VIRUS DISCOVERY AND CHARACTERIZATION IN LARGE-SCALE CANCER SEQUENCING DATA

大规模癌症测序数据中的病毒发现和表征

• 批准号: 9120839
• 负责人: Li Ding
• 金额: $ 25.52万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-08 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9120839
• 关键词: Affect; Animal Model; Bladder; Breast; Cancer Etiology; Childhood Cancer Genome Project; Clinical; Colon; Colon Carcinoma; Communities; Computer Analysis; Computing Methodologies; DNA; Data; Data Quality; Data Set; Detection; Disease Outbreaks; Epidemiologic Studies; Etiology; Event; Gene Expression; Genes; Genome; Genomics; Goals; Head and Neck Cancer; Health; Hepatitis B Virus; Hepatitis C virus; Human; Human Herpesvirus 4; Human Microbiome; Human Papillomavirus; Human papilloma virus infection; Infection; International; Large-Scale Sequencing; Lead; Link; Lung; Malignant Neoplasms; Malignant neoplasm of cervix uteri; Malignant neoplasm of lung; Malignant neoplasm of urinary bladder; Mutation; Oncornaviruses; Patients; Pattern; Population; Prevalence; Primary carcinoma of the liver cells; RNA Viruses; Research; Sampling; Satellite Viruses; Sequence Analysis; Surveys; Testing; The Cancer Genome Atlas; Thinking; Time; Variant; Viral; Viral Cancer; Viral Genes; Virus; Virus Diseases; Virus Integration; cancer genome; cancer prevention; cancer risk; cancer therapy; cancer type; clinical phenotype; computerized tools; cost effective; deep sequencing; effective therapy; epidemiology study; exome; genome sequencing; improved; insight; knowledge base; latent infection; malignant breast neoplasm; novel; novel virus; power analysis; tool; transcriptome; transcriptome sequencing; transcriptomics; tumor; tumorigenesis; viral detection; virome; virus characteristic; whole genome

DESCRIPTION (provided by applicant): Although overt and latent viral infections are widespread in human populations, only few viruses have been linked to tumorigenesis. While this may be the result of coevolution between human and viruses, it appears more likely that the low number is a reflection of the difficulties in establishing causal relations between viral infection and cancer. For the few confirmed oncoviruses (HPV, HBV, HCV, EBV, etc.), some infected patients develop cancers with variable clinical course and presentation. We hypothesize that additional essential events in the host or the viruses are required for the cancers to initiate/progress and that many more cancers than we currently know have a viral connection. A wealth of cancer sequence data is being produced by multiple large- scale cancer projects, such as the Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and Pediatric Cancer Genome Project (PCGP). In order to take advantage of these valuable data sets for testing the above hypothesis, we propose to develop a set of computational methods and analysis strategies to characterize the viromes, genomes, and transcriptomes in different cancers with known clinical features. In particular, we will first build a computational pipeline fr the identification and characterization of viruses in cancer. This effort will be augmented by developing statistical approaches to establish the association among virus characteristics, host genetic alterations, and clinical features (Aim 1). This pipeline will first be applied to detect ad characterize viruses in cancer types such as cervical cancer, head and neck cancer, and hepatocellular carcinoma, all of which are known to be associated with viral etiology. Beyond confirming the links between these cancers and their known oncoviruses, HPV and HBV/HCV respectively, we will aim at a thorough characterization of all genomic and transcriptomic changes in both host and virus. Combined with clinical features of the cancers, we expect to establish association between such changes and the status of the cancers (Aim 2). Taking a step further, we will also utilize this validated pipeline to systematically analyze sequence data from cancer types having some initial evidence of viral involvement from animal model and epidemiology studies, the aim being to perform more sensitive detections of cancer-causing viruses missed by traditional approaches and to establish the statistical association between viral infection and tumor formation using uniform and high quality data from a large number of tumor samples (Aim 3). The successful analysis of the viral and host genes, transcriptomes, and genomes of over 6,000 cancer cases from many cancer types already sequenced by several major efforts will produce, for the first time, a state-of-the-art knowledge base of the cancer virome. We anticipate that new pathogenic viruses and/or subtypes will be discovered and more cancers will be explained by viruses. This would lead to a paradigm shift for cancer prevention and treatment. Finally, both pipeline and results from this project will be made publically available, facilitating the analysis and interpretation by the research community to better discover and understand viruses in cancer.

描述（由申请人提供）：尽管人群中普遍存在明显的和潜在的病毒感染，但只有很少的病毒与肿瘤发生有关。虽然这可能是人与病毒之间共同进化的结果，但数量较低的可能是建立病毒感染与癌症之间因果关系的困难的反映。对于少数已确认的癌病毒（HPV，HBV，HCV，EBV等），一些受感染的患者会出现具有可变临床病程和表现的癌症。我们假设癌症需要其他基本事件或病毒来启动/进步所需的其他基本事件，而癌症比我们目前所知更多的癌症需要更多的癌症。多个大规模的癌症项目（例如癌症基因组图集（TCGA），国际癌症基因组联盟（ICGC），治疗疗法适用的研究以生成有效治疗（目标）和儿科癌症基因组项目（PCGP），可以生成大量的癌症序列数据。为了利用这些有价值的数据集来检验上述假设，我们建议开发一组计算方法和分析策略，以表征具有已知临床特征的不同癌症中的病毒瘤，基因组和转录组。特别是，我们将首先建立一个癌症病毒的鉴定和表征的计算管道。通过开发统计方法来建立病毒特征，宿主遗传改变和临床特征之间的关联（AIM 1），可以增强这种努力。该管道将​​首先用于检测AD的特征，以癌类型的病毒为特征，例如宫颈癌，头颈癌和肝细胞癌，所有这些都与病毒病因相关。除了确认这些癌症及其已知的肿瘤病毒之间的联系，分别是HPV和HBV/HCV之外，我们还将旨在彻底表征宿主和病毒中所有基因组和转录组变化。结合癌症的临床特征，我们希望在这种变化与癌症状态之间建立关联（AIM 2）。进一步迈出一步，我们还将利用该经过验证的管道来系统地分析来自癌症类型的序列数据，这些序列数据具有一些初步的证据，这些序列数据是动物模型和流行病学研究的一些初步证据，其目的是对传统方法遗漏的癌症引起的病毒的更敏感检测进行更敏感的检测，并在使用统一的数据和高质量的数据（以均匀的数据为单位）中建立统计相关性（以概述为统一的统计关联）（针对3次绘制数量）（构成数量数量）。对病毒和宿主基因，转录组和基因组的成功分析，这些癌症类型已经从许多癌症类型中进行的6,000例癌症病例进行了分析，这将首次产生癌症病毒群的最先进的知识基础。我们预计将发现新的病毒病毒和/或亚型，并通过病毒来解释更多的癌症。这将导致预防癌症和治疗的范式转变。最后，将公开提供该项目的管道和该项目的结果，从而促进研究界的分析和解释，以更好地发现和理解癌症的病毒。

项目成果

Divergent viral presentation among human tumors and adjacent normal tissues.

• DOI: 10.1038/srep28294
• 发表时间: 2016-06-24
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Cao S;Wendl MC;Wyczalkowski MA;Wylie K;Ye K;Jayasinghe R;Xie M;Wu S;Niu B;Grubb R 3rd;Johnson KJ;Gay H;Chen K;Rader JS;Dipersio JF;Chen F;Ding L
• 通讯作者: Ding L