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，大量的数据是通过对许多人类肿瘤的综合分子表征产生的。有效和高效地使用RNAi来评估基因靶向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弱点至关重要。肿瘤特异性脆弱性的识别为开发基于生物学的靶向治疗提供了理论基础。RNAi筛选是用于高通量基因功能发现的强大技术，其已用于鉴定肿瘤特异性脆弱性。然而，目前可用的RNAi筛选资源存在显著的限制。由于基因组覆盖和RNAi基因敲低功效的技术限制，迄今为止使用的RNAi筛选工具不能有效地靶向癌症相关基因的完整纲要。这些技术限制也会导致假阳性和假阴性的屏幕点击。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筛选而言并不具有成本效益。在这里，我们提出了克服这些限制的技术和资源，大大提高了RNAi筛选能力。我们利用基于生物学的分析和正在迅速普及的新深度测序技术的力量。我们的新方法将极大地促进癌症综合疗法的发展，为基于理性的癌症治疗开辟新的范式，充分利用人类肿瘤的基因组图谱。为了设计有效的联合癌症疗法（多药疗法），我们必须首先确定协同作用以促进肿瘤生长或治疗抗性的信号通路。然后，这些知识可以设计针对这些关键癌症“驱动”途径的疗法。开发排除或克服对靶向癌症疗法的抗性的疗法的主要障碍是，没有系统的手段来鉴定功能上合作和协同作用以驱动肿瘤生长或治疗抗性的途径。因此，对有效的癌症多种疗法的研究主要是以特定的方式进行的，仅探索了非常有限数量的潜在组合。合理设计最佳治疗组合的关键在于系统地识别当靶向时导致癌细胞特异性和协同破坏的途径。我们的新方法可以同时快速（在1-3周内）确定大量（通常为100，000）对shRNA之间功能性遗传相互作用的高精度测量，这些shRNA在任何癌症背景下靶向感兴趣的基因。这代表了一种变革性的技术，因为我们有能力系统地发现癌症相关的基因相互作用网络，这些网络驱动肿瘤生长，并且可能被开发为合理的肿瘤特异性综合疗法。 公共卫生相关性：与癌症基因组测序计划一样重要的是，识别和编目大量的变异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异是如何单独和关键性地结合在一起导致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方面的关键专业知识，以及独特的下一代shRNA筛选策略，大大提高了我们监测干扰基因组合的精确表型后果的能力。我们使用尖端的新型基因相互作用方法区分癌症驱动者和乘客以及识别癌症相关信号网络的能力与CTD 2使命高度相关，并且开发合理的联合疗法的目标可以改善遗传定义的癌症患者子集的结果。