Abstract 5508: Single-cell spatial landscape of the mutation-specific human lung tumor immune microenvironment
Authors: Rajender Nandigama, Bassem Ben Cheikh, Ning Ma, Jochen Wilhelm, Laura Klotz, Florian Eichhorn, Mark Kriegsmann, Marek Bartkuhn, Jamal Nabhanizadeh, Sascha Seidel, Thorsten Stiewe, Albrecht Stenzinger, Andreas Weigert, Mario Looso, Friedrich Grimminger, Werner Seeger, Soni Savai Pullamsetti, Niyati Jhaveri, Hauke Winter, Rajkumar Savai
Journal: Cancer Research (2024)

Abstract Background: Therapies targeting immune checkpoint have significantly transformed the treatment of several cancers, including lung cancer. However, only 20% of lung cancer patients show a response to immunotherapies. KRAS and EGFR mutations are significant factors in the development and progression of lung cancer and its response to immunotherapy. Hence, it's vital to understand intra-tumoral heterogeneity, the makeup of the Tumor Microenvironment (TME), and the cellular interactions in tumors with KRAS and EGFR mutations to uncover new insights. Methods: Lung cancer tissues from 200 patients were collected, focusing on KRAS and EGFR mutation statuses, specifically KRAS+/EGFR-, KRAS-/EGFR+, and KRAS-/EGFR-. For each patient, two samples were taken, resulting in a total of 400 tissue microarray (TMA) cores. These samples were imaged at single-cell resolution using the PhenoCycler®-Fusion system, which employed a 42-plex antibody panel targeting immune, tumor, proliferation, and apoptosis markers. The first step in the analysis was cell segmentation, performed using a fine-tuned deep learning model. This process successfully segmented a total of 2.16 million cells across the entire collection of samples. Following this, the protein expressions for each cell were calculated, and unsupervised clustering was performed, resulting in 36 distinct clusters, that were manually annotated into 14 cell phenotypes. The proportions of these cell phenotypes were then quantified for each of the 400 TMA cores and compared across the three mutation groups. The analysis also included the spatial proximity and cellular neighborhoods in relation to these mutation groups. Results: Quantitative analysis focusing on the relative abundance of cell types revealed that the ratio of tumor cells to immune cells was significantly lower in KRAS-/EGFR- tumors compared to those with KRAS+/EGFR- and KRAS-/EGFR+ mutations. Furthermore, the percentages of Regulatory T cells (Tregs) and M1 Macrophages were significantly higher in the KRAS+/EGFR- group compared to the KRAS-/EGFR+ group. Spatial proximity analysis indicated that in the KRAS-/EGFR+ group, M2 Macrophages were significantly closer to Cytotoxic T cells and Helper T cells compared to the distances observed in the other two groups. Moreover, neighborhood analysis identified 20 distinct cellular neighborhoods, each defined by specific cell-cell interactions. COX hazard analysis based on these cellular neighborhoods demonstrated notable variations among the three mutation groups, which are linked to differences in patient outcomes. Conclusions: Through single-cell spatial phenotyping, this work has shown that the spatial immune landscape of lung tumors is influenced by KRAS and EGFR mutation statuses. This presents a novel opportunity to enhance our understanding of the spatial structure of lung cancer and to identify more effective therapeutic targets. Citation Format: Rajender Nandigama, Bassem Ben Cheikh, Ning Ma, Jochen Wilhelm, Laura Klotz, Florian Eichhorn, Mark Kriegsmann, Marek Bartkuhn, Jamal Nabhanizadeh, Sascha Seidel, Thorsten Stiewe, Albrecht Stenzinger, Andreas Weigert, Mario Looso, Friedrich Grimminger, Werner Seeger, Soni Savai Pullamsetti, Niyati Jhaveri, Hauke Winter, Rajkumar Savai. Single-cell spatial landscape of the mutation-specific human lung tumor immune microenvironment [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 5508.