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

基本信息

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
项目类别：
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

项目摘要

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从人类癌症中收集前所未有的大量数据，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。在这些研究中，研究人员对 98 项与基因表达相关的癌症生存研究进行了荟萃分析，测量了从患者身上切除的原发性肿瘤的基因表达，然后将这些数据与患者治疗和结果的临床信息相关联。公共存储库中有 20,000 名患有 20 种不同肿瘤类型的患者，之前没有报道过全面的泛癌分析。我的分析结果发现，数百个基因的上调或下调与患者的病程高度相关。该基因组为人们对癌症生物学的一个鲜为人知的方面提供了非常独特的见解：虽然区分肿瘤细胞和正常细胞的分子改变已变得越来越明确，但攻击性细胞之间的差异。我的研究小组将利用这一生物信息学流程作为发现引擎来识别和表征专门负责癌症死亡率的基因，我描述了几种单基因测定和靶向筛选。评估其表达与癌症死亡相关的基因的功能。在目标 2 中，我描述了评估其表达的基因的功能的实验。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来阐明本研究中确定的基因的表达或抑制与肿瘤细胞传播之间的生物学关系。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，包括上皮间质转化，这是一种在肿瘤转移中发挥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相关的扩展分子数据。癌症生存率将被分析，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。我们的知识此外，这项工作将提供大量数据，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标，以特异性抑制肿瘤的生长。最具侵袭性的癌症。