Measuring, Modeling and Controlling Heterogeneity

测量、建模和控制异质性

• 批准号: 10166783
• 负责人: Emek Demir
• 金额: $ 185.13万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-22 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10166783
• 关键词: 3-Dimensional; Breast Cancer Cell; Cancer Control; Cancer cell line; Cell Line; Cell model; Cells; Cellular Structures; Characteristics; Clinical; Clonal Expansion; Communities; Complex; Computer Models; Custom; Data; Data Analyses; Databases; Drug Targeting; Educational Materials; Electron Microscopy; Engineering; Ensure; Environment; Evaluation; Exhibits; FDA approved; Genomics; Goals; Heterogeneity; Image; Image Analysis; Imaging technology; Immunofluorescence Immunologic; In Vitro; Infrastructure; Leadership; Learning; Link; MDA-MB-468; Machine Learning; Malignant Neoplasms; Measurement; Measures; Mediating; Metadata; Methods; Microarray Analysis; Modeling; Molecular; Pathway interactions; Patients; Periodicity; Pharmaceutical Preparations; Pharmacology; Phenotype; Pilot Projects; Plant Roots; Procedures; Proteins; Reagent; Resistance; SDZ RAD; Signal Transduction; Site; Source; Specimen; Stress; Structure; System; Systems Analysis; Systems Biology; Therapeutic; Tissues; Training; Visualization; Work; Xenograft procedure; analytical tool; bioimaging; bioprinting; cancer cell; combinatorial; computational suite; computerized tools; data integration; data management; epigenomics; experimental study; feature extraction; image visualization; improved; in vivo; light microscopy; metabolomics; multidisciplinary; network models; novel; open source; outreach; prevent; quantitative imaging; response; targeted treatment; therapy resistant; treatment response; triple-negative invasive breast carcinoma; tumor

ABSTRACT - Overall The overall goal of the Measuring, Modeling and Controlling Heterogeneity Center in the Cancer Systems Biol- ogy Consortium (M2CH-CSBC Center) is to improve management of triple negative breast cancer (TNBC) by developing systems level strategies to prevent the emergence of cancer subpopulations that are resistant to treatment. We postulate that heterogeneity arising from epigenomic instability intrinsic to cancer cells and di- verse signals from extrinsic microenvironments in which cancer cells reside are root causes of resistance. We will learn how intrinsic and extrinsic factors influence differentiation state, proliferation and therapeutic re- sponse in TNBC through experimental manipulation and computational modeling of cancer cell lines, 3D engi- neered multicellular systems, xenografts and clinical specimens. We will deploy single cell `omic and imaging technologies that allow quantitative assessment of molecular, cellular, and structural heterogeneity. We will interpret these data using computational models that define control networks and structures in heterogeneous systems as well as transitions between states of therapeutic resistance and sensitivity. This will be accom- plished in three related Projects and three Cores. Project 1 will focus on measuring and managing resistance- associated heterogeneity intrinsic to cancer cells. Project 2 will focus on identifying resistance-associated sig- nals from the microenvironment and on mitigating effects from these signals on therapeutic response. Project 3 will apply spatial systems biology approaches to TNBC specimens and multicell type models thereof to dis- cover molecular control networks that influence how cell intrinsic plasticity and microenvironment signaling al- ter therapeutic responses in complex tissues. All three Projects will include analysis of 5 core cell lines (HCC1143, HCC1599, MDA-MB-468, SUM149PT, and HCC1806), 5 patient derived cultures, and 6 FDA ap- proved, pathway-targeted drugs (afatinib, ruxolotinib, trametinib, BYL719, cabozantinib, and everolimus). The computational network discovery, data integration, spatial systems analysis and modeling approaches are the same in all Projects and serve to integrate the work of the overall M2CH-CSBC Center. Multiple integrative computational strategies are proposed to identify candidate heterogeneity control networks. These include analysis of existing genomic, epigenomic, pharmacologic response, and metabolomic characteristics of prima- ry tumors and models thereof. An Imaging Management and Analysis Core will provide infrastructure and image analytics that will enable efficient image data management, quantitative analysis of image features, and visualization of images and metadata generated using multiscale light and electron microscopy. An Outreach Core will make educational materials, experimental and computational tools and data available to the CSB Consortium, to the CSBC/PS-ON Coordinating Center and to the broader scientific community. An Administrative Core serves as the organization, integration, and evaluation hub of the M2CH-CSBC Center.

