Myeloma Bone Marrow Research Articles

Characterization and Experimental Use of Multiple Myeloma Bone Marrow Endothelial Cells and Progenitors

Multiple myeloma (MM) is a plasma cell malignancy that resides within the bone marrow microenvironment, relying heavily on interactions with its cellular components. Among these, endothelial cells (ECs) play a pivotal role in MM progression and the development of therapeutic resistance. In this study, we analyzed publicly available single-cell RNA sequencing data to identify unique pathway activations distinguishing ECs from MM patients and healthy donors. We developed a novel protocol to isolate and culture endothelial progenitor cells (EPCs) and ECs directly from MM patient bone marrow, demonstrating their ability to promote myeloma cell proliferation. Validation studies confirmed that these MM-derived ECs exhibit angiogenic potential as well as the expression of characteristic endothelial lineage markers. These findings underscore the critical role of bone marrow ECs in the MM tumor microenvironment and highlight potential new therapeutic targets to disrupt MM progression.

Expression and Function of FAM72A Gene in Multiple myelomaFAM72A.

This study aims to comprehensively investigate the role of Family Member A with sequence similarity 72-A (FAM72A) in multiple myeloma. Multiple myeloma poses significant challenges. This study delves into FAM72A's impact on key cellular processes, shedding light on potential therapeutic targets and enhancing our understanding of multiple myeloma progression. Investigate the impact of FAM72A on the proliferation, apoptosis, and bortezomib sensitivity of multiple myeloma cell line U266. qRT-PCR analyzed FAM72A expression levels in bone marrow samples from 30 patients with multiple myeloma and 10 healthy donors at the Second Hospital of Shanxi Medical University. Cell lines overexpressing FAM72A were constructed, and Cell Counting Kit 8 (CCK-8) and flow cytometry were used to assess U266 cell proliferation, apoptosis, and sensitivity to bortezomib. Biological predictions for FAM72A were performed to find transcription factors binding to the FAM72A promoter region, verified using a luciferase assay. U266 cells were transfected with si-POU2F2 (POU class 2 homeobox 2), and the impact on cell proliferation was validated. Western blot analysis detected the expression of downstream proteins in the p53 signaling pathway. In vivo, experiments established a xenograft mouse model further to study the role of FAM72A in multiple myeloma. FAM72A was upregulated in multiple myeloma bone marrow tissues. Compared to the OE-NC group, the OE-FAM72A group showed increased Mouse Double Minute 2 homolog (MDM2) expression, decreased p53 expression, increased cell proliferation, and decreased apoptosis. POU2F2 was identified as the upstream transcription factor for FAM72A. Compared to the si-NC group, the si-POU2F2 group exhibited decreased MDM2 expression, increased p53 expression, slowed cell proliferation, and increased apoptosis. Silencing POU2F2 could reverse the pro-proliferative effect of over-expressing FAM72A in U266 cells. In vivo experiments in a xenograft mouse model further studied the role of FAM72A in multiple myeloma. Overexpression of FAM72A promotes U266 cell proliferation, inhibits apoptosis, and reduces sensitivity to bortezomib by regulating the POU2F2/FAM72A/p53 signaling pathway.

Coefficient of variation and texture analysis of 18F-FDG PET/CT images for the prediction of outcome in patients with multiple myeloma

In multiple myeloma (MM) bone marrow infiltration by monoclonal plasma cells can occur in both focal and diffuse manner, making staging and prognosis rather difficult. The aim of our study was to test whether texture analysis of 18 F-2-deoxy-d-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) images can predict survival in MM patients. Forty-six patients underwent 18 F-FDG-PET/CT before treatment. We used an automated contouring program for segmenting the hottest focal lesion (FL) and a lumbar vertebra for assessing diffuse bone marrow involvement (DI). Maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean) and texture features such as Coefficient of variation (CoV), were obtained from 46 FL and 46 DI. After a mean follow-up of 51 months, 24 patients died of myeloma and were compared to the 22 survivors. At univariate analysis, FL SUVmax (p = 0.0453), FL SUVmean (p = 0.0463), FL CoV (p = 0.0211) and DI SUVmax (p = 0.0538) predicted overall survival (OS). At multivariate analysis only FL CoV and DI SUVmax were retained in the model (p = 0.0154). By Kaplan-Meier method and log-rank testing, patients with FL CoV below the cut-off had significantly better OS than those with FL CoV above the cut-off (p = 0.0003), as well as patients with DI SUVmax below the threshold versus those with DI SUVmax above the threshold (p = 0.0006). Combining FL CoV and DI SUVmax by using their respective cut-off values, a statistically significant difference was found between the resulting four survival curves (p = 0.0001). Indeed, patients with both FL CoV and DI SUVmax below their respective cut-off values showed the best prognosis. Conventional and texture parameters derived from 18F-FDG PET/CT analysis can predict survival in MM patients by assessing the heterogeneity and aggressiveness of both focal and diffuse infiltration.

