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

2016 ◽  
Author(s):  
Andrés Lanzós ◽  
Joana Carlevaro-Fita ◽  
Loris Mularoni ◽  
Ferran Reverter ◽  
Emilio Palumbo ◽  
...  
Keyword(s):  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Single Nucleotide Variants ◽  
Mutational Signatures ◽  
Single Nucleotide ◽  
Cancer Driver ◽  
Cancer Types ◽  
Distinguishing Features ◽  
Genome Projects ◽  
Excess Load

AbstractLong 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.

scholarly journals Tumour mutations in long noncoding RNAs that enhance cell fitness

2021 ◽  
Author(s):  
Roberta Esposito ◽  
Andres Lanzos ◽  
Taisia Polidori ◽  
Hugo Guillen-Ramirez ◽  
Bernard Merlin ◽  
...  
Keyword(s):  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Protein Coding ◽  
Genomic Features ◽  
Coding Regions ◽  
Lncrna Neat1

Tumour DNA contains thousands of single nucleotide variants (SNVs) in non-protein-coding regions, yet it remains unclear which are driver mutations that promote cell fitness. Amongst the most highly mutated non-coding elements are long noncoding RNAs (lncRNAs), which can promote cancer and may be targeted therapeutically. We here searched for evidence that driver mutations may act through alteration of lncRNA function. Using an integrative driver discovery algorithm, we analysed single nucleotide variants (SNVs) from 2583 primary tumours and 3527 metastases to reveal 54 candidate driver lncRNAs (FDR<0.1). Their relevance is supported by enrichment for previously-reported cancer genes and by clinical and genomic features. Using knockdown and transgene overexpression, we show that tumour SNVs in two novel lncRNAs can boost cell fitness. Researchers have noted particularly high yet unexplained mutation rates in the iconic cancer lncRNA, NEAT1. We apply in cellulo mutagenesis by CRISPR-Cas9 to identify vulnerable regions of NEAT1 where SNVs reproducibly increase cell fitness in both transformed and normal backgrounds. In particular, mutations in the 5-prime region of NEAT1 alter ribonucleoprotein assembly and boost the population of subnuclear paraspeckles. Together, this work reveals function-altering somatic lncRNA mutations as a new route to enhanced cell fitness during transformation and metastasis.

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

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Andrés Lanzós ◽  
Joana Carlevaro-Fita ◽  
Loris Mularoni ◽  
Ferran Reverter ◽  
Emilio Palumbo ◽  
...  
Keyword(s):  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Cancer Driver ◽  
Distinguishing Features

scholarly journals Systematic identification of non-coding somatic single nucleotide variants associated with altered transcription and DNA methylation in adult and pediatric cancers

NAR Cancer ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Fengju Chen ◽  
Yiqun Zhang ◽  
Chad J Creighton
Keyword(s):  
Dna Methylation ◽  
Brain Tumor ◽  
Low Grade ◽  
Single Nucleotide Variants ◽  
Multiple Cancer ◽  
Single Nucleotide ◽  
Altered Expression ◽  
Wide Range ◽  
Mutational Hotspots ◽  
Cancer Types

Abstract Whole-genome sequencing combined with transcriptomics can reveal impactful non-coding single nucleotide variants (SNVs) in cancer. Here, we developed an integrative analytical approach that, as a first step, identifies genes altered in expression or DNA methylation in association with nearby somatic SNVs, in contrast to alternative approaches that first identify mutational hotspots. Using genomic datasets from the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium and the Children's Brain Tumor Tissue Consortium (CBTTC), we identified hundreds of genes and associated CpG islands for which the nearby presence of a non-coding somatic SNV recurrently associated with altered expression or DNA methylation, respectively. Genomic regions upstream or downstream of genes, gene introns and gene untranslated regions were all involved. The PCAWG adult cancer cohort yielded different significant SNV-expression associations from the CBTTC pediatric brain tumor cohort. The SNV-expression associations involved a wide range of cancer types and histologies, as well as potential gain or loss of transcription factor binding sites. Notable genes with SNV-associated increased expression include TERT, COPS3, POLE2 and HDAC2—involving multiple cancer types—MYC, BCL2, PIM1 and IGLL5—involving lymphomas—and CYHR1—involving pediatric low-grade gliomas. Non-coding somatic SNVs show a major role in shaping the cancer transcriptome, not limited to mutational hotspots.

scholarly journals Widespread localisation of long noncoding RNAs to ribosomes: Distinguishing features and evidence for regulatory roles.

