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Immunotherapy biomarkers
Original research
Targeting myeloid checkpoint Siglec-10 reactivates antitumor immunity and improves anti-programmed cell death 1 efficacy in gastric cancer
  1. Kunpeng Lv1,
  2. Mengyao Sun1,
  3. Hanji Fang1,2,
  4. Jieti Wang3,
  5. Chao Lin2,
  6. Hao Liu2,
  7. Heng Zhang2,
  8. He Li2,
  9. Hongyong He2,
  10. Yun Gu1,4,
  11. Ruochen Li2,
  12. Fei Shao5 and
  13. Jiejie Xu1
  1. 1NHC Key Laboratory of Glycoconjugate Research, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, China
  2. 2Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
  3. 3Department of Endoscopy, Fudan University Shanghai Cancer Center, Shanghai, China
  4. 4Department of General Surgery, Shanghai Sixth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
  5. 5Department of Oncology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
  1. Correspondence to Dr Yun Gu; ygu15{at}fudan.edu.cn; Dr Ruochen Li; rcli12{at}fudan.edu.cn; Dr Fei Shao; fei.shao{at}shgh.cn; Professor Jiejie Xu; jjxufdu{at}fudan.edu.cn

Abstract

Objective Immunotherapy has not yielded satisfactory therapeutic responses in gastric cancer (GC). However, targeting myeloid checkpoints holds promise for expanding the potential of immunotherapy. This study aims to evaluate the critical role of Siglec-10+ tumor-associated macrophages (TAMs) in regulating antitumor immunity and to explore the potential of the myeloid checkpoint Siglec-10 as an interventional target.

Design Siglec-10+ TAMs were assessed based on immunohistochemistry on tumor microarrays and RNA-sequencing data. Flow cytometry, RNA sequencing, and single-cell RNA-sequencing analysis were employed to characterize the phenotypic and transcriptional features of Siglec-10+ TAMs and their impact on CD8+ T cell-mediated antitumor immunity. The effectiveness of Siglec-10 blockade, either alone or in combination with anti-programmed cell death 1 (PD-1), was evaluated using an ex vivo GC tumor fragment platform based on fresh tumor tissues.

Results Siglec-10 was predominantly expressed on TAMs in GC, and associated with tumor progression. In Zhongshan Hospital cohort, Siglec-10+ TAMs predicted unfavorable prognosis (n=446, p<0.001) and resistance to adjuvant chemotherapy (n=331, p<0.001), which were further validated in exogenous cohorts. In the Samsung Medical Center cohort, Siglec-10+ TAMs demonstrated inferior response to pembrolizumab in GC (n=45, p=0.008). Furthermore, Siglec-10+ TAMs exhibited an immunosuppressive phenotype and hindered T cell-mediated antitumor immune response. Finally, blocking Siglec-10 reinvigorated the antitumor immune response and synergistically enhances anti-PD-1 immunotherapy in an ex vivo GC tumor fragment platform.

Conclusions In GC, the myeloid checkpoint Siglec-10 contributes to the regulation of immunosuppressive property of TAMs and promotes the depletion of CD8+ T cells, ultimately facilitating immune evasion. Targeting Siglec-10 represents a potential strategy for immunotherapy in GC.

  • Tumor Microenvironment
  • Macrophages
  • Tumor Escape
  • Immunotherapy
  • Biomarkers, Tumor

Data availability statement

Data are available upon reasonable request.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/jitc-2023-007669

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    WHAT IS ALREADY KNOWN ON THIS TOPIC

    • There is an unmet medical need to identify new targets for immunotherapy and screen treatment-sensitive patients with gastric cancer (GC) .

    • Myeloid checkpoints regulate the immune response against tumors by transmitting inhibitory signals.

    • Siglec-10 has gained attention as a crucial player in the immune evasion mechanism employed by tumor cells.

    WHAT THIS STUDY ADDS

    • In GC, the myeloid checkpoint Siglec-10 was predominantly expressed in tumor-associated macrophages (TAMs), and the Siglec-10+ TAMs were associated with tumor progression, unfavorable prognosis, anti-programmed cell death 1 (PD-1) immunotherapy, and adjuvant chemotherapy resistance.

    • Siglec-10 contributes to the regulation of immunosuppressive property of TAMs and promotes the depletion of CD8+ T cells, ultimately helping the tumor immune evade immune surveillance.

    • Targeting Siglec-10 reactivates antitumor immunity and improves anti-PD-1 efficacy in GC.

    HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

    • Siglec-10+ TAMs may become a predictor of immunotherapy. Meanwhile, it might be a promising strategy to target Siglec-10 to reactivate antitumor immunity and synergistically boost the efficacy of anti-PD-1 in GC.

    Introduction

    Gastric cancer (GC) is a highly prevalent and lethal malignancy with a significant impact on global health, particularly in East Asia.1 Despite the usage of standard treatments such as surgery, chemotherapy and radiotherapy, their effectiveness remains limited, necessitating the exploration of more efficient treatment options.2 Immunotherapy, specifically immune checkpoint inhibitors (ICIs), has shown remarkable advancements as an emerging oncology treatment for advanced GC.3 However, its application is restricted to a small subset of patients, and the development of drug resistance poses a formidable challenge.4 Consequently, it is crucial to identify new targets for immunotherapy and screen treatment-sensitive patients with GC, as these factors play a vital role in improving patient prognosis.