TAZ downregulated ANXA1 expression to modulate myeloma cell interactions with bone marrow mesenchymal stromal cells

We and others have previously shown that TAZ plays a tumor suppressive role in multiple myeloma. However, recent reports suggest that molecular crosstalk between the myeloma cells and bone marrow stromal components contributes to the myeloma cell survival and drug resistance. These reports further point to reciprocal interaction via adhesion molecules as the most prominent mechanism of intercellular crosstalk between myeloma cells and bone marrow mesenchymal stromal cells (BM-MSCs). YAP/TAZ silencing/expression has been shown to correlate across all cancers with a set of adhesion/extracellular matrix proteins. Therefore, we hypothesized that TAZ may regulate myeloma cell interaction with BM stromal cells by influencing the expression of distinct cell adhesion signatures. We used previously established TAZ myeloma cell line models, including DELTA47-pLENTI or TAZ knockout DELTA47 cells cocultured with or without BM-MSCs, as our study models. Using RNA sequencing analysis, we performed the first comprehensive screen for cell adhesion-related transcriptional targets of TAZ in multiple myeloma (MM). In doing so, we uncovered an enrichment of cell adhesion-related genes in TAZ knockout DELTA47 cells relatively to pLENTI-DELTA47 cells, including 11 genes with log2 fold change > 2 (p < 0.05), namely, ANXA1, ADGRL2, NCAM1, NCAM2, ADGRL3, CXADR, ALCAM, JAM2, KIRREL1, KIRREL2, and ADGRG7, suggesting possible relationship with TAZ. We validated ANXA1 as a bona fide target of TAZ in MM. We show that TAZ represses myeloma cell migration and interaction with BM-MSCs by transcriptionally downregulating ANXA1 expression via TEAD-dependent mechanism. Our data provide new insights into the understanding of the role of TAZ in the intercellular communication signals between myeloma cells and BM-MSCs. Our findings also suggest that ANXA1 represents a putative cell adhesion target to attenuate BM-MSC driven, tumor-promoting interaction with myeloma cells.

IL-18-secreting multiantigen targeting CAR T cells eliminate antigen-low myeloma in an immunocompetent mouse model

AbstractMultiple myeloma is a plasma cell malignancy that is currently incurable with conventional therapies. Following the success of CD19-targeted chimeric antigen receptor (CAR) T cells in leukemia and lymphoma, CAR T cells targeting B-cell maturation antigen (BCMA) more recently demonstrated impressive activity in relapsed and refractory myeloma patients. However, BCMA-directed therapy can fail due to weak expression of BCMA on myeloma cells, suggesting that novel approaches to better address this antigen-low disease may improve patient outcomes. We hypothesized that engineered secretion of the proinflammatory cytokine interleukin-18 (IL-18) and multiantigen targeting could improve CAR T-cell activity against BCMA-low myeloma. In a syngeneic murine model of myeloma, CAR T cells targeting the myeloma-associated antigens BCMA and B-cell activating factor receptor (BAFF-R) failed to eliminate myeloma when these antigens were weakly expressed, whereas IL-18–secreting CAR T cells targeting these antigens promoted myeloma clearance. IL-18-secreting CAR T cells developed an effector-like T-cell phenotype, promoted interferon-gamma production, reprogrammed the myeloma bone marrow microenvironment through type-I/II interferon signaling, and activated macrophages to mediate antimyeloma activity. Simultaneous targeting of weakly-expressed BCMA and BAFF-R with dual-CAR T cells enhanced T-cell:target-cell avidity, increased overall CAR signal strength, and stimulated antimyeloma activity. Dual-antigen targeting augmented CAR T-cell secretion of engineered IL-18 and facilitated elimination of larger myeloma burdens in vivo. Our results demonstrate that combination of engineered IL-18 secretion and multiantigen targeting can eliminate myeloma with weak antigen expression through distinct mechanisms.

