Elevated TGFβ1 Drives IL4I1⁺ Macrophage–Gal-9 Signaling to Shape Tumor-Specific CD8⁺ T- Cell States in COPD-Associated NSCLC

Elevated TGFβ1 Drives IL4I1⁺ Macrophage–Gal-9 Signaling to Shape Tumor-Specific CD8⁺ T- Cell States in COPD-Associated NSCLC | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Elevated TGFβ1 Drives IL4I1⁺ Macrophage–Gal-9 Signaling to Shape Tumor-Specific CD8⁺ T- Cell States in COPD-Associated NSCLC Mingshu Xiao, Guoxin Cai, Yun Xu, Xue Zhang, Yichang Chen, Ying Yang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9365545/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Chronic obstructive pulmonary disease (COPD)-associated non-small cell lung cancer (NSCLC) exhibits distinct responses to immune checkpoint blockade, indicating a unique tumor immune microenvironment. Here, we integrate single-cell transcriptomics and T cell receptor sequencing of tumor, adjacent non-tumor, and blood samples from NSCLC patients, together with spatial transcriptomics and molecular validation. We identify an immunosuppressive niche in COPD-associated NSCLC characterized by elevated TGFβ1 expression in malignant cells and enhanced crosstalk with IL4I1⁺ tumor-associated macrophages. Notably, macrophage-derived galectin-9 (Gal-9) modulates tumor-specific CD8⁺ T-cell states, promoting CXCL13 and CD82 expression within a proliferative, tumor-reactive subset, while also being associated with features of T-cell exhaustion. These findings define a TGFβ1–IL4I1⁺ macrophage–Gal-9 axis that reshapes CD8⁺ T-cell states and contribute to differential immunotherapy responses in COPD-associated NSCLC. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Health sciences/Oncology COPD non-small cell lung cancer tumor immunology single-cell atlas Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The health burden of Chronic Obstructive Pulmonary Disease (COPD) has intensified, now ranking as the third leading cause of death worldwide 1 . Notably, a 2019 study revealed a 13.7% COPD prevalence among individuals aged 40 and above in China, affecting nearly 100 million people 2 . COPD is associated with a significantly higher risk of lung cancer (LC), with evidence indicating a 2.15-fold increased incidence of among patients with COPD 3 . Remarkably, COPD has become the second leading cause of non-cancer deaths among individuals diagnosed with LC 4 . Repeated cycles of airway injury and repair associated with COPD create favorable conditions for cancer development 5 . Immunotherapy has revolutionized the treatment of LC, particularly non-small cell lung cancer (NSCLC). Several studies have demonstrated that NSCLC patients with COPD benefit more from immunotherapy compared to those without COPD, as indicated by prolonged survival rates and higher objective response rates (ORR) 6 – 9 . This therapeutic advantage may arise from alterations in the immune microenvironment induce by COPD 6 , 7 . Nevertheless, the precise biological mechanisms underlying the increased immunotherapy responsiveness in NSCLC patients with COPD remain poorly understood. Combined single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST-seq) provide a powerful approach to characterize immune cell populations and their interactions within the tumor microenvironment 10 , 11 . Understanding the complex relationship between COPD and NSCLC is crucial for improving treatment outcomes and prognosis for patients with coexistent conditions. Building upon this premise, our research employed scRNA-seq to identify the cellular heterogeneity specific to patients with NSCLC combined with COPD compared to those without COPD. These findings highlight how chronic inflammation shapes the tumor microenvironment, paving the way for future studies aimed at understanding how COPD modulates immunotherapy efficacy in NSCLC. Additionally, our results provide a theoretical basis for the concurrent pharmacological management of NSCLC and COPD. Results COPD significantly impacts the immune environment of NSCLC To investigate the cellular composition and gene regulation within the tumor microenvironment of NSCLC patients with (LC-COPD) and without COPD (LC-only), scRNA-seq was performed on 30 tumor samples (19 COPD, 11 non-COPD), 11 NAT samples (6 COPD, 5 non-COPD), and 23 peripheral blood samples (13 COPD, 10 non-COPD) (Figure 1A and Table S1). Following quality control, 534,996 high-quality cells with an average of 1,142 detected genes were identified, which underwent automated cell-type classification and marker-gene identification (Figure 1B–C and Figure S1A). The abundance of immune cell subsets correlated with disease severity, as measured by the percent predicted forced expiratory volume in 1 second (FEV1), a key metric for evaluating lung function. Notably, increased monocyte fractions and decreased T-cell lineage populations correlated significantly with reduced FEV1%, indicating impaired lung function (Figure 1D–E and Figure S1B–G). Further analysis revealed lower proportions of T cells, macrophages, B cells and dendritic cells (DCs), but elevated monocyte fractions within tumors from patients with LC-COPD compared to those with LC-only. Conversely, NATs from LC-COPD cases displayed enriched T-cell populations (Figure 1F). Differentially expressed genes (DEG) analysis and pathway enrichment demonstrated distinct expression patterns and biological functions in malignant cells, DCs, and macrophages between the LC-COPD and LC-only groups (Figure 1G and Figure S1H). We found through cell interaction analysis that there are more interactions between immune cell subsets in LC-COPD, with macrophages showing the most prominent interactions with other cells (Figure S1I–J). Together, our scRNA-seq analysis highlights significant immune landscape heterogeneity and complexity associated with COPD in NSCLC. Malignant cells in LC-COPD exhibit increased TGFβ1 expression and immune evasion To characterize malignant cell composition and gene regulation differences in LC-only and LC-COPD, we analyzed tumor cells identified by clustering based on single-cell gene expression-derived copy number variations (CNVs) (Figure S2A–C). Malignant cells from 30 tumor samples clustered into 5 distinct subsets, each exhibiting specific enriched biological pathways (Figure 2A–B and Figure S2D). Pseudotime trajectory analysis indicated a progression of malignant cells toward distinct transcriptional states associated with immune evasion (Figure 2C–E). Malignant cells were in a state of immune evasion in LC-COPD, whereas they exhibited a state of immune surveillance in LC-only (Figure 2F–H). Specifically, transforming growth factor-beta 1 (TGFβ1)—a critical gene associated with immune escape mechanisms— showed heightened expression in LC-COPD cells, alongside reduced expression of HLA genes (Figure 2I–J), indicative of diminished immune recognition. Furthermore, increased TGFβ1 expression correlated with a decline in FEV1%, reflecting more severe disease (Figure 2K). These findings suggest that high TGFβ1 expression contributes to immune evasion in LC-COPD. The transformation of normal epithelial cells to inflammatory epithelial cells and to malignant cells may be characterized by TGFβ1 The transformation of normal epithelial cells to inflammatory epithelial cells and ultimately to malignant cells is characterized by elevated TGFβ1 expression. In LC-COPD, normal epithelial cells already exhibit higher levels of TGFβ1 compared to LC-only cases (Figure 3A). This elevated TGFβ1 expression is also critical in driving the transition to malignant cells, where TGFβ1 levels are significantly higher than in the surrounding epithelial cells (Figure 3B). The elevated TGFβ1 expression not only drives the transformation from normal to malignant cells but also reduces immune surveillance and enhances immune escape in malignant cells, contributing to the formation of a tumor-immunosuppressive microenvironment (Figure 2C–E and Figure 3C). We then prepared cigarette smoke extract (CSE) (Figure 3D) and exposed A549 cells to CSE or lipopolysaccharide (LPS), noting a dose- and time-dependent increase in TGFβ1 expression (Figure 3E–H). Overall, these findings suggest that cigarette smoke and inflammation upregulate TGFβ1 in epithelial cells, contributing to progression from healthy lungs to COPD and eventually lung cancer (Figure 3I). Exploring the role of TGFβ1 expression in malignant cells and its impact on macrophage subsets in LC-COPD Given the marked upregulation of TGFβ1 in malignant epithelial cells in LC-COPD, we next asked whether this alteration reshapes the tumor immune microenvironment through enhanced crosstalk with immune cells. Cell–cell interaction analysis revealed that interactions between malignant cells and macrophages were markedly increased in LC-COPD compared with LC-only cases (Figure S2E), with macrophages emerging as a major cellular target of tumor-derived signals.To further assess how TGFβ1 expression in malignant cells influences macrophage populations, we classified macrophages into nine clusters, including four tumor-associated macrophage (TAM) clusters (Macro_IL4I1, Macro_MS4A6A, Macro_NEAT1, and Macro_VCAN) and five additional macrophage clusters (Macro_FABP4, Macro_IFI27, Macro_CCL4, Macro_STMN1, and Macro_HSPA1A) (Figure 4A–B and Figure S3A). Among these, Macro_IL4I1 predominated within tumor tissues, representing a distinct macrophage subset (Figure 4B). We next sought to define macrophage subset characteristics through functional profiling. Traditionally, macrophages are categorized into M1 (proinflammatory or anti-tumor) and M2 (anti-inflammatory or pro-tumor) phenotypes. TAMs in LC-COPD exhibited a predominantly M2-like phenotype, whereas TAMs in LC-only samples displayed a stronger M1 signature (Figure S3B). Functional analyses further revealed that TAMs in LC-COPD showed enhanced angiogenic potential, while TAMs in LC-only demonstrated superior phagocytic capacity and T-cell recruitment ability (Figure S3C). Notably, Macro_IL4I1, classified as an M2-like subset, contributed to both angiogenesis and inflammatory processes (Figure S3D–I). Analysis of TGFβ receptor–ligand interactions between malignant cells and Macro_IL4I1 or Macro_MS4A6A demonstrated a pronounced activation of TGFβ signaling in LC-COPD (Figure 4C). Spatial transcriptomic analysis