    In recent years, there has been a growing recognition of the crucial role played by the tumor microenvironment (TME) in tumor progression and clinical outcomes.5 Tumor-infiltrating immune cells, particularly tumor-associated macrophages (TAMs), have emerged as key contributors within the diverse components of the TME.6 TAMs, being the most abundant immune cell population, exhibit remarkable plasticity and contribute to various aspects of tumorigenesis, including immune evasion, angiogenesis, and therapeutic resistance.7 While TAMs were traditionally categorized into M1 and M2 phenotypes, this binary classification system fails to capture the complexity of TAM functions.8 Therefore, it is essential to identify precise markers that define distinct functional subgroups of TAMs to develop effective therapeutic strategies for GC.

    Nowadays, the investigation of TAMs has shed light on the significance of myeloid checkpoints, which are receptors present on the surface of myeloid cells. Overall, these molecules regulate the immune response against tumors by transmitting inhibitory signals.9 Among these checkpoints, Siglec-10, an immunoglobulin-like receptor that binds to sialic acid, has gained attention as a crucial player in the immune evasion mechanism employed by tumor cells.10 Studies have demonstrated that the interaction between the glycoprotein CD24 on the tumor cell surface and Siglec-10 triggers a “don’t eat me” signal, impeding the phagocytic activity of TAMs and enabling immune surveillance evasion by tumor cells.11 Despite this knowledge, there is currently no research exploring the use of the myeloid checkpoint Siglec-10 to define specific functional subgroups of TAMs and elucidate its functional mechanism and clinical value in reshaping the TME and impacting patient outcomes in GC. Thus, this study aims to investigate the contribution of Siglec-10 expression on TAMs to immune evasion and malignant tumor development by modulating the TME. Additionally, intervention measures will be employed to assess the potential of Siglec-10 as a therapeutic target for immunotherapy in GC.

    Materials and methods

    Study cohorts

    The Zhongshan Hospital (ZSHS) cohort enrolled 496 patients with GC who received gastrectomy from Zhongshan Hospital, Fudan University (Shanghai, China) between August 2007 and December 2008. However, 50 patients were excluded from our study due to incomplete data, presence of metastatic diseases or dot loss. All patients did not receive radiation therapy, and after surgery 5-fluorouracil-based adjuvant chemotherapy was primarily given to patients according to National Comprehensive Cancer Network (NCCN) guidelines and patients’ will. Basic clinical and pathologic parameters of the ZSHS cohort based on Siglec-10+ TAMs infiltration level was summarized in online supplemental table S1. Meanwhile, we analyzed data from multiple sources, including previously published cohorts: The Cancer Genome Atlas (TCGA) cohort, the Asian Cancer Research Group (ACRG) cohort,12 the Korea University-Yonsei University-Kosin University (KYK) cohort,13 14 the Samsung Medical Center (SMC) cohort,15 and GSE84437 cohort. Among them, the KYK cohort is merged from the Korea University Guro Hospital cohort, Yonsei University Severance Hospital cohort and Kosin University College of Medicine cohort (online supplemental method S1).13 16 Additionally, we also examined single-cell RNA-sequencing (scRNA-seq) data from cohorts previously published by Kumar et al17 and Jeong et al.18

    Supplemental material

    Immunohistochemistry, EBER-RISH and immunofluorescence

    Our previous studies provided a detailed protocol for tumor microarrays construction, immunohistochemistry (IHC), Epstein-Barr virus-encoded small RNA in situ hybridization (EBER-RISH) and immunofluorescence (IFC).19 20 The associated antibodies are shown in online supplemental table S2. Two independent pathologists evaluated TMA slides under high-power magnification field (×200 magnification) to record the mean number of Siglec-10+ cells and Siglec-10+ TAMs in three representative fields. The evaluations were done blindly to the clinical data to ensure unbiased results.

    Flow cytometry analysis

    Our previous study provided a description of the flow cytometry (FCM) process that was followed.21 Comprehensive protocol and antibody information were listed (online supplemental methods S2 and table S3).

    Fluorescence activated cell sorting and sample preparation for RNA sequencing

    Freshly resected GC tumor tissues were used to prepare single-cell suspensions by treating them with Percoll (GE Healthcare) to separate mononuclear cells. Subsequently, Siglec-10– TAMs (CD45+CD14+Siglec-10–) and Siglec-10+ TAMs (CD45+CD14+Siglec-10+) were isolated using MoFlo XDP (Beckman Coulter) and lysed in Cell Lysis Buffer (provided by 10kgenomics) at a concentration of 500 cells per sample. Complementary DNA libraries were then prepared from these samples using standard Smart-Seq2 protocols,22 and messenger RNA (mRNA) profiles were generated using NovaSeq 6000 Sequencing System (Illumina).

    Development of an ex vivo gastric cancer tumor fragment platform

    Single-cell suspensions derived from patient with fresh GC tumor tissues were randomly divided into four intervention groups: IgG1+IgG4, blocking-Siglec-10+IgG4, anti-PD-1+IgG1 and blocking-Siglec-10+anti-PD-1. The concentrations of IgG1 (InvivoGen), IgG4 (BioLegend) and anti-PD-1 (Camrelizumab) were 10 µg/mL, while the concentration of blocking-Siglec-10 (Human Siglec-10 Fc Chimera, R&D systems) was 5 µg/mL. Each treatment group was cultured in Roswell Park Memorial Institute (RPMI) 1640 medium (Gibco) containing 10% fetal bovine serum (Gibco) for 12–14 hours. After intervention culture, FCM was conducted to detect the phenotype of immune cells and apoptosis of tumor cells.

