Spatial and temporal tumor-immune co-evolution and interactions that model lung adenocarcinoma development

模拟肺腺癌发展的时空肿瘤免疫协同进化和相互作用

基本信息

批准号：
10447468
负责人：
Humam Kadara
金额：
$ 45.29万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10447468
关键词：
Address Atlases B cell clonality B-Cell Antigen Receptor B-Lymphocytes Cell Communication Cell model Cells Cessation of life Clinical Communities Complex Copy Number Polymorphism Data Development Diagnosis Disease Disease Outcome Distant Early Diagnosis Early treatment Ecosystem Epithelial Event Evolution Exhibits Foundations Heterogeneity Human Immune Immunologic Receptors Immunotherapy Individual Inflammatory Intercept Knowledge Lesion Lung Lung Adenocarcinoma Malignant - descriptor Malignant Neoplasms Malignant neoplasm of lung Maps Modeling Molecular Mus Mutation Mutation Analysis Normal Cell Normal tissue morphology Pathogenesis Pathologic Pathologic Processes Patients Performance Phenotype Physiological Processes Population Prevention strategy Probability Prognosis Property RNA Receptor Cell Recurrence Role Site Smoking Somatic Mutation Structure of parenchyma of lung Technology Testing Time Training Tumor-infiltrating immune cells Work cohort data portal immunological intervention improved in vivo lung development lung lesion machine learning model mathematical model mouse model patient subsets premalignant progenitor programs single cell sequencing single cell technology spatiotemporal transcriptome sequencing transcriptomics treatment strategy tumor tumor heterogeneity tumor-immune system interactions

项目摘要

PROJECT SUMMARY Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer and accounts for most cancer deaths. Improved early detection has increased the number of LUADs diagnosed at earlier pathological stages, thus warranting strategies to treat this growing patient subpopulation. Thwarting these advances is a very poor understanding of early events that drive LUAD development and that thus would guide ideal approaches for interception. While normal lung epithelia of LUAD patients were shown to display tumor-pertinent molecular and inflammatory changes, it is not clear why a LUAD develops within a particular region in the lung. Whereas the lung is ecologically rich with many cell populations that partake in both physiological and pathological processes, we still do not know how the properties and roles of individual cell populations, such as epithelial and immune subsets, co-evolve and interact to instigate LUAD development from a specific niche in the lung. In our preliminary efforts, we found by multi-region single-cell sequencing remarkable evolution of the properties and transcriptomic features of multiple cell subsets and states (e.g., protumor immunosuppressive phenotypes) across macro-space, such that cellular ecosystems and immune cell receptor repertoires were more similar among LUADs and adjacent normal regions than with more distant normal sites. Also, such spatial properties were progressively enriched along the pathologic continuum of matched human normal lung, to preneoplasias, up to invasive LUADs. Our preliminary findings motivate the hypothesis that geospatially and temporally evolving expression programs, properties, and interplay of epithelial and immune cells model early development of LUAD from the normal and premalignant lung. In Aim 1, we will study LUADs and matched multi-region normal tissues with defined spatial proximities from the tumors by single-cell RNA and immune receptor sequencing in conjunction with analysis of mutations in the tumors to establish single-cell maps of LUAD and immune co-evolution in space. Spatially modulated cell properties and states will then be used to feed and train a machine learning model that portrays LUAD development from the lung ecosystem. In Aim 2, we will single-cell decode tumor-immune co-evolution along the pathologic continuum of normal and premalignant lung to LUAD as well as identify cell states and properties that are modulated by early immune intervention. We will use temporal information in mice, along with human matched normal lung tissues, preneoplastic lesions, and invasive LUADs, to iteratively validate and fine-tune the performance of our machine learning model to portray LUAD development in time from the normal and premalignant lung. At the end of our studies, we will have built new models that reliably portray LUAD evolution in space and time. By providing an atlas of LUAD development in an accessible data portal, we also expect that our study will offer scalable roadmaps for the scientific community to develop new strategies for treatment of this fatal disease.

项目概要肺腺癌 (LUAD) 是肺癌最常见的亚型，占癌症的大多数死亡人数。早期检测的改进增加了早期病理阶段诊断出的 LUAD 数量，因此需要制定治疗这一不断增长的患者亚群的策略。阻碍这些进步是非常糟糕的了解推动 LUAD 发展的早期事件，从而指导理想的方法拦截。而 LUAD 患者的正常肺上皮显示出肿瘤相关分子和炎症变化，目前尚不清楚为什么 LUAD 会在肺部的特定区域内发生。然而肺生态丰富，有许多参与生理和病理的细胞群过程中，我们仍然不知道单个细胞群（例如上皮细胞）的特性和作用如何和免疫亚群共同进化并相互作用，以促进肺部特定生态位的 LUAD 发展。在我们的初步努力中，我们通过多区域单细胞测序发现了多个细胞亚群和状态的特性和转录组特征（例如，促肿瘤免疫抑制表型）跨越宏观空间，使得细胞生态系统和免疫细胞受体库 LUAD 和邻近正常区域之间的相似性比更远的正常区域之间的相似性更高。还有，这样的空间特性沿着匹配的人类正常肺的病理连续体逐渐丰富，到肿瘤前期，直至侵袭性 LUAD。我们的初步发现激发了这样的假设：地理空间以及随时间变化的表达程序、特性以及上皮细胞和免疫细胞的相互作用建立正常肺和癌前肺早期发展为 LUAD 的模型。在目标 1 中，我们将研究 LUAD 并通过单细胞 RNA 将多区域正常组织与肿瘤确定的空间邻近度相匹配免疫受体测序结合肿瘤突变分析以建立单细胞空间中的 LUAD 和免疫协同进化图。空间调制的细胞属性和状态将被用于喂养和训练机器学习模型，该模型描绘了肺部生态系统的 LUAD 发展。在目标 2，我们将沿着正常和免疫的病理连续体单细胞解码肿瘤-免疫协同进化。肺癌前的 LUAD 以及识别受早期免疫调节的细胞状态和特性干涉。我们将使用小鼠的时间信息以及人类匹配的正常肺组织，肿瘤前病变和侵入性 LUAD，以迭代验证和微调我们机器的性能学习模型来及时描绘正常肺和癌前肺的 LUAD 发展。在我们的最后研究中，我们将建立新的模型来可靠地描述 LUAD 在空间和时间上的演化。通过提供通过可访问的数据门户中的 LUAD 开发图集，我们还期望我们的研究将提供可扩展的科学界制定治疗这种致命疾病的新策略的路线图。