Exhausted CD8⁺ T Cells Promote Ovarian Cancer Immunosuppression via CCL3-Driven M2 Macrophage Polarization

Exhausted CD8⁺ T Cells Promote Ovarian Cancer Immunosuppression via CCL3-Driven M2 Macrophage Polarization | 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 Exhausted CD8⁺ T Cells Promote Ovarian Cancer Immunosuppression via CCL3-Driven M2 Macrophage Polarization Wentao Yue, Yue Li, Jin Cheng, Fei Liu, Junjie Yi, Zhefeng Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8074255/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract High-grade serous ovarian cancer (HGSOC) is an aggressive malignancy marked by high recurrence rates, poor prognosis, and limited response to immune checkpoint inhibitors, primarily attributable to its immunologically “cold” tumor microenvironment. To profile the immunological landscape of HGSOC, we conducted single-cell RNA sequencing (scRNA-seq) on 84,065 cells from tumor tissues of eight treatment-naïve patients and one normal ovarian tissue, identifying six major cell clusters and revealing substantial immune cell infiltration. Further analysis of CD8⁺ T cells identified two key subpopulations—precursor and terminally exhausted T cells—and delineated their developmental trajectories. The accumulation of exhausted CD8⁺ T cells (Tex) suggested an immunosuppressive tumor microenvironment. Integrated trajectory inference and high-dimensional weighted gene co-expression network analysis (hdWGCNA) identified CCL3 as a novel hub gene specifically expressed in Tex cells. Communication analysis suggested that Tex cells may interact with M2 macrophages via the CCL3–CCR1 ligand–receptor axis. Functional validation confirmed that: (1) secretomes from Tex cells—but not effector T cells—significantly promoted M2 polarization in both THP-1 and bone marrow-derived macrophages (CD206⁺ THP-1: 83.8% vs. 51.4%, p < 0.001; CD206⁺ BMDM: 72.4% vs. 41.5%, p < 0.001); and (2) recombinant CCL3 acted synergistically with IL-4/IL-10 to further enhance M2 polarization (59.2% vs. 37.7%, p = 0.008). Collectively, our findings unveil a previously unrecognized immunoregulatory axis whereby exhausted CD8⁺ T cells drive immunosuppression via CCL3–CCR1–mediated communication with M2 macrophages, presenting a promising therapeutic target to reverse the immune-cold tumor microenvironment in HGSOC. Biological sciences/Immunology/Tumour immunology Biological sciences/Cell biology/Cell signalling/Extracellular signalling molecules Health sciences/Diseases/Cancer/Gynaecological cancer/Ovarian cancer Health sciences/Diseases/Cancer/Cancer microenvironment Health sciences/Diseases/Cancer/Tumour immunology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction High-grade serous ovarian carcinoma (HGSOC),the most common and aggressive subtype of epithelial ovarian cancer, accounts for over 70% of ovarian cancer-related deaths 1 , 2 . It is usually diagnosed at an advanced stage. Due to metastasis, recurrence, and chemoresistance, the five-year survival rate for patients is around 30%-40% 3 . Tumor immunotherapy, particularly ICIs, has achieved revolutionary success in various malignancies, including melanoma and non-small cell lung cancer 4 – 6 . However, their efficacy in HGSOC remain limited, with objective response rates generally below 10–15% 7 . This resistance is largely attributed to a highly immunosuppressive TME weakening or evading the body's antitumor immune response 7 – 9 . The HGSOC TME is a complex ecosystem comprising diverse immune and stromal cells. T-cell exhaustion is a hallmark of this immunosuppressive ecosystem 10 . Exhausted T-cells progressively lose their effector functions, including cytotoxic production and cytotoxic activity, under chronic stimulation from antigen-presenting cells or inflammatory signals. This is accompanied by the sustained overexpression of multiple co-inhibitory molecules (e.g., PD-1, TIM-3, and LAG-3) 11–13 . Extensive clinical and pathological studies demonstrate that the level of infiltrating exhausted T cells within HGSOC tissues is significantly associated with rapid disease progression, recurrence, and reduced overall patient survival. These functionally impaired T cells fail to eliminate tumor cells effectively and may also constitute a substantial barrier to ICI therapy 14 . However, the precise molecular mechanisms driving T-cell exhaustion and its crosstalk network in the HGSOC TME remain unclear. In parallel with T cell dysfunction, tumor-associated macrophages (TAMs)—particularly those skewed toward the M2 phenotype—constitute another prominent feature of the HGSOC immune microenvironment. Increasing evidence suggests that these two immunosuppressive populations are not isolated but may interact synergistically to reinforce immune evasion 15 , 16 . Understanding the potential crosstalk between exhausted T cells and M2 macrophages is thus critical for unraveling the full architecture of immune suppression in HGSOC. Concurrently, macrophages—the most abundant immune cell population in the tumor microenvironment (TME)—polarize toward an M2 phenotype in HGSOC, exhibiting immunosuppressive and pro-tumor functions 17 – 19 . These M2-TAMs promote immune evasion and tumor progression through multiple mechanisms, including highly expressing metabolic enzymes such as arginase-1 (ARG-1) and indoleamine 2,3-dioxygenase (IDO), thereby depleting nutrients essential for T cell activation, and recruiting regulatory T cells (Tregs) to further suppress immune responses 20 . Multiple clinical studies confirm that high-density infiltration of M2 TAMs in tumor tissues is an independent risk factor for poor prognosis in HGSOC patients. Traditionally, M2 TAMs were considered to be the 'cause', directly inducing and exacerbating T cell exhaustion through these mechanisms and forming the cornerstone of the immunosuppressive microenvironment 15 , 16 , 21 , 22 . However, the bidirectional nature of this interaction remains poorly defined. In this study, we reveal a previously unrecognized mechanism in which exhausted CD8⁺ T cells actively promote M2 macrophage polarization through CCL3 secretion. Our findings suggest that, within the HGSOC TME, exhausted T cells are not merely passive “victims” of immunosuppression but active “contributors” that reshape macrophage functional states. This discovery provides new insights into the complex intercellular dialogue driving immune escape in HGSOC. Results ScRNA-seq analysis of HGSOC and characterization of T-cell atlas To investigate the immune landscape of high-grade serous ovarian cancer (HGSOC), we performed single-cell RNA sequencing (scRNA-seq) on nine cases of tumor tissue from eight HGSOC patients, along with one normal ovarian tissue from one patient with uterine fibroids. After quality control and filtering ( Fig. S1 A ), a total of 84,065 cells were retained for downstream analysis. Following normalizing, batch correction, dimension reduction, and clustering, the cells were categorized into 15 distinct clusters ( Fig. S1 B ). Based on classical markers, we annotated six major cell types: epithelial cells (EPCAM, CD24, PAX8), T-lymphocytes (CD3D, CD3E, CD2), myeloid cells (CD68, CD14, FCER1G), fibroblasts (COL1A1, LUM, DCN), endothelial cells (PECAM1, VWF, CLDN5) and B-lymphocytes (CD79A, IGHM, MZB1) (Fig. 1A, 1B and 1D ). Notably, both the absolute number and proportion of T cells and myeloid cells were significantly higher in tumor tissue compared to normal tissue ( Fig. 1C ), This finding suggests that HGSOC represents a functionally cold tumor, in which immune cells are abundantly present but functionally impaired within an immunosuppressive TME. This apparent disconnect between immune cell abundance and function prompted us to further investigate the functional state of CD8⁺ T cells, the key cytotoxic effectors. We therefore performed clustering, annotation, and subpopulation analysis of CD8 + T cells. Based on classic markers, CD8⁺ T cells were categorized into four subpopulations: Tm (memory T cells), Teff (effector T cells), Tpex (precursor exhausted T cells), and Tex (exhausted T cells) ( Fig. 1E ). Feature plots illustrated the expression of memory T cell markers (IL7R, GPR183), effector T cell markers (FGFBP2, CX3CR1) and exhausted T cell markers (CTLA4, PDCD1, LAG3, HAVCR2) in T cells ( Fig. 1F ). Among these subpopulations, we focused on Tpex and Tex cells because they represent critical stages along the T cell exhaustion trajectory. Tpex cells express inhibitory receptors while retaining the potential to differentiate into terminally exhausted T cells 28 , 29 , whereas Tex cells showed high expression of exhaustion markers, consistent with a terminally exhausted phenotype. To further validate the rigor and rationality of this subpopulation, the exhaustion score for each CD8 + T cell was calculated using the AUCell R package and visualized in a feature plot and violin plot ( Fig. 1G and 1H ). These findings highlight Tpex and Tex as key players in the immune suppression observed in HGSOC, warranting further investigation into their differentiation trajectories and potential roles in shaping the tumor microenvironment. Revealing the exhaustion trajectory of CD8 T cells Next, we performed KEGG and GO enrichment analyses on each T cell subset. While Teff and Tm cells showed enrichment in expected cytotoxic and tissue-interaction pathways 30 . The function of Tpex is concentrated in proliferation-related pathways, such as the cell cycle and DNA replication. This is consistent with previous studies that have suggested that precursor-exhausted T cells have the capacity to continuously replenish exhausted T cells 28 , 29 . Tex cells were associated with weakened MAPK signaling and enhanced inhibitory phosphatase activity, collectively leading to inefficient TCR signalling transduction, which is a hallmark feature of exhaustion. ( Fig. 2A ) To further characterize the trajectory of T cell progression to exhaustion and identify the link between Tpex and Tex, we performed a pseudotime trajectory analysis of the annotated CD8 + T cells using the R package Monocle (v2.28.0). Pseudotime analysis revealed that memory T cells and effector T cells occupied the earliest positions along the pseudotime axis, while exhausted T cells and precursor exhausted T cells were located at the ends of the two trajectories ( Fig. 2B and 2C ). These results depict the evolutionary trajectory of T cells towards an exhausted state, suggesting that precursor exhausted T cells and terminal exhausted T cells represent distinct differentiation pathways rather than a linear progression. To delineate the transcriptional dynamics associated with CD8⁺ T cell exhaustion along the pseudotime trajectory, we identified gene sets exhibiting significant expression changes. These genes were clustered into five distinct patterns using the R package ClusterGVis. While modules C1 to C3 displayed transient or biphasic expression trends—either declining (C1), rising then slightly decreasing (C2), or dipping then recovering (C3)—the C4 and C5 modules exhibited a consistent upregulation throughout the process of T cell exhaustion ( Fig. 2D ), highlighting their potential role in driving functional exhaustion. We therefore focused on the C4 and C5 modules, which reflect monotonic activation during exhaustion progression. Enrichment analysis revealed that C4 is primarily involved in protein targeting, whereas C5 is significantly associated with negative regulation of immune system processes, response to calcium ions, and response to TGF-β. Given their sustained upregulation during exhaustion, we propose C4 and C5 as key candidate regulators of T cell dysfunction. Identification of hub genes involved in CD8 T cell exhaustion To further identify hub genes within these modules and validate their network centrality, we applied weighted gene co-expression network analysis (hdWGCNA). Using a soft threshold of 6, a scale-free network of CD8 + T cell subsets was created. This yielded optimal connectivity and identified 9 gene modules in total ( Fig. 3A and 3B ). Module eigengenes (MEs) were computed, and the top 5 most representative genes per module were displayed to illustrate module expression patterns ( Fig. 3C ). To identify the key modules most relevant to T cell exhaustion, we calculated the correlation between MEs of each module and the different T cell states. We then presented these correlations in bubble plots. Of all the modules, only Module 8 is positively correlated with both exhaustion subpopulations and negatively correlated with normal functional T cells ( Fig. 3D ). The UMAP projection of Module 8 gene expression displayed a spatial distribution that overlapped notably with that of the previously calculated T-cell exhaustion scores ( Fig. 3E ). To gain a preliminary understanding of Module 8's