The pan-cancer analysis of gain-of-functional mutations to identify the common oncogenic signatures in multiple cancers
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De novo loss‐ or gain‐of‐function mutations in KCNA2 cause epileptic encephalopathy Syrbe et al. (2015) Nat Genet 47(4):393–99
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We aimed to provide genetic proof for mutations in asparaginyl-tRNA synthetase (NARS1) and analyze their impact through the use of individual cell lines, neural progenitor cells, and molecular modelling.
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<p>The file contains a table demonstrating CSF3R targeted deep sequencing result</p>
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As more studies are turning to bioinformatic prediction programs to assess the potential impact of amino acid substitutions, it is relevant to evaluate the prediction results these programs give in genes that have been well-characterized for Mendelian diseases. Eight genes responsible for neurodegenerative disease with many identified mutations were sub-grouped into those that either have a gain or loss of function disease mechanism. Three prediction programs, PolyPhen, Panther and SIFT, were queried for the reported missense mutations. The mean percent of benign mutations was significantly higher in gain of function genes using the PolyPhen program (38% versus 21%, p=0.007). The probability that a gain of function mutation was predicted to have a damaging role was also significantly less using the Panther program (p=4.86x10(-12)). In contrast, there was no difference between gain and loss of function gene when the SIFT program was used. However, the most accurate distinction between gain and loss of function genes could be obtained when considering the mutations for which all three programs predicted the same result. Further, stratification of SOD1 mutations indicated that only the PolyPhen program could distinguish mutations that impaired enzymatic activity of SOD1 from those with near wildtype activity. The profile of benign and damaging changes from these genes will aid in the interpretation of bioinformatic prediction program results from missense mutations identified in novel genes.
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Carcinogenesis involves the acquisition of multiple genetic changes altering various cellular phenotypes. These changes occur within the fixed time period of a human lifespan, and mechanisms that accelerate this process are more likely to result in clinical cancers. Mutator mutations decrease genome stability and, hence, accelerate the accumulation of random mutations, including those in oncogenes and tumor suppressor genes. However, if the mutator mutation is not in itself oncogenic, acquiring that mutation would add an extra, potentially time-consuming step in carcinogenesis. We present a deterministic mathematical model that allows quantitative prediction of the efficiency of carcinogenesis with and without a mutator mutation occurring at any time point in the process. By focusing on the ratio of probabilities of pathways with and without mutator mutations within cell lineages, we can define the frequency or importance of mutator mutations in populations independently of absolute rates and circumvent the question of whether mutator mutations are "necessary" for cancers to evolve within a human lifetime. We analyze key parameters that predict the relative contribution of mutator mutants in carcinogenesis. Mechanisms of carcinogenesis involving mutator mutations are more likely if they occur early. Involvement of mutator mutations in carcinogenesis is favored by an increased initial mutation rate, by greater fold-increase in mutation rate due to the mutator mutation, by increased required steps in carcinogenesis, and by increased number of cell generations to the development of cancer.
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