Dynamic network biomarker of treg cells differentiation critical state with neoadjuvant treatment in LUSC.

Abstract

e20063 Background: Regulatory T cells (Tregs) suppress immune responses and can promote tumor growth and metastasis during cancer progression. Studies have shown that tumor-infiltrating Tregs upregulate inhibitory gene programs after immune checkpoint blockade therapy. Targeting Tregs is believed to be a valuable approach to enhance the effectiveness of immunotherapy. However, the precise role of Tregs in the tumor microenvironment, their connection to treatment resistance mechanisms, and how changes in their immune activity impact tumor treatment are still not well understood. Methods: Single-cell data and clinical information were obtained from the Gene Expression Omnibus database with the accession number GSE176021 for 4 patients diagnosed with lung squamous cell carcinoma (LUSC) who received neoadjuvant anti-PD-1 therapy, including 2 patients with major pathologic response (MPR) and 2 patients with non-MPR. BioTIP was employed to identify critical transition signals of the Treg cell differentiation trajectory constructed by Monocle2. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set variation analysis were performed to understand biological processes and pathways. Additionally, potential transcription factors were identified using Single-Cell rEgulatory Network Inference and Clustering, and CellChat analysis was conducted to evaluate possible interactions among cell populations. Results: The study constructed a pseudo-temporal differentiation trajectory of Tregs in LUSC patients, and Treg subpopulations during the critical transition period were identified based on the dynamic network biomarker method. Notably, subpopulations of Treg cells clustering MPR or non-MPR samples were located at different ends of the differentiation trajectory, showing variations in Treg-related pathways and transcription factor regulation. The critical transition subpopulation exhibited high expression of glycolysis, TCA cycle, and natural killer cell receptors, along with upregulation of the CEBPB transcription factor activity and downregulation of ARNTL activity. SELPLG-SELL, CD99-CD99, and MHC-II-CD4 were identified as potential key signals that may affect therapeutic effect, primarily influencing cell adhesion, the immune suppressive activity of Treg cells, and T cell response. Conclusions: In conclusion, this study utilized single-cell data analysis to uncover the dynamic regulation of Treg cells in LUSC, identifying critical transition Treg subpopulations. Notably, our findings suggest the involvement of the transcription factors CEBPB and ARNTL in regulating Treg activity. These novel insights shed light on potential strategies for targeted therapies and provide valuable information regarding resistance mechanisms associated with Treg cells.