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

基本信息

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
项目类别：
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

项目摘要

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建立基因组数据分析中心来分析癌症的结构变异，包括体细胞拷贝数改变（SCNA）和重新安排，解决 GDAN 要求的拷贝数能力。分析长读和链接读数据的能力，解决了第二项能力。 SCNA 分析整个癌症基因组图谱 (TCGA) 和第一次迭代我们还共同领导了结构变异工作。国际癌症基因组联盟泛癌症全基因组分析小组 (PCAWG)，我们开发了一整套工具和深厚的专业知识。具体来说，我们将实现五个目标：目标 1 和 2，我们将从短读（整个外显子组或全基因组）或长/连锁读测序数据，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。提高我们的拷贝数分辨率在目标 3 中，我们重建肿瘤基因组及其。我们评估样本异质性，包括肿瘤纯度、倍性和亚克隆改变和同源染色体的相重排——确定使用这些数据，我们可以确定 DNA 的机制。在目标 4 中，我们整合了损伤和修复并推断肿瘤进化过程中发生的事件。跨样本的数据来识别影响肿瘤进化的SV和基因组位点，检测与其他分子和临床特征的关联，并评估潜在的 SCNA 确定的此外，我们还进行了与种系的关联和混合分析。在目标 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法在 GDAN 内并在 GDAN 内和更广泛的范围内传播我们的分析结果在这个社区中，我们提供单细胞和循环 DNA 方面的辅助能力。我们的 GDAC 将对结构变异的作用进行全面分析。我们还将通过 GDAN 样本的治疗来了解癌症的发生和进展。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。