Abstract 2305: Label-free high dimensional single cell morphological profiling of different hematological malignancies by ghost cytometry

Abstract Characterizing the heterogeneity in human leukemias is critical for understanding mechanisms of disease onset, uncovering biomarkers for disease diagnosis, and guiding research into novel treatment approaches. While extensive research focus has been placed on characterizing the molecular heterogeneity in leukemia by modern omics technologies, comparatively less emphasis has been placed on studying the diversity in holistic morphological changes that occur in single cells in the context of disease. Here, we report on a novel approach to understanding and characterizing different hematological malignancies using ghost cytometry, a recently developed high-content flow cytometric method that leverages high-speed, artificial intelligence (AI) driven morphological characterization and analysis of single cells. We performed morphometric profiling on human bone marrow samples from patients with T and B-cell acute lymphoblastic leukemia (ALL, n=5), acute myeloid leukemia (AML, n=5), multiple myeloma (MM, n=5) and healthy bone marrow mononuclear cell (BM-MNC) controls (n=6). Morphological profiles from individual cells were analyzed by unsupervised machine learning (universal manifold approximation and projection, UMAP) and compared within disease subsets and against controls. We identified disease-specific cell populations from a mixture of healthy control and disease samples without any labeling, based on completely label-free information. For validation of the specific cell subsets, we overlaid known cell surface markers (e.g. CD45 for blast cells, CD10/CD19 for B-ALL, CD33/CD34 for AML, CD38/CD138 for MM). The intensity of CD45 was dimmer and disease-specific markers were highly expressed in the disease-specific cell population. We also identified a unique cell subpopulation in the disease-specific cell population that could not be distinguished by existing markers. We also analyzed three human myeloma cell lines (AMO1, KMS, and L363) for comparison to the primary MM samples. In the UMAP analysis using LF-GC data, in all three types of cell lines, Bortezomib-resistant cell lines exhibited different distributions from the control parental cell lines. In addition, we observed distinct morphological profiles between primary myeloma samples and myeloma cell lines. Here we present a novel cell characterization approach for human hematological diseases that leverages AI-based, label-free, high-speed morphological characterization of single cells. We demonstrate that the approach can be used to identify subtle morphological differences in blast cells from a range of blood cancer subtypes. Application of morphological profiling and AI can be used to identify measurable, disease-related changes in these diseases that have diagnostic, drug screening, and therapeutic monitoring potential. Citation Format: Asako Tsubouchi, Juho J. Miettinen, Keisuke Wagatsuma, Satoru Akai, Yuri An, Mika Uematsu, Philipp Sergeev, Dimitrios Tsallos, Markus J. Vähä-Koskela, Sadao Ota, Caroline A. Heckman. Label-free high dimensional single cell morphological profiling of different hematological malignancies by ghost cytometry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2305.

Gene signatures to therapeutics: Assessing the potential of ivermectin against t(4;14) multiple myeloma.

Multiple myeloma (MM) is a terminal differentiated B-cell tumor disease characterized by clonal proliferation of malignant plasma cells and excessive levels of monoclonal immunoglobulins in the bone marrow. The translocation, (t)(4;14), results in high-risk MM with limited treatment alternatives. Thus, there is an urgent need for identification and validation of potential treatments for this MM subtype. Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets. To elucidate the molecular basis and search for potential effective drugs of t(4;14) MM subtype by employing a comprehensive approach. The transcriptional signature of t(4;14) MM was sourced from the Gene Expression Omnibus. Two datasets, GSE16558 and GSE116294, which included 17 and 15 t(4;14) MM bone marrow samples, and five and four normal bone marrow samples, respectively. After the differentially expressed genes were identified, the Cytohubba tool was used to screen for hub genes. Then, the hub genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Using the STRING database and Cytoscape, protein-protein interaction networks and core targets were identified. Potential small-molecule drugs were identified and validated using the Connectivity Map database and molecular docking analysis, respectively. In this study, a total of 258 differentially expressed genes with enriched functions in cancer pathways, namely cytokine receptor interactions, nuclear factor (NF)-κB signaling pathway, lipid metabolism, atherosclerosis, and Hippo signaling pathway, were identified. Ten hub genes (cd45, vcam1, ccl3, cd56, app, cd48, btk, ccr2, cybb, and cxcl12) were identified. Nine drugs, including ivermectin, deforolimus, and isoliquiritigenin, were predicted by the Connectivity Map database to have potential therapeutic effects on t (4;14) MM. In molecular docking, ivermectin showed strong binding affinity to all 10 identified targets, especially cd45 and cybb. Ivermectin inhibited t(4;14) MM cell growth via the NF-κB pathway and induced MM cell apoptosis in vitro. Furthermore, ivermectin increased reactive oxygen species accumulation and altered the mitochondrial membrane potential in t(4;14) MM cells. Collectively, the findings offer valuable molecular insights for biomarker validation and potential drug development in t(4;14) MM diagnosis and treatment, with ivermectin emerging as a potential therapeutic alternative.

Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies.

Spatial analysis of bone marrow trephine biopsies using histology, deep learning, and tailored algorithms reveals the bone marrow architectural heterogeneity and evolution during myeloma progression and treatment.

Towards a Decision Engine for Therapeutic Optimization of Relapsed Refractory Multiple Myeloma

Introduction Substantial progress in the treatment of Multiple Myeloma (MM) extends survival for many patients (Pts), even after multiple relapses, owing to a broad and rapidly extending therapeutic arsenal, with major advances in the arena of bispecific antibodies and CART therapy. However, this also creates an increasingly complex challenge in selecting optimal therapeutic modalities for each patient, as current guidelines rely on empirical considerations. To address this, our study employs a population-scale single cell genomics database of over 250 patient samples covering all disease stages and cycles of treatment with complete patient medical records and machine learning approaches to analyze molecular signatures of myeloma cells and bone marrow immune microenvironment. Methods This study constitutes a meta-trial of RRMM patients who underwent single-cell RNA sequencing (scRNA-seq) between 6.2017 and 6.2023 in our lab. We gathered comprehensive demographic and myeloma clinical data, with a particular focus on therapeutic history and drug refractoriness, defined based on IMWG criteria (progression on or within 60 days from treatment). Our clinical scRNA-seq pipeline, includes a tailored protocol involving index sorting of CD38+ CD138+ positive cells for myeloma plasma cells (PCs) (Ledergor et al, Nat Med 2018; Cohen et al Nat Med 2020), together with CD45+ sorting and scRNA-seq cells from the tumor microenvironment for to identify tumor -immune molecular signatures associated with drug resistance. Results A total of 243 patient samples were included in the study, comprising RRMM (N=128), NDMM (N=21), SMM (N=9), and MGUS (N=4) patients with clinical annotation. Among RRMM patients, the median age was 72 (range 40-89), and 59% exhibited high-risk FISH cytogenetics (t(4:14), del17p, t(14:16), t(14:20), +1q21), while 17% had double-hit myeloma. Refractoriness to specific drugs was observed, such as bortezomib (49%), carfilzomib (27%), lenalidomide (49%), pomalidomide (33%), daratumumab (49). In total, we sequenced 158,444 myeloma/plasma cells, with an average of 880 cells per patient, along with 800 CD45+ cells. By using plasma cells from 11 healthy donors as a reference, we calculated transcriptional changes for each patient, determining the upregulated and downregulated genes profile. Clustering the patient's tumor profiles revealed 7 main tumor archetypes [TA] defined by the expression of 8 gene modules. Refractoriness score, defined as the average number of drugs to which myeloma is refractory, exhibited variations across different tumor archetypes. Notably, the expression levels of several tumor drivers and crucial targets of novel immunotherapies also showed diversity among the tumor archetypes. For instance, TA#6, primarily comprising newly diagnosed patients, displayed lower expression of GPRC5D. Conversely, in TA#1, which consisted of ultra-refractory patients with high MYC and CD47 expression, both GPRC5D and TNFRSF17 (BCMA) were downregulated. Examination of bone marrow CD45+ immune cells revealed a striking bimodal immune composition, characterized by one subtype with increased T-cell abundance and another enriched with myeloid cells. Each immune cell subtype contributed to the drug responsiveness score and provided additional insights complementary to the tumor cells' profile. Further clinical and transcriptional correlations involving both MM cells and immune cells will be presented at the meeting. Conclusions We constructed a clinically-annotated transcriptional matrix revealing the blueprint of relapsed refractory myeloma. This novel decision engine provides a basis for optimizing treatment selection by considering both tumor and tumor microenvironment characteristics. By incorporating critical information on drug resistance patterns and expression of key targets like GPRC5D, TNFRSF17 (BCMA) and FcRH5, we enable precision medicine approaches for improved management of relapsed refractory myeloma. These findings open avenues for tailored and more effective therapeutic strategies, promising better outcomes for patients with relapsed refractory myeloma.

