Dataset Information

Discovery of Cancer Driver Long Noncoding RNAs across 1112 Tumour Genomes: New Candidates and Distinguishing Features.

ABSTRACT: Long noncoding RNAs (lncRNAs) represent a vast unexplored genetic space that may hold missing drivers of tumourigenesis, but few such "driver lncRNAs" are known. Until now, they have been discovered through changes in expression, leading to problems in distinguishing between causative roles and passenger effects. We here present a different approach for driver lncRNA discovery using mutational patterns in tumour DNA. Our pipeline, ExInAtor, identifies genes with excess load of somatic single nucleotide variants (SNVs) across panels of tumour genomes. Heterogeneity in mutational signatures between cancer types and individuals is accounted for using a simple local trinucleotide background model, which yields high precision and low computational demands. We use ExInAtor to predict drivers from the GENCODE annotation across 1112 entire genomes from 23 cancer types. Using a stratified approach, we identify 15 high-confidence candidates: 9 novel and 6 known cancer-related genes, including MALAT1, NEAT1 and SAMMSON. Both known and novel driver lncRNAs are distinguished by elevated gene length, evolutionary conservation and expression. We have presented a first catalogue of mutated lncRNA genes driving cancer, which will grow and improve with the application of ExInAtor to future tumour genome projects.

SUBMITTER: Lanzos A

PROVIDER: S-EPMC5269722 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Discovery of Cancer Driver Long Noncoding RNAs across 1112 Tumour Genomes: New Candidates and Distinguishing Features.

Lanzós Andrés A Carlevaro-Fita Joana J Mularoni Loris L Reverter Ferran F Palumbo Emilio E Guigó Roderic R Johnson Rory R

Scientific reports 20170127

Long noncoding RNAs (lncRNAs) represent a vast unexplored genetic space that may hold missing drivers of tumourigenesis, but few such "driver lncRNAs" are known. Until now, they have been discovered through changes in expression, leading to problems in distinguishing between causative roles and passenger effects. We here present a different approach for driver lncRNA discovery using mutational patterns in tumour DNA. Our pipeline, ExInAtor, identifies genes with excess load of somatic single nuc ...[more]

PMID: 28128360

Similar Datasets

Project description:We analyzed several features of five currently available delta-proteobacterial genomes, including two aerobic bacteria exhibiting predatory behavior and three anaerobic sulfate-reducing bacteria. The delta genomes are distinguished from other bacteria by several properties: (i) The delta genomes contain two "giant" S1 ribosomal protein genes in contrast to all other bacterial types, which encode a single or no S1; (ii) in most delta-proteobacterial genomes the major ribosomal protein (RP) gene cluster is near the replication terminus whereas most bacterial genomes place the major RP cluster near the origin of replication; (iii) the delta genomes possess the rare combination of discriminating asparaginyl and glutaminyl tRNA synthetase (AARS) together with the amido-transferase complex (Gat CAB) genes that modify Asp-tRNA(Asn) into Asn-tRNA(Asn) and Glu-tRNA(Gln) into Gln-tRNA(Gln); (iv) the TonB receptors and ferric siderophore receptors that facilitate uptake and removal of complex metals are common among delta genomes; (v) the anaerobic delta genomes encode multiple copies of the anaerobic detoxification protein rubrerythrin that can neutralize hydrogen peroxide; and (vi) sigma(54) activators play a more important role in the delta genomes than in other bacteria. delta genomes have a plethora of enhancer binding proteins that respond to environmental and intracellular cues, often as part of two-component systems; (vii) delta genomes encode multiple copies of metallo-beta-lactamase enzymes; (viii) a host of secretion proteins emphasizing SecA, SecB, and SecY may be especially useful in the predatory activities of Myxococcus xanthus; (ix) delta proteobacteria drive many multiprotein machines in their periplasms and outer membrane, including chaperone-feeding machines, jets for slime secretion, and type IV pili. Bdellovibrio replicates in the periplasm of prey cells. The sulfate-reducing delta proteobacteria metabolize hydrogen and generate a proton gradient by electron transport. The predicted highly expressed genes from delta genomes reflect their different ecologies, metabolic strategies, and adaptations.

