Functional Role of Granulocytic Myeloid-Derived Suppressor Cells in CAR-T Therapy: Insights from Single- Cell RNA Sequencing in Multiple Myeloma

Functional Role of Granulocytic Myeloid-Derived Suppressor Cells in CAR-T Therapy: Insights from Single- Cell RNA Sequencing in Multiple Myeloma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Functional Role of Granulocytic Myeloid-Derived Suppressor Cells in CAR-T Therapy: Insights from Single- Cell RNA Sequencing in Multiple Myeloma Chao Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6437282/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 Immunotherapies, including chimeric antigen receptor T-cell (CAR-T) therapy, represent a pivotal approach in the treatment of multiple myeloma (MM). However, the complex immunosuppressive tumor microenvironment (TME) poses significant challenges to their efficacy. Among the immunosuppressive cells in the MM TME, granulocytic myeloid-derived suppressor cells (G-MDSCs) are predominant; however, their functions remain incompletely understood. In this study, a comprehensive analysis of G-MDSCs was conducted using single-cell transcriptomic data from seven MM patients before and post CAR-T therapy. The pathological activation and immunosuppressive roles of G-MDSCs were identified, and these features were found to be potentially linked to patient prognosis. Functional enrichment analysis revealed that G-MDSCs are key modulators of immune responses within the TME. GSEA analysis suggested that G-MDSCs regulate immune responses via the IFN-α/γ signaling pathway. Furthermore, G-MDSCs may facilitate immune evasion of MM cells by promoting cell proliferation through the IGF1-IGF1R axis and inhibiting T cells and other immune cells via the SIRPA-CD47 pathway. A risk prediction model based on differentially expressed genes in G-MDSCs demonstrated high prognostic accuracy (AUC = 0.94) and was validated by Kaplan-Meier survival analysis. Additionally, PTGS1 was identified as a key marker associated with high-risk groups, suggesting its potential as a therapeutic adjunct target to improve CAR-T treatment outcomes. Further in vitro experiments demonstrated that G-MDSCs may exert immunosuppressive functions through PTGS1 expression. This study provides new insights into the role of G-MDSCs in the MM TME and highlights potential therapeutic strategies to enhance CAR-T therapy efficacy. Multiple myeloma Granulocytic myeloid-derived suppressor cells single-cell RNA sequencing tumor microenvironment immune suppression CAR-T therapy prognostic biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Multiple myeloma (MM) is a hematologic malignancy characterized by the clonal expansion of malignant plasma cells within the bone marrow, leading to clinical manifestations such as anemia, osteolytic bone lesions, hypercalcemia, and renal dysfunction. Despite the availability of targeted therapies, including proteasome inhibitors, immunomodulatory drugs, and monoclonal antibodies, MM remains largely incurable for the majority of patients, with frequent relapses and a poor long-term prognosis [ 1 , 2 ]. Recent advances in immunotherapy, particularly chimeric antigen receptor T-cell (CAR-T) therapy, have shown promise in treating relapsed and refractory MM. CAR-T therapy targeting B-cell maturation antigen (BCMA) has demonstrated substantial clinical efficacy, achieving high response rates in patients who have failed multiple lines of therapy [ 3 , 4 ]. However, a significant challenge to the success of CAR-T therapy in MM is the presence of an immunosuppressive tumor microenvironment (TME). The TME is composed of a variety of cellular and molecular components that promote tumor growth and contribute to immune evasion. Among the key immunosuppressive cells within the TME, myeloid-derived suppressor cells (MDSCs), particularly the granulocytic subset (G-MDSCs), play a pivotal role in modulating immune responses. G-MDSCs suppress T-cell activation, promote regulatory T-cell (Treg) function, and interact with other immune cells and stromal components, all of which contribute to the suppression of anti-tumor immunity and the progression of MM [ 5 , 6 ]. In recent years, the role of G-MDSCs in various cancers, including MM, has been increasingly recognized as a critical barrier to effective immunotherapy [ 7 , 8 ]. These cells, through the release of immunosuppressive cytokines and engagement of inhibitory receptors, contribute to the establishment of an immune-tolerant microenvironment that hinders the effectiveness of therapies such as CAR-T. Despite their known immunosuppressive functions, the specific mechanisms by which G-MDSCs influence the immune landscape in MM, particularly in response to CAR-T therapy, remain poorly understood [ 9 , 10 ]. To address this gap, the present study utilizes single-cell RNA sequencing (scRNA-seq) to investigate the molecular and functional characteristics of G-MDSCs in the context of MM and CAR-T treatment. By analyzing patient samples obtained before and post CAR-T therapy, this study explores the activation states, signaling pathways, and prognostic relevance of G-MDSCs in MM. In particular, functional enrichment analyses revealed that G-MDSCs are key modulators of immune responses within the TME, with KEGG analysis showing the most significant enrichment in the Cytokine-cytokine receptor interaction pathway and GO analysis highlighting inflammatory response enrichment. The IFN-α/γ signaling pathways were also found to be involved in G-MDSC-mediated immune regulation. These findings suggest that G-MDSCs play an important regulatory role in immune responses during tumor progression. Moreover, G-MDSCs may contribute to immune evasion in MM by promoting cell proliferation through the IGF1-IGF1R pathway and inhibiting immune cell functions via the SIRPA-CD47 pathway [ 11 , 12 ]. This study further investigates the potential of G-MDSC-related gene signatures as prognostic markers and evaluates their clinical relevance in predicting CAR-T treatment outcomes. In particular, PTGS1 was identified as a key marker associated with poor prognosis, suggesting its potential as a therapeutic target for enhancing CAR-T efficacy. Further in vitro experiments were conducted to investigate the immunosuppressive role of PTGS1. Using a MM.1S cell model engineered to express PTGS1, co-culture assays with BCMA-CAR T cells demonstrated that PTGS1 expression significantly reduced CAR-T mediated cytotoxicity, suggesting its involvement in immune evasion. This research not only provides a deeper understanding of the tumor microenvironment in MM but also lays the foundation for novel therapeutic strategies aimed at modulating G-MDSCs to enhance the efficacy of immunotherapy, particularly CAR-T cell therapy, in MM patients. Methods Data Collection The single-cell RNA sequencing (scRNA-seq) data used in this study were obtained from the Gene Expression Omnibus (GEO) database under the accession number GSE271915 [ 13 , 14 ]. This dataset includes scRNA-seq profiles from seven multiple myeloma (MM) patients who underwent chimeric antigen receptor T-cell (CAR-T) therapy targeting B-cell maturation antigen (BCMA), with samples collected both before and post CAR-T treatment. A total of 14 samples were designated as P1_B, P1_P, P2_B, P2_P, ..., up to P7_B and P7_P, representing the seven patients before (B) and post (P) CAR-T therapy, respectively. In this study, "before treatment" corresponds to day − 4, and "post treatment" corresponds to day 28 relative to BCMA CAR-T infusion (day 0). As described in the original study, bone marrow mononuclear cells (BMMCs) were isolated using Ficoll Paque density gradient centrifugation, followed by red blood cell lysis and viability assessment. Single-cell libraries were constructed using the 10x Genomics Chromium Single Cell 3' v3 platform, and sequencing was performed on an Illumina NovaSeq6000 [ 14 ].The clinical data used to construct the risk prediction model, which includes survival information and corresponding RNA-seq data, were retrieved from the Synapse database (Synapse ID: syn6187098) [ 15 ]. To further explore potential therapeutic implications, drug ligand efficiency data were sourced from the ChEMBL database (ID: CHEMBL221) [ 16 ]. This database provides detailed information on the binding affinities and ligand efficiencies of various compounds, which were utilized to identify potential drug candidates that could be combined with CAR-T therapy to improve treatment outcomes in MM. All data used in this study were accessed and utilized in strict accordance with the respective database's terms and conditions. Processing of Single-Cell RNA-Seq Data All analyses were conducted in a Python environment (version 3.10.12) using the Scanpy package (version 1.10.4) for single-cell RNA-seq data processing [ 17 ], with supplementary packages such as Pandas (version 2.2.2), NumPy (version 1.26.4), and Matplotlib (version 3.8.0) used for data manipulation, statistical analysis, and visualization. Data quality control (QC) involved filtering out cells with fewer than 200 or more than 5000 detected genes, as well as those with more than 10% mitochondrial gene expression. Doublets were identified and removed using the Scrublet package (version 0.2.3) [ 18 ]. Following QC, normalization and standardization were performed. The log-normalization method was applied to adjust the data, and the top 4000 highly variable genes (HVGs) were selected for subsequent analysis. To address batch effects, data from all samples were merged and subjected to principal component analysis (PCA) for dimensionality reduction. Harmony (version 1.2.4015) was then employed to correct for batch effects [ 19 ], ensuring the integration of data across different batches without introducing unwanted technical variations. For clustering, dimensionality reduction was first performed using Uniform Manifold Approximation and Projection (UMAP) [ 20 ]. Cell clusters were then identified using the Leiden algorithm (resolution = 0.5) [ 21 ], and cell types were annotated based on the expression of known marker genes for each cluster. Functional enrichment analysis Functional enrichment analysis was performed on differentially expressed genes (DEGs) to identify significantly enriched pathways. DEGs were selected based on a p-value 1. Kyoto Encyclopedia of Genes and Genomes (KEGG) [ 22 ] and Gene Ontology (GO) [ 23 ] enrichment analyses were conducted using the GSEApy package (version 1.1.4) in Python [ 24 ]. Pathways with a p-value < 0.05 were considered statistically significant. Gene Set Enrichment Analysis (GSEA) Gene set enrichment analysis (GSEA) was conducted to identify statistically significant differences between biological conditions using the GSEApy package (version 1.1.4) in Python [ 24 ]. Predefined gene sets from the Molecular Signatures Database (MSigDB), including the HALLMARK gene sets, were used to evaluate the biological relevance of differentially expressed genes (DEGs) specifically in G-MDSCs compared to other cell types. DEGs were selected based on a p-value 1 [ 25 ]. Gene sets with an Adjusted p-value 1 were considered statistically significant. Single-cell level gene set enrichment analysis was performed using the AUCell package (version 3.20) in R [ 26 ]. The HALLMARK_INTERFERON_GAMMA_RESPONSE Gene Set was used to evaluate the activation of the IFN-γ signaling pathway across all cell types within the tumor microenvironment (TME) of multiple myeloma (MM). The Immunosuppressive Gene Set, a custom gene set designed to assess the immune-suppressive functions of G-MDSCs, which includes the following genes: ARG1, NOS2, IDO1, CYBB, IL10, VEGFA, CD274, ENTPD1, SOCS3, HIF1A, CXCL12 [ 27 – 29 ]; and the Pathological Activation Gene Set, a custom gene set to evaluate the pathological activation of G-MDSCs, which contributes to tumor progression, and includes the following genes: S100A8, S100A9, TGFB1, IL1B, CXCR2, CXCL1, HK2, SOCS3, ITGAM, LCN2 [ 5 , 30 ]. For each gene set, AUCell scores were computed for every individual cell to assess the degree of activation of the gene set in each cell. The AUCell package ranked cells based on the expression of the selected genes, and the results were visualized to identify subpopulations of G-MDSCs with varying immune-suppressive or pathological activation levels. Cellular Communication Analysis Cellular communication analysis was conducted using the CellPhoneDB tool (version 5.0.1) to investigate the intercellular interactions between granulocytic myeloid-derived suppressor cells (G-MDSCs) and other immune cell populations within the tumor microenvironment (TME) [ 31 ]. The analysis was based on single-cell RNA-seq data, focusing on the expression of receptor-ligand pairs