Unraveling the Metastatic Niche in Breast Cancer Bone Metastasis through Single-Cell RNA Sequencing

Unraveling the Metastatic Niche in Breast Cancer Bone Metastasis through Single-Cell RNA Sequencing | 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 Unraveling the Metastatic Niche in Breast Cancer Bone Metastasis through Single-Cell RNA Sequencing Xiangyu Li, Ziyu Gao, Meiling Yang, Ciqiu Yang, Dongyang Yang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3931288/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 Breast cancer (BRCA) is characterized by a unique metastatic pattern, often presenting with bone metastasis (BoM), posing significant clinical challenges. This study employs single-cell RNA sequencing and TCGA data analysis to comprehensively compare primary tumors (PT), lymph node metastasis (LN), and BoM. Our investigation identifies a metastatic niche in BoM marked by an increased abundance of cancer-associated fibroblasts (CAFs) and reduced immune cell presence. A distinct subtype (State 1) of BRCA BoM cells associated with adverse prognosis is identified. State 1, displaying heightened stemness traits, may represent an initiation phase for BoM in BRCA. Complex cell communications involving tumor, stromal, and immune cells are revealed. Interactions of FN1, SPP1, and MDK correlate with elevated immune cells in BoM. CD46, MDK, and PTN interactions drive myofibroblast activation and proliferation, contributing to tissue remodeling. Additionally, MDK, PTN, and FN1 interactions influence FAP + CAF activation, impacting cell adhesion and migration in BoM. These insights deepen our understanding of the metastatic niche in breast cancer BoM. Biological sciences/Cancer/Breast cancer Biological sciences/Cancer/Cancer microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Breast cancer (BRCA) represents a formidable global public health challenge, taking the forefront in 2020 as the preeminent global cancer. Approximately 5% of BRCA patients manifest bone metastases (BoM) at the initial diagnosis, with an elevated 75% risk of developing BoM over the subsequent decade 1 . Advanced BRCA exhibits a strikingly high incidence of BRCA BoM, ranging from 65–75%. Notably, bone tissue emerges as the primary site for distant metastasis in BRCA, affecting 60–75% of all metastatic BRCA cases, particularly in hormone receptor-positive BRCA patients 2 . However, due to pathophysiological impairment and lack of specificity, therapeutic agents are difficult to accumulate in metastatic bone 3 . Consequently, the analysis of pathological features and related biological parameters of bone metastases proves invaluable for predicting patient survival rates and recurrence risks. This profound understanding not only underscores the critical need for effective therapeutic strategies but also sheds light on the intricate interplay between BRCA and its metastatic cascade. Main treatment strategy for BoM is to inhibit the growth of tumor cells, while ignoring the influence of the tumor stromal microenvironment (TSM) on the progression of BoM 4 . The intricate landscape of the tumor microenvironment (TME) is composed of diverse non-cellular factors and a myriad of cell types, including cancer-associated fibroblasts (CAFs), immune cells, endothelial cells, pericytes, and adipocytes. The multifaceted crosstalk among tumor, stromal cells, and immune cells not only underlies treatment resistance but also propels tumor progression and progression to overt BoM 3 . Hence, a nuanced comprehension of these extensive interactions assumes paramount importance in advancing the efficacy of tumor treatments. Previous investigations have unveiled that CAFs, predominantly activated fibroblasts influenced by the tumor, play instrumental roles in propelling BRCA progression. Their involvement spans a spectrum of functions, including fostering tumor cell proliferation, facilitating cancer cell invasion and metastasis, orchestrating extracellular matrix remodeling and deposition, promoting angiogenesis, instigating drug resistance, generating circulating CAFs (cCAFs), and secreting pro-tumor factors. Notably, CAFs contribute to the establishment of an immunosuppressive microenvironment, thus evading immune surveillance 5 – 8 . These insights, drawn from prior studies, underscore the pivotal role of CAFs in shaping the complex intercellular network within the TME, illuminating potential avenues for therapeutic interventions. As predominant stromal constituents within the TME, CAFs intricately engage in dynamic dialogues with diverse immune cells. Employing a variety of paracrine mechanisms, CAFs meticulously secrete soluble factors that efficaciously impede anti-tumor immune responses. Playing a pivotal role, CAFs are central to the recruitment of Tumor-Associated Macrophages (TAMs), fostering a pro-tumor phenotype. Additionally, they contribute significantly to the recruitment and differentiation of Tumor-Associated Neutrophils (TANs). In advanced tumor stages, TANs facilitate metastasis through extracellular trap release, immune response suppression, and production of cytokines and proteases. Furthermore, CAFs actively promote the migration and generation of Myeloid-Derived Suppressor Cells (MDSCs) via the secretion of cytokines and chemokines, exerting immunosuppressive effects on acquired and innate immunity. Integral to immune suppression, CAFs play a key role in converting CD4 + T cells to Regulatory T cells (Tregs) and T Helper lymphocytes (Th) cells to Th2 cells. By regulating the differentiation and maturation of Dendritic Cells (DCs), CAFs inhibit antigen presentation, thus limiting T-cell activation. Moreover, CAFs hinder the infiltration of Cytotoxic T Lymphocytes (CTLs) into tumors, attenuating their tumoricidal potential. The intricate orchestration of these immunosuppressive mechanisms by CAFs, encompassing upregulation of immune checkpoint molecules, extracellular matrix remodeling via collagen, fibronectin, MMPs, and activation of the FAK signaling pathway, underscores their central role in mediating tumor immune escape through metabolic reprogramming and the production of immunosuppressive metabolites 9 – 16 . However, the precise involvement of CAFs in BRCA BoM remains elusive. In this study, we utilized single-cell RNA sequencing (scRNA-seq) and conducted an extensive analysis of The Cancer Genome Atlas (TCGA) data. Through a comparative evaluation of primary tumors (PT), lymph node metastasis (LN), and bone metastasis (BoM), our investigation reveals a distinctive metastatic niche characterized by an increase in CAFs and a reduction in immune cell populations in BoM. Notably, we identified a unique subtype of BRCA BoM cells strongly associated with an adverse prognosis. Our analysis spans the exploration of genes, signaling pathways, and variations in the immune microenvironment across PT, LN, and BoM. By uncovering intricate cellular dialogues among tumor, stromal, and immune cells, we pinpoint pivotal interactions involving FN1, SPP1, and MDK that correlate with an augmented presence of immune cells in BoM. These findings provide insights into the complexities of the immune microenvironment in BRCA BoM and offer perspectives for therapeutic interventions targeting this specific metastatic manifestation of BRCA. Materials and methods Data acquisition This study received approval from the Medical Ethics Committee of the Affiliated Cancer Hospital & Institute of Guangzhou Medical University, and all participating subjects provided informed consent before undergoing surgery. For the scRNA-seq analysis, two distinct datasets were utilized. Immunohistochemistry (IHC) staining assays were performed on formalin-fixed, paraffin-embedded tissue blocks retrieved from one BRCA BoM case in the eleventh thoracic vertebra after a thorough review of archived materials. This BoM dataset included the expression profiles of 32,738 genes across 9,181 individual cells. The dataset, GSE225600, was sourced from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo ) on October 17, 2023. It encompassed gene expression data from a total of 81,683 cells across four PT and their corresponding four paired LN, providing insights into the expression patterns of 36,601 genes. Bulk data for BRCA patients were acquired from The Cancer Genome Atlas Genomic Data Commons (TCGA GDC) via UCSC Xena ( https://xenabrowser.net/ , accessed on 2023/12/10, cohort: GDC TCGA Breast Cancer). This gene expression dataset included count data for 60,488 genes across 1,217 samples. Additionally, survival data for 1,260 BRCA samples and clinical data for 1,248 BRCA samples were obtained from the same source. Single-cell RNA-seq Data Preprocessing The high-quality reads obtained from sequencing experiments underwent meticulous processing using "Cell Ranger" (version: 3.0.2). This encompassed essential tasks such as sequence alignment, filtering, barcoding, and unique molecular index counting. The reference genome employed for this analysis was hg19. Subsequently, a thorough examination of the scRNA-seq data was conducted utilizing the "Seurat" package (version: 5.0.1; https://satijalab.org/seurat/ ) in the R software (version: 4.3.1). The comprehensive analysis unfolded in multiple stages, including data quality control, normalization, and differential gene expression analysis. Initially, each scRNA-seq dataset underwent a stringent filtering process, excluding cells with fewer than 200 genes and those with over 10% of total expressed genes being mitochondrial genes. Additionally, genes detected in fewer than 10 cells were excluded. The normalization process employed the "NormalizeData" function with default parameters. Subsequently, dimensionality reduction was performed using principal component analysis (PCA), generating a 13-dimensional output for the two datasets. The clustering analysis was accomplished using the “FindClusters” function with a resolution of 10 for the BoM data and 7 for the GEO data. The identification of doublets was addressed using the "DoubletFinder" R package (version: 2.0.3; https://github.com/chris-mcginnis-ucsf/DoubletFinder ). Finally, the "IntegrateData" function was employed to correct batch effects by integrating the data from the two datasets for subsequent analyses. Two samples from the GEO data with insufficient cells were excluded, resulting in an integrated dataset comprising 40,333 genes in 34,375 cells derived from seven samples (3 PT, 3 LN, and 1 BoM). Cell clustering and annotation The values of the integrated dataset underwent z-score conversion using the "ScaleData" command, and highly variable genes were meticulously selected through the "FindVariableGenes" function with default parameters. Principle components were calculated based on these selected genes and subsequently projected onto all other genes using the "RunPCA" function. Subsequently, the "FindNeighbors" and "FindClusters" commands were employed to detect clusters of similar cells, constructing a shared nearest neighbor map with an empirically set