Understanding the Impact of Microscale and Nanoscale Heterogeneity and Resistance

了解微米级和纳米级异质性和阻力的影响

• 批准号: 10166790
• 负责人: JOE W. GRAY
• 金额: $ 46.61万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10166790
• 关键词: 3-Dimensional; Affect; Aftercare; Architecture; Behavior; Breast Cancer Cell; Cancer Control; Cell Communication; Cell physiology; Cells; Characteristics; Clinical; Collaborations; Computer Analysis; Custom; Data; Databases; Development; Distal; Drug Controls; Drug Interactions; Drug Targeting; ERBB2 gene; Engineering; Epidermal Growth Factor Receptor; Exhibits; Exposure to; FRAP1 gene; Fluorescence Microscopy; Genomic Instability; Genomics; Goals; Heterogeneity; Image; Image Analysis; Immunofluorescence Immunologic; Individual; JAK1 gene; KDR gene; Lead; Light; Link; MEKs; Machine Learning; Measures; Mediator of activation protein; Metadata; Methods; Modeling; Molecular; Nature; Pathway interactions; Periodicity; Pharmaceutical Preparations; Pharmacology; Phenotype; Primary Neoplasm; Procedures; Proteins; Reporting; Resistance; Resistance development; Resolution; SDZ RAD; Scanning Electron Microscopy; Signal Transduction; Specimen; Stains; System; Techniques; Testing; Therapeutic; Three-dimensional analysis; Tissues; Work; Xenograft procedure; analytical method; analytical tool; base; bioprinting; cancer cell; deep learning; epigenomics; experimental study; fluorescence imaging; human tissue; learning strategy; metabolomics; nanometer resolution; nanoscale; outreach; protein expression; response; treatment response; triple-negative invasive breast carcinoma; tumor microenvironment

ABSTRACT- Project 3 The goals of this Project are to use a spatial systems approach to identify molecular networks that control development of resistance-associated heterogeneity in triple negative breast cancers (TNBCs) and to use this information to devise multidrug treatments that will be effective in heterogeneous TNBCs. Our focus is on heterogeneity that arises from epigenomic plasticity intrinsic to cancer cells and from extrinsic signals from the diverse microenvironments into which TNBC cells disperse. Individual cells within a TNBC exhibit variable phenotypes and respond variably to treatment so that establishing durable control of TNBCs is notoriously difficult. We will explore the mechanisms by which individual cells in TNBC tissues respond to perturbations induced by microenvironment interactions and/or drugs. Our approach is based on the concept that the phenotype and response to therapy of every cell in a heterogeneous TNBC tissue is influenced by its intrinsic epigenomic status and by the microenvironmental signals it receives. In short, every cancer cell- microenvironment-drug interaction in a heterogeneous experimental tissue or clinical specimen is an independent experiment of nature. We propose to analyze ensembles of such interactions in TNBC tissues before and after treatment to determine the impact of local environmental signals on cancer cell phenotype and therapeutic response. We will accomplish this using cmIF to stain cancer cells for quantitative analysis of proliferative status, differentiation state, and expression levels of proteins that report on control network activity. We will quantify cancer cell-microenvironment interactions at the microscale using multicolor fluorescence microscopy and at the nanoscale using multispectral super resolution fluorescence microscopy (MSSRM) and 3D scanning electron microscopy. We will use custom image analysis techniques developed in the Imaging Core to quantify cell and microenvironment components and machine/deep learning strategies to identify microenvironment-cancer cell interactions that influence phenotype. This work will guide development of dynamic models of spatially dependent control network-microenvironment interactions that can be used to devise therapeutic strategies to control TNBCs. The approach is statistically powerful since every tissue section contains details about tens of thousands of cell-microenvironment interactions. This work is encompassed in three Aims. Aim 1 will develop cyclic multiplex immunofluorescence (cmIF), multiscale image analysis, and machine learning procedures needed to identify molecular control networks in individual cells in TNBC tissues that respond to signals from microenvironmental cells and proteins (MEPs) and that influence phenotype and/or therapeutic response. Aim 2 will elucidate the effects of microenvironmental cells and high impact proteins on TNBC control network activity, phenotype, and therapeutic response in bioprinted tissues. Aim 3 will elucidate the effects of microenvironmental cells and high impact proteins on TNBC control network activity, phenotype, and therapeutic response in TNBC xenografts and clinical TNBC specimens.

摘要-项目3 本项目的目标是使用空间系统方法来识别控制 三阴性乳腺癌（TNBC）中耐药相关异质性的发展，并使用该 这些信息有助于设计在异质性TNBC中有效的多药物治疗。我们的重点是 这种异质性是由癌细胞固有的表观基因组可塑性和来自肿瘤细胞的外源性信号引起的。 TNBC细胞分散的不同微环境。TNBC内的单个细胞表现出可变的 表型和对治疗的反应是一致的，因此建立持久的TNBC控制是众所周知的。 难我们将探索TNBC组织中单个细胞对扰动的反应机制， 由微环境相互作用和/或药物引起。我们的方法是基于这样的概念， 异质性TNBC组织中的每个细胞的表型和对治疗的反应受其内在的 表观基因组状态和它接收的微环境信号。简而言之，每个癌细胞- 在异质性实验组织或临床标本中的微环境-药物相互作用是一种 自然的独立实验。我们建议分析TNBC组织中此类相互作用的集合 在治疗之前和之后，以确定局部环境信号对癌细胞表型的影响， 治疗反应。我们将使用cmIF对癌细胞进行染色，以定量分析癌细胞的增殖。 增殖状态、分化状态和在控制网络上报告的蛋白质的表达水平 活动我们将使用RT-PCR在微观尺度上量化癌细胞与微环境的相互作用。 荧光显微镜和在纳米级使用多光谱超分辨率荧光显微镜 （MSSRM）和3D扫描电子显微镜。我们将使用自定义图像分析技术开发的 成像核心量化细胞和微环境组件以及机器/深度学习策略， 鉴定影响表型的微环境-癌细胞相互作用。这项工作将指导发展 空间相关控制网络-微环境相互作用的动态模型，可用于 制定治疗策略来控制TNBC。这种方法在统计学上是强大的，因为每个组织 部分包含了成千上万的细胞与微环境相互作用的细节。这项工作是 包含三个目标。目的1建立循环多重免疫荧光（cmIF）、多尺度图像 分析和机器学习程序，以识别单个细胞中的分子控制网络， TNBC组织对来自微环境细胞和蛋白质（MEP）的信号做出反应，并影响 表型和/或治疗反应。目的2将阐明微环境细胞和高密度脂蛋白的作用 影响生物打印组织中TNBC控制网络活性、表型和治疗反应的蛋白质。 目的3阐明微环境细胞和高影响蛋白在TNBC控制网络中的作用 TNBC异种移植物和临床TNBC标本中的活性、表型和治疗反应。