Bay Area Cancer Target Discovery and Development Network

湾区癌症靶标发现和开发网络

• 批准号: 8676480
• 负责人: FRANK PATRICK MCCORMICK
• 金额: $ 78.75万
• 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/8676480
• 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; 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; deep sequencing; 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; screening; 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.

描述(由申请人提供)：目前，大量的数据是通过对一些人类肿瘤的全面分子表征而产生的。能够有效和高效地使用RNAi来评估基因靶标抑制的生物学后果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识别为基于生物的靶向治疗的发展提供了理论基础。RNAi筛选是一项功能强大的高通量基因功能发现技术，已被用于识别肿瘤特异性脆弱性。然而，目前可用的RNAi筛查资源有很大的局限性。由于基因组覆盖率和RNAi基因敲除效率的技术限制，迄今使用的RNAi筛选工具不能有效地针对癌症相关基因的完整纲要。这些技术限制也导致了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的高通量筛选来说并不划算。在这里，我们介绍了克服这些限制的技术和资源，极大地提高了RNAi筛选能力。我们利用基于统计的分析和新的深度测序技术的力量，这些技术正在迅速民主化。我们的新方法将极大地促进癌症综合疗法的发展，为充分利用人类肿瘤基因组图谱的合理癌症治疗开辟了一个新的范例。为了设计有效的癌症联合疗法(综合疗法)，我们必须首先确定协同作用促进肿瘤生长或治疗耐药的信号通路。这些知识使针对这些关键的癌症“驱动”途径的治疗方法的设计成为可能。阻止或克服对靶向癌症治疗的耐药性的治疗方法的发展的一个主要障碍是，没有系统的手段来确定在功能上合作和协同推动肿瘤生长或治疗耐药性的途径。因此，寻找有效的癌症综合疗法在很大程度上是以一种特别的方式进行的，只探索了非常有限的潜在组合。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对癌细胞的特异性和协同破坏的途径。我们的新方法可以同时快速地(在1-3周内)确定大量(通常是100,000对)针对任何癌症背景下的感兴趣基因的shRNA之间的功能遗传相互作用的高精度测量。这代表着一项变革性的技术，因为我们有能力系统地发现与癌症相关的基因相互作用网络，这种网络推动了肿瘤的生长，并有可能被开发为合理的、针对肿瘤的综合疗法。