Bay Area Cancer Target Discovery and Development Network

湾区癌症靶标发现和开发网络

• 批准号: 8323024
• 负责人: FRANK PATRICK MCCORMICK
• 金额: $ 79.48万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8323024
• 关键词: Area; Automobile Driving; Biochemistry; Biological; Cancer Biology; Cancer Patient; Cataloging; Catalogs; Cell Line; Cells; Clinical; Combined Modality Therapy; Complementary DNA; Complex; Data; Data Set; Dependency; Development; Dominant-Negative Mutation; Engineering; Foundations; Gene Combinations; Gene Targeting; Genes; Genetic; Genome; Genomics; Goals; Growth; Human; Human Development; Joints; Knowledge; Lead; Length; Lesion; Libraries; Malignant - descriptor; Malignant Neoplasms; Mammalian Cell; Maps; Measures; Methodology; Methods; Mining; Mission; Molecular; Monitor; Mutate; Mutation; Oncogenes; Oncogenic; Outcome; Pathway interactions; Pharmaceutical Preparations; Phenotype; Positioning Attribute; Preclinical Drug Evaluation; Property; Quantitative Genetics; RNA Interference; Reagent; Recurrence; Research Personnel; Research Project Grants; Resistance; Resources; Role; Screening procedure; Signal Pathway; Signal Transduction; System; Systems Biology; Technology; The Cancer Genome Atlas; Therapeutic; Variant; base; cancer cell; cancer genome; cancer therapy; cancer type; combination cancer therapy; cost effective; data integration; design; functional genomics; gain of function; gene function; gene interaction; genome sequencing; high throughput screening; high throughput technology; improved; inhibitor/antagonist; insight; interest; next generation; novel; novel strategies; programs; research study; response; small hairpin RNA; therapy design; therapy development; tool; tumor; tumor growth

DESCRIPTION (provided by applicant): Currently, enormous volumes of data are being generated by the comprehensive molecular characterization of a number of human tumors. The ability to effectively and efficiently use RNAi to assess the biologic consequences of gene target inhibition is of critical importance to understanding gene function and to uncover tumor-specific vulnerabilities. The identification of tumor-specific vulnerabilities provides rationale for the development of biologically-based targeted therapies. RNAi screening is a powerful technology for high- throughput gene function discovery that has been used to identify tumor-specific vulnerabilities. However there are significant limitations to the RNAi screening resources that are currently available. The RNAi screening tools used to date do not efficiently target the full compendium of cancer relevant genes due to technological limitations in genome coverage and RNAi gene knockdown efficacy. These technological limitations also lead to false-positive and false-negative screen hits. Thus, currently available RNAi screening platforms are not cost-effective for performing high-throughput screens for most labs. Here we present technologies and resources that overcome these limitations, dramatically improving RNAi screening capabilities. We take advantage of statistically-based analyses and the power of new deep sequencing technologies that are being rapidly democratized. Our new approaches will greatly facilitate the development of cancer polytherapies, opening a new paradigm for rationally-based cancer therapeutics that fully capitalize on genomic profiling of human tumors. In order to design effective combination cancer therapies (polytherapies) we must first identify the signaling pathways that act synergistically to promote tumor growth or therapeutic resistance. This knowledge then enables the design of therapies that target these key cancer "driver" pathways. A major obstacle to the development of therapies that preclude or overcome resistance to targeted cancer therapy is that there is no systematic means by which to identify pathways that functionally cooperate and synergize to drive tumor growth or therapeutic resistance. Therefore, the search for effective cancer polytherapies has been done largely in an ad hoc manner exploring only a very limited number of potential combinations. The key to rationally designing an optimal combination of therapies lies in the systematic identification of pathways that when targeted, lead to specific and synergistic destruction of cancer cells. Our new approaches can determine simultaneously and rapidly (within 1-3 weeks) high precision measures of functional genetic interactions between large numbers (typically 100,000) pairs of shRNAs that target genes of interest in the context of any cancer. This represents a transformative technology in terms of our ability to systematically uncover cancer- relevant gene interaction networks that drive tumor growth and that potentially can be exploited as rational, tumor-specific polytherapies. PUBLIC HEALTH RELEVANCE: As important as the cancer genome sequencing initiatives are, the identification and cataloguing of large numbers of variations is only the first step in efforts to provide a scientific foundation for therapeutic breakthroughs. To achieve this broader goal, we must now understand how these variations alone and critically in combination contribute to the malignant properties of human tumors. Our program aims to fill this void. Our team brings together a critical range of expertise in cancer biology, functional genomics, and systems biology as well as a unique next generation shRNA screening strategy that greatly increases our ability to monitor the precise phenotypic consequences of perturbing combinations of genes. Our ability to distinguish cancer drivers and passengers and identify cancer-relevant signaling networks using our cutting-edge novel gene interaction approach is of high-relevance to the CTD2 mission, and the goal of developing rational combination therapies that may improve outcomes for genetically-defined subsets of cancer patients.

