Article Text

Original research
Loss of Annexin A1 in macrophages restrains efferocytosis and remodels immune microenvironment in pancreatic cancer by activating the cGAS/STING pathway
  1. Zelin Hou1,2,
  2. Fengchun Lu1,
  3. Jiajing Lin1,
  4. Yuwei Wu1,2,
  5. Linjin Chen1,
  6. Haizong Fang1,
  7. Linlin Chen3,
  8. Shihan Zhang3,
  9. Heguang Huang1 and
  10. Yu Pan1,2,4
  1. 1 Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
  2. 2 Central Laboratory, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
  3. 3 Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, China
  4. 4 The Cancer Center, Fujian Medical University Union Hospital, Fuzhou, China
  1. Correspondence to Professor Yu Pan; yupan199002{at}163.com; Dr Heguang Huang; heguanghuang22{at}163.com

Abstract

Objective Pancreatic cancer is an incurable malignant disease with extremely poor prognosis and a complex tumor microenvironment. We sought to characterize the role of Annexin A1 (ANXA1) in pancreatic cancer, including its ability to promote efferocytosis and antitumor immune responses.

Methods The tumor expression of ANXA1 and cleaved Caspase-3 (c-Casp3) and numbers of tumor-infiltrating CD68+ macrophages in 151 cases of pancreatic cancer were examined by immunohistochemistry and immunofluorescence. The role of ANXA1 in pancreatic cancer was investigated using myeloid-specific ANXA1-knockout mice. The changes in tumor-infiltrating immune cell populations induced by ANXA1 deficiency in macrophages were assessed by single-cell RNA sequencing and flow cytometry.

Results ANXA1 expression in pancreatic cancer patient samples correlated with the number of CD68+ macrophages. The percentage of ANXA1+ tumor-infiltrating macrophages negatively correlated with c-Casp3 expression and was significantly associated with worse survival. In mice, myeloid-specific ANXA1 deficiency inhibited tumor growth and was accompanied by the accumulation of apoptotic cells in pancreatic tumor tissue caused by inhibition of macrophage efferocytosis, which was dependent on cGAS-STING pathway-induced type I interferon signaling. ANXA1 deficiency significantly remodeled the intratumoral lymphocyte and macrophage compartments in tumor-bearing mice by increasing the number of effector T cells and pro-inflammatory macrophages. Furthermore, combination therapy of ANXA1 knockdown with gemcitabine and anti-programmed cell death protein-1 antibody resulted in synergistic inhibition of pancreatic tumor growth.

Conclusion This research uncovers a novel role of macrophage ANXA1 in pancreatic cancer. ANXA1-mediated regulation of efferocytosis by tumor-associated macrophages promotes antitumor immune response via STING signaling, suggesting potential treatment strategies for pancreatic cancer.

  • Immunotherapy
  • Macrophage
  • Tumor microenvironment - TME

Data availability statement

Data are available upon reasonable request. All data relevant to the study are included in the article or uploaded as supplementary information.

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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/.

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WHAT IS ALREADY KNOWN ON THIS TOPIC

  • In pancreatic cancer, uncontrolled tumor cell proliferation can lead to elevated cell pressure and promote increased apoptosis of cancer cells. Then, infiltration of a large number of macrophages in the tumor microenvironment became the “scavenger”, they removed apoptosis of tumor cells, inhibiting the inflammatory and immune responses. This process is called efferocytosis. Annexin A1 (ANXA1), a member of the Annexin protein superfamily, participates in efferocytosis by recognition, binding and internalization of apoptotic cells. However, the role and mechanism of ANXA1 in tumor-associated macrophages (TAMs) of pancreatic cancer remain unclear.

WHAT THIS STUDY ADDS

  • Our results showed that blocking ANXA1 inhibited macrophage efferocytosis, leading to the accumulation of apoptotic cells in the tumor microenvironment. The apoptosis cells further necrosis and release large amounts of DNA and cyclic GMP-AMP (cGAMP) in the tumor microenvironment, these DNA and cGAMP further into the macrophages intracellular, activating the STING protein, triggering the TBK1-IRF3-dependent signaling pathway, leading to the release of type I interferon, ultimately promote antitumor immune responses. Targeting ANXA1 in macrophages in combination with chemotherapy and anti-programmed cell death protein-1 antibodies can significantly improve the antitumor effect.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

  • In this study, we investigated the regulatory role and mechanism of ANXA1 in the immune microenvironment of pancreatic cancer from the perspective of macrophages, which not only contributes to a deeper understanding of the role of TAMs and their efferocytosis in the immune-regulation of pancreatic cancer, but also provides a new strategy for antitumor immunotherapy. Targeting ANXA1 in macrophages could be a promising avenue for the treatment of pancreatic cancer.

Introduction

Pancreatic cancer is a highly malignant disease that is primarily accounted for by pancreatic ductal adenocarcinoma (PDAC).1 2 Despite considerable advances in surgery, radiotherapy, and targeted therapies, the 5-year survival rate of PDAC is still only 9%.3 4 While immune checkpoint blockers (ICBs) have revolutionized cancer treatment, sustained clinical benefits for specific cancers are observed in only a minority of patients. Intrinsic resistance to ICBs is observed in immune-desert (‘‘cold’’) tumors, characterized by low mutation load and rare infiltrating immune effector cells, as opposed to immune-inflamed (‘‘hot’’) tumors.5 Our previous study revealed that pancreatic cancer tissue is richly infiltrated with macrophages, which significantly impacts tumor growth and metastasis.6 Therefore, strategies aimed at transforming the pancreatic cancer immune microenvironment from “cold” to “hot” through macrophage targeting hold promise for enhancing the antitumor effects of immunotherapy.

Within solid tumors, uncontrolled cell proliferation induces stress and heightened apoptosis.7 8 However, abundant macrophage infiltration in pancreatic cancer tissues efficiently clears apoptotic cells before membrane damage occurs, thus suppressing intratumoral inflammation and immune responses.9 10 The clearance of apoptotic cells by macrophages involves multiple cell receptors, including Annexin A1 (ANXA1), a member of the Annexin protein superfamily that enhances the recognition, binding, and internalization of apoptotic cells via a phagocytic process known as efferocytosis.11–13 Prior research has demonstrated impaired phagocytic function and reduced apoptotic cell clearance in ANXA1-deficient macrophages, which may be related to its anti-inflammatory effects.13–15 Elevated ANXA1 expression correlates with poor overall survival (OS) in pancreatic cancer.16 Notably, ANXA1 expression in tumor cells and extracellular vesicles has been implicated in promoting macrophage M2 polarization and tumor progression.17 Nevertheless, the precise mechanism of ANXA1 in tumor-associated macrophages (TAMs) within pancreatic cancer remains unresolved.

The cGAS-STING signaling pathway is a key factor in driving the antitumor type I interferon (IFN) response.18 cGAS, a cytoplasmic DNA sensor, binds to self-DNA that invades the cytoplasm, catalyzing the synthesis of cyclic GMP-AMP, which further activates the adapter protein STING and triggers a TBK1-IRF3-dependent signaling cascade that leads to the production of pro-inflammatory cytokines and type I IFN.19 DNA released from apoptotic tumor cells can trigger cGAS-STING signaling, which is involved in the progression of pancreatic cancer.20 21 Therefore, an increased understanding of the mechanism of cGAS-STING signaling activation in pancreatic cancer may lead to the development of new therapeutic approaches.

In this study, we systematically evaluated the expression of ANXA1 using public databases and pancreatic cancer tissue staining. Our results confirm that ANXA1 expression on TAMs is associated with lower apoptosis levels and worse OS in patients with pancreatic cancer. To investigate the role of macrophage ANXA1 in pancreatic cancer, we used mouse models with myeloid-specific ANXA1-knockout, which confirmed that myeloid ANXA1 deficiency slows tumor growth. We demonstrated that ANXA1 knockout in macrophages diminishes the clearance of apoptotic cells, consequently augmenting type I IFN-dependent inflammatory responses. Furthermore, we assessed the changes in tumor-infiltrating immune cell populations in ANXA1-deficient macrophages by single-cell RNA sequencing (scRNA-seq) and flow cytometry. Finally, we determined whether ANXA1 knockdown has synergetic effects when combined with gemcitabine and anti-programmed cell death protein-1 (PD-1) antibody for slowing the progression of pancreatic cancer in mice. Our results suggest that ANXA1 may be an effective target for the treatment of pancreatic cancer.

Results

High ANXA1 expression in macrophages of patients with PDAC is associated with lower levels of cleaved Caspase-3 and poor survival

To elucidate the role of ANXA1 in TAMs in PDAC, we analyzed data from the Tumor Immune Estimation Resource (TIMER) database. The results indicate that ANXA1 was positively correlated with the infiltration of TAMs (R=0.235, p<0.001; online supplemental figure S1A). Moreover, ANXA1 expression was positively correlated with the messenger RNA (mRNA) expression of programmed death-ligand 1 (R=0.422, p<0.001; online supplemental figure S1B).

Supplemental material

To visualize ANXA1 expression in the tumor microenvironment, we retrieved tumor specimens from 151 patients with PDAC (table 1). Representative immunohistochemistry (IHC) photomicrographs of ANXA1, the macrophage marker CD68 and the apoptosis marker cleaved Caspase-3 (c-Casp3) are shown in figure 1A. The results indicate that ANXA1 was highly expressed in 57.6% of the PDAC tissues, with corresponding higher levels of CD68 (53/87, 60.9%) and lower levels of c-Casp3 (18/70, 25.7%); and that despite the higher overall CD68 levels, the ANXA1+ TAMs were low (figure 1B–D). Patients with high compared with low expression of ANXA1 had worse OS (p=0.015; figure 1E). As observed in our previous study,6 CD68 expression was also significantly associated with worse OS (p<0.001; online supplemental figure S1C). Consistently, ANXA1 expression was positively correlated with CD68 expression (R=0.34, p<0.001; figure 1C). The percentage of c-Casp3-positive cells within the tumors was not significantly associated with OS (p=0.39; online supplemental figure S1D); however, both ANXA1 and CD68 were negatively correlated with the expression of c-Casp3 (R= −0.253, p=0.002; R= −0.302, p<0.001; figure 1D).

