A single-cell transcriptomic study reveals immune suppressive cancer cell-immune cell interactions in the triple negative canine breast cancers

A single-cell transcriptomic study reveals immune suppressive cancer cell-immune cell interactions in the triple negative canine breast cancers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A single-cell transcriptomic study reveals immune suppressive cancer cell-immune cell interactions in the triple negative canine breast cancers Myung-Chul Kim, Nick Borcherding, Woo-Jin Song, Ryan Kolb, Weizhou Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3246929/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Clinical trials show promising outcomes for dogs with advanced solid tumors following treatment with immune checkpoint inhibitors (ICIs). Triple-negative breast cancer (TNBC) is very aggressive with very low response rates to ICIs. No study defines how canine TNBC interacts with the immune system within the tumor microenvironment, which is investigated in this study at the single cell level. Single cell RNA sequencing (scRNA-seq) datasets, including 6 groups of 30 dogs, were subject to integrated bioinformatic analysis. Immune modulatory TNBC subsets were identified by functional enrichment with immune-suppressive gene sets, including anti-inflammatory and M2-like macrophages. Key genes and immune-suppressive signaling pathways for TNBC included angiogenesis and leukocyte chemotaxis. Interactome analysis identified significant interactions between distinct subsets of cancer cells and effector T cells, suggesting T cell suppression. This is the first study to define immune-suppressive cancer cell subsets at the single-cell level, revealing potential mechanisms by which TNBC induces immune evasion in dogs. Biological sciences/Cancer/Cancer microenvironment Biological sciences/Immunology/Immune evasion scRNA-seq dog immune checkpoint triple-negative breast cancer interactome Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Immunotherapy has revolutionized cancer therapy and changed the paradigm of cancer treatment in many types of human cancers 1 . Among different types of immunotherapies, ICIs are the most prescribed cancer immunotherapy 1 . The major action of ICIs is to either rejuvenate pre-existing exhausted CD8 + T cells or replace old CD8 + T cell clones with new clones from blood or adjacent normal tissues 2 . ICIs have shown objective response rates of 15–45% depending on the type of cancer 3 . ICIs have demonstrated effective and durable responses and become the standard of care for various malignancies 4 . Recently, clinical trials using dogs have shown 7.7% 5 and 9.5% 6,7 of complete remission against advanced oral malignant melanoma after administration of caninized anti-PD1 and anti-PD-L1 antibodies. In addition, anti-human CCR4 antibodies targeting tumor-infiltrating regulatory T cells elicited 30% 8 and 71% 9 partial remission rates in canine models of prostate and bladder cancers, respectively. ICIs in canines are durable, although there is one report of an adverse-event-related fatality 7 . Multiple mechanisms are assumed to be involved in the immune suppressive tumor microenvironment in dogs, including infiltration of immune suppressive cells 10,11 and expression of immune checkpoints that suppress T cell activity 5–7,12–14 . Different types of immune modulatory therapies applied to dogs have shown that the tumor immune environment (TiME) among the cancer-immune cycle is the rate-limiting step for immune evasion and effective cancer immunotherapy 5–7,12,15,16 . However, little information is available regarding the composition of the tumor microenvironment, its interaction with the tumor in the context of its microenvironment, and tumor antigens in dogs 17 . Meanwhile, there is limited data on the efficacy of adoptive T-cell therapy in dogs 16,18 . Mammary gland tumors (MGT) are the most frequently diagnosed tumors in female dogs. Among MGT subtypes, TNBCs are characterized by HER2-, ER- and PR-negative with or without basal-like (cytokeratin 5/6-positive) type 19 . TNBCs are heterogeneous 20 and represent a promising model system for comparative immunotherapy research in both canine and humans 21 . scRNA-seq has just begun to unlock the secrets of veterinary diseases. Currently, there is no scRNA-seq analysis to study tumor-infiltrating immune cells in dogs. In this study, we utilized scRNA-seq studies to attest to our hypothesis that an immune modulatory subset of TNBCs is responsible for immune suppression via the interaction with effector T cells. As the first step toward cancer immunotherapy, we defined immune suppressive subsets of TNBC at the molecular level and characterized the crosstalk between cancer cells and effector CD4 + and CD8 + T cells. Our data indicate potential mechanisms by which TNBCs shape the immune suppressive tumor immune microenvironment (TiME). Our integrated scRNA-seq analysis lays out the groundwork for methodology development to study complex cell-cell interactions in the TiME of different cancer types or other immune-related syndromes in dogs. Materials and Methods scRNA-seq datasets scRNA-seq datasets in dogs are available in Gene Expression Omnibus (GEO) database and published papers, including a total of 30 scRNA-seq datasets: 10 datasets of peripheral blood mononuclear cells (PBMC) from dogs with or without atopic dermatitis (GSE144730) 22 , 3 datasets of peripheral blood T cell receptor (TCR) αβ T cells (PBT) from healthy dogs (GSE218355) 23 , 4 datasets of primary triple-negative breast cancer cells (TNBC) with or without in vitro vaccinia virus infection (GSE142184) 24 , a dataset of nuclei from lung tissue from a healthy dog (GSE183300) 25 , 4 datasets of immune cells from bronchoalveolar lavage (BAL) (E-MTAB-9265) from dogs 26 , and 8 datasets of immune cells from BAL from dogs with or without idiopathic pulmonary fibrosis (IPF) (E-MTAB-9263) 27 . These datasets were integrated and subject to bioinformatic analysis (Fig. 1 ). Each individual dataset was confirmed to have canine official gene symbols that are associated human homologs. All studies here used CanFam3.1 ( Canis lupus familiaris genome assembly) for alignment of reads to the reference genome, except for a dataset of nuclei from lung tissue that did not specify the canine genome assembly. scRNA-seq data integration and analysis Seurat objects of all 30 samples were merged and integrated into one master object, as previously described 28,29 . R toolkit Seurat (v. 4.3.0) was used for the data processing, generating the Seurat object as an input file on RStudio (v. 4.2) for subsequent bioinformatic processes. Briefly, low-quality cells with either unique feature counts of less than 200 or over 5,000 or mitochondrial counts of more than 10% were filtered out. Samples were normalized with the default setting. Preparation for integration used 3,000 anchor features. Principal component analysis (PCA) was used for linear dimensional reduction. Principal components 1 through 30 were utilized for further dimensional reduction, which was based on the most significant principal component ( P < 1E-5) from the Jackstraw substitution test algorithm and the ranking of principal components based on the percentage of variance. The t-distributed stochastic neighbor embedding (t-SNE) was used for graph-based clustering with a resolution of 2.7. scDblFinder (v. 1.4.0) R package was used to remove potential doublets, as previously described 28 . Doublet prediction was run on each study group to remove batch effects. Following singlet selection, single-cell clusters were identified and labeled based on the investigation of markers derived from the original studies where the dataset was driven, canonical markers for lineage, markers for rare and unique populations from previous publications, or SingleR (v. 1.8.1)-based unbiased cell type recognition 30 . The celldex package (v. 1.6.0) was used to leverage reference signatures of pure cell types to infer the cell of origin of every single cell. Differential gene expression analysis The likelihood-ratio test was used to find the differential expression for a single cluster, compared to all other cells. To identify cluster markers, the “ FindAllMarkers ” function was used in the Seurat package with the absolute log 2 -fold change threshold > 0.25 and minimum percentage of cells where the gene is detected in a specific cluster > 25%. To identify cluster differentially expressed genes (DEGs) for all clusters across groups, the “ FindMarkers ” function was used with the absolute log 2 -fold change threshold > 0.36 and P value < 0.05. Gene set enrichment analysis (GSEA) and Gene Ontology analysis Single-cell GSEA was performed using the escape R package (v. 1.6.0), as previously described 28 . Gene sets were derived from the Hallmark library of the Molecular Signature Database. Canine gene sets associated with cancer types were derived from a previous publication 31–34 . DEGs were also subjected to either Gene Ontology (GO) enrichment analysis using PANTHER annotation datasets with a species of Canis lupus familiaris ( http://geneontology.org/ ) or ShinyGO (v. 0.77), a graphical gene-set enrichment tool with a species of dog. GO or Kyoto Encyclopedia of Genes and Genomes (KEGG) results were filtered based on the criteria of P value < 0.05 and false discovery rate (FDR) < 0.05. DittoSeq (v. 1.4.4) and pheatmap (v. 1.0.12) R packages were used to visualize gene sets defining specific molecular and biological pathways. Cell cycle analysis Cell cycle assignment was performed by using “ CellCycleScoring ” function and calling “ cc.genes.updated . 2019 ” in Seurat. Cell-to-cell interaction analysis CellChat R package (v. 1.4.0) was used to quantitatively infer intercellular communication networks from scRNA-seq data 35 . Single cells derived from PBMC, αβ T, and TNBC groups were subject to interactome analysis. PTPRC − non-immune cells, potential doublets, and clusters that were simultaneously assigned to TNBC and immune cell groups, were not included in the interaction analysis. To find potential ligand-receptor pair, the “ netVisual_bubble ” function was used with a threshold of P value < 0.01. Data availability The raw data from scRNA-seq in this study was publicly available as described above with the accession numbers (GSE144730, GSE218355, GSE142184, GSE183300, E-MTAB-9265, and E-MTAB-9263). Statistical methods Default statistical methods available within the Seurat package were used in this study. Non-parametric Wilcoxon rank-sum test was used to compare the significance of two-sample differential expression in the “ FindAllMarkers ” function. A two-tailed unpaired Student’s t-test available within the ggpubr R package (v. 0.4.0) was used for statistical tests for the distribution of genes on count-level mRNA data. Results Standard pre-processing and quality control of the integrated scRNA-seq data. The study workflow is presented in Fig. 1 A. The standard pre-processing and rigorous quality control of scRNA-seq data integrated by studies are available ( Supplementary Fig. 1 ). The number of genes, percentage of reads that map to the mitochondrial genome, and percentage of canine Ensemble genes detected in each study are shown ( Supplementary Fig. 1A ). Mitochondrial genes were not identified in BAL and lung groups ( Supplementary