Myeloma Microenvironment Research Articles

Single-cell analysis of the multiple myeloma microenvironment after γ-secretase inhibition and CAR T-cell therapy

AbstractChimeric antigen receptor (CAR) T cells and bispecific antibodies targeting B-cell maturation antigen (BCMA) have significantly advanced the treatment of relapsed and refractory multiple myeloma. Resistance to BCMA-targeting therapies, nonetheless, remains a significant challenge. BCMA shedding by γ-secretase is a known resistance mechanism, and preclinical studies suggest that inhibition may improve anti-BCMA therapy. Leveraging a phase 1 clinical trial of the γ-secretase inhibitor (GSI), crenigacestat, with anti-BCMA CAR T cells (FCARH143), we used single-nuclei RNA sequencing and assay for transposase-accessible chromatin sequencing to characterize the effects of GSI on the tumor microenvironment. The most significant impacts of GSI involved effects on monocytes, which are known to promote tumor growth. In addition to observing a reduction in the frequency of nonclassical monocytes, we also detected significant changes in gene expression, chromatin accessibility, and inferred cell-cell interactions after exposure to GSI. Although many genes with altered expression are associated with γ-secretase–dependent signaling, such as Notch, other pathways were affected, indicating GSI has far-reaching effects. Finally, we detected monoallelic deletion of the BCMA locus in some patients with prior exposure to anti-BCMA therapy, which significantly correlated with reduced progression-free survival (PFS; median PFS, 57 vs 861 days). GSIs are being explored in combination with the full spectrum of BCMA-targeting agents, and our results reveal widespread effects of GSI on both tumor and immune cell populations, providing insight into mechanisms for enhancing BCMA-directed therapies.

Reconstitution of the Multiple Myeloma Microenvironment Following Lymphodepletion with BCMA CAR-T Therapy.

The purpose of this study was to investigate the remodeling of the multiple myeloma microenvironment after B-cell maturation antigen (BCMA)-targeted chimeric antigen receptor T (CAR-T) cell therapy. We performed single-cell RNA sequencing on paired bone marrow specimens (n = 14) from seven patients with multiple myeloma before (i.e., baseline, "day -4") and after (i.e., "day 28") lymphodepleted BCMA CAR-T cell therapy. Our analysis revealed heterogeneity in gene expression profiles among multiple myeloma cells, even those harboring the same cytogenetic abnormalities. The best overall responses of patients over the 15-month follow-up are positively correlated with the abundance and targeted cytotoxic activity of CD8+ effector CAR-T cells on day 28 after CAR-T cell infusion. Additionally, favorable responses are associated with attenuated immunosuppression mediated by regulatory T cells, enhanced CD8+ effector T-cell cytotoxic activity, and elevated type 1 conventional dendritic cell (DC) antigen presentation ability. DC re-clustering inferred intramedullary-originated type 3 conventional DCs with extramedullary migration. Cell-cell communication network analysis indicated that BCMA CAR-T therapy mitigates BAFF/GALECTIN/MK pathway-mediated immunosuppression and activates MIF pathway-mediated anti-multiple myeloma immunity. Our study sheds light on multiple myeloma microenvironment dynamics after BCMA CAR-T therapy, offering clues for predicting treatment responsivity.

Risk score constructed with neutrophil extracellular traps-related genes predicts prognosis and immune microenvironment in multiple myeloma.

Multiple myeloma (MM) exhibits considerable heterogeneity in treatment responses and survival rates, even when standardized care is administered. Ongoing efforts are focused on developing prognostic models to predict these outcomes more accurately. Recently, neutrophil extracellular traps (NETs) have emerged as a potential factor in MM progression, sparking investigation into their role in prognostication. In this study, a multi-gene risk scoring model was constructed using the intersection of NTEs and differentially expressed genes (DEGs), applying the least absolute shrinkage and selection operator (LASSO) Cox regression model. A nomogram was established, and the prognostic model's effectiveness was determined via Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). The ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) were employed to evaluate the level of immune infiltration. The sensitivity of chemotherapy drugs was assessed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Ultimately, the presence of the detected genes was confirmed through quantitative real-time polymerase chain reaction (qRT-PCR) analysis in MM cell specimens. 64 NETs-DEGs were yielded, and through univariate Cox regression and LASSO regression analysis, we constructed a risk score composed of six genes: CTSG, HSPE1, LDHA, MPO, PINK1, and VCAM1. MM patients in three independent datasets were classified into high- and low-risk groups according to the risk score. The overall survival (OS) of patients in the high-risk group was significantly reduced compared to the low-risk group. Furthermore, the risk score was an independent predictive factor for OS. In addition, interactions between the risk score, immune score, and immune cell infiltration were investigated. Further analysis indicated that patients in the high-risk group were more sensitive to a variety of chemotherapy and targeted drugs, including bortezomib. Moreover, the six genes provided insights into the progression of plasma cell disorders. This study offers novel insights into the roles of NETs in prognostic prediction, immune status, and drug sensitivity in MM, serving as a valuable supplement and enhancement to existing grading systems.

