Caerin 1.1 and 1.9 peptides halt B16 melanoma metastatic tumours via expanding cDC1 and reprogramming tumour macrophages

Caerin 1.1 and 1.9 peptides halt B16 melanoma metastatic tumours via expanding cDC1 and reprogramming tumour macrophages | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Caerin 1.1 and 1.9 peptides halt B16 melanoma metastatic tumours via expanding cDC1 and reprogramming tumour macrophages Quanlan Fu, Yuandong Luo, Junjie Li, Hejie Li, Xiaosong Liu, Zhu Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4671312/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Oct, 2024 Read the published version in Journal of Translational Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Background Cancer immunotherapy, particularly immune checkpoint inhibitors (ICBs) such as anti-PD-1 antibodies, has revolutionized cancer treatment, although response rates vary among patients. Previous studies have demonstrated that caerin 1.1 and 1.9, host-defence peptides from the Australian tree frog, enhance the effectiveness of anti-PD-1 and therapeutic vaccines in a murine TC-1 model by activating tumour-associated macrophages intratumorally. Methods We employed a murine B16 melanoma model to investigate the therapeutic potential of caerin 1.1 and 1.9 in combination with anti-CD47 and a therapeutic vaccine (triple therapy, TT). Tumour growth of caerin-injected primary tumours and distant metastatic tumours was assessed, and survival analysis conducted. Single-cell RNA sequencing (scRNAseq) of CD45 + cells isolated from distant tumours was performed to elucidate changes in the tumour microenvironment induced by TT. Results The TT treatment significantly reduced tumour volumes on the treated side compared to untreated and control groups, with notable effects observed by Day 21. Survival analysis indicated extended survival in mice receiving TT, both on the treated and distant sides. scRNAseq revealed a notable expansion of conventional type 1 dendritic cells (cDC1s) and CD4 + CD8 + T cells in the TT group. Tumour-associated macrophages in the TT group shifted toward a more immune-responsive M1 phenotype, with enhanced communication observed between cDC1s and CD8 + and CD4 + CD25 + T cells. Additionally, TT downregulated M2-like macrophage marker genes, particularly in MHCIIhi and tissue-resident macrophages, suppressing Cd68 and Arg1 expression across all macrophage types. Differential gene expression analysis highlighted pathway alterations, including upregulation of oxidative phosphorylation and MYC target V1 in Arg1 hi macrophages, and activation of pro-inflammatory pathways in MHCII hi and tissue-resident macrophages. Conclusion Our findings suggest that caerin 1.1 and 1.9, combined with immunotherapy, effectively modulate the tumour microenvironment in primary and secondary tumours, leading to reduced tumour growth and enhanced systemic immunity. Further investigation into these mechanisms could pave the way for improved combination therapies in advanced melanoma treatment. Caerin peptide B16 cell Melanoma Macrophage cDC1 CD4+CD8+ T cell cell-cell communication anti-CD47 antibody immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Melanoma is one of the most prevalent types of skin cancer, notorious for its heterogeneity and propensity to metastasise to distant organs[ 1 ]. The incidence of melanoma has been rising globally, posing a significant public health challenge. According to the World Health Organisation, approximately 132,000 new cases of melanoma occur worldwide each year[ 2 , 3 ]. In the United States alone, the American Cancer Society estimates that there were about 106,110 new cases of melanoma diagnosed in 2021, with an estimated 7,180 deaths from the disease[ 4 ]. The lifetime risk of developing melanoma has increased dramatically over the past decades. In the 1930s, the risk was about 1 in 1,500. Today, it is estimated to be about 1 in 38 for white individuals, 1 in 1,000 for Black individuals, and 1 in 167 for Hispanic individuals[ 5 ]. This rise in incidence is attributed to several factors, including increased ultraviolet (UV) radiation exposure, the use of tanning beds, and changes in lifestyle and behaviour that increase sun exposure. The five-year survival rate for localised melanoma is now around 99%, but this drops significantly to about 27% to as low as 4.7% across the subcategories of stage IV metastatic disease[ 4 ]. In recent years, the treatment landscape for melanoma has been revolutionised with the approval of new therapeutic methods, including both targeted and immune-based therapies[ 6 ]. Among these, immune checkpoint inhibitors (ICIs) have gained prominence. These inhibitors work by targeting key regulatory pathways in T cells, thereby enhancing the body's immune response against melanoma cells. Standard treatments now commonly include ICIs, such as anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4), anti-programmed death 1 (anti-PD-1), and anti-programmed death-ligand 1 (anti-PD-L1) therapies. Additionally, targeted inhibitors that focus on specific mutations in the MAPK pathway, such as BRAFV600E and MEK inhibitors, have shown efficacy in controlling tumour growth[ 7 ]. The advent of these therapies has significantly improved the survival rates of many patients with advanced melanoma, providing new hope where few options previously existed[ 8 , 9 ]. Despite these advancements, patient responses to these treatments remain highly variable. A substantial proportion of patients do not achieve long-term remission, and resistance to therapy is a common challenge[ 10 ]. This underscores the need for novel therapeutic approaches that can complement existing treatments and improve overall patient outcomes[ 11 ]. One promising avenue of research involves the use of combination therapies that can target multiple aspects of the tumour microenvironment. Caerin 1.1/1.9, a mixture of peptides derived from the skin secretions of an Australian tree frog, has demonstrated potential in this regard. Previous studies have shown that caerin 1.1/1.9 can inhibit the growth of various cancer cells, including human and mouse cervical cancer, human thyroid cancer, and human breast cancer cells in vitro [ 12 – 17 ]. In the TC-1 tumour-bearing mouse model, caerin 1.1/1.9 has been shown to attract a large number of immune cells, such as macrophages, T cells, and NK cells, to the tumour microenvironment, thereby inhibiting tumour growth[ 18 ]. Importantly, caerin 1.1/1.9 can modulate the heterogeneity of tumour-associated macrophages, promoting the polarization from M2- to M1-like macrophages, which is associated with a more robust anti-tumour immune response[ 19 ]. Given these promising preclinical findings, we hypothesised that the combination of caerin 1.1/1.9 with therapeutic vaccines and anti-CD47 (triple therapy, TT) could enhance the anti-tumour immune response and improve therapeutic efficacy. Anti-CD47 is an immune checkpoint inhibitor that blocks the "don't eat me" signal used by cancer cells to evade phagocytosis by macrophages[ 20 ]. Combining this with the immune-activating properties of therapeutic vaccines and the tumour-inhibitory effects of caerin1.1/1.9 could potentially create a synergistic effect. To test this hypothesis, we established a bilateral mouse tumour model to simulate B16 melanoma metastasis, known for its aggressive nature in skin cancer. This model enabled us to assess the impact of the Triple Therapy (TT) on both primary and secondary tumour sites, offering a comprehensive evaluation of its therapeutic efficacy. The TT treatment significantly reduced tumour growth at both sites and prolonged survival compared to untreated and control groups. Subsequently, we conducted single-cell transcriptomic analysis of CD45 + cells isolated from the three largest tumours on the distant metastatic side in each group. This analysis aimed to elucidate how TT treatment alters the tumour microenvironment compared to untreated and control groups. Our study aims to provide mechanistic insights into how caerin 1.1/1.9, in conjunction with therapeutic vaccines and anti-CD47 therapy, enhances systemic immune responses against melanoma. These findings contribute to the development of more effective combination therapies for advanced melanoma treatment. Methods Mice Female C57BL/6 mice, aged 8 to 12 weeks, were used in the experiment. These mice were purchased from the Guangdong Provincial Centre for Animal Resources and housed under specific pathogen-free (SPF) conditions in the animal facility of the First Affiliated Hospital of Guangdong Pharmaceutical University. Each cage contained five mice, which were maintained in a controlled environment at 22°C with 75% humidity and a 12-hour light/dark cycle. They were provided with standard mouse chow and water ad libitum. All experiments were conducted in accordance with the guidelines provided by the Animal Experiment Ethics Committee (Ethical Approval No.: GYFYGZR2023027). Cell culture The B16 cell line was obtained from the Shanghai Institute for Biological Sciences, Chinese Academy of Sciences. The cells were cultured in RPMI 1640 medium (Gibco) supplemented with 10% fatal bovine serum (FBS, Gibco) and 100 U/mL penicillin/100 µg/mL streptomycin (Gibco). They were grown to 70–80% confluence before being passaged, and were passaged 3 to 5 times prior to inoculation. Peptide synthesis Caerin 1.1 (referred to as F1, sequence: GLLSVLGSVAKHVLPHVVPVIAEHL-NH 2 ), caerin 1.9 (referred to as F3, sequence: GLFGVLGSIAKHVLPHVVPVIAEKL-NH 2 ), and a control peptide P3 without cytotoxic properties towards various cancer cells (GTELPSPPSVWFEAEFK-OH) were synthesised by Shanghai Qiangyao Biological Technology Co., Ltd, China. The purity of the peptides was determined to be 95% via reverse-phase high-performance liquid chromatography. The caerin and P3 peptides were stored at 4°C until use. Establishment of a bilateral mouse melanoma model When the B16 cell line reached 70–80% confluence, the cells were washed three times with phosphate-buffered saline (PBS). A total of 4×10 5 B16 cells were inoculated at the right midline of the abdomen, and 2×10 5 B16 cells were inoculated at the left midline, with a volume of 200 µL per mouse. Approximately 3 to 5 days after tumour inoculation, when tumour formation was visible to the naked eye, the mice were randomly divided into three groups: PBS + PBS + PBS (UN), P3 + V + anti-CD47 (control), and F1/F3 + V + anti-CD47 (TT). Here, V represents the therapeutic vaccine, composed of the B16 melanoma antigen (amino acid sequence: ISQAVHAAHAEINEAGRSIINFEKLSVYDFFVWL), monophosphoryl lipid A (MPLA, Sigma, L6895-5MG), and anti-interleukin-10 receptor antibody (αIL-10R, BioXcell, Cat#: BE0050). After grouping, F1/F3 peptides were injected into the right tumour, while P3 was injected as a control peptide. The therapeutic vaccine was administered intramuscularly, and the in vivo antibody was administered intraperitoneally. During the treatment, tumour size was measured every other day, and the survival of the mice was monitored. Tumour size was calculated using the formula: (length × width 2 )/2. Isolation of tumour-infiltrating CD45 + cells and single-cell transcriptome The isolation of tumour infiltrating CD45 + cells followed the protocol reported previously[ 12 , 21 ]. In brief, B16 tumours on the left sides were dissected into 2 × 2 mm pieces and digested in a solution containing RPMI 1640, Enzyme D, Enzyme R, and Enzyme A within a gentleMACS C Tube. The tissue was dissociated using a gentle MACS Dissociator from Miltenyi (Gladbach, Germany). After removing dead cells and debris, the remaining cells were labeled with CD45 microbeads (130–110–618). Flow cytometry and trypan blue staining confirmed that the viability of CD45 + cells was over 80% of total cells. The cells were washed with ice-cold PBS containing 10% fetal bovine serum after sorting and counted using a hemocytometer. Libraries were generated and sequenced from the cDNAs with Chromium Next GEM Single Cell 5’ Reagent Kits v3.1. The bioinformatic analysis workflow was reported elsewhere[ 12 , 21 ]. Briefly, the 10X Genomics Cell Ranger software (version 3.1.0) was employed to process raw BCL files, converting them into FASTQ files, and subsequently performing alignment and quantification of gene counts. Reads containing low-quality barcodes and UMIs were filtered out before mapping to the reference genome. Only reads uniquely mapped to the transcriptome, intersecting with at least 50% of an exon, were considered for UMI counting. Prior to quantification, UMI sequences underwent correction for sequencing errors, and valid barcodes were identified using the EmptyDrops method[ 22 ]. Each sample's cell-by-gene matrix was imported individually into Seurat version 3.1.1 for downstream analysis. Cells with unusually high numbers of UMIs (≥ 8000) or a high percentage of mitochondrial genes (≥ 10%) were filtered out, along with doublet GEMs. Subsequently, a global-scaling normalization method, "LogNormalize," was applied to the dataset. To mitigate batch effects and other experimental variations during clustering, the Harmony algorithm was utilized to integrate all samples. Harmony employs a PCA embedding of cells along with their batch assignments to produce a batch-corrected embedding. Seurat then employed a graph-based clustering approach where distances between cells were calculated based on previously identified principal components (PCs). Cells were embedded into a shared-nearest neighbour graph, connecting cells based on similar gene expression patterns. For visualisation of clusters, t-distributed Stochastic Neighbour Embedding (t-SNE) plots were generated using the same PCs[ 23 ]. The expression levels of each gene within a given cluster were compared against all other cells using a Wilcoxon rank sum test. Genes were considered significantly upregulated if they met several criteria: (1) at least 1.28-fold overexpression in the target cluster, (2) expression in more than 25% of cells within the target cluster, and (3) a P-value less than 0.05. This comprehensive approach facilitated the identification of genes specifically associated with each cluster, providing insights into their potential functional roles within the biological context studied. Gene ontology, KEGG pathway and GSEA analysis The enrichment of biological processes and KEGG pathways[ 24 ] was assessed for the treatments in comparison to the untreated and control groups. The genes associated with the proteins exhibiting differential expression across the three groups were subjected to analysis using Gene Set Enrichment Analysis (GSEA) with a significance threshold of P -value < 0.05. This analysis was performed using GSEA version 4.1.0[ 25 , 26 ]. Ingenuity Pathway Analysis (IPA) The release version 111725566 (2024) from Qiagen was used to analyse genes with differential expression. Gene expression within each experimental group was transformed into ratios relative to the untreated group. These ratios, along with corresponding p-values, were then input into IPA to construct the IPA database. To generate canonical pathways and regulatory networks, Fisher's Exact Test was utilized. This statistical test assessed associations between our input data and established annotations within the IPA database, helping to identify significant biological pathways and networks affected by the experimental conditions. Cell-cell communication analysis For cell-cell communication inference and analysis, we utilized the R package CellChat (version 1.1.0) with default parameters, leveraging a publicly available repository of ligands, receptors, cofactors, and their interactions[ 27 ]. Expression levels for interaction analysis were normalized relative to the total read mapping across the same set of coding genes in all transcriptomes. Expression values were averaged within each single-cell cluster or cell sample. The analysis was performed using the CellChatDB mouse database. All three sample groups were normalised collectively, followed by individual extraction and parallel comparative analysis, assuming shared cell types among the groups. Statistical analysis Statistical analysis in this study, unless otherwise specified, employed an unpaired Student’s t-test using GraphPad Prism 8 software. All experimental data underwent analysis, and graphs were generated using the same software. The determination of statistically significant means was based on a probability level of 0.05. Results The TT treatment reduces tumour size and weight, extends survival in B16 melanoma-bearing mice Our previous findings suggest that the triple therapy (TT), which combines F1/F3 peptides with therapeutic vaccines and α-CD47, can significantly inhibit tumour growth (unpublished data). To further investigate whether F1/F3 can enhance systemic immunity induced by the therapeutic vaccine, we established a bilateral tumour model to simulate tumour metastasis (Fig. 1A). Tumours on both the left and right sides of different groups were collected and measured comparatively. The results showed that in the right-side tumours (with treatments), tumour volume was reduced in the control group (P3 + V + α-CD47) compared to the untreated (UN) group (PBS only), with the maximum difference observed on Day 21 (three days after treatment completion) (Fig. 1B). However, tumour volume in the control group increased quickly and reached a level similar to the UN group by Day 29, with no significant difference detected. Throughout the entire observation period, the TT group exhibited significantly inhibited tumour growth compared to the other two groups. For the left side tumours (without treatment), no significant difference was observed between the PBS group (UN) and the control group. Notably, TT significantly inhibited tumour growth, with tumour volume decreased by approximately 60% at Day 29 (Fig. 1C). Additionally, the survival of mice in the TT group was significantly extended on both sides, with a more pronounced effect on the right side (Fig. 1D and 1E). After the treatment concluded, the mice were dissected, and the tumours on the left and right sides were weighed and compared in Fig. 1F and Figure S1 , respectively. The results clearly showed that the TT significantly reduced tumour weight on both sides compared to either the control group or the UN group, with particularly notable effects on the right side. Statistical analysis revealed significant differences in tumour weights on both sides between the TT group, the control, and the UN groups (Fig. 1G and Fig. 1H ). These findings indicate that the intra-tumoral injection of F1/F3 peptides significantly enhanced the therapeutic efficacy of the vaccine and α-CD47, effectively inhibiting tumour growth on both the primary and the metastatic tumour sides, respectively. Figure 1 Effect of F1/F3 peptides in combination with therapeutic vaccines and α-CD47 triple therapy (TT) on bilateral tumour growth and survival. ( A ) The establishment of a bilateral tumour model to simulate B16 tumour metastasis and the treatments in this study. ( B ) Tumour growth on the right side (with treatment). No significant difference was observed between the UN group (PBS only) and the control group (P3 + V + α-CD47) post-treatment. The TT group exhibited significantly inhibited tumour growth and improved therapeutic efficacy against B16 melanoma compared to the control group. ( C ) Tumour growth on the left side (without treatment). Tumour volume was reduced in the control group compared to the UN group, and the TT group demonstrated superior efficacy on both the left and right sides. ( D ) Survival of mice with tumours on the right side. Mice treated with the TT exhibited significantly extended survival compared to the control groups. ( E ) Survival of mice with tumours on the left side. Mice treated with the TT exhibited significantly extended survival compared to the control groups. Results are expressed as the mean ± standard error of the mean (SEM), and inter-group differences were statistically analysed using two-way ANOVA, where * P -value < 0.05 and ** P -value < 0.01 indicate significant differences, and ns indicates no significant difference. ( F ) Statistical graph depicting tumour weight on the right side. ( G ) Dissection diagram illustrating tumour placement on the left side of the mice. ( H ) Statistical representation of tumour weight on the left side. (See Figure S1 for the dissection diagram of tumours placement on the right side) scRNA-seq revealed the modulation of CD45 + cell heterogeneity in B16 tumour on the metastasis side Total viable CD45 + leukocytes were isolated from both sides of the UN, control, and TT groups (Fig. 2 and Table S1 ). After quality control, a total of 7,007, 8,165, and 8,307 cells were utilised for downstream analysis in the UN, control, and TT groups, respectively. Gene expression data from extracted CD45 + cells were aligned and projected into a two-dimensional space using t-stochastic neighbour embedding (t-SNE) to identify tumour-associated immune cell populations and differentially expressed genes (Fig. 3A and Figure S2 A ). This unsupervised clustering analysis identified 21 cell clusters (labelled "0" to "20"), consistently present across all three groups, indicating robust cell-type identification independent of treatments. Compared to the UN group (Fig. 3B), the control (Fig. 3C) and TT (Fig. 3D) groups showed reduced populations in clusters 2, 11, 13, and 17, while exhibiting higher populations in clusters 3, 7, 8, 10, and 12 ( Figure S2 B ). Notably, clusters 4 and 18 were significantly expanded only in the TT group ( Table S1 ). The TT group contributed more cells to clusters 3, 4, 7, 18, and 19, while contributing lest cells to clusters 9, 13, 15, and 20 ( Figure S2 C ). Figure 2 The scRNA-seq analysis of the B16 tumour tissues in the left side of mice. t-Stochastic neighbour embedding (t-SNE) representation of aligned gene expression data in CD45 + single cells extracted from the TME of B16 tumours shows partition into 21 distinct clusters, the distribution of the clusters in the UN ( A ), control ( B ) and TT ( C ) groups. ( D ) Selected enriched genes used for biological identification of each cluster and the top 5 DEGs of each cluster (in Z-score). MΦ represents macrophage; NK cell, natural killer cell; migDC, migratory DC; cDC1, conventional DC type 1; pDC, plasmacytoid dendritic cell; ASPCs, adipogenic stem