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: 10381296
• 负责人: Jacob Gardinier Scott
• 金额: $ 7.7万
• 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/10381296
• 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 年估计有 158,000 人死亡。 大多数死亡的直接原因是最终出现对最初有效疗法的耐药性。 这种耐药性的演变代表了肿瘤学中最大的未满足的需求之一。虽然大多数研究 专注于赋予这种抵抗力的单个分子改变，我们建议专注于 产生这些改变的生态进化过程。研究达尔文进化论和生态学 异质肿瘤内发生的相互作用，我们将紧密整合定制的数学模型和 旨在为他们提供信息的实验技术。聚焦EGFR突变非小细胞肺癌， 对于具有高效靶向治疗的癌症类型，我们在实验室拥有丰富的经验，我们将接近 这个问题具有三个正交的综合数学实验目标。首先，要了解 ecological interactions occurring at the inter-cellular level in heterogeneous tumors, we will couple our experience 利用进化博弈论和我们一流的进化博弈分析，我们设计的目的是 专门用于此目的。在这里，我们希望学会瞄准导致阻力的相互作用——一部小说 该策略可以开辟药物设计的全新途径。其次，我们将让进化向我们展示 它通过长期定向在面对特定选择压力时产生的趋同表型 进化。在这个长期的进化过程中，我们将以对一组药物的敏感性的形式来测量表型 定期进行化疗和靶向治疗，创造了第一个时间附带敏感性 绘制任何实体瘤的图谱。通过汇集在整个进化生命史中观察到的常见表型，我们 然后将使用交互组学和基于种子的协议来生成这些状态的分子签名 敏感性，我们将在公开数据和 PDX 细胞系库中进行验证。最后，我们将深入探讨 深入研究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。 我们计划将进化博弈论的复制子-变异子框架应用到我们所研究的空间变换中。 开创了癌症领域的先河。为了验证和参数化这些模型，我们还将测试进化稳定性 我们通过游戏分析通过进化时间进行分析来测量生态动态 在长期进化实验中。我们的三个正交目标中的每一个都得到了最近的高影响力的支持 出版物，每个都代表紧密耦合的实验和计算协议。我们的干净、良好- 精心设计的整合，加上对进化生物学直接研究的创新关注，有望 阐明了癌症研究的这一困难领域，并提供了提供普遍见解的可能性。