between G-MDSCs and other immune cells. Only interactions with a p-value 0.25 and p < 0.05. The DEGs were then used to construct the risk prediction model on the Synapse database (Synapse ID: syn6187098). To address multicollinearity among DEGs, gene filtering was performed using the Variance Inflation Factor (VIF), which reduces redundancy in gene features. Lasso regression was subsequently applied, yielding five key genes: TLR7, PDE2A, ACTN1, CCND1, and PTGS1 [ 32 ]. These five genes were used to construct multiple machine learning models, including Decision Tree, K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Logistic Regression, Naive Bayes, Support Vector Machine (SVM), and Random Forest. Model training and evaluation were implemented using the scikit-learn library (version 1.6.0) in Python [ 33 ]. The predictive performance of each model was assessed through Receiver Operating Characteristic (ROC) curve analysis, and the Area Under the Curve (AUC) was used as the primary evaluation metric. To further validate the best-performing model, Kaplan-Meier survival analysis was conducted using the lifelines library (version 0.30.0) [ 34 ]. This analysis demonstrated the model's ability to stratify patients based on survival outcomes, thereby supporting its clinical utility in predicting prognosis. Generation of PTGS1 + MM.1S Cell Line To generate PTGS1 + MM.1S cells, a lentiviral vector encoding PTGS1 was constructed and transduced into MM.1S cells (ATCC) at multiplicity of infection (MOI) of 5 followed by selection with puromycin (2 ug/mL) (Invitrogen). Generation of BCMA-CAR T Cells The BCMA-specific chimeric antigen receptor (CAR) construct was designed with the following sequence: EF1α promoter, signal peptide (SP), αBCMA scFv (derived from BCMA antibody Fab fragment) [ 35 ], CD8 hinge, CD8 transmembrane domain (TM), 4-1BB, CD3ζ, P2A self-cleaving peptide, mCherry, and WPRE. The CAR sequence was cloned into a third-generation lentiviral backbone (pLV-eGFP, Addgene) and transduced into T cells (Mingzhou Biotech) at MOI of 5. Lentivirus Production Lentivirus was produced using a three-plasmid packaging system in HEK293T cells (ATCC), using the following plasmids: psPAX2, pMD2.G, and the corresponding transfer plasmid. Viral supernatant was collected 48 hours post-transfection, filtered through a 0.45 µm filter (Millipore), and concentrated by ultracentrifugation. The concentrated virus was resuspended in Opti-MEM (Gibco) for cell transduction. Cell Culture MM.1S was maintained in RPMI 1640 medium (Gibco) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin (Gibco) and was cultured at a density of 1 × 10⁶ cells/mL and incubated at 37°C with 5% CO₂ in an incubator (Thermo Fisher). T cells was cultured in RPMI 1640 medium (Gibco) supplemented with 10% FBS, 1% penicillin-streptomycin (Gibco) and 100 U/mL IL-2 (Sigma). RT-qPCR for PTGS1 Expression Total RNA was extracted using TRIzol reagent (Invitrogen), and complementary DNA (cDNA) was synthesized using a reverse transcription kit (Thermo Fisher). PTGS1 expression was quantified by real-time quantitative PCR (RT-qPCR) using SYBR Green Master Mix (Thermo Fisher). The primers used for PTGS1 detection were forward: 5'-CGCCAGTGAATCCCTGTTGTT-3' and reverse: 5'- AAGGTGGCATTGACAAACTCC-3', with GAPDH as an internal control. Relative gene expression was calculated using the 2^(-ΔΔCt) method. Flow Cytometry Analysis Flow cytometry was performed using a BD FACSCanto™ II flow cytometer (BD Biosciences). PE Mouse Anti-Human BCMA (CD269) (BD Pharmingen™) and Anti-Human COX-1 FITC/COX-2 PE (BD™) were used to detect BCMA and Cox-1 expression in MM.1S cells, respectively. Besides, mCherry was used to detect CAR expression in T cells. Untransduced cells were used as negative controls to account for any background fluorescence. Data were analyzed using FlowJo software (TreeStar Inc.). Co-culture and Cytotoxicity Assay MM.1S target cells (BCMA + or BCMA+/PTGS1+) were co-cultured with BCMA-CAR T effector cells at an effector-to-target (E:T) ratio of 1:1. At 0 h, cells were mixed and incubated at 37°C, 5% CO₂. For resveratrol (MCE) treatment, CAR-T cells and MM.1S cells (PTGS1 + or PTGS1-) were co-cultured at an E:T ratio of 1:1 in the presence of 10 nM resveratrol for 24 hours. Control groups were treated with an equivalent volume of DMSO (MCE). Flow cytometry analysis was performed every 6 hours to assess the cytotoxicity of CAR-T cells, while cytokine levels were measured after 24 hours. Cytokine Release Assay CAR-T cells and target cells were co-cultured at 1:1 E:T ratio at 37°C for 24 h, The levels of cytokines secreted into the culture medium were measured using human lL-2, IFN-γ and TNF-αtest kits (Thermo Fisher) following the manufacturers' protocols. All the tests were conducted in triplicate and presented as mean + SDs. Statistical Analysis All statistical analyses were conducted using Python (version 3.10.12) or R (version 4.3.1). A p-value < 0.05 was considered statistically significant. Where applicable, p-values were adjusted for multiple comparisons using the Benjamini-Hochberg procedure to control the false discovery rate. Results Single-Cell RNA-seq Clustering and Cell-Type Annotation To investigate the cellular landscape of the tumor microenvironment (TME) in multiple myeloma (MM) following BCMA-targeted CAR-T therapy, single-cell RNA-sequencing (scRNA-seq) data were collected from bone marrow samples of seven MM patients (P1-P7), before and post treatment, yielding a total of 14 samples. After rigorous quality control, including normalization and batch effect correction, 74,440 high-quality cells were retained for downstream analysis. Dimensionality reduction was first performed using principal component analysis (PCA), followed by clustering using the Leiden algorithm, which identified 14 distinct clusters (Fig. 1 A). The molecular signatures of these clusters were further characterized by analyzing the expression of key marker genes, visualized in a dot plot (Fig. 1 C). These genes showed clear and distinct expression patterns across the clusters, providing strong validation for the biological relevance of the clustering approach. Based on these expression patterns, cell type identities were assigned to the clusters. The resulting cell type annotation revealed 13 distinct populations, including T cells, B cells, plasma cells, monocytes, dendritic cells, NK cells, granulocytic myeloid-derived suppressor cells (G-MDSCs), and other stromal and immune cell types (Fig. 1 B). For instance, T cells were characterized by the expression of CD3D, CD3E, CD4, and CD8A, while NK cells were identified by the presence of NCAM1, KLRD1, NKG7, GNLY, and CD16. G-MDSCs, a major focus of this study, were defined by markers such as CD33, ITGAM, ARG1, CSF1R, CD274, and CEACAM8. MM cells were identified by MZB1, TNFRSF17, IRF4, CD38, and GPRC5D [ 36 ]. This comprehensive cell-type annotation underscores the cellular heterogeneity within the MM TME and provides a solid foundation for further investigation of immune regulation, intercellular communication, and the functional changes in these populations post-therapy. G-MDSCs and Their Functional Characteristics in the Tumor Microenvironment The cellular composition and functional states within the multiple myeloma (MM) tumor microenvironment (TME) were analyzed by assessing the proportions of annotated cell types across different samples. Among all patients, P5 exhibited the highest proportion of granulocytic myeloid-derived suppressor cells (G-MDSCs). This elevated proportion of G-MDSCs persisted even after B-cell maturation antigen (BCMA)-targeted CAR-T therapy (Fig. 2 A). The sustained presence of G-MDSCs was associated with a significant residual MM cell population, indicating a potential role for G-MDSCs in tumor persistence and immune evasion. Although no correlation was observed between the proportion of G-MDSCs and MM cells, the state of G-MDSCs is still hypothesized to play a role in the tumor microenvironment. To investigate the immune dynamics within the TME, single-cell level enrichment analysis of the interferon-gamma (IFN-γ) signaling pathway was performed. The IFN-γ signaling pathway plays a crucial role in promoting anti-tumor immunity by activating T cells and enhancing their effector functions, including the production of pro-inflammatory cytokines and the upregulation of cytotoxic pathways [ 37 , 38 ]. Upon CAR-T cell infusion, a significant alteration in the IFN-γ signaling pathway within the tumor microenvironment (TME) was observed, with the degree of change closely correlated with treatment outcomes (Fig. 2 B). Notably, the enrichment of the IFN-γ signaling pathway after treatment was significantly higher in patients with stringent complete remission (sCR) (P3) and with very good partial response (VGPR) (P1, P4), compared to those with partial responses (PR), including P2, P5, P6, and P7. In the current analysis, patient P5 demonstrated the lowest enrichment scores for the IFN-γ pathway among all samples, consistent with a highly immunosuppressive TME. These findings suggest that the suppression of IFN-γ signaling may be mediated by G-MDSCs, which are known to exert their effects through various immunosuppressive mechanisms, such as PI3K-Akt/mTOR pathway [ 36 ]. Expression of ARG1, a critical marker of G-MDSC function, was evaluated across all cell types to further delineate the role of G-MDSCs. The results revealed significantly elevated ARG1 expression in G-MDSCs compared to other cell populations (Fig. 2 C). ARG1 functions by depleting L-arginine, a metabolite essential for T cell activation and proliferation, thereby inhibiting effective anti-tumor immune responses [ 39 ]. The high ARG1 expression observed in G-MDSCs suggests their active contribution to immune suppression within the MM TME. To substantiate the role of G-MDSCs in shaping the immune landscape, pathological activation and immunosuppressive scores of G-MDSCs were analyzed before and post CAR-T therapy. These results demonstrated that patients with partial response (PR), such as P2, P5, P6, and P7, had significantly higher scores for G-MDSC activation and immunosuppressive potential compared to patients with very good partial response (VGPR) (P1, P4) and stringent complete remission (sCR) (P3) (Fig. 3 A-G). Additionally, a comparison using a combined pathological activation and immunosuppressive score revealed no significant differences between the sCR and VGPR groups. However, significant differences were observed when comparing these groups with the PR group (Fig. 3 H). This suggests that G-MDSCs may be closely associated with treatment outcomes. These findings align with the hypothesis that G-MDSCs contribute to immune suppression and resistance to CAR-T therapy. These results highlight the central role of G-MDSCs in orchestrating an immunosuppressive TME in MM. The concurrent presence of elevated G-MDSC proportions, reduced IFN-γ signaling activity, high ARG1 expression, and increased immunosuppressive capacity in patients with poor therapeutic responses, such as P5, underscores their multifaceted contribution to tumor progression and therapy resistance. Further analyses will explore additional pathways and intercellular interactions mediated by G-MDSCs, offering deeper insights into their role in MM progression and immune evasion. Functional Enrichment Analysis of G-MDSCs To elucidate the functional roles of granulocytic myeloid-derived suppressor cells (G-MDSCs) within the multiple myeloma (MM) tumor microenvironment (TME), KEGG pathway analysis, Gene Ontology (GO) term enrichment, and Gene Set Enrichment Analysis (GSEA) were conducted. Through these analyses, key biological pathways and processes associated with G-MDSC activity were identified. KEGG pathway analysis indicated that the "Cytokine-cytokine receptor interaction" pathway was the most significantly enriched in G-MDSCs (Fig. 4 A). This pathway is known to mediate immune responses by facilitating communication between immune cells and regulating inflammation and immunity [ 40 ]. Within the TME, cytokine-cytokine receptor interactions are critical for recruiting and activating immunosuppressive cells, including G-MDSCs. These cells secrete immunosuppressive cytokines such as IL-10 and TGF-β, which suppress anti-tumor immune responses [ 41 , 42 ]. The enrichment of this pathway suggests that G-MDSCs in MM exploit these interactions to maintain an immunosuppressive microenvironment, promoting tumor progression. GO term enrichment analysis identified the "inflammatory response" biological process as the most significantly enriched in G-MDSCs (Fig. 4 B). This enrichment underscores the active role of G-MDSCs in modulating inflammation within the