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.

摘要