological mechanisms involving these biomarkers. Statistical tests lack power and false discovery rate is high when sample size is small. Yet, many experiments have very few samples (≤ 5). This creates the impetus for a method to discover biomarkers and mechanisms under very small sample sizes. We present a powerful method, ESSNet, that is able to identify subnetworks consistently across independent datasets of the same disease phenotypes even under very small sample sizes. The key idea of ESSNet is to fragment large pathways into smaller subnetworks and compute a statistic that discriminates the subnetworks in two phenotypes. We do not greedily select genes to be included based on differential expression but rely on gene-expression-level ranking within a phenotype, which is shown to be stable even under extremely small sample sizes. We test our subnetworks on null distributions obtained by array rotation; this preserves the gene-gene correlation structure and is suitable for datasets with small sample size allowing us to consistently predict relevant subnetworks even when sample size is small. For most other methods, this consistency drops to less than 10% when we test them on datasets with only two samples from each phenotype, whereas ESSNet is able to achieve an average consistency of 58% (72% when we consider genes within the subnetworks) and continues to be superior when sample size is large. We further show that the subnetworks identified by ESSNet are highly correlated to many references in the biological literature. ESSNet and supplementary material are available at: http://compbio.ddns.comp.nus.edu.sg:8080/essnet .
ObjectiveGenomic structural variations (SVs) causing rewiring of cis-regulatory elements remain largely unexplored in gastric cancer (GC). To identify SVs affecting enhancer elements in GC (enhancer-based SVs), we integrated epigenomic enhancer profiles revealed by paired-end H3K27ac ChIP-sequencing from primary GCs with tumour whole-genome sequencing (WGS) data (PeNChIP-seq/WGS).DesignWe applied PeNChIP-seq to 11 primary GCs and matched normal tissues combined with WGS profiles of >200 GCs. Epigenome profiles were analysed alongside matched RNA-seq data to identify tumour-associated enhancer-based SVs with altered cancer transcription. Functional validation of candidate enhancer-based SVs was performed using CRISPR/Cas9 genome editing, chromosome conformation capture assays (4C-seq, Capture-C) and Hi-C analysis of primary GCs.ResultsPeNChIP-seq/WGS revealed ~150 enhancer-based SVs in GC. The majority (63%) of SVs linked to target gene deregulation were associated with increased tumour expression. Enhancer-based SVs targeting CCNE1, a key driver of therapy resistance, occurred in 8% of patients frequently juxtaposing diverse distal enhancers to CCNE1 proximal regions. CCNE1-rearranged GCs were associated with high CCNE1 expression, disrupted CCNE1 topologically associating domain (TAD) boundaries, and novel TAD interactions in CCNE1-rearranged primary tumours. We also observed IGF2 enhancer-based SVs, previously noted in colorectal cancer, highlighting a common non-coding genetic driver alteration in gastric and colorectal malignancies.ConclusionIntegrated paired-end NanoChIP-seq and WGS of gastric tumours reveals tumour-associated regulatory SV in regions associated with both simple and complex genomic rearrangements. Genomic rearrangements may thus exploit enhancer-hijacking as a common mechanism to drive oncogene expression in GC.
Previous studies have shown that each edible oil type has its own characteristic fatty acid profile; however, no method has yet been described allowing the identification of oil types simply based on this characteristic. Moreover, the fatty acid profile of a specific oil type can be mimicked by a mixture of 2 or more oil types. This has led to fraudulent oil adulteration and intentional mislabeling of edible oils threatening food safety and endangering public health. Here, we present a machine learning method to uncover fatty acid patterns discriminative for ten different plant oil types and their intra-variability. We also describe a supervised end-to-end learning method that can be generalized to oil composition of any given mixtures. Trained on a large number of simulated oil mixtures, independent test dataset validation demonstrates that the model has a 50th percentile absolute error between 1.4–1.8% and a 90th percentile error of 4–5.4% for any 3-way mixtures of the ten oil types. The deep learning model can also be further refined with on-line training. Because oil-producing plants have diverse geographical origins and hence slightly varying fatty acid profiles, an online-training method provides also a way to capture useful knowledge presently unavailable. Our method allows the ability to control product quality, determining the fair price of purchased oils and in-turn allowing health-conscious consumers the future of accurate labeling.
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