Combating Subclonal Evolution of Resistant Cancer Phenotypes

对抗耐药癌症表型的亚克隆进化

• 批准号: 9482409
• 负责人: ANDREA Hope BILD
• 金额: $ 153.16万
• 依托单位: BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9482409
• 关键词: Combating; Subclonal; Evolution; Resistant; Cancer

Overall abstract Our Cancer Systems Biology Center of HoPE (Heterogeneity of Phenotypic Evolution) will develop a suite of systems-based methodologies to understand how genomic diversity, clonal evolution, and phenotypic change . To evaluate their potential for translation, we will integrate these dynamic models with clinical trials that will evaluate whether these phenotypic changes can be targeted for therapy. We hypothesize that acquired resistance emerges from selection acting on phenotypes during tumor evolution, and that simultaneously measuring and modeling subclone genotypes and phenotypes will identify new, and testable, therapeutic targets. Selective pressures from therapy and the tumor microenvironment can propel subclones from every patient's tumor along an evolutionary trajectory that leads to resistance. Indeed, our data shows that both genetic and phenotypic diversity among tumor subclones evolves as cancer cells progress to a resistant state. However, it is not yet known the specific phenotypes that promote that resistant state, the interactions among them, and how they converge to common resistant phenotypes seen in late stage cancer. To address these and other questions, we will develop a new class of dynamical systems models of subclone evolution to characterize the changes and development of key cell states that arise during acquired chemo-resistance and metastasis using our unique patient cohorts. These mechanistic models will identify points of therapeutic vulnerability that we will test in clinical trials aimed at blocking evolution to a resistant state by targeting critical resistant phenotypes. Our Center is comprised of an Administrative, Education/Outreach, Translational, and Computational Cores, in addition to two complementary projects. The synergies are derived from: 1) the convergent parameterization of the evolutionary models drawn from deep longitudinal patient progression studies (Project 1) and broad multisite metastatic tumor analyses (Project 2), resulting in a robust model to identify resistant states for clinical targeting; and 2) an integrated computational and experimental framework and resources for dissecting tumor heterogeneity and evolution that will contribute to an improved capacity for personalized cancer therapy. Our multidisciplinary team of systems biologists, bioinformaticians, tumor biologists, pharmacologists, mathematical biologists, and clinicians will tackle these scientific challenges. We will create programs to educate the next generation of scientists in systems biology and inform the community about the latest scientific advances and their impact on treatment strategies. And we will provide state of the art tools for the analysis of patient samples and tumor genomic complexity. These studies move beyond prior research by integrating cell population dynamics and cellular phenotypes with cellular genotypes, and will deliver approaches and a knowledge base to block or reverse the transition to a resistant state for advanced stage cancer patients. interact in the progression toward chemoresistant breast and ovarian cancer

总体摘要 我们的癌症系统生物学中心(表型进化的异质性)将会发展 一套 基于系统的方法论，以了解基因组多样性、克隆进化和表型变化 。来评估他们的潜力 我们将把这些动态模型与临床试验相结合，以评估这些 表型变化可以作为治疗的靶点。我们假设获得性抵抗力来自 在肿瘤进化过程中作用于表型的选择，以及同时测量和 对亚克隆基因和表型进行建模将确定新的、可测试的治疗靶点。 来自治疗的选择性压力和肿瘤微环境可以推动每个患者的亚克隆 肿瘤沿着导致抗药性的进化轨迹。事实上，我们的数据显示，基因和 随着癌细胞进入耐药状态，肿瘤亚克隆间的表型多样性也随之演变。然而，它 目前还不知道促进这种抗性状态的特定表型，它们之间的相互作用，以及 它们如何汇聚到晚期癌症中常见的耐药表型。要解决这些和其他问题 问题，我们将开发一类新的亚克隆进化的动力系统模型来表征 获得性化疗耐药和转移过程中关键细胞状态的变化和发展 我们独一无二的病人队列。这些机械模型将确定治疗的脆弱性，我们将 临床试验中的测试，旨在通过靶向关键的耐药表型来阻止向耐药状态的进化。 我们的中心由行政、教育/外展、翻译和计算核心组成，位于 除了两个互补的项目。协同作用源于：1)收敛的参数化 来自深度纵向患者进展研究(项目1)和BREADE的进化模型 多部位转移肿瘤分析(项目2)，为临床识别耐药状态提供了可靠的模型 靶向；以及2)用于解剖肿瘤的综合计算和实验框架和资源 异质性和进化将有助于提高个性化癌症治疗的能力。我们的 系统生物学家、生物信息学家、肿瘤生物学家、药理学家、数学家组成的多学科团队 生物学家和临床医生将解决这些科学挑战。我们将创建节目来教育下一代 一代系统生物学科学家，并向社区介绍最新的科学进展和 它们对治疗策略的影响。我们将提供最先进的工具来分析患者 样本和肿瘤基因组的复杂性。这些研究通过整合细胞，超越了先前的研究 种群动态和细胞表型与细胞基因型的关系，并将提供方法和 阻止或逆转晚期癌症患者向耐药状态转变的知识库。 化疗耐药乳腺癌和卵巢癌的相互作用