2015 ◽  
Author(s):  
Juna Carlevaro-Fita ◽  
Anisa Rahim ◽  
Roderic Guigo ◽  
Leah Vardy ◽  
Rory Johnson
Keyword(s):  
Transcriptional Regulation ◽  
Noncoding Rnas ◽  
Cell Types ◽  
Long Noncoding Rnas ◽  
Open Reading Frames ◽  
Translation Machinery ◽  
Evolutionary Selection ◽  
Regulatory Interactions ◽  
Distinguishing Features ◽  
Reading Frames

The function of long noncoding RNAs (lncRNAs) depends on their location within the cell. While most studies to date have concentrated on their nuclear roles in transcriptional regulation, evidence is mounting that lncRNA also have cytoplasmic roles. Here we comprehensively map the cytoplasmic and ribosomal lncRNA population in a human cell. Three-quarters (74%) of lncRNAs are detected in the cytoplasm, the majority of which (62%) preferentially cofractionate with polyribosomes. Ribosomal lncRNA are highly expressed across tissues, under purifying evolutionary selection, and have cytoplasmic-to-nuclear ratios comparable to mRNAs and consistent across cell types. LncRNAs may be classified into three groups by their ribosomal interaction: non-ribosomal cytoplasmic lncRNAs, and those associated with either heavy or light polysomes. A number of mRNA-like features destin lncRNA for light polysomes, including capping and 5′UTR length, but not cryptic open reading frames or polyadenylation. Surprisingly, exonic retroviral sequences antagonise recruitment. In contrast, it appears that lncRNAs are recruited to heavy polysomes through basepairing to mRNAs. Finally, we show that the translation machinery actively degrades lncRNA. We propose that light polysomal lncRNAs are translationally engaged, while heavy polysomal lncRNAs are recruited indirectly. These findings point to extensive and reciprocal regulatory interactions between lncRNA and the translation machinery.

scholarly journals SomaticSignatures: Inferring Mutational Signatures from Single Nucleotide Variants

2014 ◽  
Author(s):  
Julian S. Gehring ◽  
Bernd Fischer ◽  
Michael Lawrence ◽  
Wolfgang Huber
Keyword(s):  
Large Scale ◽  
R Package ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Mutational Signatures ◽  
Single Nucleotide ◽  
Pattern Decomposition ◽  
Bioconductor Project ◽  
Mutational Processes

Mutational signatures are patterns in the occurrence of somatic single nucleotide variants (SNVs) that can reflect underlying mutational processes. The SomaticSignatures package provides flexible, interoperable, and easy-to-use tools that identify such signatures in cancer sequencing data. It facilitates large-scale, cross-dataset estimation of mutational signatures, implements existing methods for pattern decomposition, supports extension through user-defined methods and integrates with Bioconductor workflows. The R package SomaticSignatures is available as part of the Bioconductor project (R Core Team, 2014; Gentleman et al., 2004). Its documentation provides additional details on the methodology and demonstrates applications to biological datasets.

scholarly journals Transcription-coupled repair in Drosophila melanogaster is independent of the mismatch repair pathway

2020 ◽  
Author(s):  
Lauri Törmä ◽  
Claire Burny ◽  
Viola Nolte ◽  
Kirsten-André Senti ◽  
Christian Schlötterer
Keyword(s):  
Drosophila Melanogaster ◽  
Mismatch Repair ◽  
Repair Pathway ◽  
Single Nucleotide Variants ◽  
Mutational Signatures ◽  
Data Set ◽  
Single Nucleotide ◽  
Additional Exon ◽  
Recent Demonstration ◽  
Transcription Coupled Repair