A neutrophil extracellular trap-related risk score predicts prognosis and characterizes the tumor microenvironment in multiple myeloma

Multiple myeloma (MM) is a distinguished hematologic malignancy, with existing studies elucidating its interaction with neutrophil extracellular traps (NETs), which may potentially facilitate tumor growth. However, systematic investigations into the role of NETs in MM remain limited. Utilizing the single-cell dataset GSE223060, we discerned active NET cell subgroups, namely neutrophils, monocytes, and macrophages. A transcriptional trajectory was subsequently constructed to comprehend the progression of MM. Following this, an analysis of cellular communication in MM was conducted with a particular emphasis on neutrophils, revealing an augmentation in interactions albeit with diminished strength, alongside abnormal communication links between neutrophils and NK cells within MM samples. Through the intersection of differentially expressed genes (DEGs) between NET active/inactive cells and MM versus healthy samples, a total of 316 genes were identified. This led to the development of a 13-gene risk model for prognostic prediction based on overall survival, utilizing transcriptomics dataset GSE136337. The high-risk group manifested altered immune infiltration and heightened sensitivity to chemotherapy. A constructed nomogram for predicting survival probabilities demonstrated encouraging AUCs for 1, 3, and 5-year survival predictions. Collectively, our findings unveil a novel NET-related prognostic signature for MM, thereby providing a potential avenue for therapeutic exploration.

Immune microenvironment and immunotherapy resistance in multiple myeloma

Multiple myeloma (MM) is currently treated with combined immunotherapies targeting the immune microenvironment surrounding MM cells. Although novel immunotherapeutic agents for MM have improved complete response rates and survival time, some patients experience repeated relapses and die of MM because its progression is strongly influenced by the immune microenvironment as well as cytogenetic abnormalities of MM cells. Various studies have elucidated the mechanisms underlying the immunosuppressive immune microenvironment of MM. Recently, single-cell RNA sequencing and cytometry by time-of-flight analysis have revealed spatiotemporal crosstalk between MM and immune cells in the bone marrow of patients with MM. These studies showed that immunosuppression appears in the early stages of MM and becomes stronger as the disease progresses, leading to immunotherapy resistance. Understanding the state of the immune microenvironment in patients with MM is important for developing immunotherapeutic strategies for long-term complete response and cure.

BCMA-targeted bortezomib nanotherapy improves therapeutic efficacy, overcomes resistance, and modulates the immune microenvironment in multiple myeloma

Bortezomib (BTZ) is a standard-of-care treatment in multiple myeloma (MM); however, adverse side effects and development of resistance limit its long term benefit. To improve target specificity, therapeutic efficacy, and overcome resistance, we designed nanoparticles that encapsulate BTZ and are surface-functionalized with BCMA antibodies (BCMA-BTZ-NPs). We confirmed efficient cellular internalization of the BCMA-BTZ-NPs only in BCMA-expressing MM cells, but not in BCMA-knockout (KO) cells. In addition, BCMA-BTZ-NPs showed target-specific cytotoxicity against MM cell lines and primary tumor cells from MM patients. The BCMA-BTZ-NPs entered the cell through receptor-mediated uptake, which escapes a mechanism of BTZ resistance based on upregulating P-glycoprotein. Furthermore, BCMA-BTZ-NPs induced cell death more efficiently than non-targeted nanoparticles or free BTZ, triggering potent mitochondrial depolarization followed by apoptosis. In BTZ-resistant cells, BCMA-BTZ-NPs inhibited proteasome activity more effectively than free BTZ or non-targeted nanoparticles. Additionally, BCMA-BTZ-NPs enhanced immunogenic cell death and activated the autophagic pathway more than free BTZ. Finally, we found that BCMA-BTZ-NPs selectively accumulated at the tumor site in a murine xenograft model, enhanced tumor reduction, and prolonged host survival. These results suggest BCMA-BTZ-NPs provide a promising therapeutic strategy for enhancing the efficacy of BTZ and establish a framework for their evaluation in a clinical setting.

