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

项目摘要 染色体外DNA（ecDNAs）在40%的肿瘤中发现，但在正常细胞中很少发现。重要的是， 它们含有并表达来自染色体序列的扩增的癌基因。相对于 染色体，ecDNAs在细胞分裂过程中不平等地分离到子细胞，因此可以在染色体上积累。 肿瘤内单个细胞的高拷贝数。这有助于肿瘤内异质性（ITH）， 可以使肿瘤细胞亚群具有选择性生长优势，并能够抵抗癌症治疗。而 以前的研究集中在染色体突变的ITH如何有助于肿瘤的演变，很少有 了解ecDNAs如何影响肿瘤演变和患者预后。为了解决ecDNAs 有助于ITH和肿瘤演变，Aim 1将决定来源于 患者匹配的原发性和复发性胶质母细胞瘤肿瘤，单细胞DNA测序（scDNA-seq） 和标准批量短读全基因组测序（WGS）数据已经在之前产生。到 为了克服在scDNA-seq数据中检测单个ecDNAs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”的另一种监督方法 数据作为ecDNA断裂点的替代物，并将这些数据与鉴定的ecDNA断裂点相交 序列在参考集中。这种方法将使我们能够研究ecDNA驱动的ITH和进化， 来源于纵向胶质母细胞瘤肿瘤的细胞系之间的单个细胞。我们也将应用这一点 使用现有的scDNA-seq数据集来评估ecDNAs的存在。当前的计算工具 用于预测标准批量短读WGS数据中的ecDNA的方法具有有限的确定ecDNA的能力 单细胞中的断点;因此，我们预计，我们提出的方法，而概念上简单，将有 这对提高我们对ecDNAs在肿瘤细胞内如何进化的理解产生了重大影响。在目标2中，a 代表多种癌症类型的公开可用的肿瘤块WGS数据集的大队列将被 利用更广泛地表征ecDNAs及其对肿瘤演变的影响。我们将执行 使用许多肿瘤对ecDNA和其他基因组特征进行综合分析，以表征ecDNA， 推断其形成的潜在分子机制。我们将构建一个机器学习分类器 其可以使用非WGS数据预测ecDNAs的存在（即，全外显子组和RNA测序）， 已经是对患者肿瘤进行测序的主要策略，因此， WGS我们还将系统地分析许多单个时间点和纵向肿瘤样本， 描述ecDNAs对肿瘤进化选择压力的影响。总的来说，完成这些 这些目标将极大地推进我们对ecDNAs在肿瘤演变中的理解，从而阐明如何在肿瘤中发挥作用。 ecDNAs影响患者的预后，并最终为新型癌症治疗奠定基础。

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.