biological function, we performed a Go enrichment analysis. The results showed that Module8 was enriched for immune cell differentiation and regulation, especially lymphocyte differentiation, thus further validating Module8 ( Fig. 3F ). CCL3 Orchestrates CD8⁺ T Cell Exhaustion and Macrophage-Mediated Immunosuppression To identify key genes of T-cell exhaustion, we integrated differentially expressed genes from pseudotime trajectory modules (C4/C5 module), Top 10 weighted genes from module 8 of hdWGCNA, and published exhaustion signature genes (Zheng et al.). Among overlapping candidates, CCL3 (also known as MIP-1α) emerged as a central hub gene ( Fig. 4A ). ​ ​ Validation in clinical cohorts revealed that CCL3 expression was significantly elevated in both chemotherapy non-responders (GSE222556 dataset; Fig. 4B ) and ovarian cancer tissues (TCGA database; Fig. 4C ), suggesting its clinical relevance in HGSOC immune evasion. To investigate its potential immunoregulatory role, we conducted intercellular communication analysis. Our result showed that exhausted T cells primarily communicate with macrophages ( Fig. S2 ). Next, we focused our attention on macrophage subpopulations. Myeloid cells were re-clustered into five subsets: M1 macrophages (M1_Macro), M2 macrophages (M2_Macro), proliferative macrophages (Mki67_Macro), dendritic cells (DC), and neutrophils (Neutrophils) ( Fig. 4D ). CellChat analysis revealed that Tex cells exhibited heightened cell-cell communication, with M2 macrophages as primary recipients ( Fig. 4E ). Focusing on ligand-receptor pairs,​​we found that Tex-derived CCL3 signals were predominantly received by M2 macrophages via CCR1​​ ( Fig. 4F, G ). These findings suggest that Tex secreted CCL3 promotes immunosuppression and tumor progression in HGSOC by activating M2 macrophages. Supporting this, CCL3 expression strongly correlated with M2-associated gene signatures ( Fig. 4H ), while exhausted T-cell signatures showed a positive correlation with M2 signatures ( Fig. 4I ). Functional validation of macrophage polarization​ driven by CCL3 secretion from exhausted T cells We tested the hypothesis, obtained from our bioinformatics analysis, that exhausted CD8 + T cells lead to M2 polarization of macrophages by secreting CCL3. First, we differentiated THP-1 cells into macrophages with PMA (phorbol myristate acetate, 100ng/ml) treatment and divided them into two groups: a control group and a CCL3-treated group. The control group was treated with IL-4 (10 ng/mL) and IL-10 (10 ng/mL) for 24 hours. The CCL3 group received an additional treatment of recombinant CCL3 protein (50 ng/mL) along with the aforementioned cytokines for the same 24 hours. After flow cytometry analysis, we found that the percentage of CD206-positive macrophages was higher in the CCL3-treated group (n = 5, 59.2% ± 2.9% vs. 37.7% ± 3.2%, p = 0.008) ( Fig. 5A ). Next, we examined CCL3 expression in effector and exhausted T cells. To establish an in vitro T cell exhaustion model, we first removed the spleens of OT-1 mice and sorted and cultured the CD8 + T cells using T Cell Expansion Medium containing Ova (ovalbumin, 10 ng/ml) ( Fig. 5B ). We used flow cytometry to test whether the exhaustion model was successfully constructed by detecting the proliferative capacity (Ki67) ( Fig. 5D ), the killing capacity (GZMB and IFN-γ) ( Fig. 5E and 5F ), the exhaustion state (PD-1 and Tim3) ( Fig. 5G and 5H ), and the amount of CCL3 at 0, 3, 6, 9, and 12 days ( Fig. 5C ) for T cells, respectively. The results showed that, as stimulation time increased, T cell proliferation increased in the early stage and decreased in the late stage. T cell killing capacity significantly increased in the early stage and decreased slightly in the late stage. Exhaustion markers were progressively upregulated. These results are consistent with the pattern of T cell exhaustion. Based on these findings, we discovered that CCL3 expression was significantly elevated in late-exhausted T cells on days 9–12. Using this model, we designated CD8 + T cells harvested from day 3 cultures as effector T cells (Teff) and those harvested from day 9 cultures as exhausted T cells (Tex). Next, we extracted bone marrow-derived macrophages (BMDM) from the femurs of mice. We flushed out the bone marrow with PBS, and we filtered the suspension through a 70-µm cell strainer. Erythrocytes were lysed and resuspended in 1640 medium containing M-CSF (macrophage colony-stimulating factor) at a concentration of 25 ng/ml ( Fig. 5I ). The BMDM cultured for seven days were divided into two groups and incubated with Teff and Tex culture supernatants for 24 hours. These groups were called the Teff group and the Tex group, respectively. Flow cytometry revealed that the percentage of CD206-positive BMDMs cultured with Tex supernatant was significantly higher than that of the Teff group (n = 6; 72.4% ± 0.6% vs. 41.5% ± 5.6%, p < 0.001) ( Fig. 5J ). This was also validated using a model of human-derived cell lines. In this model, CD8 + T cells were isolated from peripheral blood mononuclear cells (PBMC) and stimulated using IL-2 (20 ng/ml) and CD3/CD28 (25 µl/ml). PMA-induced THP-1 cells were cultured for 48 hours using Teff (PBMC stimulated for three days) and Tex (PBMC stimulated for 10 days) cell culture supernatants, respectively ( Fig. 5K ). Flow cytometry results showed a significantly higher percentage of CD206-positive cells in the Tex group (n = 3; 83.8% ± 1.2% vs. 51.4% ± 1.2%; p < 0.001) ( Fig. 5L ). Based on the above experimental results, we hypothesized that exhausted T cells promote M2 macrophage polarization through CCL3 secretion, contributing to tumor progression. Together, these findings demonstrate that exhausted CD8⁺ T cells functionally promote M2 macrophage polarization through CCL3 secretion in both human cell lines and murine models, suggesting a novel immunosuppressive axis that may contribute to HGSOC progression. Discussion Exhausted T cells are a central component of the TME and play a key role in shaping its immune evolution. Within the TME, these cells represent the outcome of continuous immune pressure, maintaining antigen recognition but showing reduced effector function. Through their altered cytokine production and high expression of inhibitory receptors, exhausted T cells affect the balance between immune activation and suppression, contributing to tumor persistence and immune escape 31 , 32 . In our study, we found that exhausted T cells are not only a result of immune evolution but also active participants that strengthen the immunosuppressive environment, further promoting tumor–immune coevolution and resistance to immunotherapy. The chemokine CCL3 has traditionally been recognized as a classic pro-inflammatory factor, instrumental in recruiting and activating T cells during infection or vaccine responses 33 – 37 . However, our study uncovers a previously unrecognized immunosuppressive signaling axis in HGSOC, which challenges this conventional view. This finding reveals that CCL3 undergoes a functional reprogramming in the TME—transforming from an immune-stimulating factor into a tumor-promoting, immunosuppressive molecule. This discovery leads us to propose that CCL3 plays dual roles in immune regulation, depending on the immunological context. During acute infection or certain phases of anti-tumor immunity, it likely acts as an alarmin, promoting inflammatory responses. In stark contrast, within the chronic, immunosuppressive niche of HGSOC, CCL3 is hijacked to establish an immunosuppressive barrier that supports tumor growth and immune evasion. This context-dependent functional plasticity underscores the intricacy of immune regulation in cancer and implies that the function of immune factors must be considered in the broader context of the immune system. Our study significantly enriches the understanding of CCL3 in the immune evolution of HGSOC, establishing it not only as a traditional immune mediator but also as a critical effector in Tex-driven immunosuppression. These findings carry important clinical implications. They posit that CCL3 is not a unidirectional modulator but rather a context-dependent chemokine. Similar to CCL3, several immune mediators, including CCL2, CCL5, IL-6, IFN-γ and TGF-β, exhibit context-dependent bidirectional functions 38 – 42 . Depending on the cellular and microenvironmental context, they can act as either pro- or anti-tumor factors. This functional duality likely arises from differences in receptor expression and signaling intensity, as well as microenvironmental cues that dynamically reprogram their downstream pathways within the TME 43 . Therefore, understanding the precise molecular cues that govern the switch between CCL3's pro- and anti-tumor roles could unveil novel targets for future immunomodulatory therapies, allowing us to strategically disrupt its tumor-promoting functions while preserving its beneficial immune activities. Our analysis revealed that elevated CCL3 expression and activation of the CCL3–CCR1 axis are strongly associated with resistance to platinum-based chemotherapy. CCL3–CCR1 signaling may activate the PI3K–Akt, NF-κB, and MAPK pathways, thereby enhancing cell survival, DNA damage repair, and anti-apoptotic programs in tumor or stromal cells 44 – 46 . Concurrently, CCL3-driven remodeling of the tumor microenvironment, through the recruitment and polarization of macrophages and regulatory T cells, may suppress chemotherapy-induced immunogenic cell death and foster a tolerogenic niche. Considering the pivotal role of M2 macrophages in drug resistance and immune evasion, the Tex–CCL3–M2 circuit may be a critical link between chronic T-cell dysfunction and therapeutic failure. Furthermore, since immune checkpoint blockade depends on functional effector T cells 47 , the accumulation of exhausted T cells and their M2-polarizing chemokine signals may predict poor responsiveness to immunotherapy 48 . Therefore, targeting the CCL3–CCR1 axis holds promise as a strategy to overcome chemoresistance and immune escape. This study has several limitations. First, the small sample size (n = 8) limits the generalizability of our findings and requires validation in larger groups. Second, while we showed that CCL3 promotes M2 macrophage polarization, the downstream signaling pathways following CCR1 activation are still unknown, leaving the precise molecular mechanisms unclear. Third, the triggers that prompt exhausted T cells to secrete CCL3, as well as the regulatory mechanisms that govern CCL3's dual functions within the tumor microenvironment, remain unknown. Future studies integrating multi-omics profiling and mechanistic validation are necessary to elucidate the complete CCL3–CCR1 signaling network and its role in immune modulation and therapeutic resistance. In conclusion, our study reveals a previously unrecognized tumor-promoting role of CCL3 in the HGSOC immune microenvironment: CCL3 secreted by exhausted CD8⁺ T cells induce M2 polarization of macrophages, forming positive feedback signaling axis of immunosuppression. This discovery underscores the dual nature of immune mediators—their function is not only molecule-dependent but also context-dependent. By dissecting the Tex–CCL3–M2 immunological axis, we offer a new mechanistic perspective on how chronic T cell dysfunction expands immunosuppression through myeloid cells. Given the druggable nature of this axis, targeting CCL3 or its receptor CCR1 may become a promising strategy for combining immunotherapy with resistance reversal, potentially bringing new clinical benefits to patients with HGSOC. Methods Human tissue specimens The samples of the nine HGSOC cases for scRNA-seq were obtained from patients attending the Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Patients were enrolled after signing an informed consent form. The study was carried out according to the ethical guidelines of Beijing Obstetrics and Gynecology Hospital and approved by the hospital’s Ethics Committee ethical (approval NO.2023-KY-080-01). Single-cell RNA sequencing Following the diagnosis of HGSOC by rapid pathology, ovarian cancer samples from nine patients were dissociated into single-cell suspensions for cell counting and cell viability determination by a cell counter. The prepared single-cell suspensions were then combined with a mixture of gel beads containing barcode information and enzymes, and finally encapsulated by oil droplets in microfluidic "single cross" junctions to form GEMs (Gel Bead-In-EMulsions). Subsequently, the GEMs undergo a process of cell lysis and reverse transcription, and the 10x Barcode is ligated to the cDNA product. Following this, the GEMs are broken up, and the oil droplets are fragmented for PCR amplification using the cDNA as a template. The construction of sequencing libraries was