The myeloma microenvironment and response to immunotherapy

The myeloma microenvironment and response to immunotherapy

Multimodal Single-Cell Transcriptomic and Proteomic Correlatives of Patients Outcomes Following Anti-BCMA Cellular Therapy with Ciltacabtagene Autoleucel (Cilta-cel) in Relapsed Multiple Myeloma

Introduction Multimodal single cell technologies allow us to dissect the mechanisms of therapeutic resistance by integrating transcriptomics and proteomics through the combination of antibody labeling and next-generation sequencing. Using CITE-seq, mass cytometry (CyTOF) and quantitative multiplexed proteomics (Olink), we sought to understand the determinants of chimeric antigen receptor (CAR) T cell response and the overall survival of patients with relapsed or refractory multiple myeloma (RRMM) receiving ciltacabtagene autoleucel (Cilta-Cel) cellular therapy. Methods Twenty five patients received Cilta-Cel and had bone marrow (BM) and peripheral blood (PB) samples collected at baseline and after CAR T infusion. We isolated 262,520 BM cells and 241,657 PB cells, which were later sequenced using CITE-seq. Additionally, we submitted PB samples for CyTOF analysis and acquired 10,162,426 cells used for downstream analysis. We analyzed 92 cytokines using Olink immuno-oncology panel in PB samples. Downstream analysis was performed using the R packages Seurat, CATALYST, FlowSOM and Olink Analyze. Results Our cohort had a median progression-free survival (PFS) of 732 days. To focus our correlative analyses on patients experiencing early relapse, the initial cohort was divided into 2 groups: PFS <18 months (n = 10) and PFS >18 months (n = 15). CAR-T cell expansion was observed in week 2 post-infusion and continued up to week 4. The percentage of CD4 and CD8 CAR-T cells significantly increased between weeks 1-3 and weeks 4-6 weeks (p<0.001) and significantly decreased after week seven (p<0.05). We detected novel and significant differences among four cell populations associated with PFS longer than 18 months. These patients had a higher percentage of activated CD4 Central Memory (CM) and CD4 cytotoxic cells (p<0.05) relative to the total percentage of CAR-T cells in weeks 4-6, suggesting a key role for CD4 cells in cross priming or direct cytotoxicity in this context. In patients with a PFS longer than 18 months, the CD8 CM CART cell population had a significantly higher percentage in weeks 1-3 and 4-6 (p<0.05) when compared to their counterparts, suggesting that these were the cardinal effector population in these patients. Myeloid-derived suppressor cells (MDSCs) have been shown to be a central component of the tumor microenvironment in myeloma, with subsets being capable to mount potent suppressive activity at the tumor site. In the BM CITE-seq myeloid compartment, MDSCs were significantly increased in month 1 (p<0.05) in patients with a shorter PFS. Our PB CyTOF data confirmed this finding as the percentage of MDSCs was significantly higher in weeks 1-3 (p<0.05) in the shorter PFS group when compared to their counterparts. Using a mixed linear regression model on Olink data, we detected 26 cytokines significantly different (p<0.05) between the shorter and longer PFS groups. A pseudobulk analysis of the BM CITE-seq samples for differentially expressed genes encoding the 26 cytokines revealed 22 genes differentially expressed (p<0.05) between patients with a PFS shorter than 18 months and patients with a PFS longer than 18 months in CAR-T, T-cell, NK cell and myeloid cell populations. In the shorter PFS group, VEGFA was significantly higher in CD8 TEMRA CAR-T cells when compared to their counterparts. In the longer PFS group, we observed significantly higher levels of genes involved in T cell activation, such as CD27 and CD28, and pro-inflammatory cytokines such as TNF and IL-15 in the T cell and Myeloid cell compartments. This pattern suggests that higher production of cytotoxic and pro-inflammatory cytokines, combined with enhanced T cell activation, plays an important role in prolonging the response to CAR-T therapy. Conclusions Single cell immune profiling and transcriptomic sequencing identified subpopulations of CD4 and CD8 cells which in concert may influence long term CAR-T outcomes. Our findings demonstrate an early expansion of CART, with very few CART cells surviving after 3 months, suggesting that the efficacy of this therapy is related to early dynamics of these populations. We also provide additional evidence associating immunosuppressive MDSC populations in BM and PB patients with a shorter PFS. Ongoing studies will further analyze the role of the immune microenvironment and clonal T cell dynamics in relation to patient outcomes.