    Statistical analysis

    Statistical analyses were carried out using IBM SPSS (V.26.0), GraphPad Prism (V.9.0) and R (V.4.2.1). FlowJo (V.10.8.1) software was used to analyze FCM data. The median value of the Siglec-10+ cells infiltration, SIGLEC10 mRNA expression, TAMs infiltration and Siglec-10+ TAMs infiltration was adopted as the cut-off point. Survival outcomes were analyzed through Kaplan-Meier curves, log-rank test and Cox proportional hazards regression model. For categorical variables, Pearson’s χ2 test or Fisher’s exact test was performed. While Student’s t-test, Mann-Whitney U test, One-way analysis of variance or Kruskal-Wallis test was used to assess the continuous variables. Data were shown as mean±SD. Two-tailed p<0.05 was considered as statistically significant.

    Results

    Siglec-10 was predominantly expressed in TAMs in GC

    To examine the expression patterns of Siglec-10 in GC, we first performed IHC using formalin-fixed paraffin-embedded (FFPE) samples. Siglec-10 was mainly located on stromal cells, both in tumor and peritumor tissues (figure 1A). To determine the composition of Siglec-10+ cells, we evaluated fresh tissues from tumor and peritumor tissues by multicolor FCM. It was shown that Siglec-10 was mainly expressed on CD45+ immune cells and CD45+Siglec-10+ cells were more enriched in tumor tissues compared with peritumor tissues (figure 1B). Although Siglec-10 expression could be detected on multiple immunocytes (figure 1C), quantitative analysis demonstrated that macrophages contributed to the most dominant component of Siglec-10+ cells (figure 1D,E). Furthermore, we analyzed the scRNA-seq data from two GC cohorts and found Siglec-10 was essentially expressed on macrophages across all cell types (figure 1F), which validated the findings by FCM. In addition, we confirmed the co-expression of Siglec-10 with the TAMs marker CD68 was further confirmed in FFPE GC tissues by IFC staining (figure 1G). Therefore, these data demonstrated that Siglec-10 was mainly expressed on TAMs in GC.

    Figure 1
    Figure 1

    Siglec-10 was predominantly expressed in TAMs in GC. (A) Immunohistochemistry staining of Siglec-10 in patients with GC peritumor and tumor tissues. Scale bars, 20 µm and 50 µm. (B) The expression levels of Siglec-10 on CD45– and CD45+ cells from GC peritumor (n=22) and tumor (n=30) tissues. Representative flow cytometry analysis (left) and quantitative data (right) were shown. Two-tailed unpaired t-test. (C) Representative flow cytometric histograms for the expression of Siglec-10 on Macrophages, NK cells, DCs, T cells, B cells, and neutrophils. (D) The composition of immune cells subsets in Siglec-10+CD45+ cells in each patient GC TME. (E) The percentage of immune cells subsets among Siglec-10+CD45+ cells in GC TME. Two-tailed unpaired t-test. (F) Two UMAP plots are presented, displaying clusters of cell types and the messenger RNA expression of SIGLEC10 across all cell types. The top plot shows 11 clusters from 29 GC tumor tissues (top), while the bottom plot shows 8 clusters from 10 GC tumor tissues (bottom). (G) Immunofluorescence staining of Siglec-10 (red), macrophage marker CD68 (green) and DAPI (blue) in patient with GC tumor tissue. White arrowheads, Siglec-10+ TAMs. Scale bars, 100 µm. ***p<0.001. ns, not significant. APC, allophycocyanin; DAPI, 4′,6-diamidino-2-phenylindole; GC, gastric cancer; NK, natural killer; PE, phycoerythrin; RNA-seq, RNA sequencing; TAM, tumor-associated macrophage; TME, tumor microenvironment; UMAP, uniform manifold approximation and projection.

    Siglec-10+ TAMs associated with tumor progression, unfavorable prognosis and adjuvant chemotherapy resistance in GC

    To explore the clinical relevance of Siglec-10+ TAMs in GC, we incorporate four GC cohorts for analysis (ZSHS cohort, TCGA cohort, ACRG cohort and KYK cohort). Meanwhile, fluorescence activated cell sorting and Smart-Seq2 were also combined to define Siglec-10+ TAMs at the transcriptome level (online supplemental figure S1). In the ZSHS cohort, we found that Siglec-10+ TAM infiltration was correlated with more advanced stage and higher tumor grade (figure 2A). While in the TCGA and ACRG cohorts, the infiltration level of Siglec-10+ TAMs significantly increased in the genomically stable (GS) and epithelial-to-mesenchymal transition (EMT) subtypes, which are recognized as the most aggressive subtypes of TCGA23 and ACRG12 molecular classification systems (p<0.001 and p<0.001; figure 2B), respectively.12 14 These results demonstrated that Siglec-10+ TAMs were closely correlated with tumor progression and aggressiveness.

