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 该项目的目标是使用空间系统方法来识别控制的分子网络 三重负乳腺癌（TNBC）中与抗性相关的异质性的发展并使用 信息以设计将有效的多药处理。我们的重点是 异质性来自癌细胞的表观基因组可塑性以及来自来自的外部信号 TNBC细胞分散的各种微环境。 TNBC内的单个单元显示可变 表型和对治疗的反应可观，以便众所周知，建立对TNBC的持久控制 难的。我们将探讨TNBC组织中各个细胞对扰动的响应的机制 由微环境相互作用和/或药物诱导。我们的方法基于这样的概念 表型和对异质TNBC组织中每个细胞的治疗的反应受其内在的影响 表观基因组状态以及通过其收到的微环境信号。简而言之，每个癌细胞 - 在异质实验组织或临床标本中的微环境与药物相互作用是一种 自然的独立实验。我们建议分析TNBC组织中这种相互作用的集合 治疗前后，以确定局部环境信号对癌细胞表型和 治疗反应。我们将使用CMIF对此做到这一点，以染色癌细胞，以定量分析 在控制网络上报告的蛋白质的增殖状态，分化状态和表达水平 活动。我们将使用多色在微观尺度上量化癌细胞 - 微环境的相互作用 荧光显微镜和在纳米级处使用多光谱超级分辨率荧光显微镜 （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 量化细胞和微环境组件以及机器/深度学习策略的成像核心 确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 可用于空间依赖的控制网络微环境相互作用的动态模型 制定控制TNBC的治疗策略。该方法在统计上是强大的，因为每张组织 部分包含有关数以万计的细胞微环境相互作用的详细信息。这项工作是 包含三个目标。 AIM 1将开发环状多重免疫荧光（CMIF），多尺度图像 分析和机器学习程序需要确定单个细胞中分子控制网络 TNBC组织对微环境细胞和蛋白质（MEP）的信号响应，并且影响 表型和/或治疗反应。 AIM 2将阐明微环境细胞的影响和高 影响蛋白质对生物打印组织中TNBC控制网络活性，表型和治疗反应的影响。 AIM 3将阐明微环境细胞和高影响蛋白对TNBC控制网络的影响 TNBC异种移植物和临床TNBC标本中的活性，表型和治疗反应。