Project 2: Targeting Cooperative Phenotypes Common in Spatial Heterogeneity

项目 2：针对空间异质性中常见的合作表型

• 批准号: 10207530
• 负责人: JEFFREY T CHANG
• 金额: $ 34.49万
• 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 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10207530
• 关键词: Address; Affect; Algorithms; Ascites; Automobile Driving; Autopsy; Bayesian Analysis; Biopsy; Biopsy Specimen; Breast; CDK4 gene; Cancer Etiology; Cancer Patient; Cell Communication; Cessation of life; Characteristics; Clinical; Clinical Trials; Collection; Computer Models; Coupled; DNA; DNA Sequence Alteration; Data; Data Set; Dependence; Disease; Disseminated Malignant Neoplasm; Distant; Drainage procedure; Drug Combinations; Drug resistance; Ecology; Ensure; Epithelial; Evolution; Exhibits; Gene Expression; Gene Expression Profile; Genetic; Heterogeneity; Histone Deacetylase Inhibitor; Hour; Human; In Vitro; Investigation; Knowledge; Link; Liquid substance; Malignant Neoplasms; Malignant neoplasm of ovary; Mesenchymal; Metastatic to; Modeling; Molecular Profiling; Nature; Neoplasm Metastasis; Operative Surgical Procedures; Organ; Outcome; Ovarian; Patient-Focused Outcomes; Patients; Pattern; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Phylogeny; Physiological; Population; Primary Neoplasm; Recording of previous events; Refractory; Regimen; Reporting; Research; Resistance; Resources; Sampling; Signal Transduction; Site; Structure; System; Testing; Therapeutic; Time; Tissues; Trees; Tumor stage; Tumor-Derived; Variant; advanced breast cancer; base; cancer cell; cancer site; cohort; effective therapy; effectiveness evaluation; exome sequencing; genome sequencing; in vivo; individual patient; inhibitor/antagonist; malignant breast neoplasm; mathematical model; molecular phenotype; neoplastic cell; novel; novel strategies; population based; predictive test; programs; reconstruction; refractory cancer; resistance mechanism; response; single-cell RNA sequencing; subclonal heterogeneity; tumor; tumor heterogeneity; tumorigenic; whole genome

ABSTRACT Recent studies in primary tumors have found a remarkable degree of intratumor heterogeneity, where a single tumor is comprised of a range of subclones exhibiting a diversity of phenotypes, including molecular profiles, proliferation capacity, and response to therapies. Although heterogeneity is now widely reported, few studies have investigated the heterogeneity of metastatic tumors at the end stage, despite the fact that metastatic cancer is estimated to be responsible for over 90% of cancer deaths. For breast and ovarian cancer, tumors that progress to metastasis are refractory to treatment. Therefore, there is a great need to determine the mechanisms by which subclonal diversity can affect the metastatic phenotype and underlie the difficulties in treatment. Studying metastatic tumors is difficult due to the challenges in collecting patient tissues. While primary tissues are typically obtained through biopsy, this is rarely performed for metastatic sites. To address this difficulty, we have developed both a rapid autopsy strategy where we collect fresh samples of metastatic tumors within hours of patient death, as well as collections of metastatic tumor biopsies in the clinical trial setting prior to and after drug treatment. These collections enable us to profile multiple metastatic sites and investigate the association between metastatic sites and subclonal evolution in an isogenic background. We propose to leverage this unique data set to investigate the relationship between evolution of tumor subclones during metastatic progression and the phenotypic profiles of these tumors. We hypothesize that, despite the diversity in their genetic mutation profiles, metastatic tumors exhibit clonal dynamics that ultimately leads to convergence on more common cooperative phenotypic networks, and that targeting the key dependencies within this network will lead to increased collapse of the metastatic tumor population. To investigate this, we will profile the tumors by whole genome sequencing, whole exome sequencing, and single cell RNA sequencing. This data, coupled with our newly developed algorithms for dissecting subclonal populations using tree reconstruction algorithms, for eliciting phenotypes from gene expression profiles using Bayesian statistics, and for simulating phenotypic evolution using mathematical models from ecology; will enable us to understand (Aim 1) the subclonal heterogeneity that underlies metastatic initiation and progression; (Aim 2) how cooperative functions evolve to a chemo-refractory signaling network, and therapeutic strategies to target it; and (Aim 3) how these dynamics are manifested human tumors in a clinical trial. Our investigations represent the first characterization of the clonal dynamics of a large multisite metastatic cohort, and will provide a new framework for understanding and treating end-stage tumors based on the evolution of cooperative phenotypes. We will develop these models on patient samples and test them in a unique clinical trial, ensuring the physiological, if not clinical, relevance of our findings.

抽象的 对原发性肿瘤的最新研究发现了显着的肿瘤内异质性，其中一个单一的 肿瘤由一系列的亚克隆组成，这些亚克隆表现出各种表型，包括分子特征， 增殖能力和对疗法的反应。尽管现在广泛报道了异质性，但很少有研究 尽管转移性癌症已经研究了转移性肿瘤的异质性 据估计，癌症死亡人数超过90％。对于乳腺癌和卵巢癌，肿瘤 转移的进展是治疗的难治性。因此，非常需要确定机制 亚克隆多样性可以影响转移性表型并构成治疗困难。 由于收集患者组织的挑战，研究转移性肿瘤很困难。而主要组织 通常是通过活检获得的，很少对转移部位进行。为了解决这个困难，我们 已经制定了一种快速的尸检策略，我们在几个小时内收集转移性肿瘤的新鲜样品 患者死亡以及在临床试验环境中和之后的转移性肿瘤活检的集合 药物治疗。这些集合使我们能够介绍多个转移性站点并调查关联 在异基因背景下转移部位和亚克隆进化之间。我们建议利用这种独特的 数据集以调查转移性进展过程中肿瘤亚克隆演变的关系和 这些肿瘤的表型特征。 我们假设，尽管其遗传突变谱有多样性，但转移性肿瘤表现出 克隆动力学最终导致更常见的合作表型收敛 网络以及针对该网络中的关键依赖性的网络将导致增加 转移性肿瘤种群。为了调查这一点，我们将通过整个基因组测序介绍肿瘤， 整个外显子组测序和单细胞RNA测序。这些数据与我们新开发的 使用树重建算法解剖亚克隆人群的算法，从 使用贝叶斯统计数据的基因表达谱，并用于使用数学模拟表型进化 来自生态学的模型；将使我们能够理解（目标1）基于转移性的亚克隆异质性 启动和进展； （AIM 2）合作功能如何演变为化学难治性信号网络， 和针对目标的治疗策略； （AIM 3）这些动力如何在临床中表现出人类肿瘤 审判。 我们的研究代表了大型多站点转移的克隆动力学的第一个表征 队列，并将为基于进化的终端肿瘤提供一个新的框架 合作表型。我们将在患者样品上开发这些模型，并在独特的临床上进行测试 试验，确保我们发现的生理学（即使不是临床）的相关性。