Cellular dynamics in tumour microenvironment along with lung cancer progression underscore spatial and evolutionary heterogeneity of neutrophil

• DOI: 10.1002/ctm2.1340
• Publication Date: 2023-07-26T04:20:36Z
• Authors: Haoxin Peng; Xiangrong Wu; Shaopeng Liu; Miao He; Chenshuo Tang; Yaokai Wen; Chao Xie; Ran Zhong; Caichen Li; Shan Xiong; Jun Liu; Hongbo Zheng; Jianxing He; Xu Lu; Wenhua Liang
• Keywords: Male; Cancer Immunoediting; Medicine (General); Lung Neoplasms; Neutrophils; Immunology; multiplex immunofluorescence; Datasets as Topic; Cancer research; Gene; Biochemistry; Cancer Immunotherapy; Spatial Profiling; tumour‐associated neutrophil; R5-920; single‐cell RNA sequencing; Carcinoma, Non-Small-Cell Lung; Biochemistry, Genetics and Molecular Biology; tumour‐draining lymph node; Health Sciences; Tumor Microenvironment; Gene signature; Humans; Cell Heterogeneity; Molecular Biology; Biology; Research Articles; Immunology and Microbiology; Macrophage Activation and Polarization; FOS: Clinical medicine; Life Sciences; Middle Aged; Comprehensive Integration of Single-Cell Transcriptomic Data; Prognosis; 3. Good health; Immune system; Phenotype; Oncology; Tumor microenvironment; Stromal cell; Medicine; Female; CD163; Gene expression; tumour microenvironment; Transcriptome; Algorithms

AbstractBackgroundThe cellular dynamics in the tumour microenvironment (TME) along with non‐small cell lung cancer (NSCLC) progression remain unclear.MethodsMultiplex immunofluorescence test detecting 10 immune‐related markers on 553 primary tumour (PT) samples of NSCLC was conducted and spatial information in TME was assessed by the StarDist depth learning model. The single‐cell transcriptomic atlas of PT (n = 4) and paired tumour‐draining lymph nodes (TDLNs) (n = 5 for tumour‐invaded, n = 3 for tumour‐free) microenvironment was profiled. Various bioinformatics analyses based on Gene Expression Omnibus, TCGA and Array‐Express databases were also used to validate the discoveries.ResultsSpatial distances of CD4+ T cells–CD38+ T cells, CD4+ T cells–neutrophils and CD38+ T cells–neutrophils prolonged and they were replaced by CD163+ macrophages in PT along with tumour progression. Neutrophils showed unique stage and location‐dependent prognostic effects. A high abundance of stromal neutrophils improved disease‐free survival in the early‐stage, whereas high intratumoural neutrophil infiltrates predicted poor prognosis in the mid‐to‐late‐stage. Significant molecular and functional reprogramming in PT and TDLN microenvironments was observed. Diverse interaction networks mediated by neutrophils were found between positive and negative TDLNs. Five phenotypically and functionally heterogeneous subtypes of tumour‐associated neutrophil (TAN) were further identified by pseudotime analysis, including TAN‐0 with antigen‐presenting function, TAN‐1 with strong expression of interferon (IFN)‐stimulated genes, the pro‐tumour TAN‐2 subcluster, the classical subset (TAN‐3) and the pro‐inflammatory subtype (TAN‐4). Loss of IFN‐stimulated signature and growing angiogenesis activity were discovered along the transitional trajectory. Eventually, a robust six neutrophil differentiation relevant genes‐based model was established, showing that low‐risk patients had longer overall survival time and may respond better to immunotherapy.ConclusionsThe cellular composition, spatial location, molecular and functional changes in PT and TDLN microenvironments along with NSCLC progression were deciphered, highlighting the immunoregulatory roles and evolutionary heterogeneity of TANs.