AbstractTranscription-coupled repair (TCR) removes base damage on the transcribed strand of a gene to ensure a quick resumption of transcription. Based on the absence of key enzymes for TCR and empirical evidence, TCR was thought to be missing in Drosophila melanogaster. The recent demonstration of TCR in S2 cells raises the question about the involved genes. Since the mismatch repair (MMR) pathway serves a central role in TCR, at least in Escherichia coli, we studied the mutational signatures in flies with a deletion of the MMR gene spellchecker1 (spel1), a MutS homolog. Whole-genome sequencing of mutation accumulation (MA) lines obtained 7,345 new single nucleotide variants (SNVs) and 5,672 short indel mutations, the largest data set from an MA study in D. melanogaster. Based on the observed mutational strand-asymmetries, we conclude that TCR is still active without spel1. The operation of TCR is further confirmed by a negative association between mutation rate and gene expression. Surprisingly, the TCR signatures are detected for introns, but not for exons. We propose that an additional exon-specific repair pathway is masking the signature of TCR. This study presents the first step towards understanding the molecular basis of TCR in Drosophila melanogaster.

scholarly journals Comprehensive patient-level classification and quantification of driver events in TCGA PanCanAtlas cohorts

PLoS Genetics ◽  
2022 ◽  
Vol 18 (1) ◽  
pp. e1009996
Author(s):  
Alexey D. Vyatkin ◽  
Danila V. Otnyukov ◽  
Sergey V. Leonov ◽  
Aleksey V. Belikov
Keyword(s):  
Copy Number ◽  
Human Cancer ◽  
Single Nucleotide ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Patient Level ◽  
Tumour Suppressors ◽  
Chromosome Arm ◽  
Cancer Mutations ◽  
Cancer Types

There is a growing need to develop novel therapeutics for targeted treatment of cancer. The prerequisite to success is the knowledge about which types of molecular alterations are predominantly driving tumorigenesis. To shed light onto this subject, we have utilized the largest database of human cancer mutations–TCGA PanCanAtlas, multiple established algorithms for cancer driver prediction (2020plus, CHASMplus, CompositeDriver, dNdScv, DriverNet, HotMAPS, OncodriveCLUSTL, OncodriveFML) and developed four novel computational pipelines: SNADRIF (Single Nucleotide Alteration DRIver Finder), GECNAV (Gene Expression-based Copy Number Alteration Validator), ANDRIF (ANeuploidy DRIver Finder) and PALDRIC (PAtient-Level DRIver Classifier). A unified workflow integrating all these pipelines, algorithms and datasets at cohort and patient levels was created. We have found that there are on average 12 driver events per tumour, of which 0.6 are single nucleotide alterations (SNAs) in oncogenes, 1.5 are amplifications of oncogenes, 1.2 are SNAs in tumour suppressors, 2.1 are deletions of tumour suppressors, 1.5 are driver chromosome losses, 1 is a driver chromosome gain, 2 are driver chromosome arm losses, and 1.5 are driver chromosome arm gains. The average number of driver events per tumour increases with age (from 7 to 15) and cancer stage (from 10 to 15) and varies strongly between cancer types (from 1 to 24). Patients with 1 and 7 driver events per tumour are the most frequent, and there are very few patients with more than 40 events. In tumours having only one driver event, this event is most often an SNA in an oncogene. However, with increasing number of driver events per tumour, the contribution of SNAs decreases, whereas the contribution of copy-number alterations and aneuploidy events increases.

scholarly journals SomaticSignatures: inferring mutational signatures from single-nucleotide variants: Fig. 1.

2015 ◽  
pp. btv408 ◽  
Author(s):  
Julian S. Gehring ◽  
Bernd Fischer ◽  
Michael Lawrence ◽  
Wolfgang Huber
Keyword(s):  
Single Nucleotide Variants ◽  
Mutational Signatures ◽  
Single Nucleotide
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