resolution. Upon clustering all cells within the integrated dataset, the principal components, delineating heterogeneity, were found to predominantly represent differences in tissue compartments. Consequently, clusters were grouped based on the expression of distinct cell type markers, leading to the classification into four clusters (epithelial cells, endothelial cells, immune cells, and fibroblasts). A similar analytical pipeline was applied to immune cells, where the identification of immune cell types was achieved by matching each cluster-specific gene set with known signature genes of cell populations reported in previous literature 17 – 24 . Clusters that did not significantly express marker genes were categorized based on their most differentially expressed genes. This comprehensive approach ensured a nuanced understanding of cellular heterogeneity within the integrated dataset, providing valuable insights into tissue-specific compartments and diverse cell types present in the studied samples. Quantification of epithelial cell copy number variation To assess copy number variation (CNV) in individual epithelial cells, we utilized the "infercnv" R package (version 1.16.0; https://github.com/broadinstitute/infercnv ). For this analysis, fibroblasts were designated as the reference normal cells, providing a baseline copy number for comparison. Employing a cutoff of 0.1 and setting denoising to TRUE, we systematically computed CNV scores, enabling a comprehensive evaluation of copy number alterations in the epithelial cell population. Identification and functional enrichment of differentially expressed genes Differentially expressed genes (DEGs) were identified employing the "FindMarkers" function within the "Seurat" R package, utilizing the Wilcoxon Rank Sum test with a log2 fold change threshold of 0.1. To refine the results, stringent filtering criteria were applied, necessitating an absolute average log2 fold change > 1 and a p-value < 0.05. For the analysis of Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment, we utilized the "clusterProfiler" R package (version: 4.8.3). Significantly enriched GO terms and KEGG pathways were determined based on a q-value smaller than 0.05. To investigate cancer hallmark enrichment, we obtained a reference set of 50 cancer hallmark gene sets from the Molecular Signature Database (MSigDB, accessed on 2023/11/01). Enrichment scores were calculated using the "UCell" (version: 2.7.1; https://github.com/carmonalab/UCell ) and "irGSEA" (version: 2.1.5; https://github.com/chuiqin/irGSEA ) R packages, providing a comprehensive exploration of the functional significance of differentially expressed genes in the context of cancer hallmarks. Pseudotime analysis and stemness inference Pseudotime analysis, conducted with the R package "monocle" (version: 2.28.0), harnessed variable genes to delineate pseudotime stages, pinpoint trajectory differentiation-related genes, and scrutinize alterations in branch point genes. This analysis enabled the exploration of functional disparities among pseudotime stages, defining distinct BoM states in this study. Furthermore, stemness scores for each state were estimated by leveraging established markers such as OCT4, SOX2, and NANOG, providing valuable insights into the stem cell-like characteristics associated with each pseudotime stage. Identification of Markers for Early Stage BoM To characterize state identities, we initiated the identification of DEGs specific to BoM state 1 using the "FindAllMarkers" function within the "Seurat" package. Significant genes were filtered based on a stringent threshold, requiring an absolute average log2 fold change exceeding 1.5 and an adjusted p-value below 0.05. To further refine marker selection, we employed the R package "rpart" (version: 4.1.23) to construct a tree model, utilizing the method parameter and pruning the tree with "cp = 0.01". This meticulous process resulted in the identification of markers specifically associated with the early state of BoM. Prognostic values of BoM early state markers To evaluate the prognostic significance of the identified markers, we computed enrichment scores for these marker genes in individual TCGA BRCA patients. These scores were derived by multiplying gene expression values with weights assigned by the constructed tree model. Subsequently, patients were stratified into two groups based on median values. We then assessed the prognostic effectiveness of these markers in predicting the 10-year survival of TCGA BRCA patients using the R packages "survival" (version: 3.5-7) and "survminer" (version: 0.4.9). Additionally, we explored the dynamic changes in these markers across different clinical features of TCGA BRCA samples. Cell-cell interaction analysis To investigate intercellular interactions within BRCA BoM, we utilized the R package “CellChat” (version 1.6.1; https://github.com/sqjin/CellChat/ ) specifically tailored for carcinoma cells, immune cells, and fibroblasts. This package encompasses a database of molecular signaling interactions, comprising 60% paracrine/autocrine signaling interactions, 21% extracellular matrix (ECM)-receptor interactions, and 19% cell-cell contact interactions. Through comprehensive cellular communication analysis, we obtained valuable insights into cell-cell interactions and elucidated intercellular communication networks. This approach contributes to a holistic understanding of the intricate interactions among various cell types during the development of BRCA BoM. Results Single-Cell RNA-seq Profiling of BRCA Primary Tumor, Lymph Node, and Bone Metastasis To enhance our understanding of BRCA BoM, we integrated two single-cell sequencing datasets and performed a comprehensive analysis (Fig. 1 A). Following rigorous quality control, we obtained transcriptome data from 34,375 cells, including 12,427 cells from four PT samples, 15,740 cells from four LN samples, and 6,208 cells from one BoM sample. After dimension reduction, clustering, and cell annotation (Fig. 1 B-D), we observed a predominant ratio of immune cells in LN samples. Notably, compared to PT and LN, the ratio of immune cells decreased, while epithelial cells and fibroblasts exhibited a noticeable increase. This observation leads us to hypothesize that immune cells and fibroblasts may play pivotal roles in the BoM process. Next, we employed the "infercnv" R package to infer copy number variations in epithelial cells. Comparative analysis with fibroblasts unveiled extensive mutations in epithelial cells, leading us to conclude that all epithelial cells exhibit characteristics consistent with carcinoma (Fig. 1 E). Metabolic and Immune Signaling Pathways Up-regulated in BRCA BoM Using the "FindMarkers" function within the "Seurat" R package, we identified DEGs in tumor cells originating from distinct BRCA types. In parallel, we conducted GSEA and examined cancer hallmark enrichment to gain deeper insights into these DEGs. Our analysis revealed a significant enrichment of upregulated genes in the Toll-like receptor signaling pathway, PI3K/Akt/mTOR pathway, and specific metabolic programs in BRCA cells derived from BoM (Fig. 2 A, B). These findings underscore the crucial roles of immune response and cell proliferation in the progression of BRCA BoM. Subsequently, employing pseudotime trajectory analysis on PT and BoM cells, excluding LN samples with insufficient tumor cells, we elucidated dynamic cell transitions. Our analysis identified three distinct states in BRCA BoM progression (Fig. 2 C, D). State 1, prevalent at the trajectory's outset, exhibited the highest stemness score (Fig. 2 E), while state 2, characterized by the lowest stemness score, was predominantly located at the trajectory's conclusion. Functional enrichment analysis of up-regulated KEGG pathways in these states revealed that state 1 primarily engages in biological processes encompassing cell growth, development, proliferation, differentiation, and cell adhesion (Fig. 2 F). Given the manifestation of state 1 in both PT and BoM samples early in the trajectory, displaying heightened stemness traits, we propose that state 1 may represent an initiation phase for BoM in BRCA. Identification of Marker Genes for Early Stage BRCA BoM The "rpart" R package provides a robust framework for constructing classification and regression trees. To pinpoint pivotal genes in the progression of BRCA BoM, we employed the "rpart" package to generate a recursive partitioning and regression tree. The resulting model highlighted three marker genes (ZNF831, CTLA4, and GIMAP7) and their respective positions within the decision tree model (see Fig. 3 A). TCGA BRCA samples were scored based on the expression of each gene in RNA-seq data and the corresponding weight derived from the tree model. Subsequently, BRCA samples were categorized into State 1 and non-State 1 status based on the median score (Fig. 3 B). The Kaplan-Meier plot indicates a significantly lower overall survival for BRCA patients in State 1 compared to those in non-State 1 (Fig. 3 C). The distribution of BRCA status across various clinical features reveals a fluctuating trend in State 1 percentages, indicating a dynamic progression of BRCA. This suggests that primary tumor cells may acquire the potential for distant metastasis as BRCA advances, subsequently transforming into BoM tumor cells. Critical Involvement of Immune Cells in BRCA-Driven BoM To unravel the dynamics of immune cells in BRCA BoM, we employed re-clustering techniques and marker gene annotations (Fig. 4 A,B). Seven distinct immune cell clusters emerged, with two identified clusters devoid of significant immune cell type marker gene expression, highlighted by their highest DEGs. Analysis of immune cell percentages in PT, LN, and BoM revealed a notable increase in myeloid cells and a relative rise in cytotoxic NK-T cells in BoM (Fig. 4 C). To gain comprehensive insights into the functions of myeloid cells in BoM, we further re-clustered them into six distinct subsets termed TAM 1–6 (Fig. 4 D). The bar plot illustrates that, except for TAM3, all clusters were predominantly present in BoM (Fig. 4 E), actively engaging in biological processes such as cell adhesion, immune response, and immune regulation (Fig. 4 F). Deciphering Immune Cell Interactions in BoM TME To assess the impact of immune cells on the TME in BoM, we employed "CellChat" for analyzing cell communication networks. Notably, a substantial number of interactions were observed in BoM (Fig. 5 A), with predominant pathway presence (Fig. 5 B). Our investigation unveiled that cytotoxic NK-T cells in BoM release CD8A, engaging in communication with B cells, naïve T cells, myeloid cells, regulatory T cells, and carcinoma cells through interaction with HLA-A, HLA-B, and HLA-C, potentially enhancing immune responses in BoM (Fig. 5 C). BoM state 1 carcinoma cells predominantly engage cytotoxic NK-T cells, myeloid cells, and B cells (Fig. 5 D). This interaction is facilitated through the involvement of FN1, HLA genes, and MDK, which