A Dual Model for Prioritizing Cancer Mutations in the Non-coding Genome Based on Germline and Somatic Events.
A Dual Model for Prioritizing Cancer Mutations in the Non-coding Genome Based on Germline and Somatic Events.
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DOI：
10.1371/journal.pcbi.1004583
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发表时间：
2015-11
影响因子：
4.3
通讯作者：
Gautheret D
中科院分区：
文献类型：
--
作者：
Li J;Poursat MA;Drubay D;Motz A;Saci Z;Morillon A;Michiels S;Gautheret D
我们在这里讨论肿瘤基因组中非编码突变的优先排序问题。为此，我们创建了两个独立的计算模型。第一种（种系）模型基于群体SNP数据估计纯化选择。第二种（体细胞）模型基于全基因组肿瘤测序估计肿瘤突变密度。我们表明，每个模型都反映了一组不同的约束，作用于正常或肿瘤基因组，我们确定了最有助于这些约束的特定基因组特征。重要的是，我们表明体细胞突变模型携带独立的功能信息，可用于缩小可能与癌症进展相关的非编码区域。在此基础上，我们确定了非编码rna和mrna的非编码部分的位置，这些位置在种系中处于纯化选择状态，在肿瘤中不受突变保护，从而为未来检测表达的非编码基因组中的癌症驱动元件引入了一种新的策略。癌细胞经历了一个类似于任何活细胞的突变/选择过程。癌细胞DNA的大多数突变发生在所谓的“非编码”区域，占基因组长度的98.5%。确定哪些突变有助于癌细胞的适应性，对于识别新的“癌症驱动因素”将是重要的，这可能反过来导致未来的治疗。不幸的是，预测非编码DNA改变的影响仍然非常困难。在这项研究中，我们分析了数以百万计的非编码癌症突变，并显示癌症特异性突变模式可用于预测从突变中保存下来的非编码区域，因此可能对癌细胞存活很重要。将这些信息与种群数据相结合，我们提出了一个新的评分系统，该系统应该有助于在未来的研究中优先考虑重要的非编码突变。
We address here the issue of prioritizing non-coding mutations in the tumoral genome. To this aim, we created two independent computational models. The first (germline) model estimates purifying selection based on population SNP data. The second (somatic) model estimates tumor mutation density based on whole genome tumor sequencing. We show that each model reflects a different set of constraints acting either on the normal or tumor genome, and we identify the specific genome features that most contribute to these constraints. Importantly, we show that the somatic mutation model carries independent functional information that can be used to narrow down the non-coding regions that may be relevant to cancer progression. On this basis, we identify positions in non-coding RNAs and the non-coding parts of mRNAs that are both under purifying selection in the germline and protected from mutation in tumors, thus introducing a new strategy for future detection of cancer driver elements in the expressed non-coding genome. Cancer cells undergo a mutation/selection process that resembles that of any living cell. Most mutations in cancer cell DNA occur in the so-called "non-coding" regions that represent 98.5% of the genome length. Pinning down which of these mutations contribute to the fitness of cancer cells would be important for identifying new "cancer drivers", which may in turn lead to future treatments. Unfortunately, predicting the impact of a non-coding DNA alteration remains extremely difficult. In this study, we analyze millions of non-coding cancer mutations and show cancer-specific mutational patterns can be used to predict non-coding regions that are preserved from mutations and may thus be important for cancer cell survival. Combining this information with population data, we propose a new scoring system that should help prioritize important non-coding mutations in future studies.