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.

抽象的 体拷贝数改变（SCNA）是癌症中一种突变，会影响更多的癌症 基因组比任何其他遗传事件。 SCNA经常有助于癌症的发展和进展，以及 检测它们可以有助于临床护理中诊断和治疗进步的发展。作为 癌症基因组图集（TCGA）的一部分项目我们的小组表征了10,000多个肿瘤的SCNA 在30种不同的肿瘤类型中。通过这些努力，我们开发了最先进的方法来检测和 解释SCNA，并用这些发现发现在许多肿瘤中复发的SCNA，并可能有助于 这些肿瘤的形成，候选肿瘤抑制因子和癌基因这些SCNA靶标以及新型 临床上相关的基于SCNA的癌症亚型。我们还开发了检测SCNA和 重排将它们与由高通量测序数据绑定到的类型的高通量测序数据 基因组数据分析网络（GDAN）。这些方法可以解决SCNA，即它们的机制 出现及其潜在的生物学后果，比微阵列要多得多 为TCGA生成的数据。利用我们在SCNA分析中的经验，我们建议建立基因组学 数据分析中心（GDAC）将通过SCNA的全面，高级分析为GDAN提供服务 以及将它们束缚的重排，其目标是在生物学上和临床上相关 SCNA的模式并将这些信息传播给GDAN和更广泛的研究社区。我们将： 1）为每个肿瘤生成基本和质量控制信息。我们确定癌细胞的分数 在每个肿瘤内（肿瘤纯度）和平均拷贝数全基因组（ploidy）。我们还将测试每个 推定的一对肿瘤和正常的DNA样品，以确保它们确实起源于同一人。 2）表征每个肿瘤中的SCNA和重排，包括克隆和亚克隆扩增， 删除，杂合性的丧失以及复杂的事件，例如铬骨，大火和同色粒体。 3）确定可能驱动肿瘤发展的经常性SCNA和重排 进展，以及它们可能针对的癌基因和肿瘤抑制基因。 4）通过先前鉴定的SCNA亚型对肿瘤进行分类并发现新的亚型。我们将确定 SCNA和全基因组模式与肿瘤的临床和分子特征相关。 5）与GDAN和研究社区集成。我们将将我们的分析管道与 其他GDAC；将自己沉浸在GDAN成立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；使我们的分析结果可用于其他成员 GDAN实时；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紧密合作，将我们的分析集成到其网站门户中。 我们的结果将告知SCNA如何引起癌症并指出新的诊断和治疗策略。