interact with their respective target genes. Notably, the MDK-NCL interaction (Fig. 5 E) appears to exert a more significant impact, potentially suppressing the immune response. Concurrently, myeloid cells and B cells predominantly target BoM state 1 tumor cells via interactions with FN1, SPP1, GRN, and MK and their target genes (Fig. 5 F). The activation of signaling pathways by FN1 may contribute to cell survival, metastasis, and the progression of BoM (Fig. 5 G). Decoding Fibroblast Dynamics and Interactions in BRCA BoM To unravel the intricate dynamics of fibroblast cells in BRCA BoM, we employed re-clustering techniques and marker gene annotations, revealing three distinct fibroblast cell clusters (Fig. 6 A,B). Analysis of fibroblast cell proportions in PT, LN, and BoM highlighted a significant elevation in myofibroblast cells and FAP + inflammatory cells in BoM (Fig. 6 C). Exploring the functional roles of fibroblast cells in BoM, KEGG functional enrichment unveiled active involvement of myofibroblast cells and FAP + inflammatory cells in processes such as cell proliferation, adhesion, and complement and coagulation cascades (Fig. 6 D). To assess the influence of fibroblast cells in BoM, we utilized "CellChat" for analyzing cell communication networks, revealing a significant number of interactions in BoM (Fig. 6 E). Our study unveiled that FAP + inflammatory cells in BoM release FN1, participating in communication with carcinoma cells, myofibroblast cells, FAP − inflammatory cells, and self-interactions (Fig. 6 F). In BoM, state 1 carcinoma cells exhibit a predominant interaction with myofibroblast cells and FAP + inflammatory cells (Fig. 6 G), facilitated by MDK and CD46, engaging their respective target genes (Fig. 6 H). The MDK-SDC1/SDC4 interactions exert a significant influence, potentially enhancing cell proliferation, angiogenesis, and epithelial-mesenchymal transition (EMT), thereby promoting tumor metastasis from the primary site to distant locations. Simultaneously, myofibroblast cells and FAP − inflammatory cells predominantly target BoM state 1 tumor cells through interactions involving THBS, PTN, and NOTCH pathways (Fig. 6 I). PTN activation of signaling pathways may contribute to stimulating new blood vessel formation and tumor angiogenesis (Fig. 6 J). Myofibroblast-Immune Interactions in BRCA BoM Progression Conducting an in-depth analysis of cell communications between fibroblast cells and immune cells, we unveil a substantial number of inferred interactions in BoM (Fig. 7 A). Quantitative analysis demonstrates that myofibroblast cells exhibit a higher frequency of interactions with immune cells compared to other cell types (Fig. 7 B). Predominant interactions involve cytotoxic NK-T cells and myeloid cells with myofibroblast cells (Fig. 7 C), mediated through PTN, MDK, and LAMININ signaling pathways (Fig. 7 D). Concurrently, myofibroblast cells and FAP + inflammatory cells primarily target myofibroblast cells through interactions involving PTN, MDK, and SPP1 signaling pathways (Fig. 7 E). The PTN-NCL interaction significantly influences both interactions from and towards myofibroblast cells, potentially promoting cell proliferation, angiogenesis, metastasis, and heightened resistance to apoptosis in cancer cells. Discussion The emergence of BoM is of significant prognostic importance in BRCA, underscoring the necessity to delve into the intricate pathogenesis and molecular regulatory networks governing this phenomenon. In this study, we undertook a comprehensive investigation of BRCA, with a particular focus on both LN and BoM. Our meticulous analysis provides valuable insights into the nuanced intricacies of BRCA progression, with special attention to the metastatic niche of BoM. Specifically, we observed a distinct elevation in CAFs alongside a reduction in immune cells within the bone metastatic microenvironment. These findings enhance our understanding of the disease and present potential avenues for therapeutic interventions. Several recent studies have extensively explored the intricate microenvironments within BoM, shedding light on the niche that supports tumor colonization 25 – 27 . The dynamic interplay of tumor-stromal interactions orchestrates the progression from initial seeding to the development of overt macrometastasis. Consistent with these findings, our investigation into the early-stage colonization of breast cancer bone metastasis (BRCA BoM) aligns with the observed overexpression of heterotypic adherens junctions and an up-regulation of calcium influx. A pivotal outcome of our research is the identification of a distinct subtype of BRCA BoM cells. This specific subtype demonstrates a close correlation with the occurrence of BRCA BoM and serves as an indicator of an unfavorable prognosis. Through a comparative analysis of cancer hallmarks between BoM and PT, as well as LN, we unveiled a predominant upregulation of metabolic and Toll-like receptor signaling pathways in BoM. This highlights significant molecular distinctions in the metastatic microenvironment. To gain deeper insights, we further stratified BoM into three distinct states using stemness scores. Intriguingly, State 1, characterized by the highest stemness, was found to coexist in both primary and metastatic sites, acting as the initiating point for BoM. KEGG functional enrichment analysis of State 1 underscored its involvement in critical biological processes, including cell growth, development, proliferation, differentiation, and cell adhesion. By comparing our findings with the conclusions drawn in recent publications 28 , we contribute to the ongoing discourse on bone metastatic microenvironments. Our identification of a specific BRCA BoM cell subtype and the delineation of distinct functional pathways provide novel perspectives for understanding and potentially targeting the unique aspects of metastasis within the bone microenvironment. Ma et al. recently identified a specific subset of protumorigenic macrophages which derived from CCL2-recruited inflammatory monocytes, promoting BRCA BoM in an IL-4R-dependent manner 29 . Our exploration has illuminated the intricate dynamics of communication between BRCA cells and immune cells, providing a nuanced understanding of the immune landscape. Employing distinct biomarkers for immune cell identification, we observed a significant upregulation of myeloid cells in BoM as opposed to PT and LN. Further elucidating the myeloid cell landscape through dimensionality reduction and clustering revealed that specific clusters of TAM were notably elevated in BoM. These clusters were found to predominantly engage in processes associated with cell adhesion and immune response, as substantiated by KEGG functional enrichment analysis. Remarkably, our examination of cell-cell interactions has revealed a substantial augmentation in interactions specific to the formation of the metastatic niche within BoM. Noteworthy is the identification of Major Histocompatibility Complex class I (MHC-I) as a central mediator in facilitating communication between tumor cells and immune cells, as well as orchestrating intercellular interactions among immune cells specifically within the BoM microenvironment, a phenomenon not as prominently observed in the PT or LN. Intriguingly, our focused analysis of interactions involving FN1, SPP1, and MDK with their target genes has yielded additional insights. These interactions were found to significantly contribute to an augmentation in myeloid cells, B cells, Naive T cells, and Cytotoxic T cells within the dynamic milieu of the BoM microenvironment. This intricately orchestrated interplay emphasizes the influential role of specific signaling pathways in shaping the immune landscape of BRCA BoM. Our study brings forth a nuanced understanding of the roles played by cancer-associated myofibroblasts and inflammatory CAFs within the metastatic niche. Particularly in BoM, a significant augmentation of myofibroblasts and FAP − inflammatory CAFs was observed in comparison to PT and LN, while FAP − inflammatory CAFs displayed a reduction. These identified myofibroblasts and FAP + inflammatory CAFs emerged as pivotal contributors, primarily involved in crucial cellular functions such as proliferation, adhesion, and extracellular matrix organization. The intricate interplay orchestrated by CD46, MDK, PTN, and their target genes emerged as a driving force behind the activation and proliferation of myofibroblasts, significantly contributing to tissue remodeling within BoM. Furthermore, the interactions facilitated by MDK, PTN, FN1, and their respective target genes were found to stimulate the activation and proliferation of FAP + CAFs, concurrently promoting cell adhesion and migration within the BoM microenvironment. Our in-depth exploration of immune-stromal cell communication unveiled critical genes, including PTN, MK, SPP1, and FN1. Through interactions with their target genes, these genes were implicated in fostering the activation and proliferation of myofibroblasts while concurrently playing a pivotal role in orchestrating inflammatory responses within the dynamic context of BoM. In conclusion, our investigation has meticulously constructed a comprehensive single-cell map, providing a detailed portrayal of the metastatic niche throughout the spectrum of BRCA progression, encompassing in situ conditions, LN, and BoM. The systematic delineation of the metastatic niche in BoM has uncovered distinctive features, unraveling the intricate mechanisms that govern the immunosuppression induced by cancer cells upon metastasizing to the bone. Declarations Acknowledgments We would like to express our gratitude to Professor Wei Zhao from the Zhongshan School of Medicine at Sun Yat-sen University for his guidance and assistance. This work was supported by National Natural Science Foundation of China (No.82003805, No.82002776), Guangzhou Science and Technology Project (No.2024A03J0649), and Shanxi Province Science Foundation for Youths (No.201901D211471), Young Academic and Technical Leaders Project of Changzhi Medical College (No.XSQ202101). Acknowledgement to all funding sources. Author contributions Z.G. performed the scRNA-seq analyses and wrote the materials and methods section. C.Y. and D.Y. collected and validated the data. M.Y. provided professional assistance. W.C. retrieved literature. D.W. and J.Z. provided samples of BRCA BoM case. X.L. conceptualized and designed the study. X.L. wrote the introduction and discussion section. All authors contributed to the paper and approved the work submitted. Competing interests The authors declare no conflict of interest. Data availability Publicly available datasets were analyzed in this study. The data can be found here: https://www.ncbi.nlm.nih.gov/geo/ (accessed on 17 October 2023) with access number GSE225600. The bulk data can be found here: https://xenabrowser.net/ (accessed on 10 December 2023). Ethics declarations This study was conducted in accordance with the Medical Ethics Committee of the Affiliated Cancer Hospital & Institute of Guangzhou Medical University. 