and precursor cells; NECs, neuroendocrine cells. (see Table S2 for the full list of all marker genes detected) Differentially expressed genes (DEGs) were analysed to identify cell type-specific marker genes (Fig. 2D and Table S2 ). Established canonical markers such as Cd3d , Cd3g , Cd79a , Gzma , Prf1 , Klrk1 , Cd19 , and Cd8b1 indicated lymphocyte lineages (Fig. 2E). Myeloid cell identities were supported by markers including Itgam , Adgre1 , Itgax , Csf1r , Lgals3 , Itgae , Siglec1 , Mrc1 , H2-Ab1 , S100a8/S100a9 , Ly6g1 , and Ly6c1 [ 12 , 21 ]. Clusters were annotated with predicted cell-type identities based on known marker genes from literature sources[ 28 ]. Notable macrophage subtypes included Arg1 hi MΦ (cluster 0; marker genes: Arg1 , Mmp12 , Mmp13 , and Nos2 )[ 12 ], tissue-resident macrophages (Res-like MΦ) (cluster 1; marker genes: C1qa , C1qc , Ms4a7 , and Ccl12 )[ 29 ], MHCII hi MΦ (cluster 4; marker genes: Chil3 , Ifitm6 , H2-DMb1 , and H2-DMa ), and tumour associated macrophages (TAMs) (cluster 11; marker genes: Cd209f , Lyve1 , Folr2 , and Ccl8 )[ 30 ]. T cell subsets[ 12 , 21 ] identified included, including CD4 + CD8 + T cells (cluster 3; marker genes: Ctsw , Nkg7 , and Trbc2 ), CD4 + CD25 + T cells (cluster 7; marker genes: Ctla4 , Il2ra , and Tigit ), and CD8 + T cells (cluster 12; marker genes: Cd8a , Cd8b1 , and Cd3d ). High populations of natural killer (NK) cells (cluster 8; marker genes: Nkg7 , Gzma , and Prf1 ), neutrophils (cluster 9; marker genes: Retnlg , S100a9 , and S100a8 ), and monocytes (cluster 10; marker genes: Ifit1, Cmpk2, Ifit3 , and Ifit3b ) were also detected. Cluster 13 represented B, supported by the marker genes such as Fcmr , Cd79a , and Ebf1 . Also, three clusters showed the signature of dendritic cells, i.e., migratory DCs (migDCs) (cluster 14; Ccl22 , Bcl2l14 , and Il12b ), plasmacytoid dendritic cells (pDCs) (cluster 16; Siglech , Klk1 , and Klk1b27 ), and conventional DC type 1 (cDC1s) (cluster 18; Xcr1 , Clec9a , and Mycl )[ 31 ]. Notably, cluster 5 showed characteristics of Langerhans cells, such as Camk1d , Lrmda , and Dennd1a [ 32 , 33 ]. Additionally, fibroblasts (cluster 2; marker genes: Ptgds , Cort , Cmtm5 , and Paqr6 ), erythroblasts (cluster 6; marker genes: Esco2 , H3c4 , Tk1 , and Asf1b ), adipogenic stem and precursor cells (ASPCs) (cluster 15; marker genes: Col6a2 , Col3a1 , Dpt , and Col1a1 )[ 34 ], neuronal cells (cluster 17; marker genes: Prickle2 , Grik2 , and Npas3 )[ 35 – 37 ], melanocyte ( Mlana , Pmel , and Tyrp1 )[ 38 – 40 ], and neuroendocrine cells (NECs) (cluster 20; marker genes: Alas2 , Isg20 , and Hbb-bt )[ 41 , 42 ] were identified, possibly indicating contaminants. The expressions of the marker genes of each cluster were compared, showing a relatively high correlation (score > 0.80) between Arg1 hi MΦs, Res-like MΦs, MHCII hi MΦs, erythroblasts, monocytes, TAMs, CD8 + T cells, and pDCs, respectively ( Figure S2 D ). The TT treatment reprograms tumour macrophages and expands MHCII hi population Four distinct populations of macrophages were clearly identified: Arg1 hi MΦs, Res-like MΦs, MHCII hi MΦs, and TAMs. The proportions of these macrophage populations across different groups were analysed. It was observed that Arg1hi MΦs were significantly more prevalent in the control group (55.3%) compared to the UN (37.0%) or TT (37.6%) groups (Fig. 3A and Table S2 ). Notably, MHCII hi MΦs were notably higher in the TT group (24.2%) compared to the UN (15.5%) and control (12.7%) groups. The population of TAMs showed a decrease in both the control and TT groups, with a more pronounced decrease in the control group. Differential gene expression analysis between the TT and control groups revealed that the MHCII hi MΦs exhibited the highest number of upregulated (983) and downregulated (1,140) DEGs, followed by Res-like MΦs (Fig. 3B). There was a relatively high overlap in DEGs among Arg1 hi , Res-like, and MHCII hi MΦs, indicating similarities in gene expression profiles among these populations, while TAMs showed a distinct gene expression pattern compared to other macrophage types, suggesting a unique TAM phenotype. Figure 3 Modulation of Arg1 hi and tumour-associated macrophages in the tumour microenvironment by TT treatment. ( A ) Comparison of the proportions of four macrophage (MΦ) populations among the UN, control, and TT groups. ( B ) Upset graph comparing the upregulated and downregulated differentially expressed genes (DEGs) in different macrophage populations in the TT group relative to the control group. ( C ) Beanplot showing the expression levels (in Log2 values) of selected tumour-associated macrophage marker genes across the four macrophage populations in the control and TT groups. ( D ) Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in Arg1hi macrophages of the TT group compared to the control group. ( E ) GSEA of hallmark pathways enriched in TAMs of the TT group compared to the control group. Significance levels are indicated as follows: *: P -value < 0.05, **: P -value < 0.01, ***: P -value < 0.001, and ****: P -value < 0.0001; by two-way Student’s t-test. The expression profiles of selected marker genes associated with M2-like macrophages, known for promoting tumour cell growth and inducing an immunosuppressive TME, were compared across four macrophage populations between the TT and control groups (Fig. 3C). The downregulation of these genes in treatments relative to the UN group was confirmed ( Table S3 ). Particularly noteworthy was the comparative downregulation of many of these genes in the MHCII hi and Res-like MΦs of the TT group compared to the control group. Significant suppression of Cd68 expression was evident across all macrophage populations in the TT group. Downregulation of Arg1 was observed in all macrophage types, with notably significant decreases in Res-like and Arg1 hi MΦs, which exhibited the highest baseline expression of Arg1 among all macrophages. A similar pattern was observed for Mmp12 and Mmp13 . Two hallmark pathways showed significant positive association with Arg1 hi MΦs in the TT group: 'oxidative phosphorylation' (OXPHOS) and 'MYC target V1' (Fig. 3D). Conversely, several hallmark pathways directly linked to immune response—such as IFN α/γ response, inflammatory response, and IL6/JAK/STAT3 signalling—were significantly inhibited in the control group compared to the TT group, though their activation in the TT group did not reach significance. Enrichment analysis revealed significant activation of IFN α response specifically in TAMs of the TT group, while OXPHOS remained prominently enriched (Fig. 3E). Similarly, several pathways associated with pro-inflammatory responses were suppressed in MHCII hi (Fig. 4A) and Res-like MΦs (Fig. 4B) of the control group compared to the TT group, with the latter also showing reduced apoptosis. Interestingly, the P53 pathway exhibited comparatively higher activation in these two macrophage types within the TT group. Additionally, metabolic pathways such as glycolysis, heme metabolism, and xenobiotic metabolism were downregulated in MHCII hi MΦs of the control group, along with fatty acid metabolism in Res-like MΦs. Comparative analysis with the UN group using IPA revealed that regulation of MHC class I quantity on cell surfaces was significantly activated in MHCII hi MΦs of the TT group compared to the control group, primarily modulated by Stat1 (Fig. 4C). Moreover, regulatory networks associated with inhibiting tumour growth, developing tumour cell lines, and cancer invasion were more prominent in MHCII hi MΦs of the TT group relative to those in the control group, as evidenced by downregulation of key regulators such as Arg1 , Spp1 , Adrb2 , Myc , Ilk , Gpnmb , and Eno1 (Fig. 4D). Regarding TAMs, pathways related to 'invasion of cells', particularly involving lymphocytes and leukocytes, were more activated in the control group compared to the TT group relative to the UN group ( Figure S3 A ). Similar to MHCII hi MΦs, TAMs in the TT group showed downregulation of pathways related to cancer cell growth, with further inhibition of angiogenesis and neoplasia of tumour cell lines ( Figure S3 B ). Figure 4 Modulation of MHCII hi and tissue-resident in the tumour microenvironment by TT treatment. ( A ) Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in MHCII hi MΦs of the TT group compared to the control group. ( B ) GSEA of hallmark pathways enriched in Res-like MΦs of the TT group compared to the control group. The top two most activated networks in the MHCII hi MΦs of the TT group relative to the UN group, identified by Ingenuity Pathway Analysis (IPA): ( C ) Quantity of MHC class I on cell surface and ( D ) Inhibition of tumour growth. Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue. The TT treatment recruits more immune-responsive CD4 + CD8 + and CD4 + CD25 + T cells Three types of T cells were present: CD4 + CD8 + , CD4 + CD25 + , and CD8 + T cells, with CD4 + CD8 + T cells being the most abundant ( Table S1 ). The TT treatment significantly increased the populations of CD4 + CD8 + and CD4 + CD25 + T cells compared to both the untreated (UN) and control groups (Fig. 5A). In contrast, the control group had the highest population of CD8 + T cells. In terms of functional modulation induced by the treatments, GSEA revealed that OXPHOS was activated in CD8 + T cells of the TT group, while IFN-γ and inflammatory responses were inhibited in CD8 + T cells of the control group (Fig. 5B). Similar functional modulation was observed in CD4 + CD8 + T cells between the TT and control groups ( Figure S4 A ). For CD4 + CD25 + T cells, several metabolic pathways were downregulated in the control group compared to the TT group ( Figure S4 B ). Interestingly, many marker genes associated with T cell activation, such as Lat2 , Tax1bp1 , Gzmb , and Cd8a , were upregulated in the control group relative to both the UN and TT groups (Fig. 5C). In contrast, the expression of Trbc1 and Nfat5 was more pronounced in the TT group. Figure 5 Expansion of CD4 + CD8 + and CD4 + CD25 + T cells with TT treatment in the TME on the metastatic side. ( A ) Comparison of T cell populations in the UN, the control, and the TT groups. ( B ) Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in CD8 + T cells of the TT group compared to the control group. ( C ) Bubble graphs comparing the expression of selected marker genes associated with T cell activation across different T cell populations in the untreated, control, and TT treatment groups. The bubble size represents the percentage of cells expressing each gene, while the bubble colour indicates the average expression level of the gene in each cell type. ( D ) Beanplot showing the expression levels (in Log2 values) of selected Treg marker across the three T cell populations in the control and TT groups. The most activated networks in the CD4 + CD25 + T cells of the control ( E ) and the TT group ( F ) relative to the UN group, identified by Ingenuity Pathway Analysis (IPA). Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue. The expression of selected Treg marker genes, including Foxp3 , Ctla4 , Il7r , Lag3 , and Il2ra , was compared across T cell populations (Fig. 5D). Significant downregulation of all these genes was detected in CD4 + CD25 + T cells of the TT group. Additionally, Il7r was suppressed in the other two T cell populations of the TT group compared to the control group. To understand the regulatory networks in CD4 + CD25 + T cells, we analysed the pathways activated in the control and TT groups. In the control group, the 'function of antigen presenting cells' pathway was more activated, while the 'infection of mammalia' pathway was more inhibited relative to the UN group, likely due to the high expression of activated T cell marker genes (Fig. 5E). In contrast, CD4 + CD25 + T cells in the TT group showed significant inhibition in pathways related to tumour growth, migration, and invasion, as well as the 'proliferation of connective tissue cells' pathway, which are highly relevant to metastasis (Fig. 5F). In CD8 + T cells, the 'activation of cells' pathway was most activated in the control group, while the ‘cell movement of mononuclear leukocytes' pathway was most activated in the TT group ( Figures S4 C and S4D ). For CD4 + CD8 + T cells, the 'sensitivity of tumour cell lines' pathway was highly activated in the control group ( Figure S4 E ). In contrast, the TT group showed activation of pathways inhibiting vasculogenesis, advanced malignant tumours, and invasive cancer, relative to the UN group ( Figure S4 F ). The TT expands dendritic cells (excluding Langerhans cells) and activates NK cells The populations of all three dendritic cell (DC) types expanded in the TT group, with a notable increase in cDC1s, which rose by approximately 29% and 300% compared to the UN and control groups, respectively (Fig. 6A and Table S1 ). Among the most upregulated genes in the TT group was Gm10736 (equivalent to Hla-dqb1 in human), a key MHC class II molecule (Fig. 6B). Several genes related to mitochondrial function, including Mrpl52 , Mrps21 , Mrpl33 , and Mrpl12 , were upregulated in DCs, especially in cDC1s, suggesting enhanced assembly and functioning of mitochondrial ribosomes. The expression of Cd63 and Cd302 was significantly higher in migDCs of the TT group. Additionally, several pseudogenes, such as Gm8186 , Gm3699 , Gm3511 , and Gm4149 , were substantially upregulated in cDC1s of the TT group, indicating their potential roles in modulating immune-related gene expression. Although the population of Langerhans cells slightly decreased in the TT group (4.44%) compared to the UN (4.87%) and control groups (5.62%), their phenotype was significantly modulated (Fig. 6C). Pathways supporting an inflammatory phenotype, including the IFN α/γ response, inflammatory response, and complement pathways, were negatively associated with the control group compared to the TT group. The ‘E2F targets’ pathway was the only hallmark pathway significantly activated in the TT group. Figure 6 Modulation of dendritic cells and NK cells in the tumour microenvironment by TT treatment. ( A ) Contributions to migratory dendritic cells (migDCs), plasmacytoid dendritic cells (pDCs), and conventional dendritic cells type 1 (cDC1s) from the UN, control, and TT groups. ( B ) Bubble graph comparing the expression of the top 50 upregulated DEGs in cDC1s of the TT group relative to the control group, in migDCs and pDCs. The bubble size corresponds to the percentage of expression, and the bubble colour corresponds to the average expression of the gene in each cell type. ( C ) Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in Langerhans cells of the TT group compared to the control group. ( D ) GSEA of hallmark pathways enriched in natural killer (NK) cells of the TT group compared to the control group. ( E ) Violin plots showing the expression levels of selected marker genes activating NK cells. ( F ) The most activated regulatory network, ‘biosynthesis of ribonucleotide,’ in NK cells of the TT group relative to the UN group, identified by Ingenuity Pathway Analysis (IPA). Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue. The population of NK cells was significantly elevated in both the control and TT groups compared to the UN group, with the increase being more pronounced in the control group ( Figure S1 and Table S1 ). The phenotype of NK cells in the TT group exhibited more inflammatory features compared to the control group, which showed significant downregulation of IFN-γ and inflammatory response pathways (Fig. 6D). Additionally, ‘IL2/STAT5 signalling’ was negatively associated with the control group. Interestingly, the expression levels of marker genes for priming NK cells in the TT group, such as Klrk1 , Prf1 , Gzmb , Ncr1 , Lamp1 , and Fcgr3 , were lower than those in the control or UN groups (Fig. 6E). Ingenuity Pathway Analysis (IPA) identified ‘biosynthesis of ribonucleotide’ as the top relevant network modulated by TT compared to the UN group, a network that was absent in the control group when compared to the UN group (Fig. 6E). Additionally, pathways associated with ‘cell proliferation of tumour cell lines,’ ‘cell viability of breast cancer cell lines,’ and ‘migration of cells’ were downregulated, whereas ‘sensitivity of cells’ was activated, indicating that TT treatment modulated NK cells to create an inhibitory environment for tumours. The treatments markedly reduces the B cell population The population of B cells significantly decreased, dropping from 4.52% in the UN group to 0.67% in the control group and 0.64% in the TT group ( Table S1 ). Notably, the expression of many antigen-associated genes, such as Cd86 , Cd53 , Cd68 , and Cd22 , was markedly elevated in the control group compared to the other two groups (Fig. 7A). Additionally, three MHC class II antigens, including H2-DMb2 , H2-DMa , and H2-Aa , were upregulated by the control treatment. In the TT group, the upregulation of Cd52 and a B cell marker gene Bcl2a1b was observed. GSEA indicated that energy metabolism-associated biological processes were more activated in the TT group compared to the control group (Fig. 7B). Conversely, several autophagy-related processes were comparatively less active in the control group. IPA revealed an inhibition of 'organismal death' in the B cells of the control group (Fig. 7C), while apoptosis was more activated in the B cells of the TT group ( Figure S5 ). Furthermore, the TT group showed enhanced signalling in 'differentiation of T lymphocytes,' 'immune response of antigen-presenting cells,' and 'phagocytosis,' relative to the UN group, which was not detected in the control group with high consistency. Figure 7 The effect of TT treatment on B cell phenotype and cell-cell communication in the TME. ( A ) Comparison of the expression levels of selected antigen-associated marker genes and B cell features. ( B ) Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in B cells from the TT group compared to the control group. ( C ) Identification of the most activated regulatory network in B cells of the control group relative to the UN group, as determined by Ingenuity Pathway Analysis (IPA). Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue. ( D ) Communication network among immune cells in the TT group (P-value < 0.01). ( E ) Heatmap comparing the interaction probability between cDC1s and other cell types across the three groups. (see Table S4 for cell-cell communication results in detail.) The TT induces more cDC1/CD8 + T cell communication The cell-cell communication among immune response-relevant cells was analysed, with the networks of the TT group shown in Fig. 7C . The networks of the UN and control groups are compared in Figure S5 . No communication ( P -value < 0.01) was detected between cDC1s and CD8 + T cells in the UN group, similar to the lack of communication between Langerhans cells in the treatment groups. Additionally, cDC1s interacted with TAMs, Arg1 hi , and Res-like MΦs to a lesser extent in both the control and TT groups, as well as with pDCs and B cells. The interaction probability between cDC1s and other cell types is compared in Fig. 8D, showing that most communication was reduced in the control group compared to the UN group, except with NK and CD4 + CD8 + T cells. Notably, the interaction between cDC1s and four cell types was significantly elevated by the TT, including MHCII hi MΦs (by 22% relative to the UN group), CD4 + CD25 + T cells (56%), migDCs (18%), and CD8 + T cells (by 73% relative to the control). On the other hand, the control appeared to enforce more communication between migDCs, pDCs and Langerhans cells with other cell types, such as Arg1 hi MΦs, neutrophils, B cells, NK cells, CD4 + CD8 + T cells, and CD8 + T cells, compared to the UN and the TT groups; notably, TAMs showed highest communication with these three DC types in the TT group ( Table S4 ). Discussion Melanoma metastasis poses a significant challenge due to its aggressive nature, propensity for early dissemination, and diverse pathways of spread. Traditional treatments often yield limited responses, while newer therapies can be associated with significant side effects. In our current study, we developed a triple therapy (TT) approach combining a vaccine, anti-CD47 antibody, and F1/F3, demonstrating promising efficacy against melanoma metastasis in a B16 mouse model. The TT treatment led to substantial reductions in tumour sizes on both the treated (right) and metastatic (left) sides ( P -value < 0.0001), along with a remarkable extension of survival times in the treated mice. Moreover, it significantly decreased tumour weights on both sides, with a more pronounced effect observed on the treated side. Single-cell RNA sequencing (sc-RNAseq) analysis of CD45 + cells isolated from the metastatic site identified a total of 21 cell types, including various macrophage, T cell, dendritic cell, B cell, NK cell, and monocyte populations. Notably, TT treatment comparably increased the populations of CD4 + CD8 + T cells, MHCII hi MΦs, and conventional type 1 dendritic cells (cDC1s), while also altering their functional profiles. Furthermore, TT reprogrammed Arg1 hi MΦs, TAMs, and Res-like MΦs to exhibit a more immune-responsive phenotype. Enhanced communication between cDC1s and CD8 + T cells was observed in the TT group compared to control or untreated groups, facilitating a more intensive immune response. Additionally, CD8 + T cells showed increased activation in the TT-treated mice. MHCII hi MΦs express high levels of major histocompatibility complex class II (MHCII) molecules, which have been considered crucial for the presentation of antigens to CD4 + T helper cells, thus essential for initiating and regulating immune responses. In addition, their immune regulation roles have been found in the maintenance of tolerance and preventing autoimmunity, as well as in the production of various cytokines and chemokines. In the TME, previous research has characterised that MHCII hi MΦs contribute to anti-tumour response via presenting tumour antigens to CD4 + T cells and mounting an ongoing adaptive immune response. The TT did not only markedly expand the population of MHCII hi MΦs by nearly 100% with respect to either the UN or the control, but also module their phenotype to be more inflammatory at the metastasis side, indicating they became more immune response active, which have been reported for the either