further indicated that IL4I1 expression was predominantly localized to TAMs in LC-COPD tumors (Figure 4D–E). Ligand–receptor interaction analysis revealed more extensive crosstalk between macrophages and other immune cell populations in LC-COPD, with the most prominently upregulated interaction involving LGALS9 and its receptors between macrophages and CD8⁺ T cells (Figure 4F). Consistently, we observed that TGFβ1 stimulation promoted IL4I1 expression and activated TGFβ signaling in macrophages in a dose/time-dependent manner (Figure 4G–H). Moreover, using western blotting, qPCR, and ELISA assays, we found that TGFβ1 stimulation significantly induced LGALS9 expression in macrophages (Figure 4H–J). Spatial transcriptomic analysis further revealed elevated IL4I1 expression in regions where TAMs and CD8⁺ T cells were colocalized, suggesting that IL4I1⁺ macrophages may play a critical role in mediating macrophage–CD8⁺ T-cell interactions (Figure 4K). In conclusion, our study highlights a pivotal role for malignant cell–derived TGFβ1 in shaping the LC-COPD tumor immune microenvironment by promoting the expansion and functional activation of IL4I1⁺ macrophages and enhancing their LGALS9-mediated immunomodulatory effects. Immunosuppressive phenotypes of T cells in LC-COPD We next investigated whether the TGFβ1–IL4I1⁺ TAM–LGALS9 axis in LC-COPD is associated with T-cell dysfunction in the tumor microenvironment. T cells play a crucial role in the effectiveness of current cancer immunotherapies 12 . To profile T lymphocytes at higher resolution, we subclustered them into 10 annotated CD4 + subsets—interferon-stimulated genes (ISG)–positive T cells, effector memory T cells (Tem), terminally differentiated effector memory or effector cells (Temra), follicular helper T cells (Tfh), Tfh/T helper 1 cells (TfhTh1), T helper cells 17 (Th17), memory T cells (Tm), naive T cells (Tn), regulatory T cells (Treg) and mixed T cells (Tmix). Additionally, 9 CD8 + T-cell clusters were identified, including ISG + T cells, mucosal-associated invariant T cells (MAIT), Tem, Temra, exhausted CD8 + T cells (Tex), NK-like T cells (Tk), Tm, Tn, and tissue-resident memory T cells (Trm) (Figure 5A and Figure S4A–B). We observed that the proportions of CD4_Treg and CD4_Tm were significantly higher in LC-COPD than in LC-only for both adenocarcinoma and squamous carcinoma (Figure 5B). In parallel, the proportion of CD8_Tex was markedly elevated in LC-COPD across both NSCLC subtypes (Figure 5C). These CD8_Tex cells exhibited a strong exhaustion signature, particularly in LC-COPD (Figure 5D). Flow cytometry further confirmed an increased frequency of PD1 + and TIGIT + subpopulations within CD8 + T cells from patients with LC-COPD (Figure 5E–F and Figure S5A). Because tumor-specific T cells are essential for anti-tumor immunity 13-15 , we evaluated their tumor specificity score 16 and found it to be notably higher in exhausted T-cell subsets, especially in LC-COPD (Figure S4C–D). Using a TCR-based approach that identified shared clonotypes with Tex cells and high tumor-specific T-cell gene expression scores (>0), we isolated a population of CD8 + tumor-specific T cells characterized by distinct clonotypes (Figure S4E–G). This tumor-specific subset was more abundant in LC-COPD (Figure 5G). Compared with unrelated T cells, LGALS9 displayed stronger interactions with these tumor-associated T cells, and this effect was more pronounced in LC-COPD (Figure 5H). Notably, among tumor-associated macrophage subsets, LGALS9–receptor interactions with tumor-specific CD8⁺ T cells were most prominent in the Macro_IL4I1 and Macro_MS4A6A populations in LC-COPD (Figure 5H). CXCL13 and CD82 serve as markers for exhausted, tumor-specific CD8 + T Cells associated with favorable prognosis. Based on marker genes and functional scores, we identified nine distinct tumor-specific CD8 + T-cell subtypes: CD8_Temra_GZMH, CD8_Tex_RGS1, CD8_Trm_ZNF683, CD8_Tem_GZMK, CD8_progenitor exhausted cluster_IL7R (CD8_pTex_IL7R), CD8_Tex_GZMB, CD8_proliferative_STMN1 (CD8_Tpro_STMN1), CD8_Trm_CXCR4, and CD8_Treg_FOXP3 (Figure 6A and Figure S6A). Classification accuracy for these subsets was validated by mapping them back to their original CD8 + T-cell clusters (Figure S6B). Of these, CD8_Tex_RGS1 and CD8_Tex_GZMB showed high exhaustion scores, with only the CD8_Tex_GZMB subset exhibiting strong cytotoxicity (Figure 6B). All CD8_Tex and CD8_pTex subsets were enriched in cytokine-cytokine receptor interaction pathway. Additionally, CD8_Tex_GZMB, CD8_Tex_RGS1, and CD8_pTex_IL7R were particularly abundant in IL10, TNF, and STAT signaling pathways, respectively (Figure S6C). Chemokine C-X-C motif ligand 13 (CXCL13) and CD82 emerged as shared top markers for both the CD8_Tex_GZMB and CD8_Tex_RGS1 clusters (Figure 6C). Moreover, significantly more tumor-specific CD8 + T cells co-expressed these genes compared with tumor-unrelated CD8 + T cells (Figure 6D). CXCL13 and CD82 were also expressed at higher levels in LC-COPD tumors than in NATs (Figure S6D). Consistent with their potential clinical relevance, TCGA data indicated that higher expression of both markers correlated with better PFS in patients with NSCLC (Figure 6E–F). Tumor-specific CD8 + T cells reside in an exhausted state in LC-COPD and a cytotoxic state in LC-only Building on the identification of CXCL13 and CD82 as markers of tumor-specific CD8⁺ T cells with favorable prognostic significance, we next investigated their developmental origins and state transitions. TCR analyses suggested that the two CD8_Tex subsets primarily originate from the CD8_Tpro_STMN1 cluster (Figure 6G). While CD8_Temra_GZMH, CD8_Trm_ZNF683, and CD8_Tem_GZMK were more abundant in LC-only, exhaustion-related clusters (CD8_pTex_IL7R, CD8_Tex_RGS1 and CD8_Tex_GZMB) prevailed in LC-COPD (Figure 6H). We inferred state trajectories and examined dynamic cell transitions to illustrate the distinctive immunological states of tumor specific CD8 + T cells in LC-COPD versus LC-only (Figure 6I). As cells progressed along this trajectory, cytotoxic signatures increased, while the exhaustion score reached its maximum at the fully exhausted state (Figure S6E). Pseudotime trajectory analyses positioned CD8_Tpro_STMN1 at the earliest differentiation stage (in the absence of naive T cells) and identified CD8_Tex and CD8_Temra_GZMH as terminally differentiated states (Figure 6J). Some CD8_Tex cells originated directly from CD8_Tpro_STMN1, whereas others transitioned from CD8_Tpro_STMN1 via an intermediate state involving CD8_Trm_ZNF683, CD8_Treg_FOXP3, and CD8_pTex_IL7R (Figure 6J). Additionally, the intermediate state could also transition to an effector memory state characterized by CD8_Tem_GZMK and CD8_Temra_GZMH (Figure 6J). In LC-COPD, the initial or intermediate state showed a higher propensity to transition into an exhausted CD8 + T cell state (Figure 6K). By contrast, the intermediate state in LC-only followed two distinct trajectories, one leading to exhaustion and the other toward a cytotoxic effector memory state (Figure 6L). CXCL13 and CD82 expression were elevated in both the initial proliferative and exhausted states (Figure 6L). Correlation analysis further indicated a positive association between CXCL13/CD82 expression and T-cell exhaustion (Figure S6F). Consistently, Gal-9 stimulation promoted CXCL13 and CD82 expression in CD8⁺ T cells in a dose-dependent manner (Figure 6M). Given that proliferative tumor-specific CD8⁺ T cells are considered a key reservoir sustaining effective anti-tumor immunity, these findings suggest that Gal-9 signaling may facilitate the expansion or maintenance of such proliferative CD8⁺ T-cell populations through the induction of CXCL13 and CD82. Overall, our analysis reveals distinct immune and transcriptional states during tumor-specific CD8⁺ T-cell differentiation in LC-COPD. Rather than serving solely as markers of exhaustion, CXCL13 and CD82 identify tumor-specific CD8⁺ T cells with enhanced proliferative potential. Together with the observed enrichment of these populations in LC-COPD and their association with improved patient outcomes, our findings support a model in which TGFβ-driven IL4I1⁺ macrophages shape tumor-specific CD8⁺ T-cell states through Gal-9 signaling, thereby influencing the balance between proliferation and exhaustion. Discussion Immunotherapy has become a cornerstone of NSCLC treatment 17 . Patients’ preexisting immune status significantly affects the efficacy of these therapies 18 . COPD, a common comorbidity in lung cancer, profoundly reshapes pulmonary immunity; however, how COPD alters the tumor immune microenvironment and influences immune escape mechanisms in lung cancer remains incompletely understood. In this study, we performed an integrated single-cell and spatial transcriptomic analysis of treatment-naïve NSCLC tissues to systematically compare the immune ecosystems of LC-COPD and LC-only. Our findings indicate that LC-COPD and LC-only tumors employ different immune escape mechanisms. TGF-β, a key regulator of tumor progression and immune suppression 19 , was markedly upregulated in LC-COPD. This finding is consistent with prior reports implicating aberrant TGF-β signaling in both COPD and lung cancer 20 , 21 and supports the notion that chronic airway inflammation may facilitate malignant transformation through sustained TGF-β activation 22 . Dysregulated TGF-β signaling correlates with therapy resistance in lung cancer 19 , consistent with clinical observations that patients with LC-COPD display greater resistant to anti-tumor drugs. We observed that LC-COPD tumor cells express higher levels of TGFβ1 and exhibit more frequent TGFβ-mediated crosstalk with DCs and TAMs, thus contributing to an immunosuppressive microenvironment. Although various TGF-β inhibitors are in clinical or preclinical trials 19 , 23 , 24 , their combination with PD-1/PD-L1 blockers has shown limited success, possibly due to inadequate patient selection or trial design 25 . Targeting TGF-β in LC-COPD may identify patients who benefit most and guide more effective combination therapies. A particularly notable feature of the LC-COPD tumor microenvironment is the expansion of IL4I1-expressing tumor-associated macrophages. IL4I1 (interleukin-4–induced gene 1) encodes an L-amino acid oxidase originally