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The leukocyte common antigen (CD45): a putative receptor-linked protei n tyrosine phosphatase. Proc Natl Acad Sci U S A 85, 7182–7186, doi: 10.1073/pnas.85.19.7182 . Chistiakov, D. A., Killingsworth, M. C., Myasoedova, V. A., Orekhov, A. N. & Bobryshev, Y. V. CD68/macrosialin: not just a histochemical marker. Laboratory Investigation 97, 4–13, doi: https://doi.org/10.1038/labinvest.2016.116 (2017). Li, Y. et al. Loss of Acta2 in cardiac fibroblasts does not prevent the myofibroblast differentiation or affect the cardiac repair after myocardial infarction. Journal of molecular and cellular cardiology 171, 117–132, doi: 10.1016/j.yjmcc.2022.08.003 (2022). Maetzel, D. et al. Nuclear signalling by tumour-associated antigen EpCAM. Nat Cell Biol 11, 162–171, doi: 10.1038/ncb1824 . Nurmik, M., Ullmann, P., Rodriguez, F., Haan, S. & Letellier, E. In search of definitions: Cancer-associated fibroblasts and their mark ers. Int J Cancer 146, 895–905, doi: 10.1002/ijc.32193 . Raskov, H., Orhan, A., Christensen, J. P. & Gögenur, I. Cytotoxic CD8 + T cells in cancer and cancer immunotherapy. Br J Cancer 124, 359–367, doi: 10.1038/s41416-020-01048-4 . Wang, K., Wei, G. & Liu, D. CD19: a biomarker for B cell development, lymphoma diagnosis and thera py. Exp Hematol Oncol 1, 36, doi: 10.1186/2162-3619-1-36 . Esposito, M., Guise, T. & Kang, Y. The Biology of Bone Metastasis. Cold Spring Harb Perspect Med 8, a031252, doi: 10.1101/cshperspect.a031252 . Chen, F., Han, Y. & Kang, Y. Bone marrow niches in the regulation of bone metastasis. Br J Cancer 124, 1912–1920, doi: 10.1038/s41416-021-01329-6 . Satcher, R. L. & Zhang, X. H. F. Evolving cancer-niche interactions and therapeutic targets during bone metastasis. Nat Rev Cancer 22, 85–101, doi: 10.1038/s41568-021-00406-5 . Nolan, E., Kang, Y. & Malanchi, I. Mechanisms of Organ-Specific Metastasis of Breast Cancer. Cold Spring Harb Perspect Med 13, a041326, doi: 10.1101/cshperspect.a041326 . Ma, R. Y. et al. Monocyte-derived macrophages promote breast cancer bone metastasis outgrowth. The Journal of experimental medicine 217, doi: 10.1084/jem.20191820 (2020). Additional Declarations No competing interests reported. Supplementary Files BoMrawData.zip 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-3931288","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":271155617,"identity":"5a26de80-a378-4ca9-95f6-971d22c02372","order_by":0,"name":"Xiangyu Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3OPQrCMBTA8VcCdal1rYh6BSWrIN4kRXCq4AGqBoRuOustOjqmBNolB6g4SWcH7eQgGL86ph0F84fAI7xfCIBO94PZCIC9phr9XLESYhbE+q6WkmJySFVSs/rstp+74SHLrqkPHTslRj5Tfswk0Vokbnic9LAXA26mBLW2SoIYqwexJB6MpxTcMCUmspTEoNH9SQ4CuCTLCgQxXg98+bhlrCQhvXJiEt4OGN6JCUZe7PR34rRqqUijIfDlHCzam4RnuecPunYyjnIV+cRH9D048hi0HAAshlW2dDqd7k97AEIlTqhy8a3QAAAAAElFTkSuQmCC","orcid":"","institution":"The Stem Cell and Tissue Engineering Research Center, College of Pharmacy, Changzhi Medical College","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiangyu","middleName":"","lastName":"Li","suffix":""},{"id":271155618,"identity":"afa27774-6ac4-4c07-a3b7-5b9c2433cd08","order_by":1,"name":"Ziyu Gao","email":"","orcid":"","institution":"The Stem Cell and Tissue Engineering Research Center, College of Pharmacy, Changzhi Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ziyu","middleName":"","lastName":"Gao","suffix":""},{"id":271155619,"identity":"daf44e1b-fc36-4de6-932a-16608b66e0a0","order_by":2,"name":"Meiling Yang","email":"","orcid":"","institution":"Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meiling","middleName":"","lastName":"Yang","suffix":""},{"id":271155620,"identity":"c964f86d-12f4-49cb-adaf-99547289e60e","order_by":3,"name":"Ciqiu Yang","email":"","orcid":"","institution":"Medical Oncology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ciqiu","middleName":"","lastName":"Yang","suffix":""},{"id":271155621,"identity":"7053c137-c111-43d8-9f17-a342786682b7","order_by":4,"name":"Dongyang Yang","email":"","orcid":"","institution":"Medical Oncology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dongyang","middleName":"","lastName":"Yang","suffix":""},{"id":271155622,"identity":"5332d033-6eaf-4f4d-8a92-0d9b896d0412","order_by":5,"name":"Wenhui Cui","email":"","orcid":"","institution":"The Stem Cell and Tissue Engineering Research Center, College of Pharmacy, Changzhi Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenhui","middleName":"","lastName":"Cui","suffix":""},{"id":271155623,"identity":"b5c69a11-0c18-4b68-aa72-ce7e40f4bd4e","order_by":6,"name":"Dandan Wu","email":"","orcid":"","institution":"Department of Breast Oncology Surgery, Affiliated Cancer Hospital \u0026 Institute of Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Wu","suffix":""},{"id":271155624,"identity":"7b7af278-962f-4fc7-b78b-b30c5e9c4b8c","order_by":7,"name":"Jie Zhou","email":"","orcid":"","institution":"Department of Breast Oncology Surgery, Affiliated Cancer Hospital \u0026 Institute of Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-02-05 14:59:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3931288/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3931288/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51077210,"identity":"39d495a0-6993-4bc5-8069-4eb9460351d5","added_by":"auto","created_at":"2024-02-13 18:35:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1156830,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell RNA-seq Atlas of BRCA Primary Tumor and Metastasis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Workflow overview illustrating the decoding of BRCA progression using single-cell RNA-seq (scRNA-seq). Single-cell suspensions from PT, LN, and BoM were subjected to scRNA-seq using the 10× Genomics platform.\u003c/p\u003e\n\u003cp\u003e(B) A t-distributed Stochastic Neighbor Embedding (t-SNE) plot, derived from integrated BRCA data (n=34,375 cells), visually delineates principal cell types.\u003c/p\u003e\n\u003cp\u003e(C) Proportional representation of each cell type across different tumor types in BRCA is shown in a bar chart.\u003c/p\u003e\n\u003cp\u003e(D) Dot plot presenting marker gene expression levels in indicated cell types. Dot size indicates the proportion of cells expressing the marker within the group, while color represents marker expression levels.\u003c/p\u003e\n\u003cp\u003e(E) Heatmap depicting results from “infercnv” provides insights into copy number variations across samples\u003c/p\u003e","description":"","filename":"Fig1.Res1.png","url":"https://assets-eu.researchsquare.com/files/rs-3931288/v1/dd34f16aa94632c690ab40d2.png"},{"id":51077212,"identity":"b7b2528a-bf6e-46cc-b544-2467a1007444","added_by":"auto","created_at":"2024-02-13 18:35:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":437527,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional Enrichment and Pseudotime Analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Dot plot illustrating Gene Set Enrichment Analysis (GSEA) functional enrichment results across BRCA types. Dot sizes correspond to gene set sizes, and colors indicate enrichment p values.\u003c/p\u003e\n\u003cp\u003e(B) Heatmap presenting changes in cancer hallmarks within each BRCA tumor type.\u003c/p\u003e\n\u003cp\u003e(C) Pseudotime-ordered analysis of tumor cells from PT and BoM, with the spectrum of blue indicating the temporal order.\u003c/p\u003e\n\u003cp\u003e(D) Pseudotime states are color-labeled, with each dot representing a single cell.\u003c/p\u003e\n\u003cp\u003e(E) Bar plot displaying inferred stemness scores of pseudotime states.\u003c/p\u003e\n\u003cp\u003e(F) Dot plot showcasing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment results for each state, where dot size reflects the gene set size, and color indicates the p value of the result.\u003c/p\u003e","description":"","filename":"Fig2.Res2.png","url":"https://assets-eu.researchsquare.com/files/rs-3931288/v1/c0d51a83fd92af34759cc966.png"},{"id":51077211,"identity":"c8c64e25-2142-4b52-b8e9-e2fbadc39c18","added_by":"auto","created_at":"2024-02-13 18:35:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":196868,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoM State 1 Markers Identification and Its Association with BRCA Clinical Features.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Decision tree model illustrating the identification of BoM State 1 markers.\u003c/p\u003e\n\u003cp\u003e(B) Box plot displaying the expression levels of individual markers in The Cancer Genome Atlas (TCGA) BRCA samples.\u003c/p\u003e\n\u003cp\u003e(C) Kaplan-Meier plot revealing significant differences between two BRCA states.\u003c/p\u003e\n\u003cp\u003e(D) Bar plot illustrating the distribution of the two BRCA states across distinct clinical features.\u003c/p\u003e","description":"","filename":"Fig3.Res3.png","url":"https://assets-eu.researchsquare.com/files/rs-3931288/v1/5dea212cf999033e51d61fda.png"},{"id":51077217,"identity":"484cdbaf-7bb3-40c9-8fc6-6d5fcba74158","added_by":"auto","created_at":"2024-02-13 18:36:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":603769,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune Cell Annotation and Myeloid Cell Analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Dot plot displaying marker gene expression in specified cell types, with dot size indicating the proportion of expressing cells and color representing marker expression levels.\u003c/p\u003e\n\u003cp\u003e(B) Utilizing t-SNE, a plot generated from integrated immune data visually delineates principal cell types.\u003c/p\u003e\n\u003cp\u003e(C) Bar chart illustrating the proportional representation of each immune cell type across various BRCA tumor types.\u003c/p\u003e\n\u003cp\u003e(D) Uniform Manifold Approximation and Projection (UMAP) plot depicts the primary cell types of TAM cells extracted from immune cell data.\u003c/p\u003e\n\u003cp\u003e(E) Bar chart presenting the proportional representation of each TAM cell type across diverse BRCA tumor types.\u003c/p\u003e\n\u003cp\u003e(F) Dot plot revealing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment results for each TAM cluster, with dot size indicating gene set size and color denoting the p-value of the result.\u003c/p\u003e","description":"","filename":"Fig4.Res4.png","url":"https://assets-eu.researchsquare.com/files/rs-3931288/v1/b24562bc3ed9ccdc8f83179a.png"},{"id":51077213,"identity":"45977880-fd25-46bd-b1d8-db257a72d7bf","added_by":"auto","created_at":"2024-02-13 18:35:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":522838,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteractions between BRCA Tumor Cells and Immune Cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Quantification of carcinoma cell and immune cell interactions in PT, LN, and BoM depicted in a bar plot.\u003c/p\u003e\n\u003cp\u003e(B) Bar plot presenting the cell-cell interaction count for each signaling pathway in PT, LN, and BoM.