(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）：在这里，我们研究了挑衅性问题PQD1：“使用不同类型和靶向疗法施加的选择性压力如何改变耐药性的演变？”有针对性疗法在癌症患者中延长生存的有效性受到耐药性发展的限制。癌症种群不断发展，从而使细胞的亚群适应并最终克服药物治疗。对潜在的抗药性机制及其治疗脆弱性的全面了解将构成寻找对药物耐药性肿瘤的最佳靶向治疗计划的基础。研究耐药性机制的一种常见策略是在单个选择压力下产生耐药性癌症种群，并使用人群平均测定法表征其脆弱性。但是，尚不清楚哪些选择压力应变化以鼓励未表征的药物机制的出现和演变。几乎有无限的参数可能会有所不同，目前尚不清楚哪个参数可以探索。此外，人口平均的测定在很大程度上是最合适的克隆的特征。可能会错过在实验环境中以低频出现的临床相关机制。结果，尚未系统地探索毒品的“景观”。在这里，我们建议可以通过隔离和研究在少数选择条件下衍生出的抗药性克隆来广泛调查耐药性。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，以揭示可能的抗药性机制的范围。我们的初步研究强烈表明，该策略将揭示各种药物机制。为了解决PQD1，我们评估了癌症人群中存在的抗性机制的多样性，以及这种多样性如何响应不同的选择性压力而变化。在AIM 1中，我们使用一种“ shot弹枪”方法将大量抗性克隆与接受不同靶向疗法治疗的癌症种群中分离出来。在AIM 2中，我们将克隆人群映射到由常见的治疗脆弱性定义的“抵抗类别”中。在AIM 3中，我们测试了在新的选择性压力下的抗性克隆如何发展。