Label-Free High Dimensional Single Cell Morphological Profiling of Different Hematological Malignancies By Ghost Cytometry

Characterizing the heterogeneity in human leukemias is critical for understanding mechanisms of disease onset, uncovering biomarkers for disease diagnosis, and guiding research into novel treatment approaches. While extensive research focus has been placed on characterizing the molecular heterogeneity in leukemia by modern 'omics technologies, comparatively less emphasis has been placed on studying the diversity in holistic morphological changes that occur in single cells in the context of disease. Here, we report on a novel approach to understanding and characterizing different hematological malignancies using ghost cytometry, a recently developed high-content flow cytometric method that leverages high-speed, artificial intelligence (AI) driven morphological characterization and analysis of single cells. We performed morphometric profiling on human bone marrow samples from patients with T and B-cell acute lymphoblastic leukemia (ALL, n=5), acute myeloid leukemia (AML, n=5), multiple myeloma (MM, n=5) and healthy bone marrow mononuclear cell (BM-MNC) controls (n=6) (Figure 1). Morphological profiles from individual cells were analyzed by unsupervised machine learning (universal manifold approximation and projection, UMAP) and compared within disease subsets and against controls. We identified disease-specific cell populations from a mixture of healthy control and disease samples without any labeling, based on completely label-free information (Figure 2). For validation of the specific cell subsets, we overlaid known cell surface markers (e.g. CD45 for blast cells, CD10/CD19 for B-ALL, CD33/CD34 for AML, CD38/CD138 for MM). The intensity of CD45 was dimmer and disease-specific markers were highly expressed in the disease-specific cell population. We also identified unique cell populations across blood cell populations that were distinct from the healthy BM-MNC controls. We also analyzed three human myeloma cell lines (AMO1, KMS, and L363) for comparison to the primary MM samples. In the UMAP analysis using LF-GC data, in all three types of cell lines, Bortezomib-resistant cell lines exhibited different distributions from the control parental cell lines. In addition, we observed distinct morphological profiles between primary myeloma samples and myeloma cell lines. Here we present a novel cell characterization approach for human hematological diseases that leverages AI-based, label-free, high-speed morphological characterization of single cells. We demonstrate that the approach can be used to identify subtle morphological differences in blast cells from a range of blood cancer subtypes. Application of morphological profiling and AI can be used to identify measurable, disease-related changes in these diseases that have diagnostic, drug screening, and therapeutic monitoring potential. Keywords: flow cytometry, acute leukemia, multiple myeloma, label-free cell analysis, biomarker, liquid biopsy, ghost cytometry, artificial intelligence, machine learning Figure 1. Research design overview. Bone marrow samples from patients and non-diseased controls will be analyzed by Ghost Cytometry to assess disease by conventional markers and identify novel morphometric subpopulations. Morphological information is acquired as label-free GMI signals without any labeling. The label-free GMI information is transformed into two-dimensional space by UMAP dimensionality reduction method to identify subpopulations of interest. Both samples are stained with known cell surface markers for validation of unsupervised classification. Figure 2. Identification of disease-specific cell subpopulations by unsupervised approach. Using an unsupervised machine learning technique called UMAP, label-free morphologically distinct cellular subpopulations can be identified in B-ALL, AML, MM when compared to healthy donor controls (left panels, inside the dotted circles). For validation of the subpopulations, known cell surface markers (CD45 for blast cells, CD10/CD19 for B-ALL, CD33/CD34 for AML, CD38/CD138 for MM) and cell trace marker (CellTracker) were overlaid. The intensity increases monotonically through the colormaps. The intensity of CD45 was dimmer and disease-specific markers were highly expressed in the disease-specific cell population.

Spatial Mapping of Myeloma Bone Marrow Microenvironment Using a Deep Learning Approach