topical application or intratumoral injection of drug candidates containing F1/F3 on TC-1 bearing mice models[ 16 , 21 ]. This overwrote the immunosuppressive functions exhibited by the MHCII hi MΦs in the TME of the UN or the control group. TAMs, typically polarised towards an M2-like phenotype, promote tumour cell proliferation through mechanisms that facilitate tumour growth, metastasis, and immune evasion[ 43 , 44 ]. The study identified two populations of macrophages: Arg1 hi MΦs and TAMs, both exhibiting TAM-like characteristics, with the former type showing aberrant Arg1 expression. TT significantly downregulated several TAM-associated markers, including Cd68 and Arg1 , as well as Stat6 , a marker of M2 polarisation, compared to both UN and control groups (Fig. 4). TAMs are known to secrete elevated levels of growth factors (e.g., Egf , Vegf ), cytokines (e.g., Tgfb , Tnfa , Il8 ), MMP family members (e.g., Mmp1 , Mmp2 , Mmp9 , Mmp12 , Mmp13 ), and chemokines that attract immune cells, fostering a tumour-permissive microenvironment and metastasis. Significantly, TT treatment inactivated Vegfa , Tnfaip3 , and Mmp12/13 compared to the other groups. Moreover, enzymes crucial for extracellular matrix remodelling (e.g., Ctsb , Ctsd , Ctsl , Hpse , Plau , Adam8 ) were more suppressed in the TT group, potentially hindering tumour invasion and metastasis. Notably, TT treatment modulated Arg1 hi MΦs and TAMs towards a more immunoreactive state. While the population of Arg1 hi MΦs notably increased in the control group, their function remained comparable to the UN group, suggesting a cellular response that enhances the immunosuppressive TME in response to vaccine plus anti-CD47 treatment. Furthermore, TT significantly activated OXPHOS and ‘MYC targets V1’ pathways in Arg1 hi MΦs, indicating an interplay between these pathways. MYC can upregulate genes involved in mitochondrial biogenesis and function, enhancing OXPHOS. In MYC-driven tumours, such as B16 melanoma[ 45 ], heightened OXPHOS levels induce oxidative stress, potentially activating apoptotic pathways and improving the immune milieu. This effect may contribute to enhancing the efficacy of immune checkpoint inhibitors. Additionally, MYC could influence immune evasion mechanisms, further impacting tumour progression and response to therapies. Res-like MΦs play critical roles in maintaining tissue homeostasis, responding to infections, and regulating immune responses through cytokine production and antigen presentation[ 46 ]. They recognise pathogens via pattern recognition receptors and engage in phagocytosis as part of their immune surveillance functions. It has been observed that TAMs often originate from tissue-resident macrophages, underscoring their influence on tumour progression and metastasis[ 47 ]. TT treatment modulated Res-like MΦs to exhibit a more pro-inflammatory phenotype compared to the control group. Anti-CD47 antibodies block the CD47 "don't eat me" signal on tumour cells, rendering them more susceptible to phagocytosis by macrophages and other immune cells[ 48 ]. Whereas this reprogramming of M2-like to M1-like phenotypes in multiple macrophage populations suggests they may synergistically enhance the effects of anti-CD47 antibody therapy. The presence of CD4 + CD8 + T cells represents a stage in T cell maturation where cells express both CD4 and CD8 co-receptors, which eventually differentiate into either CD4 + helper T cells or CD8 + cytotoxic T cells. These mature CD4 + CD8 + T cells are suggested to play regulatory roles, potentially contributing to immune homeostasis by combining helper and cytotoxic functions in a unique manner. In the context of our study, an increased population of CD4 + CD8 + T cells was observed in both the control and TT-treated groups, with a more pronounced increase noted in the TT group. This suggests that these cells may enhance the immune response against the tumour. The expansion of CD4 + CD8 + T cells likely reflects changes in the TME, making it more conducive to immune cell infiltration and activation. Additionally, the inclusion of F1/F3 in the treatment regimen may directly induce tumour cell death[ 12 , 15 , 16 , 19 , 21 , 49 – 51 ], releasing tumour antigens that are subsequently captured by DCs and presented to T cells. This process leads to further activation and expansion of CD4 + CD8 + T cells, indicating that F1/F3 enhances the antigen presentation pathway and boosts T cell activation. CD4 + CD25 + T cells are commonly associated with regulatory T cells (Tregs), which play a crucial role in maintaining immune tolerance and preventing autoimmune reactions. In our study, the population of CD4 + CD25 + T cells was notably increased in the TT group by approximately 18% compared to the control group and by 149% compared to the UN group. However, despite the increase in CD4 + CD25 + T cells, the TT treatment significantly downregulated the expression of marker genes associated with Tregs, such as Il2ra , Ctla4 , Il7r , Lag3 , and Foxp3 , compared to both the control and UN groups. This suggests that while the absolute number of CD4 + CD25 + T cells increased, their functional status as Tregs was reduced in the TT-treated mice. Since Tregs are known to facilitate immune evasion by suppressing anti-tumour immune responses[ 52 , 53 ], their reduction in the TT group implies a potential attenuation of these immunosuppressive effects. By diminishing Treg activity, the TT treatment may enhance anti-tumour immune responses, thereby inhibiting tumour growth and progression. Previous studies have demonstrated that topical application or intratumoral injection of F1/F3 to TC-1 tumours can enhance the recruitment of activated T cells and NK cells to the tumour sites. However, in our study, we observed that several common marker genes of NK cells were comparatively downregulated on the left side in the TT (triple therapy) group, despite achieving significantly better treatment outcomes. It is important to note that the TT treatment was administered on the right side of the mice. This discrepancy suggests that the inclusion of F1/F3 may have selectively induced migration of highly activated T and NK cells to the right side, while potentially leaving behind less activated but still effective immune cells on the left side. Similar observations have been documented in other cancer models[ 54 – 57 ], indicating a potential compartmentalized response to treatment. Another hypothesis is that TT may have elicited a more robust immune memory response, enabling the immune system to exert tumour control with fewer but more efficient immune cells. The quality of T and NK cells, rather than their activation state alone, might play a crucial role in achieving effective antitumor responses. Even with lower activation marker expression, TT-treated cells could exhibit enhanced effector functions. Moreover, F1/F3 are known to induce changes in the TME, making it less supportive of tumour growth and metastasis. This could potentially reduce the requirement for high levels of T and NK cell activation to maintain tumour control. Lastly, it is plausible that immune surveillance and tumour control mechanisms activated by TT may involve pathways that do not solely rely on heightened activation levels T, NK, and B cells. This multifaceted response underscores the complexity of immune modulation in cancer treatment and highlights the potential of including caerin peptides to induce effective antitumor immunity through diverse mechanisms beyond traditional immune cell activation paradigms. The cell-cell communication analysis revealed significantly higher levels of interplay between cDC1s with CD8 + T cells, migDCs, CD4 + CD25 + T cells, and MHCII hi macrophages in the TT group compared to the UN and control groups. This was accorded with the upregulation of marker genes positively associated with the assembly and functioning of mitochondrial ribosomes in the TT group, indicating the activation of DC function. cDC1 cells are specialised in cross-presenting extracellular antigens on MHC class I molecules to CD8 + T cells, a crucial process for initiating cytotoxic T cell responses against tumours. The increased interaction detected in the TT group suggests that cDC1 cells were more effectively presenting tumour antigens, potentially leading to the activation and expansion of CD8 + cytotoxic T lymphocytes and greater infiltration into the TME. Although the population of CD8 + T cells in the TT group was lower than in the control group (both significantly higher than that in the UN group), the CD8 + T cells in the TT group were more activated. This enhanced activation could contribute to the generation of memory T cells, providing long-term protection against tumour recurrence. In contrast, the control group's migDCs and pDCs exhibited more communication with other cell types, suggesting a broader immune response involving various cell types. This broader activation might lead to a more diverse but less targeted immune response. The inclusion of F1/F3 in the TT group likely enhances the effectiveness of cDC1s, leading to a more potent and focused anti-tumour immune response. The more intensive communications between CD8 + T cells and cDC1s in the TT group indicate a robust and potentially more effective immune response against the tumour. This enhanced communication might also correlate with improved interactions between CD8 + T cells and TAMs, resulting in a reduction in the suppressive activity of TAMs observed in the TT group. Conclusions Collectively, these findings underscore the potential of the triple therapy to enhance antitumor immunity by modulating immune cell populations and their functions within the metastatic microenvironment. The comprehensive immune modulation induced by the triple therapy highlights its promise as a therapeutic strategy for combating melanoma metastasis, addressing both tumour progression and enhancing immune surveillance. Particularly, the dual strategy of reprogramming macrophages towards a pro-inflammatory state and enhancing tumour cell phagocytosis introduced by block CD47 could potentially improve immune responses against tumours, highlighting the therapeutic potential of targeting macrophage polarisation in melanoma treatment strategies. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The scRNAseq dataset presented in this study can be found in online repository: https://singlecell.broadinstitute.org/single_cell, SCP2697. Competing interests The authors declare that they have no competing interests。 Funding This study was supported in part by the First Affiliated Hospital of Guangdong Pharmaceutical University, Deng Feng project of Foshan First People’s Hospital (2019A008), National Science Foundation of Guangdong province (2020A1515010855), National Natural Science Foundation of China (31971355). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors' contributions Conceptualisation and design: TW, GN and XL; Experimental work: QF, YL, JL and GN; Data process, curation and visualisation: QF, YL, JL and TW; Analysis and interpretation: TW, QL, XL, HL and GN; Writing-original draft preparation: QL, TW and XL; Writing-review and editing: ZC, TW, XL, HL and GN; project administration: XL and ZC. All authors reviewed the paper. Acknowledgments We thank Professor Abigail Elizur for her valuable advice and support. References Karras P, Bordeu I, Pozniak J, Nowosad A, Pazzi C, Van Raemdonck N, Landeloos E, Van Herck Y, Pedri D, Bervoets G, et al. A cellular hierarchy in melanoma uncouples growth and metastasis. Nature. 2022;610(7930):190–8. Arnold M, Singh D, Laversanne M, Vignat J, Vaccarella S, Meheus F, Cust AE, de Vries E, Whiteman DC, Bray F. Global Burden of Cutaneous Melanoma in 2020 and Projections to 2040. JAMA Dermatol. 2022;158(5):495–503. Leiter U, Keim U, Garbe C. Epidemiology of Skin Cancer: Update 2019. Adv Exp Med Biol. 2020;1268:123–39. Switzer B, Puzanov I, Skitzki JJ, Hamad L, Ernstoff MS. Managing Metastatic Melanoma in 2022: A Clinical Review. JCO Oncol Pract. 2022;18(5):335–51. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. 2021;71(1):7–33. Villani A, Potestio L, Fabbrocini G, Troncone G, Malapelle U, Scalvenzi M. The Treatment of Advanced Melanoma: Therapeutic Update. Int J Mol Sci 2022, 23(12). Ribas A, Wolchok JD. Cancer immunotherapy using checkpoint blockade. Science. 2018;359(6382):1350–5. Larkin J, Chiarion-Sileni V, Gonzalez R, Grob JJ, Cowey CL, Lao CD, Schadendorf D, Dummer R, Smylie M, Rutkowski P, et al. Combined Nivolumab and Ipilimumab or Monotherapy in Untreated Melanoma. N Engl J Med. 2015;373(1):23–34. Long GV, Weber JS, Infante JR, Kim KB, Daud A, Gonzalez R, Sosman JA, Hamid O, Schuchter L, Cebon J, et al. Overall Survival and Durable Responses in Patients With BRAF V600-Mutant Metastatic Melanoma Receiving Dabrafenib Combined With Trametinib. J Clin Oncol. 2016;34(8):871–8. Huang AC, Zappasodi R. A decade of checkpoint blockade immunotherapy in melanoma: understanding the molecular basis for immune sensitivity and resistance. Nat Immunol. 2022;23(5):660–70. Hodi FS, Chiarion-Sileni V, Gonzalez R, Grob JJ, Rutkowski P, Cowey CL, Lao CD, Schadendorf D, Wagstaff J, Dummer R, et al. Nivolumab plus ipilimumab or nivolumab alone versus ipilimumab alone in advanced melanoma (CheckMate 067): 4-year outcomes of a multicentre, randomised, phase 3 trial. Lancet Oncol. 2018;19(11):1480–92. Ni G, Liu X, Li H, Fogarty CE, Chen S, Zhang P, Liu Y, Wu X, Wei MQ, Chen G, et al. Topical Application of Temperature-Sensitive Gel Containing Caerin 1.1 and 1.9 Peptides on TC-1 Tumour-Bearing Mice Induced High-Level Immune Response in the Tumour Microenvironment. Front Oncol. 2021;11:754770. Lin R, Ma B, Liu N, Zhang L, He T, Liu X, Chen T, Liu W, Liang Y, Wang T, et al. Targeted radioimmunotherapy with the iodine-131-labeled caerin 1.1 peptide for human anaplastic thyroid cancer in nude mice. Ann Nucl Med. 2021;35(7):811–22. Chen S, Zhang P, Xiao L, Liu Y, Wu K, Ni G, Li H, Wang T, Wu X, Chen G, Liu X. Caerin 1.1 and 1.9 Peptides from Australian Tree Frog Inhibit Antibiotic-Resistant Bacteria Growth in a Murine Skin Infection Model. Microbiol Spectr. 2021;9(1):e0005121. Ni G, Chen S, Chen M, Wu J, Yang B, Yuan J, Walton SF, Li H, Wei MQ, Wang Y, et al. Host-Defense Peptides Caerin 1.1 and 1.9 Stimulate TNF-Alpha-Dependent Apoptotic Signals in Human Cervical Cancer HeLa Cells. Front Cell Dev Biol. 2020;8:676. Ma B, Yuan J, Chen S, Huang K, Wang Q, Ma J, Lin R, Zhang L, Zhou Y, Wang T, et al. Topical application of temperature-sensitive caerin 1.1 and 1.9 gel inhibits TC-1 tumor growth in mice. Am J Transl Res. 2020;12(1):191–202. Pan X, Ma B, You X, Chen S, Wu J, Wang T, Walton SF, Yuan J, Wu X, Chen G, et al. Synthesized natural peptides from amphibian skin secretions increase the efficacy of a therapeutic vaccine by recruiting more T cells to the tumour site. BMC Complement Altern Med. 2019;19(1):163. Xiao L, Yang X, Li J, Zhang P, Tang S, Cao D, Chen S, Li H, Zhang W, Chen G et al. Caerin 1 Peptides, the Potential Jack-of-All-Trades for the Multiple Antibiotic-Resistant Bacterial Infection Treatment and Cancer Immunotherapy. Biomed Res Int 2022, 2022:7841219. Yang X, Liu X, Li J, Zhang P, Li H, Chen G, Zhang W, Wang T, Frazer I, Ni G. Caerin 1.1/1.9 Enhances Antitumour Immunity by Activating the IFN-α Response Signalling Pathway of Tumour Macrophages. Cancers (Basel) 2022, 14(23). Logtenberg MEW, Scheeren FA, Schumacher TN. The CD47-SIRPα Immune Checkpoint. Immunity. 2020;52(5):742–52. Ni G, Yang X, Li J, Wu X, Liu Y, Li H, Chen S, Fogarty CE, Frazer IH, Chen G, et al. Intratumoral injection of caerin 1.1 and 1.9 peptides increases the efficacy of vaccinated TC-1 tumor-bearing mice with PD-1 blockade by modulating macrophage heterogeneity and the activation of CD8(+) T cells in the tumor microenvironment. Clin Transl Immunol. 2021;10(8):e1335. Lun ATL, Riesenfeld S, Andrews T, Dao TP, Gomes T, Marioni JC. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 2019;20(1):63. Zhou B, Jin W. Visualization of Single Cell RNA-Seq Data Using t-SNE in R. Methods Mol Biol. 2020;2117:159–67. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50. Mootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstråle M, Laurila E, et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34(3):267–73. Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH, Myung P, Plikus MV, Nie Q. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021;12(1):1088. Hu C, Li T, Xu Y, Zhang X, Li F, Bai J, Chen J, Jiang W, Yang K, Ou Q, et al. CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. Nucleic Acids Res. 2023;51(D1):D870–6. Zhao G, Lu H, Chang Z, Zhao Y, Zhu T, Chang L, Guo Y, Garcia-Barrio MT, Chen YE, Zhang J. Single-cell RNA sequencing reveals the cellular heterogeneity of aneurysmal infrarenal abdominal aorta. Cardiovasc Res. 2021;117(5):1402–16. Cassetta L, Fragkogianni S, Sims AH, Swierczak A, Forrester LM, Zhang H, Soong DYH, Cotechini T, Anur P, Lin EY, et al. Human Tumor-Associated Macrophage and Monocyte Transcriptional Landscapes Reveal Cancer-Specific Reprogramming, Biomarkers, and Therapeutic Targets. Cancer Cell. 2019;35(4):588–e602510. Guilliams M, Dutertre CA, Scott CL, McGovern N, Sichien D, Chakarov S, Van Gassen S, Chen J, Poidinger M, De Prijck S, et al. Unsupervised High-Dimensional Analysis Aligns Dendritic Cells across Tissues and Species. Immunity. 2016;45(3):669–84. Ottosson-Laakso E, Krus U, Storm P, Prasad RB, Oskolkov N, Ahlqvist E, Fadista J, Hansson O, Groop L, Vikman P. Glucose-Induced Changes in Gene Expression in Human Pancreatic Islets: Causes or Consequences of Chronic Hyperglycemia. Diabetes. 2017;66(12):3013–28. Rosenberg FM, Wardenaar R, Voorberg AN, Spierings DCJ, Schuttelaar MA. Transcriptional differences between vesicular hand eczema and atopic dermatitis. Contact Dermat. 2024;90(1):23–31. Schwalie PC, Dong H, Zachara M, Russeil J, Alpern D, Akchiche N, Caprara C, Sun W, Schlaudraff KU, Soldati G, et al. A stromal cell population that inhibits adipogenesis in mammalian fat depots. Nature. 2018;559(7712):103–8. Okuda H, Miyata S, Mori Y, Tohyama M. Mouse Prickle1 and Prickle2 are expressed in postmitotic neurons and promote neurite outgrowth. FEBS Lett. 2007;581(24):4754–60. Guzmán YF, Ramsey K, Stolz JR, Craig DW, Huentelman MJ, Narayanan V, Swanson GT. A gain-of-function mutation in the GRIK2 gene causes neurodevelopmental deficits. Neurol Genet. 2017;3(1):e129. Erbel-Sieler C, Dudley C, Zhou Y, Wu X, Estill SJ, Han T, Diaz-Arrastia R, Brunskill EW, Potter SS, McKnight SL. Behavioral and regulatory abnormalities in mice deficient in the NPAS1 and NPAS3 transcription factors. Proc Natl Acad Sci U S A. 2004;101(37):13648–53. Du J, Miller AJ, Widlund HR, Horstmann MA, Ramaswamy S, Fisher DE. MLANA/MART1 and SILV/PMEL17/GP100 are transcriptionally regulated by MITF in melanocytes and melanoma. Am J Pathol. 2003;163(1):333–43. Hellström AR, Watt B, Fard SS, Tenza D, Mannström P, Narfström K, Ekesten B, Ito S, Wakamatsu K, Larsson J, et al. Inactivation of Pmel alters melanosome shape but has only a subtle effect on visible pigmentation. PLoS Genet. 2011;7(9):e1002285. Fang D, Tsuji Y, Setaluri V. Selective down-regulation of tyrosinase family gene TYRP1 by inhibition of the activity of melanocyte transcription factor, MITF. Nucleic Acids Res. 2002;30(14):3096–106. Stankiewicz AM, Goscik J, Majewska A, Swiergiel AH, Juszczak GR. The Effect of Acute and Chronic Social Stress on the Hippocampal Transcriptome in Mice. PLoS ONE. 2015;10(11):e0142195. Wang F, Yang X, Ren Z, Chen C, Liu C. Alternative splicing in mouse brains affected by psychological stress is enriched in the signaling, neural transmission and blood-brain barrier pathways. Mol Psychiatry. 2023;28(11):4707–18. Lin Y, Xu J, Lan H. Tumor-associated macrophages in tumor metastasis: biological roles and clinical therapeutic applications. J Hematol Oncol. 2019;12(1):76. Pan Y, Yu Y, Wang X, Zhang T. Tumor-Associated Macrophages in Tumor Immunity. Front Immunol. 2020;11:583084. Yang C, Liu Y, Hu Y, Fang L, Huang Z, Cui H, Xie J, Hong Y, Chen W, Xiao N, et al. Myc inhibition tips the immune balance to promote antitumor immunity. Cell Mol Immunol. 2022;19(9):1030–41. Shapouri-Moghaddam A, Mohammadian S, Vazini H, Taghadosi M, Esmaeili SA, Mardani F, Seifi B, Mohammadi A, Afshari JT, Sahebkar A. Macrophage plasticity, polarization, and function in health and disease. J Cell Physiol. 2018;233(9):6425–40. Murray PJ, Wynn TA. Protective and pathogenic functions of macrophage subsets. Nat Rev Immunol. 2011;11(11):723–37. McCracken MN, Cha AC, Weissman IL. Molecular Pathways: Activating T Cells after Cancer Cell Phagocytosis from Blockade of CD47 Don't Eat Me Signals. Clin Cancer Res. 2015;21(16):3597–601. Liu N, He T, Xiao Z, Du J, Zhu K, Liu X, Chen T, Liu W, Ni G, Liu X, et al. (131)I-Caerin 1.1 and (131)I-Caerin 1.9 for the treatment of non-small-cell lung cancer. Front Oncol. 2022;12:861206. Wang Y, Zhong F, Xiao F, Li J, Liu X, Ni G, Wang T, Zhang W. Host-defence caerin 1.1 and 1.9 peptides suppress glioblastoma U87 and U118 cell proliferation through the modulation of mitochondrial respiration and induce the downregulation of CHI3L1. PLoS ONE. 2024;19(6):e0304149. Luo Y, Li J, Fu Q, Zhang P, Song X, Liu M, Mo R, Fu J, Tang S, Wu J. The natural defense peptide caerin1. 1/1.9 sequentially induces caspase 3/GSDME-mediated pyroptosis followed by intrinsic apoptosis of epithelial cancer cells. 2024. Facciabene A, Motz GT, Coukos G. T-regulatory cells: key players in tumor immune escape and angiogenesis. Cancer Res. 2012;72(9):2162–71. Hinz S, Pagerols-Raluy L, Oberg HH, Ammerpohl O, Grüssel S, Sipos B, Grützmann R, Pilarsky C, Ungefroren H, Saeger HD, et al. Foxp3 expression in pancreatic carcinoma cells as a novel mechanism of immune evasion in cancer. Cancer Res. 2007;67(17):8344–50. Tilburgs T, Roelen DL, van der Mast BJ, de Groot-Swings GM, Kleijburg C, Scherjon SA, Claas FH. Evidence for a selective migration of fetus-specific CD4 + CD25bright regulatory T cells from the peripheral blood to the decidua in human pregnancy. J Immunol. 2008;180(8):5737–45. Parodi M, Raggi F, Cangelosi D, Manzini C, Balsamo M, Blengio F, Eva A, Varesio L, Pietra G, Moretta L, et al. Hypoxia Modifies the Transcriptome of Human NK Cells, Modulates Their Immunoregulatory Profile, and Influences NK Cell Subset Migration. Front Immunol. 