Fig. 1B) . Representative top 10 highest variable features among a total of 3,000 features that exhibit high cell-to-cell variation in the integrated scRNA-seq dataset are shown ( Supplementary Fig. 1B ). Principal components of 20 showing strong enrichment of features with low P values were selected based on the JackStraw ( Supplementary Fig. 1C ) and Elbow ( Supplementary Fig. 1D ) plots. Following potential doublet exclusion ( Supplementary Fig. 1E ), 69,035 single cells were obtained from PBMC (GSE144730, n = 20,078), peripheral blood TCR αβ T cells (GSE218355, n = 19,796), lung (GSE183300, n = 3,694), BAL (E-MTAB-9265, n = 4,240), BAL (E-MTAB-9623, n = 16,171), and TNBC (GSE142184, n = 5,056) ( Supplementary Fig. 1F ). Identification of the major single-cell clusters by scRNA-seq Following scRNA-seq data integration, a total of 45 clusters were identified in our master Seurat object (Fig. 1 B). We assessed the clustering performance by identifying the major cell types and found a clear separation of CD45 + (or PTPRC + ) leukocytes, CD4 + T, CD8 + T (or CD8A + ), myeloid (LYZ + ), epithelial cells (EPCAM + and/or COL1A2 + ), and lung (BMP1 + ) populations (Figs. 1 C- 1 D). Out of the top 20 DEGs to define the major immune and non-immune subsets, representative genes are presented on the heatmap (Fig. 1 E). Functionally unique immune subsets from PBMC, αβ T, and BAL groups were further defined by various canonical markers and genes derived from previous studies (Fig. 1 F and Supplementary Fig. 2 ). Unbiased cell type recognition was also used to define distinct clusters, supporting our clustering performance ( Supplementary Fig. 3 ). A total of 16 clusters of CD3 + T cell were identified, which mainly consisted of single-positive CD4 + or CD8 + cells, as well as a few double-positive (clusters 11 and 25) and double-negative (clusters 7 and 22, largely derived from BAL groups) subpopulations ( Supplementary Fig. 2A ). CD4 + T cells were further classified into 5 transcriptionally unique subpopulations, including LEF1 + SELL + CCR7 + naïve (clusters 0 and 2), CXCR3 high pre-effector (cluster 8), GATA3 + Th2-like pre-effector (cluster 9), CCR4 high CXCR4 high effector memory (cluster 17), FOXP3 + regulatory T cells (Treg) (cluster 31), and uncharacterized (cluster 38). Pre-effector CD4 + T cells were defined by the increasing tendency of HOPX expression – a marker for pre-effector T cells destined for subsequent effector differentiation 36 . A total of 5 CD8 + T clusters were identified. Genes encoding killer cell lectin-like receptors, such as KLRG1 and KLRB1, and cytotoxicity, such as GZMA, PRF1, and NCR3, were highly expressed in clusters 14 and 26, suggesting cytotoxic CD8 + T cells ( Supplementary Fig. 2A ). In addition, cluster 26 specifically expressed FCER1G, a marker of an innate-like phenotype 37 . Cluster 24 resembled an exhausted progenitor phenotype based on CD7, TOX, and to some extent TCF7, but not CD27 expression. Cluster 35 showed high expression of genes associated with T cell activation, such as CD69, JUND, and KLF6, suggesting activated CD8 + T cells. Cluster 30 expressed HOPX, but less expression of cytotoxicity-related genes, thus considered pre-effector type. Cluster 18 was characterized by high CCL4, CCL5, and S100A4 expression, suggesting a migratory phenotype. Meanwhile, only a very small number of immune cells, such as gamma delta T cells, and natural killer (NK) cells, were identified based on unbiased cell type annotation, but not assigned to a single independent cluster ( Supplementary Fig. 3 ). Myeloid cells that were defined by LYZ expression ( Supplementary Fig. 2B ) were distinguishably separated on the tSNE plot by the expression of CSF3R and CD163 – the classic M1 and M2 macrophage polarization markers, respectively (Fig. 1 G). Myeloid clusters expressing CSF3R were ITGB2 high monocytes (clusters 3, 6, 10, 12, 16, and 20), mainly derived from peripheral blood (Supplementary Fig. 3B) . Myeloid clusters expressing CD163 were mainly CD68 high macrophages (clusters 1, 4, 19, 21, and 39), largely derived from BAL. Cluster 20 was further defined by IFN-related monocytes based on the high expression of genes associated with interferon signaling pathways, such as ISG15, MX1, and MX2. We also identified CD83 + CD86 + ITGAX + dendritic cells (DC) (cluster 23), FSCN1 + DC (cluster 41), and CD177 + neutrophils (cluster 40). B cells (cluster 34) and plasmablasts (clusters 28 and 33) specifically expressed MS4A1 and IRF4, respectively ( Supplementary Figs. 2A, 3A-3B ). Single cells derived from lung were not immune cells but highly expressed BMP1 gene (Fig. 1 D, Supplementary Fig. 2C ). Single cells from the TNBC group showed specific expression of epithelial cell markers, such as COL1A2, KRT14, CA2, and SPP1 ( Supplementary Fig. 2C ). Meanwhile, some immune and TNBC cells were assigned to the same clusters ( 29 , 36 , and 43 ), perhaps due to a similar global structure of RNA expression across single cells. Taken together, our integrated analysis successfully identified major distinct subsets of immune and TNBC cells at the single-cell level in dogs. Subsets of cancer cells have a distinct immune-suppressive phenotype Clinical trials have revealed the existence of an immune suppressive TiME within multiple types of canine cancers 8,9,38,39 . Whether canine TNBCs have an immune suppressive TiME is still unknown. We performed GSEA using various gene sets associated with immune-associated pathways (Fig. 2 ). Overall, we identified two distinct patterns of gene set enrichment. First, PBMC was preferentially enriched with inflammatory gene sets, such as interferon signaling, M1 macrophage, proinflammatory, and leukocyte-mediated immunity (Fig. 2 A, red boxed ). Especially, αβ T cells within the PBMC showed marked enrichment of T cell-specific gene signatures, such as Treg and terminal T cell differentiation. Second, there was preferential enrichment of immune suppressive gene sets within TNBCs, such as those related to TGF-β, TNF-α, anergy, anti-inflammatory, M2 macrophage, and exhaustion (Fig. 2 A, blue boxed ). Among them, the enrichment pattern of anti-inflammatory and M2 macrophage was particularly specific to TNBC, compared to other groups (Fig. 2 B). TNBCs were also enriched with gene sets associated with alternative metabolic pathways, oxidative stress, and importantly canine gene sets (Fig. 2 C), showing the reliability of GSEA implemented in this study. Consistent with previous scRNA-seq findings in dogs 27 , BAL affected by idiopathic pulmonary fibrosis contained single-cell clusters that were highly enriched with the M2 macrophage gene set (Figs. 2 A- 2 B). Meanwhile, lung cells showed only sporadic enrichment patterns of a few gene sets, such as TGF-β and anergy (Figs. 2 A- 2 B). There was no noticeable difference in the enrichment pattern between healthy and atopic dermatitis conditions of PBMC. Taken together, GSEA confirmed that TNBC contributes to immune suppressive TiME in dogs, providing a rationale for further analysis with a focus on identifying specific TBNC clusters that might have led to the distinct immune-suppressive phenotype. Identification and characterization of cancer cell subsets within TNBCs To scrutinize functionally unique cancer cell subpopulations, we performed sub-clustering of all cancer cells and defined a total of 11 sub-clusters (Fig. 3 A). Each cluster was clearly separated in the tSNE plot, demonstrating prominent intratumoral heterogeneity and distinct global structure of transcriptomes across clusters ( Supplementary Fig. 4 ). Representative markers used to define each cluster are available (Fig. 3 B, Supplementary Figs. 4A-4B, and Supplementary Table 1) . Genes encoding oncolytic vaccinia viral proteins, such as A12L and A7L, were specifically expressed in cluster 4, indicative of viral infection. Expression of genes associated with immune suppression, such as SPP1 and HMGA1, was mainly identified in clusters 2, 3, 5, 6, and 7. Clusters 0 and 1 were characterized by specific expressions of SFRP2 and COL2A1, respectively. DDIT4 – which has been reported to confer resistance canine TNBC against viral infection 24 – was expressed in clusters 0, 1, 8, and 9. We next performed functional enrichment analysis to investigate cancer cell subsets that can potentially contribute to immune suppression. Interestingly, GSEA identified clusters 2, 3, 5, 6, and 7 that were preferentially enriched with gene sets associated with immune suppression, such as anti-inflammatory, M2 macrophage, anergy, IL-18, TNF-α, and TGF-β (Figs. 3 C, blue boxed, 3D, and Supplementary Fig. 4C ). Interestingly, enrichment with an anti-inflammatory signature tended to increase in virus-infected clusters relative to non-viral infected cancer cell clusters (Fig. 3 D). Among them, clusters 2 and 6 showed simultaneous enrichment of anti-inflammatory and M2 macrophage gene sets, indicative of potential immune-suppressive cancer cells (Fig. 3 E). KEGG analysis using cluster markers revealed that cluster 2 was significantly associated with extracellular matrix-receptor interaction ( Supplementary Fig. 4D ). Cluster 6 was significantly associated with blood vessel development and cell migration ( Supplementary Fig. 4D ). These enrichment patterns were not associated with cell cycle alteration (Fig. 3 E). Viral infection tended to arrest cell cycle progression ( Supplementary Fig. 4E ). Based on our GSEA and expression analyses, we classified cancer cells (Fig. 3 G) into virus-susceptible, virus-resistant, and immune-modulatory – which was originally defined as bystander cancer cells in a previous study 24 (Fig. 3 F). During virus infection, bystander cancer cells can change their behavior, potentially favoring immune evasion 40 . The increasing tendency of anti-inflammatory enrichment in virus-infected, particularly immune-modulatory cancer cells, might imply genes that can play a key role in shaping an immune suppressive TiME. Among genes belonging to the immune suppressive signatures, we identified 40 DEGs across cancer cell subsets related to different viral infectious status (Fig. 3 H, Supplementary Table 2, and Supplementary Fig. 4F). Overall, these genes were significantly enriched with many biological processes, particularly angiogenesis and leukocyte chemotaxis (Fig. 3 I, Supplementary Figs. 4F-4G, and Supplementary Table 3 ). In support of this, VEGFA that belonged to these GO terms, significantly increased in virus-infected cancer cell clusters, compared to non-infected clusters (Fig. 3 J and Supplementary Fig. 4G ). Interestingly, VEGFC, which is associated with immune suppression during canine mammary cancer development 41 , was also significantly upregulated in a subset of cancer cell population ( Supplementary Fig. 4G ). Other potential immune modulatory candidate genes, such as HSD11B1, LIF, PTGS2, GADD45B, and JAG1, were present (Fig. 3 J ) . Identification and characterization of cell-to-cell interaction between cancer cells and PBMCs Tumor-immune cell interaction – a hallmark of cancer immunology – plays a critical role in T cell exhaustion within the TiME, leading to ineffective cancer immunotherapy. As a potential mechanism by which cancer cells induce the exhaustion stage of tumor-infiltrating T cells, we hypothesize that peripheral T cells interact with cancer cells via distinct