Pre-Clinical Core

临床前核心

• 批准号: 10171104
• 负责人: William Douglas Cress
• 金额: $ 33.23万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 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/10171104
• 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

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 临床前模型和病理核心 临床前核心（核心＃2）利用了几位莫菲特调查员的知识和专业知识， 核心设施并将追求三个目标，以系统地支持三个关键的计划项目 阶段； 1）模型开发，2）测量和分析结果，3）在人类人群中进行验证 和器官模型。在AIM 1中，Core＃2的任务是鼠标模型的生成和表征。这 核心将使用胚胎干细胞基因工程的鼠标模型（ESC-GEMM）方法启用 肺癌小鼠模型的快速产生。为此，将从现有的肺中建立ESCS 癌症的宝石还含有调节等位基因，以有效地靶向ESC和控制基因 表达。 ESC将用诱导表达盒作为目标，然后将其注入到胚泡中 产生嵌合动物。这种嵌合体中的Esc衍生的肺细胞携带诱导肺部必要的等位基因 肿瘤发生，嵌合体将用作实验动物，而无需进一步繁殖。这种方法 允许具有代谢的调节表达或活性的肺癌模型的非常快速的生成 感兴趣的酶。在AIM 2中，Core＃2将简化这些鼠标肿瘤负担的量化 通过提供鼠肺癌模型的数字病理（DP）分析模型。作为输入，组织学幻灯片 使用自动幻灯片扫描仪在多个宏伟元素中成像。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对处理和分析。平台 允许对整个肺部肿瘤和单个细胞的鉴定，表征和定量 肺切片。深度学习方法将用于提供肺的全面表征 肿瘤，包括肿瘤分级和免疫细胞浸润的评估。组织学样本和 程序项目创建的数据集将用于创建新算法的培训库 发展。 DP平台允许高度准确，快速的图像分析，加速 肺肿瘤模型的表征。在AIM 3中，Core＃2将提供资源和专业知识来验证 小鼠研究的结果。核心将提供庞大的，宣布良好的人 由组织微阵列和类器官模型产生的集中式工作流程代表的队列。 核心将有助于项目计划和方法开发，诊断咨询，生物标志物评分， 结果解释和手稿写作。其他病理资源将包括微观评估 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。