characterized in antigen-presenting cells and has been implicated in immune regulation through metabolic and redox-dependent mechanisms 26 . Previous studies have shown that IL4I1 can suppress T-cell proliferation and effector function by depleting essential amino acids and generating immunoregulatory metabolites, thereby contributing to immune tolerance in both inflammatory and tumor contexts 27 . In cancer, IL4I1 expression has been reported in myeloid populations and linked to poor prognosis and immune evasion, although its precise role within distinct macrophage subsets remains incompletely defined. In the context of LC-COPD, the enrichment of IL4I1⁺ TAMs suggests that chronic inflammatory cues and tumor-derived signals converge to promote a specialized immunomodulatory macrophage phenotype. Sustained TGFβ signaling, which is prominent in COPD-associated lung cancer, may favor the differentiation or stabilization of IL4I1⁺ macrophages, thereby reinforcing local immune suppression. Rather than acting solely as passive inhibitors of immunity, these macrophages appear positioned to actively shape the tumor immune ecosystem by orchestrating intercellular communication networks. One potential mechanism through which IL4I1⁺ TAMs influence tumor immunity is via the regulation of Gal-9–mediated signaling. Gal-9 has been widely recognized as an immunomodulatory ligand capable of altering T-cell activation, differentiation, and survival in a context-dependent manner 28 – 30 . While Gal-9 has often been associated with inhibitory effects on T cells, emerging evidence suggests that its impact may vary depending on the differentiation state of T cells and the broader cytokine milieu 31 . In LC-COPD tumors, IL4I1⁺ macrophages may serve as a major source of Gal-9, thereby modulating tumor-specific CD8⁺ T-cell programs rather than inducing uniform terminal dysfunction. Importantly, this macrophage-driven signaling landscape may help reconcile the apparent paradox observed in LC-COPD tumors, which exhibit features of immune suppression alongside the preservation of tumor-specific CD8⁺ T-cell populations with proliferative capacity. By influencing the balance between T-cell proliferation and differentiation, IL4I1⁺ TAMs could contribute to maintaining a reservoir of tumor-reactive CD8⁺ T cells poised for reactivation upon immune checkpoint blockade. Such a model aligns with emerging concepts that effective immunotherapy responses rely not on the complete absence of inhibitory signals, but on the presence of a sufficiently large and dynamic pool of tumor-specific T cells capable of functional reinvigoration. Our study also revealed distinct immune profiles of CD8 + T cells, especially in the presence of COPD. Patients with LC-COPD had CD8 + T cells with both heightened tumor specificity and stronger exhaustion compared to LC-only. This aligns with prior findings that the degree of T-cell exhaustion parallels tumor specificity 32 . Some research suggests that PD-L1 expression in tumors predicts the presence of tumor-reactive CD8 + T cells 12 , which may help explain improved responses to anti-PD-1/PD-L1 therapy in LC-COPD 33 . Efforts to define precursor exhausted T cells with stem-like properties have identified potential biomarkers of immunotherapy response 34 – 36 . Our data, integrating TCR and trajectory data, show that most CD8_Tex cells arise directly from a proliferative CD8_Tpro_STMN1 subset. While typical exhaustion markers such as TCF7 and IL7R did not fully characterize CD8_Tpro_STMN1, both CXCL13 and CD82—associated with T-cell dysfunction and immune therapy response—were highly expressed in this population. CXCL13 has been recognized as a marker for T-cell dysfunction and tumor antigen specificity, as well as a predictive biomarker for immunotherapy across various tumor types 37 – 41 . Moreover, CD8 + T cells expressing CXCL13 are considered to be proliferative 39 . Consistent with this notion, we observed elevated CXCL13 and CD82 expression in proliferative CD8⁺ T-cell subsets and demonstrated that Gal-9 stimulation—derived predominantly from IL4I1⁺ TAMs—induced CXCL13 and CD82 expression in a dose-dependent manner. These findings suggest that Gal-9 signaling may promote the expansion or maintenance of tumor-specific, proliferative CD8⁺ T cells, rather than solely driving terminal exhaustion. In summary, our study delineates a distinct immune architecture in LC-COPD characterized by TGFβ1-driven malignant–myeloid interactions, expansion of IL4I1⁺ TAMs, and remodeling of tumor-specific CD8⁺ T-cell differentiation trajectories. Rather than representing a uniformly immune-excluded state, LC-COPD tumors harbor a complex immune ecosystem in which immunosuppressive signals coexist with proliferative tumor-specific T-cell populations. These insights deepen our understanding of immune regulation in LC-COPD and highlight IL4I1⁺ TAMs and their downstream signaling pathways as potential modulators of immunotherapy responsiveness. Collectively, our findings provide a conceptual framework for refining patient stratification and developing tailored immunotherapeutic strategies for NSCLC patients with COPD. Methods Sample Collection and Cell Preparation Fresh lung tumor tissues and peripheral blood samples were collected from patients following ethical approval (K2022179) from The Fourth Affiliated Hospital of Zhejiang University School of Medicine and The Second Affiliated Hospital Zhejiang University School of Medicine. Informed consent was obtained from all participants. Tissue samples were mechanically dissociated and enzymatically digested to obtain single-cell suspensions. Peripheral blood mononuclear cells (PBMCs) were isolated using density gradient centrifugation. Cell viability was assessed using trypan blue staining. The clinical and pathological characteristics of the patients are listed in Table S1. Single-cell RNA Sequencing Single-cell suspensions were loaded onto microfluidic devices and processed using the Singleron GEXSCOPE® Single-Cell RNA Library Kit. Libraries were sequenced on an Illumina NovaSeq 6000 platform (150 bp paired-end reads). Raw sequencing data were processed with quality control, doublet detection, and batch effect correction using Scanpy, scDblFinder, SoupX, and Harmony. Data Analysis and Clustering Normalized and log-transformed expression matrices were subjected to principal component analysis (PCA) for dimensionality reduction, followed by UMAP visualization and Leiden clustering. Differentially expressed genes (DEGs) were identified using t-tests with false discovery rate correction (FDR < 0.05). Cell Type Annotation and Malignancy Inference Clusters were annotated using the Human Leukocyte Cell Atlas reference and scANVI transfer learning for fine T cell subtyping. Putative malignant cells were identified via inferred copy number variation (CNV) from scRNA-seq data using smoothed chromosomal expression patterns. Functional and Pathway Analysis Gene set enrichment analysis (GSEA) and overrepresentation analysis (Enrichr) were used to identify biological pathways associated with cluster-specific signatures. Functional scores for pathways or signatures were calculated per cell using a reference-based z-score approach. All signatures were got from previous studies (Table S2-4). Spatial Transcriptomics Spatial gene expression profiles were obtained using the 10× Genomics Visium platform. Cell-type deconvolution was performed with cell2location using annotated single-cell RNA-seq references, producing spot-wise cell-type abundance maps. In vitro Cell Assays A549 and THP1 cells, and isolated human CD8⁺ T cells, were cultured under standard conditions. Cells were stimulated with cytokines, cigarette smoke extract (CSE), lipopolysaccharide (LPS), or recombinant proteins for functional assays. Protein expression was assessed by Western blot, RNA levels by quantitative RT-PCR, and secreted factors by ELISA. The primers are shown in Table S5. Survival and Clinical Correlation Analysis Gene signatures were correlated with overall or progression-free survival in TCGA lung cancer cohorts using Kaplan-Meier analysis and log-rank testing. Correlations between cell-type proportions and lung function metrics (FEV1%, FEV1/FVC) were assessed using Pearson correlation after appropriate transformation. Statistical Analysis Comparisons between groups were performed using non-parametric tests (Mann–Whitney U or Kruskal–Wallis H tests), with multiple hypothesis testing controlled via the Benjamini–Hochberg procedure (adjusted p < 0.05 considered significant). Detailed experimental procedures, reagents, and protocols are provided in the Supplementary Methods. Declarations Competing interests The authors declare no competing interests. Author Contribution MX conceived the study design, performed experiments, acquired and processed the cohort data, conceptualized and implemented data analysis, and wrote the manuscript with GC and YX designed, performed, and analyzed all flow cytometry experiments. XZ and YC assisted with spatial transcriptomic data analysis. YG, YYand MZ carried out the patient phenotyping. JZ, KW and ZZ supervised the work. Acknowledgement This work is supported by the National Natural Science Foundation of China (U23A20467), National Key R&D Program of China (2024YFA1108500), the National Natural Science Foundation of China (No. 82102852) and Science and Technology program of Jinhua Science and Technology Bureau (Grant No.2023-3-058). We would like to acknowledge the Biobank staff of Second Affiliated Hospital of Zhejiang University and the staff in the departments of respiratory and thoracic surgery in The Fourth Affiliated Hospital of Zhejiang University School of Medicine for their hard work and dedication to our investigators for their support and to the study participants.We would like to thank Editage (www.editage.cn) for English language editing. Data availability The processed expression data of SC and ST reported in this study can be obtained from the China National GeneBank Database (CNGBdb) with accession number (GSA: HRA010788, HRA010787, HRA010890). This paper does not report original code. References Celli, B. R. & Wedzicha, J. A. Update on Clinical Aspects of Chronic Obstructive Pulmonary Disease. N Engl J Med 381 , 1257-1266, doi:10.1056/NEJMra1900500 (2019). Wang, C. et al. 