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.

项目概要 癌症基因组通常含有大量体细胞突变。驱动突变相对较少 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性的 中性乘客突变。在过去的十年中，对癌症驱动突变的研究主要集中在 已经开发了编码区域和几种突变显着性算法来编码突变。这 非编码调控区突变对肿瘤形成的贡献在很大程度上仍然未知， 当前的突变显着性算法并非旨在检测非编码区域中的驱动突变，因为 编码和非编码突变之间的生物学差异。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）创造了充足的机会来开发新的 专为解释非编码区域而设计的突变显着性算法。 最近，我们开发了一种新的统计方法，可以根据编码区识别驱动突变 关于核苷酸背景。至关重要的是，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域将发现新的非编码驱动因素 使用目前可用的突变显着性方法无法检测到的突变。为了 为此，我们将开发一个统计框架，其中包含编码之间的生物学差异 和非编码突变，专门设计用于检测非编码区域的驱动突变。 具体来说，我们将考虑乘客突变的上下文相关分布、模型 背景突变率，准确划分背景突变率，对序列组成进行建模 参考基因组，并解释覆盖度波动。然后我们将结合这些统计数据 通过计算其基本概率的独立乘积来划分组件。我们将得出一个意义 p值使用蒙特卡罗模拟方法，并使用FDR进行多重假设检验校正。这 该策略将使我们能够准确估计非编码基因组区域体细胞突变的重要性。 接下来我们将把这个统计框架应用到5523名肿瘤患者的全基因组测序数据中，从而 得出非编码区域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位点中过度表达，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。该项目完成后，我们将拥有 开发并应用了一个统计框架来发现非编​​码中的显着体细胞突变 区域，并定义了非编码癌症基因组的突变景观。方法的各个方面 本项目中开发和应用的项目将开源并在在线平台上开发。