Sorting and characterization of cancer cells based on metabolic phenotype

基于代谢表型的癌细胞分选和表征

基本信息

批准号：
10467279
负责人：
Cynthia A. Reinhart-King
金额：
$ 22.23万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10467279
关键词：
Biological Biosensor Blood Circulation Breast Breast Cancer Cell Breast Cancer cell line Breast cancer metastasis Cancer Intervention Cell Proliferation Cell Separation Cell physiology Cells Cellular Metabolic Process Chemicals Coupled Data Dependence Engineering Exhibits Flow Cytometry Fluorescence-Activated Cell Sorting Fluorescent Probes Foundations Future Generations Genetic Heterogeneity Glycolysis Goals Heterogeneity In Vitro Individual Knowledge Label Lead Malignant Neoplasms Metabolic Metabolic Pathway Metabolism Metastatic breast cancer Metastatic to Methods Microscopy Mitochondria Molecular NADH Neoplasm Metastasis Organ Outcome Output Oxidation-Reduction Oxidative Phosphorylation Oxygen Pathway interactions Phenotype Play Primary Neoplasm Proteins Pyruvate Reporting Research Role Site Sorting - Cell Movement Subgroup Suspensions System Testing Therapeutic Intervention Time Work base cancer cell cell motility design in vivo inhibitor innovation interest metabolic phenotype metabolic profile mouse model novel photoactivation therapeutic target therapy resistant tool transcriptomics tumor microenvironment tumor progression

项目摘要

PROJECT SUMMARY/ABSTRACT Altered metabolism is a hallmark of cancer, and therapeutic intervention of this altered feature is emerging and holds significant potential. Recent work has found that breast cancer cells exhibit dramatic differences in their glycolysis versus oxidative phosphorylation (OXPHOS) metabolic phenotype within the primary tumor and metastases, and between metastases at different organs. This heterogeneity in metabolic phenotype may be a result of genetic heterogeneity or cellular plasticity and metabolic adaptation to the local microenvironment. Metabolic heterogeneity and plasticity may contribute to therapeutic resistance to treatments that target a specific metabolic pathway. The field generally believes that cellular metabolic adaptation and plasticity facilitate their survival and colonization during metastasis. However, it not clear whether a change in metabolic phenotype in the primary tumor can predict metastatic outcome. In this project, we propose to phenotypically sort breast cancer cells into subpopulations with distinct glycolysis or OXPHOS phenotypes, and use these sorted subpopulations to test the hypothesis that the initial metabolic phenotype and heterogeneity determine the metastatic outcome against the alternative hypothesis that metabolic adaptation to the local microenvironment and phenotypical switching contribute to metastatic outcome regardless of the initial metabolic heterogeneity. By expressing a fluorescent biosensor in the cells for cellular glycolysis versus OXPHOS reliance, we have obtained preliminary data supporting the feasibility of cell sorting based on this metabolic feature. In Aim 1, we will optimize the engineering approach for cell sorting based on cellular metabolic phenotype. Fluorescence-activated cell sorting (FACS) will be coupled with metabolic biosensors, and automated microscopy, photoactivation and fluorescent labeling of cells for cell separation. In Aim 2, we will use the sorted metabolic subpopulations to test our overall hypotheses in vitro and in vivo that initial metabolic phenotype predicts metastatic outcome. Engineered systems mimicking the environmental conditions at the primary and secondary sites, and in circulation will be designed to characterize cell migration, proliferation, and survival of the subpopulations, as well as their metabolic adaptation. We will examine the metastatic potential of these subpopulations in a mouse model and determine their metabolic adaptations at different stages along the metastatic cascade. The innovative aspects of this proposal are the concept to sort by metabolic phenotype and the goal of uncovering the role of initial metabolic phenotype in the broader metastatic cascade. This project will use the novel engineered cell sorting approach to dissect the respective roles of metabolic heterogeneity and adaptability in breast cancer metastasis, thus laying the foundation for future work to identify the key molecular pathways to precisely target for cancer metabolic therapy.

项目概要/摘要代谢改变是癌症的一个标志，针对这种改变特征的治疗干预正在出现，并且拥有巨大的潜力。最近的研究发现，乳腺癌细胞在其功能方面表现出巨大的差异。原发肿瘤内的糖酵解与氧化磷酸化（OXPHOS）代谢表型和转移，以及不同器官的转移之间。这种代谢表型的异质性可能是遗传异质性或细胞可塑性以及对局部微环境的代谢适应的结果。代谢异质性和可塑性可能有助于针对特定目标的治疗产生耐药性代谢途径。该领域普遍认为细胞代谢适应和可塑性有利于其转移过程中的存活和定植。然而，尚不清楚代谢表型是否发生变化原发肿瘤可以预测转移结果。在这个项目中，我们建议对乳腺癌进行表型分类将细胞分成具有不同糖酵解或 OXPHOS 表型的亚群，并使用这些分选的亚群检验初始代谢表型和异质性决定转移结果的假设反对另一种假设，即代谢适应局部微环境和表型无论初始代谢异质性如何，转换都会导致转移结果。通过表达一个细胞中的荧光生物传感器用于细胞糖酵解与 OXPHOS 依赖性的比较，我们已经初步获得数据支持基于这种代谢特征的细胞分选的可行性。在目标 1 中，我们将优化基于细胞代谢表型的细胞分选工程方法。荧光激活细胞分选 (FACS) 将与代谢生物传感器、自动显微镜、光活化和荧光相结合细胞标记用于细胞分离。在目标 2 中，我们将使用排序后的代谢亚群来测试我们的整体情况体外和体内假设初始代谢表型预测转移结果。工程系统模拟主次场地的环境条件以及流通区域进行设计表征细胞迁移、增殖和亚群的存活及其代谢适应。我们将在小鼠模型中检查这些亚群的转移潜力并确定它们在转移级联的不同阶段的代谢适应。本次活动的创新点建议是按代谢表型分类的概念和揭示初始代谢作用的目标更广泛的转移级联反应中的表型。该项目将使用新颖的工程细胞分选方法剖析代谢异质性和适应性在乳腺癌转移中各自的作用，从而奠定为未来确定精确靶向癌症代谢的关键分子途径的工作奠定基础治疗。