Enhanced Expression of DNp73 Promotes Drug Resistance and Immune Evasion in Multiple Myeloma By Targeting the MYC and MYCN Pathways

Background Multiple myeloma (MM) is an incurable haematological malignancy with high chromosome instability and is highly dependent on the immunosuppressive bone marrow microenvironment. TP53 mutations and deletions are uncommon in MM patients, with other types of mechanisms involved in the abrogation of TP53 pathway activity. DNp73, an inhibitor of the p53 tumour suppressor family, drives drug resistance and cancer progression in several solid malignancies. However, the biological functions of DNp73, the molecular mechanisms underlying these actions and their correlations with aberrant TP53 expression in myelomagenesis remain unclear. In this study, we investigated the role of DNp73 in the drug resistance of MM, and disclosed the corresponding mechanism of how DNp73 promotes the immune escape of MM cells. Aims To disclose the role of DNp73 in promoting drug resistance and immune evasion in MM, and explore the therapeutic strategy based on the mechanism research. Methods We constructed DNp73 overexpression and sh-RNA interference plasmids to obtain stable cell lines. The effects of DNp73 on proliferation and drug sensitivity were determined by CCK8, flow cytometry and xenotransplantation model. To identify mechanisms of drug resistance, we here perform RNA-seq and CHIP-seq analyses in MM cell lines, while Western blot and RT-PCR were used to confirm the results. The DNA damage repair and invasion ability of MM cells were detected by immunofluorescence and transwell assay. To validate the role of DNp73 in immune escape, we also performed phagocytosis assays in vitro. Results Our previous study revealed that the miRNA-15a is a tumour suppressor involved in aggressive MM cell proliferation and drug resistance. Here, we found that miRNA-15a downregulation in MM cells activated NF-κB-p65, which upregulated the expression of DNp73 at the transcriptional level. In addition, the level of miR-15a was negatively correlated with the expression of DNp73 in MM cells of patients (r=-0.672, p<0.05). Enforced expression of DNp73 promoted aggressive proliferation and multidrug resistance in MM cells (p<0.001). Bulk RNA sequencing data showed that the levels of MYCN, MYC and CDK7 were increased in cells with enforced DNp73 expression. Furthermore, a ChIP-qPCR assay revealed that DNp73 bound to the MYCN gene promoter and upregulated MYCN expression. The MM cell immune evasion rate was notable when DNp73 expression was high because of CD47 and PD-L1 upregulation. Blockade of the CD47/SIRPα and PD-1/PD-L1 signalling pathways by the SIRPα-Fc fusion protein IMM01 and monoclonal antibody atezolizumab significantly re-established the anti-MM function of macrophages and T cells in the MM microenvironment, respectively (p<0.01). Notably, DNp73 dysregulation and its pathogenetic function in MM cells were found to be independent of TP53 expression. Conclusion Altogether, our study demonstrates that DNp73 is a potential oncogene involved in MM pathogenesis and promotes MM cell proliferation and immune evasion by targeting the MYC and MYCN pathways.