描述（由申请人提供）：目前，通过许多人类肿瘤的全面分子表征，正在产生大量数据。有效有效地使用RNAI评估基因靶标抑制的生物学后果的能力对于理解基因功能和发现肿瘤特异性脆弱性至关重要。肿瘤特异性脆弱性的鉴定为开发基于生物学的靶向疗法提供了理由。 RNAi筛选是一种用于高通量基因功能发现的强大技术，已用于识别肿瘤特异性脆弱性。但是，当前可用的RNAi筛选资源存在重大局限性。由于基因组覆盖范围和RNAi基因敲低功效的技术限制，用于日期的RNAi筛查工具不能有效地针对癌症相关基因的完整汇编。这些技术局限性还导致假阳性和假阴性屏幕命中率。因此，目前可用的RNAi筛选平台对于大多数实验室进行高通量屏幕并不具有成本效益。在这里，我们提出了克服这些局限性的技术和资源，从而极大地提高了RNAi筛选功能。我们利用基于统计的分析以及正在迅速民主化的新深层测序技术的力量。我们的新方法将极大地促进癌症多遍历的发展，这为基于合理的癌症治疗剂打开了新的范式，该范围充分利用了人类肿瘤的基因组分析。为了设计有效的联合癌症疗法（多延伸型），我们必须首先确定以协同作用以促进肿瘤生长或治疗性耐药性的信号通路。然后，这些知识可以设计针对这些关键癌症“驱动器”途径的疗法。阻止或克服靶向癌症治疗的抗药性的疗法发展的主要障碍是，没有系统的手段来识别功能合作并协同促进肿瘤生长或治疗性抗性的途径。因此，寻找有效的癌症多导线的搜索主要是以临时的方式进行的，仅探索了非常有限的潜在组合。合理设计疗法最佳组合的关键在于，靶向途径的系统鉴定会导致癌细胞的特定和协同破坏。我们的新方法可以同时，快速（在1-3周内）高精度测量大量的功能遗传相互作用（通常为100,000个）对靶向在任何癌症背景下靶向感兴趣的基因的SHRNA。这是我们系统地发现促进肿瘤生长的癌症相关基因相互作用网络的能力的变革技术，并且可能被用作理性的，肿瘤特异性的多层性。 公共卫生相关性：与癌症基因组测序计划一样重要，大量变化的识别和编目只是为治疗突破提供科学基础的第一步。为了实现这一更广泛的目标，我们现在必须了解这些变化是如何单独和批判性地结合起来有助于人类肿瘤的恶性特性的。我们的计划旨在填补这一空白。我们的团队汇集了癌症生物学，功能基因组学和系统生物学以及独特的下一代SHRNA筛查策略的关键专业知识，这些策略极大地提高了我们监测基因扰动组合的精确表型后果的能力。我们使用我们尖端的新型基因相互作用方法来区分癌症驱动因素和乘客并确定与癌症相关的信号网络的能力是与CTD2任务的高相关性，并且是开发有理组合疗法的目标，可以改善癌症患者遗传定义的亚群的影响。