Elucidating Mechanisms of Resistance using Genetically Engineered Mouse Models

使用基因工程小鼠模型阐明耐药机制

• 批准号: 8415139
• 负责人: LYNDA CHIN
• 金额: $ 29.4万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-12 至 2018-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8415139
• 关键词: BRAF gene; Behavior; Biological; Event; Genes; Genetic; Genetic Engineering; Genetically Engineered Mouse; Genomic Instability; Germ Lines; Human; Life; Modeling; Pathway interactions; Patients; Resistance; Testing; Therapeutic; Validation; in vivo; inhibitor/antagonist; melanoma; mouse model; mutant; novel; pre-clinical; resistance mechanism; response; tumor

Project 2: The single agent efficacy of selective mutant BRAF inhibitors in patients with advanced melanoma is uniformly short-lived, illustrating that therapeutic resistance is a paramount question in the field. Recognizing that micro-environmental context influences the biological behavior of a tumor, including response to therapy, a systematic and comprehensive effort to identify mechanisms of resistance. Thus, Project 2 brings to this P01 the uses of refined germline and non-germline genetically engineered models of BRAF-driven melanomas for discovery and validation of novel resistant genes as well as for preclinical therapeutic testing of combinations that can overcome resistance to selective BRAF inhibitor (BRAFi) in melanoma. The following 3 aims will be pursued: Aim 1: Identify genetic events conferring resistance to BRAFi in vivo. Here, using our refined BRAF^(R)¿¿^-driven genetically engineered mouse model (GEMM) ("iBIP"), we will generate a longitudinal cohort of sensitive and resistant melanomas upon long-term administration of BRAFi. These tumors will be subjected to deep genomic characterization to identify candidate lesions mediating resistance. Candidates will be prioritized and validated for/n wVo functional genetic screening based on statistical significance as well as evolutionary conservation through compahson with human genomic data from Project 1. Aim 2: Identify co-extinction targets for combination therapeutics against BRAF* melanoma. Complementing Aim 1, this aim will take a global and unbiased approach to the discovery of co-extinction targets. We will define the BRAF* regulated network in melanoma maintenance through kinetic transcriptome profiling of regressing melanomas upon genetic inactivation of mutant BF^F* in IBIP mice. Aim 3: Develop rational combination strategies for overcoming resistance to BRAFi in vivo. The goal of this aim is to generate sufficient preclinical efficacy data to motivate a novel Phase 18/11 clinical trial on a combination regimen that inhibits a co-extinction target along with BRAFi. Here, we will use mouse models to systematically screen potential combinations for efficacy; the best combination will then be tested in preclinical therapeutic trials in the IBIP GEMM. RELEVANCE (See instructions): Resistance to selective BRAF inhibitors in melanoma is a paramount challenge in the clinics today. Thus, elucidating the mechanisms modulating response and conferring resistance is timely and relevant. Bringing to bear on this challenge cutting-edged genomics and computational science as well as refined genetically engineered mouse models enable a discovery effort that is unbiased and comprehensive.

项目2：选择性突变型BRAF抑制剂对晚期晚期患者的单药疗效 黑色素瘤的寿命普遍较短，这说明治疗耐药性是该领域最重要的问题。 认识到微环境影响肿瘤的生物学行为，包括 对治疗的反应，是识别耐药机制的系统和全面的努力。 项目 2 为 P01 带来了精炼种系和非种系基因工程模型的使用 BRAF 驱动的黑色素瘤用于发现和验证新型耐药基因以及临床前研究 可以克服对选择性 BRAF 抑制剂 (BRAFi) 耐药性的组合的治疗测试 黑色素瘤。 将追求以下 3 个目标： 目标 1：识别赋予 BRAFi 抗性的遗传事件 在这里，使用我们精制的 BRAF^(R)¿ ¿ ^驱动的基因工程小鼠模型（GEMM）（“iBIP”），我们 长期施用后将产生敏感和耐药黑色素瘤的纵向队列 BRAFi。将对这些肿瘤进行深入的基因组表征，以确定候选病变。 候选人将被优先考虑并验证 wVo 功能遗传。 基于统计显着性和进化保守性的筛选 来自项目 1 的人类基因组数据。目标 2：确定组合的共同灭绝目标 针对 BRAF* 黑色素瘤的疗法是对目标 1 的补充，该目标将采取全球性和公正的态度。 我们将在 中定义 BRAF* 监管网络。 通过基因回归黑色素瘤的动力学转录组分析来维持黑色素瘤 IBIP 小鼠中突变体 BF^F* 的失活目标 3：制定合理的组合策略。 克服体内对 BRAFi 的耐药性 该目标的目标是产生足够的临床前疗效。 数据激发了一项关于抑制共灭绝的联合方案的新型 18/11 期临床试验 在这里，我们将使用鼠标模型来系统地筛选潜在的组合。 疗效；然后将在 IBIP GEMM 的临床前治疗试验中测试最佳组合。 相关性（参见说明）： 黑色素瘤对选择性 BRAF 抑制剂的耐药性是当今临床面临的首要挑战。 阐明调节反应和赋予抵抗力的机制是及时且相关的。 为了应对这一挑战，尖端的基因组学和计算科学以及精炼的遗传学 工程小鼠模型能够实现公正且全面的发现工作。