ligand-receptor pairs. To decipher coordinated tumor-immune interactions, single cell clusters from cancer cells and PBMC groups were subject to CellChat analysis ( Supplementary Fig. 5A ). While analyzing the interactome, we found two major patterns of cell-cell interaction. First, there were distinct subsets of cancer cells (clusters 13, 27, and 32) that strongly sent signals to other cells in a paracrine fashion or to themselves via an autocrine pathway (Figs. 4 A- 4 B, blue boxed ). Second, effector CD4 + (cluster 8 pre-effector CXCR3 high , cluster 17 effector memory, and cluster 31 Treg) and CD8 + (cluster 14 cytotoxic and cluster 26 innate-like cytotoxic) T cells were able to receive signals (Figs. 4 A- 4 B, red boxed, and Supplementary Fig. 5B ). We next focused on these clusters to identify significant signaling pathways and ligand and receptor pairs (Fig. 4 C). After extensive interactome analysis, we ended up identifying key ligand and receptor pairs, which were mainly derived from secretory (SPP1), cell-cell intact (APP), and extracellular matrix-receptor (FN1 and Collagen) pathways (Fig. 4 D and Supplementary Fig. 5C ). Interestingly, T cell receptors CD44 and CD74 showed high communication probability, contributing most to the outgoing signaling of the representative ligands from cancer cells ( Supplementary Fig. 5D ). The ligand and receptor genes showed high expression levels in the interacting cancer cells and T clusters (Fig. 4 E). Finally, ligands from cancer cells were also expressed in immune modulatory cancer cell subpopulations (clusters 2, 3, 5, 6, and 7) (Fig. 4 F), suggesting potential immune suppressive mechanisms mediated by cancer cell-T interaction. Discussion In the present study, we took advantage of utilizing open-source canine scRNA-seq datasets, including primary TNBC and peripheral immune cells, and investigated the mechanism by which TNBC induces immune suppression in dogs. Our integrated scRNA-seq analysis for the first time reveals that immune suppressive subsets of cancer cells, which were identified to preferentially interact with effector types of CD4 + and CD8 + T cells in dogs. Potential mechanisms by which cancer cells shape the immune suppressive TiME are suggested to regulate angiogenesis and immune cell infiltration. Leveraging the integrated scRNA-seq analysis, we were able to specify transcriptionally distinct cancer cell subsets with potentially different cancer immunity and their interaction with T cells, which cannot be discovered or addressed by bulk RNA-seq 42 . In the present study, immune modulatory cancer cell subsets were defined by unique cluster markers, such as HMGA1, SPP1, and WNT5A. Interestingly, these genes have been closely involved in the development and potential immune evasion of canine 43,44 and human TNBC 45–48 . For example, HMGA1 – a downstream gene of PD-L1 that regulates cancer immunity in human TNBC 49 – was exclusively expressed in immune-modulatory TNBC subsets. Wnt singling triggered by WNT5A promotes the immune escape of TNBC 50 . Likewise, SPP1 expressed on malignant cells contributes to the suppression of T cells by CD44-mediated binding 51 . Thus, we suggest that HMGA1 + SPP1 + or WNT5A + TNBC cells might elicit immune evasion and thus be a potential target for anticancer immunity. Functional enrichment of immune suppressive gene sets in these TNBC subpopulations also supports our notion. Immunosuppressive pathways could play a prominent role in the resistance of tumor cells to oncolytic viral infection 40 . Accordingly, we leveraged oncolytic viral infection as an anti-inflammatory factor and identified candidate genes for immune evasion and mechanisms by which TNBC escapes immune surveillance by the host. GO analysis using DEGs in cells infected by the virus identified two representative signaling pathways: angiogenesis and leukocyte chemotaxis. Angiogenesis is a hallmark of canine mammary gland tumorigenesis, and VEGF signaling is critical for the pathophysiology of canine TNBC 52 . In addition, angiogenesis is significantly correlated with immune suppression in dogs 41 . In this study, immune modulatory TNBC subsets affected by viral infection significantly upregulated VEGFA and VEGFC. Angiogenesis induces infiltration of various types of immune suppressive cells to MGT in dogs 53 . Indeed, VEGFC released by canine MGT contributes to immune suppression via the recruitment of Treg and myeloid-derived suppressive cells (MDSCs) 41 . Infiltration of Tregs 54 and tumor-associated macrophages (TAMs) 55 is promoted by VEGF signaling in canine MGT. The recruited immune suppressive cells have been strongly suggested to inhibit anti-cancer T cell activity, leading to poor prognosis in canine MGT and TNBC 41,53,56 . Thus, based on previous findings and our results, we postulate that TNBC mainly elicits cancer immunity via angiogenesis and VEGF-mediated immune cell infiltration. Future work is warranted to elucidate the mechanism by which canine TNBC modulates cancer immunity by regulating other candidate genes. For example, in this study cancer cell themselves within TNBC were strongly inferred to specifically interact each other, suggesting the potential autocrine and paracrine communications. Extrapolating from this, we propose that the candidate genes might constitute to a positive feedback loop to amplify anti-inflammatory singling pathways of cancer cells. Indeed, oncolytic virus-regulated candidate genes, such as HSD11B1, LIF, PTGS2, GADD45B, and JAG1, have been involved in autocrine and/or paracrine signaling in TiME of human TNBC 57–59 . Accordingly, exploiting therapeutic strategies for abrogating the intertumoral feedback loop, e.g., novel therapeutic modalities, such as proteolysis-targeting chimera 60 , might be effective to normalize TiME. Our interactome analysis reveals that cancer cells directly modulate T cell activity, potentially favoring immune suppression. In this study, we suggest that SPP1 + , FN1 + , or COL1A2 + cancer cells might be a key subset for T cell suppression in which CD44 is likely to be the immune checkpoint in dogs. Indeed, SPP1-CD44 interaction has been demonstrated to suppress effector T cell activity infiltrated into multiple types of cancers 51,61 . Binding of CD44 + tumor-infiltrating T cells to type I collagen induces a more aggressive phenotype of malignant melanoma 62 . In addition, FN1 + TNBC cells are positively associated with CD8 + T cell infiltration and immune suppression 63 . Although little information regarding interaction is available in dogs, substantial studies have supported the anti-inflammatory role of SPP1, FN1, and COL1A2 in canine MGT and TNBC 42,43,64 . Meanwhile, it might be also interesting to investigate the impact of TNBC-Tregs interaction. It might be associated with tumor-mediated direct induction of Tregs 65 , supporting CD4 + T cell-mediated poor prognosis of canine mammary carcinoma 66 and TNBC 56 . Future studies are warranted to demonstrate the clinical relevance of the binding of TNBC ligands to CD44 + T cells on the immune suppression of canine TNBC. The present study has limitations. Despite the well-established integrating methodology provided by Seurat, potential institutional or batch effects across scRNA-seq datasets might have occurred during analysis. Currently, a functionally validated canine gene set database is absent. Gene sets that consist of canine official gene symbol that has the associated human homolog are subjected to functional enrichment analysis to predict immunological functions. In other words, most canine Ensemble genes cannot be included in the bioinformatic analysis in this study. Although humans and dogs share a high degree of homology with the corresponding human sequences and orthologous genes, especially showing well-conserved interspecies immunological functions 67 , a more accurate assessment of immune-related functions should be made by using canine gene sets. Genomic annotation of the newly released dog reference genome CanFam6 will provide a more robust and high-resolution transcriptomic analysis, compared to CanFam3.1 68 . We did not analyze tumor-infiltrating immune cells, but peripheral ones. Although PBMCs are shown to infiltrate tumor sites, interact with tumor cells, and become exhausted by the immune-tumor interaction, future studies need to prove it in dogs, which is currently an ongoing experiment of our group to identify novel immune checkpoints. Conclusion The present study reveals that triple-negative canine breast cancer shapes immune-suppressive tumor microenvironment which is mediated by immune-TNBC interaction mainly affected by exhausted CD44 + effector CD4 + T cells. Declarations Acknowledgment We appreciate the University of Florida High-Performance Computing Center for performing HiPerGator 3.0 supercomputer, which serves as the primary working space for integrating multiple scRNA-seq datasets in this project. We thank Weizhou Zhang for sponsoring Myung-Chul to operate HiPerGator. Conflict of interest The authors declare no conflicts of interest. Funding This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (#RS-2023-00241779). Author’s contribution Conception and design: M.K. Development of methodology: M.K. Data acquisition: M.K., N.B. Bioinformatics: M.K., N.B. Analysis and interpretation of data: M.K., W.S., R.K., N.B., W.Z. Writing, review, and/or revision of the manuscript: M.K., R.K., W.S., N.B., W.Z. Study supervision: M.K. References Waldman, A. D., Fritz, J. M. & Lenardo, M. J. A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nat. Rev. Immunol. 20, 651–668 (2020). Kim, M.-C. et al. Updates on immunotherapy and immune landscape in renal clear cell carcinoma. Cancers (Basel) 13, (2021). Galluzzi, L., Humeau, J., Buqué, A., Zitvogel, L. & Kroemer, G. Immunostimulation with chemotherapy in the era of immune checkpoint inhibitors. Nat. Rev. Clin. Oncol. 17, 725–741 (2020). Yang, Y. 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Identification of Candidate Genes and Pathways Associated with Obesity-Related Traits in Canines via Gene-Set Enrichment and Pathway-Based GWAS Analysis. Animals (Basel) 10, (2020). Lee, K.-H., Park, H.-M., Son, K.-H., Shin, T.-J. & Cho, J.-Y. Transcriptome signatures of canine mammary gland tumors and its comparison to human breast cancers. Cancers (Basel) 10, (2018). Tawa, G. J. et al. Transcriptomic profiling in canines and humans reveals cancer specific gene modules and biological mechanisms common to both species. PLoS Comput. Biol. 17, e1009450 (2021). Graim, K. et al. Modeling molecular development of breast cancer in canine mammary tumors. Genome Res. 31, 337–347 (2020). Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. 12, 1088 (2021). Bourque, J., Kousnetsov, R. & Hawiger, D. Roles of Hopx in the differentiation and functions of immune cells. Eur. J. Cell Biol. 101, 151242 (2022). Morrish, E. & Ruland, J. 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Gene expression profiling of spontaneously occurring canine mammary tumours: Insight into gene networks and pathways linked to cancer pathogenesis. PLoS ONE 13, e0208656 (2018). Mohammed, S. I. et al. Ductal carcinoma in situ progression in dog model of breast cancer. Cancers (Basel) 12, (2020). Beetch, M. et al. DNA methylation landscape of triple-negative ductal carcinoma in situ (DCIS) progressing to the invasive stage in canine breast cancer. Sci. Rep. 10, 2415 (2020). Wang, X. et al. Increased expression of osteopontin in patients with triple-negative breast cancer. Eur. J. Clin. Invest. 