2018;9:2358. Kim CH, Hashimoto-Hill S, Kim M. Migration and Tissue Tropism of Innate Lymphoid Cells. Trends Immunol. 2016;37(1):68–79. Hudspeth K, Donadon M, Cimino M, Pontarini E, Tentorio P, Preti M, Hong M, Bertoletti A, Bicciato S, Invernizzi P, et al. Human liver-resident CD56(bright)/CD16(neg) NK cells are retained within hepatic sinusoids via the engagement of CCR5 and CXCR6 pathways. J Autoimmun. 2016;66:40–50. Supplementary Files FigureS1tumourweight.tif Additional File 1 Figure S1 Dissection diagram of tumour placement on the right side of the mice in different groups. FigureS2scRNAoverall.tif Additional File 2 Figure S2 (A) Bubble map illustrating the distribution of top 5 marker gene expressions across different cell cluster populations. Bubble size corresponds to the ratio of the sum of marker gene expression levels in a specific subpopulation to their sum across all cells, while bubble colour indicates the average expression of the marker genes within the cell population. (B) Proportions of the 21 cell clusters in the UN, control, and TT groups. (C) Contribution of cell fractions from different groups to each cluster. (D) Heatmap depicting correlation among the 21 clusters. FigureS3TAM.tif Additional File 3 Figure S3 Regulatory networks most activated in TAMs of the control (A) and the TT (B) group relative to the UN group, identified by Ingenuity Pathway Analysis (IPA). Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue. FigureS4Tcell.tif Additional File 4 Figure S4 Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in CD4 + CD8 + T cells (A) and CD4 + CD25 + (B) T cells between the TT and control groups. Regulatory networks most activated in CD4 + CD8 + T cells of the control (C) and the TT (D), and CD4 + CD25 + T cells of the control (E) and the TT (F) group relative to the UN group, identified by Ingenuity Pathway Analysis (IPA). Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue. FigureS5BTTapoptosis.tif Additional File 5 Figure S5 Regulatory networks most activated in B cells of the TT group relative to the UN group, identified by Ingenuity Pathway Analysis (IPA). Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue. FigureS6communication.tif Additional File 6 Figure S6 Cell-cell communication among immune cells in the UN (A) and control (B) groups. The loops along with cell type represent the interactions within the same cell type TableS1clusterstatsandAllGene.avgexpcluster.xlsx Additional File 7 Table S1 Cluster statistical analysis and the expression of genes identified in each cluster TableS2Degenes.xlsx Additional File 8 Table S2 Marker gene expression, annotation, gene ontology and KEGG analysis TableS3macropahgecomparison.xlsx Additional File 9 Table S3 Macrophage comparison between the three groups TableS4cellcommunication.xlsx Additional File 10 Table S4 Cell-cell communication analysis results Cite Share Download PDF Status: Published Journal Publication published 28 Oct, 2024 Read the published version in Journal of Translational Medicine → Version 1 posted Reviewers agreed at journal 12 Jul, 2024 Reviewers invited by journal 10 Jul, 2024 Editor assigned by journal 04 Jul, 2024 First submitted to journal 01 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4671312","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":325355115,"identity":"1e82e59f-18ff-41a1-bed6-b7aa04a84397","order_by":0,"name":"Quanlan Fu","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Quanlan","middleName":"","lastName":"Fu","suffix":""},{"id":325355116,"identity":"d59f5980-7370-4946-98de-c72d2257150d","order_by":1,"name":"Yuandong Luo","email":"","orcid":"","institution":"First People's Hospital of Foshan","correspondingAuthor":false,"prefix":"","firstName":"Yuandong","middleName":"","lastName":"Luo","suffix":""},{"id":325355117,"identity":"7e3259f3-cb54-4bb1-8d7e-080fa65000e6","order_by":2,"name":"Junjie Li","email":"","orcid":"","institution":"Zhongao Biomedical Technology (Guangdong) Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Li","suffix":""},{"id":325355118,"identity":"b1a056ec-d590-443c-88c2-5208b6d2d96f","order_by":3,"name":"Hejie Li","email":"","orcid":"","institution":"University of the Sunshine Coast","correspondingAuthor":false,"prefix":"","firstName":"Hejie","middleName":"","lastName":"Li","suffix":""},{"id":325355119,"identity":"94223188-63b1-47ae-81cb-99b1feced145","order_by":4,"name":"Xiaosong Liu","email":"","orcid":"","institution":"First People's Hospital of Foshan","correspondingAuthor":false,"prefix":"","firstName":"Xiaosong","middleName":"","lastName":"Liu","suffix":""},{"id":325355120,"identity":"ee7a93bd-70ff-449c-9ce0-7bcdc9f14730","order_by":5,"name":"Zhu Chen","email":"","orcid":"","institution":"Guiyang Stomatological Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhu","middleName":"","lastName":"Chen","suffix":""},{"id":325355121,"identity":"ec7ab4eb-54ba-4144-864b-4eebc97ad074","order_by":6,"name":"Guoying Ni","email":"","orcid":"","institution":"First People's Hospital of Foshan","correspondingAuthor":false,"prefix":"","firstName":"Guoying","middleName":"","lastName":"Ni","suffix":""},{"id":325355122,"identity":"6f3c0ff2-6272-4dbe-878f-e0cbc9d191e0","order_by":7,"name":"Tianfang Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDAC5gMg0kaOH0QlFBChg4ctAUSlGUs2gLQYEK/lcOIGsG3EaLFn4zH8XPCLmXHz+dWJHx4YMMjzix0gZAuPsfTMPjZmsxtvN0sAHWY4c3YCAS3yvRukeXt42MxunN0A0pJgcJuQFjbezb95eyR4jGec3fyDWC3bpHl+GEgY8PduI9KWY/zfrHkbEgwkbvBuswBShP3C3saWfJvnz//6/v6zm2/+qLCR55cmoAUMGNuAhARYpQQRysHgDxDzHyBW9SgYBaNgFIw0AACO1UCknf/eHgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-4876-7767","institution":"University of the Sunshine Coast","correspondingAuthor":true,"prefix":"","firstName":"Tianfang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-07-02 04:19:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4671312/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4671312/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12967-024-05763-x","type":"published","date":"2024-10-28T16:23:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62183747,"identity":"84f24be4-6c2c-408d-847c-fde328a236d4","added_by":"auto","created_at":"2024-08-10 11:37:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1718373,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of F1/F3 peptides in combination with therapeutic vaccines and α-CD47 triple therapy (TT) on bilateral tumour growth and survival. (\u003cstrong\u003eA\u003c/strong\u003e) The establishment of a bilateral tumour model to simulate B16 tumour metastasis and the treatments in this study. (\u003cstrong\u003eB\u003c/strong\u003e) Tumour growth on the right side (with treatment). No significant difference was observed between the UN group (PBS only) and the control group (P3+V+α-CD47) post-treatment. The TT group exhibited significantly inhibited tumour growth and improved therapeutic efficacy against B16 melanoma compared to the control group. (\u003cstrong\u003eC\u003c/strong\u003e) Tumour growth on the left side (without treatment). Tumour volume was reduced in the control group compared to the UN group, and the TT group demonstrated superior efficacy on both the left and right sides. (\u003cstrong\u003eD\u003c/strong\u003e) Survival of mice with tumours on the right side. Mice treated with the TT exhibited significantly extended survival compared to the control groups. (\u003cstrong\u003eE\u003c/strong\u003e) Survival of mice with tumours on the left side. Mice treated with the TT exhibited significantly extended survival compared to the control groups. Results are expressed as the mean ± standard error of the mean (SEM), and inter-group differences were statistically analysed using two-way ANOVA, where *\u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.05 and **\u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.01 indicate significant differences, and ns indicates no significant difference. (\u003cstrong\u003eF\u003c/strong\u003e) Statistical graph depicting tumour weight on the right side. (\u003cstrong\u003eG\u003c/strong\u003e) Dissection diagram illustrating tumour placement on the left side of the mice. (\u003cstrong\u003eH\u003c/strong\u003e) Statistical representation of tumour weight on the left side. (See \u003cstrong\u003eFigure S1\u003c/strong\u003e for the dissection diagram of tumours placement on the right side)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/c4900977d5f1a9a93351e27f.png"},{"id":62184595,"identity":"98ba0ddd-0582-48f5-b195-23ad3cecdb1e","added_by":"auto","created_at":"2024-08-10 11:45:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1943476,"visible":true,"origin":"","legend":"\u003cp\u003eThe scRNA-seq analysis of the B16 tumour tissues in the left side of mice. t-Stochastic neighbour embedding (t-SNE) representation of aligned gene expression data in CD45\u003csup\u003e+\u003c/sup\u003e single cells extracted from the TME of B16 tumours shows partition into 21 distinct clusters, the distribution of the clusters in the UN (\u003cstrong\u003eA\u003c/strong\u003e), control (\u003cstrong\u003eB\u003c/strong\u003e) and TT (\u003cstrong\u003eC\u003c/strong\u003e) groups. (\u003cstrong\u003eD\u003c/strong\u003e) Selected enriched genes used for biological identification of each cluster and the top 5 DEGs of each cluster (in Z-score). MΦ represents macrophage; NK cell, natural killer cell; migDC, migratory DC; cDC1, conventional DC type 1; pDC, plasmacytoid dendritic cell; ASPCs, adipogenic stem and precursor cells; NECs, neuroendocrine cells. (see \u003cstrong\u003eTable S2\u003c/strong\u003e for the full list of all marker genes detected)\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/a56ca1f6c1aca589009550de.png"},{"id":62183750,"identity":"4d3e0e25-1e08-49da-9505-20a6eee3bec9","added_by":"auto","created_at":"2024-08-10 11:37:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":746516,"visible":true,"origin":"","legend":"\u003cp\u003eModulation of Arg1\u003csup\u003ehi \u003c/sup\u003eand tumour-associated macrophages in the tumour microenvironment by TT treatment. (\u003cstrong\u003eA\u003c/strong\u003e) Comparison of the proportions of four macrophage (MΦ) populations among the UN, control, and TT groups. (\u003cstrong\u003eB\u003c/strong\u003e) Upset graph comparing the upregulated and downregulated differentially expressed genes (DEGs) in different macrophage populations in the TT group relative to the control group. (\u003cstrong\u003eC\u003c/strong\u003e) Beanplot showing the expression levels (in Log2 values) of selected tumour-associated macrophage marker genes across the four macrophage populations in the control and TT groups. (\u003cstrong\u003eD\u003c/strong\u003e) Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in Arg1hi macrophages of the TT group compared to the control group. (\u003cstrong\u003eE\u003c/strong\u003e) GSEA of hallmark pathways enriched in TAMs of the TT group compared to the control group. Significance levels are indicated as follows: *: \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.05, **: \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.01, ***: \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.001, and ****: \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.0001; by two-way Student’s t-test.\u003c/p\u003e","description":"","filename":"Figure3macrophages.png","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/32dadf1895926173d8bcfe7b.png"},{"id":62184596,"identity":"d9363fb3-5fcd-410c-a977-da7f92234a26","added_by":"auto","created_at":"2024-08-10 11:45:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3445310,"visible":true,"origin":"","legend":"\u003cp\u003eModulation of MHCII\u003csup\u003ehi\u003c/sup\u003e and tissue-resident in the tumour microenvironment by TT treatment. (\u003cstrong\u003eA\u003c/strong\u003e) Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs of the TT group compared to the control group. (\u003cstrong\u003eB\u003c/strong\u003e) GSEA of hallmark pathways enriched in Res-like MΦs of the TT group compared to the control group. The top two most activated networks in the MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs of the TT group relative to the UN group, identified by Ingenuity Pathway Analysis (IPA): (\u003cstrong\u003eC\u003c/strong\u003e) Quantity of MHC class I on cell surface and (\u003cstrong\u003eD\u003c/strong\u003e) Inhibition of tumour growth. Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue.\u003c/p\u003e","description":"","filename":"Figure4MHCRes.png","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/d01f524ff6554c00771230ca.png"},{"id":62183748,"identity":"57708ae6-d71d-43c6-a54e-d386b0f2df74","added_by":"auto","created_at":"2024-08-10 11:37:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1257633,"visible":true,"origin":"","legend":"\u003cp\u003eExpansion of CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e and CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells with TT treatment in the TME on the metastatic side. (\u003cstrong\u003eA\u003c/strong\u003e) Comparison of T cell populations in the UN, the control, and the TT groups. (\u003cstrong\u003eB\u003c/strong\u003e) Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in CD8\u003csup\u003e+\u003c/sup\u003e T cells of the TT group compared to the control group. (\u003cstrong\u003eC\u003c/strong\u003e) Bubble graphs comparing the expression of selected marker genes associated with T cell activation across different T cell populations in the untreated, control, and TT treatment groups. The bubble size represents the percentage of cells expressing each gene, while the bubble colour indicates the average expression level of the gene in each cell type. (\u003cstrong\u003eD\u003c/strong\u003e) Beanplot showing the expression levels (in Log2 values) of selected Treg marker across the three T cell populations in the control and TT groups. The most activated networks in the CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells of the control (\u003cstrong\u003eE\u003c/strong\u003e) and the TT group (\u003cstrong\u003eF\u003c/strong\u003e) relative to the UN group, identified by Ingenuity Pathway Analysis (IPA). Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue.\u003c/p\u003e","description":"","filename":"Figure5Tcell.png","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/cc75a29689d17b01183b743e.png"},{"id":62183756,"identity":"10b6c621-3b1c-45d3-8f60-64600b33bc82","added_by":"auto","created_at":"2024-08-10 11:37:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1729120,"visible":true,"origin":"","legend":"\u003cp\u003eModulation of dendritic cells and NK cells in the tumour microenvironment by TT treatment. (\u003cstrong\u003eA\u003c/strong\u003e) Contributions to migratory dendritic cells (migDCs), plasmacytoid dendritic cells (pDCs), and conventional dendritic cells type 1 (cDC1s) from the UN, control, and TT groups. (\u003cstrong\u003eB\u003c/strong\u003e) Bubble graph comparing the expression of the top 50 upregulated DEGs in cDC1s of the TT group relative to the control group, in migDCs and pDCs. The bubble size corresponds to the percentage of expression, and the bubble colour corresponds to the average expression of the gene in each cell type. (\u003cstrong\u003eC\u003c/strong\u003e) Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in Langerhans cells of the TT group compared to the control group. (\u003cstrong\u003eD\u003c/strong\u003e) GSEA of hallmark pathways enriched in natural killer (NK) cells of the TT group compared to the control group. (\u003cstrong\u003eE\u003c/strong\u003e) Violin plots showing the expression levels of selected marker genes activating NK cells. (\u003cstrong\u003eF\u003c/strong\u003e) The most activated regulatory network, ‘biosynthesis of ribonucleotide,’ in NK cells of the TT group relative to the UN group, identified by Ingenuity Pathway Analysis (IPA). Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue.\u003c/p\u003e","description":"","filename":"Figure6DCNK.png","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/00eb6f0fcab4c85ae86b6430.png"},{"id":62185491,"identity":"e5a189ec-2810-4497-b355-e11e0a6ccc6e","added_by":"auto","created_at":"2024-08-10 11:53:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1603715,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of TT treatment on B cell phenotype and cell-cell communication in the TME. (\u003cstrong\u003eA\u003c/strong\u003e) Comparison of the expression levels of selected antigen-associated marker genes and B cell features. (\u003cstrong\u003eB\u003c/strong\u003e) Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in B cells from the TT group compared to the control group. (\u003cstrong\u003eC\u003c/strong\u003e) Identification of the most activated regulatory network in B cells of the control group relative to the UN group, as determined by Ingenuity Pathway Analysis (IPA). Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue. \u0026nbsp;(\u003cstrong\u003eD\u003c/strong\u003e) Communication network among immune cells in the TT group (P-value \u0026lt; 0.01). (\u003cstrong\u003eE\u003c/strong\u003e) Heatmap comparing the interaction probability between cDC1s and other cell types across the three groups. (see \u003cstrong\u003eTable S4\u003c/strong\u003e for cell-cell communication results in detail.)\u003c/p\u003e","description":"","filename":"Figure7Bcommunication.png","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/9be855d0c876201f097f5d34.png"},{"id":68207654,"identity":"53564367-733b-44e1-8eb8-b2ef0632cf55","added_by":"auto","created_at":"2024-11-04 16:37:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12015569,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/5d90fc5c-dba3-479e-b1d1-74d0d3c42893.pdf"},{"id":62183757,"identity":"a8cf10f6-ffd2-4333-9314-ea9b76ae6fec","added_by":"auto","created_at":"2024-08-10 11:37:59","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4760716,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 1 Figure S1\u003c/strong\u003e Dissection diagram of tumour placement on the right side of the mice in different groups.\u003c/p\u003e","description":"","filename":"FigureS1tumourweight.tif","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/7b6a0a647dcdd0008ee55beb.tif"},{"id":62183754,"identity":"2884ae24-d53c-4378-83de-31c8bd2d044a","added_by":"auto","created_at":"2024-08-10 11:37:59","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":6983780,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 2 Figure S2 \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Bubble map illustrating the distribution of top 5 marker gene expressions across different cell cluster populations. Bubble size corresponds to the ratio of the sum of marker gene expression levels in a specific subpopulation to their sum across all cells, while bubble colour indicates the average expression of the marker genes within the cell population. (\u003cstrong\u003eB\u003c/strong\u003e) Proportions of the 21 cell clusters in the UN, control, and TT groups. (\u003cstrong\u003eC\u003c/strong\u003e) Contribution of cell fractions from different groups to each cluster. (\u003cstrong\u003eD\u003c/strong\u003e) Heatmap depicting correlation among the 21 clusters.\u003c/p\u003e","description":"","filename":"FigureS2scRNAoverall.tif","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/1d43f532ccda3ae3289c129c.tif"},{"id":62184598,"identity":"3fc04c8b-c483-485f-8065-cd4a140b264e","added_by":"auto","created_at":"2024-08-10 11:45:59","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":9320524,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 3 Figure S3\u003c/strong\u003e Regulatory networks most activated in TAMs of the control (\u003cstrong\u003eA\u003c/strong\u003e) and the TT (\u003cstrong\u003eB\u003c/strong\u003e) group relative to the UN group, identified by Ingenuity Pathway Analysis (IPA). Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue.\u003c/p\u003e","description":"","filename":"FigureS3TAM.tif","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/06c8121fca413f07d8226bd2.tif"},{"id":62183763,"identity":"66b7a944-1492-4251-934b-d88baa5fc3ad","added_by":"auto","created_at":"2024-08-10 11:37:59","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":10582424,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 4 Figure S4\u003c/strong\u003e Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells (A) and CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e (B) T cells between the TT and control groups. Regulatory networks most activated in CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells of the control (\u003cstrong\u003eC\u003c/strong\u003e) and the TT (\u003cstrong\u003eD\u003c/strong\u003e), and CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells of the control (\u003cstrong\u003eE\u003c/strong\u003e) and the TT (\u003cstrong\u003eF\u003c/strong\u003e) group relative to the UN group, identified by Ingenuity Pathway Analysis (IPA). Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue.\u003c/p\u003e","description":"","filename":"FigureS4Tcell.tif","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/7d4abf92e581057c0ae9a9b9.tif"},{"id":62183759,"identity":"439eb6cd-d441-4840-8f79-ed7d0d8b5dee","added_by":"auto","created_at":"2024-08-10 11:37:59","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":10034444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 5 Figure S5\u003c/strong\u003e Regulatory networks most activated in B cells of the TT group relative to the UN group, identified by Ingenuity Pathway Analysis (IPA). Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue.\u003c/p\u003e","description":"","filename":"FigureS5BTTapoptosis.tif","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/e6dc2228d6509e89bfd3a86c.tif"},{"id":62183762,"identity":"0229634d-e645-42f9-a148-4bc263cdd39d","added_by":"auto","created_at":"2024-08-10 11:37:59","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":7103712,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 6 Figure S6 \u003c/strong\u003eCell-cell communication among immune cells in the UN (\u003cstrong\u003eA\u003c/strong\u003e) and control (\u003cstrong\u003eB\u003c/strong\u003e) groups. The loops along with cell type represent the interactions within the same cell type\u003c/p\u003e","description":"","filename":"FigureS6communication.tif","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/603334d84d70cfa42b369ad6.tif"},{"id":62183761,"identity":"005da8f8-6776-4e02-9d4c-312005d599b2","added_by":"auto","created_at":"2024-08-10 11:37:59","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":16517212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 7 Table S1\u003c/strong\u003e Cluster statistical analysis and the expression of genes identified in each cluster\u003c/p\u003e","description":"","filename":"TableS1clusterstatsandAllGene.avgexpcluster.