Background: Interactions between multiple myeloma (MM) cells and the bone marrow (BM) microenvironment influence disease evolution and drug resistance. Information is largely based on BM aspirates, which lack topological information and can be affected by haemodilution. To examine the spatial relationships between tumour and immune cells in the MM immune tumour microenvironment (iTME), we used multiplex immunohistochemistry (MIHC) coupled with deep learning (DL) image analysis for detailed visualization and unbiased automated analysis of BM components. Methods: BM trephines from patients (at a single centre) with monoclonal gammopathies of undetermined significance (n=9, MGUS) and newly diagnosed MM (n=10, NDMM) with paired baseline and day100 post autologous stem cell transplantation (ASCT) were stained with MIHC panels (BLIMP-1 for tumour, CD4, CD8, FOXP3). To dissect the unique BM landscape, we developed a superpixel based DL model (MoSaicNet) to distinguish blood, bone, fat and cellular tissue. We then developed a cell abundance aware DL method for cell detection and classification (AwareNet) on whole slide images. To tackle cell abundance bias, AwareNet assigned higher attention scores to rare cell types during model training. We used an independent validation cohort consisting of 9 NDMM with paired baseline and post-treatment BM samples to further evaluate the model's performance (n=18). Validation cohort samples were obtained from 7 different hospitals and were stained using a different autostainer of the same model. A colour normalization step was added before analysis. Results: MoSaicNet and AwareNet enabled us to evaluate densities and spatial proximity between cell types. To train, validate and test MoSaicNet, 269 regions of interest (69884 superpixels) from 19 BM samples were annotated by pathologists. For AwareNet, 8004 single-cell annotations were made on 11 samples. MoSaicNet achieved an overall classification area under the curve (AUC) of 0.99, 95% confidence interval (CI) [0.989, 0.991]. Similarly, AwareNet achieved a high overall AUC of 0.98 [0.977, 0.984]. AwareNet outperformed state of the art methods such as U-net and CONCORDe-Net in detecting both abundant and rare cell types. In addition, both algorithms achieved an overall AUC of &amp;gt;0.97 when applied to the validation cohort, despite different tissue processing and staining methods. Densities of FOXP3+CD4+, FOXP3-CD4+ and CD8+ cells did not differ significantly between NDMM and MGUS samples (p=0.32, p=0.81 and p=0.74 respectively), however, there were fewer BLIMP1+ cells in proximity to CD8+ cells in MGUS samples compared with NDMM samples. In NDMM patients, post-treatment samples revealed a reduction in densities of BLIMP1+ cells (p=0.013), CD8+ T cells (p=0.004) and FOXP3+CD4+ regulatory T cells (p=0.004) when compared with baseline. The number of BLIMP1+ cells in proximity to CD8+ and CD4+ cells significantly reduced post-treatment (Corrected for densities, p&amp;lt;0.05), indicating changes in iTME. Analysis of the validation cohort provided similar density and spatial results. Conclusion: We describe the use of a DL pipeline for tissue segmentation and automated cell annotation, enabling spatial mapping of the MM iTME to address topographical questions related to immune function and cell-to-cell interactions. The high accuracy in the validation cohort suggests that our model could be applied to other independent datasets after a normalization step.

Development of Novel Invariant TCR-Anchored Bispecific Engagers to Enhance Anti-Cancer Activity of iNKT Cells

iNKT are rare, CD1d-restricted, glycolipid-reactive T cells that bridge adaptive and innate immunity. iNKT cells are characterised by an invariant TCRVa24Ja18 chain nearly always pairing with a TCRVb11 chain (iTCR). The ability of allogeneic iNKT to protect from aGVHD makes them ideal candidates for ‘off-the-shelf’ immunotherapy without need for TCR deletion. Indeed, CAR-iNKT show promise in pre-clinical models of blood cancer as well as early clinical development against B cell lymphoma and outperform CAR-T counterparts. Here we aimed to enhance the modularity of adoptive iNKT cell immunotherapy for blood cancers by designing a bispecific iNKT cell engager (biNTe) in which one arm would engage the iTCR while the other arm would engage tumour antigen(s) of interest. As a proof-of-principle we designed and synthesised a mono- and a bi-valent biNTe, one each, against the clinically validated myeloma target BCMA with purities of 90% and 70% respectively. We focused on the bivalent biNTe which we first used to demonstrate its iNKT cell binding specificity. Indeed, in flow-cytometry assays, biNTe followed by fluorescent anti-human Fc Ab, stained purified iNKT and but not T cells while in the presence of the same iTCR-binding mAb used to generate the biNTes, binding of the bivalent biNTe on iNKT cells was abrogated. This shows the iTCR engaging specificity of the bivalent biNTe. To demonstrate BCMA specificity of the other arm of the biNTe, we used it to stain iNKT cells, followed by staining with biotinylated soluble BCMA and fluorescently labelled streptavidin. This, compared to staining without soluble BCMA, showed strong staining of iNKT cells thus validating the BCMA specificity of the bivalent biNTe. Next, we set up co-cultures of pre-expanded iNKT against the BCMA-expressing and -knockout myeloma cell line MM1.S in the presence of varying concentrations of the biNTe. While, as assessed by a flow-cytometric cytotoxicity assay, only background killing of myeloma cells was observed in the absence of biNTe or when BCMA ko cell were used as targets, in the presence of biNTe, BCMA+ MM1S cells were killed, in a dose dependent manner (Figure 1A). Consistent with iNKT specificity of the biNTe similar co-cultures involving T cells showed only background killing of MM1S cells (Figure 1B). The same results were obtained using the BCMA-expressing myeloma cell line H929 and its corresponding BCMA knockout. The overall estimated EC50 for MM1.S and H929 cells were 6.56 and 3.4 nM respectively. Using the EC50 concentrations we show that biNTe very effectively kill and almost eliminate patient-derived bone marrow myeloma plasma cells (n=3). To test whether engagement of the iTCR by biNTe would impact its ability to engage with CD1d, we co-cultured iNKT and MM1.S cells transduced with CD1D in the presence of biNTe and the activating, iNKT-specific, CD1d-binding glycolipid ligand a-galactosylceramide (aGalCer). We found that compared to no biNTe control and at biNTE concentrations lower than the EC50, killing of myeloma targets cells was consistently increased. This suggests that iTCR retains its ability to engage CD1d and the efficacy of future biNTe-based immunotherapy could be enhanced by aGalCer. While in vivo validation experiments are ongoing, we conclude that biNTe constitute a novel therapeutic approach with notable pre-clinical anti-myeloma activity. We envisage that they can be used in conjunction with other therapeutic modalities such as CAR and iTCR to enhance the modularity and the efficacy of iNKT cells as an ‘off -the-shelf’ platform for the treatment of myeloma, other blood or solid tumour cancers.