\u003c/p\u003e\n\u003cp\u003e(C) Dot plot visualizing cell-cell communication probabilities within the MHC-I and CD99 pathways.\u003c/p\u003e\n\u003cp\u003e(D) Chord plot showcasing up-regulated signaling pathways originating from BoM state 1 carcinoma cells and connecting to various immune cell types.\u003c/p\u003e\n\u003cp\u003e(E) Dot plot revealing communication probabilities of BoM-specific pathways (MK, MHC-II, and FN1) from carcinoma cells.\u003c/p\u003e\n\u003cp\u003e(F) Chord plot presenting up-regulated signaling pathways targeting BoM state 1 carcinoma cells from diverse immune cell types.\u003c/p\u003e\n\u003cp\u003e(G) Dot plot demonstrating communication probabilities of BoM-specific pathways (SPP1 and FN1) targeting carcinoma cells.\u003c/p\u003e","description":"","filename":"Fig5.Res4.png","url":"https://assets-eu.researchsquare.com/files/rs-3931288/v1/2b8ff7f962a2d8df5465b0d1.png"},{"id":51077215,"identity":"bb6adb32-8e53-438e-a310-3690bf4f3e01","added_by":"auto","created_at":"2024-02-13 18:36:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":650153,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFibroblast Insights in BRCA BoM.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) t-SNE-based dimensionality reduction clustering plot of fibroblasts.\u003c/p\u003e\n\u003cp\u003e(B) Dot plot showcasing marker gene expression in specific cell types, with dot size indicating proportion and color denoting expression levels.\u003c/p\u003e\n\u003cp\u003e(C) Bar chart illustrating the proportional representation of fibroblast cell types across diverse BRCA tumor types.\u003c/p\u003e\n\u003cp\u003e(D) Dot plot revealing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment in fibroblast clusters, with dot size indicating gene set size and color representing the p-value.\u003c/p\u003e\n\u003cp\u003e(E) Bar plot quantifying interactions between carcinoma cells and fibroblasts in PT, LN, and BoM.\u003c/p\u003e\n\u003cp\u003e(F) Dot plot visualizing cell-cell communication probabilities within FN1, CD99, and LAMININ pathways.\u003c/p\u003e\n\u003cp\u003e(G) Chord plot highlighting up-regulated signaling pathways originating from BoM state 1 carcinoma cells and connecting to various fibroblast cell types.\u003c/p\u003e\n\u003cp\u003e(H) Dot plot displaying communication probabilities of BoM-specific pathways (MK, CD46, ncWNT, and TGFb) from carcinoma cells.\u003c/p\u003e\n\u003cp\u003e(I) Chord plot presenting up-regulated signaling pathways targeting BoM state 1 carcinoma cells from diverse fibroblast cell types.\u003c/p\u003e\n\u003cp\u003e(J) Dot plot demonstrating communication probabilities of BoM-specific pathways (THBS, PTN, and NOTCH) targeting carcinoma cells.\u003c/p\u003e","description":"","filename":"Fig6.Res5.png","url":"https://assets-eu.researchsquare.com/files/rs-3931288/v1/09d14fdaeaf11f23920ee14f.png"},{"id":51077322,"identity":"5a893023-9e90-40e6-84bc-03a3939c007e","added_by":"auto","created_at":"2024-02-13 18:44:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":706083,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Intricate Interactions between Fibroblast Cells and Immune Cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Bar plot quantifies interactions between fibroblasts and immune cells across PT, LN, and BoM.\u003c/p\u003e\n\u003cp\u003e(B) Visualization of up-regulated signaling pathways in BoM using a chord plot.\u003c/p\u003e\n\u003cp\u003e(C) Chord plot highlights up-regulated pathways originating from BoM myofibroblast cells, connecting with other cell types.\u003c/p\u003e\n\u003cp\u003e(D) Communication probabilities of BoM-specific pathways (PTN, MK, and LAMININ) from myofibroblast cells portrayed in a dot plot.\u003c/p\u003e\n\u003cp\u003e(E) Chord plot presents up-regulated signaling pathways targeting BoM myofibroblast cells from other cell types.\u003c/p\u003e\n\u003cp\u003e(F) Dot plot illustrating communication probabilities of BoM-specific pathways (SPP1, PTN, and MK) targeting myofibroblast cells.\u003c/p\u003e","description":"","filename":"Fig7.Res6.png","url":"https://assets-eu.researchsquare.com/files/rs-3931288/v1/f8d0b7af61deb85212b28d65.png"},{"id":51415204,"identity":"5c5286c4-11ce-4eec-833c-0ae7314d1ccd","added_by":"auto","created_at":"2024-02-21 06:44:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4256781,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3931288/v1/b15b73cc-0a4d-4e23-a864-c53c3aa1f25a.pdf"},{"id":51077218,"identity":"d4d9f00b-f079-4a92-8b35-3afa61d19dc5","added_by":"auto","created_at":"2024-02-13 18:36:06","extension":"zip","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":86976468,"visible":true,"origin":"","legend":"","description":"","filename":"BoMrawData.zip","url":"https://assets-eu.researchsquare.com/files/rs-3931288/v1/1b03f14e8d2ea6df58d4e341.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unraveling the Metastatic Niche in Breast Cancer Bone Metastasis through Single-Cell RNA Sequencing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer (BRCA) represents a formidable global public health challenge, taking the forefront in 2020 as the preeminent global cancer. Approximately 5% of BRCA patients manifest bone metastases (BoM) at the initial diagnosis, with an elevated 75% risk of developing BoM over the subsequent decade \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Advanced BRCA exhibits a strikingly high incidence of BRCA BoM, ranging from 65\u0026ndash;75%. Notably, bone tissue emerges as the primary site for distant metastasis in BRCA, affecting 60\u0026ndash;75% of all metastatic BRCA cases, particularly in hormone receptor-positive BRCA patients \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, due to pathophysiological impairment and lack of specificity, therapeutic agents are difficult to accumulate in metastatic bone \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Consequently, the analysis of pathological features and related biological parameters of bone metastases proves invaluable for predicting patient survival rates and recurrence risks. This profound understanding not only underscores the critical need for effective therapeutic strategies but also sheds light on the intricate interplay between BRCA and its metastatic cascade.\u003c/p\u003e \u003cp\u003eMain treatment strategy for BoM is to inhibit the growth of tumor cells, while ignoring the influence of the tumor stromal microenvironment (TSM) on the progression of BoM \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The intricate landscape of the tumor microenvironment (TME) is composed of diverse non-cellular factors and a myriad of cell types, including cancer-associated fibroblasts (CAFs), immune cells, endothelial cells, pericytes, and adipocytes. The multifaceted crosstalk among tumor, stromal cells, and immune cells not only underlies treatment resistance but also propels tumor progression and progression to overt BoM \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Hence, a nuanced comprehension of these extensive interactions assumes paramount importance in advancing the efficacy of tumor treatments. Previous investigations have unveiled that CAFs, predominantly activated fibroblasts influenced by the tumor, play instrumental roles in propelling BRCA progression. Their involvement spans a spectrum of functions, including fostering tumor cell proliferation, facilitating cancer cell invasion and metastasis, orchestrating extracellular matrix remodeling and deposition, promoting angiogenesis, instigating drug resistance, generating circulating CAFs (cCAFs), and secreting pro-tumor factors. Notably, CAFs contribute to the establishment of an immunosuppressive microenvironment, thus evading immune surveillance \u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. These insights, drawn from prior studies, underscore the pivotal role of CAFs in shaping the complex intercellular network within the TME, illuminating potential avenues for therapeutic interventions.\u003c/p\u003e \u003cp\u003eAs predominant stromal constituents within the TME, CAFs intricately engage in dynamic dialogues with diverse immune cells. Employing a variety of paracrine mechanisms, CAFs meticulously secrete soluble factors that efficaciously impede anti-tumor immune responses. Playing a pivotal role, CAFs are central to the recruitment of Tumor-Associated Macrophages (TAMs), fostering a pro-tumor phenotype. Additionally, they contribute significantly to the recruitment and differentiation of Tumor-Associated Neutrophils (TANs). In advanced tumor stages, TANs facilitate metastasis through extracellular trap release, immune response suppression, and production of cytokines and proteases. Furthermore, CAFs actively promote the migration and generation of Myeloid-Derived Suppressor Cells (MDSCs) via the secretion of cytokines and chemokines, exerting immunosuppressive effects on acquired and innate immunity. Integral to immune suppression, CAFs play a key role in converting CD4\u003csup\u003e+\u003c/sup\u003e T cells to Regulatory T cells (Tregs) and T Helper lymphocytes (Th) cells to Th2 cells. By regulating the differentiation and maturation of Dendritic Cells (DCs), CAFs inhibit antigen presentation, thus limiting T-cell activation. Moreover, CAFs hinder the infiltration of Cytotoxic T Lymphocytes (CTLs) into tumors, attenuating their tumoricidal potential. The intricate orchestration of these immunosuppressive mechanisms by CAFs, encompassing upregulation of immune checkpoint molecules, extracellular matrix remodeling via collagen, fibronectin, MMPs, and activation of the FAK signaling pathway, underscores their central role in mediating tumor immune escape through metabolic reprogramming and the production of immunosuppressive metabolites \u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, the precise involvement of CAFs in BRCA BoM remains elusive.\u003c/p\u003e \u003cp\u003eIn this study, we utilized single-cell RNA sequencing (scRNA-seq) and conducted an extensive analysis of The Cancer Genome Atlas (TCGA) data. Through a comparative evaluation of primary tumors (PT), lymph node metastasis (LN), and bone metastasis (BoM), our investigation reveals a distinctive metastatic niche characterized by an increase in CAFs and a reduction in immune cell populations in BoM. Notably, we identified a unique subtype of BRCA BoM cells strongly associated with an adverse prognosis. Our analysis spans the exploration of genes, signaling pathways, and variations in the immune microenvironment across PT, LN, and BoM. By uncovering intricate cellular dialogues among tumor, stromal, and immune cells, we pinpoint pivotal interactions involving FN1, SPP1, and MDK that correlate with an augmented presence of immune cells in BoM. These findings provide insights into the complexities of the immune microenvironment in BRCA BoM and offer perspectives for therapeutic interventions targeting this specific metastatic manifestation of BRCA.