Tuning the Innate Immune Multiple Myeloma Microenvironment By Modulating IRF4

The tumor microenvironment (TME) has emerged as a key contributor to cancer progression in numerous solid tumors as well as blood cancers that evolve in the bone marrow (BM). In the context of multiple myeloma (MM), survival of malignant plasma cells is supported by interactions with the BM microenvironment, which is comprised of various components including cells, extracellular matrix, and soluble factors. Therefore, the development of strategies that specifically target the MM BM microenvironment is crucial for improving current disease prognosis. Within the BM niche, M2-like myeloma-associated macrophages (MAMs) play a significant role in cancer cell survival and progression and have been implicated in producing an immune-suppressive TME by generating inflammatory mediators, growth factors, cytokines, chemokines, etc. Macrophages exhibit plasticity with regard to their polarization state, which enable them to dynamically respond and adapt to different environmental stimuli in order to fulfill important immune functions. Thus, the reprogramming of M2-like, pro-tumoral MAMs toward a M1-like, anti-tumoral phenotype represents a promising therapeutic strategy. In MM, inflammatory and anti-viral pathways promote disease progression. Interferon regulatory factor 4 (IRF4) is a transcription factor that has previously been shown to promote MM progression through enhanced survival of malignant plasma cells. Under physiological conditions, IRF4 also regulates macrophage polarization, where its expression drives alternative activation of macrophages primarily to M2-like (anti-inflammatory) phenotypes. However, the extent to which IRF4 activation or direct inhibition governs the polarization status and pro-tumoral activity of macrophages in the MM microenvironment has not been fully elucidated. In this study, we sought to further explore the role of IRF4 in macrophage polarization and investigated the potential of modulating IRF4 expression to reprogram myeloma-associated macrophages (MAMs) as a novel therapeutic strategy. First, we developed myeloma-tailored macrophage polarization models using primary human peripheral blood-derived monocytes and the THP1 monocyte cell line. Our M1 and M2 polarization models utilize a customized cocktail of myeloid growth factors and cytokines, supplemented with interleukin (IL)-6 to mimic the IL-6-enriched MM microenvironment. Characterization of M1 (CD80, TNFα, and CXCL10) and M2 (MRC1, FN1, and CCL22) markers confirmed the presence of distinct macrophage subpopulations, as well as upregulated expression of IRF4 in M2-like progeny and IRF5 in M1-like macrophages. To investigate the extent to which IRF4 activation or direct inhibition governs macrophage polarization and function, overexpression and knockdown experiments on macrophages were performed in vitro, along with whole transcriptome sequencing of IRF4-overexpressing THP1 monocytes compared to vector-transduced controls. Gene expression analyses revealed that genetic down-modulation of IRF4 in M2-like macrophages shifts the polarization status toward a hybrid phenotype that displays enhanced M1-like properties, while still retaining M2-like characteristics. This shift in polarization state was further amplified when monocytes were transduced to overexpress IRF4 prior to differentiation into M2-like macrophages and subsequent IRF4 knockdown, suggesting that IRF4 plays a key regulatory role in tuning macrophage polarization. Taken together, our preclinical findings provide groundwork for future studies on MAMs and suggest that an IRF4-mediated macrophage reprogramming strategy is a promising therapeutic approach for patients with MM and a variety of other malignancies typified by an anti-inflammatory TME.

Phenotype Molding of Monocytes in the Multiple Myeloma Microenvironment

Human monocytes are mononuclear phagocytes that comprise a major population of innate immune cells. Various monocyte-derived cells, including osteoclasts, dendritic cells, and macrophages, have been implicated in immunosuppression within the multiple myeloma (MM) tumor microenvironment (TME). Despite recent developments in understanding the link between monocyte-derived cells and immunosuppression in myeloma, there are still critical knowledge gaps in the association between monocytes themselves and immunosuppression within the context of MM. Specifically, the functional and developmental alterations of monocytes in the MM TME have not been explored based on the single-cell studies. In the present study, we generated a comprehensive single-cell map of peripheral blood (PB) and bone marrow (BM) monocytes from both healthy donors (HDs) and MM patients. Specifically, Heparin-anticoagulated PB samples or BM aspirates were collected from seven newly diagnosed MM (NDMM) patients and healthy donors (HDs). Paired BM aspirates were obtained from 10 MM patients at the time of diagnosis and after induction therapy. Differential expression analysis showed that type I interferon (IFN) response was upregulated in MM monocytes. And BM monocytes derived from MM patients exhibited impaired T cell attraction abilities and demonstrated an elevated capacity for suppressing T cell function. Moreover, immune checkpoint molecules, including CD274 (encoding PD-L1) and PDCD1LG2 (encoding PD-L2), were significantly upregulated in MM monocytes. Additionally, a positive correlation was observed between type I IFN response and T cell suppression capacity of MM monocytes. Trajectory analyses revealed an IFN-focused disruption of developmental pathways of both BM and PB monocytes in MM. Lastly, we included 10 MM patients at the time of diagnosis and after induction therapy to monitor transcriptional changes over time. Our results revealed that anti-tumor therapy successfully mitigated the excessive type-I interferon response of BM monocytes in MM. We document that MM monocytes exhibited an augmented type-I IFN response compared to monocytes from HDs, while their overall heterogeneity remained unchanged. Furthermore, we extended the knowledge on immunosuppression of the MM TME that the excessive type-I IFN response in MM had a significant impact on the differentiation of both BM and PB monocytes, leading to T-cell immunosuppression, specifically in BM monocytes. In the cohort that we longitudinally followed, we observed a decrease in the type-I IFN response across nearly all monocyte subsets in MM BM monocytes after induction therapy, accompanied by a reduction in the expression of the T-cell suppression gene set. In sum, our findings indicates that monocytes in the BM promotes the immunosuppression of the MM TME and represents potential therapeutic targets for modulating the MM TME.