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A transcriptionally and functionally distinct PD-1(+) CD8(+) T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade. Nat Med 24 , 994-1004, doi:10.1038/s41591-018-0057-z (2018). Lin, M., Huang, Z., Chen, Y., Xiao, H. & Wang, T. Lung cancer patients with chronic obstructive pulmonary disease benefit from anti-PD-1/PD-L1 therapy. Front Immunol 13 , 1038715, doi:10.3389/fimmu.2022.1038715 (2022). Gettinger, S. N. et al. A dormant TIL phenotype defines non-small cell lung carcinomas sensitive to immune checkpoint blockers. Nat Commun 9 , 3196, doi:10.1038/s41467-018-05032-8 (2018). Brummelman, J. et al. High-dimensional single cell analysis identifies stem-like cytotoxic CD8(+) T cells infiltrating human tumors. J Exp Med 215 , 2520-2535, doi:10.1084/jem.20180684 (2018). Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537 , 417-421, doi:10.1038/nature19330 (2016). Liu, B. et al. Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer. Nat Cancer 3 , 108-121, doi:10.1038/s43018-021-00292-8 (2022). Liu, B., Zhang, Y., Wang, D., Hu, X. & Zhang, Z. Single-cell meta-analyses reveal responses of tumor-reactive CXCL13(+) T cells to immune-checkpoint blockade. Nat Cancer 3 , 1123-1136, doi:10.1038/s43018-022-00433-7 (2022). Dai, S. et al. Intratumoral CXCL13(+)CD8(+)T cell infiltration determines poor clinical outcomes and immunoevasive contexture in patients with clear cell renal cell carcinoma. J Immunother Cancer 9 , doi:10.1136/jitc-2020-001823 (2021). Pichler, R. et al. A chemokine network of T cell exhaustion and metabolic reprogramming in renal cell carcinoma. Front Immunol 14 , 1095195, doi:10.3389/fimmu.2023.1095195 (2023). Wischnewski, V. et al. Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation. Nat Cancer 4 , 908-924, doi:10.1038/s43018-023-00566-3 (2023). Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS5.xlsx TableS24.xlsx SupplementaryMethods.docx Supplementary.docx Graphabstract.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 11 May, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 19 Apr, 2026 Submission checks completed at journal 12 Apr, 2026 First submitted to journal 09 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9365545","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":628465724,"identity":"f5fb3713-0385-4e00-ab0e-b216b6c4c138","order_by":0,"name":"Mingshu Xiao","email":"","orcid":"","institution":"The Fourth Affiliated Hospital of Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mingshu","middleName":"","lastName":"Xiao","suffix":""},{"id":628465725,"identity":"18ac39ce-4f8a-4bc2-be22-7f0ae3891826","order_by":1,"name":"Guoxin Cai","email":"","orcid":"","institution":"Zhejiang 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Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACNvbmgw8+VNjIMTAcAHGJ0MLPcyzZcMaZNGPitUjO8DGT5m07nNgAsZQILQY3GAwkeNiY0+c3njFg+FB2mIF/dgMBLbcbEkB6chsbzhgwzjh3mEHizgECWu4cOADSk9vMcMaAGehCoKUJhByW2ADSk84G0vKXGC2SM5IZQXoSeEBaGInRAgxkZpAewxkMxwoO9pxL55G4QUALG3v/999///2Xl59xeOODH2XWcvwzCGhBAIkD4MjkIVY9yIkNJCgeBaNgFIyCEQUAwfBHdJyabiIAAAAASUVORK5CYII=","orcid":"","institution":"The Fourth Affiliated Hospital of Zhejiang University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jiangnan","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2026-04-09 08:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9365545/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9365545/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108492863,"identity":"a4df5cf5-5cfb-4403-9338-d9e4bb15fc0f","added_by":"auto","created_at":"2026-05-05 09:58:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2147537,"visible":true,"origin":"","legend":"\u003cp\u003eLung cancer immune ecosystem characterized by single-cell transcriptomic sequencing in patients with and without chronic obstructive pulmonary disease (COPD).\u003c/p\u003e\n\u003cp\u003e(A) Schematic overview of the study design.\u003c/p\u003e\n\u003cp\u003e(B) Heatmap showing marker gene expression across identified cell types.\u003c/p\u003e\n\u003cp\u003e(C) Uniform manifold approximation and projection (UMAP) visualization of cell clusters.\u003c/p\u003e\n\u003cp\u003e(D–E) Correlation of immune cell subset abundance with lung function severity, measured by percentage forced expiratory volume in 1 s (FEV1%). Disease severity classified according to Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines.\u003c/p\u003e\n\u003cp\u003e(F) Comparison of cell fractions in tumor tissues between NSCLC patients with (LC-COPD) and without COPD (LC-only).\u003c/p\u003e\n\u003cp\u003e(G) Number of differentially expressed genes (DEGs) identified in major cell subsets comparing NSCLC with and without COPD.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9365545/v1/c79e9ce852a26a23b050aff2.png"},{"id":108493200,"identity":"4d26a4c6-c7cf-4d22-8c0d-aaf50d18de27","added_by":"auto","created_at":"2026-05-05 09:59:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1614908,"visible":true,"origin":"","legend":"\u003cp\u003eImmune suppression of malignant cells correlates with COPD.\u003c/p\u003e\n\u003cp\u003e(A) UMAP plot depicting five clusters of malignant cells.\u003c/p\u003e\n\u003cp\u003e(B) Gene set enrichment analysis of cluster-specific markers using MSigDB, GO and KEGG databases.\u003c/p\u003e\n\u003cp\u003e(C–D) UMAP plots showing expression of immune escape and immune surveillance markers.\u003c/p\u003e\n\u003cp\u003e(E) Pseudotime trajectory of malignant cells.\u003c/p\u003e\n\u003cp\u003e(F) Heatmap indicating the relative enrichment of malignant cells in the tumor microenvironment for NSCLC with and without COPD.\u003c/p\u003e\n\u003cp\u003e(G–H) Boxplots of normalized immune escape and immune surveillance scores in malignant cells from LC-COPD and LC-only.\u003c/p\u003e\n\u003cp\u003e(I) Expression patterns of HLA genes in malignant cell subsets.\u003c/p\u003e\n\u003cp\u003e(J) Boxplot comparing TGFβ1 expression between LC-COPD and LC-only.\u003c/p\u003e\n\u003cp\u003e(K) Correlation analysis between TGFβ1 expression in malignant cells and FEV1%.\u003c/p\u003e\n\u003cp\u003e*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9365545/v1/b36c7ae2107c0bfa388704d7.png"},{"id":108389469,"identity":"0e4e6d8f-ca60-437b-bd3c-132c47597d00","added_by":"auto","created_at":"2026-05-04 06:49:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":990478,"visible":true,"origin":"","legend":"\u003cp\u003eTGFβ1 expression elevation during malignant transformation of epithelial cells in NSCLC with COPD.\u003c/p\u003e\n\u003cp\u003e(A) Boxplot of TGFβ1 expression of epithelial cells in NSCLC-COPD and NSCLC-only.\u003c/p\u003e\n\u003cp\u003e(B) Boxplot of TGFβ1 expression of malignant cells and epithelial cells in NSCLC-COPD.\u003c/p\u003e\n\u003cp\u003e(C) UMAP plots showing expression of TGFβ1.\u003c/p\u003e\n\u003cp\u003e(D) Schematics of the extraction procedure by Figdraw.\u003c/p\u003e\n\u003cp\u003e(E-H) Histogram of TGFβ1 expression levels in A549 cells treated with LPS and CSE at different concentrations and times.\u003c/p\u003e\n\u003cp\u003e(I) Diagram summarizing the expression of TGFβ1 increased during malignant transformation of epithelial cells in LC-COPD by Figdraw.\u003c/p\u003e\n\u003cp\u003e* p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001, **** p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9365545/v1/2ae6129def161a16082c197b.png"},{"id":108493070,"identity":"6c553397-4502-4b8c-b2b4-45f18767aa1f","added_by":"auto","created_at":"2026-05-05 09:59:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":890392,"visible":true,"origin":"","legend":"\u003cp\u003eTGFβ1-IL4I1-mediated interactions and their impact on the tumor microenvironment of LC-COPD.\u003c/p\u003e\n\u003cp\u003e(A) UMAP plot depicting nine macrophage clusters.\u003c/p\u003e\n\u003cp\u003e(B) Proportions of these macrophage clusters in normal and tumor tissues.\u003c/p\u003e\n\u003cp\u003e(C) Bubble plot illustrating TGFβ1-receptor interactions between malignant cells and tumor-associated macrophages.\u003c/p\u003e\n\u003cp\u003e(D-E) Spatial feature plots displaying IL4I1 expression and macrophage distribution.\u003c/p\u003e\n\u003cp\u003e(F) Bubble plot demonstrating interactions between macrophages and both malignant and immune cells.\u003c/p\u003e\n\u003cp\u003e(G) IL4I1 mRNA expression in THP-1 cells treated with TGF-β1 at different doses or for different durations.\u003c/p\u003e\n\u003cp\u003e(H) Western blot images.\u003c/p\u003e\n\u003cp\u003e(I) LGALS9 mRNA expression in THP-1 cells treated with TGF-β1 at different doses or for different durations.\u003c/p\u003e\n\u003cp\u003e(J) Gal-9 secretion in THP-1 cells treated with TGF-β1 at different doses or for different durations.\u003c/p\u003e\n\u003cp\u003e(K) Spatial feature plots highlighting IL4I1 expression, as well as CD8\u003csup\u003e+ \u003c/sup\u003eT cell and TAM distribution in LC-COPD.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9365545/v1/e94f36d0faf44d12b8be53b6.png"},{"id":108389471,"identity":"cba98019-80e7-4570-bf07-6ec4ef83de74","added_by":"auto","created_at":"2026-05-04 06:49:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1383180,"visible":true,"origin":"","legend":"\u003cp\u003eIL4I1 macrophages influence CD8\u003csup\u003e+\u003c/sup\u003e T cell exhaustion and tumor-specific responses in LC-COPD.\u003c/p\u003e\n\u003cp\u003e(A) UMAP plot of T cells, visualized as 20 distinct clusters.\u003c/p\u003e\n\u003cp\u003e(B–C) Comparison of cell fractions among CD4\u003csup\u003e+\u003c/sup\u003e T cell and CD8\u003csup\u003e+\u003c/sup\u003e T-cell subtypes between NSCLC patients with (LC-COPD) and without COPD (LC-only).\u003c/p\u003e\n\u003cp\u003e(D) Box plot illustrating the distribution of exhaustion scores among CD8\u003csup\u003e+\u003c/sup\u003e T cells.\u003c/p\u003e\n\u003cp\u003e(E–F) Representative flow cytometry plots depicting PD1\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells and TIGIT\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells, along with quantitative comparisons between LC-COPD and LC-only (student’s t-test).\u003c/p\u003e\n\u003cp\u003e(G) Box plot of the fraction of tumor-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells in LC-COPD versus LC-only.