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData acquisition\u003c/h2\u003e \u003cp\u003eThis study received approval from the Medical Ethics Committee of the Affiliated Cancer Hospital \u0026amp; Institute of Guangzhou Medical University, and all participating subjects provided informed consent before undergoing surgery. For the scRNA-seq analysis, two distinct datasets were utilized.\u003c/p\u003e \u003cp\u003eImmunohistochemistry (IHC) staining assays were performed on formalin-fixed, paraffin-embedded tissue blocks retrieved from one BRCA BoM case in the eleventh thoracic vertebra after a thorough review of archived materials. This BoM dataset included the expression profiles of 32,738 genes across 9,181 individual cells.\u003c/p\u003e \u003cp\u003eThe dataset, GSE225600, was sourced from the Gene Expression Omnibus (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) on October 17, 2023. It encompassed gene expression data from a total of 81,683 cells across four PT and their corresponding four paired LN, providing insights into the expression patterns of 36,601 genes.\u003c/p\u003e \u003cp\u003eBulk data for BRCA patients were acquired from The Cancer Genome Atlas Genomic Data Commons (TCGA GDC) via UCSC Xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 2023/12/10, cohort: GDC TCGA Breast Cancer). This gene expression dataset included count data for 60,488 genes across 1,217 samples. Additionally, survival data for 1,260 BRCA samples and clinical data for 1,248 BRCA samples were obtained from the same source.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell RNA-seq Data Preprocessing\u003c/h2\u003e \u003cp\u003eThe high-quality reads obtained from sequencing experiments underwent meticulous processing using \"Cell Ranger\" (version: 3.0.2). This encompassed essential tasks such as sequence alignment, filtering, barcoding, and unique molecular index counting. The reference genome employed for this analysis was hg19. Subsequently, a thorough examination of the scRNA-seq data was conducted utilizing the \"Seurat\" package (version: 5.0.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://satijalab.org/seurat/\u003c/span\u003e\u003cspan address=\"https://satijalab.org/seurat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in the R software (version: 4.3.1).\u003c/p\u003e \u003cp\u003eThe comprehensive analysis unfolded in multiple stages, including data quality control, normalization, and differential gene expression analysis. Initially, each scRNA-seq dataset underwent a stringent filtering process, excluding cells with fewer than 200 genes and those with over 10% of total expressed genes being mitochondrial genes. Additionally, genes detected in fewer than 10 cells were excluded. The normalization process employed the \"NormalizeData\" function with default parameters. Subsequently, dimensionality reduction was performed using principal component analysis (PCA), generating a 13-dimensional output for the two datasets.\u003c/p\u003e \u003cp\u003eThe clustering analysis was accomplished using the \u0026ldquo;FindClusters\u0026rdquo; function with a resolution of 10 for the BoM data and 7 for the GEO data. The identification of doublets was addressed using the \"DoubletFinder\" R package (version: 2.0.3; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/chris-mcginnis-ucsf/DoubletFinder\u003c/span\u003e\u003cspan address=\"https://github.com/chris-mcginnis-ucsf/DoubletFinder\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Finally, the \"IntegrateData\" function was employed to correct batch effects by integrating the data from the two datasets for subsequent analyses. Two samples from the GEO data with insufficient cells were excluded, resulting in an integrated dataset comprising 40,333 genes in 34,375 cells derived from seven samples (3 PT, 3 LN, and 1 BoM).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCell clustering and annotation\u003c/h2\u003e \u003cp\u003eThe values of the integrated dataset underwent z-score conversion using the \"ScaleData\" command, and highly variable genes were meticulously selected through the \"FindVariableGenes\" function with default parameters. Principle components were calculated based on these selected genes and subsequently projected onto all other genes using the \"RunPCA\" function. Subsequently, the \"FindNeighbors\" and \"FindClusters\" commands were employed to detect clusters of similar cells, constructing a shared nearest neighbor map with an empirically set resolution.\u003c/p\u003e \u003cp\u003eUpon clustering all cells within the integrated dataset, the principal components, delineating heterogeneity, were found to predominantly represent differences in tissue compartments. Consequently, clusters were grouped based on the expression of distinct cell type markers, leading to the classification into four clusters (epithelial cells, endothelial cells, immune cells, and fibroblasts). A similar analytical pipeline was applied to immune cells, where the identification of immune cell types was achieved by matching each cluster-specific gene set with known signature genes of cell populations reported in previous literature \u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eClusters that did not significantly express marker genes were categorized based on their most differentially expressed genes. This comprehensive approach ensured a nuanced understanding of cellular heterogeneity within the integrated dataset, providing valuable insights into tissue-specific compartments and diverse cell types present in the studied samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of epithelial cell copy number variation\u003c/h2\u003e \u003cp\u003eTo assess copy number variation (CNV) in individual epithelial cells, we utilized the \"infercnv\" R package (version 1.16.0; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/broadinstitute/infercnv\u003c/span\u003e\u003cspan address=\"https://github.com/broadinstitute/infercnv\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For this analysis, fibroblasts were designated as the reference normal cells, providing a baseline copy number for comparison. Employing a cutoff of 0.1 and setting denoising to TRUE, we systematically computed CNV scores, enabling a comprehensive evaluation of copy number alterations in the epithelial cell population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and functional enrichment of differentially expressed genes\u003c/h2\u003e \u003cp\u003eDifferentially expressed genes (DEGs) were identified employing the \"FindMarkers\" function within the \"Seurat\" R package, utilizing the Wilcoxon Rank Sum test with a log2 fold change threshold of 0.1. To refine the results, stringent filtering criteria were applied, necessitating an absolute average log2 fold change\u0026thinsp;\u0026gt;\u0026thinsp;1 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eFor the analysis of Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment, we utilized the \"clusterProfiler\" R package (version: 4.8.3). Significantly enriched GO terms and KEGG pathways were determined based on a q-value smaller than 0.05.\u003c/p\u003e \u003cp\u003eTo investigate cancer hallmark enrichment, we obtained a reference set of 50 cancer hallmark gene sets from the Molecular Signature Database (MSigDB, accessed on 2023/11/01). Enrichment scores were calculated using the \"UCell\" (version: 2.7.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/carmonalab/UCell\u003c/span\u003e\u003cspan address=\"https://github.com/carmonalab/UCell\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and \"irGSEA\" (version: 2.1.5; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/chuiqin/irGSEA\u003c/span\u003e\u003cspan address=\"https://github.com/chuiqin/irGSEA\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) R packages, providing a comprehensive exploration of the functional significance of differentially expressed genes in the context of cancer hallmarks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePseudotime analysis and stemness inference\u003c/h2\u003e \u003cp\u003ePseudotime analysis, conducted with the R package \"monocle\" (version: 2.28.0), harnessed variable genes to delineate pseudotime stages, pinpoint trajectory differentiation-related genes, and scrutinize alterations in branch point genes. This analysis enabled the exploration of functional disparities among pseudotime stages, defining distinct BoM states in this study. Furthermore, stemness scores for each state were estimated by leveraging established markers such as OCT4, SOX2, and NANOG, providing valuable insights into the stem cell-like characteristics associated with each pseudotime stage.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eIdentification of Markers for Early Stage BoM\u003c/h2\u003e \u003cp\u003eTo characterize state identities, we initiated the identification of DEGs specific to BoM state 1 using the \"FindAllMarkers\" function within the \"Seurat\" package. Significant genes were filtered based on a stringent threshold, requiring an absolute average log2 fold change exceeding 1.5 and an adjusted p-value below 0.05. To further refine marker selection, we employed the R package \"rpart\" (version: 4.1.23) to construct a tree model, utilizing the method parameter and pruning the tree with \"cp\u0026thinsp;=\u0026thinsp;0.01\". This meticulous process resulted in the identification of markers specifically associated with the early state of BoM.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic values of BoM early state markers\u003c/h2\u003e \u003cp\u003eTo evaluate the prognostic significance of the identified markers, we computed enrichment scores for these marker genes in individual TCGA BRCA patients. These scores were derived by multiplying gene expression values with weights assigned by the constructed tree model. Subsequently, patients were stratified into two groups based on median values. We then assessed the prognostic effectiveness of these markers in predicting the 10-year survival of TCGA BRCA patients using the R packages \"survival\" (version: 3.5-7) and \"survminer\" (version: 0.4.9). Additionally, we explored the dynamic changes in these markers across different clinical features of TCGA BRCA samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell-cell interaction analysis\u003c/h2\u003e \u003cp\u003eTo investigate intercellular interactions within BRCA BoM, we utilized the R package \u0026ldquo;CellChat\u0026rdquo; (version 1.6.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/sqjin/CellChat/\u003c/span\u003e\u003cspan address=\"https://github.com/sqjin/CellChat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) specifically tailored for carcinoma cells, immune cells, and fibroblasts. This package encompasses a database of molecular signaling interactions, comprising 60% paracrine/autocrine signaling interactions, 21% extracellular matrix (ECM)-receptor interactions, and 19% cell-cell contact interactions. Through comprehensive cellular communication analysis, we obtained valuable insights into cell-cell interactions and elucidated intercellular communication networks. This approach contributes to a holistic understanding of the intricate interactions among various cell types during the development of BRCA BoM.