    Figure 2
    Figure 2

    Siglec-10+ TAMs correlated with tumor progression, unfavorable prognosis and adjuvant chemotherapy resistance in GC. (A) Association of Siglec-10+ TAMs abundance with TNM stage (left) and tumor grade (right). One way analysis of variance test and two-tailed unpaired t-test, respectively. (B) Distribution of Siglec-10+ TAMs across TCGA and ACRG molecular subtypes of patients with GC. Kruskal-Wallis test. (C–F) Kaplan-Meier curves for overall survival of patients in ZSHS cohort (n=446), TCGA-STAD (n=354), ACRG cohort (n=300) and KYK cohort (n=267) according to Siglec-10+ TAMs abundance. Log-rank test. (G) A test for an interaction between Siglec-10+ TAMs infiltration level and responsiveness to adjuvant chemotherapy in stage II/III patients from ZSHS cohort (n=331), ACRG cohort (n=190) and KYK cohort (n=136). Cox regression analysis. *p<0.05, **p<0.01 and ***p<0.001. ns, not significant. ACRG, Asian Cancer Research Group; ACT, adjuvant chemotherapy; CIN, chromosomal instability; EBV, Epstein-Barr virus; EMT, epithelial-to-mesenchymal transition; GC, gastric cancer; GS, genomically stable; HPF, high-power magnification field; KYK, Korea University-Yonsei University-Kosin University; MSI, microsatellite instability; STAD, stomach adenocarcinoma; TAM, tumor-associated macrophage; TCGA, The Cancer Genome Atlas; TNM, tumor, node, metastases; ZSHS, Zhongshan Hospital.

    To further investigate the prognostic value of Siglec-10+ TAM infiltration in GC, we conducted Kaplan-Meier curves and log-rank test in the four independent cohorts. A higher density of Siglec-10+ TAMs was associated with attenuated overall survival (OS) in all four cohorts (p<0.001, p=0.024, p=0.047 and p=0.002, respectively; figure 2C–F). Considering the wide application and limited therapeutic response of patients with stage II/III GC to adjuvant chemotherapy, we subsequently evaluated the potential of Siglec-10+ TAMs to be used as a stratification biomarker for clinical practice. In the ZSHS cohort, ACRG cohort and KYK cohort, the application of adjuvant chemotherapy significantly prolonged OS in stage II/III patients (p<0.001, p=0.024 and p=0.011, respectively; figure 2G). Nevertheless, after dividing patients based on Siglec-10+ TAMs infiltration level, such beneficial effect was only observed in Siglec-10+ TAMs low subgroup (p<0.001, p=0.031 and p=0.008, respectively; figure 2H), but not in Siglec-10+ TAMs low subgroup (p=0.105, p=0.279 and p=0.354, respectively; figure 2G). Consequently, the above results suggest that Siglec-10+ TAMs indicated tumor progression, poor prognosis and chemotherapeutic resistance in GC.

    Siglec-10+ TAMs predicted outcomes to anti-PD-1 immunotherapy in GC

    Although the application of immunotherapy is playing an increasingly important role in advanced cancer, only a small number of patients can benefit from it, and studies have confirmed that specific subgroups of TAMs are involved in the process of tumor resistance to immunotherapy. To evaluate the predictive value of Siglec-10+ TAMs for immunotherapy, we further enrolled another cohort from the SMC (the SMC cohort) consisting of patients treated with anti-PD-1 immunotherapy. Kaplan-Meier survival analysis indicated that patients with GC with lower Siglec-10+ TAMs signature had longer OS after receiving anti PD-1 immunotherapy (p=0.008; figure 3A). Notably, SIGLEC10 mRNA expression and TAM infiltration could not stratify patients’ survival in the SMC cohort (p=0.317 and p=0.774, respectively; figure 3A). While in terms of changes for target lesions, there is a trend that patients with low Siglec-10+ TAMs signature were more likely to obtain complete response (CR) or partial response (PR) (p=0.091; figure 3B,C), despite no statistical significance. Next, we retrospectively collected two GC cases with liver metastasis who received anti-PD-1 immunotherapy plus chemotherapy and obtained their FFPE for Siglec-10+ TAMs assessment. Interestingly, both the number and size of the liver metastasis lesions showed reduction after receiving anti-PD-1 immunotherapy plus chemotherapy. However, the liver metastasis was obviously enlarged after receiving anti-PD-1 immunotherapy plus chemotherapy (figure 3D).

    Figure 3
    Figure 3

    Siglec-10+ TAMs predicted outcomes to anti-PD-1 immunotherapy in GC. (A) Kaplan-Meier curves for overall survival of patients with GC received anti-PD-1 immunotherapy (pembrolizumab, n=43) based on SIGLEC10 mRNA expression, TAMs abundance and Siglec-10+ TAMs abundance. Log-rank test. (B and C) The waterfall plot and stacked bar plot demonstrated responsiveness to anti-PD-1 immunotherapy based on Siglec-10+ TAMs abundance in the SMC cohort (n=45). Pearson’s χ2 test. (D) CT images of patients with GC with low/high infiltration of Siglec-10+ TAMs treated with anti-PD-1 immunotherapy combined with SOX chemotherapy. (E) Forest plot for overall survival HRs and CIs for recognized predictors within subgroups according to Siglec-10+ TAMs in the SMC cohort (n=45). Pearson’s χ2 test. *p<0.05, **p<0.01 and ***p<0.001. GC, gastric cancer; GEP, gene expression profile; IFN, interferon; mRNA, messenger RNA; PD-L1, programmed death ligand 1; PD-1, programmed cell death 1; SMC, Samsung Medical Center; SOX, S-1 and oxaliplatin; TAM, tumor-associated macrophage; TGF, transforming growth factor; TLS, TIDE, tumor immune dysfunction and exclusion; TLS, tertiary lymphoid structures.

