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（由适用提供）：基因组技术的进步推动了人类癌症的前所未有的数据收集。关键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。在这些目的方面，我对98个基因表达连接的癌症生存研究进行了荟萃分析。在这些研究中，研究人员测量了患者手术中原发性肿瘤中的基因表达，然后将数据与患者的治疗和结果有关。虽然公共存储库中有20,000多名具有20种不同肿瘤类型的患者的数据可用，但以前尚无全面的PAN-CACTER分析。我的分析结果是，发现数百个基因的上调或下调与许多常见人类癌症的患者生存持续时间高度相关。该基因集为癌症生物学的理解不佳提供了独特的见解：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良好，但高度侵入性的肿瘤与良性，懒惰的肿瘤之间的差异仍然在很大程度上尚不清楚。我的研究小组将利用此生物信息学管道作为发现引擎，以识别和表征特定负责癌症死亡率的基因。在AIM 1中，我描述了几个单基因测定和靶向筛选，这些测定法将评估其表达与癌症死亡有关的基因的功能。这些测定将确定哪些基因是真正的癌基因，哪些是促进肿瘤转移的。在AIM 2中，我描述了评估基因表达与癌症患者长期生存相关的基因功能的实验。这些基因可以代表新颖的肿瘤补充剂，也可以抑制肿瘤细胞的传播。在AIM 3中，我提出了一些机械实验，以阐明将本研究中鉴定的基因表达或表达与患者存活联系起来的生物学。这些功能研究 将确定这些基因对已知癌症相关途径的影响，包括上皮 - 间质转变，这是一个发展的程序，假设在肿瘤转移中起着至关重要的作用。最后，在AIM 4中，我描述了与癌症生存有关的一组扩展的分子数据，该数据将进行分析，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鉴定与癌症相关的新基因，以进行未来的功能研究。总的来说，这些目标将大大扩展我们对区分致命和非致命性人类肿瘤的特征的了解。此外，这项工作将提供大量的数据，具有改善癌症患者风险分层的潜力，并将确定药物开发的新目标，以特别抑制最侵略性的癌症的生长。