Table 1

ANXA1, CD68 and c-Casp3 expression and clinic-pathological characteristics

Figure 1

Immunohistochemistry and immunofluorescence of ANXA1, CD68, and c-Casp3 in human PDAC. (A) Staining with an anti-ANXA1, anti-CD68, and anti-c-Casp3 antibody in the human PDAC tissue samples at×100 magnification and×400 magnification. The images do not depict simultaneous high or low expression of these three indicators within the same samples. (B) Results of IHC and double IF staining. (C) Spearman rank correlation analysis between the expression of ANXA1 and CD68 in 151 patients with PDAC. (D) Spearman rank correlation analysis between the expression of ANXA1, CD68 and c-Casp3 in 151 patients with PDAC. (E) Kaplan-Meyer plot of OS in 151 patients with PDAC with high or low tumor ANXA1 expression. (F) The immunofluorescence staining of co-expression of ANXA1 and CD68. (G) Spearman rank correlation analysis between the expression of ANXA1+ TAMs and c-Casp3 in 151 patients with PDAC. (H) Kaplan-Meyer plot of OS in 151 patients with PDAC with high or low tumor-infiltrating ANXA1+ TAMs. ANXA1, Annexin A1; c-Casp3, cleaved Caspase-3; IF, immunofluorescence; IHC, immunohistochemistry; OS, overall survival; PDAC, pancreatic ductal adenocarcinoma; TAMs, tumor-associated macrophages.

To further evaluate the relationship between ANXA1+ TAMs and apoptosis in tumor tissues of patients with PDAC, we conducted double immunofluorescence (IF) staining with CD68 and ANXA1 antibodies in tumor specimens. Representative photomicrographs are shown in figure 1F. The results indicate a significant negative correlation between the level of ANXA1+ TAMs and c-Casp3 expression (R= −0.396, p<0.001; figure 1G), suggesting that ANXA1+ TAMs may have a role in reducing the apoptosis levels in PDAC tumors. We also performed univariate and multivariate analysis, which indicated that a high frequency of ANXA1+ TAMs was an independent predictor of OS in patients with PDAC (p<0.001; figure 1H, table 2). Taken together, these results are consistent with the possibility that ANXA1 expression in TAMs may exacerbate pancreatic cancer by negatively regulating apoptosis.

Table 2

Univariate and multivariate Cox proportional analysis for overall survival (N=151)

ANXA1 deficiency in mice diminishes macrophage-mediated clearance of apoptotic cells by efferocytosis

To address the potential role of ANXA1 expression in the clearance of apoptotic cells via efferocytosis, we extracted peritoneal macrophages and thymocytes from wild-type (WT) and ANXA1 knockout (Anxa1–/–) mice. The thymocytes were treated with dexamethasone to induce apoptosis and then labeled with pHrodo, which exhibits an increase in fluorescence intensity after endocytosis. Subsequently, the apoptotic thymocytes were co-cultured with peritoneal macrophages and the phagocytosis of the thymocytes was measured according to the fluorescence level (figure 2A). After 45 min, the phagocytosis by Anxa1–/– macrophages was significantly lower than the phagocytosis by WT macrophages (figure 2B). These results indicate that ablation of ANXA1 inhibits efferocytosis.

Figure 2

Myeloid-specific Anxa1 deficiency inhibited efferocytosis of macrophages. (A) Schematic diagram of in vitro efferocytosis experiments. (B) Anxa1 deficiency inhibited the uptake of AC (red) by macrophages (green). Data are representative of three independent experiments. (C) To evaluate the clearance of thymocyte apoptosis in vivo, mice were treated with dexamethasone. Annexin-V-APC and PI were used to detect apoptotic cells and dead cells. The data presented are the means±SD. ANXA1, Annexin A1; WT, wild-type.

To further corroborate the role of ANXA1 expression in macrophages in an in vivo model system, we injected mice with dexamethasone to induce apoptosis in the thymus. A large number of apoptotic thymocytes were identified at 8 hours, most of which were eliminated by resident macrophages in WT mice at 24 hours. However, the clearance process was largely impaired in Anxa1–/– mice, for which significantly more apoptotic cells remained at 24 hours (figure 2C). These results suggest that ANXA1 regulates the efferocytosis of apoptotic cells by macrophages in vivo and in vitro.

Myeloid-specific ANXA1 deficiency inhibits tumor growth in mice

To explore the role of macrophage ANXA1 expression in PDAC progression, we generated myeloid-specific ANXA1-knockout (Anxa1ΔMɸ) and control (Anxa1WT) mice by crossing Anxa1flox/flox mice with Lyz2-Cre mice. Subsequently, we subcutaneously injected Panc02 pancreatic cancer cells into the flanks of Anxa1ΔMɸ and Anxa1WT mice. Flow cytometry results indicated that the percentage of apoptotic cells in tumor tissues of Anxa1ΔMɸ mice as compared with compared with Anxa1WT mice was significantly increased (figure 3A). Furthermore, Western blot (WB) assays verified that the level of apoptosis (c-Casp3+) in tumor tissues of Anxa1ΔMɸ mice was significantly higher than that in Anxa1WT mice (figure 3B). In addition, the cell-free DNA (cfDNA) in blood circulation, which is released by damaged or dead cells, was obviously higher in Anxa1ΔMɸ mice compared with Anxa1WT mice (figure 3C). These results indicate that macrophage ANXA1 deficiency leads to an accumulation of apoptotic cells in tumors.

Figure 3

Myeloid-specific Anxa1 deficiency causes accumulation of apoptotic cells in tumors. (A) Flow cytometry analysis of apoptotic level of tumors from Anxa1ΔMɸ and Anxa1WT mice. Annexin-V-APC and PI were used to detect apoptotic cells and dead cells. (B) Immunoblot analysis of c-Casp3 expression in tumors from Anxa1ΔMɸ and Anxa1WT mice. (C) Quantification of host-derived cfDNA in the plasma collected from Anxa1ΔMɸ and Anxa1WT Panc02-tumor-bearing mice. Each group contained four mice. (D) Representative images of tumors from Anxa1ΔMɸ and Anxa1WT mice (left). Quantification of tumor volume and weight are shown (right). Each group contained five mice. (E) Immunoblot analysis of Bcl2 and Bax expression in Panc02 tumor cells that were untreated, irradiated with 25 mJ/cm2 UV-C (UV), or treated with 100 µM BIP-V5 (Bax inhibitor) and irradiated with 25 mJ/cm2 UV-C (BIP-V5+UV). (F) Flow cytometry analysis of apoptotic level of Panc02 tumor cells that from these groups. (G) Flow cytometry analysis of apoptotic level of tumors from these groups. (H) The growth of Panc02 tumor cells that were treated with control solvent or BIP-V5 (200 ug/day) in Anxa1ΔMɸ and Anxa1WT mice. Each group contained five mice. (I) Immunoblot analysis of ANXA1 in BMDM cells that transfected with lentiviruses control (BMDM-ctrl) and carrying plasmids overexpressing ANXA1 (BMDM-ANXA1). (J) Representative images of tumors from the PDAC model that macrophages were reconstituted by BMDM-ctrl and BMDM-ANXA1 (left). Quantification of tumor volume and weight are shown (right). Each group contained five mice. (K) Immunoblot analysis of c-Casp3 expression in tumors from BMDM-ctrl and BMDM-ANXA1 mice. The data presented are the means±SD. ANXA1, Annexin A1; BIP-V5, Bax inhibitory peptide V5; BMDM, bone marrow-derived macrophage; c-Casp3, cleaved Caspase-3; cfDNA, cell-free DNA; PDAC, pancreatic ductal adenocarcinoma.

Next, we evaluated the growth of subcutaneously implanted Panc02 cells in Anxa1ΔMɸ and Anxa1WT littermates. The volume and weight were significantly lower for tumors from Anxa1ΔMɸ compared with Anxa1WT mice (figure 3D). To confirm the tumor inhibitory effects of macrophage ANXA1 deficiency, we established mice bearing orthotopic pancreatic KPC tumors. Consistently, the tumor growth was lower and the amount of apoptotic cells was higher in tumors from Anxa1ΔMɸ compared with Anxa1WT mice (online supplemental figure S1E,F). These data suggest that ANXA1 deficiency in macrophages induces antitumor effects in PDAC.

To determine whether the antitumor effect of macrophage ANXA1 deficiency is caused by the accumulation of apoptotic cells, we applied the Bax inhibitory peptide V5 (BIP-V5) to suppress Bax-mediated apoptosis of cancer cells (figure 3E). BIP-V5 treatment did not affect the proliferation of Panc02 cells in vitro (online supplemental figure S1G), but instead conferred resistance to apoptosis triggered by apoptotic stimuli (figure 3F). In the Panc02 tumor model, BIP-V5 treatment reversed the effect of macrophage ANXA1 deficiency in increasing the percentage of apoptotic cells in tumor tissues (figure 3G) and blocked its antitumor effect (figure 3H). These results suggest that deficiency of ANXA1 in macrophages causes an accumulation of apoptotic cells that promotes antitumor efficiency.

To further verify the role of macrophage ANXA1 in the tumor microenvironment, bone marrow-derived macrophages (BMDMs) overexpressing murine ANXA1 were established. WB results confirmed the obvious ANXA1 overexpression in BMDMs (figure 3I, online supplemental figure S1H). In a PDAC model, we established a system in which macrophages were depleted and subsequently reconstituted by either control or ANXA1-overexpressed BMDMs. Overexpression of ANXA1 in BMDMs increased tumor growth in vivo (figure 3J). The WB assay indicated that overexpressing ANXA1 in BMDMs decreased the levels of c-Casp3 protein in the tumor microenvironment (figure 3K, online supplemental figure S1I).