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/ca126519dd504b5ffbb53b6d.xlsx"},{"id":62185492,"identity":"1d7a730f-362d-44a3-be43-70d91b44e4a1","added_by":"auto","created_at":"2024-08-10 11:53:59","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":4432394,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 8 Table S2 \u003c/strong\u003eMarker gene expression, annotation, gene ontology and KEGG analysis\u003c/p\u003e","description":"","filename":"TableS2Degenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/f50ceafcef3736e0b5e3de4e.xlsx"},{"id":62183764,"identity":"13e0b4dc-b805-40ee-bdb2-24846b8b630d","added_by":"auto","created_at":"2024-08-10 11:37:59","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":4133009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 9 Table S3 \u003c/strong\u003eMacrophage comparison between the three groups\u003c/p\u003e","description":"","filename":"TableS3macropahgecomparison.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/8af4f6f08b234fb79c8fd751.xlsx"},{"id":62184599,"identity":"90fa87ca-882f-4d4e-867e-e0c801854100","added_by":"auto","created_at":"2024-08-10 11:45:59","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":30900,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 10 Table S4 \u003c/strong\u003eCell-cell communication analysis results\u003c/p\u003e","description":"","filename":"TableS4cellcommunication.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4671312/v1/1cc702da16ec9b8b6c9a14a5.xlsx"}],"financialInterests":"","formattedTitle":"Caerin 1.1 and 1.9 peptides halt B16 melanoma metastatic tumours via expanding cDC1 and reprogramming tumour macrophages","fulltext":[{"header":"Background","content":"\u003cp\u003eMelanoma is one of the most prevalent types of skin cancer, notorious for its heterogeneity and propensity to metastasise to distant organs[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The incidence of melanoma has been rising globally, posing a significant public health challenge. According to the World Health Organisation, approximately 132,000 new cases of melanoma occur worldwide each year[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the United States alone, the American Cancer Society estimates that there were about 106,110 new cases of melanoma diagnosed in 2021, with an estimated 7,180 deaths from the disease[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The lifetime risk of developing melanoma has increased dramatically over the past decades. In the 1930s, the risk was about 1 in 1,500. Today, it is estimated to be about 1 in 38 for white individuals, 1 in 1,000 for Black individuals, and 1 in 167 for Hispanic individuals[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This rise in incidence is attributed to several factors, including increased ultraviolet (UV) radiation exposure, the use of tanning beds, and changes in lifestyle and behaviour that increase sun exposure. The five-year survival rate for localised melanoma is now around 99%, but this drops significantly to about 27% to as low as 4.7% across the subcategories of stage IV metastatic disease[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, the treatment landscape for melanoma has been revolutionised with the approval of new therapeutic methods, including both targeted and immune-based therapies[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among these, immune checkpoint inhibitors (ICIs) have gained prominence. These inhibitors work by targeting key regulatory pathways in T cells, thereby enhancing the body's immune response against melanoma cells. Standard treatments now commonly include ICIs, such as anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4), anti-programmed death 1 (anti-PD-1), and anti-programmed death-ligand 1 (anti-PD-L1) therapies. Additionally, targeted inhibitors that focus on specific mutations in the MAPK pathway, such as BRAFV600E and MEK inhibitors, have shown efficacy in controlling tumour growth[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The advent of these therapies has significantly improved the survival rates of many patients with advanced melanoma, providing new hope where few options previously existed[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite these advancements, patient responses to these treatments remain highly variable. A substantial proportion of patients do not achieve long-term remission, and resistance to therapy is a common challenge[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This underscores the need for novel therapeutic approaches that can complement existing treatments and improve overall patient outcomes[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne promising avenue of research involves the use of combination therapies that can target multiple aspects of the tumour microenvironment. Caerin 1.1/1.9, a mixture of peptides derived from the skin secretions of an Australian tree frog, has demonstrated potential in this regard. Previous studies have shown that caerin 1.1/1.9 can inhibit the growth of various cancer cells, including human and mouse cervical cancer, human thyroid cancer, and human breast cancer cells \u003cem\u003ein vitro\u003c/em\u003e[\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the TC-1 tumour-bearing mouse model, caerin 1.1/1.9 has been shown to attract a large number of immune cells, such as macrophages, T cells, and NK cells, to the tumour microenvironment, thereby inhibiting tumour growth[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Importantly, caerin 1.1/1.9 can modulate the heterogeneity of tumour-associated macrophages, promoting the polarization from M2- to M1-like macrophages, which is associated with a more robust anti-tumour immune response[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven these promising preclinical findings, we hypothesised that the combination of caerin 1.1/1.9 with therapeutic vaccines and anti-CD47 (triple therapy, TT) could enhance the anti-tumour immune response and improve therapeutic efficacy. Anti-CD47 is an immune checkpoint inhibitor that blocks the \"don't eat me\" signal used by cancer cells to evade phagocytosis by macrophages[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Combining this with the immune-activating properties of therapeutic vaccines and the tumour-inhibitory effects of caerin1.1/1.9 could potentially create a synergistic effect.\u003c/p\u003e \u003cp\u003eTo test this hypothesis, we established a bilateral mouse tumour model to simulate B16 melanoma metastasis, known for its aggressive nature in skin cancer. This model enabled us to assess the impact of the Triple Therapy (TT) on both primary and secondary tumour sites, offering a comprehensive evaluation of its therapeutic efficacy. The TT treatment significantly reduced tumour growth at both sites and prolonged survival compared to untreated and control groups. Subsequently, we conducted single-cell transcriptomic analysis of CD45\u0026thinsp;+\u0026thinsp;cells isolated from the three largest tumours on the distant metastatic side in each group. This analysis aimed to elucidate how TT treatment alters the tumour microenvironment compared to untreated and control groups. Our study aims to provide mechanistic insights into how caerin 1.1/1.9, in conjunction with therapeutic vaccines and anti-CD47 therapy, enhances systemic immune responses against melanoma. These findings contribute to the development of more effective combination therapies for advanced melanoma treatment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMice\u003c/h2\u003e \u003cp\u003eFemale C57BL/6 mice, aged 8 to 12 weeks, were used in the experiment. These mice were purchased from the Guangdong Provincial Centre for Animal Resources and housed under specific pathogen-free (SPF) conditions in the animal facility of the First Affiliated Hospital of Guangdong Pharmaceutical University. Each cage contained five mice, which were maintained in a controlled environment at 22\u0026deg;C with 75% humidity and a 12-hour light/dark cycle. They were provided with standard mouse chow and water ad libitum. All experiments were conducted in accordance with the guidelines provided by the Animal Experiment Ethics Committee (Ethical Approval No.: GYFYGZR2023027).\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eCell culture\u003c/h2\u003e \u003cp\u003eThe B16 cell line was obtained from the Shanghai Institute for Biological Sciences, Chinese Academy of Sciences. The cells were cultured in RPMI 1640 medium (Gibco) supplemented with 10% fatal bovine serum (FBS, Gibco) and 100 U/mL penicillin/100 \u0026micro;g/mL streptomycin (Gibco). They were grown to 70\u0026ndash;80% confluence before being passaged, and were passaged 3 to 5 times prior to inoculation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePeptide synthesis\u003c/h2\u003e \u003cp\u003eCaerin 1.1 (referred to as F1, sequence: GLLSVLGSVAKHVLPHVVPVIAEHL-NH\u003csub\u003e2\u003c/sub\u003e), caerin 1.9 (referred to as F3, sequence: GLFGVLGSIAKHVLPHVVPVIAEKL-NH\u003csub\u003e2\u003c/sub\u003e), and a control peptide P3 without cytotoxic properties towards various cancer cells (GTELPSPPSVWFEAEFK-OH) were synthesised by Shanghai Qiangyao Biological Technology Co., Ltd, China. The purity of the peptides was determined to be 95% via reverse-phase high-performance liquid chromatography. The caerin and P3 peptides were stored at 4\u0026deg;C until use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of a bilateral mouse melanoma model\u003c/h2\u003e \u003cp\u003eWhen the B16 cell line reached 70\u0026ndash;80% confluence, the cells were washed three times with phosphate-buffered saline (PBS). A total of 4\u0026times;10\u003csup\u003e5\u003c/sup\u003e B16 cells were inoculated at the right midline of the abdomen, and 2\u0026times;10\u003csup\u003e5\u003c/sup\u003e B16 cells were inoculated at the left midline, with a volume of 200 \u0026micro;L per mouse. Approximately 3 to 5 days after tumour inoculation, when tumour formation was visible to the naked eye, the mice were randomly divided into three groups: PBS\u0026thinsp;+\u0026thinsp;PBS\u0026thinsp;+\u0026thinsp;PBS (UN), P3\u0026thinsp;+\u0026thinsp;V\u0026thinsp;+\u0026thinsp;anti-CD47 (control), and F1/F3\u0026thinsp;+\u0026thinsp;V\u0026thinsp;+\u0026thinsp;anti-CD47 (TT).\u003c/p\u003e \u003cp\u003eHere, V represents the therapeutic vaccine, composed of the B16 melanoma antigen (amino acid sequence: ISQAVHAAHAEINEAGRSIINFEKLSVYDFFVWL), monophosphoryl lipid A (MPLA, Sigma, L6895-5MG), and anti-interleukin-10 receptor antibody (αIL-10R, BioXcell, Cat#: BE0050). After grouping, F1/F3 peptides were injected into the right tumour, while P3 was injected as a control peptide. The therapeutic vaccine was administered intramuscularly, and the \u003cem\u003ein vivo\u003c/em\u003e antibody was administered intraperitoneally. During the treatment, tumour size was measured every other day, and the survival of the mice was monitored. Tumour size was calculated using the formula: (length \u0026times; width\u003csup\u003e2\u003c/sup\u003e)/2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIsolation of tumour-infiltrating CD45\u003csup\u003e+\u003c/sup\u003e cells and single-cell transcriptome\u003c/h2\u003e \u003cp\u003eThe isolation of tumour infiltrating CD45\u003csup\u003e+\u003c/sup\u003e cells followed the protocol reported previously[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In brief, B16 tumours on the left sides were dissected into 2 \u0026times; 2 mm pieces and digested in a solution containing RPMI 1640, Enzyme D, Enzyme R, and Enzyme A within a gentleMACS C Tube. The tissue was dissociated using a gentle MACS Dissociator from Miltenyi (Gladbach, Germany). After removing dead cells and debris, the remaining cells were labeled with CD45 microbeads (130\u0026ndash;110\u0026ndash;618). Flow cytometry and trypan blue staining confirmed that the viability of CD45\u003csup\u003e+\u003c/sup\u003e cells was over 80% of total cells. The cells were washed with ice-cold PBS containing 10% fetal bovine serum after sorting and counted using a hemocytometer. Libraries were generated and sequenced from the cDNAs with Chromium Next GEM Single Cell 5\u0026rsquo; Reagent Kits v3.1.\u003c/p\u003e \u003cp\u003eThe bioinformatic analysis workflow was reported elsewhere[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Briefly, the 10X Genomics Cell Ranger software (version 3.1.0) was employed to process raw BCL files, converting them into FASTQ files, and subsequently performing alignment and quantification of gene counts. Reads containing low-quality barcodes and UMIs were filtered out before mapping to the reference genome. Only reads uniquely mapped to the transcriptome, intersecting with at least 50% of an exon, were considered for UMI counting. Prior to quantification, UMI sequences underwent correction for sequencing errors, and valid barcodes were identified using the EmptyDrops method[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEach sample's cell-by-gene matrix was imported individually into Seurat version 3.1.1 for downstream analysis. Cells with unusually high numbers of UMIs (\u0026ge;\u0026thinsp;8000) or a high percentage of mitochondrial genes (\u0026ge;\u0026thinsp;10%) were filtered out, along with doublet GEMs. Subsequently, a global-scaling normalization method, \"LogNormalize,\" was applied to the dataset. To mitigate batch effects and other experimental variations during clustering, the Harmony algorithm was utilized to integrate all samples. Harmony employs a PCA embedding of cells along with their batch assignments to produce a batch-corrected embedding. Seurat then employed a graph-based clustering approach where distances between cells were calculated based on previously identified principal components (PCs). Cells were embedded into a shared-nearest neighbour graph, connecting cells based on similar gene expression patterns.\u003c/p\u003e \u003cp\u003eFor visualisation of clusters, t-distributed Stochastic Neighbour Embedding (t-SNE) plots were generated using the same PCs[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The expression levels of each gene within a given cluster were compared against all other cells using a Wilcoxon rank sum test. Genes were considered significantly upregulated if they met several criteria: (1) at least 1.28-fold overexpression in the target cluster, (2) expression in more than 25% of cells within the target cluster, and (3) a P-value less than 0.05. This comprehensive approach facilitated the identification of genes specifically associated with each cluster, providing insights into their potential functional roles within the biological context studied.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene ontology, KEGG pathway and GSEA analysis\u003c/h2\u003e \u003cp\u003eThe enrichment of biological processes and KEGG pathways[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] was assessed for the treatments in comparison to the untreated and control groups. The genes associated with the proteins exhibiting differential expression across the three groups were subjected to analysis using Gene Set Enrichment Analysis (GSEA) with a significance threshold of \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. This analysis was performed using GSEA version 4.1.0[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIngenuity Pathway Analysis (IPA)\u003c/h2\u003e \u003cp\u003eThe release version 111725566 (2024) from Qiagen was used to analyse genes with differential expression. Gene expression within each experimental group was transformed into ratios relative to the untreated group. These ratios, along with corresponding p-values, were then input into IPA to construct the IPA database. To generate canonical pathways and regulatory networks, Fisher's Exact Test was utilized. This statistical test assessed associations between our input data and established annotations within the IPA database, helping to identify significant biological pathways and networks affected by the experimental conditions.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eCell-cell communication analysis\u003c/h2\u003e \u003cp\u003eFor cell-cell communication inference and analysis, we utilized the R package CellChat (version 1.1.0) with default parameters, leveraging a publicly available repository of ligands, receptors, cofactors, and their interactions[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Expression levels for interaction analysis were normalized relative to the total read mapping across the same set of coding genes in all transcriptomes. Expression values were averaged within each single-cell cluster or cell sample. The analysis was performed using the CellChatDB mouse database. All three sample groups were normalised collectively, followed by individual extraction and parallel comparative analysis, assuming shared cell types among the groups.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis in this study, unless otherwise specified, employed an unpaired Student\u0026rsquo;s t-test using GraphPad Prism 8 software. All experimental data underwent analysis, and graphs were generated using the same software. The determination of statistically significant means was based on a probability level of 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe TT treatment reduces tumour size and weight, extends survival in B16 melanoma-bearing mice\u003c/h2\u003e \u003cp\u003eOur previous findings suggest that the triple therapy (TT), which combines F1/F3 peptides with therapeutic vaccines and α-CD47, can significantly inhibit tumour growth (unpublished data). To further investigate whether F1/F3 can enhance systemic immunity induced by the therapeutic vaccine, we established a bilateral tumour model to simulate tumour metastasis (Fig.\u0026nbsp;1A). Tumours on both the left and right sides of different groups were collected and measured comparatively. The results showed that in the right-side tumours (with treatments), tumour volume was reduced in the control group (P3\u0026thinsp;+\u0026thinsp;V\u0026thinsp;+\u0026thinsp;α-CD47) compared to the untreated (UN) group (PBS only), with the maximum difference observed on Day 21 (three days after treatment completion) (Fig.\u0026nbsp;1B). However, tumour volume in the control group increased quickly and reached a level similar to the UN group by Day 29, with no significant difference detected. Throughout the entire observation period, the TT group exhibited significantly inhibited tumour growth compared to the other two groups. For the left side tumours (without treatment), no significant difference was observed between the PBS group (UN) and the control group. Notably, TT significantly inhibited tumour growth, with tumour volume decreased by approximately 60% at Day 29 (Fig.\u0026nbsp;1C). Additionally, the survival of mice in the TT group was significantly extended on both sides, with a more pronounced effect on the right side (Fig.\u0026nbsp;1D and 1E).\u003c/p\u003e \u003cp\u003eAfter the treatment concluded, the mice were dissected, and the tumours on the left and right sides were weighed and compared in \u003cb\u003eFig.\u0026nbsp;1F\u003c/b\u003e and \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e, respectively. The results clearly showed that the TT significantly reduced tumour weight on both sides compared to either the control group or the UN group, with particularly notable effects on the right side. Statistical analysis revealed significant differences in tumour weights on both sides between the TT group, the control, and the UN groups (Fig.\u0026nbsp;1G and \u003cb\u003eFig.\u0026nbsp;1H\u003c/b\u003e). These findings indicate that the intra-tumoral injection of F1/F3 peptides significantly enhanced the therapeutic efficacy of the vaccine and α-CD47, effectively inhibiting tumour growth on both the primary and the metastatic tumour sides, respectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1\u003c/b\u003e Effect of F1/F3 peptides in combination with therapeutic vaccines and α-CD47 triple therapy (TT) on bilateral tumour growth and survival. (\u003cb\u003eA\u003c/b\u003e) The establishment of a bilateral tumour model to simulate B16 tumour metastasis and the treatments in this study. (\u003cb\u003eB\u003c/b\u003e) Tumour growth on the right side (with treatment). No significant difference was observed between the UN group (PBS only) and the control group (P3\u0026thinsp;+\u0026thinsp;V\u0026thinsp;+\u0026thinsp;α-CD47) post-treatment. The TT group exhibited significantly inhibited tumour growth and improved therapeutic efficacy against B16 melanoma compared to the control group. (\u003cb\u003eC\u003c/b\u003e) Tumour growth on the left side (without treatment). Tumour volume was reduced in the control group compared to the UN group, and the TT group demonstrated superior efficacy on both the left and right sides. (\u003cb\u003eD\u003c/b\u003e) Survival of mice with tumours on the right side. Mice treated with the TT exhibited significantly extended survival compared to the control groups. (\u003cb\u003eE\u003c/b\u003e) Survival of mice with tumours on the left side. Mice treated with the TT exhibited significantly extended survival compared to the control groups. Results are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM), and inter-group differences were statistically analysed using two-way ANOVA, where *\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and **\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 indicate significant differences, and ns indicates no significant difference. (\u003cb\u003eF\u003c/b\u003e) Statistical graph depicting tumour weight on the right side. (\u003cb\u003eG\u003c/b\u003e) Dissection diagram illustrating tumour placement on the left side of the mice. (\u003cb\u003eH\u003c/b\u003e) Statistical representation of tumour weight on the left side. (See \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e for the dissection diagram of tumours placement on the right side)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003escRNA-seq revealed the modulation of CD45\u003csup\u003e+\u003c/sup\u003e cell heterogeneity in B16 tumour on the metastasis side\u003c/h2\u003e \u003cp\u003eTotal viable CD45\u003csup\u003e+\u003c/sup\u003e leukocytes were isolated from both sides of the UN, control, and TT groups (Fig.