A statistical framework to systematically characterize cancer driver mutations in noncoding genomic regions

项目总结 癌症基因组通常含有大量的体细胞突变。相对较少的驱动程序突变 实际上改变了肿瘤细胞中蛋白质的功能，而大多数突变被认为是功能上的 中性乘客突变。在过去的十年里，对癌症驱动因素突变的研究主要集中在 已经开发了用于编码突变的编码区和几种突变重要性算法。这个 非编码调节区的突变在肿瘤形成中的作用在很大程度上尚不清楚， 目前的突变重要性算法不能检测非编码区的驱动突变，这是由于 编码突变和非编码突变之间的生物学差异。正在出现的大型整体式- 基因组测序数据集(例如PCAWG和HMF数据集)为开发新的 突变重要性算法是专门为解释非编码区而设计的。 最近，我们开发了一种新的统计方法，用于识别编码区中的驱动程序突变 在核苷酸的背景下。关键的是，考虑突变周围的核苷酸背景不需要 与这些突变相关的功能后果的先验知识。因此，我们假设 将我们的核苷酸上下文模型推广到非编码区将发现新的非编码驱动因素 使用目前可用的突变意义方法无法检测到的突变。为 为此，我们将开发一个统计框架，该框架结合了编码之间的生物学差异 和非编码突变，这是专门为检测非编码区的驱动突变而设计的。 具体地说，我们将考虑乘客突变的上下文相关分布， 背景突变率，准确划分背景突变率，模拟序列组成 参考基因组，并解释覆盖范围的波动。然后我们将结合这些统计数据 通过计算其潜在概率的独立乘积来计算组件。我们将衍生出一个意义重大的 P值采用蒙特卡罗模拟方法，并使用FDR进行多重假设检验修正。这 策略将使我们能够准确地估计非编码基因组区域的体细胞突变的重要性。 接下来，我们将把这个统计框架应用于5523名肿瘤患者的全基因组测序数据，从而 推导出非编码区候选驱动突变的综合列表。最后，我们将调查 非编码突变是否在转录因子结合部位过度表达，调节基因表达 水平，诱导选择性剪接，或影响表观基因组状态。这个项目完成后，我们将拥有 开发并应用了一个统计框架，用于发现非编码中的重要体细胞突变 区域，并定义了非编码癌症基因组的突变图景。方方面面的方法 在这个项目中开发和应用将成为开源的，并在一个在线平台上开发。

PROJECT SUMMARY Cancer genomes typically harbor a substantial number of somatic mutations. Relatively few driver mutations actually alter the function of proteins in tumor cells, whereas most mutations are considered to be functionally neutral passenger mutations. Over the past decade, the search for cancer driver mutations has focused on coding regions and several mutational significance algorithms have been developed for coding mutations. The contribution of mutations in noncoding regulatory regions to tumor formation largely remains unknown and current mutational significance algorithms are not designed to detect driver mutations in noncoding regions, due to biological differences between coding and noncoding mutations. The emerging availability of large whole- genome sequencing datasets (e.g. PCAWG and HMF datasets) creates an ample opportunity to develop new mutational significance algorithms that are particularly designed for the interpretation of noncoding regions. Recently, we have developed a new statistical approach that identifies driver mutations in coding regions based on the nucleotide context. Critically, consideration of the nucleotide context around mutations does not require prior knowledge for functional consequences associated with these mutations. Hence, we hypothesize that generalizing our nucleotide context model to noncoding regions will uncover novel noncoding driver mutations that cannot be detected using the mutational significance approaches currently available. For this purpose, we will develop a statistical framework that incorporates the biological differences between coding and noncoding mutations and that is specifically designed to detect driver mutations in noncoding regions. Specifically, we will consider the context-dependent distribution of passenger mutations, modeling of the background mutation rate, accurately partition the background mutation rate, model the sequence composition of the reference genome, and account for coverage fluctuation. We will then combine these statistical components by computing an independent product of their underlying probabilities. We will derive a significance p-value using a Monte-Carlo simulation approach, and use FDR for multiple hypothesis test correction. This strategy will allow us to accurately estimate the significance of somatic mutations in noncoding genomic regions. We will next apply this statistical framework to whole-genome sequencing data of 5,523 tumor patients, thereby deriving a comprehensive list of candidate driver mutations in noncoding regions. Finally, we will investigate whether noncoding mutations are overrepresented in transcription factor binding sites, regulate gene expression levels, induce alternative splicing, or affect epigenomic states. Upon the completion of this project, we will have developed and applied a statistical framework for discovery of significant somatic mutations in noncoding regions, and defined the mutational landscape of the non-coding cancer genome. All aspects of the methods developed and applied in this project will be made open source and developed in an online platform.