\u003c/p\u003e\n\u003cp\u003e(H) Bubble plot showing interactions between macrophages and CD8+ T cells.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9365545/v1/7106578e55ce3d386dbc9fcc.png"},{"id":108493286,"identity":"035acb3f-03b5-42a3-b41a-048cee1a17ba","added_by":"auto","created_at":"2026-05-05 09:59:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1656594,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization and functional analysis of tumor-specific CD8\u003csup\u003e+ \u003c/sup\u003eT cells in NSCLC with and without COPD\u003c/p\u003e\n\u003cp\u003e(A) UMAP plot of tumor-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells, colored by nine clusters.\u003c/p\u003e\n\u003cp\u003e(B) Heatmap showing the scores of six functional pathways.\u003c/p\u003e\n\u003cp\u003e(C) Venn diagram indicating the intersection of the top 40 marker genes in two Tex (exhausted) subsets.\u003c/p\u003e\n\u003cp\u003e(D) Bubble plot showing expression of CXCL13 and CD82 in tumor-specific versus tumor-unrelated CD8\u003csup\u003e+\u003c/sup\u003eT cells.\u003c/p\u003e\n\u003cp\u003e(E–F) Survival analysis showing significant differences in progression-free-survival (PFS) between high and low expression of CXCL13 or CD82.\u003c/p\u003e\n\u003cp\u003e(G) Heatmap depicting the transition of tumor-specific CD8\u003csup\u003e+ \u003c/sup\u003eT cells quantified by STARTRAC-tran indices.\u003c/p\u003e\n\u003cp\u003e(H) Heatmap of relative enrichment of tumor-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells comparing NSCLC with COPD to those without COPD.\u003c/p\u003e\n\u003cp\u003e(I) Pseudotime trajectory for tumor-specific CD8\u003csup\u003e+ \u003c/sup\u003eT cells, generated with Monocle (right), and a cell density plot illustrating the distribution of nine subtypes along the pseudotime axis (left).\u003c/p\u003e\n\u003cp\u003e(J) Heatmap of relative enrichment for each of the nine tumor-specific CD8\u003csup\u003e+\u003c/sup\u003e T-cell clusters in different states.\u003c/p\u003e\n\u003cp\u003e(K) Pseudotime plots of tumor-specific CD8\u003csup\u003e+ \u003c/sup\u003eT cells in LC-only (left) versus LC-COPD (right), alongside cell density plots displaying the distribution across the pseudotime (top).\u003c/p\u003e\n\u003cp\u003e(L) Pseudotime plot demonstrating how CXCL13, and CD82 expression levels correlate with the transition of tumor-specific CD8\u003csup\u003e+ \u003c/sup\u003eT cells.\u003c/p\u003e\n\u003cp\u003e(M) CXCL13 and CD82 mRNA expression in human CD8⁺ T cells isolated from peripheral blood mononuclear cells treated with Gal-9 at different doses.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-9365545/v1/87416f20df1577eb90c15d18.png"},{"id":108803765,"identity":"b27d34f0-6785-4d19-9189-68d3a1dc5439","added_by":"auto","created_at":"2026-05-08 15:06:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9077599,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9365545/v1/27168e8d-e61f-4b88-a1e8-452a27b16c27.pdf"},{"id":108389465,"identity":"c4125f6a-0d85-4a75-97ae-397474710ffe","added_by":"auto","created_at":"2026-05-04 06:49:40","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12663,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9365545/v1/caa449ab4ed919cb6586685c.xlsx"},{"id":108389467,"identity":"317d6ad0-e7a8-43d6-af9f-ade6eb85b402","added_by":"auto","created_at":"2026-05-04 06:49:40","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9565,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9365545/v1/f44f4a763751f936040532ac.xlsx"},{"id":108492787,"identity":"2a2e9890-79e9-45e2-90b2-41e2b418f4ae","added_by":"auto","created_at":"2026-05-05 09:58:38","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15596,"visible":true,"origin":"","legend":"","description":"","filename":"TableS24.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9365545/v1/e89a1a707fddc335d3ede4bf.xlsx"},{"id":108492374,"identity":"097ef5fb-6a2c-4a4f-9b63-6bd25cf30e66","added_by":"auto","created_at":"2026-05-05 09:57:36","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":125115,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-9365545/v1/8a3a64c8fa82c3a626c7ec0e.docx"},{"id":108389473,"identity":"efcb4aed-967a-4f5c-9c46-e3696ff579ac","added_by":"auto","created_at":"2026-05-04 06:49:40","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":3283155,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-9365545/v1/de35018d966459792e8d637d.docx"},{"id":108492382,"identity":"13f8ea7e-86dc-4e45-8f72-aaeb94d57e54","added_by":"auto","created_at":"2026-05-05 09:57:38","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":333504,"visible":true,"origin":"","legend":"","description":"","filename":"Graphabstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-9365545/v1/66f646dca91047b2961a7bbf.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elevated TGFβ1 Drives IL4I1⁺ Macrophage–Gal-9 Signaling to Shape Tumor-Specific CD8⁺ T- Cell States in COPD-Associated NSCLC","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe health burden of Chronic Obstructive Pulmonary Disease (COPD) has intensified, now ranking as the third leading cause of death worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Notably, a 2019 study revealed a 13.7% COPD prevalence among individuals aged 40 and above in China, affecting nearly 100\u0026nbsp;million people\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. COPD is associated with a significantly higher risk of lung cancer (LC), with evidence indicating a 2.15-fold increased incidence of among patients with COPD\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Remarkably, COPD has become the second leading cause of non-cancer deaths among individuals diagnosed with LC \u003csup\u003e4\u003c/sup\u003e. Repeated cycles of airway injury and repair associated with COPD create favorable conditions for cancer development\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eImmunotherapy has revolutionized the treatment of LC, particularly non-small cell lung cancer (NSCLC). Several studies have demonstrated that NSCLC patients with COPD benefit more from immunotherapy compared to those without COPD, as indicated by prolonged survival rates and higher objective response rates (ORR)\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This therapeutic advantage may arise from alterations in the immune microenvironment induce by COPD\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the precise biological mechanisms underlying the increased immunotherapy responsiveness in NSCLC patients with COPD remain poorly understood. Combined single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST-seq) provide a powerful approach to characterize immune cell populations and their interactions within the tumor microenvironment\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUnderstanding the complex relationship between COPD and NSCLC is crucial for improving treatment outcomes and prognosis for patients with coexistent conditions. Building upon this premise, our research employed scRNA-seq to identify the cellular heterogeneity specific to patients with NSCLC combined with COPD compared to those without COPD. These findings highlight how chronic inflammation shapes the tumor microenvironment, paving the way for future studies aimed at understanding how COPD modulates immunotherapy efficacy in NSCLC. Additionally, our results provide a theoretical basis for the concurrent pharmacological management of NSCLC and COPD.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eCOPD significantly impacts the immune environment of NSCLC\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eTo investigate the cellular composition and gene regulation within the tumor microenvironment of NSCLC patients with (LC-COPD) and without COPD (LC-only), scRNA-seq was performed on 30 tumor samples (19 COPD, 11 non-COPD), 11 NAT samples (6 COPD, 5 non-COPD), and 23 peripheral blood samples (13 COPD, 10 non-COPD) (Figure 1A and Table S1). Following quality control, 534,996 high-quality cells with an average of 1,142 detected genes were identified, which underwent automated cell-type classification and marker-gene identification (Figure 1B\u0026ndash;C and Figure S1A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe abundance of immune cell subsets correlated with disease severity, as measured by the percent predicted forced expiratory volume in 1 second (FEV1), a key metric for evaluating lung function. Notably, increased monocyte fractions and decreased T-cell lineage populations correlated significantly with reduced FEV1%, indicating impaired lung function (Figure 1D\u0026ndash;E and Figure S1B\u0026ndash;G). Further analysis revealed lower proportions of T cells, macrophages, B cells and dendritic cells (DCs), but elevated monocyte fractions within tumors from patients with LC-COPD compared to those with LC-only. Conversely, NATs from LC-COPD cases displayed enriched T-cell populations (Figure 1F). Differentially expressed genes (DEG) analysis and pathway enrichment demonstrated distinct expression patterns and biological functions in malignant cells, DCs, and macrophages between the LC-COPD and LC-only groups (Figure 1G and Figure S1H). We found through cell interaction analysis that there are more interactions between immune cell subsets in LC-COPD, with macrophages showing the most prominent interactions with other cells (Figure S1I\u0026ndash;J). Together, our scRNA-seq analysis highlights significant immune landscape heterogeneity and complexity associated with COPD in NSCLC.\u003c/p\u003e\n\u003ch3\u003eMalignant cells in LC-COPD exhibit increased TGF\u0026beta;1 expression and immune evasion\u003c/h3\u003e\n\u003cp\u003eTo characterize malignant cell composition and gene regulation differences in LC-only and LC-COPD, we analyzed tumor cells identified by clustering based on single-cell gene expression-derived copy number variations (CNVs) (Figure S2A\u0026ndash;C). Malignant cells from 30 tumor samples clustered into 5 distinct subsets, each exhibiting specific enriched biological pathways (Figure 2A\u0026ndash;B and Figure S2D). Pseudotime trajectory analysis indicated a progression of malignant cells toward distinct transcriptional states associated with immune evasion (Figure 2C\u0026ndash;E). Malignant cells were in a state of immune evasion in LC-COPD, whereas they exhibited a state of immune surveillance in LC-only (Figure 2F\u0026ndash;H). Specifically, transforming growth factor-beta 1 (TGF\u0026beta;1)\u0026mdash;a critical gene associated with immune escape mechanisms\u0026mdash; showed heightened expression in LC-COPD cells, alongside reduced expression of HLA genes (Figure 2I\u0026ndash;J), indicative of diminished immune recognition. Furthermore, increased TGF\u0026beta;1 expression correlated with a decline in FEV1%, reflecting more severe disease (Figure 2K). These findings suggest that high TGF\u0026beta;1 expression contributes to immune evasion in LC-COPD.