    In recent years, the search for indicators to predict immunotherapy sensitivity has received increasing attention. Thus, we analyzed the interlink between siglec-10+ TAMs signature and several recognized immunotherapy response predictors24–35 (online supplemental table S4). Interestingly, we found that the Siglec-10+ TAMs signature is positively correlated with some immunotherapy sensitivity biomarkers including inflamed signature, T cell-inflamed gene expression profile (T cell-inflamed GEP), CD8+ T effector signature, B-cell signature, and tertiary lymphatic structure signature. But at the same time, we also found a positive correlation between Siglec-10+ TAMs signature score and transforming growth factor beta (TGF-β) signature, which has been proven to indicate cancer immunotherapy resistance (online supplemental figure S2). In order to evaluate the impact of Siglec-10+ TAMs signature on the predictive efficacy of these predictors, we investigated the association between these predictors and OS among patients belonging to different Siglec-10+ TAMs subgroups. Notably, we found that only in patients with low expression of Siglec-10+ TAMs signature, programmed death ligand 1 (PD-L1) expression level, inflammatory signature, T-cell inflammatory signature, and PredictIO score can suggest better immunotherapy response (figure 3E). In summary, we concluded that Siglec-10+ TAMs were associated with worse response to anti-PD-1 immunotherapy and attenuated the predictive efficacy of recognized predictors including PD-L1 expression level, inflammatory signature, T-cell inflammatory signature, and PredictIO score.

    Siglec-10+ TAMs exhibited immunosuppressive phenotype

    First, we intended to characterize Siglec-10– TAMs and Siglec-10+ TAMs at the transcriptomic level. Hierarchical clustering indicated significant differences in gene expression between Siglec-10– TAMs and Siglec-10+ TAMs (figure 4A). Specifically, genes associated M2-polariztion, such as CSF1R, VSIR, TGFBI, CD163, TREM2, MMP12, MARCO and so on, were significantly upregulated in Siglec-10+ TAMs (figure 4B). We further extracted a single macrophage subpopulation from scRNA-seq data and found that the expression of SIGLEC10 in macrophages was coexisted with the expression of M2 polarization-related genes like CD163, CSF1R and INHBA (figure 4C). Next, we attempted to explore the differences in signaling pathway activation between two subsets. According to the gene ontology (GO) analysis for differential genes, we found that compared with Siglec-10– TAMs, immune-related signaling pathways, including immune response, inflammatory response, positive regulation of extracellular signal-regulated kinase 1/2 (ERK1/2) cascade, positive regulation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), negative regulation of interferon (IFN)-γ production, and negative regulation of tumor necrosis factor production were significantly enriched in Siglec-10+ TAMs (figure 4D).

    Figure 4
    Figure 4

    Siglec-10+ TAMs exhibited immunosuppressive phenotype. (A) Heatmap analysis showing the top 50 differentially expressed genes between Siglec-10− TAMs and Siglec-10+ TAMs using bulk RNA sequencing (n=5). (B) Volcano plot revealed the differentially expressed genes in Siglec-10+ TAMs compared with Siglec-10– TAMs using scRNA-seq (n=29). (C) UMAP plot showing the selected genes expressed in the cluster of macrophages from 29 patients with gastric cancer. (D) KEGG and GO analysis of upregulated differentially expressed genes in Siglec-10+ TAMs, compared with Siglec-10– TAMs. (E–G) Flow cytometry analysis for the expression of TAMs polarization makers (E) inflammatory cytokines (F) and anti-inflammatory cytokines (G) in the Siglec-10– TAMs and Siglec-10+ TAMs (n=24). Two-tailed unpaired t-test. *p<0.05, **p<0.01 and ***p<0.001. ns, not significant. ERK, extracellular signal-regulated kinase; GO, gene ontology; KEGG, kyoto encyclopedia of genes and genomes; IL, interleukin; MHC, major histocompatibility complex; NADP, nicotinamide adenine dinucleotide phosphate; NF, nuclear factor; NIK, nuclear factor kappa-light-chain-enhancer of activated B cells inducing kinase; PD-L1, programmed death ligand 1; scRNA-seq, single-cell RNA-sequencing; TAM, tumor-associated macrophage; TGF, transforming growth factor; TNF, tumor necrosis factor; UMAP, uniform manifold approximation and projection.

    Through multicolor FCM, we further validated significant differences between the two TAM subsets in terms of phenotypic markers and cytokine secretions. Siglec-10+ TAMs expressed significant higher levels of the classical M2 macrophage surface marker CD206 but lower levels of classical M1 macrophage surface markers CD80 compared with Siglec-10– counterparts (figure 4E). Furthermore, significantly lower level of tumor necrosis factor-alpha (TNF-α) and interleukin (IL)-12 while higher level of TGF-β, IL-10 and PD-L1 were found in Siglec-10+ TAMs, suggesting an anti-inflammatory phenotype of Siglec-10+ TAMs (figure 4F,G). Taken together, the above results suggested that Siglec-10+ TAMs exhibited more similar to M2-polarized phenotypes with immunosuppressive properties.

