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首页> 外文期刊>BMC Genomics >Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers
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Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers

机译：使用预测的癌症突变功能影响揭示癌症中的选择。申请提名癌症驾驶员

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Every malignant tumor has a unique spectrum of genomic alterations including numerous protein mutations. There are also hundreds of personal germline variants to be taken into account. The combinatorial diversity of potential cancer-driving events limits the applicability of statistical methods to determine tumor-specific "driver" alterations among an overwhelming majority of "passengers". An alternative approach to determining driver mutations is to assess the functional impact of mutations in a given tumor and predict drivers based on a numerical value of the mutation impact in a particular context of genomic alterations.Recently, we introduced a functional impact score, which assesses the mutation impact by the value of entropic disordering of the evolutionary conservation patterns in proteins. The functional impact score separates disease-associated variants from benign polymorphisms with an accuracy of ~80%. Can the score be used to identify functionally important non-recurrent cancer-driver mutations? Assuming that cancer-drivers are positively selected in tumor evolution, we investigated how the functional impact score correlates with key features of natural selection in cancer, such as the non-uniformity of distribution of mutations, the frequency of affected tumor suppressors and oncogenes, the frequency of concurrent alterations in regions of heterozygous deletions and copy gain; as a control, we used presumably non-selected silent mutations. Using mutations of six cancers studied in TCGA projects, we found that predicted high-scoring functional mutations as well as truncating mutations tend to be evolutionarily selected as compared to low-scoring and silent mutations. This result justifies prediction of mutations-drivers using a shorter list of predicted high-scoring functional mutations, rather than the "long tail" of all mutations.
机译：每个恶性肿瘤都有独特的基因组变化谱，其中包括许多蛋白质突变。还有数百种个人种系变体要考虑在内。潜在的癌症驾驶事件的组合多样性限制了统计方法在绝大多数“乘客”中确定肿瘤特异性“驾驶员”改变的适用性。确定驱动程序突变的另一种方法是评估给定肿瘤中突变的功能影响并根据基因组改变的特定情况下突变影响的数值预测驱动程序。最近，我们引入了功能影响评分，用于评估熵影响无序的蛋白质进化保守模式的价值影响突变。功能影响评分可将疾病相关变异与良性多态性分开，准确度约为80％。该分数可以用于识别功能上重要的非复发性癌症驱动基因突变吗？假设在癌症发展过程中积极选择了癌症驱动因子，我们研究了功能影响评分与癌症自然选择的关键特征如何相关，例如突变分布的不均匀性，受影响的肿瘤抑制基因和癌基因的频率，杂合缺失和复制增益区域同时发生改变的频率;作为对照，我们使用了未选择的沉默突变。使用在TCGA项目中研究的六种癌症的突变，我们发现与低得分和沉默突变相比，预测性的高得分功能突变以及截短突变倾向于在进化上进行选择。该结果使用较短的预测的高分功能突变列表，而不是所有突变的“长尾巴”来证明对突变驱动因子的预测是正确的。

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