TME. In MM, inflammation is frequently dysregulated, facilitating immune evasion by recruiting additional suppressive cells and inhibiting effector immune cell activity [ 28 , 43 ]. These findings highlight the intricate role of G-MDSCs in shaping immune dynamics within the MM TME. GSEA revealed significant enrichment of gene sets associated with the "Interferon gamma response" and "Interferon alpha response" in G-MDSCs (Fig. 4 C). Interferon signaling is critical for orchestrating immune surveillance through the activation of effector T cells, natural killer cells, and antigen-presenting cells [ 37 ]. However, evidence suggests that G-MDSCs may suppress these pathways by secreting immunosuppressive mediators and expressing inhibitory molecules such as PD-L1 [ 28 ]. This suppression impairs anti-tumor immune responses and fosters immune evasion. The enrichment of these pathways in G-MDSCs suggests their role in attenuating interferon signaling within the TME, further contributing to tumor progression. These findings provide compelling evidence that G-MDSCs serve as pivotal modulators of immune suppression in MM. By leveraging cytokine signaling and interferon pathways, G-MDSCs inhibit anti-tumor immunity and promote tumor survival. Cellular Communication in the Tumor Microenvironment To investigate the functional role of granulocytic myeloid-derived suppressor cells (G-MDSCs) within the multiple myeloma (MM) tumor microenvironment (TME), cellular communication analysis was conducted using CellPhoneDB. This computational framework leverages single-cell RNA sequencing data to infer ligand-receptor interactions between diverse cell types, offering insights into how G-MDSCs contribute to immune suppression and tumor progression in MM. The overall cellular interaction network within the TME is visualized in Fig. 5 A, which highlights the frequency and distribution of ligand-receptor interactions among various cell types. Particular attention was directed to interactions involving G-MDSCs and other cell populations, given their critical role in shaping the immunosuppressive microenvironment in MM. Detailed analysis suggested that G-MDSCs may interact with MM cells through the IGF1-IGF1R axis, with IGF1 being highly expressed in MM cells and IGF1R exhibiting high expression in G-MDSCs (Fig. 5 B, Fig. 5 C). The IGF1-IGF1R axis has been implicated in promoting tumor cell proliferation, survival, and resistance to apoptosis, particularly in hematologic malignancies [ 44 ]. Within the TME, IGF1 signaling may influence G-MDSCs by activating downstream pathways such as PI3K/AKT and MAPK, potentially enhancing their immunosuppressive function [ 45 ]. These observations suggest that the IGF1-IGF1R interaction could contribute to a feedback mechanism wherein tumor cells and G-MDSCs mutually support each other’s survival and activity. However, further experimental validation is required to confirm these functional connections. Another key interaction identified involves SIRPA, predominantly expressed on G-MDSCs, and CD47, which is widely expressed on immune cells, such as T cells and NK cells (Fig. 5 B, Fig. 5 D). The CD47-SIRPA axis is known as an immune checkpoint pathway that inhibits macrophage phagocytosis through the “don’t eat me” signal [ 46 ]. In the MM TME, this interaction likely contributes to reduced macrophage-mediated clearance of tumor cells and impaired antigen presentation by dendritic cells. Furthermore, SIRPA engagement by CD47 may polarize macrophages toward an M2-like phenotype, which secretes anti-inflammatory cytokines such as IL-10 and TGF-β, thereby fostering an immunosuppressive environment [ 47 ]. While preclinical studies in other cancer models suggest that blocking this pathway can restore phagocytic function and enhance anti-tumor immunity [ 48 ], its precise role in MM remains to be fully elucidated. In addition to these interactions, other pathways potentially involving G-MDSCs were considered, such as those mediated by ANXA1. ANXA1, previously associated with immunosuppressive functions in other cancer contexts, may modulate TME dynamics by promoting regulatory T cell expansion and inhibiting effector T cell activity [ 49 ]. While ANXA1 expression was observed in G-MDSCs, its functional impact within the MM TME requires further investigation. Taken together, these findings highlight the complex network of cellular communication involving G-MDSCs in the MM TME. Interactions such as IGF1-IGF1R and CD47-SIRPA suggest mechanisms by which G-MDSCs contribute to immune evasion and tumor persistence. Risk Prediction Model Construction Based on previous findings suggesting that G-MDSCs might be closely related to the treatment outcomes of CAR-T therapy (Fig. 3 H), machine learning risk prediction models were constructed using clinical data from syn6187098 to further validate this association. Differential gene expression was compared between the sCR, VGPR, and PR groups (Fig. 6 A), and differentially expressed genes (DEGs) were selected based on a p-value 1. To mitigate the impact of multicollinearity on subsequent analyses, the variance inflation factor (VIF) method was applied to filter out genes with high collinearity, ensuring that only independent and relevant features were retained for further model construction. Finally, 15 genes were selected for the next phase of analysis. Lasso regression was then employed to identify the most predictive genes by applying regularization, which helped select genes with the greatest impact while minimizing overfitting. The Lasso path analysis (Fig. 6 B) was conducted to visualize the regularization process as the lambda value decreased, and the Lasso coefficients (Fig. 6 C) highlighted the most relevant genes. Through this process, five key genes, TLR7, PDE2A, ACTN1, CCND1, and PTGS1 were selected, as they demonstrated significant association with MM progression and patient prognosis. To assess the predictive power of these genes, several machine learning models were constructed, including Decision Tree, K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Logistic Regression, Naive Bayes, Support Vector Machine (SVM), and Random Forest. The Random Forest model demonstrated the highest performance with an area under the curve (AUC) of 0.94 (Fig. 6 D), indicating its robustness in predicting patient outcomes. Kaplan-Meier survival analysis further validated the predictive capability of the Random Forest model, clearly differentiating high-risk from low-risk patient groups (Fig. 6 E). Additionally, PTGS1 expression was found to be significantly higher in the high-risk group, suggesting its potential as a therapeutic target in MM. To explore this possibility, ligand efficiencies of various compounds targeting PTGS1 were analyzed using ChEMBL data (ID: CHEMBL221) (Fig. 6 F). Among the compounds analyzed, Resveratrol stood out due to its strong binding affinity for PTGS1. Most importantly, resveratrol is currently in Phase 3 clinical trials and has shown promising anti-cancer effects in multiple tumor types, such as breast and liver cancers [ 50 , 51 ]. Given its clinical development and demonstrated efficacy in other cancers, Resveratrol may serve as a potential adjunct to CAR-T therapy in MM, thereby improving treatment outcomes. PTGS1 Expression in MM.1S Cells Inhibits BCMA-CAR T Cytotoxicity Previous studies have suggested that G-MDSCs-expressing PTGS1 may be associated with immunosuppression. In previous analysis (Fig. 6 C), PTGS1 was found to have the strongest correlation with the high-risk group, indicating that G-MDSCs in the tumor microenvironment may suppress immune responses through the expression of PTGS1. To determine whether PTGS1 is one of the key genes mediating the immunosuppressive function of G-MDSCs in the tumor microenvironment, MM.1S, a classical MM cell line with high BCMA expression (Fig. 7 A), was used as an in vitro model to assess its inhibitory effect. First, an MM.1S cell line expressing PTGS1 was successfully constructed. Figure 7 C shows the expression level of PTGS1 mRNA, as detected by RT-qPCR. Figures 7 D illustrates the expression level of Cox-1, the protein encoded by PTGS1. Additionally, a CAR-T cell targeting BCMA was successfully constructed (Fig. 7 B). Subsequently, an in vitro co-culture experiment was performed with MM.1S cells and CAR-T cells. The cells were cultured at an effector-to-target ratio of 1:1 for 24 hours, with flow cytometry conducted every 6 hours. The results showed that CAR-T cells exhibited significantly reduced killing effects on PTGS1 + MM.1S cells compared to MM.1S cells that did not express PTGS1 (Figs. 8 A, B). Furthermore, after co-culturing CAR-T cells with MM.1S cells for 24 hours, cytokine analysis revealed that the expression levels of IL-2 and TNF-α in CAR-T cells co-cultured with PTGS1 + MM.1S cells were significantly reduced (Fig. 8 C). As mentioned before, resveratrol is a potential drug targeting PTGS1 (Fig. 6 F). Therefore, when 10 nM resveratrol was used in the co-culture system of CAR-T cells and PTGS1 + MM.1S cells, it was observed that although PTGS1 + MM.1S cells initially exhibited some resistance to CAR-T cells from 0 to 12 hours, after 24 hours of incubation, the killing effect reached a level comparable to that of PTGS1- MM.1S cells (Figs. 9 A, B). Moreover, cytokine analysis revealed an increase in the expression levels of IL-2 and IFN-γ in CAR-T cells co-cultured with PTGS1 + MM.1S cells in the presence of resveratrol (Fig. 9 C). This finding suggests that PTGS1 may play a role in suppressing CAR-T cell function, and it also implies that G-MDSCs in vivo may exert immune-suppressive effects through the expression of PTGS1. Discussion In this study, the immunosuppressive role of granulocytic myeloid-derived suppressor cells (G-MDSCs) within the tumor microenvironment (TME) of multiple myeloma (MM) was investigated using single-cell RNA sequencing (scRNA-seq) data from patients treated with CAR-T therapy. The findings highlight the complex mechanisms through which G-MDSCs contribute to immune evasion and treatment resistance, offering potential avenues for improving immunotherapy efficacy in MM. G-MDSCs were identified as a prominent immunosuppressive cell population in the MM TME, consistent with previous reports that associate their abundance with progressive disease and poor therapeutic outcomes [ 52 ]. Their high enrichment in the TME may reflect the establishment of a more robust immunosuppressive environment, potentially correlating with the suppression of key immune responses, such as those mediated by interferon-γ (IFN-γ) [ 37 ]. IFN-γ plays a critical role in the activation of anti-tumor immunity, and is known to be attenuated by G-MDSCs. The heightened presence of G-MDSCs in the TME could, therefore, be linked to the disruption of this critical immune pathway, which is commonly associated with poor prognosis in MM. These observations highlight the potential of G-MDSCs as a critical determinant of immune evasion in MM and suggest that their suppression may correlate with worse therapeutic responses. Pathway enrichment analyses revealed significant involvement of G-MDSCs in cytokine-cytokine receptor interactions, a pathway crucial for immune regulation and tumor immune escape. The expression of ARG1 in G-MDSCs, which depletes arginine—an amino acid essential for T-cell function—was noted as a key mechanism of immune suppression. The upregulation of ARG1 in G-MDSCs may thus contribute directly to the impairment of anti-tumor immunity by hindering T-cell responses, further corroborating previous studies that link ARG1 activity to reduced efficacy of immune responses and unfavorable prognosis in MM. These findings point to the critical role of metabolic reprogramming in G-MDSCs and suggest that strategies targeting ARG1 could potentially improve immune functionality and therapeutic outcomes in MM. Cell-cell communication analysis further suggested that G-MDSCs mediate their effects via the IGF1-IGF1R signaling axis. IGF1 is highly expressed in MM cells, while IGF1R is expressed in G-MDSCs (Fig. 5 C). The IGF1-IGF1R axis is known to be involved in regulating tumor progression and immune modulation [ 44 ]. However, its role in G-MDSC-mediated immune suppression in MM remains an area of active investigation. The interaction between G-MDSCs and immune cells through this axis could play a pivotal role in promoting tumor growth and therapy resistance by limiting effective immune surveillance. Similarly, interactions between G-MDSCs and immune cells, such as the SIRPA/CD47 axis (Fig. 5 D), represent additional mechanisms through which G-MDSCs inhibit macrophage-mediated phagocytosis, a process crucial for the elimination of tumor cells. These findings suggest