Low Levels of Circulating Tumor Cells Correlate with Favorable Clinical Outcome and Unique Biological Features in Newly Diagnosed Multiple Myeloma Patients

Introduction Circulating tumor cells (CTCs) have been proposed as a prognostic biomarker in newly-diagnosed MM (NDMM) but their low frequency questions their use in clinical practice. Lower CTC levels (cut-off &amp;lt;0.01-0.07%) correlate with a favorable prognostic outcomes in transplant-eligible (TE) patients (pts); however, several issues remain to be addressed. On clinical grounds, it remains unclear whether there is a similar prognostic cut-off value for transplant-ineligible (TI) pts. Furthermore, it is not clear whether the CTCs mimic the characteristics of a single (main)-site bone marrow (ΒΜ) myeloma cells or if they represent the total disease burden, i.e. in cases with extramedullary or macrofocal disease. Moreover, there is limited information of the biological factors that may lead myeloma cells to egress their primary BM site. Methods BM and matched peripheral blood (PB) samples from 550 NDMM pts (210 TE, 340 TI) were evaluated with NGF (median LOD 2.1x10 -6) for the presence of myeloma cells. Various multivariable regression models including CTCs, R-ISS, cytogenetic status and LDH were assessed to define the optimal clinical CTC cut-off. The BM niche profiling was examined with mass cytometry (CyTOF, n=15 pts, 8 with no CTCs, 7 with CTCs higher than the cut-off) and various multicolor flow cytometry panels (n=199 pts including 56 pts with no CTCs) that allowed the detection of 32 distinct immune populations. Results The median value of CTCs was 0.01% of total PB cells (range: 0.0002%-63%) and in 57/550 (10.4%) pts CTCs could not be detected. Increased levels of CTCs correlated with ISS III stage (median: 0.037%, 0.007% and 0.002% for pts with ISS-3, ISS-2 and ISS-1, respectively; p&amp;lt;0.0001), high risk cytogenetics (median: 0.038% vs. 0.006% in standard risk, p&amp;lt;0.0001), and higher levels of b2-microglobulin and BM infiltration. Median follow-up was 41 months (range: 5-66 months). PFS and OS were gradually worsened towards a CTCs log increment, independently of ISS, cytogenetic status and LDH. Hence, pts with undetectable CTCs had the most favorable prognosis with a 5-year PFS and OS of 83% and 97% respectively. The optimal clinical CTCs cut-off that stratified pts into two distinct prognostic groups was defined at the level of 0.02%, which was common for both TE (median PFS: NR in both high and low CTC subsets; p&amp;lt;0.01) and TI subgroups (median PFS: 47 for low vs. 23 months for high CTCs subset, p&amp;lt;0.0001). Importantly, this cut-off, together with the phenotypic classification of pts (MGUS-like, MM-like etc.) could be incorporated in the established R-ISS system, providing an improved ptnts' stratification. The 2-year achievement of MRD negativity was 45%, 67% and 73% for pts with high, low and undetectable CTCs and median time for MRD negativity was 34, 17 and 12 months, respectively (p&amp;lt;0.001). Of note, pts with high CTCs who achieved MRD negativity, modulated their unfavorable risk status and showed similar PFS with those who had low CTCs. In 73/493 (14.8%) pts with detectable CTCs, there was an obvious phenotypic inconsistency between BM myeloma cells and CTCs, either in regard to the relative ratio of the detected (sub)clones, or the appearance of ≥1 phenotypically distinct clonal populations, which were present only in BM or PB. Pts with phenotypic inconsistencies had higher CTC levels and more often diffuse MRI patterns than those with a phenotypic agreement (frequency: 40% vs. 10%; p&amp;lt;0.01). To better understand the heterogeneous dissemination of myeloma cells to PB, we compared the BM niche profiles of pts with high CTC levels (&amp;gt;10 -3) and those with no CTCs. The analyses highlighted clear differences in the BM composition of the two groups with the BM of pts with no CTCs showing significantly lower levels of T cells, NK cells, TAMs and an increased naïve/memory B cell ratio. Of note, no differences were observed in the prevalence of immunosuppressive cell subsets (Tregs, MDSCs) between the two groups. Discussion The presence of CTCs at levels &amp;gt;0.02% confers an adverse prognostic factor for NDMM pts, irrespective of their transplant status. On the contrary, pts with no CTCs have the more favorable outcome and share unique microenvironmental features, which may eliminate CTCs dissemination. Since the liquid biopsy seems to be a better representative of the entire tumor load than a single tissue biopsy, the CTC analysis serves as the new hallmark for the real-time evaluation of a patient's disease and immune status.

