Pre-Clinical Core

临床前核心

基本信息

批准号：
10438718
负责人：
William Douglas Cress
金额：
$ 34.22万
依托单位：
H. LEE MOFFITT CANCER CTR & RES INST
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-25 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10438718
关键词：
Adenocarcinoma Adenocarcinoma Cell Alleles Animals Antibodies Artificial Intelligence Biological Markers Brassicaceae Breeding Cancer Center Cancer Model Cells Chimera organism Clustered Regularly Interspaced Short Palindromic Repeats Collaborations Complex Consultations Core Facility Critical Pathways DNA cassette Data Data Set Development Diagnosis Diagnostic Ensure Environment Enzymes Evaluation Experimental Designs Fertilization in Vitro Gene Expression Gene Targeting Generations Genes Genetically Engineered Mouse Genotype Histology Histopathology Human Image Image Analysis Immune Immunofluorescence Immunologic Immunohistochemistry Individual Infiltration Injections Investigation Knowledge Libraries Lung Lung Neoplasms Malignant Neoplasms Malignant neoplasm of lung Manuscripts Measurement Metabolic Microscopic Modeling Molecular Mus Organoids Outcome Pathologic Pathologist Pathology Patients Pre-Clinical Model Process Production Research Research Personnel Resolution Resource Sharing Resources Services Slide Specimen Squamous cell carcinoma Structure Tissue Microarray Training Transgenic Organisms Tumor Burden Validation Writing algorithm development animal tissue blastocyst cancer imaging cohort data dictionary deep learning digital digital pathology embryo cryopreservation embryonic stem cell fluorescence imaging high resolution imaging histological specimens homologous recombination human tissue in vivo inducible gene expression innovation interest learning strategy lung small cell carcinoma lung tumorigenesis machine learning algorithm method development model development mouse model novel pre-clinical predictive modeling programs quantitative imaging repository service learning sperm cryopreservation success tissue resource tumor tumor growth tumor metabolism zygote

项目摘要

CORE 2 PROJECT SUMMARY PRECLINICAL MODELS AND PATHOLOGY CORE The Preclinical Core (Core #2) leverages the knowledge and expertise from several Moffitt investigators and core facilities and will pursue three aims that systematically support the Program Projects at three critical stages; 1) model development, 2) measurement and analysis of outcome, and 3) validation in human cohorts and organoid models. In the Aim 1, Core #2 is tasked with mouse model generation and characterization. The Core will use the embryonic stem cell-genetically engineered mouse model (ESC-GEMM) approach to enable the rapid generation of lung cancer mouse models. To this end, ESCs will be established from existing lung cancer GEMMs that also harbor regulatory alleles for efficient targeting of ESCs and control of gene expression. ESCs will be targeted with inducible expression cassettes, followed by injection into blastocysts to generate chimeric animals. ESC-derived lung cells in such chimeras carry the alleles necessary to induce lung tumorigenesis, and chimeras will be used as experimental animals without further breeding. This approach allows for the very rapid generation of lung cancer models having modulated expression or activity of metabolic enzymes of interest. In Aim 2, Core #2 will streamline the quantification of tumor burden in these mouse models by providing digital pathological (DP) analysis of murine lung cancer models. As input, histology slides are imaged at multiple magnifications using automated slide scanners. The resulting high-resolution images are processed and analyzed using innovative machine learning algorithms we have developed. The platform allows for the identification, characterization, and quantification of lung tumors and individual cells within whole lung sections. Deep learning methods will be applied to provide a comprehensive characterization of lung tumors, including tumor grading and the assessment of immune cell infiltration. Histology samples and datasets created by the program projects will be used to create training libraries for new algorithm development. The DP platform allows for highly accurate and rapid image analysis, accelerating the characterization of lung tumor models. In Aim 3, Core #2 will provide resources and expertise to validate outcomes from the mouse studies of the first two Aims. The Core will provide large, well-annotated human cohorts represented by tissue microarrays and a centralized workflow for the generation of organoid models. The Core will assist with project planning and method development, diagnostic consultation, biomarker scoring, result interpretation, and manuscript writing. Additional pathology resources will include microscopic evaluation of animal and human tissues for adequacy and diagnosis, antibody selection guidance, optimization for immunohistochemistry (IHC), and multiplexed immunofluorescence imaging.

核心 2 项目摘要临床前模型和病理学核心临床前核心（核心#2）利用了几位莫菲特研究人员的知识和专业知识，核心设施，并将追求三个目标，系统地支持三个关键的计划项目阶段； 1) 模型开发，2) 结果测量和分析，3) 人类队列验证和类器官模型。在目标 1 中，核心 #2 的任务是小鼠模型生成和表征。这 Core 将使用胚胎干细胞基因工程小鼠模型 (ESC-GEMM) 方法来实现肺癌小鼠模型的快速生成。为此，将从现有的肺中建立 ESC 癌症 GEMM 还含有调节等位基因，可有效靶向 ESC 并控制基因表达。 ESC 将被诱导表达盒靶向，然后注射到囊胚中产生嵌合动物。此类嵌合体中的 ESC 衍生肺细胞携带诱导肺功能所需的等位基因肿瘤发生，嵌合体将用作实验动物，无需进一步饲养。这种做法允许非常快速地生成具有调节的代谢表达或活性的肺癌模型感兴趣的酶。在目标 2 中，核心 #2 将简化这些小鼠肿瘤负荷的量化通过提供小鼠肺癌模型的数字病理 (DP) 分析来建立模型。作为输入，组织学幻灯片使用自动幻灯片扫描仪以多个放大倍数成像。由此产生的高分辨率图像使用我们开发的创新机器学习算法进行处理和分析。平台允许对肺肿瘤和整个细胞内的单个细胞进行识别、表征和定量肺切片。将应用深度学习方法来提供肺部的全面表征肿瘤，包括肿瘤分级和免疫细胞浸润的评估。组织学样本和项目创建的数据集将用于创建新算法的训练库发展。 DP 平台可实现高精度和快速的图像分析，从而加速肺肿瘤模型的表征。在目标 3 中，核心#2 将提供资源和专业知识来验证前两个目标的小鼠研究结果。核心将提供大型、注释良好的人类以组织微阵列和用于生成类器官模型的集中工作流程为代表的队列。核心将协助项目规划和方法开发、诊断咨询、生物标志物评分、结果解释和手稿写作。其他病理学资源将包括显微镜评估动物和人体组织的充分性和诊断、抗体选择指导、优化免疫组织化学 (IHC) 和多重免疫荧光成像。