that disruption of these signaling pathways could serve as a therapeutic strategy for overcoming G-MDSC-mediated immune evasion in MM. The risk prediction model developed in this study identified five key genes (TLR7, PDE2A, ACTN1, CCND1, and PTGS1). Among these, PTGS1 emerged as a potential therapeutic target, given its role in immune suppression and tumor progression. Resveratrol, a known PTGS1 inhibitor, represents a promising adjunctive therapy, although preclinical validation is necessary [ 53 ]. The high predictive accuracy of the random forest model (AUC = 0.94) underscores its potential utility in stratifying patients for personalized treatment strategies, although prospective validation in independent cohorts is required to confirm its clinical applicability. To further validate the immunosuppressive function of PTGS1, additional in vitro experiments were conducted. BCMA + MM.1S cells with high PTGS1 expression were co-cultured with BCMA-CAR T cells, and the results demonstrated that PTGS1 overexpression significantly inhibited CAR-T-mediated cytotoxicity (Fig. 7 ). This finding provides direct evidence supporting the role of PTGS1 in immune suppression, further reinforcing its potential as a therapeutic target. Although this study explores the inhibitory role of G-MDSCs in the tumor microenvironment and identifies several potential immune-suppressive pathways and interactions between G-MDSCs and immune cells, these findings are still preliminary and require further biological validation. While the identification of these pathways offers valuable insights, additional in vitro experiments, including the validation of other potential immunosuppressive markers and functional assays, are necessary to establish a comprehensive understanding of the mechanisms through which G-MDSCs modulate immune responses. Furthermore, although the combined analysis of clinical data revealed several genes associated with patient survival, and in vitro validation confirmed the role of PTGS1 in suppressing immune function, it is important to note that these observations need to be confirmed in vivo. Animal model studies are critical to determine the physiological relevance of these findings, assess the systemic effects of PTGS1 and other identified pathways, and evaluate their potential as therapeutic targets in multiple myeloma. Further investigation into these mechanisms in preclinical models will be essential to confirm their biological relevance and pave the way for future clinical applications. In conclusion, this study underscores the pivotal role of G-MDSCs in promoting immune suppression and therapy resistance in MM. By elucidating the molecular pathways and interactions underlying their function, the findings provide a foundation for developing novel therapeutic strategies aimed at overcoming G-MDSC mediated immune evasion. The in vitro validation of PTGS1 further highlights its potential as a therapeutic target. Future research should focus on validating these targets and integrating them into combination immunotherapy approaches to improve clinical outcomes in MM. In conclusion, based on scRNA-seq data, this study provides a detailed analysis of the immunosuppressive function of G-MDSCs within the tumor microenvironment for the first time. It reveals that G-MDSCs are associated with CAR-T therapy outcomes. Additionally, through extensive clinical data analysis, G-MDSCs-related genes were found to effectively predict patient survival. In vitro experiments further suggest that G-MDSCs may exert their immunosuppressive effects partially through PTGS1. Overall, this study expands the understanding of immune microenvironment changes during CAR-T and other immunotherapies, offering new insights for improving therapeutic strategies in MM. By incorporating these findings into future treatment strategies, we may improve patient outcomes and reduce resistance to current therapies, ultimately paving the way for more effective and personalized treatments in multiple myeloma. Declarations Ethics, Consent to Participate, and Consent to Publish declarations: not applicable. Conflict of interest statement It is declared that no known competing financial interests or personal relationships exist that could have appeared to influence the work reported in this paper. Author Contribution C.Z. conceived the study, downloaded and processed the scRNA-seq data, performed all analyses, interpreted the results, prepared the figures, and wrote the manuscript. The author read and approved the final manuscript. Data Availability The single-cell RNA sequencing (scRNA-seq) data analyzed in this study are publicly available from the Gene Expression Omnibus (GEO) under accession number GSE271915. Clinical data, including survival information and bulk RNA-seq profiles, were retrieved from the Synapse database (Synapse ID: syn6187098). Drug ligand efficiency data were obtained from the ChEMBL database (ChEMBL ID: CHEMBL221). References Rajkumar SV, Multiple myeloma. 2024 update on diagnosis, risk-stratification, and management. Am J Hematol. 2024;99(9):1802–24. 10.1002/ajh.27422 . Dima D, et al. Management of Relapsed-Refractory Multiple Myeloma in the Era of Advanced Therapies: Evidence-Based Recommendations for Routine Clinical Practice. Cancers (Basel). 2023;15(7). 10.3390/cancers15072160 . Sheykhhasan M, et al. CAR T therapies in multiple myeloma: unleashing the future. Cancer Gene Ther. 2024;31(5):667–86. 10.1038/s41417-024-00750-2 . Mishra AK, et al. 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J Nucleic Acids. 2011;2011:102431. 10.4061/2011/102431 . Additional Declarations No competing interests reported. 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-6437282","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447060914,"identity":"a6442935-6cb7-42f1-9434-591ac962f4e4","order_by":0,"name":"Chao Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYDADfmbmww9I0yLZzpZmQJoWg/M8ChLEqbx9Ok3i54478saHeRgMGGpsoglrOZe7TbL3zDPDbYd5DzxgOJaW20BQyxnebRK8bYcZtx3mSzBgbDhMnBbJv22H7Tc38xhIEK1FGmhL4gZmYrVInuHdbC3bdjh5xmFgICcQ4xe+M7wbb75tO2zb33/48IMPNTaEtQABCyI6EohQDgLMH4hUOApGwSgYBSMVAADLYUBCZT07uAAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":true,"prefix":"","firstName":"Chao","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-04-13 04:53:10","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6437282/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6437282/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82120878,"identity":"097c08e2-00ea-409d-9e28-ee30e2aa688d","added_by":"auto","created_at":"2025-05-07 03:21:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":579379,"visible":true,"origin":"","legend":"\u003cp\u003eCell clustering analysis. (A) UMAP representation of the Leiden clustering results showing the distribution of different cell populations. (B) UMAP visualization of cell types annotated based on marker gene expression. (C) Dot plot showing the expression levels of marker genes in each Leiden cluster.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6437282/v1/e54510e0735afd06203d0778.png"},{"id":82118018,"identity":"16b7ee10-a66e-443e-9545-87fd2d5a586b","added_by":"auto","created_at":"2025-05-07 03:05:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":319204,"visible":true,"origin":"","legend":"\u003cp\u003eImmune cell composition and gene set enrichment analysis. (A) Stacked bar plot showing the immune cell composition across all samples. (B) Ridge plot showing single-cell enrichment scores for the HALLMARK_INTERFERON_GAMMA_RESPONSE gene set across individual samples. (C) Violin plot showing ARG1 expression across different cell types. Statistical significance was determined using the Kruskal-Wallis test (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6437282/v1/f78c773ffe0f397cd561d64f.png"},{"id":82118022,"identity":"36023c27-f705-4c88-8566-fcf15d1a8615","added_by":"auto","created_at":"2025-05-07 03:05:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1716350,"visible":true,"origin":"","legend":"\u003cp\u003ePathological activation and immune suppression scores of G-MDSCs. (A-G) Contour plots showing the pathological activation and immune suppression scores of G-MDSCs before and post treatment for patients P1–P7. (H) Significance analysis of the combined score of pathological activation and immune suppression among different response levels (including sCR, VGPR, and PR) in post-treatment G-MDSCs. Statistical significance was determined using the log-rank test (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6437282/v1/096ae62a9d36ef9dc23c7bcb.png"},{"id":82122443,"identity":"528ab8a8-389c-408a-ab51-02615fdb8db1","added_by":"auto","created_at":"2025-05-07 03:29:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":443113,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis. (A) KEGG pathway analysis showing the top enriched signaling pathways associated with G-MDSCs. (B) GO analysis showing enriched biological processes, cellular components, and molecular functions related to G-MDSCs. (C) GSEA analysis showing significant pathways enriched in G-MDSCs.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6437282/v1/61848e6d61eafddfc734fb51.png"},{"id":82120880,"identity":"e30068cd-57eb-4534-915f-710dcfbc73ce","added_by":"auto","created_at":"2025-05-07 03:21:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":547180,"visible":true,"origin":"","legend":"\u003cp\u003eCell-cell communication analysis. (A) Heatmap showing the interaction counts between cell types identified by CellPhoneDB. (B) Dot plot showing interaction pairs between G-MDSCs and other immune cell types. (C) UMAP visualization showing the expression of IGF1 and IGF1R in identified interaction pairs. (D) UMAP visualization showing the expression of SIRPA and CD47 in identified interaction pairs.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6437282/v1/f31d4136550dc39287c22fbb.png"},{"id":82118035,"identity":"588bf666-e7d2-4077-bb3c-be3a9a69c78b","added_by":"auto","created_at":"2025-05-07 03:05:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":442997,"visible":true,"origin":"","legend":"\u003cp\u003eRisk prediction model development. (A) Lasso path plot showing the selection of key genes based on Lasso regression. (B) Coefficient values of feature genes included in the risk prediction model. (C) ROC curves showing predictive performance for five machine learning models. (D) Kaplan-Meier survival curves stratifying patients into high-risk and low-risk groups using the random forest model. Statistical significance was determined using the log-rank test (p \u0026lt; 0.05). (E) Ligand efficiencies for the PTGS1 target, showing the binding affinities of potential drugs.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6437282/v1/2cd64248b66a76028bd0e364.png"},{"id":82120073,"identity":"f24a5fac-056a-452b-b860-d2b6c133ea91","added_by":"auto","created_at":"2025-05-07 03:13:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":196043,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of BCMA-CAR T cells and PTGS1 expressing MM.1S cells. (A) Flow cytometry analysis of BCMA expression in MM.1S cells. (B) Flow cytometry analysis of the transduction of BCMA-CAR T cells. (C) Relative gene expression of PTGS1 in MM.1S cells, assessed by RT-qPCR. Biological replicates (n=3) were performed. Statistical significance was determined using a t-test (p \u0026lt; 0.0001). (D) . Flow cytometry analysis of Cox-1 expression in MM.1S cells.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6437282/v1/9d144f23ba1b4490ac893431.png"},{"id":82123944,"identity":"d25c8a5d-12c3-4ae5-b27f-51b5e2db9fea","added_by":"auto","created_at":"2025-05-07 03:37:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":436100,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Flow cytometry analysis of co-culture experiments. At 0 hours, MM.1S and CAR-T cells were co-cultured at an E:T ratio of 1:1. After 24 hours, flow cytometry was performed to assess cytotoxicity. (B) Quantification of the ratio of MM.1S cells at 0 h, 6 h, 12 h and 24 h in three independent co-culture experiments (n=3). Statistical significance was determined using a t-test (p \u0026lt; 0.001). (C) Relative cytokine expression of CAR-T cells in three independent co-culture experiments (n=3). Statistical significance was determined using a t-test (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6437282/v1/842cad4b4a31c2217f59ca57.png"},{"id":82120885,"identity":"57d8a389-59f2-48ca-b1fe-292c5112ff06","added_by":"auto","created_at":"2025-05-07 03:21:11","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":655021,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-6437282/v1/bba397caae9772c16766fadc.png"},{"id":83607559,"identity":"acaa83f1-1431-4c32-9778-93e8bc215e76","added_by":"auto","created_at":"2025-05-29 11:17:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5850832,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6437282/v1/2d8c93d2-3207-4ba4-b392-631b30c11827.