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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：
10651836
负责人：
Humam Kadara
金额：
$ 42.87万
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/10651836
关键词：
Acceleration 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 Dimensions Disease Disease Outcome Distant Early Diagnosis Early treatment Ecosystem Epithelium Event Evolution Exhibits Foundations Heterogeneity Human Immune Immunologic Receptors Immunotherapy Individual Inflammatory 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 Work cohort data portal immunological intervention improved in vivo lung development lung lesion machine learning model mathematical model mouse model neoplastic novel therapeutic intervention 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.

项目总结肺腺癌(LUAD)是肺癌中最常见的亚型，占大多数癌症的比例死亡。改进的早期检测增加了在早期病理阶段诊断的LUAD的数量，因此，有必要采取策略来治疗这一不断增长的患者亚群。阻挠这些进步是非常糟糕的了解推动LUAD发展的早期事件，从而将指导理想的方法拦截。而LUAD患者的正常肺上皮细胞显示与肿瘤相关的分子与炎症性变化有关，目前尚不清楚为什么LUAD发生在肺部的特定区域。鉴于肺具有丰富的生态功能，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体的属性和作用，如上皮细胞和免疫亚群，共同进化和相互作用，从肺中的特定生态位激发LUAD的发展。在我们的初步努力中，我们发现通过多区域单细胞测序获得了显著的进化多种细胞亚群和状态的性质和转录特征(例如，促肿瘤免疫抑制表型)，如细胞生态系统和免疫细胞受体谱系 LUAD和邻近正常区域之间的相似性比距离更远的正常区域更相似。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，到创业家，到侵入性LUAD。我们的初步发现支持这样一种假设，即在地理空间上以及上皮细胞和免疫细胞的时间进化的表达程序、性质和相互作用从正常肺和癌前病变肺组织建立LUAD早期模型。在目标1中，我们将研究LUAD 并通过单细胞RNA匹配与肿瘤具有特定空间邻近度的多区域正常组织免疫受体测序结合肿瘤中突变的分析建立单细胞 LUAD和免疫在太空中共同进化的地图。空间调制的小区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……里面目的2，我们将单细胞破译肿瘤免疫协同进化沿着正常和正常的病理连续体。癌前肺到LUAD以及识别受早期免疫调节的细胞状态和特性干预。我们将在小鼠身上使用时间信息，以及人类匹配的正常肺组织，癌前病变和侵袭性LUAD，反复验证和微调我们机器的性能学习模型，从正常肺和癌前病变的肺组织中及时描绘LUAD的发展。在我们的最后通过研究，我们将建立新的模型，可靠地描绘LUAD在空间和时间上的演变。通过提供一个在可访问的数据门户中开发LUAD地图集，我们还预计我们的研究将提供可扩展的为科学界制定治疗这一致命疾病的新战略的路线图。