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. 2023 Jul;13(7):e1340.
doi: 10.1002/ctm2.1340.

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

Haoxin Peng  1   2   3 , Xiangrong Wu  1   2   4 , Shaopeng Liu  5   6 , Miao He  1   2 , Chenshuo Tang  5 , Yaokai Wen  7   8 , Chao Xie  5 , Ran Zhong  1 , Caichen Li  1 , Shan Xiong  1 , Jun Liu  1 , Hongbo Zheng  9 , Jianxing He  1 , Xu Lu  5   6 , Wenhua Liang  1   10
Affiliations

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

Haoxin Peng et al. Clin Transl Med. 2023 Jul.

Abstract

Background: The cellular dynamics in the tumour microenvironment (TME) along with non-small cell lung cancer (NSCLC) progression remain unclear.

Methods: Multiplex 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.

Results: Spatial 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.

Conclusions: The 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.

Keywords: multiplex immunofluorescence; single-cell RNA sequencing; tumour microenvironment; tumour-associated neutrophil; tumour-draining lymph node.

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Conflict of interest statement

The authors declare no potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Dynamic changes of cellular composition and spatial location in the tumour microenvironment along with non‐small cell lung cancer (NSCLC) progression. Radar map comparing the cellular composition (A) and spatial distribution patterns (B) between early and middle‐to‐late‐stage NSCLC by Wilcoxon t‐test, highlighting significant findings. Representative multiplex immunofluorescence graphs depicting the changes in infiltration patterns (C) of CD66b+ neutrophils, CD133+ cells and CD163+ macrophages and spatial distances (D) of CD4+ T cells–CD66b+ neutrophils, CD38+ T cells–CD66b+ neutrophils and CD4+ T cells–CD38+ T cells between early and middle‐to‐late‐stage NSCLC within two fields from one tissue section. *p < 0.05.
FIGURE 2
FIGURE 2
Neutrophils showed unique stage and location‐dependent prognostic effects in the tumour microenvironment. Forest plots showing the prognostic effects of infiltrating levels of neutrophils (A) and spatial relationships between neutrophils and other cell types (B), as evaluated by multivariate Cox regression analyses. Kaplan–Meier curves and log‐rank test demonstrating the disease‐free survival differences between tumour‐low and stroma‐high and tumour‐high and stroma‐low neutrophil infiltrates in the overall group (C), IA–IIA (D) and IIB–IIIB (E) subgroups. Different expressing levels of representative genes in neutrophils between different cTNM stages (F) and spatial location (G) based on re‐analyses of GSE123904, GSE200563 and E‐MTAB‐6149 datasets. Gene set variation analyses estimating the pathway activity of neutrophils in different cTNM stages (stage II–IV vs. stage I) (H) and spatial location (tumour core vs. tumour edge) (I).
FIGURE 3
FIGURE 3
Microenvironment landscape in the primary tumour (PT) and tumour‐draining lymph node (TDLN) of non‐small cell lung cancer. UMAP plots demonstrated the identified cell lineages and cellular composition in PT (A and D) and paired positive (B and E) and negative (C and F) TDLN microenvironment. Cells in TDLNs were coloured upon the tumour invasion status (G) and cTNM stage (H). Comparison of cell contents in PT and paired TDLN microenvironment by Wilcoxon t‐test (I and J). *p < 0.05; ns, non‐significant.
FIGURE 4
FIGURE 4
Differences in the transcriptomic atlas and interaction networks between primary tumour (PT) and tumour‐draining lymph node (TDLN) based on single‐cell RNA sequencing data of neutrophils. Volcano plots displaying differentially expressed genes (DEGs) of neutrophils in tumour‐invaded TDLN than PT (A) and tumour‐free TDLN (B). Violin plots demonstrating the differences in expression of representative function genes (C). Gene set variation analyses comparing pathway activity among PT, positive and negative TDLN by enrichment scores (D). Gene ontology analysis showing enriched biological process terms of DEGs in tumour‐invaded TDLN than PT (E). The numbers of remarkable receptor‐ligand communications between neutrophils as signal transmitters (F) or receivers (G) and other cell populations in positive and negative TDLN microenvironments. Comparing the relative (H) and overall (I) information flow of each signal pathway with neutrophils as signal transmitters between tumour‐invaded and tumour‐free TDLNs. Depiction of interaction probabilities with neutrophils as signal transmitters and other cell types as signal receivers mediated by ligand–receptor pairs in positive (J) and negative (K) TDLN.