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Functional Role of Granulocytic Myeloid-Derived Suppressor Cells in CAR-T Therapy: Insights from Single- Cell RNA Sequencing in Multiple Myeloma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple myeloma (MM) is a hematologic malignancy characterized by the clonal expansion of malignant plasma cells within the bone marrow, leading to clinical manifestations such as anemia, osteolytic bone lesions, hypercalcemia, and renal dysfunction. Despite the availability of targeted therapies, including proteasome inhibitors, immunomodulatory drugs, and monoclonal antibodies, MM remains largely incurable for the majority of patients, with frequent relapses and a poor long-term prognosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recent advances in immunotherapy, particularly chimeric antigen receptor T-cell (CAR-T) therapy, have shown promise in treating relapsed and refractory MM. CAR-T therapy targeting B-cell maturation antigen (BCMA) has demonstrated substantial clinical efficacy, achieving high response rates in patients who have failed multiple lines of therapy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, a significant challenge to the success of CAR-T therapy in MM is the presence of an immunosuppressive tumor microenvironment (TME). The TME is composed of a variety of cellular and molecular components that promote tumor growth and contribute to immune evasion. Among the key immunosuppressive cells within the TME, myeloid-derived suppressor cells (MDSCs), particularly the granulocytic subset (G-MDSCs), play a pivotal role in modulating immune responses. G-MDSCs suppress T-cell activation, promote regulatory T-cell (Treg) function, and interact with other immune cells and stromal components, all of which contribute to the suppression of anti-tumor immunity and the progression of MM [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, the role of G-MDSCs in various cancers, including MM, has been increasingly recognized as a critical barrier to effective immunotherapy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These cells, through the release of immunosuppressive cytokines and engagement of inhibitory receptors, contribute to the establishment of an immune-tolerant microenvironment that hinders the effectiveness of therapies such as CAR-T. Despite their known immunosuppressive functions, the specific mechanisms by which G-MDSCs influence the immune landscape in MM, particularly in response to CAR-T therapy, remain poorly understood [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo address this gap, the present study utilizes single-cell RNA sequencing (scRNA-seq) to investigate the molecular and functional characteristics of G-MDSCs in the context of MM and CAR-T treatment. By analyzing patient samples obtained before and post CAR-T therapy, this study explores the activation states, signaling pathways, and prognostic relevance of G-MDSCs in MM. In particular, functional enrichment analyses revealed that G-MDSCs are key modulators of immune responses within the TME, with KEGG analysis showing the most significant enrichment in the Cytokine-cytokine receptor interaction pathway and GO analysis highlighting inflammatory response enrichment. The IFN-α/γ signaling pathways were also found to be involved in G-MDSC-mediated immune regulation. These findings suggest that G-MDSCs play an important regulatory role in immune responses during tumor progression. Moreover, G-MDSCs may contribute to immune evasion in MM by promoting cell proliferation through the IGF1-IGF1R pathway and inhibiting immune cell functions via the SIRPA-CD47 pathway [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This study further investigates the potential of G-MDSC-related gene signatures as prognostic markers and evaluates their clinical relevance in predicting CAR-T treatment outcomes. In particular, PTGS1 was identified as a key marker associated with poor prognosis, suggesting its potential as a therapeutic target for enhancing CAR-T efficacy. Further in vitro experiments were conducted to investigate the immunosuppressive role of PTGS1. Using a MM.1S cell model engineered to express PTGS1, co-culture assays with BCMA-CAR T cells demonstrated that PTGS1 expression significantly reduced CAR-T mediated cytotoxicity, suggesting its involvement in immune evasion.\u003c/p\u003e \u003cp\u003eThis research not only provides a deeper understanding of the tumor microenvironment in MM but also lays the foundation for novel therapeutic strategies aimed at modulating G-MDSCs to enhance the efficacy of immunotherapy, particularly CAR-T cell therapy, in MM patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eThe single-cell RNA sequencing (scRNA-seq) data used in this study were obtained from the Gene Expression Omnibus (GEO) database under the accession number GSE271915 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This dataset includes scRNA-seq profiles from seven multiple myeloma (MM) patients who underwent chimeric antigen receptor T-cell (CAR-T) therapy targeting B-cell maturation antigen (BCMA), with samples collected both before and post CAR-T treatment. A total of 14 samples were designated as P1_B, P1_P, P2_B, P2_P, ..., up to P7_B and P7_P, representing the seven patients before (B) and post (P) CAR-T therapy, respectively. In this study, \"before treatment\" corresponds to day \u0026minus;\u0026thinsp;4, and \"post treatment\" corresponds to day 28 relative to BCMA CAR-T infusion (day 0). As described in the original study, bone marrow mononuclear cells (BMMCs) were isolated using Ficoll Paque density gradient centrifugation, followed by red blood cell lysis and viability assessment. Single-cell libraries were constructed using the 10x Genomics Chromium Single Cell 3' v3 platform, and sequencing was performed on an Illumina NovaSeq6000 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].The clinical data used to construct the risk prediction model, which includes survival information and corresponding RNA-seq data, were retrieved from the Synapse database (Synapse ID: syn6187098) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo further explore potential therapeutic implications, drug ligand efficiency data were sourced from the ChEMBL database (ID: CHEMBL221) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This database provides detailed information on the binding affinities and ligand efficiencies of various compounds, which were utilized to identify potential drug candidates that could be combined with CAR-T therapy to improve treatment outcomes in MM.\u003c/p\u003e \u003cp\u003eAll data used in this study were accessed and utilized in strict accordance with the respective database's terms and conditions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcessing of Single-Cell RNA-Seq Data\u003c/h3\u003e\n\u003cp\u003eAll analyses were conducted in a Python environment (version 3.10.12) using the Scanpy package (version 1.10.4) for single-cell RNA-seq data processing [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], with supplementary packages such as Pandas (version 2.2.2), NumPy (version 1.26.4), and Matplotlib (version 3.8.0) used for data manipulation, statistical analysis, and visualization. Data quality control (QC) involved filtering out cells with fewer than 200 or more than 5000 detected genes, as well as those with more than 10% mitochondrial gene expression. Doublets were identified and removed using the Scrublet package (version 0.2.3) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFollowing QC, normalization and standardization were performed. The log-normalization method was applied to adjust the data, and the top 4000 highly variable genes (HVGs) were selected for subsequent analysis. To address batch effects, data from all samples were merged and subjected to principal component analysis (PCA) for dimensionality reduction. Harmony (version 1.2.4015) was then employed to correct for batch effects [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], ensuring the integration of data across different batches without introducing unwanted technical variations.\u003c/p\u003e \u003cp\u003eFor clustering, dimensionality reduction was first performed using Uniform Manifold Approximation and Projection (UMAP) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Cell clusters were then identified using the Leiden algorithm (resolution\u0026thinsp;=\u0026thinsp;0.5) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and cell types were annotated based on the expression of known marker genes for each cluster.\u003c/p\u003e\n\u003ch3\u003eFunctional enrichment analysis\u003c/h3\u003e\n\u003cp\u003eFunctional enrichment analysis was performed on differentially expressed genes (DEGs) to identify significantly enriched pathways. DEGs were selected based on a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a log2 fold change (Log2FC)\u0026thinsp;\u0026gt;\u0026thinsp;1. Kyoto Encyclopedia of Genes and Genomes (KEGG) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and Gene Ontology (GO) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] enrichment analyses were conducted using the GSEApy package (version 1.1.4) in Python [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Pathways with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\n\u003ch3\u003eGene Set Enrichment Analysis (GSEA)\u003c/h3\u003e\n\u003cp\u003eGene set enrichment analysis (GSEA) was conducted to identify statistically significant differences between biological conditions using the GSEApy package (version 1.1.4) in Python [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Predefined gene sets from the Molecular Signatures Database (MSigDB), including the HALLMARK gene sets, were used to evaluate the biological relevance of differentially expressed genes (DEGs) specifically in G-MDSCs compared to other cell types. DEGs were selected based on a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a log2 fold change (Log2FC)\u0026thinsp;\u0026gt;\u0026thinsp;1 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Gene sets with an Adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and Normalized Enrichment Score (NES)\u0026thinsp;\u0026gt;\u0026thinsp;1 were considered statistically significant.\u003c/p\u003e \u003cp\u003eSingle-cell level gene set enrichment analysis was performed using the AUCell package (version 3.20) in R [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The HALLMARK_INTERFERON_GAMMA_RESPONSE Gene Set was used to evaluate the activation of the IFN-γ signaling pathway across all cell types within the tumor microenvironment (TME) of multiple myeloma (MM). The Immunosuppressive Gene Set, a custom gene set designed to assess the immune-suppressive functions of G-MDSCs, which includes the following genes: ARG1, NOS2, IDO1, CYBB, IL10, VEGFA, CD274, ENTPD1, SOCS3, HIF1A, CXCL12 [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]; and the Pathological Activation Gene Set, a custom gene set to evaluate the pathological activation of G-MDSCs, which contributes to tumor progression, and includes the following genes: S100A8, S100A9, TGFB1, IL1B, CXCR2, CXCL1, HK2, SOCS3, ITGAM, LCN2 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For each gene set, AUCell scores were computed for every individual cell to assess the degree of activation of the gene set in each cell. The AUCell package ranked cells based on the expression of the selected genes, and the results were visualized to identify subpopulations of G-MDSCs with varying immune-suppressive or pathological activation levels.