Center for the comprehensive analysis of somatic copy-number alterations in cancer

癌症体细胞拷贝数改变综合分析中心

• 批准号: 9764290
• 负责人: RAMEEN BEROUKHIM
• 金额: $ 44.03万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9764290
• 关键词: Affect; Algorithms; Alleles; Biological; Cancer Diagnostics; Cancer Etiology; Characteristics; Chromosomal Instability; Clinical; Collaborations; Communities; Complex; Computing Methodologies; Custom; DNA; Data; Data Set; Development; Diagnosis; Diagnostic; Ensure; Epigenetic Process; Event; Expression Profiling; Generations; Genome; Genome Data Analysis Center; Genome Data Analysis Network; Genotype; Goals; High-Throughput Nucleotide Sequencing; Human; Hybrids; Imagery; Inflammatory Infiltrate; International; Isochromosomes; Lead; Light; Loss of Heterozygosity; Malignant Neoplasms; Methods; Methylation; Molecular; Mutation; Oncogenes; Other Genetics; Pattern; Persons; Ploidies; Population; Quality Control; Recording of previous events; Recurrence; Research; Research Personnel; Resolution; Sampling; Services; Structure; Testing; The Cancer Genome Atlas; Therapeutic; Time; Tumor Suppressor Genes; Tumor Suppressor Proteins; Update; Variant; arm; base; cancer cell; cancer genome; cancer subtypes; chromothripsis; clinical care; clinical phenotype; clinically relevant; exome sequencing; experience; genome analysis; genome sequencing; genome-wide; genomic data; improved; innovation; insight; member; molecular subtypes; novel; novel diagnostics; novel therapeutic intervention; transcriptome sequencing; tumor; web portal; whole genome; working group

Abstract Somatic copy number alterations (SCNAs) are a type of mutation in cancer that affect more of the cancer genome than any other genetic event. SCNAs often contribute to cancer development and progression, and detecting them can contribute to the development of diagnostic and therapeutic advances in clinical care. As part of The Cancer Genome Atlas (TCGA) project our group characterized SCNAs for over 10,000 tumors across 30 different tumor types. Through these efforts we developed state-of-the-art methods to detect and interpret SCNAs, and used these to discover SCNAs that recur across many tumors and likely contribute to the formation of these tumors, the candidate tumor suppressors and oncogenes these SCNAs target, and novel clinically relevant SCNA-based cancer subtypes. We have also developed methods to detect SCNAs and the rearrangements that bound them from high-throughput sequencing data of the type being collected by the Genomics Data Analysis Network (GDAN). These methods resolve SCNAs, the mechanisms by which they arise, and their potential biological consequences, in much greater detail than could be done with microarray data generated for TCGA. Leveraging our experience in SCNA analysis, we propose to establish a Genomics Data Analysis Center (GDAC) that will service the GDAN with comprehensive, advanced analyses of SCNAs and the rearrangements that bound them, with the goals of identifying biologically and clinically relevant patterns of SCNA and disseminating this information to the GDAN and wider research community. We will: 1) Generate basic and quality control information for each tumor. We determine the fraction of cancer cells within each tumor (tumor purity) and the average copy number genomewide (ploidy). We will also test every putative pair of tumor and normal DNA samples to ensure that they did originate in the same person. 2) Characterize SCNAs and rearrangements in each tumor, including clonal and subclonal amplifications, deletions, loss of heterozygosity, and complex events like chromothripsis, firestorms, and isochromosomes. 3) Identify recurrent SCNAs and rearrangements that are likely to drive tumor development and progression, and the oncogenes and tumor suppressor genes they likely target. 4) Classify tumors by previously identified SCNA subtypes and discover new subtypes. We will identify SCNAs and genomewide patterns of SCNA that correlate with clinical and molecular features of tumors. 5) Integrate with the GDAN and research community. We will integrate our analytic pipelines with those of other GDACs; immerse ourselves in cooperative Analysis Working Groups formed by the GDAN to refine those analyses in light of the most important questions; make our analysis results available to other members of the GDAN in real time; and disseminate those results to the wider research community through our existing web portal and by working closely with other GDACs to integrate our analyses into their web portals. Our results will inform how SCNAs cause cancer and indicate new diagnostic and therapeutic strategies.

摘要 体细胞拷贝数改变（SCNAs）是一种癌症突变，影响更多的癌症。 基因组比任何其他遗传事件。SCNA通常会促进癌症的发展和进展， 检测它们有助于临床护理中诊断和治疗进展的发展。作为 作为癌症基因组图谱（TCGA）项目的一部分，我们的研究小组对超过10，000种肿瘤的SCNA进行了表征 30种不同的肿瘤类型。通过这些努力，我们开发了最先进的方法来检测和 解释SCNA，并利用这些发现SCNA，在许多肿瘤复发，并可能有助于 这些肿瘤的形成、这些SCNA靶向的候选肿瘤抑制因子和癌基因，以及新的 临床相关的基于DNA的癌症亚型。我们还开发了检测SCNAs的方法， 这些重排使它们与由测序仪收集的类型的高通量测序数据结合。 基因组学数据分析网络（GDAN）。这些方法解决了SCNA，即它们 以及它们潜在的生物学后果，比微阵列能做得更详细。 为TCGA生成的数据。利用我们在SCNA分析方面的经验，我们建议建立基因组学 数据分析中心（GDAC）将为GDAN提供全面、先进的SCNA分析服务 以及结合它们的重排，目的是识别生物学和临床相关的 SCNA的模式，并将此信息传播给GDAN和更广泛的研究界。我们将： 1)生成每个肿瘤的基本和质量控制信息。我们测定癌细胞的比例 每个肿瘤内的平均拷贝数（肿瘤纯度）和全基因组的平均拷贝数（倍性）。我们还将测试 一对假定的肿瘤和正常DNA样本，以确保它们确实起源于同一个人。 2)表征每个肿瘤中的SCNA和重排，包括克隆和亚克隆扩增， 缺失、杂合性丢失和复杂事件，如染色体断裂、火灾和等染色体。 3)识别可能导致肿瘤发展的复发性SCNA和重排， 进展，以及它们可能靶向的癌基因和肿瘤抑制基因。 4)通过先前确定的SCNA亚型对肿瘤进行分类并发现新的亚型。我们将确定 SCNA和SCNA的全基因组模式与肿瘤的临床和分子特征相关。 5)整合GDAN和研究社区。我们将把我们的分析管道与 其他GDAC;沉浸在由GDAN组成的合作分析工作组中，以完善这些工作组 根据最重要的问题进行分析;将我们的分析结果提供给其他成员， 真实的时间GDAN;并通过我们现有的网络将这些结果传播给更广泛的研究社区 门户网站，并与其他GDAC密切合作，将我们的分析整合到他们的门户网站。 我们的研究结果将为SCNAs如何导致癌症提供信息，并指出新的诊断和治疗策略。