preceded by the implementation of a quality control procedure on the amplified products. The subsequent analysis of the data was performed using the Illumina NovaSeq sequencing platform. Quality control and data integration The sequence data were used to map the reads to the human genome (GRCh38) employing Cellranger 7.0.0 ( http://10xgenomics.com ). Subsequently, the data were analyzed using the Seurat package (Ver. 4.3.0). The Seurat objects were initially created using the read10x function, with the data from each of the six samples subsequently undergoing quality control. Subsequently, the data were log-normalized, and homogenization and principal component analysis were performed after identifying highly variable genes. The RunHarmony function from the harmony package (Ver. 1.1.0) 23 was employed to eliminate batch effects. Subsequently, the number of principal components was determined, and UMAP downscaling was performed. Subpopulations were categorised and annotated based on markers characteristic of distinct cell types. The annotation process was facilitated by the SingleR package (Ver. 2.0.0) 24 . Pseudotime analysis Use the Monocle2 R package (Ver. 2.28.0) 25 for pseudotime analysis to reconstruct cell trajectories. Briefly, gene expression matrices were converted to Monocle-compatible CellDataSet objects. The DDRTree algorithm was used to reduce the dimensionality and construct the trajectory graph. The root node of the trajectory is set manually based on the biological significance of cell subpopulations. Cells are then sorted along the trajectory, and a pseudo-time value is assigned to each cell. Enrichment analysis and visualization of significantly changed genes using the ClusterGVis R package (Ver. 0.1.4). Cell–cell communication analysis We used the CellChat R package (Ver. 2.1.0) 26 to analyze communication between two cell types: T cells and myeloid cells. First, we constructed cellchat objects and then used CellChatDB V2 as a ligand database. After filtering out populations of less than 10 cells, we inferred the intercellular communication network. hdWGCNA analysis HdWGNCA identifies modules of highly co-expressed genes and provides context for these modules via statistical testing and biological knowledge sources 27 . Setting the threshold of the scale-free topology model fit as > 0.8, a soft threshold was selected 6 for the best connectivity. Correlations between modules and T-cell status were evaluated by the Spearman test. Statistical analysis Statistical analyses were performed using R (Ver. 4.4.2), and GraphPad Prism (Ver. 9.0). Differences between the two groups were assessed using Student's t-test or the Wilcoxon rank sum test. Survival data were analyzed using the log-rank test. All experiments were repeated at least three times. Data are expressed as the mean ± standard deviation (SD). A p-value of less than 0.05 was considered statistically significant for all statistical comparisons. Modeling of T cell exhaustion Mouse Spleens were isolated from OT-1 mice and ground under a clean bench. The tissue homogenate was filtered using a 200 µm nylon filter and subjected to erythrocyte lysis. Next, Naïve CD8 + T-cells were isolated using Naïve CD8a + T Cell Isolation Kit (Cat: 130-104-075, Miltenyibiotec). Finally, the isolated cells were cultured using ImmunoCult™-XF T Cell Expansion Medium (Cat: # 10981, STEMCELL) and stimulated with 10 ng/mL ovalbumen (Cat: 763606, Biolegend). Cells were counted and replaced to maintain optimal cell density with fresh stimulation-supplemented medium every three days. Human CD8 + T cells were isolated from PBMC (Cat: 130-096-495, Miltenyibiotec) and stimulated using IL2(Cat: HY-P7037B, MCE) and CD3/CD28(Cat: #100–0784, STEMCELL) to construct T cell activation models and exhaustion models. Stimuli were replenished every 3–4 days. Bone marrow-derived macrophages (BMDMs) induction Firstly, female mice (6–8 weeks old) were sacrificed and sterilized in 75% alcohol for 10 min. Subsequently, the BM in the femur and tibia was flushed out and treated with red blood cell lysis buffer (Cat: R1010, Solarbio). BM cells were then cultured in 90% RPMI1640, 10% FBS medium with macrophage colony-stimulating factor (MCSF, Cat: HY-P7085A, MCE), and 1% streptomycin/penicillin at a density of 3×10 6 per 6-well cell culture plate for 6 days, and the medium was changed every 2–3 days. After 6 days of induction, the obtained BMDMs were used for subsequent experiments. Flow cytometry The cells to be tested were collected, and then, after centrifugation at 500 g for five minutes, the cells were resuspended in staining buffer (RPMI 1640 + 2% FBS medium) and labeled with surface antibodies. Then, the cells were fixed with Cytofix Fixation Buffer (Cat: 554655, BD), permeabilized with Perm/Wash Buffer (Cat: 554723, BD), and labeled with intracellular antibodies. Online analysis was then performed. The following antibodies were used: PD-1 (Cat: 135219, biolegend), Tim3 (119725), Ki67 (Cat: 563757, BD), CCL3 (Cat: IC450A, bio-techne), IFN-γ (Cat: 505841, biolegend), GZMB (Cat: 372230, biolegend), CD206 (Cat: 321105, biolegend), CD86 (Cat: 305411, biolegend), CD11b (Cat: 301329, biolegend). Declarations Acknowledgements Not applicable. Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential competing interest. Author Contribution Statement YL, MJ, XTZ, and WTY conceived and designed the study. YL carried out all experiments and wrote the manuscript. YL, and FL participated in bioinformatics analysis. JC, ZFL, JJY, and YHY supported the study. XTZ and WTY supervised the study. XTZ and WTY revised the manuscript. All authors read and approved the final manuscript . Ethics Statement The study was conducted in accordance with the Declaration of Helsinki, as revised in 2013. The study was approved by the medical ethics committee of the Beijing Obstetrics and Gynecology Hospital Capital Medical University. We have obtained informed consent from all patients. Funding Statement The study is supported by the National Natural Science Foundation of China (No. 82373397), the Capital’s Funds for Health Improvement and Research (No.2022–2-2116), the Beijing Hospitals Authority’s Ascent Plan, Code: DFL20241401, the Beijing Nova Program, and the Laboratory for Clinical Medicine, Capital Medical University. Data Availability Statement The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. References Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68 : 394–424. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71 : 209–249. Kurnit KC, Fleming GF, Lengyel E. Updates and new options in advanced epithelial ovarian cancer treatment. Obstet Gynecol 2021; 137 : 108–121. Larkin J, Chiarion-Sileni V, Gonzalez R, Grob J-J, Rutkowski P, Lao CD et al. Five-year survival with combined nivolumab and ipilimumab in advanced melanoma. N Engl J Med 2019; 381 : 1535–1546. Robert C, Schachter J, Long GV, Arance A, Grob JJ, Mortier L et al. Pembrolizumab versus ipilimumab in advanced melanoma. N Engl J Med 2015; 372 : 2521–2532. Robert C. A decade of immune-checkpoint inhibitors in cancer therapy. Nat Commun 2020; 11 : 3801. How J, Patel A, Jazaeri A. Immuno-oncology for gynecologic malignancies. Adv Exp Med Biol 2020; 1244 : 149–182. Matulonis UA, Shapira-Frommer R, Santin AD, Lisyanskaya AS, Pignata S, Vergote I et al. Antitumor activity and safety of pembrolizumab in patients with advanced recurrent ovarian cancer: Results from the phase II KEYNOTE-100 study. Ann Oncol 2019; 30 : 1080–1087. Ke M, Zhu H, Lin Y, Zhang Y, Tang T, Xie Y et al. Actin-related protein 2/3 complex subunit 1B promotes ovarian cancer progression by regulating the AKT/PI3K/mTOR signaling pathway. J Transl Int Med ; 12 : 406–423. Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med 2018; 24 : 541–550. Wherry EJ. T cell exhaustion. Nat Immunol 2011; 12 : 492–499. Pauken KE, Wherry EJ. Overcoming T cell exhaustion in infection and cancer. Trends Immunol 2015; 36 : 265–276. Philip M, Fairchild L, Sun L, Horste EL, Camara S, Shakiba M et al. Chromatin states define tumour-specific T cell dysfunction and reprogramming. Nature 2017; 545 : 452–456. Zou Y, Zhang H, Liu F, Chen Z-S, Tang H. Intratumoral microbiota in orchestrating cancer immunotherapy response. J Transl Int Med ; 12 : 540–542. Chen Y, Ouyang D, Wang Y, Pan Q, Zhao J, Chen H et al. EBV promotes TCR-T-cell therapy resistance by inducing CD163+M2 macrophage polarization and MMP9 secretion. J Immunother Cancer 2024; 12 : e008375. Pu J, Xu Z, Nian J, Fang Q, Yang M, Huang Y et al. M2 macrophage-derived extracellular vesicles facilitate CD8+T cell exhaustion in hepatocellular carcinoma via the miR-21-5p/YOD1/YAP/β-catenin pathway. Cell Death Discov 2021; 7 : 182. Wu K, Lin K, Li X, Yuan X, Xu P, Ni P et al. Redefining tumor-associated macrophage subpopulations and functions in the tumor microenvironment. Front Immunol 2020; 11 : 1731. Zhang W, Wang M, Ji C, Liu X, Gu B, Dong T. Macrophage polarization in the tumor microenvironment: Emerging roles and therapeutic potentials. Biomed Pharmacother 2024; 177 : 116930. Mantovani A, Sozzani S, Locati M, Allavena P, Sica A. Macrophage polarization: Tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes. Trends Immunol 2002; 23 : 549–555. Gao J, Liang Y, Wang L. Shaping polarization of tumor-associated macrophages in cancer immunotherapy. Front Immunol 2022; 13 : 888713. Kamat K, Krishnan V, Dorigo O. Macrophage-derived CCL23 upregulates expression of T-cell exhaustion markers in ovarian cancer. Br J Cancer 2022; 127 : 1026–1033. Launonen I-M, Niemiec I, Hincapié-Otero M, Erkan EP, Junquera A, Afenteva D et al. Chemotherapy induces myeloid-driven spatially confined T cell exhaustion in ovarian cancer. Cancer Cell 2024; 42 : 2045-2063.e10. Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K et al. Fast, sensitive and accurate integration of single-cell data with harmony. Nat Methods 2019; 16 : 1289–1296. Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol 2019; 20 : 163–172. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 2014; 32 : 381–386. Jin S, Plikus MV, Nie Q. CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics. Nat Protoc 2025; 20 : 180–219. Morabito S, Reese F, Rahimzadeh N, Miyoshi E, Swarup V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep Methods 2023; 3 : 100498. Kallies A, Zehn D, Utzschneider DT. Precursor exhausted T cells: key to successful immunotherapy? Nat Rev Immunol 2020; 20 : 128–136. Wherry EJ, Kurachi M. Molecular and cellular insights into T cell exhaustion. Nat Rev Immunol 2015; 15 : 486–499. Gan J, Zhang X, Guo J. The role of platelets in tumor immune evasion and metastasis: Mechanisms and therapeutic implications. Cancer Cell Int 2025; 25 : 258. Gubin MM, Vesely MD. Cancer immunoediting in the era of immuno-oncology. Clin Cancer Res 2022; : clincanres.1804.2022. Ariyasu R, Nishikawa H. [cancer immunoediting in the development of cancer immunotherapy]. Gan To Kagaku Ryoho 2019; 46 : 407–411. Patterson SJ, Pesenacker AM, Wang AY, Gillies J, Mojibian M, Morishita K et al. T regulatory cell chemokine production mediates pathogenic T cell attraction and suppression. J Clin Invest 2016; 126 : 1039–1051. Song Q, Shang J, Zhang C, Chen J, Zhang L, Wu X. Transcription factor RUNX3 promotes CD8+ T cell recruitment by CCL3 and CCL20 in lung adenocarcinoma immune microenvironment. J Cell Biochem 2020; 121 : 3208–3220. Yang J, Lu X, Cai Q, Liu M, Xia T, Hong D et al. Loss of TACC2 impairs chemokine CCL3 and CCL4 expression and reduces response to anti-PD-1 therapy in soft tissue sarcoma. Mol Cancer 2025; 24 : 158. Trifilo MJ, Bergmann CC, Kuziel WA, Lane TE. CC chemokine ligand 3 (CCL3) regulates CD8(+)-T-cell effector function and migration following viral infection. J Virol 2003; 77 : 4004–4014. Harlin H, Meng Y, Peterson AC, Zha Y, Tretiakova M, Slingluff C et al. Chemokine expression in melanoma metastases associated with CD8+ T-cell recruitment. Cancer Res 2009; 69 : 3077–3085. Li M, Knight DA, A Snyder L, Smyth MJ, Stewart TJ. A role for CCL2 in both tumor progression and immunosurveillance. Oncoimmunology 2013; 2 : e25474. Aldinucci D, Borghese C, Casagrande N. The CCL5/CCR5 axis in cancer progression. Cancers (Basel) 2020; 12 : 1765. Soler MF, Abaurrea A, Azcoaga P, Araujo AM, Caffarel MM. New perspectives in cancer immunotherapy: Targeting IL-6 cytokine family. J Immunother Cancer 2023; 11 : e007530. Jorgovanovic D, Song M, Wang L, Zhang Y. Roles of IFN-γ in tumor progression and regression: A review. Biomarker Res 2020; 8 : 49. Derynck R, Turley SJ, Akhurst RJ. TGFβ biology in cancer progression and immunotherapy. Nat Rev Clin Oncol 2021; 18 : 9–34. Goenka A, Khan F, Verma B, Sinha P, Dmello CC, Jogalekar MP et al. Tumor microenvironment signaling and therapeutics in cancer progression. Cancer Commun 2023; 43 : 525–561. Vallet