Integrative Spatial and Transcriptomic Profiling of Tumor Immune Interactions in Lung Cancer

Immune checkpoint inhibitors (ICIs) have reshaped the treatment of non-small cell lung cancer (NSCLC) by harnessing the immune system’s ability to recognize and eliminate tumor cells. While these therapies have led to durable responses in some patients, many fail to benefit, and current biomarkers offer limited prognostic and predictive value. This dissertation leverages advancements in spatial imaging, transcriptomic profiling, and data science to characterize the NSCLC tumor microenvironment and identify spatial and molecular features associated with immune engagement and overall survival. In the first part of this work, I develop and apply a computational pipeline for single-cell analysis of multiplexed immunofluorescence images. This platform enables the quantification of cellular phenotypes, spatial neighborhoods, and tissue architecture, providing a flexible framework for spatial systems immunology. The second part of the dissertation focuses on tumor-intrinsic expression of major histocompatibility complex class II (MHC II) molecules, a pathway with known roles in antigen presentation and immune activation but limited characterization in lung cancer. I integrate multiplexed imaging, spatial transcriptomics, bulk RNA sequencing, and single-cell RNA sequencing to characterize the heterogeneity of MHC II expression in lung adenocarcinoma and assess its relationship with immune microenvironment features, tumor cell states, and patient survival. Together, this body of work advances both computational tools and biological insight into NSCLC immunobiology. It highlights the prognostic relevance of spatially resolved, cell-intrinsic features and offers a framework for future studies aimed at improving patient stratification and informing biomarker development.