Phenotype Heterogeneity and Dynamics in SCLC

SCLC 的表型异质性和动态

• 批准号: 9901484
• 负责人: Vito Quaranta
• 金额: $ 173.3万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-13 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9901484
• 关键词: Address; Aftercare; Automobile Driving; Biological Models; CD44 gene; Cancer Biology; Cancer cell line; Cell Communication; Cell Shape; Cell model; Cells; Cellular Morphology; Cellular biology; Collaborations; Communication; Complex; Cytometry; DNA Sequence Alteration; Data; Data Analyses; Ecosystem; Educational workshop; Epigenetic Process; Exhibits; Experimental Models; Genes; Genetic; Genetically Engineered Mouse; Genotype; Goals; Health; Heterogeneity; Human; Human Engineering; Information Systems; Knowledge; Leadership; Logic; Malignant Neoplasms; Measurement; Measures; Mediating; Methods; Mission; Modeling; Molecular; Morphologic artifacts; Mutation; Oncology; Outcome; Pathologic; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Physiological; Population Dynamics; Predisposition; Process; Production; Property; Protein p53; Publications; Recurrence; Relapse; Research Personnel; Resource Sharing; Role; Side; Site; Source; System; Systems Biology; Technology; Testing; Thinking; Tissues; Treatment Failure; Universities; Variant; base; cancer cell; cancer heterogeneity; cancer therapy; cancer type; cell behavior; chemoradiation; computerized tools; data exchange; design; drug sensitivity; effective therapy; exosome; experimental study; high dimensionality; improved; innovation; lung small cell carcinoma; mathematical model; meetings; mouse model; multidimensional data; neoplastic cell; non-genetic; novel; online course; outreach; outreach program; programs; resistance mutation; response; single cell analysis; single cell technology; single-cell RNA sequencing; success; symposium; therapy resistant; tool; transcriptomics; treatment strategy; tumor; tumor heterogeneity; virtual; web site

Heterogeneity is a pervasive feature of cancer, central to progression and therapy failure. Both genetic and nongenetic factors, cell-cell and cell-microenvironment interactions contribute to tumor cell phenotypic variation. Thus, heterogeneity is a complex multiscale problem difficult to study by reductionist approaches but well suited to systems thinking. A mechanistic, system-level understanding of heterogeneity would spawn fundamental advances in cancer treatment strategies. Major challenges include definition of relevant tumor cell phenotypes, phenotype dynamics emergence from single-cell behavior and interactions, and effective targeting strategies. In our Center, we will use systems biology approaches to tackle these challenges, focusing on small cell lung cancer (SCLC), in which the impact of intratumor heterogeneity is particularly compelling. Thus, SCLC tumors, while histo-pathologically homogeneous with classic “small blue round cell” morphology, are comprised of phenotypic subpopulations (e.g., tumor-propagating cells; Hes1+ cells; CD44+ cells) that cooperate to form a tumor ecosystem adaptive to drug treatment. In SCLC genetically engineered mouse models (GEMMs) and patient tumors, accumulated genetic alterations (e.g., MYC amplification, NOTCH mutations) may bias phenotypic compositions and consequent drug sensitivity, underscoring the combined role of genetic and nongenetic sources. The overall goal of our proposed Center is a system-level understanding of the impact of SCLC phenotype heterogeneity in drug evasion, that will open avenues to novel treatment strategies. In two highly integrated Projects, we will combine experimentation with mathematical modeling to generate a comprehensive blueprint of SCLC phenotypic space and identify complex phenotype dynamics underlying treatment resistance. Project 1 will use high-dimensional cytometry and transcriptomics data to define human and GEMM core SCLC phenotypes, their susceptibility to genomic alteration bias, and their drug response plasticity. Project 2 will study SCLC phenotype dynamics initiated by cell-cell and secreted factors or exosomes in SCLC tumor ecosystems, using models of cell population dynamics driving tumor aggressiveness. Projects will be supported by a Single-Cell Biology and Data Analysis Shared Resource equipped with a vast palette of state-of-the-art single-cell technologies, including mass cytometry, scRNA-Seq and multidimensional data analysis. The Center will be located at Vanderbilt University and a satellite site at Stanford contributing highly-regarded SCLC GEMMs. Experimental systems will encompass human and GEMM SCLC cell lines and tumors. Our Center investigators have a track record of co-authored publications in SCLC and/or systems biology. The Administrative Core will provide leadership and communication across the Center while the Outreach Core will promote collaborations and disseminate knowledge and tools on SCLC and cancer heterogeneity. We expect that iteration between experiments and modeling will bring about a system-level understanding of SCLC tumor heterogeneity with lessons generally applicable to any cancer type.

异质性是癌症的一个普遍特征，是疾病进展和治疗失败的关键。遗传和 非遗传因素、细胞-细胞和细胞-微环境相互作用有助于肿瘤细胞表型变异。 因此，异质性是一个复杂的多尺度问题，很难用还原论的方法来研究， 适合系统思考。对异质性的机械的、系统级的理解将产生 癌症治疗策略的根本性进展。主要挑战包括相关肿瘤细胞的定义 表型，表型动力学从单细胞行为和相互作用中出现，以及有效的靶向 战略布局在我们的中心，我们将使用系统生物学方法来应对这些挑战，重点是 小细胞肺癌（SCLC），其中肿瘤内异质性的影响特别引人注目。因此，在本发明中， SCLC肿瘤，虽然组织病理学上具有典型的“小蓝圆细胞”形态， 包括表型亚群（例如，肿瘤增殖细胞; Hes 1+细胞; CD 44+细胞）， 协同形成适应药物治疗的肿瘤生态系统。SCLC基因工程小鼠 模型（GEMM）和患者肿瘤，累积的遗传改变（例如，MYC扩增，NOTCH 突变）可能会偏向表型组成和随之而来的药物敏感性，强调了联合作用， 基因和非基因来源。我们建议的中心的总体目标是系统级的理解， SCLC表型异质性在药物逃避中的影响，这将为新的治疗开辟途径 战略布局在两个高度集成的项目中，我们将联合收割机实验与数学建模相结合， 生成SCLC表型空间的综合蓝图并识别复杂的表型动态 潜在的治疗抵抗。项目1将使用高维细胞计数和转录组学数据， 定义人类和GEMM核心SCLC表型，它们对基因组改变偏倚的易感性，以及它们的药物 反应可塑性项目2将研究由细胞-细胞和分泌因子启动的SCLC表型动力学， SCLC肿瘤生态系统中的外来体，使用驱动肿瘤侵袭性的细胞群体动力学模型。 项目将得到单细胞生物学和数据分析共享资源的支持， 一系列最先进的单细胞技术，包括质谱细胞术、scRNA-Seq和多维 数据分析该中心将设在范德比尔特大学和斯坦福大学的一个卫星站， 备受推崇的SCLC GEMM实验系统将包括人和GEMM SCLC细胞系， 肿瘤的我们中心的研究人员在SCLC和/或系统中有共同撰写的出版物的跟踪记录 生物学行政核心将在整个中心提供领导和沟通， 外展核心将促进合作，传播关于小细胞肺癌和癌症的知识和工具 异质性我们期望实验和建模之间的迭代将带来系统级的 了解SCLC肿瘤异质性，经验教训通常适用于任何癌症类型。