Core 2: Ecological Core

核心2：生态核心

基本信息

批准号：
10730409
负责人：
Joel Brown
金额：
$ 33.79万
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：
2023
资助国家：
美国
起止时间：
2023-09-15 至 2028-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10730409
关键词：
3-Dimensional Behavior Biological Markers Calibration Cataloging Cells Clinical Complex Computer software Data Development Dimensions Ecological Change Ecology Ecosystem Environment Feedback Generations Genotype Heterogeneity Histology Image Immune Immunofluorescence Immunologic Investigation Maintenance Malignant Neoplasms Malignant neoplasm of lung Methods Modeling Non-Small-Cell Lung Carcinoma Pattern Recording of previous events Resolution Sampling Shelter facility Slice Speed Stains Techniques Testing Thinking Time Tissues Visualization cancer therapy computational pipelines data pipeline high dimensionality histological specimens image registration imaging modality improved insight interest mathematical model multimodality multiplexed imaging novel therapeutic intervention spatiotemporal three dimensional structure tissue reconstruction tool treatment response tumor tumor behavior tumor heterogeneity tumor microenvironment tumor progression tumor-immune system interactions whole slide imaging

项目摘要

Summary – Ecological Core Recent advances in spatial ‘omics’ have led to an increased interest in studying and predicting emergent aggregate behavior and interactions between the tumor and its microenvironment. Tumors are dynamic, rapidly evolving and undergoing ecological shifts; therefore, the understanding of tumor progression and response to therapy will benefit from studying eco-evolutionary interactions over time. Investigation of the spatial and spatiotemporal interactions within the tumor immune microenvironment requires computational approaches to identify and investigate the diverse ecological patterns that emerge by virtue of these complex dynamics. The Ecological Analysis Core will cater to the generation, investigation, and application of the spatial ecological models and techniques to support the two non-small cell lung cancer (NSCLC)-focused Projects of the proposal. Given that ecological change over space and/or time (the Δ-Ecology) is a central theme shared by both Projects, the Core is poised to support and tailor the tools to the requirements of each Project. The Core will have the capability to the ability to manage, visualize, store, share, and perform quantitative analyses of the high-dimensional and high-resolution clinical and experimental histology images. We will also build important computational approaches and developmental frameworks to improve our understanding and prediction of emergent tumor behavior using the images. In summary, the Core will develop and apply spatial ecological models in concert with mathematical models from the Mathematical Modeling Core to understand the evolutionary ecology underlying the diversity and maintenance of tumor heterogeneity, and how this can be leveraged to guide new therapeutic strategies for lung cancer.

总结——生态核心空间“组学”的最新进展引起了人们对研究和预测新兴事件的兴趣增加肿瘤与其微环境之间的聚集行为和相互作用是动态的。因此，对肿瘤进展的理解和对治疗的反应将受益于随着时间的推移研究生态进化相互作用。肿瘤免疫微环境内的空间和时空相互作用需要计算识别和研究这些复合体所出现的不同生态模式的方法生态分析核心将满足空间的生成、调查和应用。支持两个以非小细胞肺癌（NSCLC）为重点的项目的生态模型和技术鉴于空间和/或时间上的生态变化（Δ-生态）是共同的中心主题。通过这两个项目，核心准备支持并根据每个项目的要求定制工具。 Core 将具有管理、可视化、存储、共享和执行定量分析的能力我们还将构建高维和高分辨率的临床和实验组织学图像。重要的计算方法和发展框架，以提高我们的理解和总之，核心将开发和应用空间。生态模型与数学建模核心的数学模型相结合，以理解多样性和异质性维持背后的进化生态学，以及如何实现这一点用于指导肺癌的新治疗策略。