Center for the Comprehensive Analysis of Cancer Somatic Copy-Number Alterations, Rearrangements, and Long-Read Sequencing Data

摘要 我们建议继续建立基因组学数据分析中心， 癌症中的结构变异，包括体细胞拷贝数改变（SCNA）， 重新安排，解决GDAN要求的副本编号能力。我们还添加 分析长读和链接读数据的能力，解决第二个能力。我们领导 整个癌症基因组图谱（TCGA）和第一次迭代的SCNA分析 基因组学数据分析网络（GDAN）。我们还共同领导了结构变化工作 国际癌症基因组联盟全基因组泛癌分析小组 （PCAWG）。为了实现这些目标，我们开发了一整套工具和深厚的专业知识 涵盖了癌症中SV分析的所有方面。具体而言，我们将实现五个目标： 目的1和2，我们将确定SCNA和重排概况，从短读（整体） 外显子组或全基因组）或长/连接读段测序数据，并确定种系 基因型、亲本单倍型和祖先群体。单倍型信息将用于 提高拷贝数分辨率。在目标3中，我们重建了肿瘤基因组及其 进化史我们评估样本的异质性，包括肿瘤纯度，倍性， 亚克隆改变和同源染色体的相位重排 所有对基因座之间的体细胞距离。利用这些数据，我们确定了DNA 损伤和修复，并推断肿瘤演变过程中发生的事件。在目标4中， 跨样本的数据，以识别影响肿瘤演变的SV和基因组位点，检测 与其他分子和临床特征的相关性，并评估潜在的DNA决定的 子类。此外，我们进行了关联和混合分析与生殖系 Aim 1中检测到的基因型。在目标5中，我们指出了我们将沉浸其中的方式 在GDAN内部，并在GDAN内部和更广泛的范围内传播我们的分析结果 社区在此范围内，我们提供单细胞和循环DNA的次要能力 分析.我们的GDAC将提供一个全面的分析结构变化的作用， 在GDAN样品中通过治疗的癌症发展和进展。我们还将 优化与更广泛的GDAN和科学界的互动，以最大限度地利用这些 数据

Abstract We propose to continue our established Genomics Data Analysis Center for the analysis of structural variants in cancer, including somatic copy-number alterations (SCNAs) and rearrangements, addressing the Copy Number competency required by GDAN. We also add capabilities to analyze long- and linked-read data, addressing a second competency. We led SCNA analyses throughout The Cancer Genome Atlas (TCGA) and the first iteration of the Genomics Data Analysis Network (GDAN). We also co-led the Structural Variations Working Group of the International Cancer Genome Consortium Pan-Cancer Analysis of Whole Genomes (PCAWG). For these efforts, we have developed a large suite of tools and deep expertise covering all aspects of analysis of SVs in cancer. Specifically, we will accomplish five Aims: In Aims 1 and 2, we will determine SCNA and rearrangement profiles from either short-read (whole exome or whole genome) or long-/linked-read sequencing data, and determine germline genotypes, parental haplotypes, and ancestry groups. The haplotype information will be used to improve our copy-number resolution. In Aim 3, we reconstruct the tumor genome and its evolutionary history. We evaluate sample heterogeneity including tumor purity, ploidy, and subclonal alterations, and phase rearrangements to homologous chromosomes—determining somatic distances between all pairs of loci. Using these data, we determine mechanisms of DNA damage and repair and infer the events that occurred over tumor evolution. In Aim 4, we integrate data across samples to identify SVs and genomic loci that impact tumor evolution, detect associations with other molecular and clinical features, and evaluate potential SCNA-determined subclasses. Moreover, we perform association and admixture analyses with the germline genotypes detected in Aim 1. In Aim 5, we indicate ways in which we will immerse ourselves within the GDAN and disseminate our analysis results both within the GDAN and to the wider community. Within this, we offer secondary competencies in single-cell and circulating DNA analysis. Our GDAC will provide a comprehensive analysis of the roles of structural variations in cancer development and progression through treatment among GDAN samples. We will also optimize interactions with the wider GDAN and scientific community to make maximal use of these data.