Macrophage ANXA1 deficiency activates the cGAS-STING pathway to induce type I IFN in tumors

To explore the mechanism by which macrophage ANXA1 deficiency induces antitumor responses, we performed RNA sequencing of tumors from Anxa1ΔMɸ and Anxa1WT mice. Volcano plots revealed the expression patterns of the differentially expressed genes (DEGs), most of which were upregulated in tumors from Anxa1ΔMɸ as compared with Anxa1WT mice (online supplemental figure S1J). Specifically, among the 365 DEGs that were identified, 291 were upregulated and 74 were downregulated. Gene ontology (GO) enrichment analysis demonstrated that tumors of Anxa1ΔMɸ mice were differentially enriched for pathways associated with immune response and cellular response to IFN-β (figure 4A), which was further confirmed by gene set enrichment analysis (GSEA) (online supplemental figure S1K). We also performed quantitative proteomics analyses of tumors from Anxa1ΔMɸ and Anxa1WT mice. Consistent with RNA sequencing, 243 differentially expressed proteins (DEPs) were identified in tumors from Anxa1ΔMɸ as compared with Anxa1WT mice, including 175 that were upregulated and 68 that were downregulated (online supplemental figure S1L). GO enrichment analysis of the DEPs verified that tumors of Anxa1ΔMɸ mice were enriched for pathways associated with cellular response to IFN-β (figure 4B).

Figure 4

Macrophage Anxa1 deficiency activates cGAS-STING pathway to induce type I interferon in tumors. (A) GO enrichment analysis of upregulated genes in tumors from Anxa1ΔMɸ versus Anxa1WT mice. (B) GO enrichment analysis of upregulated proteins in tumors from Anxa1ΔMɸ versus Anxa1WT mice. (C) qPCR analysis of mRNA expression of Ifnb1, Usp18, Oas3 and Ifit1 in tumors from Anxa1ΔMɸ and Anxa1WT mice. Results represent the averages of three independent experiments. (D) Measurement of IFN-β protein in tumor homogenates. Tumors were collected 3 weeks after tumor cells implanted. (E, G) Immunoblot analysis of IFN-β, cGAS, STING, P65, p-P65TBK1, p-TBK1, IRF3 and p-IRF3 in tumor tissues from Anxa1ΔMɸ and Anxa1WT mice. (F) Shown are the image of the subcutaneous tumor of Anxa1ΔMɸ mice treated with IgG or anti-IFNAR1 (left). Quantification of tumor volume and weight are shown (right). Each group contained five mice. (H) qPCR analysis of mRNA expression of Ifnb1, Usp18, Oas3 and Ifit1 in tumors of Anxa1ΔMɸ mice treated with control solvent or H151. Results represent the averages of three independent experiments. (I) Measurement of IFN-β protein in tumor homogenates from Anxa1ΔMɸ mice treated with control solvent or H151. (J) Shown are the image of the subcutaneous tumor of Anxa1ΔMɸ mice treated with control solvent or H151 (left). Quantification of tumor volume and weight are shown (right). Each group contained five mice. (K) qPCR analysis of mRNA expression of Ifnb1, Usp18, Oas3 and Ifit1 in tumors of Anxa1ΔMɸ mice treated with control solvent or RU521. Results represent the averages of three independent experiments. (L) Measurement of IFN-β protein in tumor homogenates from Anxa1ΔMɸ mice treated with control solvent or RU521. (M) Shown are the image of the subcutaneous tumors of Anxa1ΔMɸ mice treated with control solvent or RU521 (left). Quantification of tumor volume and weight are shown (right). Each group contained five mice. (N) Shown are the image of the subcutaneous tumor size of Anxa1ΔMɸ mice treated with IgG or anti-CSF1R. Each group contained five mice. Quantification of tumor volume and weight that from Anxa1ΔMɸ mice treated with IgG or anti-CSF1R are shown. (O) qPCR analysis of mRNA expression of Ifnb1, Usp18, Oas3 and Ifit1 in tumors from Anxa1ΔMɸ mice treated with IgG or anti-CSF1R. Results represent the averages of three independent experiments. (P) Measurement of IFN-β protein in tumor homogenates from Anxa1ΔMɸ mice treated with IgG or anti-CSF1R. (Q) Immunoblot analysis of c-Casp3 and STING in tumor tissues from Anxa1ΔMɸ that treated IgG and anti-CSF1R mice. (R) Immunoblot analysis of ANXA1 in THP1 cells that transfected with lentiviruses control (THP1-ctrl) and carrying plasmids knockdown ANXA1 (THP1-shANXA1). (S) Representative images of tumors from the humanized mouse model of PDAC that ANXA1 was knocked down in macrophages (left). Quantification of tumor volume and weight are shown (right). Each group contained five mice. (T) Immunoblot analysis of c-Casp3, STING and IFN-β expression in tumors from THP1-ctrl and THP1-shANXA1 mice. (U) Immunoblot analysis of ANXA1 in THP1 cells that transfected with lentiviruses control (THP1-ctrl) and carrying plasmids overexpressing ANXA1 (THP1-ANXA1). (V) Representative images of tumors from the humanized mouse model of PDAC that ANXA1 was overexpressed in macrophages (left). Quantification of tumor volume and weight are shown (right). Each group contained five mice. (W) Immunoblot analysis of c-Casp3, STING and IFN-β expression in tumors from THP1-ctrl and THP1-ANXA1 mice. The Panc02 cells line was used in the mouse PDAC tumor model involved in (A–Q). The data presented are the means±SD. ANXA1, Annexin A1; c-Casp3, cleaved Caspase-3; CSF1R, colony-stimulating factor 1 receptor; GO, gene ontology; IFN, interferon; IFNAR1, IFN-alpha/beta receptor 1; mRNA, messenger RNA; PDAC, pancreatic ductal adenocarcinoma; qPCR, quantitative PCR; THP1, human monocytic cell line.

For additional confirmation of the role of IFN signaling, we performed reverse-transcriptase (RT)-quantitative real-time PCR (qPCR) assays. The results showed that the mRNA levels of multiple IFN-stimulated genes (ISGs), including Ifnb1, Usp18, Oas3, and Ifit1, were robustly increased in tumors of Anxa1ΔMɸ as compared with Anxa1WT mice (figure 4C). ELISA and WB assays further confirmed that IFN-β protein levels were significantly increased in tumors from Anxa1ΔMɸ mice (figure 4D,E, online supplemental figure S1M). In addition, we established a mouse pancreatic cancer model using the KPC cell line in Anxa1ΔMɸ and Anxa1WT mice. As expected, downregulated expression of ANXA1 in macrophages led to significantly suppressed tumor growth in vivo and increased IFN-β protein levels (online supplemental figure S2A,B). To determine whether the increased type I IFN signaling in Anxa1ΔMɸ tumors is functionally relevant in terms of the antitumor effect, we used a function-blocking antibody against IFN-alpha/beta receptor 1 (IFNAR1). Anti-IFNAR1 treatment completely abolished the antitumor activity of macrophage ANXA1 deficiency in both Panc02 and KPC tumor-bearing mice (figure 4F, online supplemental figure S2C). Anti-IFN treatment had no effect on the attenuation of efferocytosis caused by the lack of ANXA1 in macrophages, and WB results also confirmed that c-Casp3 protein levels were not changed significantly after anti-IFNAR1 treatment (online supplemental figure S2D). Therefore, the antitumor effect of ANXA1 deficiency in macrophages is mediated via enhanced type I IFN signaling.

Damage to the apoptotic cell membrane integrity is known to induce cGAMP release into the tumor microenvironment, which triggers the STING pathway.22–24 Furthermore, the cGAS-STING signaling pathway has been reported to represent a key mechanism driving antitumor type I IFN response.18 Therefore, we speculated that the cGAS-STING pathway may be involved in macrophage ANXA1 deficiency-induced type I IFN response in tumors. To assess this possibility, we performed a WB analysis of tumor tissues from both Panc02 and KPC tumor-bearing mice. The protein levels of cGAS, STING, p-TBK1, p-IRF3, and p-P65 were significantly increased in tumor tissues of Anxa1ΔMɸ compared with Anxa1WT mice (figure 4G, online supplemental figure S2E–I). To evaluate the role of cGAS-STING signaling in deficiency-induced type I IFN response, Anxa1ΔMɸ mice bearing subcutaneous Panc02 and KPC tumors were subjected to administration of H151, a selective and covalent antagonist of STING, or RU521, a potent and selective cGAS inhibitor. Consistently, the mRNA expression of ISGs and the levels of IFN-β protein in tumors were obviously decreased by H151 treatment (figure 4H,I, online supplemental figure S2J). Furthermore, H151 suppressed the antitumor effect induced by ANXA1 deficiency (figure 4J, online supplemental figure S2K). The type I IFN response in Anxa1ΔMɸ mouse tumors was obviously decreased after RU521 treatment (figure 4K,L, online supplemental figure S2L). Moreover, treatment with RU521 suppressed the antitumor effects caused by ANXA1 deficiency (figure 4M, online supplemental figure S2M). Therefore, these results support the role of the cGAS-STING pathway in mediating the type I IFN response induced by ANXA1 deficiency.