Project description:BACKGROUND: We are developing a cross-species comparison strategy to distinguish between cancer driver- and passenger gene alteration candidates, by utilizing the difference in genomic location of orthologous genes between the human and other mammals. As an initial test of this strategy, we conducted a pilot study with human colorectal cancer (CRC) and its mouse model C57BL/6J ApcMin/+, focusing on human 5q22.2 and 18q21.1-q21.2. METHODS: We first performed bioinformatics analysis on the evolution of 5q22.2 and 18q21.1-q21.2 regions. Then, we performed exon-targeted sequencing, real time quantitative polymerase chain reaction (qPCR), and real time quantitative reverse transcriptase PCR (qRT-PCR) analyses on a number of genes of both regions with both human and mouse colon tumors. RESULTS: These two regions (5q22.2 and 18q21.1-q21.2) are frequently deleted in human CRCs and encode genuine colorectal tumor suppressors APC and SMAD4. They also encode genes such as MCC (mutated in colorectal cancer) with their role in CRC etiology unknown. We have discovered that both regions are evolutionarily unstable, resulting in genes that are clustered in each human region being found scattered at several distinct loci in the genome of many other species. For instance, APC and MCC are within 200 kb apart in human 5q22.2 but are 10 Mb apart in the mouse genome. Importantly, our analyses revealed that, while known CRC driver genes APC and SMAD4 were disrupted in both human colorectal tumors and tumors from ApcMin/+ mice, the questionable MCC gene was disrupted in human tumors but appeared to be intact in mouse tumors. CONCLUSIONS: These results indicate that MCC may not actually play any causative role in early colorectal tumorigenesis. We also hypothesize that its disruption in human CRCs is likely a mere result of its close proximity to APC in the human genome. Expanding this pilot study to the entire genome may identify more questionable genes like MCC, facilitating the discovery of new CRC driver gene candidates.

Project description:BackgroundIn somatic cancer genomes, delineating genuine driver mutations against a background of multiple passenger events is a challenging task. The difficulty of determining function from sequence data and the low frequency of mutations are increasingly hindering the search for novel, less common cancer drivers. The accumulation of extensive amounts of data on somatic point and copy number alterations necessitates the development of systematic methods for driver mutation analysis.ResultsWe introduce a framework for detecting driver mutations via functional network analysis, which is applied to individual genomes and does not require pooling multiple samples. It probabilistically evaluates 1) functional network links between different mutations in the same genome and 2) links between individual mutations and known cancer pathways. In addition, it can employ correlations of mutation patterns in pairs of genes. The method was used to analyze genomic alterations in two TCGA datasets, one for glioblastoma multiforme and another for ovarian carcinoma, which were generated using different approaches to mutation profiling. The proportions of drivers among the reported de novo point mutations in these cancers were estimated to be 57.8% and 16.8%, respectively. The both sets also included extended chromosomal regions with synchronous duplications or losses of multiple genes. We identified putative copy number driver events within many such segments. Finally, we summarized seemingly disparate mutations and discovered a functional network of collagen modifications in the glioblastoma. In order to select the most efficient network for use with this method, we used a novel, ROC curve-based procedure for benchmarking different network versions by their ability to recover pathway membership.ConclusionsThe results of our network-based procedure were in good agreement with published gold standard sets of cancer genes and were shown to complement and expand frequency-based driver analyses. On the other hand, three sequence-based methods applied to the same data yielded poor agreement with each other and with our results. We review the difference in driver proportions discovered by different sequencing approaches and discuss the functional roles of novel driver mutations. The software used in this work and the global network of functional couplings are publicly available at http://research.scilifelab.se/andrej_alexeyenko/downloads.html.

Project description:BACKGROUND:Most statistical methods used to identify cancer driver genes are either biased due to choice of assumed parametric models or insensitive to directional relationships important for causal inference. To overcome modeling biases and directional insensitivity, a recent statistical functional chi-squared test (FunChisq) detects directional association via model-free functional dependency. FunChisq examines patterns pointing from independent to dependent variables arising from linear, non-linear, or many-to-one functional relationships. Meanwhile, the Functional Annotation of Mammalian Genome 5 (FANTOM5) project surveyed gene expression at over 200,000 transcription start sites (TSSs) in nearly all human tissue types, primary cell types, and cancer cell lines. The data cover TSSs originated from both coding and noncoding genes. For the vast uncharacterized human TSSs that may exhibit complex patterns in cancer versus normal tissues, the model-free property of FunChisq provides us an unprecedented opportunity to assess the evidence for a gene's directional effect on human cancer. RESULTS:We first evaluated FunChisq and six other methods using 719 curated cancer genes on the FANTOM5 data. FunChisq performed best in detecting known cancer driver genes from non-cancer genes. We also show the capacity of FunChisq to reveal non-monotonic patterns of functional association, to which typical differential analysis methods such as t-test are insensitive. Further applying FunChisq to screen unannotated TSSs in FANTOM5, we predicted 1108 putative cancer driver noncoding RNAs, stronger than 90% of curated cancer driver genes. Next, we compared leukemia samples against other samples in FANTOM5 and FunChisq predicted 332/79 potential biomarkers for lymphoid/myeloid leukemia, stronger than the TSSs of all 87/100 known driver genes in lymphoid/myeloid leukemia. CONCLUSIONS:This study demonstrated the advantage of FunChisq in revealing directional association, especially in detecting non-monotonic patterns. Here, we also provide the most comprehensive catalog of high-quality biomarkers that may play a causative role in human cancers, including putative cancer driver noncoding RNAs and lymphoid/myeloid leukemia specific biomarkers.

Dataset Information

Discovery of Cancer Driver Long Noncoding RNAs across 1112 Tumour Genomes: New Candidates and Distinguishing Features.

Publications

Discovery of Cancer Driver Long Noncoding RNAs across 1112 Tumour Genomes: New Candidates and Distinguishing Features.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets