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

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.

项目概要 染色体外 DNA (ecDNA) 存在于 40% 的肿瘤中，但在正常细胞中很少发现。重要的是， 它们含有并表达源自染色体序列的扩增癌基因。相比之下 染色体、ecDNA 在细胞分裂过程中不均匀地分离到子细胞，因此可以在 肿瘤内单个细胞的高拷贝数。这导致了肿瘤内异质性（ITH）， 可以赋予肿瘤细胞亚群选择性生长优势并使其对癌症治疗产生抵抗力。尽管 之前的研究主要集中在染色体突变的ITH如何促进肿瘤进化，但很少有研究 了解 ecDNA 如何影响肿瘤进化和患者预后。解决 ecDNA 如何 有助于 ITH 和肿瘤进化，目标 1 将确定源自细胞系的 ecDNA 的 ITH 患者匹配的原发性和复发性胶质母细胞瘤，采用单细胞 DNA 测序 (scDNA-seq) 之前已经生成了标准批量短读长全基因组测序（WGS）数据。到 为了克服在 scDNA-seq 数据中检测单个 ecDNA 的技术挑战，我们将采用 在 scDNA-seq 中高拷贝数片段之间使用“断点”的替代监督方法 数据作为 ecDNA 断点的替代，并将这些数据与已识别的 ecDNA 断点相交 参考集中的序列。这种方法将使我们能够研究 ecDNA 驱动的 ITH 和进化 来自纵向胶质母细胞瘤的细胞系之间的单细胞。我们也将应用这个 方法利用现有的 scDNA-seq 数据集来评估 ecDNA 的存在。当前的计算工具 用于预测标准批量短读长 WGS 数据中的 ecDNA 的 ecDNA 测定能力有限 单细胞断点；因此，我们预计我们提出的方法虽然概念上很简单，但将会 这对提高我们对 ecDNA 如何在肿瘤细胞内进化的理解产生了重大影响。在目标 2 中， 代表多种癌症类型的大量公开可用的肿瘤批量 WGS 数据集将 用于更广泛地表征 ecDNA 及其对肿瘤进化的影响。我们将表演 使用许多肿瘤对 ecDNA 和其他基因组特征进行综合分析来表征 ecDNA 并 推断其形成背后的潜在分子机制。我们将构建一个机器学习分类器 可以使用非 WGS 数据（即全外显子组和 RNA 测序）预测 ecDNA 的存在 已成为对患者肿瘤进行测序的主要策略，因此比 全基因组测序。我们还将系统地分析许多单时间点和纵向肿瘤样本，以 描述 ecDNA 对肿瘤进化选择压力的影响。总体而言，完成这些 目标将极大地增进我们对肿瘤进化中 ecDNA 的理解，从而揭示如何 ecDNA 影响患者的治疗结果并最终为新型癌症疗法奠定基础。