Pre-Clinical Core

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，并 核心设施，并将追求三个目标，在三个关键方面系统地支持方案项目 阶段：1)模型开发，2)结果的测量和分析，以及3)人类队列的验证 还有器官模型。在目标1中，核心2的任务是生成和表征老鼠模型。这个 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，将从现有的肺建立胚胎干细胞。 也含有有效靶向ESCs和控制基因的调节等位基因的癌症GEMM 表情。ESCs将被诱导表达框作为靶点，然后注射到囊胚中 产生嵌合动物。这种嵌合体中的胚胎干细胞来源的肺细胞携带诱导肺的必要等位基因。 肿瘤发生，嵌合体将被用作实验动物，不需要进一步繁殖。这种方法 允许非常快速地产生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，Core#2将简化这些小鼠肿瘤负担的量化 通过提供对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 使用自动载玻片扫描仪以多倍放大进行成像。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。站台 允许识别、表征和量化肺肿瘤和整体内的单个细胞 肺切片。将应用深度学习方法来提供肺部的全面特征 肿瘤，包括肿瘤分级和免疫细胞浸润的评估。组织学标本和 计划项目创建的数据集将用于创建新算法的训练库 发展。DP平台允许进行高精度和快速的图像分析，从而加速 肺肿瘤模型的特征。在目标3中，核心2号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。核心将提供体型庞大、注释良好的人类 以组织微阵列为代表的队列和用于生成有机模型的集中式工作流程。 核心将协助项目规划和方法开发、诊断咨询、生物标记物评分、 成果解读、稿件撰写。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，抗体选择指南，最优化 免疫组织化学(IHC)和多重免疫荧光成像。

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.