To further corroborate the predominant role of TAMs in producing IFN-β, we injected mice with an antibody against colony-stimulating factor 1 receptor (CSF1R), which is essential for macrophage survival. Indeed, CSF1R inhibitor-mediated macrophage depletion abrogated the antitumor activity induced by ANXA1 deficiency in both Panc02 and KPC tumor-bearing mice (figure 4N, online supplemental figure S2N). Furthermore, the mRNA expression of ISGs was obviously decreased in anti-CSF1R tumors (figure 4O). Consistently, treatment with anti-CSF1R antibody decreased the IFN-β protein levels in tissue homogenates prepared from freshly dissected tumors (figure 4P, online supplemental figure S2O). WB results demonstrated an elevation of c-Casp3 in tumors from Anxa1ΔMɸ mice following treatment with anti-CSF1R, while STING protein levels were reduced. (figure 4Q, online supplemental figure S2P,Q). These results confirm that TAMs are the major source of IFN-β in Anxa1ΔMɸ mouse tumors.

Additionally, a human monocytic cell line (THP1) was used to confirm the roles of ANXA1. THP1 cells were transfected with a lentiviral control and lentiviral shANXA1. The WB result confirmed that the expression of ANXA1 was effectively modulated in THP1 cells (figure 4R, online supplemental figure S3A). On induction to M0, the cells were co-inoculated with PANC-1 cells subcutaneously into immunodeficient mice, establishing a humanized mouse model of pancreatic cancer.25–27 Consistent with the results in both Panc02 and KPC tumor-bearing mice, downregulated expression of ANXA1 in macrophages led to significantly suppressed tumor growth in the humanized mouse model of PDAC (online supplemental figure 4S). WB assay results validated that the downregulation of ANXA1 by macrophages within tumors resulted in significant upregulation of c-Casp3, STING, and IFN-β protein levels (figure 4T, online supplemental figure S3B–D). To further verify the modulatory role of ANXA1 on macrophage function in the humanized mouse model, a THP1 cell line overexpressing hominine ANXA1 and a control were constructed. WB results confirmed the successful overexpression of ANXA1 (figure 4U, online supplemental figure S3E). As expected, overexpression of ANXA1 in macrophages led to significantly promoted tumor growth (figure 4V). The WB assay further validated that tumors with macrophages overexpressing ANXA1 exhibited significantly decreased levels of c-Casp3, STING, and IFN-β protein (figure 4W, online supplemental figure S3F–H).

Macrophage ANXA1 deficiency promotes remodeling of T cells in the tumor microenvironment

To identify changes in the tumor microenvironment caused by macrophage ANXA1 deficiency, we harvested tumors on 3 weeks of the Panc02 tumor model using syngeneic Anxa1ΔMɸ and Anxa1WT mice and performed scRNA-seq. Next, we applied Uniform Manifold Approximation and Projection (UMAP) technology for dimensionality reduction clustering and cell cluster annotation based on canonical markers, which revealed nine main groups: cancer cells, endothelial cells, fibroblasts, B cells, T and natural killer (NK) cells, neutrophils, mast cells, mononuclear phagocytes, and plasmacytoid dendritic cells (online supplemental figure S4A). The proportions of each of these cell subpopulations and the bubble map of marker genes are shown in online supplemental figure S4B,C.

For a more detailed understanding of the immune populations in the tumor microenvironment, we computationally separated the UMAP clusters and reanalyzed the data. The T and NK cells were categorized into eight distinct subclusters broadly defined by the distribution of classical marker genes (figure 5A). Of note, the proportions of NK cells, CD8 naive T cells and CD8 effector T cells (Teff) were higher in tumors from Anxa1ΔMɸ as compared with Anxa1WT mice, while the proportion of CD8 exhausted T cells (Tex) was lower (figure 5B). To gain further insight into the relationships among CD8+ T cells, we used Monocle 2 to infer the temporal dynamics of biological processes from the transcriptional similarities among cells. Pseudotime analysis for CD8+ T cells indicated that the starting point corresponded to CD8 naive T cells, followed by CD8 Teff, and ending with CD8 Tex (figure 5C). The group density stream verified the temporal order of these CD8 cell subpopulations and further confirmed that CD8 Teff cells were mainly distributed in Anxa1ΔMɸ mice, while CD8 Tex cells were mainly distributed in Anxa1WT mice (figure 5D). Furthermore, GSEA revealed that NK and CD8 Teff cells displayed a higher cytotoxicity score and a lower exhaustion score in Anxa1ΔMɸ mice (figure 5E,F). These results suggest that the T cells in Anxa1ΔMɸ mice tended toward higher toxicity than the T cells in Anxa1WT mice.

Figure 5

scRNA-seq analysis reveals the changes of T cells, NK cells and macrophages in tumor induced by macrophage Anxa1 deficiency. (A) UMAP plot showing T and NK cell clusters in tumor. (B) Fraction of eight clusters in each group. (C) tSNE plot with analysis of T and NK cells by Monocle 2 (the small picture in the middle). T and NK subpopulations overlaid on Monocle 2 pseudotime plot. (D) Pseudotime plot showed the proportion of each CD8 T cell type at different time points. The vertical coordinate is the pseudotime series, and the horizontal coordinate is the proportion of cell types at different time points. (E–F) Comparative cytotoxicity and exhaustion scores. The bar graph illustrates the cytotoxicity and exhaustion scores of two groups, across four cell types: CD8 Tex, CD8 naive T, CD8 Teff and NK cells. The y-axis represents the cytotoxicity score. (G) The number and proportion of clone types in different groups are displayed. The size of the circle represents the percentage of clone types (in the legend on the right). Single represents a single clone type; medium indicates that the clone frequency is greater than one and less than or equal to 10. Large indicates that the frequency of clone types is greater than 10. (H) Representative flow cytometry plot (up) and tabulated percentages (below) of NK cells in CD45+ TILs and Ki67, GZMB, IFN-γ in CD8+ TILs (n=4 per group). The data presented are the means±SD. Anxa1, Annexin A1; IFN, interferon; NK, natural killer; scRNA-seq, single-cell RNA sequencing; TILs, tumour-infiltrating lymphocytes; UMAP, Uniform Manifold Approximation and Projection.

For additional verification of the T-cell populations in Anxa1ΔMɸ mice, we performed an integrative analysis of the T cell receptor (TCR) repertoires represented in the scRNA-seq data. There were more clonotypes with a clonal frequency of greater than 10 in Anxa1ΔMɸ mice than in Anxa1WT mice, especially for the Teff cells, which is indicative of enhanced clonal expansion of effector T cells in Anxa1ΔMɸ mice (figure 5G). Using the cell–cell communication analytical tool in CellPhoneDB, we further found that there were more interactions between Teff cells and cancer cells in Anxa1ΔMɸ mice than in Anxa1WT mice, which is indicative of an enhanced immune response mechanism (online supplemental figure S4D). We also used flow cytometry to detect the status of NK and T cells in the tumors of two groups of mice (online supplemental figure S5A). The tumors of Anxa1ΔMɸ compared with Anxa1WT mice had a significantly higher percentage of NK cells (figure 5H). There was no significant change in the percentage of intratumoral T cells, CD4+ T cells or CD8+ T cells among the groups, but the expression of GZMB, IFN-γ, and Ki67 in CD8+ tumour-infiltrating lymphocytes (TILs) was significantly higher in tumors from Anxa1ΔMɸ mice (online supplemental figure S5B–D, figure 5H). These results confirm that the proportions of NK and CD8 Teff cells are more extensive in Anxa1ΔMɸ mice.

Deletion of ANXA1 promotes the polarization of macrophages towards antitumoral M1 type

Next, we classified the macrophages from Anxa1ΔMɸ and Anxa1WT mice into seven subgroups (online supplemental figure S4E,F). Subgroups 1, 3, 5, and 6 highly expressed the M2 macrophage genes CD68, Mrc1, and Arg1 and had higher M2 gene set scores. Therefore, we defined them as the M2-like subcluster. Subgroups 2, 4, and 7 highly expressed the M1 macrophage gene CD86 and were defined as the M1-like subcluster (online supplemental figure S4G–I). Interestingly, the ratio of the M1-like cluster for tumors from Anxa1ΔMɸ mice was greater than that for tumors from Anxa1WT mice (figure 6A, online supplemental figure S4J). GO enrichment analysis further revealed that the M1-like cluster displayed upregulation of pathways associated with immune and inflammatory responses, as well as cell–cell adhesion and Toll-like receptor signaling (online supplemental figure S4K–M). For a more detailed understanding of the temporal dynamics of the macrophage clusters, we performed pseudotime analysis, which demonstrated that the starting point corresponded to cells within the M2-like cluster, and the endpoint corresponded to cells within the M1-like cluster (figure 6B). The Arg1 gene, which is a characteristic marker of M2 macrophages, showed reduced expression at the terminal stage of pseudotime analysis, suggesting a decline in immunosuppressive activity.28 To verify the scRNA-seq results, we also applied flow cytometry analysis, which confirmed that the proportion of M1 macrophages increased and the proportion of M2 macrophages decreased in tumors of Anxa1ΔMɸ mice (online supplemental figure S5A), figure 6C). These findings suggest that the deficiency of ANXA1 in macrophages remodels the proportions of M1 and M2 macrophages in pancreatic tumors.

Figure 6

Deletion of Anxa1 promotes the polarization of macrophages towards M1 type. (A) UMAP plot showing macrophage clusters in tumor (left). Fraction of macrophage clusters in each group (right). (B) tSNE plot with analysis of macrophage by Monocle 2 (the small picture in the upper). Macrophage subpopulations overlaid on Monocle 2 pseudotime plot. (C) Representative flow cytometry plot (above) and tabulated percentages (below) of CD80+CD206 cells and CD80CD206+ cells in macrophages (n=4 per group). (D–E) BMDMs from WT and Anxa1–/– mice were stimulated with LPS+IFN-γ (M1) or IL-4 (M2). After the above stimulation, the transcription level and release changes of inflammatory factors related to M1-macrophage and M2-macrophage were detected. The mRNA expression of M1 and M2 markers was determined by qRT-PCR. (F–G) The production of cytokine was measured by ELISA. The experiments were repeated three times. The data presented are the means±SD. Anxa1, Annexin A1; BMDM, bone marrow-derived macrophages; IFN, interferon; iNOS, nitric oxide (NO) synthase (iNOS); mRNA, messenger RNA; qRT, quantitative reverse-transcriptase; UMAP, Uniform Manifold Approximation and Projection; WT, wild-type.

