Exploiting Ecology and Evolution to Prevent Therapy Resistance in EGFR-Driven Lung Cancer

利用生态学和进化来预防 EGFR 驱动的肺癌的治疗耐药性

• 批准号: 10312107
• 负责人: Jacob Gardinier Scott
• 金额: $ 44.76万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10312107
• 关键词: Area; Automobile Driving; Biological Assay; Biology; Cancer Etiology; Cause of Death; Cell Line; Cessation of life; Clinic; Clinical; Clinical Oncology; Computer Models; Coupled; Data; Dependence; Directed Molecular Evolution; Disease; Drug Design; Drug resistance; Ecology; Environment; Epidermal Growth Factor Receptor; Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor; Equation; Evolution; Family; Fibroblasts; Game Theory; Goals; Individual; Investigation; Knowledge; Laboratories; Learning; Libraries; Light; Malignant Neoplasms; Malignant neoplasm of lung; Maps; Mathematics; Measures; Modeling; Molecular; Molecular Profiling; Mutate; Mutation; Non-Small-Cell Lung Carcinoma; Oncology; Outcome; Patients; Pharmaceutical Preparations; Phenotype; Play; Population; Process; Protocols documentation; Publications; Research; Research Project Grants; Resistance; Secondary to; Seeds; Solid Neoplasm; Techniques; Testing; Time; Toxic effect; Translating; Translations; Tyrosine Kinase Inhibitor; anticancer research; base; cancer cell; cancer type; clinical application; clinical development; clinically relevant; design; drug discovery; drug sensitivity; effective therapy; experience; experimental study; improved; in vivo; innovation; insight; life history; mathematical model; multidisciplinary; neoplastic cell; novel strategies; novel therapeutics; objective response rate; personalized medicine; pressure; prevent; replicator; resistance mechanism; targeted agent; targeted treatment; time use; treatment strategy; tumor; tumor heterogeneity; tumor progression

ABSTRACT Lung cancer is the leading cause of cancer deaths in the USA, with an estimated 158,000 deaths in 2016. The direct cause of the majority of these deaths is the eventual emergence of resistance to initially effective therapies. This evolution of drug resistance represents one of the greatest unmet needs in oncology. While most research is focused on the individual molecular alterations that confer this resistance, we instead propose to focus on the eco-evolutionary processes that generate these alterations. To study the Darwinian evolution and ecological interactions occurring within heterogeneous tumors, we will tightly integrate bespoke mathematical models and experimental techniques designed to inform them. Focusing on EGFR mutated non-small cell lung cancer, a cancer type with a highly efficacious targeted therapy with which we have experience in our lab, we will approach this problem with three, orthogonal, integrated mathematical-experimental Aims. First, to understand the ecological interactions occurring at the inter-cellular level in heterogeneous tumors, we will couple our experience with evolutionary game theory with our first-in-class evolutionary game assay, which we have designed to specifically for this purpose. Here, we hope to learn to target the interactions that drive resistance – a novel strategy which could open up entirely new avenues of drug design. Second, we will allow evolution to show us the convergent phenotypes it creates in the face of specific selective pressures through long-term directed evolution. During this long-term evolution, we will measure phenotype, in the form of drug sensitivity to a panel of chemotherapeutics and targeted therapies at regular intervals, creating the first temporal collateral sensitivity map in any solid tumor. By pooling common phenotypes observed throughout the evolutionary life history, we will then use interactomic and seed-based protocols to generate molecular signatures of these states of sensitivity, which we will validate in publicly available data and in an in library of PDX lines. Finally, we will delve deeply into the relevant time scales of the ecological and evolutionary processes we study in the first two aims. We plan to apply the replicator-mutator framework of evolutionary game theory to a spatial transform that we pioneered in cancer. To validate and parameterize these models, we will also test the evolutionary stability of the ecological dynamics we measure with our game assay by performing the assay through evolutionary time during a long-term evolution experimental. Each of our three orthogonal aims is supported by recent high impact publications, and each represent tightly coupled experimental and computational protocols. Our clean, well- designed integration, together with innovative focus on the direct study of the evolutionary biology, promises to shed light on this difficult area of cancer research, and offers the possibility of providing generalizable insights.

抽象的 肺癌是美国癌症死亡的主要原因，2016年估计死亡158,000例。 这些死亡中大多数的直接原因是对最初有效疗法的抗药性最终出现。 这种耐药性的演变代表了肿瘤学中最大的未满足需求之一。大多数研究 专注于涉及这种抗性的个体分子改变，我们建议专注于 生成这些改变的生态进化过程。研究达尔文人的进化和生态学 在异质肿瘤中发生的相互作用，我们将紧密整合定制的数学模型和 旨在告知他们的实验技术。专注于EGFR突变的非小细胞肺癌，A 癌症类型具有高效的目标疗法，我们在实验室中有经验，我们将接近 这是三个正交的，综合数学实验目标的问题。首先，了解 生态相互作用发生在异质肿瘤的细胞间水平上，我们将融入我们的经验 通过我们的第一类进化游戏测定，通过进化游戏理论，我们已经为此设计了 专门用于此目的。在这里，我们希望学会针对驱动抵抗力的互动 - 一本小说 可以打开全新药物设计途径的策略。第二，我们将允许进化向我们展示 它通过长期定向在特定的选择压力上创建的收敛表型 进化。在这种长期演变中，我们将以药物对面板的敏感性的形式测量表型 定期进行化学治疗剂和靶向疗法的疗法，从而产生第一个临时侧支灵敏度 在任何实体瘤中地图。通过在整个进化寿命中观察到的共同表型，我们 然后将使用基于相互作用和种子的方案来生成这些状态的分子特征 灵敏度，我们将在公开可用的数据和PDX线库中验证。最后，我们将探究 深入了解我们在前两个目标中研究的生态和进化过程的相关时间尺度。 我们计划将进化游戏理论的复制器 - 突击器框架应用于我们的空间变换 在癌症中开创性。为了验证和参数化这些模型，我们还将测试 通过进化时间执行测定法，我们通过游戏分析测量的生态动力学 在长期进化实验中。最近的高影响力支持我们三个正交的目标 出版物，每个都代表了与实验和计算协议紧密耦合的。我们干净，良好 设计的整合以及对进化生物学的直接研究的创新关注都有望 阐明了癌症研究的这一困难领域，并提供了提供可推广的见解的可能性。