\u003c/p\u003e\n\u003ch3\u003eCellular Communication Analysis\u003c/h3\u003e\n\u003cp\u003eCellular communication analysis was conducted using the CellPhoneDB tool (version 5.0.1) to investigate the intercellular interactions between granulocytic myeloid-derived suppressor cells (G-MDSCs) and other immune cell populations within the tumor microenvironment (TME) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The analysis was based on single-cell RNA-seq data, focusing on the expression of receptor-ligand pairs between G-MDSCs and other immune cells. Only interactions with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRisk Prediction Model Construction\u003c/h2\u003e \u003cp\u003eDifferentially expressed genes (DEGs) were identified from G-MDSCs based on single-cell RNA-seq data, using thresholds of logFC\u0026thinsp;\u0026gt;\u0026thinsp;0.25 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The DEGs were then used to construct the risk prediction model on the Synapse database (Synapse ID: syn6187098). To address multicollinearity among DEGs, gene filtering was performed using the Variance Inflation Factor (VIF), which reduces redundancy in gene features. Lasso regression was subsequently applied, yielding five key genes: TLR7, PDE2A, ACTN1, CCND1, and PTGS1 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese five genes were used to construct multiple machine learning models, including Decision Tree, K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Logistic Regression, Naive Bayes, Support Vector Machine (SVM), and Random Forest. Model training and evaluation were implemented using the scikit-learn library (version 1.6.0) in Python [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The predictive performance of each model was assessed through Receiver Operating Characteristic (ROC) curve analysis, and the Area Under the Curve (AUC) was used as the primary evaluation metric.\u003c/p\u003e \u003cp\u003eTo further validate the best-performing model, Kaplan-Meier survival analysis was conducted using the lifelines library (version 0.30.0) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This analysis demonstrated the model's ability to stratify patients based on survival outcomes, thereby supporting its clinical utility in predicting prognosis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGeneration of PTGS1 + MM.1S Cell Line\u003c/h3\u003e\n\u003cp\u003eTo generate PTGS1\u0026thinsp;+\u0026thinsp;MM.1S cells, a lentiviral vector encoding PTGS1 was constructed and transduced into MM.1S cells (ATCC) at multiplicity of infection (MOI) of 5 followed by selection with puromycin (2 ug/mL) (Invitrogen).\u003c/p\u003e\n\u003ch3\u003eGeneration of BCMA-CAR T Cells\u003c/h3\u003e\n\u003cp\u003eThe BCMA-specific chimeric antigen receptor (CAR) construct was designed with the following sequence: EF1α promoter, signal peptide (SP), αBCMA scFv (derived from BCMA antibody Fab fragment) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], CD8 hinge, CD8 transmembrane domain (TM), 4-1BB, CD3ζ, P2A self-cleaving peptide, mCherry, and WPRE. The CAR sequence was cloned into a third-generation lentiviral backbone (pLV-eGFP, Addgene) and transduced into T cells (Mingzhou Biotech) at MOI of 5.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLentivirus Production\u003c/h2\u003e \u003cp\u003eLentivirus was produced using a three-plasmid packaging system in HEK293T cells (ATCC), using the following plasmids: psPAX2, pMD2.G, and the corresponding transfer plasmid. Viral supernatant was collected 48 hours post-transfection, filtered through a 0.45 \u0026micro;m filter (Millipore), and concentrated by ultracentrifugation. The concentrated virus was resuspended in Opti-MEM (Gibco) for cell transduction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell Culture\u003c/h2\u003e \u003cp\u003eMM.1S was maintained in RPMI 1640 medium (Gibco) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin (Gibco) and was cultured at a density of 1 \u0026times; 10⁶ cells/mL and incubated at 37\u0026deg;C with 5% CO₂ in an incubator (Thermo Fisher). T cells was cultured in RPMI 1640 medium (Gibco) supplemented with 10% FBS, 1% penicillin-streptomycin (Gibco) and 100 U/mL IL-2 (Sigma).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRT-qPCR for PTGS1 Expression\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted using TRIzol reagent (Invitrogen), and complementary DNA (cDNA) was synthesized using a reverse transcription kit (Thermo Fisher). PTGS1 expression was quantified by real-time quantitative PCR (RT-qPCR) using SYBR Green Master Mix (Thermo Fisher). The primers used for PTGS1 detection were forward: 5'-CGCCAGTGAATCCCTGTTGTT-3' and reverse: 5'- AAGGTGGCATTGACAAACTCC-3', with GAPDH as an internal control. Relative gene expression was calculated using the 2^(-ΔΔCt) method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFlow Cytometry Analysis\u003c/h2\u003e \u003cp\u003eFlow cytometry was performed using a BD FACSCanto\u0026trade; II flow cytometer (BD Biosciences). PE Mouse Anti-Human BCMA (CD269) (BD Pharmingen\u0026trade;) and Anti-Human COX-1 FITC/COX-2 PE (BD\u0026trade;) were used to detect BCMA and Cox-1 expression in MM.1S cells, respectively. Besides, mCherry was used to detect CAR expression in T cells. Untransduced cells were used as negative controls to account for any background fluorescence. Data were analyzed using FlowJo software (TreeStar Inc.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCo-culture and Cytotoxicity Assay\u003c/h2\u003e \u003cp\u003eMM.1S target cells (BCMA\u0026thinsp;+\u0026thinsp;or BCMA+/PTGS1+) were co-cultured with BCMA-CAR T effector cells at an effector-to-target (E:T) ratio of 1:1. At 0 h, cells were mixed and incubated at 37\u0026deg;C, 5% CO₂. For resveratrol (MCE) treatment, CAR-T cells and MM.1S cells (PTGS1\u0026thinsp;+\u0026thinsp;or PTGS1-) were co-cultured at an E:T ratio of 1:1 in the presence of 10 nM resveratrol for 24 hours. Control groups were treated with an equivalent volume of DMSO (MCE). Flow cytometry analysis was performed every 6 hours to assess the cytotoxicity of CAR-T cells, while cytokine levels were measured after 24 hours.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCytokine Release Assay\u003c/h2\u003e \u003cp\u003eCAR-T cells and target cells were co-cultured at 1:1 E:T ratio at 37\u0026deg;C for 24 h, The levels of cytokines secreted into the culture medium were measured using human lL-2, IFN-γ and TNF-αtest kits (Thermo Fisher) following the manufacturers' protocols. All the tests were conducted in triplicate and presented as mean\u0026thinsp;+\u0026thinsp;SDs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using Python (version 3.10.12) or R (version 4.3.1). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Where applicable, p-values were adjusted for multiple comparisons using the Benjamini-Hochberg procedure to control the false discovery rate.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSingle-Cell RNA-seq Clustering and Cell-Type Annotation\u003c/h2\u003e \u003cp\u003eTo investigate the cellular landscape of the tumor microenvironment (TME) in multiple myeloma (MM) following BCMA-targeted CAR-T therapy, single-cell RNA-sequencing (scRNA-seq) data were collected from bone marrow samples of seven MM patients (P1-P7), before and post treatment, yielding a total of 14 samples. After rigorous quality control, including normalization and batch effect correction, 74,440 high-quality cells were retained for downstream analysis. Dimensionality reduction was first performed using principal component analysis (PCA), followed by clustering using the Leiden algorithm, which identified 14 distinct clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eThe molecular signatures of these clusters were further characterized by analyzing the expression of key marker genes, visualized in a dot plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). These genes showed clear and distinct expression patterns across the clusters, providing strong validation for the biological relevance of the clustering approach. Based on these expression patterns, cell type identities were assigned to the clusters. The resulting cell type annotation revealed 13 distinct populations, including T cells, B cells, plasma cells, monocytes, dendritic cells, NK cells, granulocytic myeloid-derived suppressor cells (G-MDSCs), and other stromal and immune cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). For instance, T cells were characterized by the expression of CD3D, CD3E, CD4, and CD8A, while NK cells were identified by the presence of NCAM1, KLRD1, NKG7, GNLY, and CD16. G-MDSCs, a major focus of this study, were defined by markers such as CD33, ITGAM, ARG1, CSF1R, CD274, and CEACAM8. MM cells were identified by MZB1, TNFRSF17, IRF4, CD38, and GPRC5D [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This comprehensive cell-type annotation underscores the cellular heterogeneity within the MM TME and provides a solid foundation for further investigation of immune regulation, intercellular communication, and the functional changes in these populations post-therapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eG-MDSCs and Their Functional Characteristics in the Tumor Microenvironment\u003c/h2\u003e \u003cp\u003eThe cellular composition and functional states within the multiple myeloma (MM) tumor microenvironment (TME) were analyzed by assessing the proportions of annotated cell types across different samples. Among all patients, P5 exhibited the highest proportion of granulocytic myeloid-derived suppressor cells (G-MDSCs). This elevated proportion of G-MDSCs persisted even after B-cell maturation antigen (BCMA)-targeted CAR-T therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The sustained presence of G-MDSCs was associated with a significant residual MM cell population, indicating a potential role for G-MDSCs in tumor persistence and immune evasion. Although no correlation was observed between the proportion of G-MDSCs and MM cells, the state of G-MDSCs is still hypothesized to play a role in the tumor microenvironment.\u003c/p\u003e \u003cp\u003eTo investigate the immune dynamics within the TME, single-cell level enrichment analysis of the interferon-gamma (IFN-γ) signaling pathway was performed. The IFN-γ signaling pathway plays a crucial role in promoting anti-tumor immunity by activating T cells and enhancing their effector functions, including the production of pro-inflammatory cytokines and the upregulation of cytotoxic pathways [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Upon CAR-T cell infusion, a significant alteration in the IFN-γ signaling pathway within the tumor microenvironment (TME) was observed, with the degree of change closely correlated with treatment outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Notably, the enrichment of the IFN-γ signaling pathway after treatment was significantly higher in patients with stringent complete remission (sCR) (P3) and with very good partial response (VGPR) (P1, P4), compared to those with partial responses (PR), including P2, P5, P6, and P7. In the current analysis, patient P5 demonstrated the lowest enrichment scores for the IFN-γ pathway among all samples, consistent with a highly immunosuppressive TME. These findings suggest that the suppression of IFN-γ signaling may be mediated by G-MDSCs, which are known to exert their effects through various immunosuppressive mechanisms, such as PI3K-Akt/mTOR pathway [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExpression of ARG1, a critical marker of G-MDSC function, was evaluated across all cell types to further delineate the role of G-MDSCs. The results revealed significantly elevated ARG1 expression in G-MDSCs compared to other cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). ARG1 functions by depleting L-arginine, a metabolite essential for T cell activation and proliferation, thereby inhibiting effective anti-tumor immune responses [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The high ARG1 expression observed in G-MDSCs suggests their active contribution to immune suppression within the MM TME.\u003c/p\u003e \u003cp\u003eTo substantiate the role of G-MDSCs in shaping the immune landscape, pathological activation and immunosuppressive scores of G-MDSCs were analyzed before and post CAR-T therapy. These results demonstrated that patients with partial response (PR), such as P2, P5, P6, and P7, had significantly higher scores for G-MDSC activation and immunosuppressive potential compared to patients with very good partial response (VGPR) (P1, P4) and stringent complete remission (sCR) (P3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-G). Additionally, a comparison using a combined pathological activation and immunosuppressive score revealed no significant differences between the sCR and VGPR groups. However, significant differences were observed when comparing these groups with the PR group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). This suggests that G-MDSCs may be closely associated with treatment outcomes. These findings align with the hypothesis that G-MDSCs contribute to immune suppression and resistance to CAR-T therapy.