To further verify the effect of ANXA1 on macrophage polarization, we extracted BMDMs from both WT and Anxa1–/– mice. The macrophages were induced into either the M1 or M2 phenotype through the application of Lipopolysaccharide (LPS) in conjunction with IFN-γ or IL-4 stimulation. RT-qPCR evaluation of macrophage marker genes suggested that ANXA1 knockout in macrophages increased the expression of M1 phenotype markers IL-1β, IL-6, and nitric oxide (NO) synthase (iNOS), while also significantly inhibiting the expression of M2 marker genes Arg1, IL-10, and Ym1 (figure 6A,B). To confirm these results, we performed ELISA to measure the production of M1 cytokines (IL1-β, IL-6, and TNF-α); and M2 cytokines (IL10 and CCL2). The results confirm that macrophage ANXA1 deficiency increased the M1 cytokines and significantly inhibited the M2 cytokines (figure 6C,D). These results verify that the ablation of ANXA1 affects macrophage polarization.

Combining shANXA1 with anti-PD-1/gemcitabine therapy synergistically enhances the antitumor response

To investigate whether inhibition of ANXA1 expression synergistically enhances the effect of gemcitabine and anti-PD-1 in inhibiting tumor growth in mice, we employed adeno-associated virus (AAV) expressing short hairpin RNA (shRNA) that targets ANXA1. WB and flow cytometry results confirmed that shAnxa1AAV decreased the expression of ANXA1 in tumors, with approximately 90% inhibition in macrophages after treatment (figure 7A,B). The shAnxa1AAV, anti-PD-1 antibody and gemcitabine groups each exhibited modestly decreased tumor volumes and weights compared with those of the untreated control group. Moreover, shAnxa1AAV combined with either anti-PD-1 or gemcitabine reduced the tumor burden more than the single agents did, while combination therapy with shAnxa1AAV, anti-PD-1, and gemcitabine had the best overall antitumor effect (figure 7C–E).

Figure 7

shAnxa1 enhances antitumor response. (A) Immunoblot analysis of Anxa1 in tumor tissues that treated ctrlAAV and shAnxa1AAV. (B) Representative flow cytometry plot (left) and percentage bar graph (right) of the Anxa1+ cells in CD45+F4/80+ macrophages (n=4 per group). (C) Shown are the image of the subcutaneous tumor sizes under different treatments. The treatments were shown in materials and methods. (D) Bar graph depicting tumor volume for each treatment group. (E) Bar graph showing tumor weight for each treatment group. (F) Bar graph representing the percentage of CD80+CD206 macrophages (M1) in tumors under different treatments. (G) Bar graph representing the percentage of CD80CD206+ macrophages (M2) in tumors under different treatments. (H) Bar graph showing the M1/M2 macrophage ratio in tumors for each treatment condition. (I) Bar graph depicting the percentage of GZMB+ CD8 T cells in tumors under different treatments. The data presented are the means±SD. AAV, adeno-associated virus; Anxa1, Annexin A1.

To determine whether shAnxa1AAV combined with anti-PD-1 and gemcitabine therapy activates T cells and alters macrophage polarization, we performed additional flow cytometry analysis for immune cell markers. Our results showed that treatment with shAnxa1AAV, anti-PD-1, or gemcitabine alone resulted in an increased proportion of M1 (CD80+CD206) macrophages, a decreased proportion of M2 (CD80CD206+) macrophages, an increased M1/M2 ratio, and an increased proportion of GZMB+CD8+ TILs. Furthermore, when compared with the single agent treatments, the combination of shAnxa1AAV and anti-PD-1 antibody or gemcitabine led to further increases in the percentage of M1 macrophages, GZMB+CD8+ TILs, and the M1/M2 ratio, while the combination of the three treatments had the greatest overall effect (figure 7F–I). ELISA assays further confirmed that shAnxa1AAV treatment elevated IFN-β levels, which were further increased by adding anti-PD-1 antibody or gemcitabine. The combination of all three treatments was the most effective (online supplemental figure S5E). Taken together, these results indicate that shAnxa1AAV combined with anti-PD-1 and gemcitabine regulates the tumor immune microenvironment and synergistically enhances the antitumor response.

Discussion

Immunotherapy exhibits remarkable efficacy for various malignant tumors. Within the tumor microenvironment of pancreatic cancer, macrophages play a pivotal role as tumor-infiltrating immune cells.29 In this study, we elucidated an immune evasion mechanism in pancreatic cancer whereby TAMs suppress tumor immune activation by phagocytosing and eliminating apoptotic tumor cells in an ANXA1-dependent manner. Additionally, we demonstrated that ANXA1 depletion in macrophages triggers a type I IFN response within tumors, enhances T-cell function, modulates macrophage polarization, and augments the effectiveness of anti-PD-1 and gemcitabine chemotherapy.

ANXA1 has been reported to function as an engulfment ligands involved in efferocytosis.14 30 31 Indeed, we found that ANXA1+ TAMs regulate the apoptosis level in PDAC tumor tissues. Interestingly, several studies have shown that a lack of ANXA1 in macrophages is associated with the inability of phagocytic capacity. Moreover, Dalli et al reported that ANXA1 promotes the clearance of apoptotic leukocytes by macrophages.32 Similarly, macrophage ANXA1 expression may enhance the uptake of apoptotic cells by phagocytes through interaction with surface-exposed phosphatidylserine.31 Consistently, our results show that ANXA1 regulates the efferocytosis of apoptotic cells by macrophages in vivo and in vitro. We also found that macrophage ANXA1 deficiency leads to an accumulation of apoptotic cells in tumors of pancreatic cancer, which promotes immune-mediated clearance.

If phagocytic removal of apoptotic cells fails, apoptotic cells progress to secondary necrosis, which may result in compromised plasma membrane integrity and release of cellular contents. Dying tumor cells release DNA and cGAMP, activating the STING pathway and enhancing innate immunity within tumors.22 In tumor progression, rapid disposal of dying tumor cells by TAMs prevents immune system alarm, effectively eliminating the source of extracellular cGAMP. Our data demonstrate that the STING pathway is activated in pancreatic tumors with an increase in apoptotic cells, and this effect diminishes on inhibition of apoptosis. These results suggest that the principal mechanism for exogenous STING activation in pancreatic cancer may be attributable to DNA liberated from apoptotic cells. STING can be activated by tumor DNA, leading to type I IFN production via the STING-IRF3 axis, or by cytosolic DNA, initiating the cGAS-STING pathway and type I IFN synthesis.33 The antitumor activity of STING agonists has been observed in many studies, some of which show promising results.34–36 In addition, several studies have demonstrated that pancreatic cancer progression is potently inhibited by STING agonist.37–39 Our results indicate that activating the STING pathway within tumors inhibits tumor growth; conversely, inhibiting STING pathway activation abolishes this tumor-suppressive effect. We observed that the activation of the STING pathway was accompanied by changes in the tumor immune microenvironment, with an increase in inflammatory macrophages and enhanced T-cell activity. These findings are consistent with the results of previous studies demonstrating that STING agonists can reactivate the immunogenicity of PDAC.37 40

The anti-inflammatory role of ANXA1 in the innate immune system is well-documented, yet its function in adaptive immunity is still under debate.12 ANXA1 has been shown to influence immune cells and the tumor microenvironment by enhancing regulatory T-cell function,41 enhancing the polarization and activation of M2 TAMs,42 suppressing dendritic cell activation, and impairing CD8+ T-cell antitumor immunity.43 Studies have also shown that ANXA1 inhibits polymorphonuclear leukocyte (PMN) recruitment under flow conditions and is able to reduce firm adhesion of human PMNs.44 Moreover, ANXA1 deficiency affects the activation of the NLRP3 inflammasome in neutrophils, and it inhibits the migration of both neutrophils and monocytes during inflammation.45 We investigated the regulatory role and mechanism of ANXA1 in the immune microenvironment from the perspective of immune cells, providing a deeper understanding of the mechanism and treatment of pancreatic cancer. Our results indicate that ANXA1 promotes remodeling of T cells and M2 polarization of macrophages. Furthermore, shRNA-mediated knockdown of ANXA1 functions synergistically with anti-PD-1 therapy and gemcitabine, with the best antitumor efficacy achieved by the application of the three agents in combination. Therefore, our study elucidates the regulatory role and mechanism of ANXA1 in the immune microenvironment of pancreatic cancer and provides a rational basis for combinations of shANXA1 and ICBs, which have the potential to translate into clinical practice.

There are some limitations in this study. First, the subcutaneous tumor model in mice may not faithfully represent the tumor immune microenvironment of human pancreatic cancer. Second, the process through which ANXA1 modulates the transformation between M1 and M2 macrophages remains to be elucidated. Third, the AAV used in our study did not specifically target macrophages. Though it remains a significant challenge, the precise targeting of TAM therapy could help to improve the applicability of shRNA therapy.

In summary, our findings elucidate the role of ANXA1 expressed on macrophages in pancreatic cancer. Within pancreatic cancer tumor tissues, ANXA1 deficiency in macrophages disrupts apoptotic cell clearance, subsequently activating the cGAS-STING signaling pathway and inducing type I IFN production—a process crucial for tumor suppression. Consequently, targeting ANXA1 on macrophages presents a promising avenue for pancreatic cancer treatment.

Materials and methods

Patients and tissue samples

Pancreatic cancer tumor samples were collected from patients who underwent surgery at Fujian Medical University Union Hospital in Fuzhou, China, between November 2013 and January 2021. All patients had histologically confirmed diagnoses of PDAC. Exclusion criteria included patients with neoadjuvant treatment, inflammatory diseases, or active infections. A total of 151 patients with PDAC were enrolled in the study. Disease stage was assessed using the American Joint Committee on Cancer V.8. Informed consent was obtained prior to sample collection. Formalin-fixed paraffin-embedded samples were used for IHC analysis.