Spatial Dissection of the Bone Marrow Microenvironment in Multiple Myeloma By High Dimensional Multiplex Tissue Imaging

Introduction Multiple myeloma (MM) is shaped by interactions between immune, stromal and tumor cells in the bone marrow (BM) microenvironment (BMME). Our understanding of these processes is based on high parametric flow cytometry and single cell transcriptomics, which lack spatial resolution. Thus, crucial aspects of the in situ tumor ecosystem, including cell-cell interactions, can only be predicted. Novel high dimensional, multiplex tissue imaging methods are able to capture similar granularity while also preserving spatial organization. By optimizing antibody selection, as well as staining, imaging and data processing protocols we have established a BMME-targeted CODEX workflow based on a 60 marker panel. Here, we conducted the first comprehensive multiplex protein imaging survey of the BMME across the spectrum of MM and precursor stages and leveraged paired FISH and whole genome sequencing (WGS) to elucidate the interplay of tumor intrinsic features and the BMME. Methods We imaged 489 BM trephine biopsies in tissue microarray format on the CODEX (Akoya Biosciences) platform. After quality control (≥750 cells per core, <25% unclassifiable cells) we retained data from 12 non-malignant controls, 13 MGUS, 20 smoldering MM, 349 uniformly treated newly diagnosed MM (NDMM) patients from a phase III clinical trial, as well as 22 relapsed MM samples with a median of 3 (range 1-6) tissue cores per sample, comprising a total of 3.1 million single cells. CODEX data was processed and analyzed using custom scripts. Cellular neighborhoods were computed by clustering the cell type composition of sliding windows (n=10 nearest neighboring cells) across the tissue. WGS (n=212) and FISH (n=309) were performed on CD138-purified BM mononuclear cells and analyzed using in-house pipelines. Results We identified 40 cell types, encompassing the relevant lineages (myeloid, lymphoid, stromal, endothelial, tumor), subtypes (e.g. T, B, NK cells) and functional states (e.g GRZB, Ki67, and PD1) of the BMME. The degree of plasma cell infiltration determined by CODEX correlated with the clinical pathologist's quantification based on classical immunohistology (r=0.77, p<0.0001), and the kappa/lambda status of MM cells matched the reported light chain restriction, supporting the validity of our data. At the compositional level, we observed shifts in relative cell type abundance from precursor to MM stages. Major changes in the BMME included enriched stromal, endothelial and CD8+ T cells as well as depleted polymorphonuclear (PMN), erythropoietic and CD4+ T cells in advanced stages. The highest proportion of monocytoid dendritic cells, monocytes, mast cells and cytotoxic CD8+ T cells was seen in relapsed MM. Leveraging the spatial resolution of the CODEX data, we identified multiple distinct cellular neighborhoods (CNs) defined by recurrent local cell type composition. CNs enriched for plasma cells in combination with different immune cell subsets including exhausted T cells increased towards the MM stage, while CNs dominated by PMNs, erythropoietic cells and CD206 negative macrophages decreased. To investigate links between MM subtypes and BMME patterns, we correlated CODEX data with MM-initiating and driver events. While patients with t(4;14) showed a significant increase in eosinophils, patients with gain1q had higher levels of endothelial and stromal subsets colocalizing in the same CN. Distinct types of stromal cells were also enriched in patients with double hits (≥2 FISH high risk features). RAS mutations and biallelic inactivation of tumor suppressor genes were associated with altered T cell subset composition. Further corroborating the importance of BMME architecture, a CN with colocalization of plasma and exhausted CD8 T cells was associated with unfavorable prognosis in NDMM patients. Conclusions High dimensional, multiplex tissue imaging of the BMME enables interrogation of cellular interactions and tissue architecture in situ in clinical samples at single cell resolution. To our knowledge this is the first highly multiplexed spatial study of the human BMME in a large clinical cohort and the first such study in multiple myeloma. We track characteristic shifts in tissue composition associated with disease progression, reveal genotype-phenotype associations in the BMME as a correlate of tumor-microenvironment co-evolution and detect prognostically relevant tissue architectural features.