\u003c/p\u003e \u003cp\u003eThese results highlight the central role of G-MDSCs in orchestrating an immunosuppressive TME in MM. The concurrent presence of elevated G-MDSC proportions, reduced IFN-γ signaling activity, high ARG1 expression, and increased immunosuppressive capacity in patients with poor therapeutic responses, such as P5, underscores their multifaceted contribution to tumor progression and therapy resistance. Further analyses will explore additional pathways and intercellular interactions mediated by G-MDSCs, offering deeper insights into their role in MM progression and immune evasion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis of G-MDSCs\u003c/h2\u003e \u003cp\u003eTo elucidate the functional roles of granulocytic myeloid-derived suppressor cells (G-MDSCs) within the multiple myeloma (MM) tumor microenvironment (TME), KEGG pathway analysis, Gene Ontology (GO) term enrichment, and Gene Set Enrichment Analysis (GSEA) were conducted. Through these analyses, key biological pathways and processes associated with G-MDSC activity were identified.\u003c/p\u003e \u003cp\u003eKEGG pathway analysis indicated that the \"Cytokine-cytokine receptor interaction\" pathway was the most significantly enriched in G-MDSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This pathway is known to mediate immune responses by facilitating communication between immune cells and regulating inflammation and immunity [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Within the TME, cytokine-cytokine receptor interactions are critical for recruiting and activating immunosuppressive cells, including G-MDSCs. These cells secrete immunosuppressive cytokines such as IL-10 and TGF-β, which suppress anti-tumor immune responses [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The enrichment of this pathway suggests that G-MDSCs in MM exploit these interactions to maintain an immunosuppressive microenvironment, promoting tumor progression.\u003c/p\u003e \u003cp\u003eGO term enrichment analysis identified the \"inflammatory response\" biological process as the most significantly enriched in G-MDSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). This enrichment underscores the active role of G-MDSCs in modulating inflammation within the TME. In MM, inflammation is frequently dysregulated, facilitating immune evasion by recruiting additional suppressive cells and inhibiting effector immune cell activity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These findings highlight the intricate role of G-MDSCs in shaping immune dynamics within the MM TME.\u003c/p\u003e \u003cp\u003eGSEA revealed significant enrichment of gene sets associated with the \"Interferon gamma response\" and \"Interferon alpha response\" in G-MDSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Interferon signaling is critical for orchestrating immune surveillance through the activation of effector T cells, natural killer cells, and antigen-presenting cells [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, evidence suggests that G-MDSCs may suppress these pathways by secreting immunosuppressive mediators and expressing inhibitory molecules such as PD-L1 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This suppression impairs anti-tumor immune responses and fosters immune evasion. The enrichment of these pathways in G-MDSCs suggests their role in attenuating interferon signaling within the TME, further contributing to tumor progression.\u003c/p\u003e \u003cp\u003eThese findings provide compelling evidence that G-MDSCs serve as pivotal modulators of immune suppression in MM. By leveraging cytokine signaling and interferon pathways, G-MDSCs inhibit anti-tumor immunity and promote tumor survival.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eCellular Communication in the Tumor Microenvironment\u003c/h2\u003e \u003cp\u003eTo investigate the functional role of granulocytic myeloid-derived suppressor cells (G-MDSCs) within the multiple myeloma (MM) tumor microenvironment (TME), cellular communication analysis was conducted using CellPhoneDB. This computational framework leverages single-cell RNA sequencing data to infer ligand-receptor interactions between diverse cell types, offering insights into how G-MDSCs contribute to immune suppression and tumor progression in MM.\u003c/p\u003e \u003cp\u003eThe overall cellular interaction network within the TME is visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, which highlights the frequency and distribution of ligand-receptor interactions among various cell types. Particular attention was directed to interactions involving G-MDSCs and other cell populations, given their critical role in shaping the immunosuppressive microenvironment in MM. Detailed analysis suggested that G-MDSCs may interact with MM cells through the IGF1-IGF1R axis, with IGF1 being highly expressed in MM cells and IGF1R exhibiting high expression in G-MDSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The IGF1-IGF1R axis has been implicated in promoting tumor cell proliferation, survival, and resistance to apoptosis, particularly in hematologic malignancies [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Within the TME, IGF1 signaling may influence G-MDSCs by activating downstream pathways such as PI3K/AKT and MAPK, potentially enhancing their immunosuppressive function [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These observations suggest that the IGF1-IGF1R interaction could contribute to a feedback mechanism wherein tumor cells and G-MDSCs mutually support each other\u0026rsquo;s survival and activity. However, further experimental validation is required to confirm these functional connections.\u003c/p\u003e \u003cp\u003eAnother key interaction identified involves SIRPA, predominantly expressed on G-MDSCs, and CD47, which is widely expressed on immune cells, such as T cells and NK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The CD47-SIRPA axis is known as an immune checkpoint pathway that inhibits macrophage phagocytosis through the \u0026ldquo;don\u0026rsquo;t eat me\u0026rdquo; signal [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In the MM TME, this interaction likely contributes to reduced macrophage-mediated clearance of tumor cells and impaired antigen presentation by dendritic cells. Furthermore, SIRPA engagement by CD47 may polarize macrophages toward an M2-like phenotype, which secretes anti-inflammatory cytokines such as IL-10 and TGF-β, thereby fostering an immunosuppressive environment [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. While preclinical studies in other cancer models suggest that blocking this pathway can restore phagocytic function and enhance anti-tumor immunity [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], its precise role in MM remains to be fully elucidated.\u003c/p\u003e \u003cp\u003eIn addition to these interactions, other pathways potentially involving G-MDSCs were considered, such as those mediated by ANXA1. ANXA1, previously associated with immunosuppressive functions in other cancer contexts, may modulate TME dynamics by promoting regulatory T cell expansion and inhibiting effector T cell activity [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. While ANXA1 expression was observed in G-MDSCs, its functional impact within the MM TME requires further investigation.\u003c/p\u003e \u003cp\u003eTaken together, these findings highlight the complex network of cellular communication involving G-MDSCs in the MM TME. Interactions such as IGF1-IGF1R and CD47-SIRPA suggest mechanisms by which G-MDSCs contribute to immune evasion and tumor persistence.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eRisk Prediction Model Construction\u003c/h2\u003e \u003cp\u003eBased on previous findings suggesting that G-MDSCs might be closely related to the treatment outcomes of CAR-T therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eH), machine learning risk prediction models were constructed using clinical data from syn6187098 to further validate this association. Differential gene expression was compared between the sCR, VGPR, and PR groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), and differentially expressed genes (DEGs) were selected based on a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a log2 fold change (Log2FC)\u0026thinsp;\u0026gt;\u0026thinsp;1. To mitigate the impact of multicollinearity on subsequent analyses, the variance inflation factor (VIF) method was applied to filter out genes with high collinearity, ensuring that only independent and relevant features were retained for further model construction. Finally, 15 genes were selected for the next phase of analysis.\u003c/p\u003e \u003cp\u003eLasso regression was then employed to identify the most predictive genes by applying regularization, which helped select genes with the greatest impact while minimizing overfitting. The Lasso path analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) was conducted to visualize the regularization process as the lambda value decreased, and the Lasso coefficients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) highlighted the most relevant genes. Through this process, five key genes, TLR7, PDE2A, ACTN1, CCND1, and PTGS1 were selected, as they demonstrated significant association with MM progression and patient prognosis.\u003c/p\u003e \u003cp\u003eTo assess the predictive power of these genes, several machine learning models were constructed, including Decision Tree, K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Logistic Regression, Naive Bayes, Support Vector Machine (SVM), and Random Forest. The Random Forest model demonstrated the highest performance with an area under the curve (AUC) of 0.94 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), indicating its robustness in predicting patient outcomes. Kaplan-Meier survival analysis further validated the predictive capability of the Random Forest model, clearly differentiating high-risk from low-risk patient groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eAdditionally, PTGS1 expression was found to be significantly higher in the high-risk group, suggesting its potential as a therapeutic target in MM. To explore this possibility, ligand efficiencies of various compounds targeting PTGS1 were analyzed using ChEMBL data (ID: CHEMBL221) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Among the compounds analyzed, Resveratrol stood out due to its strong binding affinity for PTGS1. Most importantly, resveratrol is currently in Phase 3 clinical trials and has shown promising anti-cancer effects in multiple tumor types, such as breast and liver cancers [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Given its clinical development and demonstrated efficacy in other cancers, Resveratrol may serve as a potential adjunct to CAR-T therapy in MM, thereby improving treatment outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003ePTGS1 Expression in MM.1S Cells Inhibits BCMA-CAR T Cytotoxicity\u003c/h2\u003e \u003cp\u003ePrevious studies have suggested that G-MDSCs-expressing PTGS1 may be associated with immunosuppression. In previous analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), PTGS1 was found to have the strongest correlation with the high-risk group, indicating that G-MDSCs in the tumor microenvironment may suppress immune responses through the expression of PTGS1. To determine whether PTGS1 is one of the key genes mediating the immunosuppressive function of G-MDSCs in the tumor microenvironment, MM.1S, a classical MM cell line with high BCMA expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), was used as an in vitro model to assess its inhibitory effect.\u003c/p\u003e \u003cp\u003eFirst, an MM.1S cell line expressing PTGS1 was successfully constructed. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC shows the expression level of PTGS1 mRNA, as detected by RT-qPCR. Figures\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD illustrates the expression level of Cox-1, the protein encoded by PTGS1. Additionally, a CAR-T cell targeting BCMA was successfully constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Subsequently, an in vitro co-culture experiment was performed with MM.1S cells and CAR-T cells. The cells were cultured at an effector-to-target ratio of 1:1 for 24 hours, with flow cytometry conducted every 6 hours. The results showed that CAR-T cells exhibited significantly reduced killing effects on PTGS1\u0026thinsp;+\u0026thinsp;MM.1S cells compared to MM.1S cells that did not express PTGS1 (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B). Furthermore, after co-culturing CAR-T cells with MM.1S cells for 24 hours, cytokine analysis revealed that the expression levels of IL-2 and TNF-α in CAR-T cells co-cultured with PTGS1\u0026thinsp;+\u0026thinsp;MM.1S cells were significantly reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eAs mentioned before, resveratrol is a potential drug targeting PTGS1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Therefore, when 10 nM resveratrol was used in the co-culture system of CAR-T cells and PTGS1\u0026thinsp;+\u0026thinsp;MM.1S cells, it was observed that although PTGS1\u0026thinsp;+\u0026thinsp;MM.1S cells initially exhibited some resistance to CAR-T cells from 0 to 12 hours, after 24 hours of incubation, the killing effect reached a level comparable to that of PTGS1- MM.1S cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, B). Moreover, cytokine analysis revealed an increase in the expression levels of IL-2 and IFN-γ in CAR-T cells co-cultured with PTGS1\u0026thinsp;+\u0026thinsp;MM.1S cells in the presence of resveratrol (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eThis finding suggests that PTGS1 may play a role in suppressing CAR-T cell function, and it also implies that G-MDSCs in vivo may exert immune-suppressive effects through the expression of PTGS1.