\u003c/p\u003e\n\u003ch2\u003eThe transformation of normal epithelial cells to inflammatory epithelial cells and to malignant cells may be characterized by TGF\u0026beta;1\u003c/h2\u003e\n\u003cp\u003eThe transformation of normal epithelial cells to inflammatory epithelial cells and ultimately to malignant cells is characterized by elevated TGF\u0026beta;1 expression. In LC-COPD, normal epithelial cells already exhibit higher levels of TGF\u0026beta;1 compared to LC-only cases (Figure 3A). This elevated TGF\u0026beta;1 expression is also critical in driving the transition to malignant cells, where TGF\u0026beta;1 levels are significantly higher than in the surrounding epithelial cells (Figure 3B). The elevated TGF\u0026beta;1 expression not only drives the transformation from normal to malignant cells but also reduces immune surveillance and enhances immune escape in malignant cells, contributing to the formation of a tumor-immunosuppressive microenvironment (Figure 2C\u0026ndash;E and Figure 3C). We then prepared cigarette smoke extract (CSE) (Figure 3D) and exposed A549 cells to CSE or lipopolysaccharide (LPS), noting a dose- and time-dependent increase in TGF\u0026beta;1 expression (Figure 3E\u0026ndash;H). Overall, these findings suggest that cigarette smoke and inflammation upregulate TGF\u0026beta;1 in epithelial cells, contributing to progression from healthy lungs to COPD and eventually lung cancer (Figure 3I).\u003c/p\u003e\n\u003ch3\u003eExploring the role of TGF\u0026beta;1 expression in malignant cells and its impact on macrophage subsets in LC-COPD\u003c/h3\u003e\n\u003cp\u003eGiven the marked upregulation of TGF\u0026beta;1 in malignant epithelial cells in LC-COPD, we next asked whether this alteration reshapes the tumor immune microenvironment through enhanced crosstalk with immune cells. Cell\u0026ndash;cell interaction analysis revealed that interactions between malignant cells and macrophages were markedly increased in LC-COPD compared with LC-only cases (Figure S2E), with macrophages emerging as a major cellular target of tumor-derived signals.To further assess how TGF\u0026beta;1 expression in malignant cells influences macrophage populations, we classified macrophages into nine clusters, including four tumor-associated macrophage (TAM) clusters (Macro_IL4I1, Macro_MS4A6A, Macro_NEAT1, and Macro_VCAN) and five additional macrophage clusters (Macro_FABP4, Macro_IFI27, Macro_CCL4, Macro_STMN1, and Macro_HSPA1A) (Figure 4A\u0026ndash;B and Figure S3A). Among these, Macro_IL4I1 predominated within tumor tissues, representing a distinct macrophage subset (Figure 4B).\u003c/p\u003e\n\u003cp\u003eWe next sought to define macrophage subset characteristics through functional profiling. Traditionally, macrophages are categorized into M1 (proinflammatory or anti-tumor) and M2 (anti-inflammatory or pro-tumor) phenotypes. TAMs in LC-COPD exhibited a predominantly M2-like phenotype, whereas TAMs in LC-only samples displayed a stronger M1 signature (Figure S3B). Functional analyses further revealed that TAMs in LC-COPD showed enhanced angiogenic potential, while TAMs in LC-only demonstrated superior phagocytic capacity and T-cell recruitment ability (Figure S3C). Notably, Macro_IL4I1, classified as an M2-like subset, contributed to both angiogenesis and inflammatory processes (Figure S3D\u0026ndash;I). Analysis of TGF\u0026beta; receptor\u0026ndash;ligand interactions between malignant cells and Macro_IL4I1 or Macro_MS4A6A demonstrated a pronounced activation of TGF\u0026beta; signaling in LC-COPD (Figure 4C). Spatial transcriptomic analysis further indicated that IL4I1 expression was predominantly localized to TAMs in LC-COPD tumors (Figure 4D\u0026ndash;E).\u003c/p\u003e\n\u003cp\u003eLigand\u0026ndash;receptor interaction analysis revealed more extensive crosstalk between macrophages and other immune cell populations in LC-COPD, with the most prominently upregulated interaction involving LGALS9 and its receptors between macrophages and CD8⁺ T cells (Figure 4F). Consistently, we observed that TGF\u0026beta;1 stimulation promoted IL4I1 expression and activated TGF\u0026beta; signaling in macrophages in a dose/time-dependent manner (Figure 4G\u0026ndash;H). Moreover, using western blotting, qPCR, and ELISA assays, we found that TGF\u0026beta;1 stimulation significantly induced LGALS9 expression in macrophages (Figure 4H\u0026ndash;J). Spatial transcriptomic analysis further revealed elevated IL4I1 expression in regions where TAMs and CD8⁺ T cells were colocalized, suggesting that IL4I1⁺ macrophages may play a critical role in mediating macrophage\u0026ndash;CD8⁺ T-cell interactions (Figure 4K).\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study highlights a pivotal role for malignant cell\u0026ndash;derived TGF\u0026beta;1 in shaping the LC-COPD tumor immune microenvironment by promoting the expansion and functional activation of IL4I1⁺ macrophages and enhancing their LGALS9-mediated immunomodulatory effects.\u003c/p\u003e\n\u003ch2\u003eImmunosuppressive phenotypes of T cells in LC-COPD\u003c/h2\u003e\n\u003cp\u003eWe next investigated whether the TGF\u0026beta;1\u0026ndash;IL4I1⁺ TAM\u0026ndash;LGALS9 axis in LC-COPD is associated with T-cell dysfunction in the tumor microenvironment. T cells play a crucial role in the effectiveness of current cancer immunotherapies\u003csup\u003e12\u003c/sup\u003e. To profile T lymphocytes at higher resolution, we subclustered them into 10 annotated CD4\u003csup\u003e+\u003c/sup\u003e subsets\u0026mdash;interferon-stimulated genes (ISG)\u0026ndash;positive T cells, effector memory T cells (Tem), terminally differentiated effector memory or effector cells (Temra), follicular helper T cells (Tfh), Tfh/T helper 1 cells (TfhTh1), T helper cells 17 (Th17), memory T cells (Tm), naive T cells (Tn), regulatory T cells (Treg) and mixed T cells (Tmix). Additionally, 9 CD8\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT-cell clusters were identified, including ISG\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT cells, mucosal-associated invariant T cells (MAIT), Tem, Temra, exhausted CD8\u003csup\u003e+\u003c/sup\u003e T cells (Tex), NK-like T cells (Tk), Tm, Tn, and tissue-resident memory T cells (Trm) (Figure 5A and Figure S4A\u0026ndash;B).\u003c/p\u003e\n\u003cp\u003eWe observed that the proportions of CD4_Treg and CD4_Tm were significantly higher in LC-COPD than in LC-only for both adenocarcinoma and squamous carcinoma (Figure 5B). In parallel, the proportion of CD8_Tex was markedly elevated in LC-COPD across both NSCLC subtypes (Figure 5C). These CD8_Tex cells exhibited a strong exhaustion signature, particularly in LC-COPD (Figure 5D). Flow cytometry further confirmed an increased frequency of PD1\u003csup\u003e+\u003c/sup\u003e and TIGIT\u003csup\u003e+\u003c/sup\u003e subpopulations within CD8\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT cells from patients with LC-COPD (Figure 5E\u0026ndash;F and Figure S5A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBecause tumor-specific T cells are essential for anti-tumor immunity\u003csup\u003e13-15\u003c/sup\u003e, we evaluated their tumor specificity score\u003csup\u003e16\u003c/sup\u003e and found it to be notably higher in exhausted T-cell subsets, especially in LC-COPD (Figure S4C\u0026ndash;D). Using a TCR-based approach that identified shared clonotypes with Tex cells and high tumor-specific T-cell gene expression scores (\u0026gt;0), we isolated a population of CD8\u003csup\u003e+\u003c/sup\u003e tumor-specific T cells characterized by distinct clonotypes (Figure S4E\u0026ndash;G). This tumor-specific subset was more abundant in LC-COPD (Figure 5G). Compared with unrelated T cells, LGALS9 displayed stronger interactions with these tumor-associated T cells, and this effect was more pronounced in LC-COPD (Figure 5H). Notably, among tumor-associated macrophage subsets, LGALS9\u0026ndash;receptor interactions with tumor-specific CD8⁺ T cells were most prominent in the Macro_IL4I1 and Macro_MS4A6A populations in LC-COPD (Figure 5H).\u003c/p\u003e\n\u003ch3\u003eCXCL13 and CD82 serve as markers for exhausted, tumor-specific CD8\u003csup\u003e+\u003c/sup\u003e T Cells associated with favorable prognosis.\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eBased on marker genes and functional scores, we identified nine distinct tumor-specific CD8\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT-cell subtypes:\u0026nbsp;CD8_Temra_GZMH, CD8_Tex_RGS1, CD8_Trm_ZNF683, CD8_Tem_GZMK, CD8_progenitor exhausted cluster_IL7R (CD8_pTex_IL7R), CD8_Tex_GZMB, CD8_proliferative_STMN1 (CD8_Tpro_STMN1), CD8_Trm_CXCR4, and CD8_Treg_FOXP3 (Figure 6A and Figure S6A). Classification accuracy for these subsets was validated by mapping them back to their original CD8\u003csup\u003e+\u003c/sup\u003e T-cell clusters (Figure S6B).\u003c/p\u003e\n\u003cp\u003eOf these, CD8_Tex_RGS1 and CD8_Tex_GZMB showed high exhaustion scores, with only the CD8_Tex_GZMB subset exhibiting strong cytotoxicity (Figure 6B). All CD8_Tex and CD8_pTex subsets were enriched in cytokine-cytokine receptor interaction pathway. Additionally, CD8_Tex_GZMB, CD8_Tex_RGS1, and CD8_pTex_IL7R were particularly abundant in IL10, TNF, and STAT signaling pathways, respectively (Figure S6C). Chemokine C-X-C motif ligand 13 (CXCL13) and CD82 emerged as shared top markers for both the CD8_Tex_GZMB and CD8_Tex_RGS1 clusters (Figure 6C). Moreover, significantly more tumor-specific CD8\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT cells co-expressed these genes compared with tumor-unrelated CD8\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT cells (Figure 6D). CXCL13 and CD82 were also expressed at higher levels in LC-COPD tumors than in NATs (Figure S6D). Consistent with their potential clinical relevance, TCGA data indicated that higher expression of both markers correlated with better PFS in patients with NSCLC (Figure 6E\u0026ndash;F).