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

总体抽象 我们的癌症系统希望中心（表型进化的异质性）将发展 一套 基于系统的方法，以了解基因组多样性，克隆进化和表型变化如何 。评估他们的潜力 翻译，我们将将这些动态模型与临床试验相结合，以评估这些模型是否 表型变化可以用于治疗。我们假设从 选择作用于肿瘤进化过程中表型的选择，并同时测量和 建模亚克隆基因型和表型将确定新的，可检验的治疗靶标。 疗法和肿瘤微环境的选择性压力可以推动每个患者的亚克隆 沿进化轨迹的肿瘤导致抗性。确实，我们的数据表明遗传和 肿瘤亚克隆中的表型多样性随着癌细胞发展到抗性状态而演变。但是，它 尚不知道促进这种抗性状态，它们之间的相互作用以及 它们如何融合到晚期癌症中观察到的常见抗性表型。解决这些和其他 问题，我们将开发一个新的动态系统模型，以表征 在获得化学抗性和转移期间出现的关键细胞状态的变化和开发使用 我们独特的病人队列。这些机械模型将确定我们将要确定的治疗脆弱性点 在临床试验中，旨在通过靶向关键的抗性表型将进化阻止到抗性状态的测试。 我们的中心由行政，教育/外展，翻译和计算核心组成 补充两个补充项目。协同作用源自：1） 从深度纵向患者进展研究（项目1）和广泛的进化模型中得出的进化模型 多站点转移性肿瘤分析（项目2），导致了一个强大的模型，以鉴定临床的抗性状态 定位； 2）用于剖析肿瘤的集成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，数学的多学科团队 生物学家和临床医生将应对这些科学挑战。我们将创建计划以教育下一个 在系统生物学中生成科学家，并向社区告知最新的科学进步和 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 样品和肿瘤基因组复杂性。这些研究通过整合细胞而超越先前的研究 种群动力学和具有细胞基因型的细胞表型，并将提供方法和A 知识库阻止或扭转了晚期癌症患者的过渡到抗性状态。 在向化学抗性乳腺癌和卵巢癌的进展中相互作用