Abstract
Background
Esophageal squamous cell carcinoma (ESCC) is a common gastrointestinal malignancy with poor patient prognosis. Current treatment for ESCC, including immunotherapy, is only beneficial for a small subset of patients. Better characterization of the tumor microenvironment (TME) and the development of novel therapeutic targets are urgently needed.
Methods
In the present study, we hypothesized that integration of single-cell transcriptomic sequencing and large microarray sequencing of ESCC biopsies would reveal the key cell subtypes and therapeutic targets that determine the prognostic and tumorigenesis of ESCC. We characterized the gene expression profiles, gene sets enrichment, and the TME landscape of a microarray cohort including 84 ESCC tumors and their paired peritumor samples. We integrated single-cell transcriptomic sequencing and bulk microarray sequencing of ESCC to reveal key cell subtypes and druggable targets that determine the prognostic and tumorigenesis of ESCC. We then designed and screened a blocking peptide targeting Chemokine C–C motif ligand 18 (CCL18) derived from tumor associated macrophages and validated its potency by MTT assay. The antitumor activity of CCL18 blocking peptide was validated in vivo by using 4-nitroquinoline-1-oxide (4-NQO) induced spontaneous ESCC mouse model.
Results
Comparative gene expression and cell–cell interaction analyses revealed dysregulated chemokine and cytokine pathways during ESCC carcinogenesis. TME deconvolution and cell interaction analyses allow us to identify the chemokine CCL18 secreted by tumor associated macrophages could promote tumor cell proliferation via JAK2/STAT3 signaling pathway and lead to poor prognosis of ESCC. The peptide Pep3 could inhibit the proliferation of EC-109 cells promoted by CCL18 and significantly restrain the tumor progression in 4-NQO-induced spontaneous ESCC mouse model.
Conclusions
For the first time, we discovered and validated that CCL18 blockade could significantly prevent ESCC progression. Our study revealed the comprehensive cell–cell interaction network in the TME of ESCC and provided novel therapeutic targets and strategies to ESCC treatment.
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Background
Esophageal cancer ranked the sixth among malignant tumors worldwide. Over 90% of the tumor cases are esophageal squamous cell carcinoma (ESCC) [1, 2]. Despite recent advances in the personalized therapies, molecular subty** and development of targeted drugs such as epithermal growth factor receptor (EGFR) inhibitor Gefitinib and anti-programed cell death protein-1 (anti-PD-1) pembrolizumab [47] which may be overlooked by previous studies focusing on only one or several types of TME cells [48, 49]. In ESCC tumors, the increased cell interactions occurred mostly within and between the epithelial, fibroblasts and immune cells. Moreover, differential cell interaction analysis identified signaling from macrophages, DCs, and monocytes to epithelial/tumor and T cells was strengthened in ESCC tumors (Fig. 3D).
Combing clinical information in the ESCC microarray cohort, we were able to uncover prognostically relevant cell subtypes and cell communication ligands by dissecting the ESCC TME. In multiple solid tumors, TAMs are actively recruited to the TME through paracrine communication and chemotaxis with the tumor cells [16, 19, 36]. CCL18 is mainly secreted by antigen-presenting cells of the innate immune system, such as DCs, monocytes, and macrophages. It can act on the adaptive immune cells, and recruit naïve T cells, Tregs, or even B cells [50, 51]. TAMs were thought to closely resemble the M2 phenotype; however, recent findings suggested that this binary polarization model was oversimplified and a spectrum model of TAM phenotypes has been proposed instead [52]. TAM is highly plastic within TME, where they display different characteristics and functions and has mixed expression profiles ranging from M1 to M2 [53].