38, 438–446 (2008). Bourdakou, M. M., Athanasiadis, E. I. & Spyrou, G. M. Discovering gene re-ranking efficiency and conserved gene-gene relationships derived from gene co-expression network analysis on breast cancer data. Sci. Rep. 6, 20518 (2016). Zanin, R. et al. HMGA1 promotes breast cancer angiogenesis supporting the stability, nuclear localization and transcriptional activity of FOXM1. J. Exp. Clin. Cancer Res. 38, 313 (2019). Chantada-Vázquez, M. D. P. et al. Protein Corona Gold Nanoparticles Fingerprinting Reveals a Profile of Blood Coagulation Proteins in the Serum of HER2-Overexpressing Breast Cancer Patients. Int. J. Mol. Sci. 21, (2020). Chang, X. et al. Targeting HMGA1 contributes to immunotherapy in aggressive breast cancer while suppressing EMT. Biochem. Pharmacol. 212, 115582 (2023). Merikhian, P., Eisavand, M. R. & Farahmand, L. Triple-negative breast cancer: understanding Wnt signaling in drug resistance. Cancer Cell Int. 21, 419 (2021). Shurin, M. R. Osteopontin controls immunosuppression in the tumor microenvironment. J. Clin. Invest. 128, 5209–5212 (2018). Amirkhani Namagerdi, A. et al. Triple-Negative Breast Cancer Comparison With Canine Mammary Tumors From Light Microscopy to Molecular Pathology. Front. Oncol. 10, 563779 (2020). Muscatello, L. V. et al. Standardized approach for evaluating tumor infiltrating lymphocytes in canine mammary carcinoma: Spatial distribution and score as relevant features of tumor malignancy. Vet. J. 283–284, 105833 (2022). Carvalho, M. I. et al. Intratumoral FoxP3 expression is associated with angiogenesis and prognosis in malignant canine mammary tumors. Vet. Immunol. Immunopathol. 178, 1–9 (2016). Raposo, T. P. et al. Tumour-associated macrophages are associated with vascular endothelial growth factor expression in canine mammary tumours. Vet. Comp. Oncol. 13, 464–474 (2015). Franzoni, M. S. et al. Tumor-infiltrating CD4 + and CD8 + lymphocytes and macrophages are associated with prognostic factors in triple-negative canine mammary complex type carcinoma. Res. Vet. Sci. 126, 29–36 (2019). Christianson, J., Oxford, J. T. & Jorcyk, C. L. Emerging perspectives on leukemia inhibitory factor and its receptor in cancer. Front. Oncol. 11, 693724 (2021). Somasundaram, V. et al. Systemic Nos2 Depletion and Cox inhibition limits TNBC disease progression and alters lymphoid cell spatial orientation and density. Redox Biol. 58, 102529 (2022). Meng, J. et al. Tumor-derived Jagged1 promotes cancer progression through immune evasion. Cell Rep. 38, 110492 (2022). Kolb, R. et al. Proteolysis-targeting chimera against BCL-XL destroys tumor-infiltrating regulatory T cells. Nat. Commun. 12, 1281 (2021). Klement, J. D. et al. An osteopontin/CD44 immune checkpoint controls CD8 + T cell activation and tumor immune evasion. J. Clin. Invest. 128, 5549–5560 (2018). Weimann, T. K., Wagner, C., Goos, M. & Wagner, S. N. CD44 variant isoform v10 is expressed on tumor-infiltrating lymphocytes and mediates hyaluronan-independent heterotypic cell-cell adhesion to melanoma cells. Exp. Dermatol. 12, 204–212 (2003). Zhang, X.-X., Luo, J.-H. & Wu, L.-Q. FN1 overexpression is correlated with unfavorable prognosis and immune infiltrates in breast cancer. Front. Genet. 13, 913659 (2022). Monteiro, L. N. et al. Osteopontin expression and its relationship with prognostic biomarkers in canine mammary carcinomas. Pesq. Vet. Bras. 40, 210–219 (2020). Oliveira, G. et al. Landscape of helper and regulatory antitumour CD4 + T cells in melanoma. Nature 605, 532–538 (2022). Estrela-Lima, A. et al. Immunophenotypic features of tumor infiltrating lymphocytes from mammary carcinomas in female dogs associated with prognostic factors and survival rates. BMC Cancer 10, 256 (2010). Wang, C. et al. A novel canine reference genome resolves genomic architecture and uncovers transcript complexity. Commun. Biol. 4, 185 (2021). Jagannathan, V. et al. Dog10K_Boxer_Tasha_1.0: A Long-Read Assembly of the Dog Reference Genome. Genes (Basel) 12, (2021). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.xlsx Supplementary Table1 . List of top 20 cluster markers defining TNBC subpopulations. SupplementrayTable2.xlsx Supplementary Table 2. List of DEGs defining immune suppressive TNBC subsets and their enrichment gene sets. SupplementaryTable3.xlsx Supplementary Table 3. A list of GO biological processes using DEGs is listed in Supplementary Table 2. SupplementaryFiguresandFigureLegend.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3246929","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":225586581,"identity":"f240b593-ef6d-48c2-a5dc-8df06701481b","order_by":0,"name":"Myung-Chul Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACCWYGxgcfDCTs2NgbiNfCbDijwCaZn+cAsVoYGNikeT6kMc6ckUCkFsl27mTDGQaHmQ1uvj34mafiHgN/ezd+zdLMvBuBfjnMZ3A7L1ma50wxg8SZsxvwapFj5t0MseV2joHkzLYEBgOJXIJatknzGBxm3HDzjPHPmf+I0CIN0QLyPo+ZxMcGIrRINoMdBgrkHDOLD8cSeAj6ReL8WaD3/4Ci8ozxjYSaBDn+9l78WjAAD2nKR8EoGAWjYBRgBQDUNUU4YOBjoAAAAABJRU5ErkJggg==","orcid":"","institution":"Jeju National University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Myung-Chul","middleName":"","lastName":"Kim","suffix":""},{"id":225586582,"identity":"9988bfac-280c-4df4-9e0c-5ad93d3ca59d","order_by":1,"name":"Nick Borcherding","email":"","orcid":"","institution":"Washington University in St Louis","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nick","middleName":"","lastName":"Borcherding","suffix":""},{"id":225586583,"identity":"ba9bdc9b-5497-4647-876a-2f12b82f061e","order_by":2,"name":"Woo-Jin Song","email":"","orcid":"","institution":"Jeju National University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Woo-Jin","middleName":"","lastName":"Song","suffix":""},{"id":225586584,"identity":"97788c67-f4d6-4667-90f6-22b5c937697d","order_by":3,"name":"Ryan Kolb","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ryan","middleName":"","lastName":"Kolb","suffix":""},{"id":225586585,"identity":"b1aad0ea-31b2-44a4-80c0-0ec691a1ba66","order_by":4,"name":"Weizhou Zhang","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Weizhou","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2023-08-08 23:44:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3246929/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3246929/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":41724936,"identity":"9cfc8542-fe24-4139-8b0b-4fba77669015","added_by":"auto","created_at":"2023-08-17 18:01:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":877589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy scheme and identification of major immune and TNBC clusters by integrated scRNA-seq analysis in dogs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Study scheme. scRNA-seq datasets used in this study are present along with downstream analysis (B) A total of 45 clusters identified from the integrated Seurat dataset are presented on the tSNE plot. (C) Single-cell tSNE distribution by the group. Overall, there is distinct tSNE distribution of single cells across groups. (D) Canonical markers used to evaluate cell lineage are present on the tSNE plot. (E) Among the top 20 DEGs to define each major single cell subset, representative genes are presented on the heatmap. (F) Identification of functionally distinct immune cells on the tSNE plot. (G) Simultaneous visualization of co-expression of CD163 and CSF3R on the tSNE plot. Abbreviations: PBMC, peripheral blood mononuclear cells; PBT, peripheral blood TCR αβ T cells; BAL, bronchoalveolar lavage; Mo, monocytes; MP, macrophages; M1, M1-polarized; M2, M2-polarized; DC, dendritic cells; pre-eff, pre-effector; Eff, effector; Mem, memory; act, activated; Pro exh, progenitor exhausted; Treg, regulatory T cells; Cyto, cytotoxic.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3246929/v1/3fa17d6b27820cbda632fde3.png"},{"id":41724933,"identity":"d10c2a43-7d0c-4371-95c2-662d8aa295b7","added_by":"auto","created_at":"2023-08-17 18:01:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":317046,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene set enrichment analysis of gene signatures associated with immune-related pathways\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) GSEA. Immune-related pathways are present across subsets and clusters. TNBC clusters show marked enrichment with gene sets associated with metabolism, cytokine, and immune suppression, particularly including anti-inflammatory, M2 macrophage, and anergy signatures (Blue boxed). Note specific enrichment of canine gene set associated with mammary gland tumor in TNBC. Contrary to TNBC, PBMC and BAL shows a general increase in the enrichment with gene sets associated with inflammatory responses, such as interferons, M1 macrophage, and proinflammatory (Red boxed). (B) Hex density enrichment plot reveals the enrichment pattern of the indicated immune-related pathways across groups. TNBC group exhibits a more anti-inflammatory phenotype than other groups, as evidenced by the TNBC-preferential shift toward the anti-inflammatory and M2 macrophage signatures. Note intrinsic, significant enrichment of M2 macrophage signature in single cells from BAL group. (C) GSEA. Enrichment of canine gene signatures are present across subsets and clusters. Abbreviations: GSEA, gene set enrichment analysis; PBMC; peripheral blood mononuclear cells; PB_T, peripheral blood TCR αβ T cells; TNBC, triple-negative breast cancer; BAL, bronchoalveolar lavage; TNBC_infected, TNBC subset infected with oncolytic vaccinia virus; Dog_Module 1, gene set defining canine pulmonary carcinoma; Dog_Module 2, gene set associated with canine malignant melanoma; Dog_Module 3, gene set associated with canine osteosarcoma; Dog_Module 4, gene set associated with canine B cell lymphoma; Dog_Module 5, gene set associated with canine T cell lymphoma; Dog_IMHA, gene signature associated with canine immune-mediated hemolytic anemia.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3246929/v1/9d3ee7187131b60a4ab3cfca.png"},{"id":41726071,"identity":"3a1d7383-c075-4686-85cf-11bdea7e0fc7","added_by":"auto","created_at":"2023-08-17 18:09:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1090024,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and characterization of immune suppressive TNBC subsets and potential candidates by scRNA-seq\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) A total of 11 major TNBC clusters are presented on the tSNE plot.(B) Representative markers to define clusters are present on the feature plot. (C) GSEA shows preferential enrichment patterns for gene sets associated with M2 macrophage, TGF-β, IL18, anergy, anti-inflammatory, and TNF-α in clusters 2, 3, 5, 6, and 7 TNBC clusters. (D) Hex density enrichment plot reveals that indicated TNBC subsets are more anti-inflammatory, compared to the entire population of TNBC. Red and blue numbers on each quadrant illustrate upward and downward trends, respectively, compared to control. (E) Simultaneous visualization of co-enrichment of anti-inflammatory and M2 macrophage signatures on the tSNE plot. (F) Cell cycle profile across TNBC subsets. (G) Molecular classification of TNBC clusters according to viral infection, transcriptional features, and functional enrichment. (H) Differentially upregulated genes that belong to gene sets highlighted in the Fig 3C are present across TNBC clusters. Numbers in the parenthesis indicate the origin of statistically significant DEGs in TNBC clusters. (I) Gene Ontology analysis using DEGs affected by viral infection. Highly enriched and statistically significant representative GO terms are shown, which is mainly associated with the regulation of angiogenesis and leukocyte chemotaxis. (J) Violin plot reveals differential expression of indicated genes in TNBC subsets. A comparison between the two groups of interest by a two-tailed unpaired Student’s t-test.