(PQD1) An Iterative Approach for Overcoming Evolving Targeted Therapy Resistance

(PQD1) 克服不断变化的靶向治疗耐药性的迭代方法

• 批准号: 8687271
• 负责人: LANI F WU
• 金额: $ 32.79万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8687271
• 关键词: Address; Biological Assay; Biological Markers; Cancer Patient; Cells; Clinical; Data; Development; Disease; Dose; Drug resistance; Drug toxicity; Effectiveness; Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor; Evolution; Frequencies; Heterogeneity; Individual; Malignant Neoplasms; Maps; Molecular; Nature; Pattern; Pharmaceutical Preparations; Population; Positioning Attribute; Resistance; Resistance development; Shotguns; Surveys; Testing; Therapeutic; Time; Toxic effect; Translating; Tumor Burden; base; cancer cell; cancer therapy; chemotherapy; clinically relevant; drug mechanism; empowered; fitness; pressure; public health relevance; reagent testing; resistance mechanism; response; treatment planning; treatment strategy; tumor

DESCRIPTION (provided by applicant): Here, we investigate Provocative Question PQD1: "How does the selective pressure imposed by the use of different types and doses of targeted therapies modify the evolution of drug resistance?" The effectiveness of targeted therapy to prolong survival in cancer patients is limited by the inevitable development of drug resistance. Cancer populations constantly evolve, enabling subpopulations of cells to adapt and ultimately overcome drug treatment. A comprehensive understanding of potential drug-resistance mechanisms and their therapeutic vulnerabilities will form the basis for finding optimal targeted treatment plans of drug-resistant tumors. A common strategy for studying mechanisms of drug resistance is to generate a drug-resistant cancer population under a single selective pressure, and characterize its vulnerabilities using population-averaged assays. However, it is unclear which selective pressures should be varied to encourage the emergence and evolution of uncharacterized drug mechanisms. There are a virtually limitless number of parameters that could be varied, and it is unclear which would be productive to explore. Further, population-averaged assays largely characterize the fittest clones; clinically relevant mechanisms, which may appear at low frequencies in experimental settings, will be missed. As a result, the drug-resistance "landscape" has not been systematically explored. Here, we propose that drug resistance can be broadly surveyed instead by isolating and studying individual drug-resistant clones derived under a small number of selection conditions. We leverage the natural heterogeneity of cancer, traditionally viewed as an impediment for understanding the disease, to reveal the range of possible resistance mechanisms. Our preliminary studies strongly suggest that this strategy will unmask diverse drug mechanisms. To address PQD1, we assess the diversity of resistance mechanisms present in a cancer population and how this diversity changes in response to different selective pressures. In Aim 1, we use a "shotgun" approach for isolating large numbers of resistant clones from cancer populations treated with different targeted therapies. In Aim 2, we map our clonal populations into "resistance classes" defined by common therapeutic vulnerabilities. In Aim 3, we test how our resistant clones evolve under new selective pressures.

描述（由申请人提供）：在此，我们研究了挑衅性问题PQD 1：“使用不同类型和剂量的靶向治疗所施加的选择性压力如何改变耐药性的演变？“靶向治疗延长癌症患者生存期的有效性受到不可避免的耐药性发展的限制。癌症群体不断进化，使细胞亚群能够适应并最终克服药物治疗。全面了解潜在的耐药机制及其治疗漏洞将成为寻找耐药肿瘤最佳靶向治疗方案的基础。研究耐药性机制的一个常见策略是在单一选择压力下产生耐药性癌症群体，并使用群体平均测定来表征其脆弱性。然而，目前还不清楚，选择性的压力应该是不同的，以鼓励出现和演变的非特征化的药物机制。实际上，可以改变的参数数量是无限的，目前还不清楚探索哪些参数会有成效。此外，群体平均测定主要表征最适合的克隆;临床相关的机制，这可能会出现在实验环境中的低频率，将被错过。因此，对耐药性“情况”没有进行系统的探讨。在这里，我们建议，耐药性可以广泛调查，而不是通过分离和研究个别耐药克隆下的少数选择条件。我们利用癌症的自然异质性，传统上被视为理解疾病的障碍，以揭示可能的耐药机制的范围。我们的初步研究强烈表明，这种策略将揭示不同的药物机制。为了解决PQD 1，我们评估了癌症人群中存在的耐药机制的多样性，以及这种多样性如何响应不同的选择压力而变化。在目标1中，我们使用“鸟枪”方法从接受不同靶向治疗的癌症人群中分离出大量耐药克隆。在目标2中，我们将我们的克隆群体映射到由常见治疗弱点定义的“抗性类别”中。在目标3中，我们测试了我们的抗性克隆如何在新的选择压力下进化。