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

癌症体细胞拷贝数改变、重排和长读长测序数据综合分析中心

• 批准号: 10491146
• 负责人: RAMEEN BEROUKHIM
• 金额: $ 37.63万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-20 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10491146
• 关键词: Address; Admixture; Algorithms; Automobile Driving; Cancer Diagnostics; Cells; Chromosome Structures; Chromosome abnormality; Chromosomes; Classification Scheme; Clinical; Clinical Trials; Communities; Competence; Complex; Custom; DNA; DNA Repair; DNA Sequence Rearrangement; DNA analysis; Data; Development; Diagnosis; Event; Evolution; Genome; Genome Data Analysis Center; Genome Data Analysis Network; Genomics; Genotype; Haplotypes; Heterogeneity; Human; Human Resources; Immersion; Inflammatory Infiltrate; International; Leadership; Link; Malignant Neoplasms; Methods; Methylation; Molecular; Mutation; Mutation Analysis; Patients; Pattern; Phase; Physicians; Play; Ploidies; RNA; Recording of previous events; Recurrence; Repetitive Sequence; Research; Resolution; Role; SNP genotyping; Sampling; Scientist; Shapes; Structure; The Cancer Genome Atlas; Therapeutic; Variant; Visualization software; Work; admixture mapping; arm; base; cancer genome; cancer subtypes; cell free DNA; chromothripsis; circulating DNA; exome; experience; genome sequencing; genome wide association study; genomic locus; improved; innovation; insight; molecular subtypes; nanopore; novel therapeutic intervention; reconstruction; repaired; single cell analysis; tool; tumor; whole genome; working group

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.

抽象的 我们建议继续我们建立的基因组数据分析中心，以分析 癌症的结构变异，包括体细胞拷贝数（SCNA）和 重新安排，解决GDAN要求的拷贝数能力。我们还添加 分析长期和链接的读取数据的功能，以解决第二能力。我们领导 SCNA在整个癌症基因组图集（TCGA）中进行了分析和第一次迭代 基因组数据分析网络（GDAN）。我们还共同领导了工作的结构变化 全基因组的国际癌症基因组泛财团分析 （PCAWG）。为了这些努力，我们开发了大量工具和深厚的专业知识 涵盖癌症中SVS分析的所有方面。具体来说，我们将实现五个目标： 目的1和2，我们将确定SCNA和重新排列的轮廓。 外显子或整个基因组）或长/链接阅读的测序数据，并确定种系 基因型，父母单倍型和祖先。单倍型信息将用于 改善我们的副本分辨率。在AIM 3中，我们重建肿瘤基因组及其ITS基因组 进化史。我们评估样品异质性，包括肿瘤纯度，倍性和 亚克隆的改变和同源染色体的相位重排 - 确定 所有对基因座之间的躯体距离。使用这些数据，我们确定DNA的机制 损坏和修复并推断出在肿瘤演化中发生的事件。在AIM 4中，我们整合 跨样品的数据，以识别影响肿瘤进化的SV和基因组基因素基因局，检测 与其他分子和临床特征的关联，并确定潜在的SCNA确定 子类。此外，我们与种系进行关联和混合分析 在AIM 1中检测到的基因型。在AIM 5中，我们指出了我们沉浸自己的方法 在GDAN内部，并在GDAN内部和Wilder内传播我们的分析结果 社区。在此中，我们提供单细胞和循环DNA的次要能力 分析。我们的GDAC将对结构变化的作用进行全面分析 癌症的发展和通过GDAN样品中的治疗进行进展。我们也会 优化与更广泛的GDAN和科学界的互动以最大程度地利用这些 数据。