\u003c/p\u003e\n\u003ch3\u003eTumor-specific CD8\u003csup\u003e+\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eT cells reside in an exhausted state in LC-COPD and a cytotoxic state in LC-only\u003c/h3\u003e\n\u003cp\u003eBuilding on the identification of CXCL13 and CD82 as markers of tumor-specific CD8⁺ T cells with favorable prognostic significance, we next investigated their developmental origins and state transitions. TCR analyses suggested that the two CD8_Tex subsets primarily originate from the CD8_Tpro_STMN1 cluster (Figure 6G). While CD8_Temra_GZMH, CD8_Trm_ZNF683, and CD8_Tem_GZMK were more abundant in LC-only, exhaustion-related clusters (CD8_pTex_IL7R, CD8_Tex_RGS1 and CD8_Tex_GZMB) prevailed in LC-COPD (Figure 6H).\u0026nbsp;We inferred state trajectories and examined dynamic cell transitions to illustrate the distinctive immunological states of tumor specific CD8\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT cells in LC-COPD versus LC-only (Figure 6I). As cells progressed along this trajectory, cytotoxic signatures increased, while the exhaustion score reached its maximum at the fully exhausted state (Figure S6E). Pseudotime trajectory analyses positioned CD8_Tpro_STMN1 at the earliest differentiation stage (in the absence of naive T cells) and identified CD8_Tex and CD8_Temra_GZMH as terminally differentiated states (Figure 6J). Some CD8_Tex cells originated directly from CD8_Tpro_STMN1, whereas others transitioned from CD8_Tpro_STMN1 via an intermediate state involving CD8_Trm_ZNF683, CD8_Treg_FOXP3, and CD8_pTex_IL7R (Figure 6J). Additionally, the intermediate state could also transition to an effector memory state characterized by CD8_Tem_GZMK and CD8_Temra_GZMH (Figure 6J).\u003c/p\u003e\n\u003cp\u003eIn LC-COPD, the initial or intermediate state showed a higher propensity to transition into an exhausted CD8\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT cell state (Figure 6K). By contrast, the intermediate state in LC-only followed two distinct trajectories, one leading to exhaustion and the other toward a cytotoxic effector memory state (Figure 6L). CXCL13 and CD82 expression were elevated in both the initial proliferative and exhausted states (Figure 6L). Correlation analysis further indicated a positive association between CXCL13/CD82 expression and T-cell exhaustion (Figure S6F). Consistently, Gal-9 stimulation promoted CXCL13 and CD82 expression in CD8⁺ T cells in a dose-dependent manner (Figure 6M). Given that proliferative tumor-specific CD8⁺ T cells are considered a key reservoir sustaining effective anti-tumor immunity, these findings suggest that Gal-9 signaling may facilitate the expansion or maintenance of such proliferative CD8⁺ T-cell populations through the induction of CXCL13 and CD82.\u003c/p\u003e\n\u003cp\u003eOverall, our analysis reveals distinct immune and transcriptional states during tumor-specific CD8⁺ T-cell differentiation in LC-COPD. Rather than serving solely as markers of exhaustion, CXCL13 and CD82 identify tumor-specific CD8⁺ T cells with enhanced proliferative potential. Together with the observed enrichment of these populations in LC-COPD and their association with improved patient outcomes, our findings support a model in which TGF\u0026beta;-driven IL4I1⁺ macrophages shape tumor-specific CD8⁺ T-cell states through Gal-9 signaling, thereby influencing the balance between proliferation and exhaustion.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eImmunotherapy has become a cornerstone of NSCLC treatment\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Patients\u0026rsquo; preexisting immune status significantly affects the efficacy of these therapies\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. COPD, a common comorbidity in lung cancer, profoundly reshapes pulmonary immunity; however, how COPD alters the tumor immune microenvironment and influences immune escape mechanisms in lung cancer remains incompletely understood. In this study, we performed an integrated single-cell and spatial transcriptomic analysis of treatment-na\u0026iuml;ve NSCLC tissues to systematically compare the immune ecosystems of LC-COPD and LC-only.\u003c/p\u003e \u003cp\u003eOur findings indicate that LC-COPD and LC-only tumors employ different immune escape mechanisms. TGF-β, a key regulator of tumor progression and immune suppression\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, was markedly upregulated in LC-COPD. This finding is consistent with prior reports implicating aberrant TGF-β signaling in both COPD and lung cancer\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and supports the notion that chronic airway inflammation may facilitate malignant transformation through sustained TGF-β activation\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Dysregulated TGF-β signaling correlates with therapy resistance in lung cancer\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, consistent with clinical observations that patients with LC-COPD display greater resistant to anti-tumor drugs. We observed that LC-COPD tumor cells express higher levels of TGFβ1 and exhibit more frequent TGFβ-mediated crosstalk with DCs and TAMs, thus contributing to an immunosuppressive microenvironment. Although various TGF-β inhibitors are in clinical or preclinical trials\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, their combination with PD-1/PD-L1 blockers has shown limited success, possibly due to inadequate patient selection or trial design\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Targeting TGF-β in LC-COPD may identify patients who benefit most and guide more effective combination therapies.\u003c/p\u003e \u003cp\u003eA particularly notable feature of the LC-COPD tumor microenvironment is the expansion of IL4I1-expressing tumor-associated macrophages. IL4I1 (interleukin-4\u0026ndash;induced gene 1) encodes an L-amino acid oxidase originally characterized in antigen-presenting cells and has been implicated in immune regulation through metabolic and redox-dependent mechanisms\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Previous studies have shown that IL4I1 can suppress T-cell proliferation and effector function by depleting essential amino acids and generating immunoregulatory metabolites, thereby contributing to immune tolerance in both inflammatory and tumor contexts\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In cancer, IL4I1 expression has been reported in myeloid populations and linked to poor prognosis and immune evasion, although its precise role within distinct macrophage subsets remains incompletely defined.\u003c/p\u003e \u003cp\u003eIn the context of LC-COPD, the enrichment of IL4I1⁺ TAMs suggests that chronic inflammatory cues and tumor-derived signals converge to promote a specialized immunomodulatory macrophage phenotype. Sustained TGFβ signaling, which is prominent in COPD-associated lung cancer, may favor the differentiation or stabilization of IL4I1⁺ macrophages, thereby reinforcing local immune suppression. Rather than acting solely as passive inhibitors of immunity, these macrophages appear positioned to actively shape the tumor immune ecosystem by orchestrating intercellular communication networks.\u003c/p\u003e \u003cp\u003eOne potential mechanism through which IL4I1⁺ TAMs influence tumor immunity is via the regulation of Gal-9\u0026ndash;mediated signaling. Gal-9 has been widely recognized as an immunomodulatory ligand capable of altering T-cell activation, differentiation, and survival in a context-dependent manner\u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. While Gal-9 has often been associated with inhibitory effects on T cells, emerging evidence suggests that its impact may vary depending on the differentiation state of T cells and the broader cytokine milieu\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In LC-COPD tumors, IL4I1⁺ macrophages may serve as a major source of Gal-9, thereby modulating tumor-specific CD8⁺ T-cell programs rather than inducing uniform terminal dysfunction.\u003c/p\u003e \u003cp\u003eImportantly, this macrophage-driven signaling landscape may help reconcile the apparent paradox observed in LC-COPD tumors, which exhibit features of immune suppression alongside the preservation of tumor-specific CD8⁺ T-cell populations with proliferative capacity. By influencing the balance between T-cell proliferation and differentiation, IL4I1⁺ TAMs could contribute to maintaining a reservoir of tumor-reactive CD8⁺ T cells poised for reactivation upon immune checkpoint blockade. Such a model aligns with emerging concepts that effective immunotherapy responses rely not on the complete absence of inhibitory signals, but on the presence of a sufficiently large and dynamic pool of tumor-specific T cells capable of functional reinvigoration.\u003c/p\u003e \u003cp\u003eOur study also revealed distinct immune profiles of CD8\u003csup\u003e+\u003c/sup\u003e T cells, especially in the presence of COPD. Patients with LC-COPD had CD8\u003csup\u003e+\u003c/sup\u003e T cells with both heightened tumor specificity and stronger exhaustion compared to LC-only. This aligns with prior findings that the degree of T-cell exhaustion parallels tumor specificity\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Some research suggests that PD-L1 expression in tumors predicts the presence of tumor-reactive CD8\u003csup\u003e+\u003c/sup\u003e T cells\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, which may help explain improved responses to anti-PD-1/PD-L1 therapy in LC-COPD\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Efforts to define precursor exhausted T cells with stem-like properties have identified potential biomarkers of immunotherapy response\u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Our data, integrating TCR and trajectory data, show that most CD8_Tex cells arise directly from a proliferative CD8_Tpro_STMN1 subset. While typical exhaustion markers such as TCF7 and IL7R did not fully characterize CD8_Tpro_STMN1, both CXCL13 and CD82\u0026mdash;associated with T-cell dysfunction and immune therapy response\u0026mdash;were highly expressed in this population. CXCL13 has been recognized as a marker for T-cell dysfunction and tumor antigen specificity, as well as a predictive biomarker for immunotherapy across various tumor types\u003csup\u003e\u003cspan additionalcitationids=\"CR38 CR39 CR40\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Moreover, CD8\u003csup\u003e+\u003c/sup\u003e T cells expressing CXCL13 are considered to be proliferative\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Consistent with this notion, we observed elevated CXCL13 and CD82 expression in proliferative CD8⁺ T-cell subsets and demonstrated that Gal-9 stimulation\u0026mdash;derived predominantly from IL4I1⁺ TAMs\u0026mdash;induced CXCL13 and CD82 expression in a dose-dependent manner. These findings suggest that Gal-9 signaling may promote the expansion or maintenance of tumor-specific, proliferative CD8⁺ T cells, rather than solely driving terminal exhaustion.