Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, NY 10065, USA.
BMC Genomics. 2013;14 Suppl 3(Suppl 3):S8. doi: 10.1186/1471-2164-14-S3-S8. Epub 2013 May 28.
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.
每个恶性肿瘤都具有独特的基因组改变谱，包括许多蛋白质突变。还有数百个人种系变异需要考虑。潜在致癌事件的组合多样性限制了统计方法在确定绝大多数“乘客”中肿瘤特异性“驱动”改变的适用性。确定驱动突变的另一种方法是评估给定肿瘤中突变的功能影响，并根据特定基因组改变背景下突变影响的数值来预测驱动突变。最近，我们引入了一种功能影响评分，通过评估蛋白质进化保守模式的熵混乱值来评估突变的影响。功能影响评分可以将疾病相关变体与良性多态性区分开来，准确率约为 80%。评分能否用于识别功能重要的非重现性癌症驱动突变？假设癌症驱动因素在肿瘤进化中受到积极选择，我们研究了功能影响评分与癌症中自然选择的关键特征（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、杂合性缺失和拷贝增益区域中并发改变的频率）之间的相关性；作为对照，我们使用了推测的非选择沉默突变。使用 TCGA 项目研究的六种癌症的突变，我们发现与低评分和沉默突变相比，预测的高分功能突变和截断突变往往受到进化选择。这一结果证明了使用预测的高分功能突变的更短列表而不是所有突变的“长尾”来预测突变驱动是合理的。