Comparative analysis of cell communication further screened prognostic ligand receptor pairs regulating ESCC TME including CCL3/CCR1, CCL3/CCR5 and CCL18/PITPNM3 (Fig. S1). We finally confirmed CCL18-PITPNM3 signaling network showed strongest interaction occurred between epithelial/tumor cells and macrophages based on cell interaction network inferred from ESCC scRNA-seq data (Fig. 4E). Chemokines play important roles during the cell migration between different organs and tissues. Therefore, it would be helpful to develop potential cancer treatments involving the immunostimulatory action of cytokines in order to address cytokine imbalance. They can regulate host response to cancer by directing leukocytes or other cells into the TME to elicit anti-tumor or tumor-promoting effects [33, 34]. The role of CCL18 in cancer progression is controversial, it was reported that CCL18 could directly promote invasion, metastasis and angiogenesis in breast cancer, pancreatic cancer and ovarian cancer [16, 17], but CCL18 was associated with prolonged survival in patients with gastric cancer [54]. Here we found that CCL18 was elevated in tumor tissues of ESCC, negatively correlated with the patient survival, and positively correlated with tumor develo** stage (Table 1), which corresponded with its high expression level on TAMs infiltration (Fig. 2B). As a receptor of CCL18, PITPNM3 mainly expressed in human retina, brain, spleen, and breast cancer cells [55]. It was reported that CCL18/PITPNM3 interaction could also promote liver cancer migration, invasion, EMT, and the progression of pancreatic ductal carcinoma [56]. Similarly, we discovered that CCL18 could promote proliferation of esophageal cancer cells via its interaction with PITPNM3. Although we discovered the CCL18 was mainly expressed by M1 macrophages, the role of M2 macrophages shall not be fully ruled out. It was uncovered that CCL18 caused the maturation of cultured monocytes to macrophages in the M2 spectrum [57]. Therefore, CCL18 secreted by macrophages in the tumor tissues of ESCC could promote the proliferation of esophageal cancer cells and led the polarization of macrophages toward M2 phenotype.
Considering the high similarities of structures and sequences of CCL3 and CCL18, we designed and evaluated blocking peptides based on the functional fragments of both chemokines. The Pep3 constructed from the N terminus of hCCL18 was selected due to its greatest inhibition effect on EC-109 cell proliferation (Fig. 6). The antitumor efficacy of CCL18 blocking peptide Pep3 was further validated in vitro and in vivo, using a spontaneous ESCC mice model induced by 4-NQO. Pep3 is, to our knowledge, the first peptide to inhibit CCL18 and greatly reduce the ESCC cancer progression. This peptide may reduce esophageal carcinogenesis by inhibiting TAM infiltration and recruitment via CCL18/PITPNM3 or CCL3/CCR1/CCR5 signaling, hence enhancing the CD8+ T cell response (Fig. 7).
Furthermore, the pan-cancer analyses between CCL18 and immune checkpoints expression have important implications for cancer immunotherapy. As a number of inhibitory immunoreceptors have been identified and studied, 40 known immune checkpoint genes were collected, and were correlated with the expression of CCL18 (Fig. S9). We found 26 checkpoints were highly co-expressed with CCL18 including but not limited to PD-L1 (CD274), LAG3, TIGIT and CTLA4. Compared with other 33 cancer types in The Cancer Genome Atlas (TCGA) (Fig. S10), CCL18 was co-expressed with multiple immune checkpoints in 14 cancers. This demonstrated the great potential of CCL18 to be served as a pan-cancer target for future immunotherapy.
There are major efforts to develop therapeutic strategies to overcome immune resistance by using a combination of checkpoint blockers, and to improve drug efficacies and response rate of patients. Recent reports have indicated the strategies to improve phagocytic ability and reduce tumor growth via blocking PD-1 on TAMs [58] or promoting M2 macrophage polarization [59, 60]. TME changes not only influence tumor progression, but also dramatically influence the efficacy of cancer therapy. Monocyte derived macrophages build an essential inflammatory niche sha** tumor immune microenvironment. Recent progress defined the molecular landscapes and mechanisms of macrophage differentiation [61] held the promise to uncover its heterogeneity and functional roles within tumors. TAMs secrete cytokines and chemokines that can suppress T cell recruitment and activation, thereby promoting resistance to immune checkpoint inhibition. Novel therapeutic techniques, as demonstrated here, have the potential to synergize with checkpoint inhibitors, chemotherapy, and/or radiation therapy to enhance the overall efficacy of cancer treatment.