    Siglec-10+ TAMs jeopardized T cell-mediated antitumor immune response

    Since Siglec-10+ TAMs expressed high level of immunosuppressive factors, we investigated whether they had an impact on the TME in GC. In the TCGA cohort, Siglec-10+ TAMs signature high subgroup correlated with increased infiltration of naive B cells, M2 macrophages and resting mast cell infiltration but decreased infiltration of resting natural killer cells and M0 TAMs. Moreover, we discovered significantly higher expression of immune checkpoint molecules such as PDCD1, CD274, LAG3, HAVCR2, CTLA-4 and TIGIT as well as immunosuppressive molecules such as TGFB1, IL-10, IDO1 and CCL22 in Siglec-10+ TAMs high subgroup (figure 5A). We further performed IHC on the tissue microarray of ZSHS cohort patients. As shown in figure 5B and online supplemental figure S3, the infiltration of TAMs, M2-TAMs, Treg cells, and CD8+ T cells significantly increased in GC with high infiltration of Siglec-10+ TAMs. At the same time, PD-1+CD8+ T cells and CXCL13+CD8+ T cells, which have been identified as functionally exhausted T-cell subpopulations in our previous studies,36 37 significantly increased in Siglec-10+ TAMs high infiltration subgroup. By multicolor FCM, we observed that the accumulation of Siglec-10+ TAMs in GC would lead to an increase in the proportion of CD8+ T cells in general CD3+ T cells and Tregs in CD4+T cells (figure 5C). However, there was a reduction for the proportion of Th1 cells in CD4+ T cells as well as the Th1-to-Th2 ration in GC with Siglec-10+ TAMs low infiltration (figure 5C,D). The above findings indicated that Siglec-10+ TAMs were associated with the differentiation process towards protumor subsets of T lymphocytes.

    Figure 5
    Figure 5

    Siglec-10+ TAMs jeopardized T cell-mediated antitumor immune response. (A and B) Heatmap demonstrated the comprehensive immune landscape according to Siglec-10+ TAMs abundance in the TCGA (A) and ZSHS (B) cohort. (C and D) Flow cytometry analysis for T-cell subset distribution in GC tissues from the Siglec-10+ TAMs low (n=15) and high (n=15) subgroups. (E) Flow cytometry analysis for Ki-67, CD44 and ICOS expression in CD4+ and CD8+ T cells from the Siglec-10+ TAMs low (n=15) and high (n=15) subgroups. (F) Flow cytometry analysis for expression of cytotoxic factors including IFN-γ, TNF-α, granzyme B and perforin in CD8+ T cells from the Siglec-10+ TAMs low (n=15) and high (n=15) subgroups. (G) GSEA revealed an enrichment of negative regulation of T cell-mediated immunity and exhausted CD8+ T cells signature by Siglec-10+ TAMs in patients with high GC in the TCGA cohort. (H) Flow cytometry analysis for the frequency of PD-1+, LAG-3+, and PD-1+ LAG-3+ cells in CD4+ and CD8+ T cells from Siglec-10+ TAMs low (n=15) and high (n=15) subgroups. Statistical analysis by two-tailed unpaired t-test. *p<0.05, **p<0.01 and ***p<0.001. ns, not significant. GC, gastric cancer; IFN, interferon; IL, interleukin; NK, natural killer; TAM, tumor-associated macrophage; TCGA, The Cancer Genome Atlas; TNM, tumor, node, metastases; ZSHS, Zhongshan Hospital.

    Next, we planned to explore the impact of Siglec-10+ TAMs on T-cell activation and functional status. It was shown that the expression of activation markers CD44 and inducible T cell co-stimulator (ICOS) in CD4+ T cells and CD8+ T cells was significantly downregulated in the Siglec-10+ TAMs high infiltration subgroup (figure 5E). Interestingly, no difference of Ki-67 expression level was found between the T cells from the two subgroups of GC (figure 5E). Subsequently, we analyzed the effector factors and killer-related molecules expressed by CD8+ T cells and found that CD8+ T cells from Siglec-10+ TAMs high tumors exhibit significantly decreased expression of IFN-γ, TNF-α, granzyme B and perforin compared with those from Siglec-10+ TAMs low tumors (figure 5F). Besides, gene analysis enrichment analysis demonstrated that T-cell immune negative regulation (GO: 0061517) and exhausted CD8+ T signature38–40 were significantly enriched in Siglec-10+ TAMs signature high subgroup (figure 5G). Considering the important role of co-inhibitory receptors in T-cell dysfunction, we also examined the expression of co-inhibitory receptors in CD4+ T cells and CD8+ T cells. The results showed that the proportion of PD-1+, LAG-3+ as well as PD-1+LAG-3+ cells in both CD4+ and CD8+ T cells was significantly increased in Siglec-10+ TAMs high subgroup, while the expression levels of T cell immunoglobulin and mucin-domain containing-3 (Tim-3), CTLA-4 and TIGIT did not change (figure 5G and online supplemental figure S4). Moreover, the spatial proximity between Siglec-10-expressing TAMs and CD8+ T cells were further substantiated by multiplex IFC staining (online supplemental figure S5). Taken together, we concluded that Siglec-10+ TAMs jeopardized T cell-mediated antitumor immune response in GC.