S, Raje N, Ishitsuka K, Hideshima T, Podar K, Chhetri S et al. MLN3897, a novel CCR1 inhibitor, impairs osteoclastogenesis and inhibits the interaction of multiple myeloma cells and osteoclastsMLN3897. Blood 2007; 110 : 3744–3752. Hsu C-J, Wu M-H, Chen C-Y, Tsai C-H, Hsu H-C, Tang C-H. AMP-activated protein kinase activation mediates CCL3-induced cell migration and matrix metalloproteinase-2 expression in human chondrosarcoma. Cell Communication and Signaling 2013; 11 : 68. Yan J, Zuo G, Sherchan P, Huang L, Ocak U, Xu W et al. CCR1 activation promotes neuroinflammation through CCR1/TPR1/ERK1/2 signaling pathway after intracerebral hemorrhage in mice. Neurotherapeutics 2020; 17 : 1170–1183. Paijens ST, Vledder A, de Bruyn M, Nijman HW. Tumor-infiltrating lymphocytes in the immunotherapy era. Cell Mol Immunol 2021; 18 : 842–859. Chen S, Saeed AFUH, Liu Q, Jiang Q, Xu H, Xiao GG et al. Macrophages in immunoregulation and therapeutics. Signal Transduct Target Ther 2023; 8 : 207. Additional Declarations (Not answered) Supplementary Files Fig.S1.tif Supplemental figure1 Fig.S2.tif Supplemental figure2 Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: revise 10 Feb, 2026 Review # 1 received at journal 07 Dec, 2025 Reviewer # 1 agreed at journal 04 Dec, 2025 Reviewers invited by journal 04 Dec, 2025 Submission checks completed at journal 21 Nov, 2025 First submitted to journal 20 Nov, 2025 Unknown event 11 Nov, 2025 Editor assigned by journal 10 Nov, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8074255","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":555337070,"identity":"ce9d5d4b-13f6-4279-b292-609466a1b014","order_by":0,"name":"Wentao Yue","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYNCCCijNQ7SOA2dI1nKwjRQtBsd7zKQ/zrOT152RwPjgbRuDvDlBLWfOmEkc3JZsuO1GArPh3DYGw50NhLTcyDG7cXDbgQSzGwls0rxtDAkGBwhpuf8GqGUOWAv7b+K03OABammA2MJMlBbJM2nlP84cA/rlzMNmyTnnJAw3ENLCd/zwZoOKGjt5s+PJBz+8KbORJ2iLAkIBYwOQkCCgHgjkGwirGQWjYBSMgpEOAB54RLRKDtYdAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Obstetrics and Gynecology Hospital; Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing","correspondingAuthor":true,"prefix":"","firstName":"Wentao","middleName":"","lastName":"Yue","suffix":""},{"id":555337071,"identity":"bb0bab16-50f1-42a0-931e-95c70ae59ddb","order_by":1,"name":"Yue Li","email":"","orcid":"","institution":"Beijing Obstetrics and Gynecology Hospital; Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Li","suffix":""},{"id":555337072,"identity":"c6726b42-0d63-4ac5-8dae-b394064bdaab","order_by":2,"name":"Jin Cheng","email":"","orcid":"","institution":"Beijing Obstetrics and Gynecology Hospital; Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Cheng","suffix":""},{"id":555337073,"identity":"879a864f-8588-45af-af50-8ac1da4559c7","order_by":3,"name":"Fei Liu","email":"","orcid":"","institution":"Beijing Obstetrics and Gynecology Hospital; Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Liu","suffix":""},{"id":555337074,"identity":"f3aaf06c-9aeb-4927-b0ee-1ff90ee0ff70","order_by":4,"name":"Junjie Yi","email":"","orcid":"","institution":"Beijing Obstetrics and Gynecology Hospital; Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Yi","suffix":""},{"id":555337075,"identity":"17e8a9ef-90d3-4f93-bdbe-8bd4b9365348","order_by":5,"name":"Zhefeng Li","email":"","orcid":"","institution":"Beijing Obstetrics and Gynecology Hospital; Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Zhefeng","middleName":"","lastName":"Li","suffix":""},{"id":555337076,"identity":"53628639-50a2-48c8-a63b-b7713c8cca3a","order_by":6,"name":"Yanheng Yu","email":"","orcid":"","institution":"Beijing Obstetrics and Gynecology Hospital; Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Yanheng","middleName":"","lastName":"Yu","suffix":""},{"id":555337077,"identity":"3acf3337-d88e-4261-8c6a-1f272b899d91","order_by":7,"name":"Mei Jiang","email":"","orcid":"","institution":"Beijing Obstetrics and Gynecology Hospital; Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"","lastName":"Jiang","suffix":""},{"id":555337078,"identity":"f1732061-2be7-43f7-9970-a14a64d86337","order_by":8,"name":"Xiaoting Zhao","email":"","orcid":"","institution":"Beijing Obstetrics and Gynecology Hospital; Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Xiaoting","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-11-10 07:56:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8074255/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8074255/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97894987,"identity":"ecb292be-84da-485e-8242-3229cf2653de","added_by":"auto","created_at":"2025-12-10 15:33:18","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":99131,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptsubmission.docx","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/09ced3c64fae6add7e50c4f9.docx"},{"id":97705403,"identity":"e976c5fc-0b4b-4531-9b96-17936a14b79c","added_by":"auto","created_at":"2025-12-08 12:53:41","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4560348,"visible":true,"origin":"","legend":"","description":"","filename":"Fig1umapT.tif","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/b2032982721cd8a1b59ee3a5.tif"},{"id":97705292,"identity":"7c3f57ec-d4a1-47dd-87b8-de9ef6e5887d","added_by":"auto","created_at":"2025-12-08 12:53:37","extension":"json","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11771,"visible":true,"origin":"","legend":"","description":"","filename":"CDDIS257176.json","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/9d148c2fb392e2cca01076fc.json"},{"id":97705237,"identity":"9f7f9a44-bf09-42a2-8e8d-30c5c2df679d","added_by":"auto","created_at":"2025-12-08 12:53:36","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1925472,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/6a2e90323d070e03cfa0d972.tif"},{"id":97705329,"identity":"f376c61d-9089-46ad-86d3-3129d18a6233","added_by":"auto","created_at":"2025-12-08 12:53:38","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":695852,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/0904d3f41fdb2c129fed1491.tif"},{"id":97705406,"identity":"aa5dddf4-6eb2-448f-81a5-2af24fe0ebea","added_by":"auto","created_at":"2025-12-08 12:53:41","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116284,"visible":true,"origin":"","legend":"","description":"","filename":"CDDIS2571760enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/7329d7afa189184e9f0edae9.xml"},{"id":97705220,"identity":"e4fb50bf-f848-4947-9a88-df954bae960b","added_by":"auto","created_at":"2025-12-08 12:53:35","extension":"tif","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4560348,"visible":true,"origin":"","legend":"","description":"","filename":"Fig1umapT.tif","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/58f88534e8488aed8cde4b88.tif"},{"id":97705332,"identity":"9096b7d0-304b-47d6-a337-3158b37de02d","added_by":"auto","created_at":"2025-12-08 12:53:38","extension":"tif","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6456024,"visible":true,"origin":"","legend":"","description":"","filename":"Fig2monocle.tif","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/48d7eefa3d70a1ed339e2c91.tif"},{"id":97705338,"identity":"a0c4a484-3cb7-4d06-ad4a-4c0697c37faa","added_by":"auto","created_at":"2025-12-08 12:53:39","extension":"tif","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2650984,"visible":true,"origin":"","legend":"","description":"","filename":"Fig3WGCNA.tif","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/e529b047cf42cbcf581021d7.tif"},{"id":97705412,"identity":"381497d3-3450-4cfc-94e9-95d0a49a0631","added_by":"auto","created_at":"2025-12-08 12:53:41","extension":"tif","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2980588,"visible":true,"origin":"","legend":"","description":"","filename":"Fig4CCL3cellchat.tif","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/0fcd4b72299c3ca6cbfdc1db.tif"},{"id":97705306,"identity":"dadd552b-56fd-4850-b6c0-3e31b2705bed","added_by":"auto","created_at":"2025-12-08 12:53:38","extension":"tif","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2523556,"visible":true,"origin":"","legend":"","description":"","filename":"Fig5CCL3M2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/70bf341c316ed9b460a59f7b.tif"},{"id":97705327,"identity":"1248b3d7-bb92-4c5b-9392-ca7575664d68","added_by":"auto","created_at":"2025-12-08 12:53:38","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111089,"visible":true,"origin":"","legend":"","description":"","filename":"CDDIS2571760structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/dc4c6cfca4542c76dbbdb6d1.xml"},{"id":97705410,"identity":"7213ed31-cbf8-42b1-9314-ecc8449c49f2","added_by":"auto","created_at":"2025-12-08 12:53:41","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126047,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/e018eeb3958007ce424dc6bc.html"},{"id":97705357,"identity":"f2cda6f6-8c10-4c81-ba0a-2537cc9c2918","added_by":"auto","created_at":"2025-12-08 12:53:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3878996,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not provided with this version\u003c/p\u003e","description":"","filename":"Fig1umapT.png","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/1dab71e913b4ed1252574564.png"},{"id":97705355,"identity":"949ab556-f2ea-46dd-b703-911a28da0545","added_by":"auto","created_at":"2025-12-08 12:53:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4503425,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not provided with this version\u003c/p\u003e","description":"","filename":"Fig2monocle.png","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/ca3f47b1a1179717e0bef2fb.png"},{"id":97705222,"identity":"0497c485-6cd8-4596-9701-7ccdb6d1ef61","added_by":"auto","created_at":"2025-12-08 12:53:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1711142,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not provided with this version\u003c/p\u003e","description":"","filename":"Fig3WGCNA.png","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/155a37cf95c00b8f966b28c5.png"},{"id":97705361,"identity":"012545fb-bf14-4c51-b4a0-083ef6f81ce3","added_by":"auto","created_at":"2025-12-08 12:53:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2451287,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not provided with this version\u003c/p\u003e","description":"","filename":"Fig4CCL3cellchat.png","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/f8093c98a4cfdf3d697e140c.png"},{"id":97705248,"identity":"1d2d4d8a-a73d-4029-9b73-248607aa5fd7","added_by":"auto","created_at":"2025-12-08 12:53:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1583472,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not provided with this version\u003c/p\u003e","description":"","filename":"Fig5CCL3M2.png","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/63d011fa3a39d9e35165ec8e.png"},{"id":97902551,"identity":"651707d5-ac94-4fe7-a3a3-1b4e15ddb2fa","added_by":"auto","created_at":"2025-12-10 15:52:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14623254,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/a8a24668-6567-4bc0-a83e-a855cc58f790.pdf"},{"id":97705259,"identity":"58c25a46-700c-47dd-af81-a889b38a9dda","added_by":"auto","created_at":"2025-12-08 12:53:37","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1925472,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental figure1\u003c/p\u003e","description":"","filename":"Fig.S1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/ad4b626f8add876838b836ac.tif"},{"id":97705228,"identity":"b4aa1bf4-bfad-4dd5-9234-5ab1d16b5b01","added_by":"auto","created_at":"2025-12-08 12:53:35","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":695852,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental figure2\u003c/p\u003e","description":"","filename":"Fig.S2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8074255/v1/8f6b62ab30aacca142a0889e.tif"}],"financialInterests":"(Not answered)","formattedTitle":"Exhausted CD8⁺ T Cells Promote Ovarian Cancer Immunosuppression via CCL3-Driven M2 Macrophage Polarization","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHigh-grade serous ovarian carcinoma (HGSOC),the most common and aggressive subtype of epithelial ovarian cancer, accounts for over 70% of ovarian cancer-related deaths\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. It is usually diagnosed at an advanced stage. Due to metastasis, recurrence, and chemoresistance, the five-year survival rate for patients is around 30%-40%\u003csup\u003e3\u003c/sup\u003e. Tumor immunotherapy, particularly ICIs, has achieved revolutionary success in various malignancies, including melanoma and non-small cell lung cancer \u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, their efficacy in HGSOC remain limited, with objective response rates generally below 10\u0026ndash;15%\u003csup\u003e7\u003c/sup\u003e. This resistance is largely attributed to a highly immunosuppressive TME weakening or evading the body's antitumor immune response\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe HGSOC TME is a complex ecosystem comprising diverse immune and stromal cells. T-cell exhaustion is a hallmark of this immunosuppressive ecosystem \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Exhausted T-cells progressively lose their effector functions, including cytotoxic production and cytotoxic activity, under chronic stimulation from antigen-presenting cells or inflammatory signals. This is accompanied by the sustained overexpression of multiple co-inhibitory molecules (e.g., PD-1, TIM-3, and LAG-3)\u003csup\u003e11\u0026ndash;13\u003c/sup\u003e. Extensive clinical and pathological studies demonstrate that the level of infiltrating exhausted T cells within HGSOC tissues is significantly associated with rapid disease progression, recurrence, and reduced overall patient survival. These functionally impaired T cells fail to eliminate tumor cells effectively and may also constitute a substantial barrier to ICI therapy\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, the precise molecular mechanisms driving T-cell exhaustion and its crosstalk network in the HGSOC TME remain unclear.