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3246929/v1/11ff544bc260e67cf441e355.png"},{"id":41724938,"identity":"4e5add58-269a-4537-b2f0-be9f67611ef0","added_by":"auto","created_at":"2023-08-17 18:01:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":644089,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCellChat-based identification of immune-to-TNBC interactions by scRNA-seq.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Institutive visualization of the dominant signal senders (blue box) and receivers (red box) on the scatter plot. (B) Overall outgoing and incoming signaling patterns of significant pathways across clusters are presented. Bars refer to the sum of the original computed interaction strength in each column and row. (C) Visualization of cell-cell communication from indicated TNBC to T clusters is present on the chord diagram. The inner thinner bar colors represent the targets that receive signal from the corresponding outer bar. (D) Identification of significant interactions and key ligand and receptor pairs from indicated TNBC to T clusters on the bubble plot.(E) Expression of representative ligand and receptor genes that belongs to SPP1, FN1, APP and Collagen signaling pathways is present across clusters in the violin plot. (F) Identification of TNBC ligand gene expression on the functionally unique TNBC subpopulations from sub-clustered group.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3246929/v1/ac8f6077c3d2b7bee4266444.png"},{"id":43087302,"identity":"742180e0-47b7-4ab8-9417-df78775ce000","added_by":"auto","created_at":"2023-09-13 17:37:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3157483,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3246929/v1/b60716b2-d140-443a-8c2f-33b8aea5ed62.pdf"},{"id":41726069,"identity":"bcbf21c2-e11c-43b9-9bfa-064a7fe311eb","added_by":"auto","created_at":"2023-08-17 18:09:21","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24185,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table1 .\u003c/strong\u003e List of top 20 cluster markers defining TNBC subpopulations.\u003c/p\u003e","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3246929/v1/3d27f1be32ed7b1ad6cb4d99.xlsx"},{"id":41724932,"identity":"fda03227-82bc-4968-b5d7-5b6b9a999cc4","added_by":"auto","created_at":"2023-08-17 18:01:21","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23083,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 2.\u003c/strong\u003e List of\u003cstrong\u003e \u003c/strong\u003eDEGs defining immune suppressive TNBC subsets and their enrichment gene sets.\u003c/p\u003e","description":"","filename":"SupplementrayTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3246929/v1/05be7aa033bad7ea2d0da310.xlsx"},{"id":41726070,"identity":"2c0763fc-df96-41ce-ae01-29712e7079b9","added_by":"auto","created_at":"2023-08-17 18:09:21","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15359,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 3.\u003c/strong\u003e A list of GO biological processes using DEGs is listed in Supplementary Table 2.\u003c/p\u003e","description":"","filename":"SupplementaryTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3246929/v1/32412a180850f8a186d01a33.xlsx"},{"id":41724943,"identity":"858c828e-edf4-4292-8385-dfa81f8824c6","added_by":"auto","created_at":"2023-08-17 18:01:22","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3289984,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresandFigureLegend.docx","url":"https://assets-eu.researchsquare.com/files/rs-3246929/v1/ba17aa70e12db26d3258a98a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A single-cell transcriptomic study reveals immune suppressive cancer cell-immune cell interactions in the triple negative canine breast cancers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eImmunotherapy has revolutionized cancer therapy and changed the paradigm of cancer treatment in many types of human cancers \u003csup\u003e1\u003c/sup\u003e. Among different types of immunotherapies, ICIs are the most prescribed cancer immunotherapy \u003csup\u003e1\u003c/sup\u003e. The major action of ICIs is to either rejuvenate pre-existing exhausted CD8\u003csup\u003e+\u003c/sup\u003e T cells or replace old CD8\u003csup\u003e+\u003c/sup\u003e T cell clones with new clones from blood or adjacent normal tissues \u003csup\u003e2\u003c/sup\u003e. ICIs have shown objective response rates of 15\u0026ndash;45% depending on the type of cancer \u003csup\u003e3\u003c/sup\u003e. ICIs have demonstrated effective and durable responses and become the standard of care for various malignancies \u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecently, clinical trials using dogs have shown 7.7% \u003csup\u003e5\u003c/sup\u003e and 9.5% \u003csup\u003e6,7\u003c/sup\u003e of complete remission against advanced oral malignant melanoma after administration of caninized anti-PD1 and anti-PD-L1 antibodies. In addition, anti-human CCR4 antibodies targeting tumor-infiltrating regulatory T cells elicited 30% \u003csup\u003e8\u003c/sup\u003e and 71% \u003csup\u003e9\u003c/sup\u003e partial remission rates in canine models of prostate and bladder cancers, respectively. ICIs in canines are durable, although there is one report of an adverse-event-related fatality \u003csup\u003e7\u003c/sup\u003e. Multiple mechanisms are assumed to be involved in the immune suppressive tumor microenvironment in dogs, including infiltration of immune suppressive cells \u003csup\u003e10,11\u003c/sup\u003e and expression of immune checkpoints that suppress T cell activity \u003csup\u003e5\u0026ndash;7,12\u0026ndash;14\u003c/sup\u003e. Different types of immune modulatory therapies applied to dogs have shown that the tumor immune environment (TiME) among the cancer-immune cycle is the rate-limiting step for immune evasion and effective cancer immunotherapy \u003csup\u003e5\u0026ndash;7,12,15,16\u003c/sup\u003e. However, little information is available regarding the composition of the tumor microenvironment, its interaction with the tumor in the context of its microenvironment, and tumor antigens in dogs \u003csup\u003e17\u003c/sup\u003e. Meanwhile, there is limited data on the efficacy of adoptive T-cell therapy in dogs \u003csup\u003e16,18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMammary gland tumors (MGT) are the most frequently diagnosed tumors in female dogs. Among MGT subtypes, TNBCs are characterized by HER2-, ER- and PR-negative with or without basal-like (cytokeratin 5/6-positive) type \u003csup\u003e19\u003c/sup\u003e. TNBCs are heterogeneous \u003csup\u003e20\u003c/sup\u003e and represent a promising model system for comparative immunotherapy research in both canine and humans \u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003escRNA-seq has just begun to unlock the secrets of veterinary diseases. Currently, there is no scRNA-seq analysis to study tumor-infiltrating immune cells in dogs. In this study, we utilized scRNA-seq studies to attest to our hypothesis that an immune modulatory subset of TNBCs is responsible for immune suppression via the interaction with effector T cells. As the first step toward cancer immunotherapy, we defined immune suppressive subsets of TNBC at the molecular level and characterized the crosstalk between cancer cells and effector CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells. Our data indicate potential mechanisms by which TNBCs shape the immune suppressive tumor immune microenvironment (TiME). Our integrated scRNA-seq analysis lays out the groundwork for methodology development to study complex cell-cell interactions in the TiME of different cancer types or other immune-related syndromes in dogs.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003escRNA-seq datasets\u003c/h2\u003e \u003cp\u003escRNA-seq datasets in dogs are available in Gene Expression Omnibus (GEO) database and published papers, including a total of 30 scRNA-seq datasets: 10 datasets of peripheral blood mononuclear cells (PBMC) from dogs with or without atopic dermatitis (GSE144730) \u003csup\u003e22\u003c/sup\u003e, 3 datasets of peripheral blood T cell receptor (TCR) αβ T cells (PBT) from healthy dogs (GSE218355) \u003csup\u003e23\u003c/sup\u003e, 4 datasets of primary triple-negative breast cancer cells (TNBC) with or without \u003cem\u003ein vitro\u003c/em\u003e vaccinia virus infection (GSE142184) \u003csup\u003e24\u003c/sup\u003e, a dataset of nuclei from lung tissue from a healthy dog (GSE183300) \u003csup\u003e25\u003c/sup\u003e, 4 datasets of immune cells from bronchoalveolar lavage (BAL) (E-MTAB-9265) from dogs \u003csup\u003e26\u003c/sup\u003e, and 8 datasets of immune cells from BAL from dogs with or without idiopathic pulmonary fibrosis (IPF) (E-MTAB-9263) \u003csup\u003e27\u003c/sup\u003e. These datasets were integrated and subject to bioinformatic analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each individual dataset was confirmed to have canine official gene symbols that are associated human homologs. All studies here used CanFam3.1 (\u003cem\u003eCanis lupus familiaris\u003c/em\u003e genome assembly) for alignment of reads to the reference genome, except for a dataset of nuclei from lung tissue that did not specify the canine genome assembly.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003escRNA-seq data integration and analysis\u003c/h2\u003e \u003cp\u003eSeurat objects of all 30 samples were merged and integrated into one master object, as previously described \u003csup\u003e28,29\u003c/sup\u003e. R toolkit Seurat (v. 4.3.0) was used for the data processing, generating the Seurat object as an input file on RStudio (v. 4.2) for subsequent bioinformatic processes. Briefly, low-quality cells with either unique feature counts of less than 200 or over 5,000 or mitochondrial counts of more than 10% were filtered out. Samples were normalized with the default setting. Preparation for integration used 3,000 anchor features. Principal component analysis (PCA) was used for linear dimensional reduction. Principal components 1 through 30 were utilized for further dimensional reduction, which was based on the most significant principal component (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1E-5) from the Jackstraw substitution test algorithm and the ranking of principal components based on the percentage of variance. The t-distributed stochastic neighbor embedding (t-SNE) was used for graph-based clustering with a resolution of 2.7.