    Blocking Siglec-10 reinvigorated antitumor immune response and synergistically enhanced anti-PD-1 immunotherapy

    In order to clarify the potential targeting value of Siglec-10 in GC, we constructed an in vitro tumor fragment platform based on freshly excised tumor tissue from patients with GC for intervention experiments (figure 6A). After Siglec-10 blocking, we found that two pro-inflammatory factors TNF-α and IL-12 were upregulated in TAMs. Meanwhile, Siglec-10 blockade significantly reduced the expression of anti-inflammatory factors including TGF-β, IL-10 and PD-L1 by TAMs (figure 6B). To assess the functional significance of these changes in regulating T cell-mediated antitumor response, we further assessed the subset distribution and functional status of intratumoral T cells after Siglec-10 blockade. As shown in figure 6C, in spite of no difference in Th1 and Th2 balance, there was a significant increase of CD8+ T cells but a decrease of Tregs. In addition, CD8+T cells were functionally restored after Siglec-10 blockade, as evidenced by elevated expression of effector molecule TNF-α, IFN-γ and granzyme B (figure 6D). Thus, these results indicate that targeting Siglec-10 could reprogram TAMs and therefore promote both cytotoxic adaptive immune responses.

    Figure 6
    Figure 6

    Blocking Siglec-10 reinvigorated antitumor immune response and synergistically enhanced anti-PD-1 immunotherapy. (A) Schematic diagram of the construction of an ex vivo GC tumor fragment platform. (B) Flow cytometry analysis for the expression of TNF-α, IL-12, TGF-β, IL-10 and PD-L1 on Siglec-10+ tumor-associated macrophages after Isotype or Siglec-10 blockade treatment (n=12). Two-tailed paired t-test. (C) Flow cytometry analysis for T-cell subset distribution after Isotype or Siglec-10 blockade treatment (n=12). Two-tailed unpaired t-test. (D) Flow cytometry analysis for the expression of cytotoxic factors on CD8+ T cells after Isotype or Siglec-10 blockade treatment (n=12). Two-tailed unpaired t-test. (E) Correlation between Siglec-10 and PD-L1 mRNA expression level in the TCGA (left) and GSE84437 (right) queue. Pearson’s correlation test. (F) Flow cytometry analysis for the frequency of apoptotic tumor cells after treatment with IgG1+IgG4, blocking-Siglec-10+IgG4, anti-PD-1+IgG1 and blocking-Siglec-10+anti-PD-1 in tumor tissues of GC (n=12). (G) Flow cytometry analysis for the frequency of Ki-67+CD8+ T cells after treatment with IgG1+IgG4, blocking-Siglec-10+IgG4, anti-PD-1+IgG1 and blocking-Siglec-10+anti-PD-1 in tumor tissues of GC (n=12). RM one-way analysis of variance followed by Tukey’s multiple comparisons test (H–I). *p<0.05, **p<0.01 and ***p<0.001. ns, not significant. CAFs, cancer associated fibroblasts; DCs, dendritic cells; GC, gastric cancer; IFN, interferon; IL, interleukin; mRNA, messenger RNA; NK, natural killer; PD-L1, programmed death ligand 1; PD-1, programmed cell death 1; STAD, stomach adenocarcinoma; TGF, transforming growth factor; TCGA, The Cancer Genome Atlas; TNF, tumor necrosis factor.

    In the past few years, PD-1 monoclonal antibodies have been the most widely used immunotherapy drugs in clinical practice. Interestingly, we found a significantly positive correlation between Siglec-10 expression and PD-L1 mRNA expression (figure 6E), which is recognized as a biomarker for PD-1 monoclonal antibody application. Thus, we wondered whether Siglec-10 blockade would synergize with PD-1 monoclonal antibody and achieve better therapeutic effects. In our tumor fragment platform model, the combination of Siglec-10 blockade with the anti-PD-1 monoclonal antibody resulted in a significant increase of apoptotic tumor cells, as compared with the effect of each treatment alone and the control group (figure 6F). In addition, the combination treatment led to a higher frequency of Ki-67+CD8+ T cells in tumors compared with other three groups (figure 6G). From all of these experiments, we concluded that the Siglec-10 blockade/anti-PD-1 combination had synergistic antitumor effect in GC.

    Discussion

    Recently, the emergence of immunotherapy has revolutionized cancer treatment, particularly in GC. However, despite the approval of ICIs as the first-line treatment for patients with advanced GC in current medical guidelines, there are still challenges in clinical practice, including low objective response rates and frequent treatment resistance.4 While T-cell checkpoint immunotherapy can partially reactivate the body’s antitumor immune response, its effectiveness is influenced by various components of TME, including myeloid cells.41 Among these, TAMs are the most abundant immune cells in the TME and exhibit remarkable phenotypic plasticity, playing a crucial role in immune regulation within the context of GC.42 Therefore, accurately defining the functional subgroups of TAMs and comprehending their functional mechanisms and clinical significance in immune regulation can significantly enhance the efficacy of immunotherapy in the treatment of GC.

    Researches have demonstrated that myeloid checkpoints, receptors located on the surface of myeloid cells, play a role in regulating cell function and activity, enabling tumor immune evasion.9 Specifically, studies have identified the Siglec family as inhibitory receptors found on innate immune cells.43 These receptors, on binding to cancer-associated abnormal sialylated glycans, transmit inhibitory signals that facilitate tumor progression and metastasis.44 In our investigation, we discovered that the myeloid checkpoint Siglec-10 could identify a distinct functional subset of TAMs in GC. Transcriptomic analysis revealed that Siglec-10+ TAMs displayed a greater polarization toward M2 macrophages, but also exhibited antigen processing, presentation, and activation of pro-inflammatory signaling pathways. Thus, we hypothesize that Siglec-10+ TAMs are likely in an intermediate state of M1–M2 macrophage polarization, which confirms that the phenotypic differentiation of TAMs exists within a continuous spectrum, consistent with previous findings.45 Interestingly, we also found that Siglec-10+ TAMs have an immunosuppressive functional phenotype, as evidenced by reduced expression of the pro-inflammatory factors TNF-α and IL-12 and increased expression of the anti-inflammatory factors TGF-β and IL-10, suggesting that myeloid checkpoints, rather than the M1/M2 binary classification model, can precisely define a subpopulation of TAMs that exert tumor-promoting immunosuppressive effects.