\u003c/p\u003e\u003cp\u003eIn parallel with T cell dysfunction, tumor-associated macrophages (TAMs)\u0026mdash;particularly those skewed toward the M2 phenotype\u0026mdash;constitute another prominent feature of the HGSOC immune microenvironment. Increasing evidence suggests that these two immunosuppressive populations are not isolated but may interact synergistically to reinforce immune evasion\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Understanding the potential crosstalk between exhausted T cells and M2 macrophages is thus critical for unraveling the full architecture of immune suppression in HGSOC.\u003c/p\u003e\u003cp\u003eConcurrently, macrophages\u0026mdash;the most abundant immune cell population in the tumor microenvironment (TME)\u0026mdash;polarize toward an M2 phenotype in HGSOC, exhibiting immunosuppressive and pro-tumor functions\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These M2-TAMs promote immune evasion and tumor progression through multiple mechanisms, including highly expressing metabolic enzymes such as arginase-1 (ARG-1) and indoleamine 2,3-dioxygenase (IDO), thereby depleting nutrients essential for T cell activation, and recruiting regulatory T cells (Tregs) to further suppress immune responses\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Multiple clinical studies confirm that high-density infiltration of M2 TAMs in tumor tissues is an independent risk factor for poor prognosis in HGSOC patients.\u003c/p\u003e\u003cp\u003eTraditionally, M2 TAMs were considered to be the 'cause', directly inducing and exacerbating T cell exhaustion through these mechanisms and forming the cornerstone of the immunosuppressive microenvironment\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, the bidirectional nature of this interaction remains poorly defined. In this study, we reveal a previously unrecognized mechanism in which exhausted CD8⁺ T cells actively promote M2 macrophage polarization through CCL3 secretion. Our findings suggest that, within the HGSOC TME, exhausted T cells are not merely passive \u0026ldquo;victims\u0026rdquo; of immunosuppression but active \u0026ldquo;contributors\u0026rdquo; that reshape macrophage functional states. This discovery provides new insights into the complex intercellular dialogue driving immune escape in HGSOC.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eScRNA-seq analysis of HGSOC and characterization of T-cell atlas\u003c/h2\u003e\u003cp\u003eTo investigate the immune landscape of high-grade serous ovarian cancer (HGSOC), we performed single-cell RNA sequencing (scRNA-seq) on nine cases of tumor tissue from eight HGSOC patients, along with one normal ovarian tissue from one patient with uterine fibroids. After quality control and filtering (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u003c/b\u003e), a total of 84,065 cells were retained for downstream analysis. Following normalizing, batch correction, dimension reduction, and clustering, the cells were categorized into 15 distinct clusters (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB\u003c/b\u003e). Based on classical markers, we annotated six major cell types: epithelial cells (EPCAM, CD24, PAX8), T-lymphocytes (CD3D, CD3E, CD2), myeloid cells (CD68, CD14, FCER1G), fibroblasts (COL1A1, LUM, DCN), endothelial cells (PECAM1, VWF, CLDN5) and B-lymphocytes (CD79A, IGHM, MZB1) \u003cb\u003e(Fig.\u0026nbsp;1A, 1B and 1D\u003c/b\u003e). Notably, both the absolute number and proportion of T cells and myeloid cells were significantly higher in tumor tissue compared to normal tissue (\u003cb\u003eFig.\u0026nbsp;1C\u003c/b\u003e), This finding suggests that HGSOC represents a functionally cold tumor, in which immune cells are abundantly present but functionally impaired within an immunosuppressive TME. This apparent disconnect between immune cell abundance and function prompted us to further investigate the functional state of CD8⁺ T cells, the key cytotoxic effectors.\u003c/p\u003e\u003cp\u003eWe therefore performed clustering, annotation, and subpopulation analysis of CD8\u003csup\u003e+\u003c/sup\u003e T cells. Based on classic markers, CD8⁺ T cells were categorized into four subpopulations: Tm (memory T cells), Teff (effector T cells), Tpex (precursor exhausted T cells), and Tex (exhausted T cells) (\u003cb\u003eFig.\u0026nbsp;1E\u003c/b\u003e). Feature plots illustrated the expression of memory T cell markers (IL7R, GPR183), effector T cell markers (FGFBP2, CX3CR1) and exhausted T cell markers (CTLA4, PDCD1, LAG3, HAVCR2) in T cells (\u003cb\u003eFig.\u0026nbsp;1F\u003c/b\u003e). Among these subpopulations, we focused on Tpex and Tex cells because they represent critical stages along the T cell exhaustion trajectory. Tpex cells express inhibitory receptors while retaining the potential to differentiate into terminally exhausted T cells\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, whereas Tex cells showed high expression of exhaustion markers, consistent with a terminally exhausted phenotype. To further validate the rigor and rationality of this subpopulation, the exhaustion score for each CD8\u0026thinsp;+\u0026thinsp;T cell was calculated using the AUCell R package and visualized in a feature plot and violin plot (\u003cb\u003eFig.\u0026nbsp;1G and 1H\u003c/b\u003e). These findings highlight Tpex and Tex as key players in the immune suppression observed in HGSOC, warranting further investigation into their differentiation trajectories and potential roles in shaping the tumor microenvironment.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRevealing the exhaustion trajectory of CD8 T cells\u003c/h3\u003e\n\u003cp\u003eNext, we performed KEGG and GO enrichment analyses on each T cell subset. While Teff and Tm cells showed enrichment in expected cytotoxic and tissue-interaction pathways\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The function of Tpex is concentrated in proliferation-related pathways, such as the cell cycle and DNA replication. This is consistent with previous studies that have suggested that precursor-exhausted T cells have the capacity to continuously replenish exhausted T cells\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Tex cells were associated with weakened MAPK signaling and enhanced inhibitory phosphatase activity, collectively leading to inefficient TCR signalling transduction, which is a hallmark feature of exhaustion. (\u003cb\u003eFig.\u0026nbsp;2A\u003c/b\u003e)\u003c/p\u003e\u003cp\u003eTo further characterize the trajectory of T cell progression to exhaustion and identify the link between Tpex and Tex, we performed a pseudotime trajectory analysis of the annotated CD8\u0026thinsp;+\u0026thinsp;T cells using the R package Monocle (v2.28.0). Pseudotime analysis revealed that memory T cells and effector T cells occupied the earliest positions along the pseudotime axis, while exhausted T cells and precursor exhausted T cells were located at the ends of the two trajectories (\u003cb\u003eFig.\u0026nbsp;2B and 2C\u003c/b\u003e). These results depict the evolutionary trajectory of T cells towards an exhausted state, suggesting that precursor exhausted T cells and terminal exhausted T cells represent distinct differentiation pathways rather than a linear progression.\u003c/p\u003e\u003cp\u003eTo delineate the transcriptional dynamics associated with CD8⁺ T cell exhaustion along the pseudotime trajectory, we identified gene sets exhibiting significant expression changes. These genes were clustered into five distinct patterns using the R package ClusterGVis. While modules C1 to C3 displayed transient or biphasic expression trends\u0026mdash;either declining (C1), rising then slightly decreasing (C2), or dipping then recovering (C3)\u0026mdash;the C4 and C5 modules exhibited a consistent upregulation throughout the process of T cell exhaustion (\u003cb\u003eFig.\u0026nbsp;2D\u003c/b\u003e), highlighting their potential role in driving functional exhaustion.\u003c/p\u003e\u003cp\u003eWe therefore focused on the C4 and C5 modules, which reflect monotonic activation during exhaustion progression. Enrichment analysis revealed that C4 is primarily involved in protein targeting, whereas C5 is significantly associated with negative regulation of immune system processes, response to calcium ions, and response to TGF-β. Given their sustained upregulation during exhaustion, we propose C4 and C5 as key candidate regulators of T cell dysfunction.\u003c/p\u003e\n\u003ch3\u003eIdentification of hub genes involved in CD8 T cell exhaustion\u003c/h3\u003e\n\u003cp\u003eTo further identify hub genes within these modules and validate their network centrality, we applied weighted gene co-expression network analysis (hdWGCNA). Using a soft threshold of 6, a scale-free network of CD8\u0026thinsp;+\u0026thinsp;T cell subsets was created. This yielded optimal connectivity and identified 9 gene modules in total (\u003cb\u003eFig.\u0026nbsp;3A and 3B\u003c/b\u003e). Module eigengenes (MEs) were computed, and the top 5 most representative genes per module were displayed to illustrate module expression patterns (\u003cb\u003eFig.\u0026nbsp;3C\u003c/b\u003e). To identify the key modules most relevant to T cell exhaustion, we calculated the correlation between MEs of each module and the different T cell states. We then presented these correlations in bubble plots. Of all the modules, only Module 8 is positively correlated with both exhaustion subpopulations and negatively correlated with normal functional T cells (\u003cb\u003eFig.\u0026nbsp;3D\u003c/b\u003e). The UMAP projection of Module 8 gene expression displayed a spatial distribution that overlapped notably with that of the previously calculated T-cell exhaustion scores (\u003cb\u003eFig.\u0026nbsp;3E\u003c/b\u003e). To gain a preliminary understanding of Module 8's biological function, we performed a Go enrichment analysis. The results showed that Module8 was enriched for immune cell differentiation and regulation, especially lymphocyte differentiation, thus further validating Module8 (\u003cb\u003eFig.\u0026nbsp;3F\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003eCCL3 Orchestrates CD8⁺ T Cell Exhaustion and Macrophage-Mediated Immunosuppression\u003c/h3\u003e\n\u003cp\u003eTo identify key genes of T-cell exhaustion, we integrated differentially expressed genes from pseudotime trajectory modules (C4/C5 module), Top 10 weighted genes from module 8 of hdWGCNA, and published exhaustion signature genes (Zheng et al.). Among overlapping candidates, CCL3 (also known as MIP-1α) emerged as a central hub gene (\u003cb\u003eFig.\u0026nbsp;4A\u003c/b\u003e).\u003cb\u003e​\u003c/b\u003e​ Validation in clinical cohorts revealed that CCL3 expression was significantly elevated in both chemotherapy non-responders (GSE222556 dataset; \u003cb\u003eFig.\u0026nbsp;4B\u003c/b\u003e) and ovarian cancer tissues (TCGA database; \u003cb\u003eFig.\u0026nbsp;4C\u003c/b\u003e), suggesting its clinical relevance in HGSOC immune evasion.\u003c/p\u003e\u003cp\u003eTo investigate its potential immunoregulatory role, we conducted intercellular communication analysis. Our result showed that exhausted T cells primarily communicate with macrophages (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). Next, we focused our attention on macrophage subpopulations. Myeloid cells were re-clustered into five subsets: M1 macrophages (M1_Macro), M2 macrophages (M2_Macro), proliferative macrophages (Mki67_Macro), dendritic cells (DC), and neutrophils (Neutrophils) (\u003cb\u003eFig.