\u003c/p\u003e \u003cp\u003escDblFinder (v. 1.4.0) R package was used to remove potential doublets, as previously described \u003csup\u003e28\u003c/sup\u003e. Doublet prediction was run on each study group to remove batch effects. Following singlet selection, single-cell clusters were identified and labeled based on the investigation of markers derived from the original studies where the dataset was driven, canonical markers for lineage, markers for rare and unique populations from previous publications, or SingleR (v. 1.8.1)-based unbiased cell type recognition \u003csup\u003e30\u003c/sup\u003e. The celldex package (v. 1.6.0) was used to leverage reference signatures of pure cell types to infer the cell of origin of every single cell.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene expression analysis\u003c/h2\u003e \u003cp\u003eThe likelihood-ratio test was used to find the differential expression for a single cluster, compared to all other cells. To identify cluster markers, the \u0026ldquo;\u003cem\u003eFindAllMarkers\u003c/em\u003e\u0026rdquo; function was used in the Seurat package with the absolute log\u003csub\u003e2\u003c/sub\u003e-fold change threshold\u0026thinsp;\u0026gt;\u0026thinsp;0.25 and minimum percentage of cells where the gene is detected in a specific cluster\u0026thinsp;\u0026gt;\u0026thinsp;25%. To identify cluster differentially expressed genes (DEGs) for all clusters across groups, the \u0026ldquo;\u003cem\u003eFindMarkers\u003c/em\u003e\u0026rdquo; function was used with the absolute log\u003csub\u003e2\u003c/sub\u003e-fold change threshold\u0026thinsp;\u0026gt;\u0026thinsp;0.36 and P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGene set enrichment analysis (GSEA) and Gene Ontology analysis\u003c/h2\u003e \u003cp\u003eSingle-cell GSEA was performed using the escape R package (v. 1.6.0), as previously described \u003csup\u003e28\u003c/sup\u003e. Gene sets were derived from the Hallmark library of the Molecular Signature Database. Canine gene sets associated with cancer types were derived from a previous publication \u003csup\u003e31\u0026ndash;34\u003c/sup\u003e. DEGs were also subjected to either Gene Ontology (GO) enrichment analysis using PANTHER annotation datasets with a species of Canis lupus familiaris (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://geneontology.org/\u003c/span\u003e\u003cspan address=\"http://geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) or ShinyGO (v. 0.77), a graphical gene-set enrichment tool with a species of dog. GO or Kyoto Encyclopedia of Genes and Genomes (KEGG) results were filtered based on the criteria of \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. DittoSeq (v. 1.4.4) and pheatmap (v. 1.0.12) R packages were used to visualize gene sets defining specific molecular and biological pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCell cycle analysis\u003c/h2\u003e \u003cp\u003eCell cycle assignment was performed by using \u0026ldquo;\u003cem\u003eCellCycleScoring\u003c/em\u003e\u0026rdquo; function and calling \u0026ldquo;\u003cem\u003ecc.genes.updated\u003c/em\u003e.\u003cem\u003e2019\u003c/em\u003e\u0026rdquo; in Seurat.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCell-to-cell interaction analysis\u003c/h2\u003e \u003cp\u003eCellChat R package (v. 1.4.0) was used to quantitatively infer intercellular communication networks from scRNA-seq data \u003csup\u003e35\u003c/sup\u003e. Single cells derived from PBMC, αβ T, and TNBC groups were subject to interactome analysis. PTPRC\u003csup\u003e\u0026minus;\u003c/sup\u003e non-immune cells, potential doublets, and clusters that were simultaneously assigned to TNBC and immune cell groups, were not included in the interaction analysis. To find potential ligand-receptor pair, the \u0026ldquo;\u003cem\u003enetVisual_bubble\u003c/em\u003e\u0026rdquo; function was used with a threshold of P value\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe raw data from scRNA-seq in this study was publicly available as described above with the accession numbers (GSE144730, GSE218355, GSE142184, GSE183300, E-MTAB-9265, and E-MTAB-9263).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eDefault statistical methods available within the Seurat package were used in this study. Non-parametric Wilcoxon rank-sum test was used to compare the significance of two-sample differential expression in the \u0026ldquo;\u003cem\u003eFindAllMarkers\u003c/em\u003e\u0026rdquo; function. A two-tailed unpaired Student\u0026rsquo;s t-test available within the ggpubr R package (v. 0.4.0) was used for statistical tests for the distribution of genes on count-level mRNA data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eStandard pre-processing and quality control of the integrated scRNA-seq data.\u003c/b\u003e The study workflow is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. The standard pre-processing and rigorous quality control of scRNA-seq data integrated by studies are available (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). The number of genes, percentage of reads that map to the mitochondrial genome, and percentage of canine Ensemble genes detected in each study are shown (\u003cb\u003eSupplementary Fig.\u0026nbsp;1A\u003c/b\u003e). Mitochondrial genes were not identified in BAL and lung groups (\u003cb\u003eSupplementary Fig.\u0026nbsp;1B)\u003c/b\u003e. Representative top 10 highest variable features among a total of 3,000 features that exhibit high cell-to-cell variation in the integrated scRNA-seq dataset are shown (\u003cb\u003eSupplementary Fig.\u0026nbsp;1B\u003c/b\u003e). Principal components of 20 showing strong enrichment of features with low \u003cem\u003eP\u003c/em\u003e values were selected based on the JackStraw (\u003cb\u003eSupplementary Fig.\u0026nbsp;1C\u003c/b\u003e) and Elbow (\u003cb\u003eSupplementary Fig.\u0026nbsp;1D\u003c/b\u003e) plots. Following potential doublet exclusion (\u003cb\u003eSupplementary Fig.\u0026nbsp;1E\u003c/b\u003e), 69,035 single cells were obtained from PBMC (GSE144730, n\u0026thinsp;=\u0026thinsp;20,078), peripheral blood TCR αβ T cells (GSE218355, n\u0026thinsp;=\u0026thinsp;19,796), lung (GSE183300, n\u0026thinsp;=\u0026thinsp;3,694), BAL (E-MTAB-9265, n\u0026thinsp;=\u0026thinsp;4,240), BAL (E-MTAB-9623, n\u0026thinsp;=\u0026thinsp;16,171), and TNBC (GSE142184, n\u0026thinsp;=\u0026thinsp;5,056) (\u003cb\u003eSupplementary Fig.\u0026nbsp;1F\u003c/b\u003e).\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of the major single-cell clusters by scRNA-seq\u003c/h2\u003e \u003cp\u003eFollowing scRNA-seq data integration, a total of 45 clusters were identified in our master Seurat object (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). We assessed the clustering performance by identifying the major cell types and found a clear separation of CD45\u003csup\u003e+\u003c/sup\u003e (or PTPRC\u003csup\u003e+\u003c/sup\u003e) leukocytes, CD4\u003csup\u003e+\u003c/sup\u003e T, CD8\u003csup\u003e+\u003c/sup\u003e T (or CD8A\u003csup\u003e+\u003c/sup\u003e), myeloid (LYZ\u003csup\u003e+\u003c/sup\u003e), epithelial cells (EPCAM\u003csup\u003e+\u003c/sup\u003e and/or COL1A2\u003csup\u003e+\u003c/sup\u003e), and lung (BMP1\u003csup\u003e+\u003c/sup\u003e) populations (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Out of the top 20 DEGs to define the major immune and non-immune subsets, representative genes are presented on the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Functionally unique immune subsets from PBMC, αβ T, and BAL groups were further defined by various canonical markers and genes derived from previous studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF \u003cb\u003eand Supplementary Fig.\u0026nbsp;2\u003c/b\u003e). Unbiased cell type recognition was also used to define distinct clusters, supporting our clustering performance (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eA total of 16 clusters of CD3\u003csup\u003e+\u003c/sup\u003e T cell were identified, which mainly consisted of single-positive CD4\u003csup\u003e+\u003c/sup\u003e or CD8\u003csup\u003e+\u003c/sup\u003e cells, as well as a few double-positive (clusters 11 and 25) and double-negative (clusters 7 and 22, largely derived from BAL groups) subpopulations (\u003cb\u003eSupplementary Fig.\u0026nbsp;2A\u003c/b\u003e). CD4\u003csup\u003e+\u003c/sup\u003e T cells were further classified into 5 transcriptionally unique subpopulations, including LEF1\u003csup\u003e+\u003c/sup\u003eSELL\u003csup\u003e+\u003c/sup\u003eCCR7\u003csup\u003e+\u003c/sup\u003e na\u0026iuml;ve (clusters 0 and 2), CXCR3\u003csup\u003ehigh\u003c/sup\u003e pre-effector (cluster 8), GATA3\u003csup\u003e+\u003c/sup\u003e Th2-like pre-effector (cluster 9), CCR4\u003csup\u003ehigh\u003c/sup\u003eCXCR4\u003csup\u003ehigh\u003c/sup\u003e effector memory (cluster 17), FOXP3\u003csup\u003e+\u003c/sup\u003e regulatory T cells (Treg) (cluster 31), and uncharacterized (cluster 38). Pre-effector CD4\u003csup\u003e+\u003c/sup\u003e T cells were defined by the increasing tendency of HOPX expression \u0026ndash; a marker for pre-effector T cells destined for subsequent effector differentiation \u003csup\u003e36\u003c/sup\u003e. A total of 5 CD8\u003csup\u003e+\u003c/sup\u003e T clusters were identified. Genes encoding killer cell lectin-like receptors, such as KLRG1 and KLRB1, and cytotoxicity, such as GZMA, PRF1, and NCR3, were highly expressed in clusters 14 and 26, suggesting cytotoxic CD8\u003csup\u003e+\u003c/sup\u003e T cells (\u003cb\u003eSupplementary Fig.\u0026nbsp;2A\u003c/b\u003e). In addition, cluster 26 specifically expressed FCER1G, a marker of an innate-like phenotype \u003csup\u003e37\u003c/sup\u003e. Cluster 24 resembled an exhausted progenitor phenotype based on CD7, TOX, and to some extent TCF7, but not CD27 expression. Cluster 35 showed high expression of genes associated with T cell activation, such as CD69, JUND, and KLF6, suggesting activated CD8\u003csup\u003e+\u003c/sup\u003e T cells. Cluster 30 expressed HOPX, but less expression of cytotoxicity-related genes, thus considered pre-effector type. Cluster 18 was characterized by high CCL4, CCL5, and S100A4 expression, suggesting a migratory phenotype. Meanwhile, only a very small number of immune cells, such as gamma delta T cells, and natural killer (NK) cells, were identified based on unbiased cell type annotation, but not assigned to a single independent cluster (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eMyeloid cells that were defined by LYZ expression (\u003cb\u003eSupplementary Fig.\u0026nbsp;2B\u003c/b\u003e) were distinguishably separated on the tSNE plot by the expression of CSF3R and CD163 \u0026ndash; the classic M1 and M2 macrophage polarization markers, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). Myeloid clusters expressing CSF3R were ITGB2\u003csup\u003ehigh\u003c/sup\u003e monocytes (clusters 3, 6, 10, 12, 16, and 20), mainly derived from peripheral blood \u003cb\u003e(Supplementary Fig.\u0026nbsp;3B)\u003c/b\u003e. Myeloid clusters expressing CD163 were mainly CD68\u003csup\u003ehigh\u003c/sup\u003e macrophages (clusters 1, 4, 19, 21, and 39), largely derived from BAL. Cluster 20 was further defined by IFN-related monocytes based on the high expression of genes associated with interferon signaling pathways, such as ISG15, MX1, and MX2. We also identified CD83\u003csup\u003e+\u003c/sup\u003eCD86\u003csup\u003e+\u003c/sup\u003eITGAX\u003csup\u003e+\u003c/sup\u003e dendritic cells (DC) (cluster 23), FSCN1\u003csup\u003e+\u003c/sup\u003e DC (cluster 41), and CD177\u003csup\u003e+\u003c/sup\u003e neutrophils (cluster 40).