\u0026nbsp;2 and \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). After quality control, a total of 7,007, 8,165, and 8,307 cells were utilised for downstream analysis in the UN, control, and TT groups, respectively. Gene expression data from extracted CD45\u003csup\u003e+\u003c/sup\u003e cells were aligned and projected into a two-dimensional space using t-stochastic neighbour embedding (t-SNE) to identify tumour-associated immune cell populations and differentially expressed genes (Fig.\u0026nbsp;3A and \u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA\u003c/b\u003e). This unsupervised clustering analysis identified 21 cell clusters (labelled \"0\" to \"20\"), consistently present across all three groups, indicating robust cell-type identification independent of treatments. Compared to the UN group (Fig.\u0026nbsp;3B), the control (Fig.\u0026nbsp;3C) and TT (Fig.\u0026nbsp;3D) groups showed reduced populations in clusters 2, 11, 13, and 17, while exhibiting higher populations in clusters 3, 7, 8, 10, and 12 (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB\u003c/b\u003e). Notably, clusters 4 and 18 were significantly expanded only in the TT group (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The TT group contributed more cells to clusters 3, 4, 7, 18, and 19, while contributing lest cells to clusters 9, 13, 15, and 20 (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2\u003c/b\u003e The scRNA-seq analysis of the B16 tumour tissues in the left side of mice. t-Stochastic neighbour embedding (t-SNE) representation of aligned gene expression data in CD45\u003csup\u003e+\u003c/sup\u003e single cells extracted from the TME of B16 tumours shows partition into 21 distinct clusters, the distribution of the clusters in the UN (\u003cb\u003eA\u003c/b\u003e), control (\u003cb\u003eB\u003c/b\u003e) and TT (\u003cb\u003eC\u003c/b\u003e) groups. (\u003cb\u003eD\u003c/b\u003e) Selected enriched genes used for biological identification of each cluster and the top 5 DEGs of each cluster (in Z-score). MΦ represents macrophage; NK cell, natural killer cell; migDC, migratory DC; cDC1, conventional DC type 1; pDC, plasmacytoid dendritic cell; ASPCs, adipogenic stem and precursor cells; NECs, neuroendocrine cells. (see \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e for the full list of all marker genes detected)\u003c/p\u003e \u003cp\u003eDifferentially expressed genes (DEGs) were analysed to identify cell type-specific marker genes (Fig.\u0026nbsp;2D and \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). Established canonical markers such as \u003cem\u003eCd3d\u003c/em\u003e, \u003cem\u003eCd3g\u003c/em\u003e, \u003cem\u003eCd79a\u003c/em\u003e, \u003cem\u003eGzma\u003c/em\u003e, \u003cem\u003ePrf1\u003c/em\u003e, \u003cem\u003eKlrk1\u003c/em\u003e, \u003cem\u003eCd19\u003c/em\u003e, and \u003cem\u003eCd8b1\u003c/em\u003e indicated lymphocyte lineages (Fig.\u0026nbsp;2E). Myeloid cell identities were supported by markers including \u003cem\u003eItgam\u003c/em\u003e, \u003cem\u003eAdgre1\u003c/em\u003e, \u003cem\u003eItgax\u003c/em\u003e, \u003cem\u003eCsf1r\u003c/em\u003e, \u003cem\u003eLgals3\u003c/em\u003e, \u003cem\u003eItgae\u003c/em\u003e, \u003cem\u003eSiglec1\u003c/em\u003e, \u003cem\u003eMrc1\u003c/em\u003e, \u003cem\u003eH2-Ab1\u003c/em\u003e, \u003cem\u003eS100a8/S100a9\u003c/em\u003e, \u003cem\u003eLy6g1\u003c/em\u003e, and \u003cem\u003eLy6c1\u003c/em\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Clusters were annotated with predicted cell-type identities based on known marker genes from literature sources[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Notable macrophage subtypes included Arg1\u003csup\u003ehi\u003c/sup\u003e MΦ (cluster 0; marker genes: \u003cem\u003eArg1\u003c/em\u003e, \u003cem\u003eMmp12\u003c/em\u003e, \u003cem\u003eMmp13\u003c/em\u003e, and \u003cem\u003eNos2\u003c/em\u003e)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], tissue-resident macrophages (Res-like MΦ) (cluster 1; marker genes: \u003cem\u003eC1qa\u003c/em\u003e, \u003cem\u003eC1qc\u003c/em\u003e, \u003cem\u003eMs4a7\u003c/em\u003e, and \u003cem\u003eCcl12\u003c/em\u003e)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], MHCII\u003csup\u003ehi\u003c/sup\u003e MΦ (cluster 4; marker genes: \u003cem\u003eChil3\u003c/em\u003e, \u003cem\u003eIfitm6\u003c/em\u003e, \u003cem\u003eH2-DMb1\u003c/em\u003e, and \u003cem\u003eH2-DMa\u003c/em\u003e), and tumour associated macrophages (TAMs) (cluster 11; marker genes: \u003cem\u003eCd209f\u003c/em\u003e, \u003cem\u003eLyve1\u003c/em\u003e, \u003cem\u003eFolr2\u003c/em\u003e, and \u003cem\u003eCcl8\u003c/em\u003e)[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eT cell subsets[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] identified included, including CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells (cluster 3; marker genes: \u003cem\u003eCtsw\u003c/em\u003e, \u003cem\u003eNkg7\u003c/em\u003e, and \u003cem\u003eTrbc2\u003c/em\u003e), CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells (cluster 7; marker genes: \u003cem\u003eCtla4\u003c/em\u003e, \u003cem\u003eIl2ra\u003c/em\u003e, and \u003cem\u003eTigit\u003c/em\u003e), and CD8\u003csup\u003e+\u003c/sup\u003e T cells (cluster 12; marker genes: \u003cem\u003eCd8a\u003c/em\u003e, \u003cem\u003eCd8b1\u003c/em\u003e, and \u003cem\u003eCd3d\u003c/em\u003e). High populations of natural killer (NK) cells (cluster 8; marker genes: \u003cem\u003eNkg7\u003c/em\u003e, \u003cem\u003eGzma\u003c/em\u003e, and \u003cem\u003ePrf1\u003c/em\u003e), neutrophils (cluster 9; marker genes: \u003cem\u003eRetnlg\u003c/em\u003e, \u003cem\u003eS100a9\u003c/em\u003e, and \u003cem\u003eS100a8\u003c/em\u003e), and monocytes (cluster 10; marker genes: \u003cem\u003eIfit1, Cmpk2, Ifit3\u003c/em\u003e, and \u003cem\u003eIfit3b\u003c/em\u003e) were also detected. Cluster 13 represented B, supported by the marker genes such as \u003cem\u003eFcmr\u003c/em\u003e, \u003cem\u003eCd79a\u003c/em\u003e, and \u003cem\u003eEbf1\u003c/em\u003e. Also, three clusters showed the signature of dendritic cells, i.e., migratory DCs (migDCs) (cluster 14; \u003cem\u003eCcl22\u003c/em\u003e, \u003cem\u003eBcl2l14\u003c/em\u003e, and \u003cem\u003eIl12b\u003c/em\u003e), plasmacytoid dendritic cells (pDCs) (cluster 16; \u003cem\u003eSiglech\u003c/em\u003e, \u003cem\u003eKlk1\u003c/em\u003e, and \u003cem\u003eKlk1b27\u003c/em\u003e), and conventional DC type 1 (cDC1s) (cluster 18; \u003cem\u003eXcr1\u003c/em\u003e, \u003cem\u003eClec9a\u003c/em\u003e, and \u003cem\u003eMycl\u003c/em\u003e)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Notably, cluster 5 showed characteristics of Langerhans cells, such as \u003cem\u003eCamk1d\u003c/em\u003e, \u003cem\u003eLrmda\u003c/em\u003e, and \u003cem\u003eDennd1a\u003c/em\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, fibroblasts (cluster 2; marker genes: \u003cem\u003ePtgds\u003c/em\u003e, \u003cem\u003eCort\u003c/em\u003e, \u003cem\u003eCmtm5\u003c/em\u003e, and \u003cem\u003ePaqr6\u003c/em\u003e), erythroblasts (cluster 6; marker genes: \u003cem\u003eEsco2\u003c/em\u003e, \u003cem\u003eH3c4\u003c/em\u003e, \u003cem\u003eTk1\u003c/em\u003e, and \u003cem\u003eAsf1b\u003c/em\u003e), adipogenic stem and precursor cells (ASPCs) (cluster 15; marker genes: \u003cem\u003eCol6a2\u003c/em\u003e, \u003cem\u003eCol3a1\u003c/em\u003e, \u003cem\u003eDpt\u003c/em\u003e, and \u003cem\u003eCol1a1\u003c/em\u003e)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], neuronal cells (cluster 17; marker genes: \u003cem\u003ePrickle2\u003c/em\u003e, \u003cem\u003eGrik2\u003c/em\u003e, and \u003cem\u003eNpas3\u003c/em\u003e)[\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], melanocyte (\u003cem\u003eMlana\u003c/em\u003e, \u003cem\u003ePmel\u003c/em\u003e, and \u003cem\u003eTyrp1\u003c/em\u003e)[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and neuroendocrine cells (NECs) (cluster 20; marker genes: \u003cem\u003eAlas2\u003c/em\u003e, \u003cem\u003eIsg20\u003c/em\u003e, and \u003cem\u003eHbb-bt\u003c/em\u003e)[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] were identified, possibly indicating contaminants. The expressions of the marker genes of each cluster were compared, showing a relatively high correlation (score\u0026thinsp;\u0026gt;\u0026thinsp;0.80) between Arg1\u003csup\u003ehi\u003c/sup\u003e MΦs, Res-like MΦs, MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs, erythroblasts, monocytes, TAMs, CD8\u003csup\u003e+\u003c/sup\u003e T cells, and pDCs, respectively (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eD\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe TT treatment reprograms tumour macrophages and expands MHCII\u003csup\u003ehi\u003c/sup\u003e population\u003c/h2\u003e \u003cp\u003eFour distinct populations of macrophages were clearly identified: Arg1\u003csup\u003ehi\u003c/sup\u003e MΦs, Res-like MΦs, MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs, and TAMs. The proportions of these macrophage populations across different groups were analysed. It was observed that Arg1hi MΦs were significantly more prevalent in the control group (55.3%) compared to the UN (37.0%) or TT (37.6%) groups (Fig.\u0026nbsp;3A and \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). Notably, MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs were notably higher in the TT group (24.2%) compared to the UN (15.5%) and control (12.7%) groups. The population of TAMs showed a decrease in both the control and TT groups, with a more pronounced decrease in the control group. Differential gene expression analysis between the TT and control groups revealed that the MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs exhibited the highest number of upregulated (983) and downregulated (1,140) DEGs, followed by Res-like MΦs (Fig.\u0026nbsp;3B). There was a relatively high overlap in DEGs among Arg1\u003csup\u003ehi\u003c/sup\u003e, Res-like, and MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs, indicating similarities in gene expression profiles among these populations, while TAMs showed a distinct gene expression pattern compared to other macrophage types, suggesting a unique TAM phenotype.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 3\u003c/b\u003e Modulation of Arg1\u003csup\u003ehi\u003c/sup\u003e and tumour-associated macrophages in the tumour microenvironment by TT treatment. (\u003cb\u003eA\u003c/b\u003e) Comparison of the proportions of four macrophage (MΦ) populations among the UN, control, and TT groups. (\u003cb\u003eB\u003c/b\u003e) Upset graph comparing the upregulated and downregulated differentially expressed genes (DEGs) in different macrophage populations in the TT group relative to the control group. (\u003cb\u003eC\u003c/b\u003e) Beanplot showing the expression levels (in Log2 values) of selected tumour-associated macrophage marker genes across the four macrophage populations in the control and TT groups. (\u003cb\u003eD\u003c/b\u003e) Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in Arg1hi macrophages of the TT group compared to the control group. (\u003cb\u003eE\u003c/b\u003e) GSEA of hallmark pathways enriched in TAMs of the TT group compared to the control group. Significance levels are indicated as follows: *: \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **: \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***: \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and ****: \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; by two-way Student\u0026rsquo;s t-test.\u003c/p\u003e \u003cp\u003eThe expression profiles of selected marker genes associated with M2-like macrophages, known for promoting tumour cell growth and inducing an immunosuppressive TME, were compared across four macrophage populations between the TT and control groups (Fig.\u0026nbsp;3C). The downregulation of these genes in treatments relative to the UN group was confirmed (\u003cb\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). Particularly noteworthy was the comparative downregulation of many of these genes in the MHCII\u003csup\u003ehi\u003c/sup\u003e and Res-like MΦs of the TT group compared to the control group. Significant suppression of \u003cem\u003eCd68\u003c/em\u003e expression was evident across all macrophage populations in the TT group. Downregulation of \u003cem\u003eArg1\u003c/em\u003e was observed in all macrophage types, with notably significant decreases in Res-like and Arg1\u003csup\u003ehi\u003c/sup\u003e MΦs, which exhibited the highest baseline expression of \u003cem\u003eArg1\u003c/em\u003e among all macrophages. A similar pattern was observed for \u003cem\u003eMmp12\u003c/em\u003e and \u003cem\u003eMmp13\u003c/em\u003e. Two hallmark pathways showed significant positive association with Arg1\u003csup\u003ehi\u003c/sup\u003e MΦs in the TT group: 'oxidative phosphorylation' (OXPHOS) and 'MYC target V1' (Fig.\u0026nbsp;3D). Conversely, several hallmark pathways directly linked to immune response\u0026mdash;such as IFN α/γ response, inflammatory response, and IL6/JAK/STAT3 signalling\u0026mdash;were significantly inhibited in the control group compared to the TT group, though their activation in the TT group did not reach significance. Enrichment analysis revealed significant activation of IFN α response specifically in TAMs of the TT group, while OXPHOS remained prominently enriched (Fig.\u0026nbsp;3E).\u003c/p\u003e \u003cp\u003eSimilarly, several pathways associated with pro-inflammatory responses were suppressed in MHCII\u003csup\u003ehi\u003c/sup\u003e (Fig.\u0026nbsp;4A) and Res-like MΦs (Fig.\u0026nbsp;4B) of the control group compared to the TT group, with the latter also showing reduced apoptosis. Interestingly, the P53 pathway exhibited comparatively higher activation in these two macrophage types within the TT group. Additionally, metabolic pathways such as glycolysis, heme metabolism, and xenobiotic metabolism were downregulated in MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs of the control group, along with fatty acid metabolism in Res-like MΦs. Comparative analysis with the UN group using IPA revealed that regulation of MHC class I quantity on cell surfaces was significantly activated in MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs of the TT group compared to the control group, primarily modulated by \u003cem\u003eStat1\u003c/em\u003e (Fig.\u0026nbsp;4C). Moreover, regulatory networks associated with inhibiting tumour growth, developing tumour cell lines, and cancer invasion were more prominent in MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs of the TT group relative to those in the control group, as evidenced by downregulation of key regulators such as \u003cem\u003eArg1\u003c/em\u003e, \u003cem\u003eSpp1\u003c/em\u003e, \u003cem\u003eAdrb2\u003c/em\u003e, \u003cem\u003eMyc\u003c/em\u003e, \u003cem\u003eIlk\u003c/em\u003e, \u003cem\u003eGpnmb\u003c/em\u003e, and \u003cem\u003eEno1\u003c/em\u003e (Fig.\u0026nbsp;4D). Regarding TAMs, pathways related to 'invasion of cells', particularly involving lymphocytes and leukocytes, were more activated in the control group compared to the TT group relative to the UN group (\u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA\u003c/b\u003e). Similar to MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs, TAMs in the TT group showed downregulation of pathways related to cancer cell growth, with further inhibition of angiogenesis and neoplasia of tumour cell lines (\u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 4\u003c/b\u003e Modulation of MHCII\u003csup\u003ehi\u003c/sup\u003e and tissue-resident in the tumour microenvironment by TT treatment. (\u003cb\u003eA\u003c/b\u003e) Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs of the TT group compared to the control group. (\u003cb\u003eB\u003c/b\u003e) GSEA of hallmark pathways enriched in Res-like MΦs of the TT group compared to the control group. The top two most activated networks in the MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs of the TT group relative to the UN group, identified by Ingenuity Pathway Analysis (IPA): (\u003cb\u003eC\u003c/b\u003e) Quantity of MHC class I on cell surface and (\u003cb\u003eD\u003c/b\u003e) Inhibition of tumour growth. Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eThe TT treatment recruits more immune-responsive CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e and CD4\u0026thinsp;+\u0026thinsp;CD25\u0026thinsp;+\u0026thinsp;T cells\u003c/h2\u003e \u003cp\u003eThree types of T cells were present: CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e, CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e, and CD8\u003csup\u003e+\u003c/sup\u003e T cells, with CD4\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cells being the most abundant (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The TT treatment significantly increased the populations of CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e and CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells compared to both the untreated (UN) and control groups (Fig.\u0026nbsp;5A). In contrast, the control group had the highest population of CD8\u003csup\u003e+\u003c/sup\u003e T cells. In terms of functional modulation induced by the treatments, GSEA revealed that OXPHOS was activated in CD8\u003csup\u003e+\u003c/sup\u003e T cells of the TT group, while IFN-γ and inflammatory responses were inhibited in CD8\u003csup\u003e+\u003c/sup\u003e T cells of the control group (Fig.\u0026nbsp;5B). Similar functional modulation was observed in CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells between the TT and control groups (\u003cb\u003eFigure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA\u003c/b\u003e). For CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells, several metabolic pathways were downregulated in the control group compared to the TT group (\u003cb\u003eFigure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eB\u003c/b\u003e). Interestingly, many marker genes associated with T cell activation, such as \u003cem\u003eLat2\u003c/em\u003e, \u003cem\u003eTax1bp1\u003c/em\u003e, \u003cem\u003eGzmb\u003c/em\u003e, and \u003cem\u003eCd8a\u003c/em\u003e, were upregulated in the control group relative to both the UN and TT groups (Fig.\u0026nbsp;5C). In contrast, the expression of \u003cem\u003eTrbc1\u003c/em\u003e and \u003cem\u003eNfat5\u003c/em\u003e was more pronounced in the TT group.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 5\u003c/b\u003e Expansion of CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e and CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells with TT treatment in the TME on the metastatic side. (\u003cb\u003eA\u003c/b\u003e) Comparison of T cell populations in the UN, the control, and the TT groups. (\u003cb\u003eB\u003c/b\u003e) Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in CD8\u003csup\u003e+\u003c/sup\u003e T cells of the TT group compared to the control group. (\u003cb\u003eC\u003c/b\u003e) Bubble graphs comparing the expression of selected marker genes associated with T cell activation across different T cell populations in the untreated, control, and TT treatment groups. The bubble size represents the percentage of cells expressing each gene, while the bubble colour indicates the average expression level of the gene in each cell type. (\u003cb\u003eD\u003c/b\u003e) Beanplot showing the expression levels (in Log2 values) of selected Treg marker across the three T cell populations in the control and TT groups. The most activated networks in the CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells of the control (\u003cb\u003eE\u003c/b\u003e) and the TT group (\u003cb\u003eF\u003c/b\u003e) relative to the UN group, identified by Ingenuity Pathway Analysis (IPA). Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue.\u003c/p\u003e \u003cp\u003eThe expression of selected Treg marker genes, including \u003cem\u003eFoxp3\u003c/em\u003e, \u003cem\u003eCtla4\u003c/em\u003e, \u003cem\u003eIl7r\u003c/em\u003e, \u003cem\u003eLag3\u003c/em\u003e, and \u003cem\u003eIl2ra\u003c/em\u003e, was compared across T cell populations (Fig.\u0026nbsp;5D). Significant downregulation of all these genes was detected in CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells of the TT group. Additionally, \u003cem\u003eIl7r\u003c/em\u003e was suppressed in the other two T cell populations of the TT group compared to the control group. To understand the regulatory networks in CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells, we analysed the pathways activated in the control and TT groups. In the control group, the 'function of antigen presenting cells' pathway was more activated, while the 'infection of mammalia' pathway was more inhibited relative to the UN group, likely due to the high expression of activated T cell marker genes (Fig.\u0026nbsp;5E). In contrast, CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells in the TT group showed significant inhibition in pathways related to tumour growth, migration, and invasion, as well as the 'proliferation of connective tissue cells' pathway, which are highly relevant to metastasis (Fig.\u0026nbsp;5F). In CD8\u003csup\u003e+\u003c/sup\u003e T cells, the 'activation of cells' pathway was most activated in the control group, while the \u0026lsquo;cell movement of mononuclear leukocytes' pathway was most activated in the TT group (\u003cb\u003eFigures \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eC\u003c/b\u003e and \u003cb\u003eS4D\u003c/b\u003e). For CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells, the 'sensitivity of tumour cell lines' pathway was highly activated in the control group (\u003cb\u003eFigure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eE\u003c/b\u003e). In contrast, the TT group showed activation of pathways inhibiting vasculogenesis, advanced malignant tumours, and invasive cancer, relative to the UN group (\u003cb\u003eFigure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eF\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eThe TT expands dendritic cells (excluding Langerhans cells) and activates NK cells\u003c/h2\u003e \u003cp\u003eThe populations of all three dendritic cell (DC) types expanded in the TT group, with a notable increase in cDC1s, which rose by approximately 29% and 300% compared to the UN and control groups, respectively (Fig.