IHC and IF

IHC experiments were conducted on large tissue sections obtained from paraffin-embedded samples. The IHC protocol, as previously described,46 involved the following steps: Deparaffinization and pretreatment in 1 mmol/L EDTA at 95°C for 20 min for antigen retrieval. Incubation with a 0.3% hydrogen peroxide solution for 15 min to block endogenous peroxidase activity. Primary antibodies were employed to detect ANXA1 (1:500), CD68 (1:500), and c-Casp3 (1:250). For visualization, we used the Elivision super (Mouse/Rabbit) IHC Kit and DAB Kit (20×), following the manufacturer’s instructions. Counterstaining was performed using hematoxylin. IHC analysis of CD68 status was performed as previously described.6 The ANXA1 and c-Casp3 expression levels were categorized based on intensity scores: 0 for negative, 1 for weak, 2 for moderate, or 3 for strong. Additionally, the percentage of cells stained positively for ANXA1 and c-Casp3 were assessed using scores of 1–3, representing<10%, 10–30%, and>30% of cells. Samples were considered to exhibit high expression if the score was≥3. All specimens were evaluated by two pathologists who were blinded to the patients’ clinical information.

IF was performed by our previous protocol.46 The antibodies used were summarized in online supplemental table S1.

Isolation of mouse peritoneal macrophages

Isolation of mouse peritoneal macrophages was referred to previously published articles.47 Intraperitoneal injections of 5 mL phosphate-buffered saline (PBS) were administered to mice. Subsequently, cells were harvested from the mouse peritoneal cavity in PBS. Then, cells were cultured in high-glucose Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 units/mL penicillin, and 100 mg/mL streptomycin. After 12 hours of incubation, non-adherent cells were discarded, and the adherent macrophages were used for further study.

In vitro and in vivo efferocytosis assay

The thymus derived from mice was subjected to grinding and filtration processes to yield a single-cell suspension. Thymocyte apoptosis was triggered by the application of 2 mM dexamethasone (MedChemExpress, USA) and incubated at a temperature of 37°C for a duration of 4 hours. The apoptotic thymocytes were subsequently labeled with 1 mg/mL of pHrodo red succinimidyl 1 ester (MedChemExpress, USA). These labeled apoptotic cells were introduced to peritoneal macrophages extracted from Anxa1WT or Anxa1−/− mice. Following an incubation period of 45 min, apoptotic cells that remained unengulfed were removed through a washing process. The macrophages were then labeled with an anti-CD11b, AF488 antibody (eBioscience, clone M1/70). The process of phagocytosis of the apoptotic cells was monitored and quantified using a fluorescence microscope.

To evaluate in vivo clearance of apoptotic cells in the thymus. Dexamethasone (0.2 mg/25 g) was dosed by intraperitoneal injection to induce thymocyte apoptosis. Then the thymus tissues were collected at 8 hours and 24 hours later, and dissociated into single-cell suspension. Apoptotic cells and dead cells were then detected using the Annexin V-FITC Apoptosis Detection Kit (Solarbio) by flow cytometry.

Cell lines, mice and reagents

The murine PDAC cell line (Panc02) was procured from Shanghai Aolu Biological Technology (Shanghai, China). THP1 were procured from Cellverse (Shanghai, China). The primary mouse PDAC cell line KPC (LSL-KrasG12D; LSL-Trp53R172H; Pdx1-Cre) was obtained from professor Xiaolong Liu. Both Panc02 and KPC are syngeneic to C57BL/6 mice. The cell lines underwent genotyping by the Cell Bank of the Chinese Academy of Sciences and were subjected to rigorous testing to eliminate the possibility of mycoplasma contamination.

WT C57BL/6J mice, NOD-SCID (NOD/ShiLtJGpt-Prkdcem26Cd52/Gpt) mice, Anxa1flox/flox mice (C57BL/6J genetic background) and Lyz2-Cre mice (C57BL/6J) were purchased from GemPharmatech (Nanjing, China, Strain NO. T003822, Strain NO. T016067). Myeloid-specific ANXA1-knockout mice (Anxa1ΔMɸ, Mɸ: macrophage; Δ: Specific knockout) were generated by crossing Anxa1flox/flox mice with Lyz2-Cre mice. The mice were maintained under specific-pathogen-free conditions at the laboratory animal room of Fujian Medical University. Male mice of 8–12 weeks were used for all tumor studies. All animal experiments were approved by the Ethics Committee for Animal Research of Fujian Medical University (IACUC FJMU 2023–0087). The protocol for the care and utilization of animals was in strict compliance with the National Regulations for the Administration of Affairs Concerning Experimental Animals.

To establish a PDAC model, both Panc02 and KPC cells (1×106) were subcutaneously implanted into Anxa1flox/flox and Anxa1ΔMɸ mice, respectively. When the tumor reached 100 mm3, tumor-bearing mice were randomly divided into groups for further study (4–6 mice, respectively). The groups were treated as follows.

  1. Control solvent (10% DMSO, 40% PEG300, 5% Tween-80, 45% Saline), BIP-V5 (5 mg/kg, daily intraperitoneally (i.p.));

  2. Mouse IgG1 (200 µg/day i.p., Clone No. MOPC-21, Bio X Cell), anti-IFNAR1 (200 µg/2 days i.p., Clone No. MAR1-5A3, Bio X Cell);

  3. Control solvent (10% DMSO, 40% PEG300, 5% Tween-80, 45% Saline), H151 (750 nmol/2 days, i.p., Clone No. HY-112693, MCE);

  4. Control solvent (10% DMSO, 40% PEG300, 5% Tween-80, 45% Saline), RU521 (5 mg/kg, once every 2 days, i.p., Clone No. HY-114180, MCE);

  5. Rat IgG2a (200 µg/day i.p., Clone No. 2A3, Bio X Cell), anti-CSF1R (200 µg/2 days i.p., Clone No. AFS98, Bio X Cell);

  6. Anti-PD-1 (200 µg/2 days i.p., Clone No. RMP1-14, Bio X Cell), gemcitabine (100 mg/kg, two times a week, Clone No. HY-17026, MCE), shAnxa1AAV (5×1010 Vector genomes (Vg)/week, injected intratumorally, OBiO), ctrlAAV (5×1010 Vector genomes (Vg)/week, injected intratumorally, OBiO). shAnxa1AAV and control virus (ctrlAAV) were designed and synthesized by OBiO Technology (Shanghai, China, online supplemental table S2).

After treatment, the mice were sacrificed, and tumors were removed and weighed.

Luciferase-expressing KPC cells (1×106) were injected into the pancreatic capsule of Anxa1flox/flox and Anxa1ΔMɸ mice to establish the PDAC tumor in situ model. After 4 weeks, the mice were intraperitoneally injected with D-Luciferin potassium salt (2 mg/mouse, 122799, Revvity), and the tumor growth was monitored according to the IVIS Spectrum (PerkinElmer). Then, the mice were sacrificed and the tumors were recorded.

A humanized mouse tumor model was generated as described previously.25–27 Lentiviruses carrying plasmids overexpressing or knocking down ANXA1 (Ubi-ANXA1-3FLAG-CBh-gcGFP-IRES-puromycin; Ubi-Anxa1-3FLAG-CBh-gcGFP-IRES-puromycin; hU6-ANXA1-CBh-gcGFP-IRES-puromycin) were designed and synthesized by GeneChem (Shanghai, China). The lentiviruses were infected into THP1 to construct a stable cell line for subsequent experiments. To study the role of ANXA1 in macrophages in vivo, we stimulated THP1-Ctrl, THP1-shANXA1, and THP1-ANXA1 cells to M0 polarization, and mixed them with PANC-1, and inoculated them subcutaneously on the backs of immunodeficient mice. In polarization-related experiments, THP1 cells were stimulated with phorbol 12-myristate 13-acetate (200 ng/mL, MCE, HY-18739) for 48 hours and then used in follow-up experiments. 20 NOD-SCID mice were classified into four groups. A control group overexpressing ANXA1 was subcutaneously implanted with 1×106 PANC-1 cells and 1×105 THP1-Ctrl cells, while the THP1-ANXA1 group was subcutaneously implanted with 1×106 PANC-1 cells and 1×105 THP1-ANXA1 cells. A control group with ANXA1 knocked down was subcutaneously implanted with 1×106 PANC-1 cells and 1×105 THP1-Ctrl cells, while a THP1-shANXA1 group was subcutaneously implanted with 1×106 PANC-1 cells and 1×105 THP1-shANXA1 cells.

Macrophage depletion and reconstitution in mice were conducted as outlined in prior studies.48–50 Bone marrow cells were harvested from the femur and tibia of C57BL/6 mice. The cells were cultured in DMEM supplemented with 10% FBS for 12 hours. Non-adherent monocytes were collected and transfected with a lentivirus overexpressing ANXA1 or a control for 2 days. The transduced cells were then differentiated into BMDMs by treating with 50 ng/mL M-CSF for 7 days. To deplete mice of macrophages, 2 days following subcutaneous tumor formation with KPC cells, PDAC model mice were pretreated with clodronate liposomes (200 µl per mouse; Yeasen, Shanghai, China) via intraperitoneal injection. Three days post-clodronate liposome treatment, lentivirus-transduced BMDMs were intravenously administered to the macrophage-depleted mice to achieve reconstitution. Then, mice were sacrificed 2 weeks later and tumor specimens were dissected for subsequent analysis.

