Identification and characterization of genomic features affecting survival duration in cancer

影响癌症生存期的基因组特征的鉴定和表征

• 批准号: 9146424
• 负责人: Jason Sheltzer
• 金额: $ 47.08万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-18 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9146424
• 关键词: Adverse event; Affect; Apoptosis; Benign; Bioinformatics; Biological; Biological Assay; Biological Markers; Biology; Cancer Biology; Cancer Patient; Cell Cycle Checkpoint; Cell Proliferation; Cells; Cessation of life; Clinical; Clinical Data; Collaborations; Collection; Computer software; Copy Number Polymorphism; DNA copy number; Data; Data Set; Development; Down-Regulation; Epithelial; Freezing; Future; Gene Expression; Genes; Genetic; Genomic Segment; Genomics; Goals; Growth; Health; Human; Indolent; Investigation; Knockout Mice; Knowledge; Length; Light; Link; MAP Kinase Gene; Malignant Neoplasms; Measures; Mesenchymal; Meta-Analysis; Methylation; Modeling; Molecular; Mortality Map; Mutation; Neoplasm Metastasis; Normal Cell; Oncogenes; Operative Surgical Procedures; Outcome; Pathway interactions; Patient Care; Patient risk; Patients; Phenotype; Play; Primary Neoplasm; Process; RNA Splicing; Recurrence; Reporting; Repression; Research; Research Personnel; Role; Series; Signal Transduction; Software Engineering; Stimulus; Stratification; Surveys; Technology; Time; Tissues; Translating; Treatment outcome; Tumor Suppressor Genes; Tumor Suppressor Proteins; Untranslated RNA; Up-Regulation; Variant; Work; addiction; cancer cell; cancer genome; cancer survival; cancer type; cell growth; cell transformation; drug development; gene function; gene repression; genomic data; human data; improved; in vivo; insight; knock-down; mortality; neoplastic cell; novel; novel therapeutic intervention; outcome forecast; patient stratification; programs; repository; research study; tumor; tumor progression; tumorigenesis; tumorigenic

 DESCRIPTION (provided by applicant): Advances in genomic technology have driven the collection of unprecedented amounts of data from human cancers. Critical challenges include extracting biological insights from this data and then translating these findings into improved patient care. Toward those ends, I performed a meta-analysis on 98 gene expression-linked cancer survival studies. In these studies, investigators measured gene expression in primary tumors surgically excised from patients and then correlated that data with clinical information on the patient's treatment and outcome. While data from over 20,000 patients with 20 distinct tumor types are available in public repositories, no comprehensive pan-cancer analysis has previously been reported. My analysis resulted in the discovery that the up-regulation or down regulation of hundreds of genes is highly correlated with the duration of patient survival across many common human cancers. This gene set provides unique insight into a poorly-understood facet of cancer biology: while the molecular alterations that distinguish tumor cells from normal cells have become increasingly well-characterized, the differences between highly-aggressive tumors and benign, indolent tumors remain largely unknown. My research group will utilize this bioinformatics pipeline as a discovery engine to identify and characterize the genes specifically responsible for cancer mortality. In Aim 1, I describe several single-gene assays and targeted screens that will assess the function of genes whose expression is associated with death from cancer. These assays will determine which of these genes are bona fide oncogenes and which promote tumor metastasis. In Aim 2, I describe experiments to assess the functions of genes whose expression correlates with prolonged survival in cancer patients. These genes could represent novel tumor suppressors or could otherwise inhibit tumor cell dissemination. In Aim 3, I propose several mechanistic experiments to elucidate the biology that connects the expression or repression of the genes identified in this study with patient survival. These functional studies will determine the effects of these genes on known cancer-related pathways, including the epithelial-mesenchymal transition, a developmental program hypothesized to play a crucial role in tumor metastasis. Lastly, in Aim 4, I describe an expanded set of molecular data linked to cancer survival that will be analyzed in order to create a "mortality map" of genomic features that drive or suppress tumor progression. This broader analysis will be used to identify new cancer-associated genes for future functional studies. Collectively, these Aims will greatly expand our knowledge of the features that differentiate fatal and non-fatal human tumors. Additionally, this work will provide an abundance of data with the potential to improve the stratification of cancer patient risk, and will identify novel targets for drug development to specifically inhibit the growth of the most aggressive cancers.

 描述（由申请人提供）：基因组技术的进步推动了人类癌症前所未有的数据收集。关键的挑战包括从这些数据中提取生物学见解，然后将这些发现转化为改善的患者护理。为此，我对98项与基因表达相关的癌症生存研究进行了荟萃分析。在这些研究中，研究人员测量了从患者手术切除的原发性肿瘤中的基因表达，然后将该数据与患者治疗和结果的临床信息相关联。虽然公共数据库中有20种不同肿瘤类型的20，000多名患者的数据，但以前没有全面的泛癌症分析报告。我的分析发现，数百个基因的上调或下调与许多常见人类癌症患者的生存时间高度相关。该基因集为癌症生物学的一个知之甚少的方面提供了独特的见解：虽然区分肿瘤细胞与正常细胞的分子改变已经变得越来越好，但高度侵袭性肿瘤和良性惰性肿瘤之间的差异仍然很大程度上未知。我的研究小组将利用这个生物信息学管道作为发现引擎，以识别和表征专门负责癌症死亡率的基因。在目标1中，我描述了几种单基因检测和靶向筛选，这些检测和筛选将评估其表达与癌症死亡相关的基因的功能。这些检测将确定这些基因中哪些是真正的癌基因，哪些促进肿瘤转移。在目标2中，我描述了一些实验来评估与癌症患者生存期延长相关的基因的功能。这些基因可能代表新的肿瘤抑制因子，或者可以抑制肿瘤细胞的传播。在目标3中，我提出了几个机制实验，以阐明生物学，在这项研究中确定的基因的表达或抑制与患者的生存。这些功能研究 将确定这些基因对已知的癌症相关途径的影响，包括上皮-间质转化，这是一种假设在肿瘤转移中起关键作用的发育程序。最后，在目标4中，我描述了一组与癌症生存相关的分子数据，这些数据将被分析，以创建驱动或抑制肿瘤进展的基因组特征的“死亡率图”。这种更广泛的分析将用于识别新的癌症相关基因，用于未来的功能研究。总的来说，这些目标将大大扩展我们对区分致命性和非致命性人类肿瘤的特征的了解。此外，这项工作将提供丰富的数据，有可能改善癌症患者风险的分层，并将确定药物开发的新靶点，以特异性抑制最具侵袭性的癌症的生长。