\u0026nbsp;6A and \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Among the most upregulated genes in the TT group was \u003cem\u003eGm10736\u003c/em\u003e (equivalent to \u003cem\u003eHla-dqb1\u003c/em\u003e in human), a key MHC class II molecule (Fig.\u0026nbsp;6B). Several genes related to mitochondrial function, including \u003cem\u003eMrpl52\u003c/em\u003e, \u003cem\u003eMrps21\u003c/em\u003e, \u003cem\u003eMrpl33\u003c/em\u003e, and \u003cem\u003eMrpl12\u003c/em\u003e, were upregulated in DCs, especially in cDC1s, suggesting enhanced assembly and functioning of mitochondrial ribosomes. The expression of \u003cem\u003eCd63\u003c/em\u003e and \u003cem\u003eCd302\u003c/em\u003e was significantly higher in migDCs of the TT group. Additionally, several pseudogenes, such as \u003cem\u003eGm8186\u003c/em\u003e, \u003cem\u003eGm3699\u003c/em\u003e, \u003cem\u003eGm3511\u003c/em\u003e, and \u003cem\u003eGm4149\u003c/em\u003e, were substantially upregulated in cDC1s of the TT group, indicating their potential roles in modulating immune-related gene expression. Although the population of Langerhans cells slightly decreased in the TT group (4.44%) compared to the UN (4.87%) and control groups (5.62%), their phenotype was significantly modulated (Fig.\u0026nbsp;6C). Pathways supporting an inflammatory phenotype, including the IFN α/γ response, inflammatory response, and complement pathways, were negatively associated with the control group compared to the TT group. The \u0026lsquo;E2F targets\u0026rsquo; pathway was the only hallmark pathway significantly activated in the TT group.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 6\u003c/b\u003e Modulation of dendritic cells and NK cells in the tumour microenvironment by TT treatment. (\u003cb\u003eA\u003c/b\u003e) Contributions to migratory dendritic cells (migDCs), plasmacytoid dendritic cells (pDCs), and conventional dendritic cells type 1 (cDC1s) from the UN, control, and TT groups. (\u003cb\u003eB\u003c/b\u003e) Bubble graph comparing the expression of the top 50 upregulated DEGs in cDC1s of the TT group relative to the control group, in migDCs and pDCs. The bubble size corresponds to the percentage of expression, and the bubble colour corresponds to the average expression of the gene in each cell type. (\u003cb\u003eC\u003c/b\u003e) Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in Langerhans cells of the TT group compared to the control group. (\u003cb\u003eD\u003c/b\u003e) GSEA of hallmark pathways enriched in natural killer (NK) cells of the TT group compared to the control group. (\u003cb\u003eE\u003c/b\u003e) Violin plots showing the expression levels of selected marker genes activating NK cells. (\u003cb\u003eF\u003c/b\u003e) The most activated regulatory network, \u0026lsquo;biosynthesis of ribonucleotide,\u0026rsquo; in NK cells of the TT group relative to the UN group, identified by Ingenuity Pathway Analysis (IPA). Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue.\u003c/p\u003e \u003cp\u003eThe population of NK cells was significantly elevated in both the control and TT groups compared to the UN group, with the increase being more pronounced in the control group (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e and \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The phenotype of NK cells in the TT group exhibited more inflammatory features compared to the control group, which showed significant downregulation of IFN-γ and inflammatory response pathways (Fig.\u0026nbsp;6D). Additionally, \u0026lsquo;IL2/STAT5 signalling\u0026rsquo; was negatively associated with the control group. Interestingly, the expression levels of marker genes for priming NK cells in the TT group, such as \u003cem\u003eKlrk1\u003c/em\u003e, \u003cem\u003ePrf1\u003c/em\u003e, \u003cem\u003eGzmb\u003c/em\u003e, \u003cem\u003eNcr1\u003c/em\u003e, \u003cem\u003eLamp1\u003c/em\u003e, and \u003cem\u003eFcgr3\u003c/em\u003e, were lower than those in the control or UN groups (Fig.\u0026nbsp;6E). Ingenuity Pathway Analysis (IPA) identified \u0026lsquo;biosynthesis of ribonucleotide\u0026rsquo; as the top relevant network modulated by TT compared to the UN group, a network that was absent in the control group when compared to the UN group (Fig.\u0026nbsp;6E). Additionally, pathways associated with \u0026lsquo;cell proliferation of tumour cell lines,\u0026rsquo; \u0026lsquo;cell viability of breast cancer cell lines,\u0026rsquo; and \u0026lsquo;migration of cells\u0026rsquo; were downregulated, whereas \u0026lsquo;sensitivity of cells\u0026rsquo; was activated, indicating that TT treatment modulated NK cells to create an inhibitory environment for tumours.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eThe treatments markedly reduces the B cell population\u003c/h2\u003e \u003cp\u003eThe population of B cells significantly decreased, dropping from 4.52% in the UN group to 0.67% in the control group and 0.64% in the TT group (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Notably, the expression of many antigen-associated genes, such as \u003cem\u003eCd86\u003c/em\u003e, \u003cem\u003eCd53\u003c/em\u003e, \u003cem\u003eCd68\u003c/em\u003e, and \u003cem\u003eCd22\u003c/em\u003e, was markedly elevated in the control group compared to the other two groups (Fig.\u0026nbsp;7A). Additionally, three MHC class II antigens, including \u003cem\u003eH2-DMb2\u003c/em\u003e, \u003cem\u003eH2-DMa\u003c/em\u003e, and \u003cem\u003eH2-Aa\u003c/em\u003e, were upregulated by the control treatment. In the TT group, the upregulation of \u003cem\u003eCd52\u003c/em\u003e and a B cell marker gene \u003cem\u003eBcl2a1b\u003c/em\u003e was observed. GSEA indicated that energy metabolism-associated biological processes were more activated in the TT group compared to the control group (Fig.\u0026nbsp;7B). Conversely, several autophagy-related processes were comparatively less active in the control group. IPA revealed an inhibition of 'organismal death' in the B cells of the control group (Fig.\u0026nbsp;7C), while apoptosis was more activated in the B cells of the TT group (\u003cb\u003eFigure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e\u003c/b\u003e). Furthermore, the TT group showed enhanced signalling in 'differentiation of T lymphocytes,' 'immune response of antigen-presenting cells,' and 'phagocytosis,' relative to the UN group, which was not detected in the control group with high consistency.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 7\u003c/b\u003e The effect of TT treatment on B cell phenotype and cell-cell communication in the TME. (\u003cb\u003eA\u003c/b\u003e) Comparison of the expression levels of selected antigen-associated marker genes and B cell features. (\u003cb\u003eB\u003c/b\u003e) Gene Set Enrichment Analysis (GSEA) of hallmark pathways enriched in B cells from the TT group compared to the control group. (\u003cb\u003eC\u003c/b\u003e) Identification of the most activated regulatory network in B cells of the control group relative to the UN group, as determined by Ingenuity Pathway Analysis (IPA). Cellular events/canonical pathways/regulators that were activated are indicated in orange, while others that were suppressed are indicated in blue. (\u003cb\u003eD\u003c/b\u003e) Communication network among immune cells in the TT group (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). (\u003cb\u003eE\u003c/b\u003e) Heatmap comparing the interaction probability between cDC1s and other cell types across the three groups. (see \u003cb\u003eTable \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e for cell-cell communication results in detail.)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe TT induces more cDC1/CD8\u003csup\u003e+\u003c/sup\u003e T cell communication\u003c/h2\u003e \u003cp\u003eThe cell-cell communication among immune response-relevant cells was analysed, with the networks of the TT group shown in \u003cb\u003eFig.\u0026nbsp;7C\u003c/b\u003e. The networks of the UN and control groups are compared in \u003cb\u003eFigure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e\u003c/b\u003e. No communication (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) was detected between cDC1s and CD8\u003csup\u003e+\u003c/sup\u003e T cells in the UN group, similar to the lack of communication between Langerhans cells in the treatment groups. Additionally, cDC1s interacted with TAMs, Arg1\u003csup\u003ehi\u003c/sup\u003e, and Res-like MΦs to a lesser extent in both the control and TT groups, as well as with pDCs and B cells. The interaction probability between cDC1s and other cell types is compared in Fig.\u0026nbsp;8D, showing that most communication was reduced in the control group compared to the UN group, except with NK and CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells. Notably, the interaction between cDC1s and four cell types was significantly elevated by the TT, including MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs (by 22% relative to the UN group), CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells (56%), migDCs (18%), and CD8\u003csup\u003e+\u003c/sup\u003e T cells (by 73% relative to the control). On the other hand, the control appeared to enforce more communication between migDCs, pDCs and Langerhans cells with other cell types, such as Arg1\u003csup\u003ehi\u003c/sup\u003e MΦs, neutrophils, B cells, NK cells, CD4\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cells, and CD8\u0026thinsp;+\u0026thinsp;T cells, compared to the UN and the TT groups; notably, TAMs showed highest communication with these three DC types in the TT group (\u003cb\u003eTable \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMelanoma metastasis poses a significant challenge due to its aggressive nature, propensity for early dissemination, and diverse pathways of spread. Traditional treatments often yield limited responses, while newer therapies can be associated with significant side effects. In our current study, we developed a triple therapy (TT) approach combining a vaccine, anti-CD47 antibody, and F1/F3, demonstrating promising efficacy against melanoma metastasis in a B16 mouse model. The TT treatment led to substantial reductions in tumour sizes on both the treated (right) and metastatic (left) sides (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), along with a remarkable extension of survival times in the treated mice. Moreover, it significantly decreased tumour weights on both sides, with a more pronounced effect observed on the treated side.\u003c/p\u003e \u003cp\u003eSingle-cell RNA sequencing (sc-RNAseq) analysis of CD45\u003csup\u003e+\u003c/sup\u003e cells isolated from the metastatic site identified a total of 21 cell types, including various macrophage, T cell, dendritic cell, B cell, NK cell, and monocyte populations. Notably, TT treatment comparably increased the populations of CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells, MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs, and conventional type 1 dendritic cells (cDC1s), while also altering their functional profiles. Furthermore, TT reprogrammed Arg1\u003csup\u003ehi\u003c/sup\u003e MΦs, TAMs, and Res-like MΦs to exhibit a more immune-responsive phenotype. Enhanced communication between cDC1s and CD8\u003csup\u003e+\u003c/sup\u003e T cells was observed in the TT group compared to control or untreated groups, facilitating a more intensive immune response. Additionally, CD8\u0026thinsp;+\u0026thinsp;T cells showed increased activation in the TT-treated mice.\u003c/p\u003e \u003cp\u003eMHCII\u003csup\u003ehi\u003c/sup\u003e MΦs express high levels of major histocompatibility complex class II (MHCII) molecules, which have been considered crucial for the presentation of antigens to CD4\u0026thinsp;+\u0026thinsp;T helper cells, thus essential for initiating and regulating immune responses. In addition, their immune regulation roles have been found in the maintenance of tolerance and preventing autoimmunity, as well as in the production of various cytokines and chemokines. In the TME, previous research has characterised that MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs contribute to anti-tumour response via presenting tumour antigens to CD4\u003csup\u003e+\u003c/sup\u003e T cells and mounting an ongoing adaptive immune response. The TT did not only markedly expand the population of MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs by nearly 100% with respect to either the UN or the control, but also module their phenotype to be more inflammatory at the metastasis side, indicating they became more immune response active, which have been reported for the either topical application or intratumoral injection of drug candidates containing F1/F3 on TC-1 bearing mice models[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This overwrote the immunosuppressive functions exhibited by the MHCII\u003csup\u003ehi\u003c/sup\u003e MΦs in the TME of the UN or the control group.\u003c/p\u003e \u003cp\u003eTAMs, typically polarised towards an M2-like phenotype, promote tumour cell proliferation through mechanisms that facilitate tumour growth, metastasis, and immune evasion[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The study identified two populations of macrophages: Arg1\u003csup\u003ehi\u003c/sup\u003e MΦs and TAMs, both exhibiting TAM-like characteristics, with the former type showing aberrant Arg1 expression. TT significantly downregulated several TAM-associated markers, including \u003cem\u003eCd68\u003c/em\u003e and \u003cem\u003eArg1\u003c/em\u003e, as well as \u003cem\u003eStat6\u003c/em\u003e, a marker of M2 polarisation, compared to both UN and control groups (Fig.\u0026nbsp;4). TAMs are known to secrete elevated levels of growth factors (e.g., \u003cem\u003eEgf\u003c/em\u003e, \u003cem\u003eVegf\u003c/em\u003e), cytokines (e.g., \u003cem\u003eTgfb\u003c/em\u003e, \u003cem\u003eTnfa\u003c/em\u003e, \u003cem\u003eIl8\u003c/em\u003e), MMP family members (e.g., \u003cem\u003eMmp1\u003c/em\u003e, \u003cem\u003eMmp2\u003c/em\u003e, \u003cem\u003eMmp9\u003c/em\u003e, \u003cem\u003eMmp12\u003c/em\u003e, \u003cem\u003eMmp13\u003c/em\u003e), and chemokines that attract immune cells, fostering a tumour-permissive microenvironment and metastasis. Significantly, TT treatment inactivated \u003cem\u003eVegfa\u003c/em\u003e, \u003cem\u003eTnfaip3\u003c/em\u003e, and \u003cem\u003eMmp12/13\u003c/em\u003e compared to the other groups. Moreover, enzymes crucial for extracellular matrix remodelling (e.g., \u003cem\u003eCtsb\u003c/em\u003e, \u003cem\u003eCtsd\u003c/em\u003e, \u003cem\u003eCtsl\u003c/em\u003e, \u003cem\u003eHpse\u003c/em\u003e, \u003cem\u003ePlau\u003c/em\u003e, \u003cem\u003eAdam8\u003c/em\u003e) were more suppressed in the TT group, potentially hindering tumour invasion and metastasis. Notably, TT treatment modulated Arg1\u003csup\u003ehi\u003c/sup\u003e MΦs and TAMs towards a more immunoreactive state. While the population of Arg1\u003csup\u003ehi\u003c/sup\u003e MΦs notably increased in the control group, their function remained comparable to the UN group, suggesting a cellular response that enhances the immunosuppressive TME in response to vaccine plus anti-CD47 treatment.\u003c/p\u003e \u003cp\u003eFurthermore, TT significantly activated OXPHOS and \u0026lsquo;MYC targets V1\u0026rsquo; pathways in Arg1\u003csup\u003ehi\u003c/sup\u003e MΦs, indicating an interplay between these pathways. MYC can upregulate genes involved in mitochondrial biogenesis and function, enhancing OXPHOS. In MYC-driven tumours, such as B16 melanoma[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], heightened OXPHOS levels induce oxidative stress, potentially activating apoptotic pathways and improving the immune milieu. This effect may contribute to enhancing the efficacy of immune checkpoint inhibitors. Additionally, MYC could influence immune evasion mechanisms, further impacting tumour progression and response to therapies.\u003c/p\u003e \u003cp\u003eRes-like MΦs play critical roles in maintaining tissue homeostasis, responding to infections, and regulating immune responses through cytokine production and antigen presentation[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. They recognise pathogens via pattern recognition receptors and engage in phagocytosis as part of their immune surveillance functions. It has been observed that TAMs often originate from tissue-resident macrophages, underscoring their influence on tumour progression and metastasis[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. TT treatment modulated Res-like MΦs to exhibit a more pro-inflammatory phenotype compared to the control group. Anti-CD47 antibodies block the CD47 \"don't eat me\" signal on tumour cells, rendering them more susceptible to phagocytosis by macrophages and other immune cells[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Whereas this reprogramming of M2-like to M1-like phenotypes in multiple macrophage populations suggests they may synergistically enhance the effects of anti-CD47 antibody therapy.\u003c/p\u003e \u003cp\u003eThe presence of CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells represents a stage in T cell maturation where cells express both CD4 and CD8 co-receptors, which eventually differentiate into either CD4\u0026thinsp;+\u0026thinsp;helper T cells or CD8\u0026thinsp;+\u0026thinsp;cytotoxic T cells. These mature CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells are suggested to play regulatory roles, potentially contributing to immune homeostasis by combining helper and cytotoxic functions in a unique manner. In the context of our study, an increased population of CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells was observed in both the control and TT-treated groups, with a more pronounced increase noted in the TT group. This suggests that these cells may enhance the immune response against the tumour. The expansion of CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells likely reflects changes in the TME, making it more conducive to immune cell infiltration and activation. Additionally, the inclusion of F1/F3 in the treatment regimen may directly induce tumour cell death[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], releasing tumour antigens that are subsequently captured by DCs and presented to T cells. This process leads to further activation and expansion of CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells, indicating that F1/F3 enhances the antigen presentation pathway and boosts T cell activation.\u003c/p\u003e \u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells are commonly associated with regulatory T cells (Tregs), which play a crucial role in maintaining immune tolerance and preventing autoimmune reactions. In our study, the population of CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells was notably increased in the TT group by approximately 18% compared to the control group and by 149% compared to the UN group. However, despite the increase in CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells, the TT treatment significantly downregulated the expression of marker genes associated with Tregs, such as \u003cem\u003eIl2ra\u003c/em\u003e, \u003cem\u003eCtla4\u003c/em\u003e, \u003cem\u003eIl7r\u003c/em\u003e, \u003cem\u003eLag3\u003c/em\u003e, and \u003cem\u003eFoxp3\u003c/em\u003e, compared to both the control and UN groups. This suggests that while the absolute number of CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells increased, their functional status as Tregs was reduced in the TT-treated mice. Since Tregs are known to facilitate immune evasion by suppressing anti-tumour immune responses[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], their reduction in the TT group implies a potential attenuation of these immunosuppressive effects. By diminishing Treg activity, the TT treatment may enhance anti-tumour immune responses, thereby inhibiting tumour growth and progression.\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated that topical application or intratumoral injection of F1/F3 to TC-1 tumours can enhance the recruitment of activated T cells and NK cells to the tumour sites. However, in our study, we observed that several common marker genes of NK cells were comparatively downregulated on the left side in the TT (triple therapy) group, despite achieving significantly better treatment outcomes. It is important to note that the TT treatment was administered on the right side of the mice. This discrepancy suggests that the inclusion of F1/F3 may have selectively induced migration of highly activated T and NK cells to the right side, while potentially leaving behind less activated but still effective immune cells on the left side. Similar observations have been documented in other cancer models[\u003cspan additionalcitationids=\"CR55 CR56\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], indicating a potential compartmentalized response to treatment.\u003c/p\u003e \u003cp\u003eAnother hypothesis is that TT may have elicited a more robust immune memory response, enabling the immune system to exert tumour control with fewer but more efficient immune cells. The quality of T and NK cells, rather than their activation state alone, might play a crucial role in achieving effective antitumor responses. Even with lower activation marker expression, TT-treated cells could exhibit enhanced effector functions. Moreover, F1/F3 are known to induce changes in the TME, making it less supportive of tumour growth and metastasis. This could potentially reduce the requirement for high levels of T and NK cell activation to maintain tumour control. Lastly, it is plausible that immune surveillance and tumour control mechanisms activated by TT may involve pathways that do not solely rely on heightened activation levels T, NK, and B cells. This multifaceted response underscores the complexity of immune modulation in cancer treatment and highlights the potential of including caerin peptides to induce effective antitumor immunity through diverse mechanisms beyond traditional immune cell activation paradigms.