FIGURE 5
FIGURE 5
Tumour‐associated neutrophils (TAN) consist of phenotypically and functionally different subsets in the positive tumour‐draining lymph node microenvironment. Heatmap (A) and violin (B) plot showing the differential transcriptome spectrums of different TAN subsets. Gene set variation analyses comparing pathway activity among different TAN subtypes by enrichment scores (C). Two‐dimensional plots demonstrating the dynamic expressing levels of antigen presentation‐relevant genes (CD74 and HLA‐DRA), interferon‐γ stimulated gene (GBP1), costimulatory molecular‐related gene (TNFRSF9), immune regulation‐relevant gene (LGALS3) and angiogenesis related‐gene (VEGFA) along pseudotime trajectory (D and E). The trajectory of TANs along pseudotime in a two‐dimensional space was evaluated by the Monocle approach, with each point corresponding to a single cell (F). Heatmap displaying genes with dynamic expression levels along pseudotime, among which the differentially expressed genes could be hierarchically clustered into four groups with distinct enriched pathways (G). The SCANPY method validated the pseudotime analysis findings of TANs by the Monocle approach (H and I).
FIGURE 6
FIGURE 6
Infiltrating patterns and prognostic significance of different tumour‐associated neutrophil (TAN) subclusters in the non‐small cell lung cancer microenvironment. Comparing the contents of different TAN subtypes in different tissue types (A and B) and cTNM stages (C). Prognostic effects of different TAN signatures as evaluated by the log‐rank test (D–H) and multivariate Cox regression (I) analysis. The proposed model summarises the spatial and evolutionary heterogeneity of TANs (J). TAN‐1 with interferon‐stimulated function was abundant in the stroma of primary tumour, while TAN‐2 with pro‐tumour functions was abundant in the tumour nest. TAN‐3 with classical neutrophil features was the dominant TAN subtype in the negative tumour‐draining lymph node (TDLN), while TAN‐2 and TAN‐0 subtypes were abundant in the tumour‐invaded TDLN. The evolutionary trajectory was designated to start with TAN‐1, through TAN‐3 as the intermediate states, and eventually reached a terminal differentiation state characterised as TAN‐2. Features of TAN‐0 maintain along the trajectory. Comparison of two‐group data by Wilcoxon t‐test, *p < 0.05; ***p < 0.001; ns, non‐significant.
FIGURE 7
FIGURE 7
Construction of the neutrophil differentiation expressed gene score (NDEGS) model in the training cohort. Venn plot demonstrating the intersection and combination of neutrophil differentially expressed genes (NDEGs) among primary tumour, positive and negative tumour‐draining lymph node (A). Six robust NDEGs, including CTSZ, PLAUR, NME2, NPM1, EIF3E and PPIA, were selected by the LASSO Cox regression model (B and C), with prognostic effects in overall survival (OS) as evaluated by the univariate Cox regression analysis (D). Kaplan–Meier curve showing the overall survival (OS) rate differences between high and low‐NDEGS groups (E). Time‐dependent ROC curves and AUC values evaluate the prognostic performance of the NDEGS model at 1, 3 and 5 years (F and G). Forest plot implying the prognostic effects of the NDEGS model, as evaluated by the multivariate Cox regression analysis (H). Differences in clinicopathologic features (I–N), mutational (O) and immune infiltrating landscapes (P) between high and low‐NDEGS groups. Tumour mutational burden differences between low and high‐NDEGS groups (Q and R). p Values of the ANOVA and chi‐square tests between different groups. *p < 0.05; **p < 0.01; ****p < 0.0001; ns, non‐significant.
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