Characterization of extrachromosomal DNAs in tumors through computational analysis of single-cell and bulk sequencing data

通过单细胞和批量测序数据的计算分析来表征肿瘤中的染色体外 DNA

• 批准号: 10810168
• 负责人: Roel GW Verhaak
• 金额: $ 7.21万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-23 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10810168
• 关键词: Characterization; extrachromosomal; DNAs; tumors; through; Characterization; extrachromosomal; DNAs; tumors; through

PROJECT SUMMARY Extrachromosomal DNAs (ecDNAs) are found in 40% of tumors but rarely found in normal cells. Importantly, they contain and express amplified oncogenes derived from chromosomal sequences. In contrast to the chromosomes, ecDNAs segregate unequally to daughter cells during cell division and thus can accumulate at high copy numbers in individual cells within a tumor. This contributes to intratumor heterogeneity (ITH), which can give subsets of tumor cells a selective growth advantage and enable resistance to cancer treatment. While previous studies have focused on how ITH of chromosomal mutations contributes to tumor evolution, little is known about how ecDNAs might impact tumor evolution and patient outcomes. To address how ecDNAs contribute to ITH and tumor evolution, Aim 1 will determine the ITH of ecDNAs for cell lines derived from patient-matched primary and recurrent glioblastoma tumors for which single-cell DNA sequencing (scDNA-seq) and standard bulk short-read whole-genome sequencing (WGS) data have been previously generated. To overcome the technical challenge of detecting individual ecDNAs in scDNA-seq data, we will employ an alternative supervised approach of using `breakpoints' between high-copy number segments in the scDNA-seq data as surrogates for the ecDNA breakpoints and intersect these with the identified ecDNA breakpoint sequences in the reference sets. This approach will enable us to study ecDNA-driven ITH and evolution in single cells between the cell lines derived from the longitudinal glioblastoma tumors. We will also apply this approach to existing scDNA-seq datasets to assess the presence of ecDNAs. Current computational tools used to predict ecDNAs in standard bulk short-read WGS data have limited ability to determine the ecDNA breakpoints in single cells; thus, we anticipate that our proposed approach, while conceptually simple, will have a major impact on improving our understanding of how ecDNAs evolve within the cells of a tumor. In Aim 2, a large cohort of publicly available tumor bulk WGS datasets representing multiple cancer types will be leveraged to characterize ecDNAs more broadly and their effects on tumor evolution. We will perform integrative analysis of ecDNAs and other genomic features using many tumors to characterize ecDNAs and to infer the potential molecular mechanisms underlying their formation. We will build a machine learning classifier that can predict the presence of ecDNAs using non-WGS data (i.e., whole-exome and RNA sequencing) that have been a primary strategy for sequencing patient tumors, and therefore, are more widely available than WGS. We will also systematically analyze many single time point and longitudinal tumor samples to characterize the effects of ecDNAs on evolutionary selection pressures in tumors. Overall, completion of these Aims will greatly advance our understanding of ecDNAs in tumor evolution, thereby shedding light on how ecDNAs impact patient outcomes and ultimately establishing a basis for novel cancer therapeutics.

项目摘要 肿瘤外DNA（ECDNA）在40％的肿瘤中发现，但在正常细胞中很少发现。重要的是， 它们包含并表达从染色体序列衍生的肿瘤基因。与 染色体，ECDNA在细胞分裂过程中不平等地与子细胞分离，因此可以在 肿瘤中单个细胞中的高拷贝数。这有助于肿瘤内异质性（ITH），这 可以使肿瘤细胞子集具有选择性的生长优势，并能够抵抗癌症治疗。尽管 先前的研究集中于染色体突变的ITH如何促进肿瘤的演化，几乎没有 知道ECDNA如何影响肿瘤进化和患者结局。解决如何ecdnas AIM 1有助于ITH和肿瘤的演变，将确定来自ECDNA的ITH 单细胞DNA测序（SCDNA-SEQ）的患者匹配的原发性和复发性胶质母细胞瘤肿瘤 先前已经生成了标准的批量短阅读全基因组测序（WGS）数据。到 克服检测SCDNA-Seq数据中单个eCDNA的技术挑战，我们将采用 在scDNA-seq中使用“断点”段之间使用“断点”的替代监督方法 数据作为ECDNA断点的替代物，并将其与已识别的ECDNA断点相交 参考集中的序列。这种方法将使我们能够研究以ECDNA驱动的ITH和进化 来自纵向胶质母细胞瘤肿瘤的细胞系之间的单细胞。我们还将应用此 现有SCDNA-SEQ数据集的方法来评估ECDNA的存在。当前的计算工具 用于预测标准批量短阅读WGS数据中的ECDNA的确定能力有限 单细胞中的断点；因此，我们预计我们提出的方法在概念上很简单，但 对我们对肿瘤细胞内ECDNA如何发展的理解的重大影响。在AIM 2中 大量代表多种癌症类型的公共可用肿瘤散装WGS数据集将是 杠杆化以更广泛地表征ECDNA及其对肿瘤进化的影响。我们将表演 使用许多肿瘤来表征ECDNA的ECDNA和其他基因组特征的综合分析 推断出形成的潜在分子机制。我们将构建机器学习分类器 可以使用非WGS数据（即全异常和RNA测序）来预测ECDNA的存在 已经是对患者肿瘤进行测序的主要策略，因此比 WGS。我们还将系统地分析许多单个时间点和纵向肿瘤样本 表征ECDNA对肿瘤进化选择压力的影响。总体而言，这些完成 目标将大大提高我们对肿瘤进化中ECDNA的理解，从而阐明如何 ECDNA会影响患者的结果，并最终为新型癌症治疗剂建立基础。