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

系统地表征非编码基因组区域中癌症驱动突变的统计框架

• 批准号: 10260680
• 负责人: Eliezer M Van Allen
• 金额: $ 17.7万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10260680
• 关键词: Address; Affect; Algorithms; Alternative Splicing; Attention; Binding Sites; Biological; Biological Process; Biology; Clinical; Code; Communities; Computational Biology; Computer Models; Data; Data Set; Development; Gene Expression; Gene Expression Regulation; Genomic Segment; Immunotherapy; Knowledge; Machine Learning; Malignant Neoplasms; Mediating; Methods; Microsatellite Instability; Modeling; Monte Carlo Method; Mutation; Nucleic Acid Regulatory Sequences; Nucleotides; Outcome; Patients; Pattern; Play; Positioning Attribute; Probability; Process; Role; Somatic Mutation; Statistical Models; Stratification; Testing; The Cancer Genome Atlas; Untranslated RNA; base; cancer genome; cancer immunotherapy; checkpoint therapy; clinical application; clinical effect; cohort; design; driver mutation; epigenomics; exome sequencing; genome sequencing; genome-wide; immune checkpoint blockade; immunogenicity; large datasets; malignant breast neoplasm; melanoma; mutant; neoantigens; neoplastic cell; novel; open source; predicting response; promoter; protein function; reference genome; response; targeted treatment; transcription factor; tumor; whole genome

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.

项目摘要 癌症基因组通常具有大量的体细胞突变。相对较少的驾驶员突变 实际上改变了蛋白质在肿瘤细胞中的功能，而大多数突变在功能上被认为是 中性乘客突变。在过去的十年中，寻找癌症驾驶员突变的重点是 编码区域和几种突变意义算法已开发用于编码突变。这 非编码调节区域中突变对肿瘤形成的贡献在很大程度上是未知的，并且 当前的突变意义算法并非旨在检测非编码区域的驾驶员突变， 在编码和非编码突变之间存在生物学差异。大型整体的新兴可用性 基因组测序数据集（例如PCAWG和HMF数据集）创造了足够的机会开发新的 突变意义算法是专门为解释非编码区域的解释而设计的。 最近，我们开发了一种新的统计方法，该方法识别基于编码区域的驱动程序突变 在核苷酸上下文。至关重要的是，考虑突变周围的核苷酸环境不需要 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域将发现新的非编码驱动器 无法使用当前可用的突变意义方法检测到的突变。为了 此目的，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，并专门设计用于检测非编码区域的驾驶员突变。 具体而言，我们将考虑乘客突变的上下文依赖性分布，建模 背景突变率，准确地分配背景突变速率，对序列组成进行建模 参考基因组，并解释覆盖率波动。然后，我们将结合这些统计 通过计算其潜在概率的独立产品来组件。我们将获得意义 使用Monte-Carlo模拟方法的P值，并使用FDR进行多种假设检验校正。这 策略将使我们能够准确估计不编码基因组区域体细胞突变的重要性。 接下来，我们将将此统计框架应用于5,523名肿瘤患者的全基因组测序数据，从而 得出非编码区域中候选驾驶员突变的全面列表。最后，我们将调查 非编码突变是否在转录因子结合位点过多代表，调节基因表达 水平，诱导替代剪接或影响表观基因组状态。该项目完成后，我们将拥有 开发并应用了一个统计框架，以发现非编码的重要体细胞突变 区域，并定义了非编码癌症基因组的突变景观。方法的所有方面 在该项目中开发和应用将被开源并在在线平台中开发。