\u003c/p\u003e \u003cp\u003eThe cell-cell communication analysis revealed significantly higher levels of interplay between cDC1s with CD8\u003csup\u003e+\u003c/sup\u003e T cells, migDCs, CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells, and MHCII\u003csup\u003ehi\u003c/sup\u003e macrophages in the TT group compared to the UN and control groups. This was accorded with the upregulation of marker genes positively associated with the assembly and functioning of mitochondrial ribosomes in the TT group, indicating the activation of DC function. cDC1 cells are specialised in cross-presenting extracellular antigens on MHC class I molecules to CD8\u003csup\u003e+\u003c/sup\u003e T cells, a crucial process for initiating cytotoxic T cell responses against tumours. The increased interaction detected in the TT group suggests that cDC1 cells were more effectively presenting tumour antigens, potentially leading to the activation and expansion of CD8\u003csup\u003e+\u003c/sup\u003e cytotoxic T lymphocytes and greater infiltration into the TME. Although the population of CD8\u003csup\u003e+\u003c/sup\u003e T cells in the TT group was lower than in the control group (both significantly higher than that in the UN group), the CD8\u003csup\u003e+\u003c/sup\u003e T cells in the TT group were more activated. This enhanced activation could contribute to the generation of memory T cells, providing long-term protection against tumour recurrence.\u003c/p\u003e \u003cp\u003eIn contrast, the control group's migDCs and pDCs exhibited more communication with other cell types, suggesting a broader immune response involving various cell types. This broader activation might lead to a more diverse but less targeted immune response. The inclusion of F1/F3 in the TT group likely enhances the effectiveness of cDC1s, leading to a more potent and focused anti-tumour immune response. The more intensive communications between CD8\u003csup\u003e+\u003c/sup\u003e T cells and cDC1s in the TT group indicate a robust and potentially more effective immune response against the tumour. This enhanced communication might also correlate with improved interactions between CD8\u003csup\u003e+\u003c/sup\u003e T cells and TAMs, resulting in a reduction in the suppressive activity of TAMs observed in the TT group.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCollectively, these findings underscore the potential of the triple therapy to enhance antitumor immunity by modulating immune cell populations and their functions within the metastatic microenvironment. The comprehensive immune modulation induced by the triple therapy highlights its promise as a therapeutic strategy for combating melanoma metastasis, addressing both tumour progression and enhancing immune surveillance. Particularly, the dual strategy of reprogramming macrophages towards a pro-inflammatory state and enhancing tumour cell phagocytosis introduced by block CD47 could potentially improve immune responses against tumours, highlighting the therapeutic potential of targeting macrophage polarisation in melanoma treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe scRNAseq dataset presented in this study can be found in online repository: https://singlecell.broadinstitute.org/single_cell, SCP2697.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests。\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported in part by the First Affiliated Hospital of Guangdong Pharmaceutical University, Deng Feng project of Foshan First People\u0026rsquo;s Hospital (2019A008), National Science Foundation of Guangdong province (2020A1515010855), National Natural Science Foundation of China (31971355). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualisation and design: TW, GN and XL; Experimental work: QF, YL, JL and GN; Data process, curation and visualisation: QF, YL, JL and TW; Analysis and interpretation: TW, QL, XL, HL and GN; Writing-original draft preparation: QL, TW and XL; Writing-review and editing: ZC, TW, XL, HL and GN; project administration: XL and ZC. All authors reviewed the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Professor Abigail Elizur for her valuable advice and support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKarras P, Bordeu I, Pozniak J, Nowosad A, Pazzi C, Van Raemdonck N, Landeloos E, Van Herck Y, Pedri D, Bervoets G, et al. A cellular hierarchy in melanoma uncouples growth and metastasis. Nature. 2022;610(7930):190\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArnold M, Singh D, Laversanne M, Vignat J, Vaccarella S, Meheus F, Cust AE, de Vries E, Whiteman DC, Bray F. Global Burden of Cutaneous Melanoma in 2020 and Projections to 2040. JAMA Dermatol. 2022;158(5):495\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeiter U, Keim U, Garbe C. Epidemiology of Skin Cancer: Update 2019. Adv Exp Med Biol. 2020;1268:123\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwitzer B, Puzanov I, Skitzki JJ, Hamad L, Ernstoff MS. Managing Metastatic Melanoma in 2022: A Clinical Review. JCO Oncol Pract. 2022;18(5):335\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. 2021;71(1):7\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillani A, Potestio L, Fabbrocini G, Troncone G, Malapelle U, Scalvenzi M. The Treatment of Advanced Melanoma: Therapeutic Update. Int J Mol Sci 2022, 23(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRibas A, Wolchok JD. Cancer immunotherapy using checkpoint blockade. Science. 2018;359(6382):1350\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarkin J, Chiarion-Sileni V, Gonzalez R, Grob JJ, Cowey CL, Lao CD, Schadendorf D, Dummer R, Smylie M, Rutkowski P, et al. Combined Nivolumab and Ipilimumab or Monotherapy in Untreated Melanoma. N Engl J Med. 2015;373(1):23\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong GV, Weber JS, Infante JR, Kim KB, Daud A, Gonzalez R, Sosman JA, Hamid O, Schuchter L, Cebon J, et al. Overall Survival and Durable Responses in Patients With BRAF V600-Mutant Metastatic Melanoma Receiving Dabrafenib Combined With Trametinib. J Clin Oncol. 2016;34(8):871\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang AC, Zappasodi R. A decade of checkpoint blockade immunotherapy in melanoma: understanding the molecular basis for immune sensitivity and resistance. Nat Immunol. 2022;23(5):660\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHodi FS, Chiarion-Sileni V, Gonzalez R, Grob JJ, Rutkowski P, Cowey CL, Lao CD, Schadendorf D, Wagstaff J, Dummer R, et al. Nivolumab plus ipilimumab or nivolumab alone versus ipilimumab alone in advanced melanoma (CheckMate 067): 4-year outcomes of a multicentre, randomised, phase 3 trial. Lancet Oncol. 2018;19(11):1480\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNi G, Liu X, Li H, Fogarty CE, Chen S, Zhang P, Liu Y, Wu X, Wei MQ, Chen G, et al. Topical Application of Temperature-Sensitive Gel Containing Caerin 1.1 and 1.9 Peptides on TC-1 Tumour-Bearing Mice Induced High-Level Immune Response in the Tumour Microenvironment. Front Oncol. 2021;11:754770.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin R, Ma B, Liu N, Zhang L, He T, Liu X, Chen T, Liu W, Liang Y, Wang T, et al. Targeted radioimmunotherapy with the iodine-131-labeled caerin 1.1 peptide for human anaplastic thyroid cancer in nude mice. Ann Nucl Med. 2021;35(7):811\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Zhang P, Xiao L, Liu Y, Wu K, Ni G, Li H, Wang T, Wu X, Chen G, Liu X. Caerin 1.1 and 1.9 Peptides from Australian Tree Frog Inhibit Antibiotic-Resistant Bacteria Growth in a Murine Skin Infection Model. Microbiol Spectr. 2021;9(1):e0005121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNi G, Chen S, Chen M, Wu J, Yang B, Yuan J, Walton SF, Li H, Wei MQ, Wang Y, et al. Host-Defense Peptides Caerin 1.1 and 1.9 Stimulate TNF-Alpha-Dependent Apoptotic Signals in Human Cervical Cancer HeLa Cells. Front Cell Dev Biol. 2020;8:676.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa B, Yuan J, Chen S, Huang K, Wang Q, Ma J, Lin R, Zhang L, Zhou Y, Wang T, et al. Topical application of temperature-sensitive caerin 1.1 and 1.9 gel inhibits TC-1 tumor growth in mice. Am J Transl Res. 2020;12(1):191\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan X, Ma B, You X, Chen S, Wu J, Wang T, Walton SF, Yuan J, Wu X, Chen G, et al. Synthesized natural peptides from amphibian skin secretions increase the efficacy of a therapeutic vaccine by recruiting more T cells to the tumour site. BMC Complement Altern Med. 2019;19(1):163.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao L, Yang X, Li J, Zhang P, Tang S, Cao D, Chen S, Li H, Zhang W, Chen G et al. Caerin 1 Peptides, the Potential Jack-of-All-Trades for the Multiple Antibiotic-Resistant Bacterial Infection Treatment and Cancer Immunotherapy. \u003cem\u003eBiomed Res Int\u003c/em\u003e 2022, 2022:7841219.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang X, Liu X, Li J, Zhang P, Li H, Chen G, Zhang W, Wang T, Frazer I, Ni G. Caerin 1.1/1.9 Enhances Antitumour Immunity by Activating the IFN-α Response Signalling Pathway of Tumour Macrophages. \u003cem\u003eCancers (Basel)\u003c/em\u003e 2022, 14(23).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLogtenberg MEW, Scheeren FA, Schumacher TN. The CD47-SIRPα Immune Checkpoint. Immunity. 2020;52(5):742\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNi G, Yang X, Li J, Wu X, Liu Y, Li H, Chen S, Fogarty CE, Frazer IH, Chen G, et al. Intratumoral injection of caerin 1.1 and 1.9 peptides increases the efficacy of vaccinated TC-1 tumor-bearing mice with PD-1 blockade by modulating macrophage heterogeneity and the activation of CD8(+) T cells in the tumor microenvironment. Clin Transl Immunol. 2021;10(8):e1335.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLun ATL, Riesenfeld S, Andrews T, Dao TP, Gomes T, Marioni JC. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 2019;20(1):63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou B, Jin W. Visualization of Single Cell RNA-Seq Data Using t-SNE in R. Methods Mol Biol. 2020;2117:159\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstr\u0026aring;le M, Laurila E, et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34(3):267\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH, Myung P, Plikus MV, Nie Q. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021;12(1):1088.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu C, Li T, Xu Y, Zhang X, Li F, Bai J, Chen J, Jiang W, Yang K, Ou Q, et al. CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. Nucleic Acids Res. 2023;51(D1):D870\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao G, Lu H, Chang Z, Zhao Y, Zhu T, Chang L, Guo Y, Garcia-Barrio MT, Chen YE, Zhang J. Single-cell RNA sequencing reveals the cellular heterogeneity of aneurysmal infrarenal abdominal aorta. Cardiovasc Res. 2021;117(5):1402\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCassetta L, Fragkogianni S, Sims AH, Swierczak A, Forrester LM, Zhang H, Soong DYH, Cotechini T, Anur P, Lin EY, et al. Human Tumor-Associated Macrophage and Monocyte Transcriptional Landscapes Reveal Cancer-Specific Reprogramming, Biomarkers, and Therapeutic Targets. Cancer Cell. 2019;35(4):588\u0026ndash;e602510.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuilliams M, Dutertre CA, Scott CL, McGovern N, Sichien D, Chakarov S, Van Gassen S, Chen J, Poidinger M, De Prijck S, et al. Unsupervised High-Dimensional Analysis Aligns Dendritic Cells across Tissues and Species. Immunity. 2016;45(3):669\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOttosson-Laakso E, Krus U, Storm P, Prasad RB, Oskolkov N, Ahlqvist E, Fadista J, Hansson O, Groop L, Vikman P. Glucose-Induced Changes in Gene Expression in Human Pancreatic Islets: Causes or Consequences of Chronic Hyperglycemia. Diabetes. 2017;66(12):3013\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenberg FM, Wardenaar R, Voorberg AN, Spierings DCJ, Schuttelaar MA. Transcriptional differences between vesicular hand eczema and atopic dermatitis. Contact Dermat. 2024;90(1):23\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwalie PC, Dong H, Zachara M, Russeil J, Alpern D, Akchiche N, Caprara C, Sun W, Schlaudraff KU, Soldati G, et al. A stromal cell population that inhibits adipogenesis in mammalian fat depots. Nature. 2018;559(7712):103\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkuda H, Miyata S, Mori Y, Tohyama M. Mouse Prickle1 and Prickle2 are expressed in postmitotic neurons and promote neurite outgrowth. FEBS Lett. 2007;581(24):4754\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuzm\u0026aacute;n YF, Ramsey K, Stolz JR, Craig DW, Huentelman MJ, Narayanan V, Swanson GT. A gain-of-function mutation in the GRIK2 gene causes neurodevelopmental deficits. Neurol Genet. 2017;3(1):e129.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErbel-Sieler C, Dudley C, Zhou Y, Wu X, Estill SJ, Han T, Diaz-Arrastia R, Brunskill EW, Potter SS, McKnight SL. Behavioral and regulatory abnormalities in mice deficient in the NPAS1 and NPAS3 transcription factors. Proc Natl Acad Sci U S A. 2004;101(37):13648\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu J, Miller AJ, Widlund HR, Horstmann MA, Ramaswamy S, Fisher DE. MLANA/MART1 and SILV/PMEL17/GP100 are transcriptionally regulated by MITF in melanocytes and melanoma. Am J Pathol. 2003;163(1):333\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHellstr\u0026ouml;m AR, Watt B, Fard SS, Tenza D, Mannstr\u0026ouml;m P, Narfstr\u0026ouml;m K, Ekesten B, Ito S, Wakamatsu K, Larsson J, et al. Inactivation of Pmel alters melanosome shape but has only a subtle effect on visible pigmentation. PLoS Genet. 2011;7(9):e1002285.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang D, Tsuji Y, Setaluri V. Selective down-regulation of tyrosinase family gene TYRP1 by inhibition of the activity of melanocyte transcription factor, MITF. Nucleic Acids Res. 2002;30(14):3096\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStankiewicz AM, Goscik J, Majewska A, Swiergiel AH, Juszczak GR. The Effect of Acute and Chronic Social Stress on the Hippocampal Transcriptome in Mice. PLoS ONE. 2015;10(11):e0142195.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang F, Yang X, Ren Z, Chen C, Liu C. Alternative splicing in mouse brains affected by psychological stress is enriched in the signaling, neural transmission and blood-brain barrier pathways. Mol Psychiatry. 2023;28(11):4707\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin Y, Xu J, Lan H. Tumor-associated macrophages in tumor metastasis: biological roles and clinical therapeutic applications. J Hematol Oncol. 2019;12(1):76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan Y, Yu Y, Wang X, Zhang T. Tumor-Associated Macrophages in Tumor Immunity. Front Immunol. 2020;11:583084.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang C, Liu Y, Hu Y, Fang L, Huang Z, Cui H, Xie J, Hong Y, Chen W, Xiao N, et al. Myc inhibition tips the immune balance to promote antitumor immunity. Cell Mol Immunol. 2022;19(9):1030\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShapouri-Moghaddam A, Mohammadian S, Vazini H, Taghadosi M, Esmaeili SA, Mardani F, Seifi B, Mohammadi A, Afshari JT, Sahebkar A. Macrophage plasticity, polarization, and function in health and disease. J Cell Physiol. 2018;233(9):6425\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurray PJ, Wynn TA. Protective and pathogenic functions of macrophage subsets. Nat Rev Immunol. 2011;11(11):723\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCracken MN, Cha AC, Weissman IL. Molecular Pathways: Activating T Cells after Cancer Cell Phagocytosis from Blockade of CD47 Don't Eat Me Signals. Clin Cancer Res. 2015;21(16):3597\u0026ndash;601.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu N, He T, Xiao Z, Du J, Zhu K, Liu X, Chen T, Liu W, Ni G, Liu X, et al. (131)I-Caerin 1.1 and (131)I-Caerin 1.9 for the treatment of non-small-cell lung cancer. Front Oncol. 2022;12:861206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Zhong F, Xiao F, Li J, Liu X, Ni G, Wang T, Zhang W. Host-defence caerin 1.1 and 1.9 peptides suppress glioblastoma U87 and U118 cell proliferation through the modulation of mitochondrial respiration and induce the downregulation of CHI3L1. PLoS ONE. 2024;19(6):e0304149.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo Y, Li J, Fu Q, Zhang P, Song X, Liu M, Mo R, Fu J, Tang S, Wu J. The natural defense peptide caerin1. 1/1.9 sequentially induces caspase 3/GSDME-mediated pyroptosis followed by intrinsic apoptosis of epithelial cancer cells. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFacciabene A, Motz GT, Coukos G. T-regulatory cells: key players in tumor immune escape and angiogenesis. Cancer Res. 2012;72(9):2162\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHinz S, Pagerols-Raluy L, Oberg HH, Ammerpohl O, Gr\u0026uuml;ssel S, Sipos B, Gr\u0026uuml;tzmann R, Pilarsky C, Ungefroren H, Saeger HD, et al. Foxp3 expression in pancreatic carcinoma cells as a novel mechanism of immune evasion in cancer. Cancer Res. 2007;67(17):8344\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTilburgs T, Roelen DL, van der Mast BJ, de Groot-Swings GM, Kleijburg C, Scherjon SA, Claas FH. Evidence for a selective migration of fetus-specific CD4\u0026thinsp;+\u0026thinsp;CD25bright regulatory T cells from the peripheral blood to the decidua in human pregnancy. J Immunol. 2008;180(8):5737\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParodi M, Raggi F, Cangelosi D, Manzini C, Balsamo M, Blengio F, Eva A, Varesio L, Pietra G, Moretta L, et al. Hypoxia Modifies the Transcriptome of Human NK Cells, Modulates Their Immunoregulatory Profile, and Influences NK Cell Subset Migration. Front Immunol. 2018;9:2358.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim CH, Hashimoto-Hill S, Kim M. Migration and Tissue Tropism of Innate Lymphoid Cells. Trends Immunol. 2016;37(1):68\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHudspeth K, Donadon M, Cimino M, Pontarini E, Tentorio P, Preti M, Hong M, Bertoletti A, Bicciato S, Invernizzi P, et al. Human liver-resident CD56(bright)/CD16(neg) NK cells are retained within hepatic sinusoids via the engagement of CCR5 and CXCR6 pathways. J Autoimmun. 2016;66:40\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Caerin peptide, B16 cell, Melanoma, Macrophage, cDC1, CD4+CD8+ T cell, cell-cell communication, anti-CD47 antibody, immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-4671312/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4671312/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCancer immunotherapy, particularly immune checkpoint inhibitors (ICBs) such as anti-PD-1 antibodies, has revolutionized cancer treatment, although response rates vary among patients. Previous studies have demonstrated that caerin 1.1 and 1.9, host-defence peptides from the Australian tree frog, enhance the effectiveness of anti-PD-1 and therapeutic vaccines in a murine TC-1 model by activating tumour-associated macrophages intratumorally.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe employed a murine B16 melanoma model to investigate the therapeutic potential of caerin 1.1 and 1.9 in combination with anti-CD47 and a therapeutic vaccine (triple therapy, TT). Tumour growth of caerin-injected primary tumours and distant metastatic tumours was assessed, and survival analysis conducted. Single-cell RNA sequencing (scRNAseq) of CD45\u003csup\u003e+\u003c/sup\u003e cells isolated from distant tumours was performed to elucidate changes in the tumour microenvironment induced by TT.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe TT treatment significantly reduced tumour volumes on the treated side compared to untreated and control groups, with notable effects observed by Day 21. Survival analysis indicated extended survival in mice receiving TT, both on the treated and distant sides. scRNAseq revealed a notable expansion of conventional type 1 dendritic cells (cDC1s) and CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells in the TT group. Tumour-associated macrophages in the TT group shifted toward a more immune-responsive M1 phenotype, with enhanced communication observed between cDC1s and CD8\u003csup\u003e+\u003c/sup\u003e and CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e T cells. Additionally, TT downregulated M2-like macrophage marker genes, particularly in MHCIIhi and tissue-resident macrophages, suppressing \u003cem\u003eCd68\u003c/em\u003e and \u003cem\u003eArg1\u003c/em\u003e expression across all macrophage types. Differential gene expression analysis highlighted pathway alterations, including upregulation of oxidative phosphorylation and MYC target V1 in Arg1\u003csup\u003ehi\u003c/sup\u003e macrophages, and activation of pro-inflammatory pathways in MHCII\u003csup\u003ehi\u003c/sup\u003e and tissue-resident macrophages.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings suggest that caerin 1.1 and 1.9, combined with immunotherapy, effectively modulate the tumour microenvironment in primary and secondary tumours, leading to reduced tumour growth and enhanced systemic immunity. Further investigation into these mechanisms could pave the way for improved combination therapies in advanced melanoma treatment.\u003c/p\u003e","manuscriptTitle":"Caerin 1.1 and 1.9 peptides halt B16 melanoma metastatic tumours via expanding cDC1 and reprogramming tumour macrophages","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-10 11:37:54","doi":"10.21203/rs.3.rs-4671312/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-07-12T09:09:56+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-10T17:10:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-04T11:23:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2024-07-02T00:18:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9b0f7fd6-3105-4707-908c-7ea770f104b5","owner":[],"postedDate":"August 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-04T16:31:23+00:00","versionOfRecord":{"articleIdentity":"rs-4671312","link":"https://doi.org/10.1186/s12967-024-05763-x","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2024-10-28 16:23:00","publishedOnDateReadable":"October 28th, 2024"},"versionCreatedAt":"2024-08-10 11:37:54","video":"","vorDoi":"10.1186/s12967-024-05763-x","vorDoiUrl":"https://doi.org/10.1186/s12967-024-05763-x","workflowStages":[]},"version":"v1","identity":"rs-4671312","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4671312","identity":"rs-4671312","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}