\u0026nbsp;4D\u003c/b\u003e). CellChat analysis revealed that Tex cells exhibited heightened cell-cell communication, with M2 macrophages as primary recipients (\u003cb\u003eFig.\u0026nbsp;4E\u003c/b\u003e). Focusing on ligand-receptor pairs,​​we found that Tex-derived CCL3 signals were predominantly received by M2 macrophages via CCR1​​ (\u003cb\u003eFig.\u0026nbsp;4F, G\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eThese findings suggest that Tex secreted CCL3 promotes immunosuppression and tumor progression in HGSOC by activating M2 macrophages. Supporting this, CCL3 expression strongly correlated with M2-associated gene signatures (\u003cb\u003eFig.\u0026nbsp;4H\u003c/b\u003e), while exhausted T-cell signatures showed a positive correlation with M2 signatures (\u003cb\u003eFig.\u0026nbsp;4I\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003eFunctional validation of macrophage polarization​ driven by CCL3 secretion from exhausted T cells\u003c/h3\u003e\n\u003cp\u003eWe tested the hypothesis, obtained from our bioinformatics analysis, that exhausted CD8\u003csup\u003e+\u003c/sup\u003e T cells lead to M2 polarization of macrophages by secreting CCL3. First, we differentiated THP-1 cells into macrophages with PMA (phorbol myristate acetate, 100ng/ml) treatment and divided them into two groups: a control group and a CCL3-treated group. The control group was treated with IL-4 (10 ng/mL) and IL-10 (10 ng/mL) for 24 hours. The CCL3 group received an additional treatment of recombinant CCL3 protein (50 ng/mL) along with the aforementioned cytokines for the same 24 hours. After flow cytometry analysis, we found that the percentage of CD206-positive macrophages was higher in the CCL3-treated group (n\u0026thinsp;=\u0026thinsp;5, 59.2% \u0026plusmn; 2.9% vs. 37.7% \u0026plusmn; 3.2%, p\u0026thinsp;=\u0026thinsp;0.008) (\u003cb\u003eFig.\u0026nbsp;5A\u003c/b\u003e). Next, we examined CCL3 expression in effector and exhausted T cells. To establish an in vitro T cell exhaustion model, we first removed the spleens of OT-1 mice and sorted and cultured the CD8\u003csup\u003e+\u003c/sup\u003e T cells using T Cell Expansion Medium containing Ova (ovalbumin, 10 ng/ml) (\u003cb\u003eFig.\u0026nbsp;5B\u003c/b\u003e). We used flow cytometry to test whether the exhaustion model was successfully constructed by detecting the proliferative capacity (Ki67) (\u003cb\u003eFig.\u0026nbsp;5D\u003c/b\u003e), the killing capacity (GZMB and IFN-γ) (\u003cb\u003eFig.\u0026nbsp;5E and 5F\u003c/b\u003e), the exhaustion state (PD-1 and Tim3) (\u003cb\u003eFig.\u0026nbsp;5G and 5H\u003c/b\u003e), and the amount of CCL3 at 0, 3, 6, 9, and 12 days (\u003cb\u003eFig.\u0026nbsp;5C\u003c/b\u003e) for T cells, respectively. The results showed that, as stimulation time increased, T cell proliferation increased in the early stage and decreased in the late stage. T cell killing capacity significantly increased in the early stage and decreased slightly in the late stage. Exhaustion markers were progressively upregulated. These results are consistent with the pattern of T cell exhaustion. Based on these findings, we discovered that CCL3 expression was significantly elevated in late-exhausted T cells on days 9\u0026ndash;12. Using this model, we designated CD8\u003csup\u003e+\u003c/sup\u003e T cells harvested from day 3 cultures as effector T cells (Teff) and those harvested from day 9 cultures as exhausted T cells (Tex).\u003c/p\u003e\u003cp\u003eNext, we extracted bone marrow-derived macrophages (BMDM) from the femurs of mice. We flushed out the bone marrow with PBS, and we filtered the suspension through a 70-\u0026micro;m cell strainer. Erythrocytes were lysed and resuspended in 1640 medium containing M-CSF (macrophage colony-stimulating factor) at a concentration of 25 ng/ml (\u003cb\u003eFig.\u0026nbsp;5I\u003c/b\u003e). The BMDM cultured for seven days were divided into two groups and incubated with Teff and Tex culture supernatants for 24 hours. These groups were called the Teff group and the Tex group, respectively. Flow cytometry revealed that the percentage of CD206-positive BMDMs cultured with Tex supernatant was significantly higher than that of the Teff group (n\u0026thinsp;=\u0026thinsp;6; 72.4% \u0026plusmn; 0.6% vs. 41.5% \u0026plusmn; 5.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cb\u003eFig.\u0026nbsp;5J\u003c/b\u003e). This was also validated using a model of human-derived cell lines. In this model, CD8\u003csup\u003e+\u003c/sup\u003e T cells were isolated from peripheral blood mononuclear cells (PBMC) and stimulated using IL-2 (20 ng/ml) and CD3/CD28 (25 \u0026micro;l/ml). PMA-induced THP-1 cells were cultured for 48 hours using Teff (PBMC stimulated for three days) and Tex (PBMC stimulated for 10 days) cell culture supernatants, respectively (\u003cb\u003eFig.\u0026nbsp;5K\u003c/b\u003e). Flow cytometry results showed a significantly higher percentage of CD206-positive cells in the Tex group (n\u0026thinsp;=\u0026thinsp;3; 83.8% \u0026plusmn; 1.2% vs. 51.4% \u0026plusmn; 1.2%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cb\u003eFig.\u0026nbsp;5L\u003c/b\u003e). Based on the above experimental results, we hypothesized that exhausted T cells promote M2 macrophage polarization through CCL3 secretion, contributing to tumor progression. Together, these findings demonstrate that exhausted CD8⁺ T cells functionally promote M2 macrophage polarization through CCL3 secretion in both human cell lines and murine models, suggesting a novel immunosuppressive axis that may contribute to HGSOC progression.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eExhausted T cells are a central component of the TME and play a key role in shaping its immune evolution. Within the TME, these cells represent the outcome of continuous immune pressure, maintaining antigen recognition but showing reduced effector function. Through their altered cytokine production and high expression of inhibitory receptors, exhausted T cells affect the balance between immune activation and suppression, contributing to tumor persistence and immune escape\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In our study, we found that exhausted T cells are not only a result of immune evolution but also active participants that strengthen the immunosuppressive environment, further promoting tumor\u0026ndash;immune coevolution and resistance to immunotherapy.\u003c/p\u003e\u003cp\u003eThe chemokine CCL3 has traditionally been recognized as a classic pro-inflammatory factor, instrumental in recruiting and activating T cells during infection or vaccine responses\u003csup\u003e\u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. However, our study uncovers a previously unrecognized immunosuppressive signaling axis in HGSOC, which challenges this conventional view. This finding reveals that CCL3 undergoes a functional reprogramming in the TME\u0026mdash;transforming from an immune-stimulating factor into a tumor-promoting, immunosuppressive molecule. This discovery leads us to propose that CCL3 plays dual roles in immune regulation, depending on the immunological context. During acute infection or certain phases of anti-tumor immunity, it likely acts as an alarmin, promoting inflammatory responses. In stark contrast, within the chronic, immunosuppressive niche of HGSOC, CCL3 is hijacked to establish an immunosuppressive barrier that supports tumor growth and immune evasion. This context-dependent functional plasticity underscores the intricacy of immune regulation in cancer and implies that the function of immune factors must be considered in the broader context of the immune system. Our study significantly enriches the understanding of CCL3 in the immune evolution of HGSOC, establishing it not only as a traditional immune mediator but also as a critical effector in Tex-driven immunosuppression. These findings carry important clinical implications. They posit that CCL3 is not a unidirectional modulator but rather a context-dependent chemokine. Similar to CCL3, several immune mediators, including CCL2, CCL5, IL-6, IFN-γ and TGF-β, exhibit context-dependent bidirectional functions\u003csup\u003e\u003cspan additionalcitationids=\"CR39 CR40 CR41\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Depending on the cellular and microenvironmental context, they can act as either pro- or anti-tumor factors. This functional duality likely arises from differences in receptor expression and signaling intensity, as well as microenvironmental cues that dynamically reprogram their downstream pathways within the TME\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Therefore, understanding the precise molecular cues that govern the switch between CCL3's pro- and anti-tumor roles could unveil novel targets for future immunomodulatory therapies, allowing us to strategically disrupt its tumor-promoting functions while preserving its beneficial immune activities.\u003c/p\u003e\u003cp\u003eOur analysis revealed that elevated CCL3 expression and activation of the CCL3\u0026ndash;CCR1 axis are strongly associated with resistance to platinum-based chemotherapy. CCL3\u0026ndash;CCR1 signaling may activate the PI3K\u0026ndash;Akt, NF-κB, and MAPK pathways, thereby enhancing cell survival, DNA damage repair, and anti-apoptotic programs in tumor or stromal cells\u003csup\u003e\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Concurrently, CCL3-driven remodeling of the tumor microenvironment, through the recruitment and polarization of macrophages and regulatory T cells, may suppress chemotherapy-induced immunogenic cell death and foster a tolerogenic niche. Considering the pivotal role of M2 macrophages in drug resistance and immune evasion, the Tex\u0026ndash;CCL3\u0026ndash;M2 circuit may be a critical link between chronic T-cell dysfunction and therapeutic failure. Furthermore, since immune checkpoint blockade depends on functional effector T cells\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, the accumulation of exhausted T cells and their M2-polarizing chemokine signals may predict poor responsiveness to immunotherapy\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Therefore, targeting the CCL3\u0026ndash;CCR1 axis holds promise as a strategy to overcome chemoresistance and immune escape.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the small sample size (n\u0026thinsp;=\u0026thinsp;8) limits the generalizability of our findings and requires validation in larger groups. Second, while we showed that CCL3 promotes M2 macrophage polarization, the downstream signaling pathways following CCR1 activation are still unknown, leaving the precise molecular mechanisms unclear. Third, the triggers that prompt exhausted T cells to secrete CCL3, as well as the regulatory mechanisms that govern CCL3's dual functions within the tumor microenvironment, remain unknown. Future studies integrating multi-omics profiling and mechanistic validation are necessary to elucidate the complete CCL3\u0026ndash;CCR1 signaling network and its role in immune modulation and therapeutic resistance.\u003c/p\u003e\u003cp\u003eIn conclusion, our study reveals a previously unrecognized tumor-promoting role of CCL3 in the HGSOC immune microenvironment: CCL3 secreted by exhausted CD8⁺ T cells induce M2 polarization of macrophages, forming positive feedback signaling axis of immunosuppression. This discovery underscores the dual nature of immune mediators\u0026mdash;their function is not only molecule-dependent but also context-dependent. By dissecting the Tex\u0026ndash;CCL3\u0026ndash;M2 immunological axis, we offer a new mechanistic perspective on how chronic T cell dysfunction expands immunosuppression through myeloid cells. Given the druggable nature of this axis, targeting CCL3 or its receptor CCR1 may become a promising strategy for combining immunotherapy with resistance reversal, potentially bringing new clinical benefits to patients with HGSOC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eHuman tissue specimens\u003c/h2\u003e\u003cp\u003eThe samples of the nine HGSOC cases for scRNA-seq were obtained from patients attending the Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Patients were enrolled after signing an informed consent form. The study was carried out according to the ethical guidelines of Beijing Obstetrics and Gynecology Hospital and approved by the hospital\u0026rsquo;s Ethics Committee ethical (approval NO.2023-KY-080-01).