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the immunosuppressive role of granulocytic myeloid-derived suppressor cells (G-MDSCs) within the tumor microenvironment (TME) of multiple myeloma (MM) was investigated using single-cell RNA sequencing (scRNA-seq) data from patients treated with CAR-T therapy. The findings highlight the complex mechanisms through which G-MDSCs contribute to immune evasion and treatment resistance, offering potential avenues for improving immunotherapy efficacy in MM.\u003c/p\u003e \u003cp\u003eG-MDSCs were identified as a prominent immunosuppressive cell population in the MM TME, consistent with previous reports that associate their abundance with progressive disease and poor therapeutic outcomes [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Their high enrichment in the TME may reflect the establishment of a more robust immunosuppressive environment, potentially correlating with the suppression of key immune responses, such as those mediated by interferon-γ (IFN-γ) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. IFN-γ plays a critical role in the activation of anti-tumor immunity, and is known to be attenuated by G-MDSCs. The heightened presence of G-MDSCs in the TME could, therefore, be linked to the disruption of this critical immune pathway, which is commonly associated with poor prognosis in MM. These observations highlight the potential of G-MDSCs as a critical determinant of immune evasion in MM and suggest that their suppression may correlate with worse therapeutic responses.\u003c/p\u003e \u003cp\u003ePathway enrichment analyses revealed significant involvement of G-MDSCs in cytokine-cytokine receptor interactions, a pathway crucial for immune regulation and tumor immune escape. The expression of ARG1 in G-MDSCs, which depletes arginine\u0026mdash;an amino acid essential for T-cell function\u0026mdash;was noted as a key mechanism of immune suppression. The upregulation of ARG1 in G-MDSCs may thus contribute directly to the impairment of anti-tumor immunity by hindering T-cell responses, further corroborating previous studies that link ARG1 activity to reduced efficacy of immune responses and unfavorable prognosis in MM. These findings point to the critical role of metabolic reprogramming in G-MDSCs and suggest that strategies targeting ARG1 could potentially improve immune functionality and therapeutic outcomes in MM.\u003c/p\u003e \u003cp\u003eCell-cell communication analysis further suggested that G-MDSCs mediate their effects via the IGF1-IGF1R signaling axis. IGF1 is highly expressed in MM cells, while IGF1R is expressed in G-MDSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The IGF1-IGF1R axis is known to be involved in regulating tumor progression and immune modulation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, its role in G-MDSC-mediated immune suppression in MM remains an area of active investigation. The interaction between G-MDSCs and immune cells through this axis could play a pivotal role in promoting tumor growth and therapy resistance by limiting effective immune surveillance. Similarly, interactions between G-MDSCs and immune cells, such as the SIRPA/CD47 axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), represent additional mechanisms through which G-MDSCs inhibit macrophage-mediated phagocytosis, a process crucial for the elimination of tumor cells. These findings suggest that disruption of these signaling pathways could serve as a therapeutic strategy for overcoming G-MDSC-mediated immune evasion in MM.\u003c/p\u003e \u003cp\u003eThe risk prediction model developed in this study identified five key genes (TLR7, PDE2A, ACTN1, CCND1, and PTGS1). Among these, PTGS1 emerged as a potential therapeutic target, given its role in immune suppression and tumor progression. Resveratrol, a known PTGS1 inhibitor, represents a promising adjunctive therapy, although preclinical validation is necessary [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The high predictive accuracy of the random forest model (AUC\u0026thinsp;=\u0026thinsp;0.94) underscores its potential utility in stratifying patients for personalized treatment strategies, although prospective validation in independent cohorts is required to confirm its clinical applicability.\u003c/p\u003e \u003cp\u003eTo further validate the immunosuppressive function of PTGS1, additional in vitro experiments were conducted. BCMA\u0026thinsp;+\u0026thinsp;MM.1S cells with high PTGS1 expression were co-cultured with BCMA-CAR T cells, and the results demonstrated that PTGS1 overexpression significantly inhibited CAR-T-mediated cytotoxicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). This finding provides direct evidence supporting the role of PTGS1 in immune suppression, further reinforcing its potential as a therapeutic target.\u003c/p\u003e \u003cp\u003eAlthough this study explores the inhibitory role of G-MDSCs in the tumor microenvironment and identifies several potential immune-suppressive pathways and interactions between G-MDSCs and immune cells, these findings are still preliminary and require further biological validation. While the identification of these pathways offers valuable insights, additional in vitro experiments, including the validation of other potential immunosuppressive markers and functional assays, are necessary to establish a comprehensive understanding of the mechanisms through which G-MDSCs modulate immune responses. Furthermore, although the combined analysis of clinical data revealed several genes associated with patient survival, and in vitro validation confirmed the role of PTGS1 in suppressing immune function, it is important to note that these observations need to be confirmed in vivo. Animal model studies are critical to determine the physiological relevance of these findings, assess the systemic effects of PTGS1 and other identified pathways, and evaluate their potential as therapeutic targets in multiple myeloma. Further investigation into these mechanisms in preclinical models will be essential to confirm their biological relevance and pave the way for future clinical applications. In conclusion, this study underscores the pivotal role of G-MDSCs in promoting immune suppression and therapy resistance in MM. By elucidating the molecular pathways and interactions underlying their function, the findings provide a foundation for developing novel therapeutic strategies aimed at overcoming G-MDSC mediated immune evasion. The in vitro validation of PTGS1 further highlights its potential as a therapeutic target. Future research should focus on validating these targets and integrating them into combination immunotherapy approaches to improve clinical outcomes in MM.\u003c/p\u003e \u003cp\u003eIn conclusion, based on scRNA-seq data, this study provides a detailed analysis of the immunosuppressive function of G-MDSCs within the tumor microenvironment for the first time. It reveals that G-MDSCs are associated with CAR-T therapy outcomes. Additionally, through extensive clinical data analysis, G-MDSCs-related genes were found to effectively predict patient survival. In vitro experiments further suggest that G-MDSCs may exert their immunosuppressive effects partially through PTGS1. Overall, this study expands the understanding of immune microenvironment changes during CAR-T and other immunotherapies, offering new insights for improving therapeutic strategies in MM. By incorporating these findings into future treatment strategies, we may improve patient outcomes and reduce resistance to current therapies, ultimately paving the way for more effective and personalized treatments in multiple myeloma.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics, Consent to Participate, and Consent to Publish declarations: not applicable.\u003c/p\u003e\n\u003ch2\u003eConflict of interest statement\u003c/h2\u003e\n\u003cp\u003eIt is declared that no known competing financial interests or personal relationships exist that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eC.Z. conceived the study, downloaded and processed the scRNA-seq data, performed all analyses, interpreted the results, prepared the figures, and wrote the manuscript. The author read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe single-cell RNA sequencing (scRNA-seq) data analyzed in this study are publicly available from the Gene Expression Omnibus (GEO) under accession number GSE271915. Clinical data, including survival information and bulk RNA-seq profiles, were retrieved from the Synapse database (Synapse ID: syn6187098). Drug ligand efficiency data were obtained from the ChEMBL database (ChEMBL ID: CHEMBL221).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRajkumar SV, Multiple myeloma. 2024 update on diagnosis, risk-stratification, and management. 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J Nucleic Acids. 2011;2011:102431. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4061/2011/102431\u003c/span\u003e\u003cspan address=\"10.4061/2011/102431\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Multiple myeloma, Granulocytic myeloid-derived suppressor cells, single-cell RNA sequencing, tumor microenvironment, immune suppression, CAR-T therapy, prognostic biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-6437282/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6437282/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImmunotherapies, including chimeric antigen receptor T-cell (CAR-T) therapy, represent a pivotal approach in the treatment of multiple myeloma (MM). However, the complex immunosuppressive tumor microenvironment (TME) poses significant challenges to their efficacy. Among the immunosuppressive cells in the MM TME, granulocytic myeloid-derived suppressor cells (G-MDSCs) are predominant; however, their functions remain incompletely understood. In this study, a comprehensive analysis of G-MDSCs was conducted using single-cell transcriptomic data from seven MM patients before and post CAR-T therapy. The pathological activation and immunosuppressive roles of G-MDSCs were identified, and these features were found to be potentially linked to patient prognosis. Functional enrichment analysis revealed that G-MDSCs are key modulators of immune responses within the TME. GSEA analysis suggested that G-MDSCs regulate immune responses via the IFN-α/γ signaling pathway. Furthermore, G-MDSCs may facilitate immune evasion of MM cells by promoting cell proliferation through the IGF1-IGF1R axis and inhibiting T cells and other immune cells via the SIRPA-CD47 pathway. A risk prediction model based on differentially expressed genes in G-MDSCs demonstrated high prognostic accuracy (AUC\u0026thinsp;=\u0026thinsp;0.94) and was validated by Kaplan-Meier survival analysis. Additionally, PTGS1 was identified as a key marker associated with high-risk groups, suggesting its potential as a therapeutic adjunct target to improve CAR-T treatment outcomes. Further in vitro experiments demonstrated that G-MDSCs may exert immunosuppressive functions through PTGS1 expression. This study provides new insights into the role of G-MDSCs in the MM TME and highlights potential therapeutic strategies to enhance CAR-T therapy efficacy.\u003c/p\u003e","manuscriptTitle":"Functional Role of Granulocytic Myeloid-Derived Suppressor Cells in CAR-T Therapy: Insights from Single- Cell RNA Sequencing in Multiple Myeloma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 03:05:06","doi":"10.21203/rs.3.rs-6437282/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":"45874e48-b10b-4766-9ed8-d5a29c626093","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-29T11:09:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 03:05:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6437282","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6437282","identity":"rs-6437282","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}