Integrative Multi-Omics Analysis of Tumor-Immune Microenvironment in Multiple Myeloma: Novel Insights and Therapeutic Implications

Despite notable therapeutic advances that have improved the survival of multiple myeloma (MM) patients, development of drug resistance remains a major problem. Transcriptomic analysis provides an opportunity to dissect the complexity of tumors, including the surrounding microenvironment, which has a significant impact on MM tumor progression and patients' response to treatment, as demonstrated by the effectiveness of immunomodulatory therapies. To improve the tailoring of targeted and immune based therapeutic strategies, it is crucial to decipher the tumor-immune microenvironment profile in MM patients. We used a multi-omics data integration approach, including RNAseq-based gene expression for MM tumor cells (MMCs) (n=196) and for the corresponding purified tumor microenvironment (TME) (n=124), single nucleotide variant data from whole exome sequencing of MMCs (n=100), deconvolution of TME immune subtypes (n=124), and relevant clinical metadata. This allowed us to thoroughly characterize both the tumor and its TME. Applying Multiomics Factor Analysis (MOFA) identified 13 principal factors capturing the diversity of the disease, encompassing both MM malignant and microenvironment components. Factors 1 and 2 specifically represented the heterogeneity in the TME, incorporating gene expression profiles and deconvolution-based immune cell subtypes. On the other hand, Factors 6, 9, 11, and 12 were specific to variations in the gene expression profile of the malignant component. Importantly, the analysis of the variables linked to these main factors uncovers not only genes with established functions in myeloma but also novel candidates with potential importance. Based on the top-weight in the gene expression and deconvolution data, Factor 1 showed a positive association with MMP8 expression by TME cells and a negative correlation with the presence of CD8 positive T cells in the TME. Factor 2, on the other hand, exhibited positive alignments with the expression of BHLHA15, TNFRSF17 (BCMA), KLF15, and IGF1 by TME cells. Regarding Factor 6 and Factor 11, which capture tumor fraction heterogeneity, Factor 6 showed alignment with the expression of cancer testis antigens ( MAGEA6, MAGEA3), CXCL12, and KLF4. Meanwhile, Factor 11 was associated with the expression of genes such as SULF2, as well as CRP levels, percentage of CD138+ cells, and the percentage of cells in cell cycle phase S. Next, we investigated the predictive potential of the latent factors inferred by MOFA in models of clinical outcomes. Interestingly, 6 out of the 13 factors identified by MOFA showed significant associations with patients' overall survival (cox regression, p <= 0.05). These factors include Factor 1 and 2, which are related to the composition of the TME, and Factor 6 and 9, which are associated with myeloma progression status, osteolysis, t(11;14), t(4;14), and MM molecular classification. Finally, performing unsupervised clustering using the latent MOFA factors, we identified two main groups with distinct TME subtypes. These groups can be further divided into nine subgroups, each exhibiting unique TME characteristics. Notably, the TME subtypes showed correlations with essential gene expression signatures related to immune response regulation, T cell activation, and natural killer mediated immunity. Additionally, the significant presence of deconvolution-based immune cell types, including T cell CD8+, T cell regulatory, T cell CD4+ (non-regulatory), NK cells and Monocytes was observed. Moreover, the TME subtypes were associated with patients' overall survival and response to daratumumab. Patients presenting TME enriched in immune cells were associated with a significant better outcome ( p = 0.0026). These findings highlight the importance of TME analysis in predicting treatment response and MM patients' outcome in the context of immune-based therapies. Our integrative multi-omics analysis revealed comprehensive MM heterogeneity and distinct immune subtypes that could be of therapeutic interest for personalized therapeutic approaches.

Myeloperoxidase creates a permissive microenvironmental niche for the progression of multiple myeloma.

