Investigate heterogeneous neutrophils in NSCLC

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To target a subgroup of tumor-infiltrating neutrophils (SiglecF+ neutrophils) in the GEM model of NSCLC, we decided to create a mouse model lacking SiglecF expression in neutrophils. Unfortunately, despite multiple attempts, the mouse mode supposed only to lose SiglecF expression in neutrophils has an unforeseen defect: it can't express SiglecF in any cell type, likely due to unknown epigenetic effects. Therefore, we decided to find upstream regulators modulating intratumor neutrophil development and identified RUNX1 as a potential transcription factor based on our single-cell RNA sequencing with single-cell ATAC (Assay for Transposase-Accessible Chromatin) sequencing data. We generated a mouse model lacking RUNX1 only in matured neutrophils (Ly6G-Cre; RUNX1-flox) and discovered a significant increase of SiglecF+ neutrophils in tumor-infiltrating neutrophils. This is the first transcription factor that can regulate tumor-infiltrating neutrophil development, laying the groundwork for understanding the heterogeneous tumor-infiltrating neutrophils. To further study its impact on tumor progression in a GEM model, we have created a bone marrow chimera GEM model with an immune system lacking RUNX1 in matured neutrophils. To have a more precise and unbiased measurement of tumor burden in the GEM model of NSCLC, we collaborated with CCR Artificial Intelligence Resource. We developed an artificial intelligence algorithm for automatic tumor quantification. Our manuscript, "Artificial Intelligence-based Tumor Segmentation in Mouse Models of Lung Adenocarcinoma," was published in the Journal of Pathology Informatics.