Conclusion
In summary, our study provided a comprehensive landscape of TME in ESCC by characterizing the cell–cell interactions dynamics of ESCC TME, thus provided novel insights to understand the co-evolution of tumor and immune system in ESCC. We identified that CCL18, mainly secreted by macrophages, and may serve as a target for the diagnosis and immunotherapy of ESCC by combining bioinformatic analysis and in vitro validation of the associated signaling pathway. We designed and screened a CCL18 blockade peptide and validated its antitumor activity in vitro and in vivo. As summarized in Fig. 8, CCL18 blockade might inhibit esophageal carcinogenesis by preventing TAMs recruitment and cancer cell proliferation.
Availability of data and materials
The dataset and associated source codes supporting the conclusions of this article is available from the corresponding author upon request.
Abbreviations
- ESCC:
-
Esophageal squamous cell carcinoma
- TME:
-
Tumor microenvironment
- EMT:
-
Epithelial mesenchymal transformation
- CCL18:
-
Chemokine C–C motif ligand 18
- IL-17:
-
Interleukin-17
- TCGA:
-
The Cancer Genome Atlas
- TAMs:
-
Tumor associated macrophages
- DCs:
-
Dendritic cells
- FACS:
-
Fluorescence-activated cell sorting
- IHC:
-
Immunohistochemistry
- eBayes:
-
Empirical Bayes
- DEGs:
-
Differentially expressed genes
- UMAP:
-
Uniform manifold approximation and projection
- FBS:
-
Fetal bovine serum
- PITPNM3:
-
PITPNM Family Member 3
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Acknowledgements
We thank the National Natural Science Foundation of China, Shenzhen Science and Technology Program and the Key Incubation Fund of Sun Yat-sen University to Dr. Yanfeng Gao and Dr. **nghua Sui.
Funding
This work was supported by the National Natural Science Foundation of China (Nos. U20A20369, 81901687, U1604286), Shenzhen Science and Technology Program (JCYJ20190807154819245, KQTD20190929173853397), and the “Pearl River Talent Plan” Innovation and Entrepreneurship Team Project of Guangdong Province (2019ZT08Y464).
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XS, CC, XZ, XW, YG, and YL conceived and performed analysis and supervised the project. CS obtained primary tumors from the operating room during surgery. CC, XZ, XW, YY, WZ and ZH performed wet lab experiments and validation. XW and CS extracted and curated clinical information from patient records. XS analyzed all scRNA-seq and microarray data, designed and conducted integration analysis and cell communication analysis. XZ, XW, YL and YG supervised and oversaw immunohistochemistry experiments and validation. XZ and CC designed and synthesized peptides. CC performed animal experiments. XS, CC, XZ, XW, YL and YG wrote the paper with contributions from all authors. All authors approved submission of the final manuscript.
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All patient samples in this study were collected with informed consent for research use and approved by the institutional research ethics committee of the Affiliated Cancer Hospital of Zhengzhou University / Henan Cancer Hospital (protocol numbers 2017407) in accordance with the Declaration of Helsinki. Animal experiments in this study were approved by the Ethics Committee of the Zhengzhou University (ethical approval code number: ZZUIRB2021-32).