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSingle-cell RNA sequencing\u003c/h2\u003e\u003cp\u003eFollowing the diagnosis of HGSOC by rapid pathology, ovarian cancer samples from nine patients were dissociated into single-cell suspensions for cell counting and cell viability determination by a cell counter. The prepared single-cell suspensions were then combined with a mixture of gel beads containing barcode information and enzymes, and finally encapsulated by oil droplets in microfluidic \"single cross\" junctions to form GEMs (Gel Bead-In-EMulsions). Subsequently, the GEMs undergo a process of cell lysis and reverse transcription, and the 10x Barcode is ligated to the cDNA product. Following this, the GEMs are broken up, and the oil droplets are fragmented for PCR amplification using the cDNA as a template. The construction of sequencing libraries was preceded by the implementation of a quality control procedure on the amplified products. The subsequent analysis of the data was performed using the Illumina NovaSeq sequencing platform.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eQuality control and data integration\u003c/h2\u003e\u003cp\u003eThe sequence data were used to map the reads to the human genome (GRCh38) employing Cellranger 7.0.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://10xgenomics.com\u003c/span\u003e\u003cspan address=\"http://10xgenomics.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Subsequently, the data were analyzed using the Seurat package (Ver. 4.3.0). The Seurat objects were initially created using the read10x function, with the data from each of the six samples subsequently undergoing quality control. Subsequently, the data were log-normalized, and homogenization and principal component analysis were performed after identifying highly variable genes. The RunHarmony function from the harmony package (Ver. 1.1.0)\u003csup\u003e23\u003c/sup\u003e was employed to eliminate batch effects. Subsequently, the number of principal components was determined, and UMAP downscaling was performed. Subpopulations were categorised and annotated based on markers characteristic of distinct cell types. The annotation process was facilitated by the SingleR package (Ver. 2.0.0)\u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePseudotime analysis\u003c/h2\u003e\u003cp\u003eUse the Monocle2 R package (Ver. 2.28.0)\u003csup\u003e25\u003c/sup\u003e for pseudotime analysis to reconstruct cell trajectories. Briefly, gene expression matrices were converted to Monocle-compatible CellDataSet objects. The DDRTree algorithm was used to reduce the dimensionality and construct the trajectory graph. The root node of the trajectory is set manually based on the biological significance of cell subpopulations. Cells are then sorted along the trajectory, and a pseudo-time value is assigned to each cell. Enrichment analysis and visualization of significantly changed genes using the ClusterGVis R package (Ver. 0.1.4).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eCell\u0026ndash;cell communication analysis\u003c/h2\u003e\u003cp\u003eWe used the CellChat R package (Ver. 2.1.0)\u003csup\u003e26\u003c/sup\u003e to analyze communication between two cell types: T cells and myeloid cells. First, we constructed cellchat objects and then used CellChatDB V2 as a ligand database. After filtering out populations of less than 10 cells, we inferred the intercellular communication network.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003ehdWGCNA analysis\u003c/h2\u003e\u003cp\u003eHdWGNCA identifies modules of highly co-expressed genes and provides context for these modules via statistical testing and biological knowledge sources\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Setting the threshold of the scale-free topology model fit as \u0026gt;\u0026thinsp;0.8, a soft threshold was selected 6 for the best connectivity. Correlations between modules and T-cell status were evaluated by the Spearman test.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using R (Ver. 4.4.2), and GraphPad Prism (Ver. 9.0). Differences between the two groups were assessed using Student's t-test or the Wilcoxon rank sum test. Survival data were analyzed using the log-rank test. All experiments were repeated at least three times. Data are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). A p-value of less than 0.05 was considered statistically significant for all statistical comparisons.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eModeling of T cell exhaustion\u003c/h2\u003e\u003cp\u003eMouse\u003c/p\u003e\u003cp\u003eSpleens were isolated from OT-1 mice and ground under a clean bench. The tissue homogenate was filtered using a 200 \u0026micro;m nylon filter and subjected to erythrocyte lysis. Next, Na\u0026iuml;ve CD8\u003csup\u003e+\u003c/sup\u003e T-cells were isolated using Na\u0026iuml;ve CD8a\u003csup\u003e+\u003c/sup\u003e T Cell Isolation Kit (Cat: 130-104-075, Miltenyibiotec). Finally, the isolated cells were cultured using ImmunoCult\u0026trade;-XF T Cell Expansion Medium (Cat: # 10981, STEMCELL) and stimulated with 10 ng/mL ovalbumen (Cat: 763606, Biolegend). Cells were counted and replaced to maintain optimal cell density with fresh stimulation-supplemented medium every three days.\u003c/p\u003e\u003cp\u003eHuman\u003c/p\u003e\u003cp\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells were isolated from PBMC (Cat: 130-096-495, Miltenyibiotec) and stimulated using IL2(Cat: HY-P7037B, MCE) and CD3/CD28(Cat: #100\u0026ndash;0784, STEMCELL) to construct T cell activation models and exhaustion models. Stimuli were replenished every 3\u0026ndash;4 days.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eBone marrow-derived macrophages (BMDMs) induction\u003c/h2\u003e\u003cp\u003eFirstly, female mice (6\u0026ndash;8 weeks old) were sacrificed and sterilized in 75% alcohol for 10 min. Subsequently, the BM in the femur and tibia was flushed out and treated with red blood cell lysis buffer (Cat: R1010, Solarbio). BM cells were then cultured in 90% RPMI1640, 10% FBS medium with macrophage colony-stimulating factor (MCSF, Cat: HY-P7085A, MCE), and 1% streptomycin/penicillin at a density of 3\u0026times;10\u003csup\u003e6\u003c/sup\u003e per 6-well cell culture plate for 6 days, and the medium was changed every 2\u0026ndash;3 days. After 6 days of induction, the obtained BMDMs were used for subsequent experiments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eFlow cytometry\u003c/h2\u003e\u003cp\u003eThe cells to be tested were collected, and then, after centrifugation at 500 g for five minutes, the cells were resuspended in staining buffer (RPMI 1640\u0026thinsp;+\u0026thinsp;2% FBS medium) and labeled with surface antibodies. Then, the cells were fixed with Cytofix Fixation Buffer (Cat: 554655, BD), permeabilized with Perm/Wash Buffer (Cat: 554723, BD), and labeled with intracellular antibodies. Online analysis was then performed. The following antibodies were used: PD-1 (Cat: 135219, biolegend), Tim3 (119725), Ki67 (Cat: 563757, BD), CCL3 (Cat: IC450A, bio-techne), IFN-γ (Cat: 505841, biolegend), GZMB (Cat: 372230, biolegend), CD206 (Cat: 321105, biolegend), CD86 (Cat: 305411, biolegend), CD11b (Cat: 301329, biolegend).\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYL, MJ, XTZ, and WTY conceived and designed the study. YL carried out all experiments and wrote the manuscript. YL, and FL participated in bioinformatics analysis. JC, ZFL, JJY, and YHY supported the study. XTZ and WTY supervised the study. XTZ and WTY revised the manuscript. All authors read and approved the final manuscript\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, as revised in 2013. The study was approved by the medical ethics committee of the Beijing Obstetrics and Gynecology Hospital Capital Medical University. We have obtained informed consent from all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is supported by the National Natural Science Foundation of China (No. 82373397), the Capital’s Funds for Health Improvement and Research (No.2022–2-2116), the Beijing Hospitals Authority’s Ascent Plan, Code: DFL20241401, the Beijing Nova Program, and the Laboratory for Clinical Medicine, Capital Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e 2018; \u003cstrong\u003e68\u003c/strong\u003e: 394\u0026ndash;424.\u003c/li\u003e\n \u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A \u003cem\u003eet al.\u003c/em\u003e Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e 2021; \u003cstrong\u003e71\u003c/strong\u003e: 209\u0026ndash;249.\u003c/li\u003e\n \u003cli\u003eKurnit KC, Fleming GF, Lengyel E. Updates and new options in advanced epithelial ovarian cancer treatment. \u003cem\u003eObstet Gynecol\u003c/em\u003e 2021; \u003cstrong\u003e137\u003c/strong\u003e: 108\u0026ndash;121.\u003c/li\u003e\n \u003cli\u003eLarkin J, Chiarion-Sileni V, Gonzalez R, Grob J-J, Rutkowski P, Lao CD \u003cem\u003eet al.\u003c/em\u003e Five-year survival with combined nivolumab and ipilimumab in advanced melanoma. \u003cem\u003eN Engl J Med\u003c/em\u003e 2019; \u003cstrong\u003e381\u003c/strong\u003e: 1535\u0026ndash;1546.\u003c/li\u003e\n \u003cli\u003eRobert C, Schachter J, Long GV, Arance A, Grob JJ, Mortier L \u003cem\u003eet al.\u003c/em\u003e Pembrolizumab versus ipilimumab in advanced melanoma. \u003cem\u003eN Engl J Med\u003c/em\u003e 2015; \u003cstrong\u003e372\u003c/strong\u003e: 2521\u0026ndash;2532.\u003c/li\u003e\n \u003cli\u003eRobert C. A decade of immune-checkpoint inhibitors in cancer therapy. \u003cem\u003eNat Commun\u003c/em\u003e 2020; \u003cstrong\u003e11\u003c/strong\u003e: 3801.\u003c/li\u003e\n \u003cli\u003eHow J, Patel A, Jazaeri A. Immuno-oncology for gynecologic malignancies. \u003cem\u003eAdv Exp Med Biol\u003c/em\u003e 2020; \u003cstrong\u003e1244\u003c/strong\u003e: 149\u0026ndash;182.\u003c/li\u003e\n \u003cli\u003eMatulonis UA, Shapira-Frommer R, Santin AD, Lisyanskaya AS, Pignata S, Vergote I \u003cem\u003eet al.\u003c/em\u003e Antitumor activity and safety of pembrolizumab in patients with advanced recurrent ovarian cancer: Results from the phase II KEYNOTE-100 study. \u003cem\u003eAnn Oncol\u003c/em\u003e 2019; \u003cstrong\u003e30\u003c/strong\u003e: 1080\u0026ndash;1087.\u003c/li\u003e\n \u003cli\u003eKe M, Zhu H, Lin Y, Zhang Y, Tang T, Xie Y \u003cem\u003eet al.\u003c/em\u003e Actin-related protein 2/3 complex subunit 1B promotes ovarian cancer progression by regulating the AKT/PI3K/mTOR signaling pathway. \u003cem\u003eJ Transl Int Med\u003c/em\u003e; \u003cstrong\u003e12\u003c/strong\u003e: 406\u0026ndash;423.\u003c/li\u003e\n \u003cli\u003eBinnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M \u003cem\u003eet al.\u003c/em\u003e Understanding the tumor immune microenvironment (TIME) for effective therapy. \u003cem\u003eNat Med\u003c/em\u003e 2018; \u003cstrong\u003e24\u003c/strong\u003e: 541\u0026ndash;550.\u003c/li\u003e\n \u003cli\u003eWherry EJ. T cell exhaustion. \u003cem\u003eNat Immunol\u003c/em\u003e 2011; \u003cstrong\u003e12\u003c/strong\u003e: 492\u0026ndash;499.\u003c/li\u003e\n \u003cli\u003ePauken KE, Wherry EJ. Overcoming T cell exhaustion in infection and cancer. \u003cem\u003eTrends Immunol\u003c/em\u003e 2015; \u003cstrong\u003e36\u003c/strong\u003e: 265\u0026ndash;276.\u003c/li\u003e\n \u003cli\u003ePhilip M, Fairchild L, Sun L, Horste EL, Camara S, Shakiba M \u003cem\u003eet al.\u003c/em\u003e Chromatin states define tumour-specific T cell dysfunction and reprogramming. \u003cem\u003eNature\u003c/em\u003e 2017; \u003cstrong\u003e545\u003c/strong\u003e: 452\u0026ndash;456.\u003c/li\u003e\n \u003cli\u003eZou Y, Zhang H, Liu F, Chen Z-S, Tang H. Intratumoral microbiota in orchestrating cancer immunotherapy response. \u003cem\u003eJ Transl Int Med\u003c/em\u003e; \u003cstrong\u003e12\u003c/strong\u003e: 540\u0026ndash;542.\u003c/li\u003e\n \u003cli\u003eChen Y, Ouyang D, Wang Y, Pan Q, Zhao J, Chen H \u003cem\u003eet al.\u003c/em\u003e EBV promotes TCR-T-cell therapy resistance by inducing CD163+M2 macrophage polarization and MMP9 secretion. \u003cem\u003eJ Immunother Cancer\u003c/em\u003e 2024; \u003cstrong\u003e12\u003c/strong\u003e: e008375.\u003c/li\u003e\n \u003cli\u003ePu J, Xu Z, Nian J, Fang Q, Yang M, Huang Y \u003cem\u003eet al.\u003c/em\u003e M2 macrophage-derived extracellular vesicles facilitate CD8+T cell exhaustion in hepatocellular carcinoma via the miR-21-5p/YOD1/YAP/\u0026beta;-catenin pathway. \u003cem\u003eCell Death Discov\u003c/em\u003e 2021; \u003cstrong\u003e7\u003c/strong\u003e: 182.\u003c/li\u003e\n \u003cli\u003eWu K, Lin K, Li X, Yuan X, Xu P, Ni P \u003cem\u003eet al.\u003c/em\u003e Redefining tumor-associated macrophage subpopulations and functions in the tumor microenvironment. \u003cem\u003eFront Immunol\u003c/em\u003e 2020; \u003cstrong\u003e11\u003c/strong\u003e: 1731.\u003c/li\u003e\n \u003cli\u003eZhang W, Wang M, Ji C, Liu X, Gu B, Dong T. Macrophage polarization in the tumor microenvironment: Emerging roles and therapeutic potentials. \u003cem\u003eBiomed Pharmacother\u003c/em\u003e 2024; \u003cstrong\u003e177\u003c/strong\u003e: 116930.