\u003c/p\u003e \u003cp\u003eB cells (cluster 34) and plasmablasts (clusters 28 and 33) specifically expressed MS4A1 and IRF4, respectively (\u003cb\u003eSupplementary Figs.\u0026nbsp;2A, 3A-3B\u003c/b\u003e). Single cells derived from lung were not immune cells but highly expressed BMP1 gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, \u003cb\u003eSupplementary Fig.\u0026nbsp;2C\u003c/b\u003e). Single cells from the TNBC group showed specific expression of epithelial cell markers, such as COL1A2, KRT14, CA2, and SPP1 (\u003cb\u003eSupplementary Fig.\u0026nbsp;2C\u003c/b\u003e). Meanwhile, some immune and TNBC cells were assigned to the same clusters (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, and \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), perhaps due to a similar global structure of RNA expression across single cells. Taken together, our integrated analysis successfully identified major distinct subsets of immune and TNBC cells at the single-cell level in dogs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSubsets of cancer cells have a distinct immune-suppressive phenotype\u003c/h2\u003e \u003cp\u003eClinical trials have revealed the existence of an immune suppressive TiME within multiple types of canine cancers \u003csup\u003e8,9,38,39\u003c/sup\u003e. Whether canine TNBCs have an immune suppressive TiME is still unknown. We performed GSEA using various gene sets associated with immune-associated pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Overall, we identified two distinct patterns of gene set enrichment. First, PBMC was preferentially enriched with inflammatory gene sets, such as interferon signaling, M1 macrophage, proinflammatory, and leukocyte-mediated immunity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cb\u003ered boxed\u003c/b\u003e). Especially, αβ T cells within the PBMC showed marked enrichment of T cell-specific gene signatures, such as Treg and terminal T cell differentiation. Second, there was preferential enrichment of immune suppressive gene sets within TNBCs, such as those related to TGF-β, TNF-α, anergy, anti-inflammatory, M2 macrophage, and exhaustion (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cb\u003eblue boxed\u003c/b\u003e). Among them, the enrichment pattern of anti-inflammatory and M2 macrophage was particularly specific to TNBC, compared to other groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). TNBCs were also enriched with gene sets associated with alternative metabolic pathways, oxidative stress, and importantly canine gene sets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), showing the reliability of GSEA implemented in this study. Consistent with previous scRNA-seq findings in dogs \u003csup\u003e27\u003c/sup\u003e, BAL affected by idiopathic pulmonary fibrosis contained single-cell clusters that were highly enriched with the M2 macrophage gene set (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Meanwhile, lung cells showed only sporadic enrichment patterns of a few gene sets, such as TGF-β and anergy (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). There was no noticeable difference in the enrichment pattern between healthy and atopic dermatitis conditions of PBMC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTaken together, GSEA confirmed that TNBC contributes to immune suppressive TiME in dogs, providing a rationale for further analysis with a focus on identifying specific TBNC clusters that might have led to the distinct immune-suppressive phenotype.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and characterization of cancer cell subsets within TNBCs\u003c/h2\u003e \u003cp\u003eTo scrutinize functionally unique cancer cell subpopulations, we performed sub-clustering of all cancer cells and defined a total of 11 sub-clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Each cluster was clearly separated in the tSNE plot, demonstrating prominent intratumoral heterogeneity and distinct global structure of transcriptomes across clusters (\u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e). Representative markers used to define each cluster are available (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cb\u003eSupplementary Figs.\u0026nbsp;4A-4B, and Supplementary Table\u0026nbsp;1)\u003c/b\u003e. Genes encoding oncolytic vaccinia viral proteins, such as A12L and A7L, were specifically expressed in cluster 4, indicative of viral infection. Expression of genes associated with immune suppression, such as SPP1 and HMGA1, was mainly identified in clusters 2, 3, 5, 6, and 7. Clusters 0 and 1 were characterized by specific expressions of SFRP2 and COL2A1, respectively. DDIT4 \u0026ndash; which has been reported to confer resistance canine TNBC against viral infection \u003csup\u003e24\u003c/sup\u003e \u0026ndash; was expressed in clusters 0, 1, 8, and 9.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next performed functional enrichment analysis to investigate cancer cell subsets that can potentially contribute to immune suppression. Interestingly, GSEA identified clusters 2, 3, 5, 6, and 7 that were preferentially enriched with gene sets associated with immune suppression, such as anti-inflammatory, M2 macrophage, anergy, IL-18, TNF-α, and TGF-β (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cb\u003eblue boxed, 3D, and Supplementary Fig.\u0026nbsp;4C\u003c/b\u003e). Interestingly, enrichment with an anti-inflammatory signature tended to increase in virus-infected clusters relative to non-viral infected cancer cell clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Among them, clusters 2 and 6 showed simultaneous enrichment of anti-inflammatory and M2 macrophage gene sets, indicative of potential immune-suppressive cancer cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). KEGG analysis using cluster markers revealed that cluster 2 was significantly associated with extracellular matrix-receptor interaction (\u003cb\u003eSupplementary Fig.\u0026nbsp;4D\u003c/b\u003e). Cluster 6 was significantly associated with blood vessel development and cell migration (\u003cb\u003eSupplementary Fig.\u0026nbsp;4D\u003c/b\u003e). These enrichment patterns were not associated with cell cycle alteration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Viral infection tended to arrest cell cycle progression (\u003cb\u003eSupplementary Fig.\u0026nbsp;4E\u003c/b\u003e). Based on our GSEA and expression analyses, we classified cancer cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG) into virus-susceptible, virus-resistant, and immune-modulatory \u0026ndash; which was originally defined as bystander cancer cells in a previous study \u003csup\u003e24\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). During virus infection, bystander cancer cells can change their behavior, potentially favoring immune evasion \u003csup\u003e40\u003c/sup\u003e. The increasing tendency of anti-inflammatory enrichment in virus-infected, particularly immune-modulatory cancer cells, might imply genes that can play a key role in shaping an immune suppressive TiME. Among genes belonging to the immune suppressive signatures, we identified 40 DEGs across cancer cell subsets related to different viral infectious status (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH, \u003cb\u003eSupplementary Table\u0026nbsp;2, and Supplementary Fig.\u0026nbsp;4F).\u003c/b\u003e Overall, these genes were significantly enriched with many biological processes, particularly angiogenesis and leukocyte chemotaxis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI, \u003cb\u003eSupplementary Figs.\u0026nbsp;4F-4G, and Supplementary Table\u0026nbsp;3\u003c/b\u003e). In support of this, VEGFA that belonged to these GO terms, significantly increased in virus-infected cancer cell clusters, compared to non-infected clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ \u003cb\u003eand Supplementary Fig.\u0026nbsp;4G\u003c/b\u003e). Interestingly, VEGFC, which is associated with immune suppression during canine mammary cancer development \u003csup\u003e41\u003c/sup\u003e, was also significantly upregulated in a subset of cancer cell population (\u003cb\u003eSupplementary Fig.\u0026nbsp;4G\u003c/b\u003e). Other potential immune modulatory candidate genes, such as HSD11B1, LIF, PTGS2, GADD45B, and JAG1, were present (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and characterization of cell-to-cell interaction between cancer cells and PBMCs\u003c/h2\u003e \u003cp\u003eTumor-immune cell interaction \u0026ndash; a hallmark of cancer immunology \u0026ndash; plays a critical role in T cell exhaustion within the TiME, leading to ineffective cancer immunotherapy. As a potential mechanism by which cancer cells induce the exhaustion stage of tumor-infiltrating T cells, we hypothesize that peripheral T cells interact with cancer cells via distinct ligand-receptor pairs. To decipher coordinated tumor-immune interactions, single cell clusters from cancer cells and PBMC groups were subject to CellChat analysis (\u003cb\u003eSupplementary Fig.\u0026nbsp;5A\u003c/b\u003e). While analyzing the interactome, we found two major patterns of cell-cell interaction. First, there were distinct subsets of cancer cells (clusters 13, 27, and 32) that strongly sent signals to other cells in a paracrine fashion or to themselves via an autocrine pathway (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, \u003cb\u003eblue boxed\u003c/b\u003e). Second, effector CD4\u003csup\u003e+\u003c/sup\u003e (cluster 8 pre-effector CXCR3\u003csup\u003ehigh\u003c/sup\u003e, cluster 17 effector memory, and cluster 31 Treg) and CD8\u003csup\u003e+\u003c/sup\u003e (cluster 14 cytotoxic and cluster 26 innate-like cytotoxic) T cells were able to receive signals (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, \u003cb\u003ered boxed, and Supplementary Fig.\u0026nbsp;5B\u003c/b\u003e). We next focused on these clusters to identify significant signaling pathways and ligand and receptor pairs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter extensive interactome analysis, we ended up identifying key ligand and receptor pairs, which were mainly derived from secretory (SPP1), cell-cell intact (APP), and extracellular matrix-receptor (FN1 and Collagen) pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD \u003cb\u003eand Supplementary Fig.\u0026nbsp;5C\u003c/b\u003e). Interestingly, T cell receptors CD44 and CD74 showed high communication probability, contributing most to the outgoing signaling of the representative ligands from cancer cells (\u003cb\u003eSupplementary Fig.\u0026nbsp;5D\u003c/b\u003e). The ligand and receptor genes showed high expression levels in the interacting cancer cells and T clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Finally, ligands from cancer cells were also expressed in immune modulatory cancer cell subpopulations (clusters 2, 3, 5, 6, and 7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF), suggesting potential immune suppressive mechanisms mediated by cancer cell-T interaction.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we took advantage of utilizing open-source canine scRNA-seq datasets, including primary TNBC and peripheral immune cells, and investigated the mechanism by which TNBC induces immune suppression in dogs. Our integrated scRNA-seq analysis for the first time reveals that immune suppressive subsets of cancer cells, which were identified to preferentially interact with effector types of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells in dogs. Potential mechanisms by which cancer cells shape the immune suppressive TiME are suggested to regulate angiogenesis and immune cell infiltration.