摘要 - 总体 癌症系统生物测量、建模和控制异质性中心的总体目标 ogy 联盟（M2CH-CSBC 中心）旨在通过以下方式改善三阴性乳腺癌 (TNBC) 的管理： 制定系统级策略以防止出现耐药的癌症亚群 治疗。我们假设，异质性是由癌细胞固有的表观基因组不稳定性引起的，并且 来自癌细胞所在的外在微环境的反向信号是耐药性的根本原因。我们 将了解内在和外在因素如何影响分化状态、增殖和治疗再 通过癌细胞系的实验操作和计算模型、3D 工程在 TNBC 中做出反应 需要多细胞系统、异种移植物和临床标本。我们将部署单细胞组学和成像 允许定量评估分子、细胞和结构异质性的技术。我们将 使用定义异构控制网络和结构的计算模型来解释这些数据 系统以及治疗抵抗和敏感性状态之间的转变。这将适应 完成了三个相关项目和三个核心。项目 1 将重点关注测量和管理阻力 - 癌细胞固有的相关异质性。项目2将重点识别与耐药性相关的信号 来自微环境的结果以及减轻这些信号对治疗反应的影响。项目 3 将空间系统生物学方法应用于 TNBC 标本及其多细胞类型模型，以揭示 涵盖影响细胞内在可塑性和微环境信号传导的分子控制网络 复杂组织中的治疗反应。所有三个项目都将包括 5 个核心细胞系的分析 （HCC1143、HCC1599、MDA-MB-468、SUM149PT 和 HCC1806）、5 个患者来源的培养物和 6 个 FDA AP- 经证实的通路靶向药物（阿法替尼、鲁索洛替尼、曲美替尼、BYL719、卡博替尼和依维莫司）。这 计算网络发现、数据集成、空间系统分析和建模方法是 所有项目都相同，并用于整合整个 M2CH-CSBC 中心的工作。多元综合 提出了计算策略来识别候选异质性控制网络。这些包括 分析原始基因组、表观基因组、药理学反应和代谢组学特征 ry肿瘤及其模型。影像管理和分析核心将提供基础设施和 图像分析将实现高效的图像数据管理、图像特征的定量分析，以及 使用多尺度光学和电子显微镜生成的图像和元数据的可视化。外展活动 Core 将为 CSB 提供教育材料、实验和计算工具以及数据 联盟、CSBC/PS-ON 协调中心以及更广泛的科学界。一个 管理核心是 M2CH-CSBC 中心的组织、集成和评估中心。

项目成果

Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach.

用序贯多重免疫组织化学和稀疏子空间聚类方法解读食管腺癌和癌前病变的免疫复杂性。

• 发表时间: 2022
• 作者: Sundaram, Srinand;Kim, Eun Na;Jones, Georgina M;Sivagnanam, Shamilene;Tripathi, Monika;Miremadi, Ahmad;Di Pietro, Massimiliano;Coussens, Lisa M;Fitzgerald, Rebecca C;Chang, Young Hwan;Zhuang, Lizhe
• 通讯作者: Zhuang, Lizhe

A multiplex implantable microdevice assay identifies synergistic combinations of cancer immunotherapies and conventional drugs.

多重植入式微装置测定可识别癌症免疫疗法和常规药物的协同组合。

• 发表时间: 2022-12
• 影响因子: 46.9
• 作者: Tatarova, Zuzana;Blumberg, Dylan C;Korkola, James E;Heiser, Laura M;Muschler, John L;Schedin, Pepper J;Ahn, Sebastian W;Mills, Gordon B;Coussens, Lisa M;Jonas, Oliver;Gray, Joe W
• 通讯作者: Gray, Joe W

A lineage tree-based hidden Markov model quantifies cellular heterogeneity and plasticity.

基于谱系树的隐马尔可夫模型量化细胞异质性和可塑性。

• 发表时间: 2022-11-17
• 影响因子: 5.9
• 作者: Mohammadi, Farnaz;Visagan, Shakthi;Gross, Sean M;Karginov, Luka;Lagarde, J C;Heiser, Laura M;Meyer, Aaron S
• 通讯作者: Meyer, Aaron S

Multiplexed and Millimeter-Scale Fluorescence Nanoscopy of Cells and Tissue Sections via Prism-Illumination and Microfluidics-Enhanced DNA-PAINT.

通过棱镜照明和微流体增强 DNA-PAINT 对细胞和组织切片进行多重和毫米级荧光纳米显微镜检查。

• 发表时间: 2023-12-25
• 作者: Rames, Matthew J;Kenison, John P;Heineck, Daniel;Civitci, Fehmi;Szczepaniak, Malwina;Zheng, Ting;Shangguan, Julia;Zhang, Yujia;Tao, Kai;Esener, Sadik;Nan, Xiaolin
• 通讯作者: Nan, Xiaolin

3D multiplexed tissue imaging reconstruction and optimized region of interest (ROI) selection through deep learning model of channels embedding.

通过通道嵌入的深度学习模型进行 3D 多重组织成像重建和优化感兴趣区域 (ROI) 选择。

• 发表时间: 2023
• 作者: Burlingame, Erik;Ternes, Luke;Lin, Jia-Ren;Chen, Yu-An;Kim, Eun Na;Gray, Joe W;Chang, Young Hwan
• 通讯作者: Chang, Young Hwan