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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