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSingle-Cell RNA-seq Profiling of BRCA Primary Tumor, Lymph Node, and Bone Metastasis\u003c/h2\u003e \u003cp\u003eTo enhance our understanding of BRCA BoM, we integrated two single-cell sequencing datasets and performed a comprehensive analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Following rigorous quality control, we obtained transcriptome data from 34,375 cells, including 12,427 cells from four PT samples, 15,740 cells from four LN samples, and 6,208 cells from one BoM sample. After dimension reduction, clustering, and cell annotation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-D), we observed a predominant ratio of immune cells in LN samples. Notably, compared to PT and LN, the ratio of immune cells decreased, while epithelial cells and fibroblasts exhibited a noticeable increase. This observation leads us to hypothesize that immune cells and fibroblasts may play pivotal roles in the BoM process.\u003c/p\u003e \u003cp\u003eNext, we employed the \"infercnv\" R package to infer copy number variations in epithelial cells. Comparative analysis with fibroblasts unveiled extensive mutations in epithelial cells, leading us to conclude that all epithelial cells exhibit characteristics consistent with carcinoma (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic and Immune Signaling Pathways Up-regulated in BRCA BoM\u003c/h2\u003e \u003cp\u003eUsing the \"FindMarkers\" function within the \"Seurat\" R package, we identified DEGs in tumor cells originating from distinct BRCA types. In parallel, we conducted GSEA and examined cancer hallmark enrichment to gain deeper insights into these DEGs. Our analysis revealed a significant enrichment of upregulated genes in the Toll-like receptor signaling pathway, PI3K/Akt/mTOR pathway, and specific metabolic programs in BRCA cells derived from BoM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). These findings underscore the crucial roles of immune response and cell proliferation in the progression of BRCA BoM.\u003c/p\u003e \u003cp\u003eSubsequently, employing pseudotime trajectory analysis on PT and BoM cells, excluding LN samples with insufficient tumor cells, we elucidated dynamic cell transitions. Our analysis identified three distinct states in BRCA BoM progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D). State 1, prevalent at the trajectory's outset, exhibited the highest stemness score (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), while state 2, characterized by the lowest stemness score, was predominantly located at the trajectory's conclusion. Functional enrichment analysis of up-regulated KEGG pathways in these states revealed that state 1 primarily engages in biological processes encompassing cell growth, development, proliferation, differentiation, and cell adhesion (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Given the manifestation of state 1 in both PT and BoM samples early in the trajectory, displaying heightened stemness traits, we propose that state 1 may represent an initiation phase for BoM in BRCA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Marker Genes for Early Stage BRCA BoM\u003c/h2\u003e \u003cp\u003eThe \"rpart\" R package provides a robust framework for constructing classification and regression trees. To pinpoint pivotal genes in the progression of BRCA BoM, we employed the \"rpart\" package to generate a recursive partitioning and regression tree. The resulting model highlighted three marker genes (ZNF831, CTLA4, and GIMAP7) and their respective positions within the decision tree model (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). TCGA BRCA samples were scored based on the expression of each gene in RNA-seq data and the corresponding weight derived from the tree model. Subsequently, BRCA samples were categorized into State 1 and non-State 1 status based on the median score (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The Kaplan-Meier plot indicates a significantly lower overall survival for BRCA patients in State 1 compared to those in non-State 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eThe distribution of BRCA status across various clinical features reveals a fluctuating trend in State 1 percentages, indicating a dynamic progression of BRCA. This suggests that primary tumor cells may acquire the potential for distant metastasis as BRCA advances, subsequently transforming into BoM tumor cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCritical Involvement of Immune Cells in BRCA-Driven BoM\u003c/h2\u003e \u003cp\u003eTo unravel the dynamics of immune cells in BRCA BoM, we employed re-clustering techniques and marker gene annotations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA,B). Seven distinct immune cell clusters emerged, with two identified clusters devoid of significant immune cell type marker gene expression, highlighted by their highest DEGs. Analysis of immune cell percentages in PT, LN, and BoM revealed a notable increase in myeloid cells and a relative rise in cytotoxic NK-T cells in BoM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). To gain comprehensive insights into the functions of myeloid cells in BoM, we further re-clustered them into six distinct subsets termed TAM 1\u0026ndash;6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The bar plot illustrates that, except for TAM3, all clusters were predominantly present in BoM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), actively engaging in biological processes such as cell adhesion, immune response, and immune regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDeciphering Immune Cell Interactions in BoM TME\u003c/h2\u003e \u003cp\u003eTo assess the impact of immune cells on the TME in BoM, we employed \"CellChat\" for analyzing cell communication networks. Notably, a substantial number of interactions were observed in BoM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), with predominant pathway presence (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Our investigation unveiled that cytotoxic NK-T cells in BoM release CD8A, engaging in communication with B cells, na\u0026iuml;ve T cells, myeloid cells, regulatory T cells, and carcinoma cells through interaction with HLA-A, HLA-B, and HLA-C, potentially enhancing immune responses in BoM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBoM state 1 carcinoma cells predominantly engage cytotoxic NK-T cells, myeloid cells, and B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). This interaction is facilitated through the involvement of FN1, HLA genes, and MDK, which interact with their respective target genes. Notably, the MDK-NCL interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE) appears to exert a more significant impact, potentially suppressing the immune response. Concurrently, myeloid cells and B cells predominantly target BoM state 1 tumor cells via interactions with FN1, SPP1, GRN, and MK and their target genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). The activation of signaling pathways by FN1 may contribute to cell survival, metastasis, and the progression of BoM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDecoding Fibroblast Dynamics and Interactions in BRCA BoM\u003c/h2\u003e \u003cp\u003eTo unravel the intricate dynamics of fibroblast cells in BRCA BoM, we employed re-clustering techniques and marker gene annotations, revealing three distinct fibroblast cell clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA,B). Analysis of fibroblast cell proportions in PT, LN, and BoM highlighted a significant elevation in myofibroblast cells and FAP\u003csup\u003e+\u003c/sup\u003e inflammatory cells in BoM (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Exploring the functional roles of fibroblast cells in BoM, KEGG functional enrichment unveiled active involvement of myofibroblast cells and FAP\u003csup\u003e+\u003c/sup\u003e inflammatory cells in processes such as cell proliferation, adhesion, and complement and coagulation cascades (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo assess the influence of fibroblast cells in BoM, we utilized \"CellChat\" for analyzing cell communication networks, revealing a significant number of interactions in BoM (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Our study unveiled that FAP\u003csup\u003e+\u003c/sup\u003e inflammatory cells in BoM release FN1, participating in communication with carcinoma cells, myofibroblast cells, FAP\u003csup\u003e\u0026minus;\u003c/sup\u003e inflammatory cells, and self-interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eIn BoM, state 1 carcinoma cells exhibit a predominant interaction with myofibroblast cells and FAP\u003csup\u003e+\u003c/sup\u003e inflammatory cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG), facilitated by MDK and CD46, engaging their respective target genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). The MDK-SDC1/SDC4 interactions exert a significant influence, potentially enhancing cell proliferation, angiogenesis, and epithelial-mesenchymal transition (EMT), thereby promoting tumor metastasis from the primary site to distant locations. Simultaneously, myofibroblast cells and FAP\u003csup\u003e\u0026minus;\u003c/sup\u003e inflammatory cells predominantly target BoM state 1 tumor cells through interactions involving THBS, PTN, and NOTCH pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). PTN activation of signaling pathways may contribute to stimulating new blood vessel formation and tumor angiogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMyofibroblast-Immune Interactions in BRCA BoM Progression\u003c/h2\u003e \u003cp\u003eConducting an in-depth analysis of cell communications between fibroblast cells and immune cells, we unveil a substantial number of inferred interactions in BoM (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Quantitative analysis demonstrates that myofibroblast cells exhibit a higher frequency of interactions with immune cells compared to other cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Predominant interactions involve cytotoxic NK-T cells and myeloid cells with myofibroblast cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC), mediated through PTN, MDK, and LAMININ signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Concurrently, myofibroblast cells and FAP\u003csup\u003e+\u003c/sup\u003e inflammatory cells primarily target myofibroblast cells through interactions involving PTN, MDK, and SPP1 signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). The PTN-NCL interaction significantly influences both interactions from and towards myofibroblast cells, potentially promoting cell proliferation, angiogenesis, metastasis, and heightened resistance to apoptosis in cancer cells.