    The concept of personalized tumor treatment aims to accurately classify patients by using available diagnostic markers and subsequently recommend tailored treatment strategies for each specific subtype of patients with tumor.46 By characterizing Siglec-10+ TAMs at both the transcriptional and protein levels, we found that patients with high levels of Siglec-10+ TAMs tended to have poorer survival outcomes, while patients with low levels of Siglec-10+ TAMs were more likely to benefit from anti-PD-1 immunotherapy and adjuvant chemotherapy (ACT). These findings suggest that Siglec-10+ TAMs have the potential to serve as a biomarker for predicting prognosis and therapeutic responsiveness in patients with GC, that patients exhibiting limited Siglec-10+ TAM infiltration may respond better to standard ACT and ICI strategies, whereas patients with high infiltration of Siglec-10+ TAMs may have difficulty benefiting from available therapies.

    On this basis, our endeavor centered on exploring potential therapeutic interventions for patients characterized by elevated Siglec-10+ TAM infiltration. Through intervention in the aforementioned model, we discovered that blocking Siglec-10 reversed the immunosuppressive functional phenotype exhibited by Siglec-10+ TAMs. Moreover, Siglec-10 blockade stimulated the antitumor immune response in CD8+ T cells, leading to increased secretion of cytotoxic factors. Thus, we speculate that Siglec-10 blockade may be able to promote reprogramming of TAMs and participate in remodeling the TME, thus having the potential to improve survival outcomes of patients with GC.

    Furthermore, we aimed to investigate whether Siglec-10 blockade in combination with existing immunotherapy strategies would yield improved efficacy in GC. Our previous findings have demonstrated a significant correlation between Siglec-10 and PD-1 expression levels and the results of combined intervention experiments have shown that Siglec-10 blockade and administering anti-PD-1 both significantly stimulate the proliferation of CD8+ T cells and promote cancer cell apoptosis, with the best outcomes observed in the combined treatment. These findings suggest that Siglec-10 blockade may enhance the effectiveness of anti-PD-1 therapy, offering hope for the treatment of patients who are not sensitive to existing therapies. Moreover, a recent study reported the antitumor effect of a novel dual-targeting D-peptide to block Siglec-10 and PD-1 signaling in mice models, which further confirms the clinical application prospect of dual blockade of Siglec-10 and PD-1.47

    Although the ligand of Siglec-10 in GC was not mentioned in our study, it remains an important issue to be explored. As a member of sialic acid-binding immunoglobulin type lectins, Siglec-10 displays affinity for sialic acid on the termini of several glycoconjugates. The two known ligands for Siglec-10 were sialylated CD24 and CD52.11 48 However, only CD24 but not CD52 has been reported to be overexpressed in GC so far.49 50 Thus, we supposed that CD24 might serve as a ligand involved in Siglec-10 signaling in GC, while the role of CD52 needs to be explored in the following studies.

    A major limitation of this study is the absence of validation from in vivo mouse models due to the unavailability of a suitable mouse-derived cell line for GC that would allow the creation of an immune competent mouse model. We strongly encourage future research to validate the potential of targeting Siglec-10 in combination with PD-1 inhibitors to enhance antitumor efficacy through more extensive, multicentered clinical trials.

    Supplemental material

    Data availability statement

    Data are available upon reasonable request.

    Ethics statements

    Patient consent for publication

    Ethics approval

    The study was approved by the Clinical Research Ethics Committee of Zhongshan Hospital, Fudan University, with the approval number Y2015-054. Written informed consent was obtained from each patient included and this study was performed in accordance with the Declaration of Helsinki.

    Acknowledgments

    We would like to thank Dr Lingli Chen (Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China) and Dr Yunyi Kong (Department of Pathology, Shanghai Cancer Center, Fudan University, Shanghai, China) for their excellent pathological technology help. We thank Dr Jeeyun Lee (Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine) for providing support with the usage of Samsung Medical Center cohort data (PRJEB25780).

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    Footnotes

    • Contributors KL for the acquisition of data, interpretation of data, statistical analysis, and drafting of the manuscript. MS, HF, JW, CL, HLiu, HZ, HLi and HH for technical and material support. YG, RL, FS and JX for study concept and design, analysis and interpretation of data, drafting of the manuscript, obtaining funding, and study supervision. All authors read and approved the final manuscript. JX was responsible for the overall content as the guarantor.

    • Funding This study was funded by grants from National Natural Science Foundation of China (81972219, 82003019, 82103313, 82203201, 82272786, 82273192, 82303966, 82373417), Shanghai Municipal Natural Science Foundation (23ZR1409900), Shanghai Rising-Star Program (22QA1401700) and Shanghai Sailing Program (21YF1407600). All these study sponsors have no roles in the study design, in the collection, analysis and interpretation of data.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.