Expression of myeloperoxidase (MPO), a key inflammatory enzyme restricted to myeloid cells, is negatively associated with the development of solid tumours. Activated myeloid cell populations are increased in multiple myeloma (MM); however, the functional consequences of myeloid-derived MPO within the myeloma microenvironment are unknown. Here, the role of MPO in MM pathogenesis was investigated, and the capacity for pharmacological inhibition of MPO to impede MM progression was evaluated. In the 5TGM1-KaLwRij mouse model of myeloma, the early stages of tumour development were associated with an increase in CD11b+ myeloid cell populations and an increase in Mpo expression within the bone marrow (BM). Interestingly, MM tumour cell homing was increased towards sites of elevated myeloid cell numbers and MPO activity within the BM. Mechanistically, MPO induced the expression of key MM growth factors, resulting in tumour cell proliferation and suppressed cytotoxic T-cell activity. Notably, tumour growth studies in mice treated with a small-molecule irreversible inhibitor of MPO (4-ABAH) demonstrated a significant reduction in overall MM tumour burden. Taken together, our data demonstrate that MPO contributes to MM tumour growth, and that MPO-specific inhibitors may provide a new therapeutic strategy to limit MM disease progression.

P-415 Profiling of patient bone marrow adipocytes in the myeloma microenvironment

P-415 Profiling of patient bone marrow adipocytes in the myeloma microenvironment

Intratumor microbiota: Occult participants in the microenvironment of multiple myeloma.

More recently, microbiota was detected in several tumorous tissues including multiple myeloma (MM), but the roles of which is still under-studied as paucity of research on tumor biology. Moreover, we also detected the presence of microbiota in the bone marrow of patients with MM by 2bRAD-M sequencing technology, which is an incurable hematological malignancy characterized by accumulation of abnormal plasma cells in the bone marrow. However, the roles of intratumor microbiota in tumor disease remains poorly understood. In this review, we critically reviewed recent literature about microbiota in the tumorigenesis and progression of MM. Importantly, we proposed that the emergence of microbiota in the microenvironment of multiple myeloma may be attributed to microbial dysbiosis and impaired intestinal barrier, due to the increased prevalence of MM in patients with obesity and diabetes, of which the characteristic phenotype is gut microbial dysbiosis and impaired intestinal barrier. When the intestinal barrier is damaged, dysbiotic microbiota and their metabolites, as well as dysregulated immune cells, may participate in the reshaping of the local immune microenvironment, and play pivotal roles in the tumorigenesis and development of multiple myeloma, probably by migrating to the bone marrow microenvironment from intestine. We also discuss the emerging microbiological manipulation strategies to improve long-term outcomes of MM, as well as the prospective of the state-of-the-art techniques to advance our knowledge about the biological implication in the microbiome in MM.

P-382 Dysregulations of the immune tumor microenvironment in multiple myeloma

P-382 Dysregulations of the immune tumor microenvironment in multiple myeloma

MM-123 Determination of T Lymphocyte Subgroups in the Bone Marrow Microenvironment in Multiple Myeloma: Can Tumor-Directed T-Cells Play a Role in Fighting Cancer?

MM-123 Determination of T Lymphocyte Subgroups in the Bone Marrow Microenvironment in Multiple Myeloma: Can Tumor-Directed T-Cells Play a Role in Fighting Cancer?

POSTER: MM-123 Determination of T Lymphocyte Subgroups in the Bone Marrow Microenvironment in Multiple Myeloma: Can Tumor-Directed T-Cells Play a Role in Fighting Cancer?

POSTER: MM-123 Determination of T Lymphocyte Subgroups in the Bone Marrow Microenvironment in Multiple Myeloma: Can Tumor-Directed T-Cells Play a Role in Fighting Cancer?

Resolving therapy resistance mechanisms in multiple myeloma by multiomics subclone analysis

AbstractIntratumor heterogeneity as a clinical challenge becomes most evident after several treatment lines, when multidrug-resistant subclones accumulate. To address this challenge, the characterization of resistance mechanisms at the subclonal level is key to identify common vulnerabilities. In this study, we integrate whole-genome sequencing, single-cell (sc) transcriptomics (scRNA sequencing), and chromatin accessibility (scATAC sequencing) together with mitochondrial DNA mutations to define subclonal architecture and evolution for longitudinal samples from 15 patients with relapsed or refractory multiple myeloma. We assess transcriptomic and epigenomic changes to resolve the multifactorial nature of therapy resistance and relate it to the parallel occurrence of different mechanisms: (1) preexisting epigenetic profiles of subclones associated with survival advantages, (2) converging phenotypic adaptation of genetically distinct subclones, and (3) subclone-specific interactions of myeloma and bone marrow microenvironment cells. Our study showcases how an integrative multiomics analysis can be applied to track and characterize distinct multidrug-resistant subclones over time for the identification of molecular targets against them.