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe emergence of BoM is of significant prognostic importance in BRCA, underscoring the necessity to delve into the intricate pathogenesis and molecular regulatory networks governing this phenomenon. In this study, we undertook a comprehensive investigation of BRCA, with a particular focus on both LN and BoM. Our meticulous analysis provides valuable insights into the nuanced intricacies of BRCA progression, with special attention to the metastatic niche of BoM. Specifically, we observed a distinct elevation in CAFs alongside a reduction in immune cells within the bone metastatic microenvironment. These findings enhance our understanding of the disease and present potential avenues for therapeutic interventions.\u003c/p\u003e \u003cp\u003eSeveral recent studies have extensively explored the intricate microenvironments within BoM, shedding light on the niche that supports tumor colonization \u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The dynamic interplay of tumor-stromal interactions orchestrates the progression from initial seeding to the development of overt macrometastasis. Consistent with these findings, our investigation into the early-stage colonization of breast cancer bone metastasis (BRCA BoM) aligns with the observed overexpression of heterotypic adherens junctions and an up-regulation of calcium influx. A pivotal outcome of our research is the identification of a distinct subtype of BRCA BoM cells. This specific subtype demonstrates a close correlation with the occurrence of BRCA BoM and serves as an indicator of an unfavorable prognosis. Through a comparative analysis of cancer hallmarks between BoM and PT, as well as LN, we unveiled a predominant upregulation of metabolic and Toll-like receptor signaling pathways in BoM. This highlights significant molecular distinctions in the metastatic microenvironment.\u003c/p\u003e \u003cp\u003eTo gain deeper insights, we further stratified BoM into three distinct states using stemness scores. Intriguingly, State 1, characterized by the highest stemness, was found to coexist in both primary and metastatic sites, acting as the initiating point for BoM. KEGG functional enrichment analysis of State 1 underscored its involvement in critical biological processes, including cell growth, development, proliferation, differentiation, and cell adhesion. By comparing our findings with the conclusions drawn in recent publications \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, we contribute to the ongoing discourse on bone metastatic microenvironments. Our identification of a specific BRCA BoM cell subtype and the delineation of distinct functional pathways provide novel perspectives for understanding and potentially targeting the unique aspects of metastasis within the bone microenvironment.\u003c/p\u003e \u003cp\u003eMa et al. recently identified a specific subset of protumorigenic macrophages which derived from CCL2-recruited inflammatory monocytes, promoting BRCA BoM in an IL-4R-dependent manner \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Our exploration has illuminated the intricate dynamics of communication between BRCA cells and immune cells, providing a nuanced understanding of the immune landscape. Employing distinct biomarkers for immune cell identification, we observed a significant upregulation of myeloid cells in BoM as opposed to PT and LN. Further elucidating the myeloid cell landscape through dimensionality reduction and clustering revealed that specific clusters of TAM were notably elevated in BoM. These clusters were found to predominantly engage in processes associated with cell adhesion and immune response, as substantiated by KEGG functional enrichment analysis.\u003c/p\u003e \u003cp\u003eRemarkably, our examination of cell-cell interactions has revealed a substantial augmentation in interactions specific to the formation of the metastatic niche within BoM. Noteworthy is the identification of Major Histocompatibility Complex class I (MHC-I) as a central mediator in facilitating communication between tumor cells and immune cells, as well as orchestrating intercellular interactions among immune cells specifically within the BoM microenvironment, a phenomenon not as prominently observed in the PT or LN. Intriguingly, our focused analysis of interactions involving FN1, SPP1, and MDK with their target genes has yielded additional insights. These interactions were found to significantly contribute to an augmentation in myeloid cells, B cells, Naive T cells, and Cytotoxic T cells within the dynamic milieu of the BoM microenvironment. This intricately orchestrated interplay emphasizes the influential role of specific signaling pathways in shaping the immune landscape of BRCA BoM.\u003c/p\u003e \u003cp\u003eOur study brings forth a nuanced understanding of the roles played by cancer-associated myofibroblasts and inflammatory CAFs within the metastatic niche. Particularly in BoM, a significant augmentation of myofibroblasts and FAP\u003csup\u003e\u0026minus;\u003c/sup\u003e inflammatory CAFs was observed in comparison to PT and LN, while FAP\u003csup\u003e\u0026minus;\u003c/sup\u003e inflammatory CAFs displayed a reduction. These identified myofibroblasts and FAP\u003csup\u003e+\u003c/sup\u003e inflammatory CAFs emerged as pivotal contributors, primarily involved in crucial cellular functions such as proliferation, adhesion, and extracellular matrix organization. The intricate interplay orchestrated by CD46, MDK, PTN, and their target genes emerged as a driving force behind the activation and proliferation of myofibroblasts, significantly contributing to tissue remodeling within BoM. Furthermore, the interactions facilitated by MDK, PTN, FN1, and their respective target genes were found to stimulate the activation and proliferation of FAP\u003csup\u003e+\u003c/sup\u003e CAFs, concurrently promoting cell adhesion and migration within the BoM microenvironment. Our in-depth exploration of immune-stromal cell communication unveiled critical genes, including PTN, MK, SPP1, and FN1. Through interactions with their target genes, these genes were implicated in fostering the activation and proliferation of myofibroblasts while concurrently playing a pivotal role in orchestrating inflammatory responses within the dynamic context of BoM.\u003c/p\u003e \u003cp\u003eIn conclusion, our investigation has meticulously constructed a comprehensive single-cell map, providing a detailed portrayal of the metastatic niche throughout the spectrum of BRCA progression, encompassing in situ conditions, LN, and BoM. The systematic delineation of the metastatic niche in BoM has uncovered distinctive features, unraveling the intricate mechanisms that govern the immunosuppression induced by cancer cells upon metastasizing to the bone.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to Professor Wei Zhao from the Zhongshan School of Medicine at Sun Yat-sen University for his guidance and assistance. This work was supported by National Natural Science Foundation of China (No.82003805, No.82002776), Guangzhou Science and Technology Project (No.2024A03J0649), and Shanxi Province Science Foundation for Youths (No.201901D211471), Young Academic and Technical Leaders Project of Changzhi Medical College (No.XSQ202101). \u0026nbsp;Acknowledgement to all funding sources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZ.G. performed the scRNA-seq analyses and wrote the materials and methods section. C.Y. and D.Y. collected and validated the data. M.Y. provided professional assistance. W.C. retrieved literature. D.W. and J.Z. provided samples of BRCA BoM case. X.L. conceptualized and designed the study. X.L. wrote the introduction and discussion section. All authors contributed to the paper and approved the work submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. The data can be found here: https://www.ncbi.nlm.nih.gov/geo/ (accessed on 17 October 2023) with access number GSE225600. The bulk data can be found here: https://xenabrowser.net/ (accessed on 10 December 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Medical Ethics Committee of the Affiliated Cancer Hospital \u0026amp; Institute of Guangzhou Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoMrawData: The eleventh thoracic vertebra of the BRCA BoM case in the BoM dataset contains expression profiles of 32,738 genes across 9,181 individual cells.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnwar, S. L., Avanti, W. S., Dwianingsih, E. K., Cahyono, R. \u0026amp; Suwardjo, S. Risk Factors, Patterns, and Distribution of Bone Metastases and Skeletal-Related Events in High-Risk Breast Cancer Patients. 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The Journal of experimental medicine 217, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1084/jem.20191820\u003c/span\u003e\u003cspan address=\"10.1084/jem.20191820\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-3931288/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3931288/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreast cancer (BRCA) is characterized by a unique metastatic pattern, often presenting with bone metastasis (BoM), posing significant clinical challenges. This study employs single-cell RNA sequencing and TCGA data analysis to comprehensively compare primary tumors (PT), lymph node metastasis (LN), and BoM. Our investigation identifies a metastatic niche in BoM marked by an increased abundance of cancer-associated fibroblasts (CAFs) and reduced immune cell presence. A distinct subtype (State 1) of BRCA BoM cells associated with adverse prognosis is identified. State 1, displaying heightened stemness traits, may represent an initiation phase for BoM in BRCA. Complex cell communications involving tumor, stromal, and immune cells are revealed. Interactions of FN1, SPP1, and MDK correlate with elevated immune cells in BoM. CD46, MDK, and PTN interactions drive myofibroblast activation and proliferation, contributing to tissue remodeling. Additionally, MDK, PTN, and FN1 interactions influence FAP\u003csup\u003e+\u003c/sup\u003e CAF activation, impacting cell adhesion and migration in BoM. These insights deepen our understanding of the metastatic niche in breast cancer BoM.\u003c/p\u003e","manuscriptTitle":"Unraveling the Metastatic Niche in Breast Cancer Bone Metastasis through Single-Cell RNA Sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-13 18:35:54","doi":"10.21203/rs.3.rs-3931288/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":"a20e8115-1b20-4897-b05d-1b10038bc627","owner":[],"postedDate":"February 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28579029,"name":"Biological sciences/Cancer/Breast cancer"},{"id":28579030,"name":"Biological sciences/Cancer/Cancer microenvironment"}],"tags":[],"updatedAt":"2024-02-21T06:44:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-13 18:35:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3931288","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3931288","identity":"rs-3931288","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}