Abstract 5507: High-resolution spatial atlas reveals insight into spatial landscape of lung cancer and chronic lung diseases
Authors: Nandigama Rajender Nandigama , Cheikh Bassem Ben Cheikh , Nabhanizadeh Jamal , Grimminger Friedrich , Seeger Werner , Jhaveri Niyati , Pullamsetti Soni Savai , Savai Rajkumar 
Published: March 2024

Abstract
Background: Patients with lung cancer often have a high prevalence of severe comorbidities, primarily due to the significant correlation with cigarette smoking and the aging process. Dysregulations of the immune response are the cause of chronic inflammatory diseases such as chronic obstructive pulmonary disease (COPD), pulmonary arterial hypertension (PAH), pulmonary fibrosis (PF) and lung cancer. In addition, the lung microenvironment in these chronic inflammatory diseases has not yet been sufficiently investigated. Therefore, we aimed to investigate the spatio-temporal localization and cellular distribution in the lung microenvironment and to compare lung cancer with the concomitant diseases of lung cancer.
Methods: FFPE lung sections, each 3µm in thickness, were collected from patients with diagnoses of lung cancer, COPD, PAH, PF, and from healthy donors, with three samples taken for each category. These sections were subsequently imaged using the PhenoCycler®-Fusion system, utilizing a comprehensive panel of 45 antibodies. including immune, epithelial, vascular, proliferation and apoptosis markers. The first step of the analysis workflow consists of cell segmentation using a fine-tuned deep learning model leading to a total of 5.2 million cells across the whole dataset. Protein expressions were then calculated from the segmented cells, and unsupervised clustering was performed based on the normalized expression values. The resulting clusters were manually annotated into 16 cell phenotypes based on their protein expression patterns as displayed on a hierarchical clustering heatmap. The percentages of cell phenotypes were then calculated for each sample and compared between disease groups. Furthermore, the spatial proximity and cellular neighborhood analyses were performed to study the spatial organization of the different cell phenotypes in the tissues.
Results: Quantitative assessment revealed unique characteristics specific to each disease, identified by the varying abundance of different immune cell subtypes. Additionally, when comparing lungs from healthy donors with those affected by lung diseases, notable differences were observed in the spatial arrangement of immune cells and their proximity to lung blood vessels.
Conclusions: This study presents the first architectural map of lung cancer, COPD, PAH, and PF linking various immune cell types to their spatial location within the tissue. Such in-depth spatial analyses will aid in comprehending the spatial and temporal interplay within the lung's microenvironment in diverse lung conditions, as well as in developing targeted therapies for specific cell types.
Citation Format: Rajender Nandigama Nandigama, Bassem Ben Cheikh Cheikh, Jamal Nabhanizadeh, Friedrich Grimminger, Werner Seeger, Niyati Jhaveri, Soni Savai Pullamsetti, Rajkumar Savai. High-resolution spatial atlas reveals insight into spatial landscape of lung cancer and chronic lung diseases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5507.