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Additional file 1:
FigureS1. Expression,distribution, and prognostic significance of CCL3, CCL18 and their receptors inESCC TME. (A) Bubble heatmap showing expression levels of CCL3,CCL18 and their receptors in different cell types and locations based on ESCC scRNA-seqdataset. Dot size indicates percentage of expressing cells, colored based onnormalized expression levels. (B) Kaplan-Meier curves of overall survival ofESCC microarray cohort stratified by the expression of CCL3 and its receptorsCCR1 and CCR5. (C) Kaplan-Meier curves of overall survival of ESCC microarraycohort stratified by the expression of CCL18 receptors ACKR1, ERG28 and CCR8. FigureS2. Cell-cell communication network of ESCC. Differential number of possible interactions and interaction strength(edge weight) between the five major cell types between peri- and tumor sites(A and B). Number of possible interactions between the 11 cell types in peri-and tumor sites (C and D). Figure S3. Cell-cellcommunication in 4-nitroquinoline 1-oxide (4NQO) inducedmouse ESCC model. (A) UMAP visualization of 19,829 cells from 3 normal mice, 3 carcinoma in situ(CIS) mice and 3 invasive carcinoma (ICA) mice (CRA002118). (B) Differential numberof possible interactions between any two cell populations. Red (positivevalues) and blue (negative values) in the color bar indicate higher number ofpredicted interactions in normal and tumor mice samples, respectively. (C)A circle plotshowing the number of possible interactions in normal mice estimated byCellChat. (D) A circle plot showing the number of possible interactions in CIS and ICAmice estimated by CellChat. (E)A circle plot showing the cell-cell communicationnetwork of CCL pathway estimated by CellChat. (F)The significantly related ligand–receptor interactions of CCL pathway in theESCC mouse model inferred by CellChat analysis. Figure S4. The expression of CCL3 or its receptor inthe ESCC tissues or macrophages. (A)The mRNA expression level of CCL3, CCR1 and CCR5 in the ESCC tissues of 4-NQOinduced mice with ESCC by qPCR. (B) The expression of CCL3 and co-localizationwith macrophages (F4/80) in the mouse ESCC tissues. Representative views were shown. (C) The expression of CCL3 in BMDM polarized M1 or M2 macrophages inmRNA and protein levels. (*P < 0.05, ***P < 0.001). Figure S5.The cell cycle and apoptosis of EC-109, EC-109 Vector and EC-109 shPITPNM3cells. (A) Cell cycles of EC-109, EC-109 Vectorand EC-109 shPITPNM3 cells treated with or without 40ng/mL CCL18 for 72 h. *P< 0.05. (B) Flow cytometry analysis of the apoptosis of EC-109, EC-109Vector and EC-109 shPITPNM3 cells treated with or without 40ng/mL CCL18 for 72h. n.s., no significance. Figure S6. The proliferation, colony formation and migration analysis ofEC-109, EC-109 Vector and EC-109 shPITPNM3 cells. (A) The cell growth curve and cell viability of EC-109, EC-109 Vector andEC-109 shPITPNM3 cells determined by the cell number counting and MTT assay. (B)The colony formation of EC-109, EC-109 Vector and EC-109 shPITPNM3 cells toform clones. (C) Representative results of the Transwell assay of EC-109,EC-109 Vector and EC-109 shPITPNM3 cells at 48 h. Bar = 200 μm. (D) Representativeimages of the wound-healing assay of EC-109, EC-109 Vector and EC-109 shPITPNM3cells after 48 h. n.s., no significance. Figure S7. (A)Representative results of the Transwell assay of EC-109 cells in the presenceor absence of rhCCL18 at increasing concentrations (10–40 ng/mL) for 48 h. (B)Representative images of the wound-healing assay of EC-109 cells in thepresence or absence of rhCCL18 at increasing concentrations (10–40 ng/mL) at 0h and 48 h. n.s., no significance. Figure S8. The expression of PITPNM3 inthe ESCC tissues with or without Pep3 treatment. (A) The mRNA expression level of PITPNM3 in the ESCC tissues of 4-NQOinduced mouse model by qPCR. (B) Representative images of PITPNM3 staining inthe ESCC tissues, bar = 50 μm. ***P < 0.001. Figure S9. Correlationof CCL18 with immune scores, CD8 T cells and 40 known immune checkpoints in ESCC. The correlationmap between 40 common immune checkpoint genes andthe expression of CCL18 is as follows: *** P < 0.001, ** P< 0.01, * P < 0.05. Most correlated immune checkpoint genes arehighlighted in red. Figure S10. Correlation of CCL18 with known immunecheckpoints. Calculated from RNA-seqexpression data from The Cancer Genome Atlas (TCGA)datasets across 33 different cancer types. *** P < 0.001, ** P< 0.01, * P < 0.05.
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Sui, X., Chen, C., Zhou, X. et al. Integrative analysis of bulk and single-cell gene expression profiles to identify tumor-associated macrophage-derived CCL18 as a therapeutic target of esophageal squamous cell carcinoma. J Exp Clin Cancer Res 42, 51 (2023). https://doi.org/10.1186/s13046-023-02612-5
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DOI: https://doi.org/10.1186/s13046-023-02612-5