\u003c/li\u003e\n \u003cli\u003eMantovani A, Sozzani S, Locati M, Allavena P, Sica A. Macrophage polarization: Tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes. \u003cem\u003eTrends Immunol\u003c/em\u003e 2002; \u003cstrong\u003e23\u003c/strong\u003e: 549\u0026ndash;555.\u003c/li\u003e\n \u003cli\u003eGao J, Liang Y, Wang L. Shaping polarization of tumor-associated macrophages in cancer immunotherapy. \u003cem\u003eFront Immunol\u003c/em\u003e 2022; \u003cstrong\u003e13\u003c/strong\u003e: 888713.\u003c/li\u003e\n \u003cli\u003eKamat K, Krishnan V, Dorigo O. Macrophage-derived CCL23 upregulates expression of T-cell exhaustion markers in ovarian cancer. \u003cem\u003eBr J Cancer\u003c/em\u003e 2022; \u003cstrong\u003e127\u003c/strong\u003e: 1026\u0026ndash;1033.\u003c/li\u003e\n \u003cli\u003eLaunonen I-M, Niemiec I, Hincapi\u0026eacute;-Otero M, Erkan EP, Junquera A, Afenteva D \u003cem\u003eet al.\u003c/em\u003e Chemotherapy induces myeloid-driven spatially confined T cell exhaustion in ovarian cancer. \u003cem\u003eCancer Cell\u003c/em\u003e 2024; \u003cstrong\u003e42\u003c/strong\u003e: 2045-2063.e10.\u003c/li\u003e\n \u003cli\u003eKorsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K \u003cem\u003eet al.\u003c/em\u003e Fast, sensitive and accurate integration of single-cell data with harmony. \u003cem\u003eNat Methods\u003c/em\u003e 2019; \u003cstrong\u003e16\u003c/strong\u003e: 1289\u0026ndash;1296.\u003c/li\u003e\n \u003cli\u003eAran D, Looney AP, Liu L, Wu E, Fong V, Hsu A \u003cem\u003eet al.\u003c/em\u003e Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. \u003cem\u003eNat Immunol\u003c/em\u003e 2019; \u003cstrong\u003e20\u003c/strong\u003e: 163\u0026ndash;172.\u003c/li\u003e\n \u003cli\u003eTrapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M \u003cem\u003eet al.\u003c/em\u003e The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. \u003cem\u003eNat Biotechnol\u003c/em\u003e 2014; \u003cstrong\u003e32\u003c/strong\u003e: 381\u0026ndash;386.\u003c/li\u003e\n \u003cli\u003eJin S, Plikus MV, Nie Q. CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics. \u003cem\u003eNat Protoc\u003c/em\u003e 2025; \u003cstrong\u003e20\u003c/strong\u003e: 180\u0026ndash;219.\u003c/li\u003e\n \u003cli\u003eMorabito S, Reese F, Rahimzadeh N, Miyoshi E, Swarup V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. \u003cem\u003eCell Rep Methods\u003c/em\u003e 2023; \u003cstrong\u003e3\u003c/strong\u003e: 100498.\u003c/li\u003e\n \u003cli\u003eKallies A, Zehn D, Utzschneider DT. Precursor exhausted T cells: key to successful immunotherapy? \u003cem\u003eNat Rev Immunol\u003c/em\u003e 2020; \u003cstrong\u003e20\u003c/strong\u003e: 128\u0026ndash;136.\u003c/li\u003e\n \u003cli\u003eWherry EJ, Kurachi M. Molecular and cellular insights into T cell exhaustion. \u003cem\u003eNat Rev Immunol\u003c/em\u003e 2015; \u003cstrong\u003e15\u003c/strong\u003e: 486\u0026ndash;499.\u003c/li\u003e\n \u003cli\u003eGan J, Zhang X, Guo J. The role of platelets in tumor immune evasion and metastasis: Mechanisms and therapeutic implications. \u003cem\u003eCancer Cell Int\u003c/em\u003e 2025; \u003cstrong\u003e25\u003c/strong\u003e: 258.\u003c/li\u003e\n \u003cli\u003eGubin MM, Vesely MD. Cancer immunoediting in the era of immuno-oncology. \u003cem\u003eClin Cancer Res\u003c/em\u003e 2022; : clincanres.1804.2022.\u003c/li\u003e\n \u003cli\u003eAriyasu R, Nishikawa H. [cancer immunoediting in the development of cancer immunotherapy]. \u003cem\u003eGan To Kagaku Ryoho\u003c/em\u003e 2019; \u003cstrong\u003e46\u003c/strong\u003e: 407\u0026ndash;411.\u003c/li\u003e\n \u003cli\u003ePatterson SJ, Pesenacker AM, Wang AY, Gillies J, Mojibian M, Morishita K \u003cem\u003eet al.\u003c/em\u003e T regulatory cell chemokine production mediates pathogenic T cell attraction and suppression. \u003cem\u003eJ Clin Invest\u003c/em\u003e 2016; \u003cstrong\u003e126\u003c/strong\u003e: 1039\u0026ndash;1051.\u003c/li\u003e\n \u003cli\u003eSong Q, Shang J, Zhang C, Chen J, Zhang L, Wu X. Transcription factor RUNX3 promotes CD8+ T cell recruitment by CCL3 and CCL20 in lung adenocarcinoma immune microenvironment. \u003cem\u003eJ Cell Biochem\u003c/em\u003e 2020; \u003cstrong\u003e121\u003c/strong\u003e: 3208\u0026ndash;3220.\u003c/li\u003e\n \u003cli\u003eYang J, Lu X, Cai Q, Liu M, Xia T, Hong D \u003cem\u003eet al.\u003c/em\u003e Loss of TACC2 impairs chemokine CCL3 and CCL4 expression and reduces response to anti-PD-1 therapy in soft tissue sarcoma. \u003cem\u003eMol Cancer\u003c/em\u003e 2025; \u003cstrong\u003e24\u003c/strong\u003e: 158.\u003c/li\u003e\n \u003cli\u003eTrifilo MJ, Bergmann CC, Kuziel WA, Lane TE. CC chemokine ligand 3 (CCL3) regulates CD8(+)-T-cell effector function and migration following viral infection. \u003cem\u003eJ Virol\u003c/em\u003e 2003; \u003cstrong\u003e77\u003c/strong\u003e: 4004\u0026ndash;4014.\u003c/li\u003e\n \u003cli\u003eHarlin H, Meng Y, Peterson AC, Zha Y, Tretiakova M, Slingluff C \u003cem\u003eet al.\u003c/em\u003e Chemokine expression in melanoma metastases associated with CD8+ T-cell recruitment. \u003cem\u003eCancer Res\u003c/em\u003e 2009; \u003cstrong\u003e69\u003c/strong\u003e: 3077\u0026ndash;3085.\u003c/li\u003e\n \u003cli\u003eLi M, Knight DA, A Snyder L, Smyth MJ, Stewart TJ. A role for CCL2 in both tumor progression and immunosurveillance. \u003cem\u003eOncoimmunology\u003c/em\u003e 2013; \u003cstrong\u003e2\u003c/strong\u003e: e25474.\u003c/li\u003e\n \u003cli\u003eAldinucci D, Borghese C, Casagrande N. The CCL5/CCR5 axis in cancer progression. \u003cem\u003eCancers (Basel)\u003c/em\u003e 2020; \u003cstrong\u003e12\u003c/strong\u003e: 1765.\u003c/li\u003e\n \u003cli\u003eSoler MF, Abaurrea A, Azcoaga P, Araujo AM, Caffarel MM. New perspectives in cancer immunotherapy: Targeting IL-6 cytokine family. \u003cem\u003eJ Immunother Cancer\u003c/em\u003e 2023; \u003cstrong\u003e11\u003c/strong\u003e: e007530.\u003c/li\u003e\n \u003cli\u003eJorgovanovic D, Song M, Wang L, Zhang Y. Roles of IFN-\u0026gamma; in tumor progression and regression: A review. \u003cem\u003eBiomarker Res\u003c/em\u003e 2020; \u003cstrong\u003e8\u003c/strong\u003e: 49.\u003c/li\u003e\n \u003cli\u003eDerynck R, Turley SJ, Akhurst RJ. TGF\u0026beta; biology in cancer progression and immunotherapy. \u003cem\u003eNat Rev Clin Oncol\u003c/em\u003e 2021; \u003cstrong\u003e18\u003c/strong\u003e: 9\u0026ndash;34.\u003c/li\u003e\n \u003cli\u003eGoenka A, Khan F, Verma B, Sinha P, Dmello CC, Jogalekar MP \u003cem\u003eet al.\u003c/em\u003e Tumor microenvironment signaling and therapeutics in cancer progression. \u003cem\u003eCancer Commun\u003c/em\u003e 2023; \u003cstrong\u003e43\u003c/strong\u003e: 525\u0026ndash;561.\u003c/li\u003e\n \u003cli\u003eVallet S, Raje N, Ishitsuka K, Hideshima T, Podar K, Chhetri S \u003cem\u003eet al.\u003c/em\u003e MLN3897, a novel CCR1 inhibitor, impairs osteoclastogenesis and inhibits the interaction of multiple myeloma cells and osteoclastsMLN3897. \u003cem\u003eBlood\u003c/em\u003e 2007; \u003cstrong\u003e110\u003c/strong\u003e: 3744\u0026ndash;3752.\u003c/li\u003e\n \u003cli\u003eHsu C-J, Wu M-H, Chen C-Y, Tsai C-H, Hsu H-C, Tang C-H. AMP-activated protein kinase activation mediates CCL3-induced cell migration and matrix metalloproteinase-2 expression in human chondrosarcoma. \u003cem\u003eCell Communication and Signaling\u003c/em\u003e 2013; \u003cstrong\u003e11\u003c/strong\u003e: 68.\u003c/li\u003e\n \u003cli\u003eYan J, Zuo G, Sherchan P, Huang L, Ocak U, Xu W \u003cem\u003eet al.\u003c/em\u003e CCR1 activation promotes neuroinflammation through CCR1/TPR1/ERK1/2 signaling pathway after intracerebral hemorrhage in mice. \u003cem\u003eNeurotherapeutics\u003c/em\u003e 2020; \u003cstrong\u003e17\u003c/strong\u003e: 1170\u0026ndash;1183.\u003c/li\u003e\n \u003cli\u003ePaijens ST, Vledder A, de Bruyn M, Nijman HW. Tumor-infiltrating lymphocytes in the immunotherapy era. \u003cem\u003eCell Mol Immunol\u003c/em\u003e 2021; \u003cstrong\u003e18\u003c/strong\u003e: 842\u0026ndash;859.\u003c/li\u003e\n \u003cli\u003eChen S, Saeed AFUH, Liu Q, Jiang Q, Xu H, Xiao GG \u003cem\u003eet al.\u003c/em\u003e Macrophages in immunoregulation and therapeutics. \u003cem\u003eSignal Transduct Target Ther\u003c/em\u003e 2023; \u003cstrong\u003e8\u003c/strong\u003e: 207.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cell-death-and-disease","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cddis","sideBox":"Learn more about [Cell Death \u0026 Disease](http://www.nature.com/cddis/)","snPcode":"41419","submissionUrl":"https://mts-cddis.nature.com/cgi-bin/main.plex","title":"Cell Death \u0026 Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8074255/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8074255/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh-grade serous ovarian cancer (HGSOC) is an aggressive malignancy marked by high recurrence rates, poor prognosis, and limited response to immune checkpoint inhibitors, primarily attributable to its immunologically \u0026ldquo;cold\u0026rdquo; tumor microenvironment. To profile the immunological landscape of HGSOC, we conducted single-cell RNA sequencing (scRNA-seq) on 84,065 cells from tumor tissues of eight treatment-na\u0026iuml;ve patients and one normal ovarian tissue, identifying six major cell clusters and revealing substantial immune cell infiltration. Further analysis of CD8⁺ T cells identified two key subpopulations\u0026mdash;precursor and terminally exhausted T cells\u0026mdash;and delineated their developmental trajectories. The accumulation of exhausted CD8⁺ T cells (Tex) suggested an immunosuppressive tumor microenvironment. Integrated trajectory inference and high-dimensional weighted gene co-expression network analysis (hdWGCNA) identified CCL3 as a novel hub gene specifically expressed in Tex cells. Communication analysis suggested that Tex cells may interact with M2 macrophages via the CCL3\u0026ndash;CCR1 ligand\u0026ndash;receptor axis. Functional validation confirmed that: (1) secretomes from Tex cells\u0026mdash;but not effector T cells\u0026mdash;significantly promoted M2 polarization in both THP-1 and bone marrow-derived macrophages (CD206⁺ THP-1: 83.8% vs. 51.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; CD206⁺ BMDM: 72.4% vs. 41.5%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); and (2) recombinant CCL3 acted synergistically with IL-4/IL-10 to further enhance M2 polarization (59.2% vs. 37.7%, p\u0026thinsp;=\u0026thinsp;0.008). Collectively, our findings unveil a previously unrecognized immunoregulatory axis whereby exhausted CD8⁺ T cells drive immunosuppression via CCL3\u0026ndash;CCR1\u0026ndash;mediated communication with M2 macrophages, presenting a promising therapeutic target to reverse the immune-cold tumor microenvironment in HGSOC.\u003c/p\u003e","manuscriptTitle":"Exhausted CD8⁺ T Cells Promote Ovarian Cancer Immunosuppression via CCL3-Driven M2 Macrophage Polarization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 12:53:16","doi":"10.21203/rs.3.rs-8074255/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-02-10T16:11:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-12-07T08:20:24+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-12-05T02:14:40+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-12-04T14:54:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-21T10:03:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cell Death \u0026 Disease","date":"2025-11-21T02:36:06+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-11-11T13:14:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-10T07:52:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cell-death-and-disease","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cddis","sideBox":"Learn more about [Cell Death \u0026 Disease](http://www.nature.com/cddis/)","snPcode":"41419","submissionUrl":"https://mts-cddis.nature.com/cgi-bin/main.plex","title":"Cell Death \u0026 Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c65f4d01-f940-4e41-82fb-874e09db2d76","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59105341,"name":"Biological sciences/Immunology/Tumour immunology"},{"id":59105342,"name":"Biological sciences/Cell biology/Cell signalling/Extracellular signalling molecules"},{"id":59105343,"name":"Health sciences/Diseases/Cancer/Gynaecological cancer/Ovarian cancer"},{"id":59105344,"name":"Health sciences/Diseases/Cancer/Cancer microenvironment"},{"id":59105345,"name":"Health sciences/Diseases/Cancer/Tumour immunology"}],"tags":[],"updatedAt":"2026-04-13T08:47:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 12:53:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8074255","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8074255","identity":"rs-8074255","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}