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

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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.