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Tobacco smoking causes lung cancer1-3, a process that is driven by more than 60 carcinogens in cigarette smoke that directly damage and mutate DNA4,5. The profound effects of tobacco on the genome of lung cancer cells are well-documented6-10, but equivalent data for normal bronchial cells are lacking. Here we sequenced whole genomes of 632 colonies derived from single bronchial epithelial cells across 16 subjects. Tobacco smoking was the major influence on mutational burden, typically adding from 1,000 to 10,000 mutations per cell; massively increasing the variance both within and between subjects; and generating several distinct mutational signatures of substitutions and of insertions and deletions. A population of cells in individuals with a history of smoking had mutational burdens that were equivalent to those expected for people who had never smoked: these cells had less damage from tobacco-specific mutational processes, were fourfold more frequent in ex-smokers than current smokers and had considerably longer telomeres than their more-mutated counterparts. Driver mutations increased in frequency with age, affecting 4-14% of cells in middle-aged subjects who had never smoked. In current smokers, at least 25% of cells carried driver mutations and 0-6% of cells had two or even three drivers. Thus, tobacco smoking increases mutational burden, cell-to-cell heterogeneity and driver mutations, but quitting promotes replenishment of the bronchial epithelium from mitotically quiescent cells that have avoided tobacco mutagenesis.
Pubmed ID: 31996850 RIS Download
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Database to store and display somatic mutation information and related details and contains information relating to human cancers. The mutation data and associated information is extracted from the primary literature. In order to provide a consistent view of the data a histology and tissue ontology has been created and all mutations are mapped to a single version of each gene. The data can be queried by tissue, histology or gene and displayed as a graph, as a table or exported in various formats.
Some key features of COSMIC are:
* Contains information on publications, samples and mutations. Includes samples which have been found to be negative for mutations during screening therefore enabling frequency data to be calculated for mutations in different genes in different cancer types.
* Samples entered include benign neoplasms and other benign proliferations, in situ and invasive tumours, recurrences, metastases and cancer cell lines.
Software tool as single nucleotide variant expectation maximisation based mutation calling algorithm aimed at detecting somatic mutations in paired tumour and normal cancer samples. Algorithms for calling somatic mutations.
View all literature mentionsSoftware tool as cancer genome project insertion or deletion detection workflow for Pindel.
View all literature mentionsSoftware tool for analysis of one or more related BAM files of paired end sequencing to determine potential rearrangement breakpoints. Identifies breaks and attempts to assemble rearrangements.
View all literature mentionsSoftware R package as suite of dN/dS methods to quantify selection in cancer and somatic evolution. Contains functions to quantify dN/dS ratios for missense, nonsense and essential splice mutations, at level of individual genes, groups of genes or at whole genome level. Used to detect cancer driver genes on datasets.
View all literature mentionsSoftware tool for sequence mapping.The next version of BWA-MEM. Used for aligning sequencing reads against large reference genome.
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
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