Bone marrow cells were harvested from the femur and tibia of C57BL/6 mice. These cells were cultured in DMEM medium supplemented with 10% FBS for 12 hours. Non-adherent monocytes were collected and transfected with lentiviruses control and overexpressing ANXA1 for 2 days. The transduced cells were then differentiated into BMDMs treated with 50 ng/mL M-CSF for 7 days. For deplete macrophages, 2 days following subcutaneous tumor formation with KPC cells, PDAC model mice were pretreated with clodronate liposomes (200 µl per mouse; Yeasen, Shanghai, China) via intraperitoneal injection. Three days post clodronate liposome treatment, lentivirus-transduced BMDMs were intravenously administered to the macrophage-depleted mice to achieve reconstitution. Then mice were sacrificed 2 weeks later and tumor specimens were dissected for following analysis.

Immunoblotting

Tumor tissues underwent lysis with RIPA Lysis Buffer (Beyotime Biotechnology, P0013B), which was supplemented with a Protease and Phosphatase Inhibitor Cocktail (Thermo Scientific, 78440). This was followed by protein quantification using a BCA Protein Assay kit (Beyotime, P0009). The samples were then subjected to equal loading onto a 10% SDS-PAGE and subsequently transferred to polyvinylidene fluoride (PVDF) membranes (Beyotime, FFP33). The membranes were blocked and subsequently incubated with the appropriate primary antibody at a temperature of 4°C overnight. On the following day, the membranes were incubated with HRP-linked secondary antibodies (Cell Signaling Technology, 7074 and 7076). Visualization of the blot was achieved using an enhanced chemiluminescence reagent (Thermo Scientific) on ChemiDoc Imagers (Bio-Rad). The quantification of the WB bands was executed using the ImageJ software. All antibodies used for WB are listed in online supplemental table S1.

Quantification of cfDNA

Panc02 tumor cells were inoculated into Anxa1ΔMɸ and Anxa1WT mice, and allowed tumors to establish. Four weeks post inoculation, whole blood was collected by cardiac puncture into cfDNA tubes. Plasma was obtained by a double spin procedure (1,600 g for 10 min, separation, followed by 16,000 g for 10 min). Commence by taking a 1.5 mL centrifuge tube and adding 1 mL of plasma, followed by the addition of 100 µL of 10%SDS and 30 µL of Proteinase K. Invert the mixture for thorough mixing, then place it in an incubator set at 65°C for a duration of 20 min. Next, take a 5 mL centrifuge tube and add 15 µL of MagMAX Cell-Free DNA Magnetic Beads and 1.25 mL of MagMAX Cell-Free DNA Lysis/Binding Solution. Ensure complete mixing of the contents. Place the centrifuge tube in a QT-1 vortex mixer and vortex thoroughly for 10 min. Subsequently, place the centrifuge tube on a DynaMag Magnet magnetic separation rack and let it stand for 5 min or until the solution becomes clear and the beads concentrate on the wall of the centrifuge tube. Carefully remove the supernatant using a pipettor. Resuspend the magnetic beads in 1 mL of MagMAX Cell-Free DNA Wash Solution and transfer the suspension to a new 1.5 mL centrifuge tube. Place the 1.5 mL centrifuge tube on a DynaMag-2 Magnet magnetic separation rack and let it stand for 20 s. Repeat the standing process for an additional 2 min or until the solution becomes clear and the beads concentrate on the wall of the centrifuge tube. Remove the supernatant using a 1 mL pipettor and remove the residual supernatant with a 200 µL pipettor. Remove the centrifuge tube from the magnetic separation rack, add 1 mL of MagMAX Cell-Free DNA Wash Solution, and vortex for 30 s. Remove the centrifuge tube from the magnetic separation rack again, add freshly prepared 80% alcohol, and vortex for 30 s. Place the centrifuge tube on a DynaMag-2 Magnet and let it stand for 2 min, then remove the supernatant. Remove the residual alcohol, and allow it to dry at room temperature for 5 min. Remove a centrifuge tube from the magnetic separation rack, add 22 µL of MagMAX Cell-Free DNA Elution Solution, and vortex for 5 min. Return the centrifuge tube to the magnetic separation rack and let it stand for 2 min, or until all magnetic beads are adsorbed on the wall of the centrifuge tube to make the solution clear. Finally, transfer 20 µL of the supernatant (cfDNA lysate) into a centrifuge tube and store at −20°C. Sample analysis was performed using an Agilent 2100, and cfDNA (ng/mL of plasma) was calculated as the quantitative outcome.

Cell proliferation assay

Per hole of 96-well plate was seeded with 3×103 viable Panc02 cells and grew for 24 hours. Next, the cells were allowed to grow in complete media for 5 days with treatment of BIP-V5 or control solvent (DMSO). The viability of Panc02 cells were determined using the Cell Counting Kit-8 (Dojindo, Japan) and measured at 450 nM with the BioTek Gen5 system (BioTek, Winooski, Vermont, USA).

ELISA analysis

Tumor tissues were subjected to a homogenization process in a solution of PBS, which was supplemented with Halt Protease and Phosphatase Inhibitor Cocktail (Thermo Fisher Scientific). This process was conducted in tubes using a three-dimensional cryogenic grinder (Servicebio, KZ-5F-3D), adhering strictly to the manufacturer’s protocol. For each 100 mg of tumor tissue, a volume of 3 mL of buffer was employed. Tumor homogenates were subsequently clarified through a centrifugation process at a force of 12,000 g for a duration of 20 min at a temperature of 4°C. ELISA was performed for the measurement of IFN-β (Multi Sciences, Zhejiang, China) in tumor tissue homogenates according to the manufacturer’s instructions.

The BMDMs obtained from Anxa1ΔMɸ and Anxa1WT mice were polarized into M1 and M2 macrophages, respectively. The supernatants were clarified by centrifugation at 12,000 g for 20 min at 4°C. For each sample, an equal volume was employed. IL-1β, IL-6, TNF-α, IL-10 and CCL2 (Multi Sciences, Zhejiang, China) were assayed in supernatants according to the manufacturer’s instructions.

qPCR for general

For the execution of qPCR assays on samples, the total RNA was initially extracted using TRIzol (Invitrogen, 15596026). This was followed by a RT-PCR process conducted with the PrimeScript RT Reagent Kit (Takara, RR047A). The resulting complementary DNA (cDNA) was then subjected to a qPCR assay using the SYBR Green qPCR Master Mix (HY-K0501A, MCE), according to the manufacturer’s recommended protocol. All the qPCR assays were performed at least three times independently on the Applied Biosystems 7500 Real-Time PCR System. Sequences for qPCR primers are provided in online supplemental table S3.

Bulk RNA sequencing

Tumor tissues from the mouse were subjected to a grinding process and subsequently lysed using TRIzol (Invitrogen). The total RNA was then extracted using an RNA extraction kit (procured from Vazyme), strictly adhering to the manufacturer’s protocol. The sequencing and analysis process was performed by OE Biotech, Shanghai.

Proteome analysis in Liquid Chromatography-Mass Spectrometry/Mass Spectrometry analysis

Samples were added to the SDT buffer and transferred to 2 mL tubes containing a specific amount of quartz sand. The lysate was homogenized using an MP Fastprep-24 Automated Homogenizer at 6.0M/S for 30 s, repeated twice. The homogenate was then sonicated and boiled for 10 min. Following centrifugation at 14,000 g for 15 min, the supernatant was filtered using 0.22 µm filters. The filtrate was quantified using the BCA Protein Assay Kit (P0012, Beyotime). For each sample, 20 µg of proteins were mixed with 6× loading buffer and boiled for 5 min. The proteins were then separated on a 12% sodium dodecyl sulfate – polyacrylamide gel electrophoresis (SDS-PAGE) gel and protein bands were visualized by Coomassie Blue R-250 staining. For each sample, 50–200 µg of proteins were reduced with 100 mM Dithiothreitol (DTT) for 5 min at 100°C. Subsequently, the detergent, DTT, and other low-molecular-weight components were removed using UA buffer (8 M Urea, 150 mM Tris-HCl pH 8.5) through repeated ultrafiltration (Sartorius, 30 kD). A solution of 100 µl iodoacetamide (100 mM IAA in UA buffer) was added to block reduced cysteine residues, and the samples were incubated for 30 min in darkness. The filters were washed three times with 100 µl UA buffer and twice with 100 µl 50 mM NH4HCO3 buffer. Finally, the protein suspensions were digested with 4 µg trypsin (Promega) in 40 µl 50 mM NH4HCO3 buffer overnight at 37°C, and the resulting peptides were collected as a filtrate. The peptide segment was desalted using a C18 column. The peptide content was estimated by UV light spectral density at 280 nm, using an extinction coefficient of 1.1 of 0.1% (g/l) solution that was calculated based on the frequency of tryptophan and tyrosine in vertebrate proteins. LC-MS/MS analysis was performed on an Orbitrap Exploris 480 mass spectrometer (Thermo Fisher Scientific) coupled to EASY-nLC (Thermo Fisher Scientific). The MS data were analyzed using MaxQuant software V.1.6.17.0. The cut-off of the global false discovery rate for peptide and protein identification was set to 0.01. Protein abundance was calculated based on the normalized spectral protein intensity (LFQ intensity). Proteins with a fold change greater than 2 or less than 0.5 and a p value (Student’s t-test) less than 0.05 were considered to be DEPs. All protein sequences were aligned to the database using NCBI BLAST on a Linux server, retaining only the sequences in the top 10 and with an E-value less than or equal to 1e-3. The GO term of the sequence with the top bit-score was selected by Blast2GO. Annotation from GO terms to proteins was completed by Blast2GO Command Line. After the initial annotation, InterProScan was used to search the European Bioinformatics Institute (EBI) database by motif and then add the functional information of the motif to proteins to improve annotation. Further improvement of annotation and connection between GO terms were carried out by ANNEX. Fisher’s Exact Test was used to enrich GO terms by comparing the number of DEPs and total proteins correlated to GO terms. Pathway analysis was also performed using the KEGG database. Fisher’s exact test was used to identify the significantly enriched pathways by comparing the number of DEPs and total proteins correlated to pathways.