\u003c/p\u003e \u003cp\u003eLeveraging the integrated scRNA-seq analysis, we were able to specify transcriptionally distinct cancer cell subsets with potentially different cancer immunity and their interaction with T cells, which cannot be discovered or addressed by bulk RNA-seq \u003csup\u003e42\u003c/sup\u003e. In the present study, immune modulatory cancer cell subsets were defined by unique cluster markers, such as HMGA1, SPP1, and WNT5A. Interestingly, these genes have been closely involved in the development and potential immune evasion of canine \u003csup\u003e43,44\u003c/sup\u003e and human TNBC \u003csup\u003e45\u0026ndash;48\u003c/sup\u003e. For example, HMGA1 \u0026ndash; a downstream gene of PD-L1 that regulates cancer immunity in human TNBC \u003csup\u003e49\u003c/sup\u003e \u0026ndash; was exclusively expressed in immune-modulatory TNBC subsets. Wnt singling triggered by WNT5A promotes the immune escape of TNBC \u003csup\u003e50\u003c/sup\u003e. Likewise, SPP1 expressed on malignant cells contributes to the suppression of T cells by CD44-mediated binding \u003csup\u003e51\u003c/sup\u003e. Thus, we suggest that HMGA1\u003csup\u003e+\u003c/sup\u003eSPP1\u003csup\u003e+\u003c/sup\u003e or WNT5A\u003csup\u003e+\u003c/sup\u003e TNBC cells might elicit immune evasion and thus be a potential target for anticancer immunity. Functional enrichment of immune suppressive gene sets in these TNBC subpopulations also supports our notion.\u003c/p\u003e \u003cp\u003eImmunosuppressive pathways could play a prominent role in the resistance of tumor cells to oncolytic viral infection \u003csup\u003e40\u003c/sup\u003e. Accordingly, we leveraged oncolytic viral infection as an anti-inflammatory factor and identified candidate genes for immune evasion and mechanisms by which TNBC escapes immune surveillance by the host. GO analysis using DEGs in cells infected by the virus identified two representative signaling pathways: angiogenesis and leukocyte chemotaxis. Angiogenesis is a hallmark of canine mammary gland tumorigenesis, and VEGF signaling is critical for the pathophysiology of canine TNBC \u003csup\u003e52\u003c/sup\u003e. In addition, angiogenesis is significantly correlated with immune suppression in dogs \u003csup\u003e41\u003c/sup\u003e. In this study, immune modulatory TNBC subsets affected by viral infection significantly upregulated VEGFA and VEGFC. Angiogenesis induces infiltration of various types of immune suppressive cells to MGT in dogs \u003csup\u003e53\u003c/sup\u003e. Indeed, VEGFC released by canine MGT contributes to immune suppression via the recruitment of Treg and myeloid-derived suppressive cells (MDSCs) \u003csup\u003e41\u003c/sup\u003e. Infiltration of Tregs \u003csup\u003e54\u003c/sup\u003e and tumor-associated macrophages (TAMs) \u003csup\u003e55\u003c/sup\u003e is promoted by VEGF signaling in canine MGT. The recruited immune suppressive cells have been strongly suggested to inhibit anti-cancer T cell activity, leading to poor prognosis in canine MGT and TNBC \u003csup\u003e41,53,56\u003c/sup\u003e. Thus, based on previous findings and our results, we postulate that TNBC mainly elicits cancer immunity via angiogenesis and VEGF-mediated immune cell infiltration. Future work is warranted to elucidate the mechanism by which canine TNBC modulates cancer immunity by regulating other candidate genes. For example, in this study cancer cell themselves within TNBC were strongly inferred to specifically interact each other, suggesting the potential autocrine and paracrine communications. Extrapolating from this, we propose that the candidate genes might constitute to a positive feedback loop to amplify anti-inflammatory singling pathways of cancer cells. Indeed, oncolytic virus-regulated candidate genes, such as HSD11B1, LIF, PTGS2, GADD45B, and JAG1, have been involved in autocrine and/or paracrine signaling in TiME of human TNBC \u003csup\u003e57\u0026ndash;59\u003c/sup\u003e. Accordingly, exploiting therapeutic strategies for abrogating the intertumoral feedback loop, e.g., novel therapeutic modalities, such as proteolysis-targeting chimera \u003csup\u003e60\u003c/sup\u003e, might be effective to normalize TiME.\u003c/p\u003e \u003cp\u003eOur interactome analysis reveals that cancer cells directly modulate T cell activity, potentially favoring immune suppression. In this study, we suggest that SPP1\u003csup\u003e+\u003c/sup\u003e, FN1\u003csup\u003e+\u003c/sup\u003e, or COL1A2\u003csup\u003e+\u003c/sup\u003e cancer cells might be a key subset for T cell suppression in which CD44 is likely to be the immune checkpoint in dogs. Indeed, SPP1-CD44 interaction has been demonstrated to suppress effector T cell activity infiltrated into multiple types of cancers \u003csup\u003e51,61\u003c/sup\u003e. Binding of CD44\u003csup\u003e+\u003c/sup\u003e tumor-infiltrating T cells to type I collagen induces a more aggressive phenotype of malignant melanoma \u003csup\u003e62\u003c/sup\u003e. In addition, FN1\u003csup\u003e+\u003c/sup\u003e TNBC cells are positively associated with CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration and immune suppression \u003csup\u003e63\u003c/sup\u003e. Although little information regarding interaction is available in dogs, substantial studies have supported the anti-inflammatory role of SPP1, FN1, and COL1A2 in canine MGT and TNBC \u003csup\u003e42,43,64\u003c/sup\u003e. Meanwhile, it might be also interesting to investigate the impact of TNBC-Tregs interaction. It might be associated with tumor-mediated direct induction of Tregs \u003csup\u003e65\u003c/sup\u003e, supporting CD4\u003csup\u003e+\u003c/sup\u003e T cell-mediated poor prognosis of canine mammary carcinoma \u003csup\u003e66\u003c/sup\u003e and TNBC \u003csup\u003e56\u003c/sup\u003e. Future studies are warranted to demonstrate the clinical relevance of the binding of TNBC ligands to CD44\u003csup\u003e+\u003c/sup\u003e T cells on the immune suppression of canine TNBC.\u003c/p\u003e \u003cp\u003eThe present study has limitations. Despite the well-established integrating methodology provided by Seurat, potential institutional or batch effects across scRNA-seq datasets might have occurred during analysis. Currently, a functionally validated canine gene set database is absent. Gene sets that consist of canine official gene symbol that has the associated human homolog are subjected to functional enrichment analysis to predict immunological functions. In other words, most canine Ensemble genes cannot be included in the bioinformatic analysis in this study. Although humans and dogs share a high degree of homology with the corresponding human sequences and orthologous genes, especially showing well-conserved interspecies immunological functions \u003csup\u003e67\u003c/sup\u003e, a more accurate assessment of immune-related functions should be made by using canine gene sets. Genomic annotation of the newly released dog reference genome CanFam6 will provide a more robust and high-resolution transcriptomic analysis, compared to CanFam3.1 \u003csup\u003e68\u003c/sup\u003e. We did not analyze tumor-infiltrating immune cells, but peripheral ones. Although PBMCs are shown to infiltrate tumor sites, interact with tumor cells, and become exhausted by the immune-tumor interaction, future studies need to prove it in dogs, which is currently an ongoing experiment of our group to identify novel immune checkpoints.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study reveals that triple-negative canine breast cancer shapes immune-suppressive tumor microenvironment which is mediated by immune-TNBC interaction mainly affected by exhausted CD44\u003csup\u003e+\u003c/sup\u003e effector CD4\u003csup\u003e+\u003c/sup\u003e T cells.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate the University of Florida High-Performance Computing Center for performing HiPerGator 3.0 supercomputer, which serves as the primary working space for integrating multiple scRNA-seq datasets in this project. We thank Weizhou Zhang for sponsoring Myung-Chul to operate HiPerGator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (#RS-2023-00241779).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: M.K.\u003c/p\u003e\n\u003cp\u003eDevelopment of methodology: M.K.\u003c/p\u003e\n\u003cp\u003eData acquisition: M.K., N.B.\u003c/p\u003e\n\u003cp\u003eBioinformatics: M.K., N.B.\u003c/p\u003e\n\u003cp\u003eAnalysis and interpretation of data: M.K., W.S., R.K., N.B., W.Z.\u003c/p\u003e\n\u003cp\u003eWriting, review, and/or revision of the manuscript: M.K., R.K., W.S., N.B., W.Z.\u003c/p\u003e\n\u003cp\u003eStudy supervision: M.K.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eWaldman, A. 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Genes (Basel) 12, (2021).\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"scRNA-seq, dog, immune checkpoint, triple-negative breast cancer, interactome","lastPublishedDoi":"10.21203/rs.3.rs-3246929/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3246929/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClinical trials show promising outcomes for dogs with advanced solid tumors following treatment with immune checkpoint inhibitors (ICIs). Triple-negative breast cancer (TNBC) is very aggressive with very low response rates to ICIs. No study defines how canine TNBC interacts with the immune system within the tumor microenvironment, which is investigated in this study at the single cell level. Single cell RNA sequencing (scRNA-seq) datasets, including 6 groups of 30 dogs, were subject to integrated bioinformatic analysis. Immune modulatory TNBC subsets were identified by functional enrichment with immune-suppressive gene sets, including anti-inflammatory and M2-like macrophages. Key genes and immune-suppressive signaling pathways for TNBC included angiogenesis and leukocyte chemotaxis. Interactome analysis identified significant interactions between distinct subsets of cancer cells and effector T cells, suggesting T cell suppression. This is the first study to define immune-suppressive cancer cell subsets at the single-cell level, revealing potential mechanisms by which TNBC induces immune evasion in dogs.\u003c/p\u003e","manuscriptTitle":"A single-cell transcriptomic study reveals immune suppressive cancer cell-immune cell interactions in the triple negative canine breast cancers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-17 18:01:16","doi":"10.21203/rs.3.rs-3246929/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"40eb6abb-6e09-41ec-82b5-30fe495434a6","owner":[],"postedDate":"August 17th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":23962712,"name":"Biological sciences/Cancer/Cancer microenvironment"},{"id":23962713,"name":"Biological sciences/Immunology/Immune evasion"}],"tags":[],"updatedAt":"2023-09-13T17:29:12+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-17 18:01:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3246929","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3246929","identity":"rs-3246929","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}