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 最近对原发性肿瘤的研究发现了显着程度的肿瘤内异质性，其中单个肿瘤 肿瘤由一系列表现出多种表型的亚克隆组成，包括分子谱、 增殖能力和对治疗的反应。尽管异质性现在已被广泛报道，但很少有研究 研究了末期转移性肿瘤的异质性，尽管事实上转移性癌症 据估计，90%以上的癌症死亡都是由它造成的。对于乳腺癌和卵巢癌，肿瘤 进展为转移是治疗难治性的。因此，非常需要确定机制 亚克隆多样性可以影响转移表型并造成治疗困难。 由于收集患者组织的挑战，研究转移性肿瘤很困难。而原代组织 通常通过活检获得，很少对转移部位进行活检。为了解决这个困难，我们 开发了一种快速尸检策略，我们可以在数小时内收集转移性肿瘤的新鲜样本 患者死亡的情况，以及在临床试验前后收集转移性肿瘤活检 药物治疗。这些集合使我们能够分析多个转移位点并调查其关联 同基因背景下转移位点和亚克隆进化之间的关系。我们建议利用这一独特的 用于研究转移进展过程中肿瘤亚克隆进化与 这些肿瘤的表型特征。 我们假设，尽管转移性肿瘤的基因突变谱存在多样性，但它们表现出 最终导致更常见的合作表型趋同的克隆动力学 网络，并且针对该网络内的关键依赖关系将导致网络崩溃加剧 转移性肿瘤群体。为了研究这一点，我们将通过全基因组测序来分析肿瘤， 全外显子组测序和单细胞RNA测序。这些数据加上我们新开发的 使用树重建算法解剖亚克隆群体的算法，用于从 使用贝叶斯统计分析基因表达谱，并使用数学模拟表型进化 生态学模型；将使我们能够理解（目标 1）转移性潜在的亚克隆异质性 启动和进展； （目标 2）协作功能如何演化为化学难治性信号网络， 以及针对它的治疗策略； （目标 3）这些动态如何在临床中表现出人类肿瘤 审判。 我们的研究首次表征了大型多位点转移瘤的克隆动力学 队列，并将为基于进化的理解和治疗终末期肿瘤提供新的框架 的合作表型。我们将在患者样本上开发这些模型，并在独特的临床中对其进行测试 试验，确保我们的研究结果具有生理相关性（如果不是临床相关性）。