\u003c/p\u003e \u003cp\u003eIn summary, our study delineates a distinct immune architecture in LC-COPD characterized by TGFβ1-driven malignant\u0026ndash;myeloid interactions, expansion of IL4I1⁺ TAMs, and remodeling of tumor-specific CD8⁺ T-cell differentiation trajectories. Rather than representing a uniformly immune-excluded state, LC-COPD tumors harbor a complex immune ecosystem in which immunosuppressive signals coexist with proliferative tumor-specific T-cell populations. These insights deepen our understanding of immune regulation in LC-COPD and highlight IL4I1⁺ TAMs and their downstream signaling pathways as potential modulators of immunotherapy responsiveness. Collectively, our findings provide a conceptual framework for refining patient stratification and developing tailored immunotherapeutic strategies for NSCLC patients with COPD.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eSample Collection and Cell Preparation\u003c/h3\u003e\n\u003cp\u003eFresh lung tumor tissues and peripheral blood samples were collected from patients following ethical approval (K2022179) from The Fourth Affiliated Hospital of Zhejiang University School of Medicine and The Second Affiliated Hospital Zhejiang University School of Medicine. Informed consent was obtained from all participants. Tissue samples were mechanically dissociated and enzymatically digested to obtain single-cell suspensions. Peripheral blood mononuclear cells (PBMCs) were isolated using density gradient centrifugation. Cell viability was assessed using trypan blue staining. The clinical and pathological characteristics of the patients are listed in Table S1.\u003c/p\u003e\n\n\u003ch3\u003eSingle-cell RNA Sequencing\u003c/h3\u003e\n\u003cp\u003eSingle-cell suspensions were loaded onto microfluidic devices and processed using the Singleron GEXSCOPE\u0026reg; Single-Cell RNA Library Kit. Libraries were sequenced on an Illumina NovaSeq 6000 platform (150 bp paired-end reads). Raw sequencing data were processed with quality control, doublet detection, and batch effect correction using Scanpy, scDblFinder, SoupX, and Harmony.\u003c/p\u003e\n\n\u003ch3\u003eData Analysis and Clustering\u003c/h3\u003e\n\u003cp\u003eNormalized and log-transformed expression matrices were subjected to principal component analysis (PCA) for dimensionality reduction, followed by UMAP visualization and Leiden clustering. Differentially expressed genes (DEGs) were identified using t-tests with false discovery rate correction (FDR \u0026lt; 0.05).\u003c/p\u003e\n\n\u003ch3\u003eCell Type Annotation and Malignancy Inference\u003c/h3\u003e\n\u003cp\u003eClusters were annotated using the Human Leukocyte Cell Atlas reference and scANVI transfer learning for fine T cell subtyping. Putative malignant cells were identified via inferred copy number variation (CNV) from scRNA-seq data using smoothed chromosomal expression patterns.\u003c/p\u003e\n\n\u003ch3\u003eFunctional and Pathway Analysis\u003c/h3\u003e\n\u003cp\u003eGene set enrichment analysis (GSEA) and overrepresentation analysis (Enrichr) were used to identify biological pathways associated with cluster-specific signatures. Functional scores for pathways or signatures were calculated per cell using a reference-based z-score approach. All signatures were got from previous studies (Table S2-4).\u003c/p\u003e\n\n\u003ch3\u003eSpatial Transcriptomics\u003c/h3\u003e\n\u003cp\u003eSpatial gene expression profiles were obtained using the 10\u0026times; Genomics Visium platform. Cell-type deconvolution was performed with cell2location using annotated single-cell RNA-seq references, producing spot-wise cell-type abundance maps.\u003c/p\u003e\n\n\u003ch3\u003eIn vitro Cell Assays\u003c/h3\u003e\n\u003cp\u003eA549 and THP1 cells, and isolated human CD8⁺ T cells, were cultured under standard conditions. Cells were stimulated with cytokines, cigarette smoke extract (CSE), lipopolysaccharide (LPS), or recombinant proteins for functional assays. Protein expression was assessed by Western blot, RNA levels by quantitative RT-PCR, and secreted factors by ELISA. The primers are shown in Table S5.\u003c/p\u003e\n\n\u003ch3\u003eSurvival and Clinical Correlation Analysis\u003c/h3\u003e\n\u003cp\u003eGene signatures were correlated with overall or progression-free survival in TCGA lung cancer cohorts using Kaplan-Meier analysis and log-rank testing. Correlations between cell-type proportions and lung function metrics (FEV1%, FEV1/FVC) were assessed using Pearson correlation after appropriate transformation.\u003c/p\u003e\n\n\u003ch3\u003eStatistical Analysis\u003c/h3\u003e\n\u003cp\u003eComparisons between groups were performed using non-parametric tests (Mann\u0026ndash;Whitney U or Kruskal\u0026ndash;Wallis H tests), with multiple hypothesis testing controlled via the Benjamini\u0026ndash;Hochberg procedure (adjusted p \u0026lt; 0.05 considered significant).\u003c/p\u003e\n\n\u003cp\u003eDetailed experimental procedures, reagents, and protocols are provided in the Supplementary Methods.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMX conceived the study design, performed experiments, acquired and processed the cohort data, conceptualized and implemented data analysis, and wrote the manuscript with GC and YX designed, performed, and analyzed all flow cytometry experiments. XZ and YC assisted with spatial transcriptomic data analysis. YG, YYand MZ carried out the patient phenotyping. JZ, KW and ZZ supervised the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work is supported by the National Natural Science Foundation of China (U23A20467), National Key R\u0026amp;D Program of China (2024YFA1108500), the National Natural Science Foundation of China (No. 82102852) and Science and Technology program of Jinhua Science and Technology Bureau (Grant No.2023-3-058). We would like to acknowledge the Biobank staff of Second Affiliated Hospital of Zhejiang University and the staff in the departments of respiratory and thoracic surgery in The Fourth Affiliated Hospital of Zhejiang University School of Medicine for their hard work and dedication to our investigators for their support and to the study participants.We would like to thank Editage (www.editage.cn) for English language editing.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe processed expression data of SC and ST reported in this study can be obtained from the China National GeneBank Database (CNGBdb) with accession number (GSA: HRA010788, HRA010787, HRA010890). This paper does not report original code.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCelli, B. R. \u0026amp; Wedzicha, J. A. 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Here, we integrate single-cell transcriptomics and T cell receptor sequencing of tumor, adjacent non-tumor, and blood samples from NSCLC patients, together with spatial transcriptomics and molecular validation. We identify an immunosuppressive niche in COPD-associated NSCLC characterized by elevated TGFβ1 expression in malignant cells and enhanced crosstalk with IL4I1⁺ tumor-associated macrophages. Notably, macrophage-derived galectin-9 (Gal-9) modulates tumor-specific CD8⁺ T-cell states, promoting CXCL13 and CD82 expression within a proliferative, tumor-reactive subset, while also being associated with features of T-cell exhaustion. These findings define a TGFβ1\u0026ndash;IL4I1⁺ macrophage\u0026ndash;Gal-9 axis that reshapes CD8⁺ T-cell states and contribute to differential immunotherapy responses in COPD-associated NSCLC.\u003c/p\u003e","manuscriptTitle":"Elevated TGFβ1 Drives IL4I1⁺ Macrophage–Gal-9 Signaling to Shape Tumor-Specific CD8⁺ T- Cell States in COPD-Associated NSCLC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 06:49:34","doi":"10.21203/rs.3.rs-9365545/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-11T10:05:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234420533163747184438560934158230018063","date":"2026-04-23T15:02:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T13:44:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T02:03:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-13T03:55:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Precision Oncology","date":"2026-04-09T08:22:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-precision-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjprecisiononcology","sideBox":"Learn more about [npj Precision Oncology](http://www.nature.com/npjprecisiononcology/)","snPcode":"41698","submissionUrl":"https://submission.springernature.com/new-submission/41698/3","title":"npj Precision Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"920bedae-e84c-4fc0-9c14-55c02b8c3a10","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-11T10:05:47+00:00","index":25,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66892588,"name":"Biological sciences/Cancer"},{"id":66892589,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":66892590,"name":"Biological sciences/Immunology"},{"id":66892591,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-05-04T06:49:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 06:49:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9365545","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9365545","identity":"rs-9365545","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}