Tissue digestion

Single cells were isolated from tumor tissues by using gentle enzymatic dissociation with a Tumor Dissociation Kit (Miltenyi Biotec, 130-096-730), as previously described.51 Then, single-cell suspension was resuspended in PBS with 0.04% BSA at a density of 1×106 cells/mL in preparation for further analysis.

Flow cytometry

The discrimination between live and dead cells was conducted using the Fixable Viability Stain 700. Single-cell suspensions underwent incubation with anti-mouse CD16/CD32 to inhibit the non-specific binding of the Fc receptor, followed by staining with antibodies in accordance with established protocols.52 For the labeling of intracellular cytokines, cells were subjected to stimulation with a Leukocyte Activation Cocktail supplemented with GolgiPlug (550583, BD Pharmingen) for a duration of 4 hours at a temperature of 37°C and 5% CO2 concentration in RPMI medium. The Transcription Factor Buffer Set (562574, BD Pharmingen) was employed for intracellular staining, adhering to the guidelines provided by the manufacturer. Online supplemental figure S3A illustrates the representative gating for flow cytometry. Flow cytometric assays were executed using BD FACSCelesta, and the resultant data were analyzed employing FlowJo software (Treestar). A comprehensive list of all antibodies used for flow cytometry is provided in the online supplemental table S1.

scRNA-seq of tumor tissues from mouse models

Single-cell RNA sequencing

The pipeline of scRNA-seq was similar to those previously described.53 Single-cell suspensions in PBS were loaded onto a microwell chip using the Singleron Matrix single-cell processing system. Subsequently, barcoding beads were collected from the microwell chip, followed by reverse transcription of the mRNA captured by the barcoding beads to obtain cDNA, and PCR amplification. The amplified cDNA was then fragmented and ligated with sequencing adapters. The scRNA-seq libraries were constructed according to the protocol of the GEXSCOPE Single Cell RNA Library Kits (Singleron). Individual libraries were diluted to 4 nM, pooled, and sequenced on an Illumina NovaSeq 6000 with 150 bp paired-end reads.

Data preprocessing

The raw reads obtained from scRNA-seq were subjected to a processing pipeline using CeleSCOPE V.1.9.0 (Singleron Biotechnologies) to yield gene expression matrices. Initially, the raw reads were processed using CeleSCOPE, which employed Cutadapt V.1.17 to eliminate low-quality reads and trim polyA tail and adapter sequences. This step also involved the extraction of the cell barcode and unique molecular identifier (UMI). Subsequently, the reads were mapped to the reference genome GRCm38 (Ensembl V.92 annotation) using STAR V.2.6.1a. The UMI counts and gene counts for each cell were obtained using FeatureCounts V.2.0.1 software. These counts were then used to generate expression matrix files, which were used for further analysis.

Seurat pipeline

Scanpy V.1.8.2 was used for quality control, dimensionality reduction and clustering under Python V.3.9.10.54 For each sample dataset, we filtered the expression matrix by the following criteria: (1) Cells with a gene count less than 200 or with a top 2% gene count were excluded; (2) cells with a top 2% UMI count were excluded; (3) cells with mitochondrial content>10% were excluded; (4) genes expressed in less than 5 cells were excluded. After filtering, 1,41,071 cells were retained for the downstream analyses, with an average 1,674 genes and 4,019 UMIs per cell. The raw count matrix was normalized by total counts per cell and logarithmically transformed into a normalized data matrix. Top 2,000 variable genes were selected by setting flavor = “seurat”. Principal component analysis was performed on the scaled variable gene matrix, and the top 20 principle components were used for clustering and dimensional reduction. Cells were separated into 29 clusters by using the Louvain algorithm and setting the resolution parameter at 1.2. Cell clusters were visualized by using UMAP.

DEGs analysis

The DEGs analysis method was performed as described before.53 To identify DEGs, we used the scanpy.tl.rank_genes_groups() function based on the Wilcoxon rank-sum test with default parameters, and selected the genes expressed in more than 10% of the cells in either of the compared groups of cells and with an average log (fold change) value greater than 1 as DEGs. Adjusted p value was calculated by Benjamini–Hochberg correction and the value 0.05 was used as the criterion to evaluate the statistical significance.

Pathway enrichment analysis

In order to explore the potential functions of the DEGs, analyses were conducted using GO and the Kyoto Encyclopedia of Genes and Genomes (KEGG) in conjunction with the “clusterProfiler” R package V.3.16.1.55 Pathways with an adjusted p value (p_adj) of less than 0.05 were deemed to be significantly enriched.

Pseudotime trajectory analysis

The trajectory of cell differentiation for monocyte subtypes was reconstructed using Monocle 2 V.2.10.0.56 The construction of this trajectory involved the selection of the top 2,000 highly variable genes using the FindVariableFeatures() function from Seurat V.3.1.2. Dimension reduction was subsequently performed using the DDRTree() function. The resulting trajectory was visualized using the plot_cell_trajectory() function in Monocle 2.

Cell type recognition with cell-ID

Cell-ID is a multivariate approach that extracts gene signatures for each individual cell and performs cell identity recognition using hypergeometric tests (HGT).55 Dimensionality reduction was performed on a normalized gene expression matrix through multiple correspondence analysis, where both cells and genes were projected in the same low-dimensional space. Then a gene ranking was calculated for each cell to obtain the most featured gene sets of that cell. HGT were performed on these gene sets against reference from SynEcoSys database, which contains all cell type’s featured genes in the specific organ/tissue. Identity of each cell was determined as the cell type has the minimal HGT p value. For cluster annotation, frequency of each cell type was calculated in each cluster, and cell type with the highest frequency was chosen as the cluster’s identity.53 The cell type identification of each cluster was determined according to the expression of canonical markers from the reference database SynEcoSys (Singleron Biotechnology). SynEcoSys contains collections of canonical cell type markers for single-cell seq data, from CellMakerDB, PanglaoDB and recently published literatures.

Cell–cell interaction analysis (CellPhoneDB)

The cell–cell interaction analysis was performed by CellPhoneDB V.2.1.057 based on known receptor–ligand interactions between two cell types/subtypes, as described previously.

Immune Repertoire analysis

The pipeline of TCR clonotypes assignment was conducted using Cell Ranger (V.4.0.0) as a reference. A TCR/BCR diversity metric, encompassing clonotype frequency and barcode information, was procured. For the TCR, only cells possessing one productive TCR α-chain (TRA) and one productive TCR β-chain (TRB) were retained for subsequent analysis. Each unique pair of TRA(s)-TRB(s) was designated as a clonotype. If a clonotype was present in a minimum of two cells, the cells containing this clonotype were deemed to be clonal. The number of cells with such pairs served as an indicator of the degree of clonality of the clonotype.

Isolation and stimulation of mouse BMDMs

Bone marrow cells were isolated from the femur and tibia of mice and cultured with culture medium (high-glucose DMEM supplemented with 10% fetal bovine serum, 100 units/mL penicillin and 100 mg/mL streptomycin, 10 ng/mL M-CSF (MedChemExpress, USA)) for 5 days, allowed to obtain the adherent BMDMs. Then, these macrophages were induced to different phenotypes. The obtained BMDMs were stimulated with 100 ng/mL LPS and 20 ng/mL IFN-γ for 12 hours for inducing BMDMs into M1 macrophages, while stimulated with 20 ng/mL IL-4 for 24 hours for inducing BMDMs polarized into M2 macrophages.

Statistics and bioinformatics

All data were represented as the mean±SD deviation (SD). Each experiment was conducted a minimum of three times for consistency. The specific statistical details pertaining to the experiments are delineated in the associated legend or methods section. The two-tailed Student’s t-test was employed to ascertain the statistical significance between two distinct groups. For the purpose of correlation analysis, the Spearman Rank correlation test was used. The comparison of categorical variables was conducted using either the χ² test or the Fisher’s exact test. The measurement of OS was taken from the date of diagnosis until the date of death from any cause, or until the last censored follow-up. The methodologies used for survival analysis have been delineated in our preceding study. The data were analyzed using either the Statistical Package for Social Science V.26.0 or GraphPad Prism (V.8.3.0.). p<0.05 was considered statistically significant, with *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. ANXA1 expression data were obtained from the Cancer Genome Atlas (https://tcga-data.nci.nih.gov/tcga/) and TIMER databases (http://timer.comp-genomics.org/timer/).

Supplemental material

Data availability statement

Data are available upon reasonable request. All data relevant to the study are included in the article or uploaded as supplementary information.

Ethics statements

Patient consent for publication

Ethics approval

Human sample collection and the study protocol were approved by the Committee for the Ethical Review of Research, Fujian Medical University Union Hospital (2021KJCX060). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

We thank Professor Xiaolong Liu (Mengchao Hepatobiliary Hospital of Fujian Medical University), for generously sharing their primary mouse pancreatic ductal adenocarcinoma cell line KPC. We thank Jingru Liu for her help in the analysis of the flow cytometry experiments.

References

Supplementary materials

  • Supplementary Data

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Footnotes

  • ZH, FL and JL contributed equally.

  • Contributors YP and ZH conceived the research. ZH and FL designed the methodology. FL, JL, YW, Linjin Chen, HF, Linlin Chen, and SZ performed the experiments. ZH wrote the original draft of the manuscript. YP reviewed and edited the manuscript. HH and YP supervised the study. YP is the guarantor of this research.

  • Funding This study was supported by the National Natural Science Foundation of China (No. 82103310), Joint Funds of Scientific and Technological Innovation Program of Fujian Province (2020Y9081), Natural Science Foundation of Fujian Province (2021J01774), Excellent Young Scholars Cultivation Project of Fujian Medical University Union Hospital (2022XH025), and Fujian provincial health technology project (2020GGA029).

  • 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.