High Dimensional Profiling of Patient-Derived Multiple Myeloma Bone Marrow Specimens Treated with an FcRH5-Targeted Bispecific Ex Vivo

Background: Cevostamab is an FcRH5 (Fc receptor-homolog 5) targeted T cell-dependent bispecific (TDB) currently being evaluated in the clinic, with promising activity and favorable safety profile as a monotherapy in patients (pts) with heavily pre-treated Relapsed/Refractory multiple myeloma (MM) (NCT03275103, Trudel et al. ASH 2021). Cevostamab selectively engages and activates T cells in the proximity of MM cells in the bone marrow (BM) to drive tumor cell killing. To dissect the transcriptional and functional impact of cevostamab treatment on the MM BM tumor microenvironment (TME) and identify factors linked to enhanced killing, we established an ex vivo TDB killing assay with MM pt BMMC (BM mononuclear cells) samples followed by detailed characterization of immune and tumor cells. Methods: We tested commercially procured frozen BMMC samples from 7 pts with Stage I or Stage III Newly Diagnosed Multiple Myeloma (NDMM) with no known prior treatments (except a paired, longitudinally collected sample) and median age of 67.5 years. BMMC samples containing endogenous T and MM cells were treated ex vivo with 10 ug/mL of either a non-targeted TDB (considered as baseline) or a research grade cevostamab surrogate (FcRH5 TDB) for 16 to 48 hours (hr). T cell enumeration and MM killing (CD138 + CD38 +) was evaluated at 48 hours by flow cytometry. Ex vivo response was normalized by T cell quantity and defined as % tumor cell killing per 10 3 T cells present in the assay, which was used to classify samples as Responders (R) (&amp;gt;= 10%) or Suboptimal Responders (SoR) (&amp;lt; 10%). Single cell RNA-seq (scRNA-seq) was performed at 16, 24 and 48 hr post treatment. Tumor-specific genes enriched in SoRs at baseline were investigated for prognostic value by examining their bulk gene expression profile in baseline tumor samples (CD138-enriched) from pts (N=1150) treated with immunomodulatory drug, proteasome inhibitors, or combinations thereof (NCT01454297, MMRF CoMMpass IA22). Results: We first examined the T cell compartment by scRNA-seq profiling. Upon FcRH5 TDB treatment, clustering analysis suggested that CD4 and CD8 T cells undergo extensive transcriptional changes compared to control TDB (Fig 1A). A TNF and LTA-expressing CD4 memory cluster was enriched upon treatment in both SoRs and Rs, but to a greater extent in Rs. A similar trend was observed with an IFNG and CCL4-enriched CD8 effector-like proliferating T cell cluster. Transcriptional profiles of MM cells suggested inter- and intra-pt tumor heterogeneity at baseline. MM cells in SoRs at baseline had higher expression of genes like CD44, SRGN, CCND2 and SLAMF1. Of these genes, higher expression of CD44 or SRGN was found to be significantly associated with worse outcome to 1L treatments. Exposure to FcRH5 TDB altered tumor cells from all pts (Figure 1B), resulting in significant upregulation of genes such as HLA-E, SOCS1, and TNFSF10. Increase in CXCL9 expression upon treatment was found to be higher in Rs than SoRs. Discussion: Our work provides a novel ex vivo approach for unbiased, high dimensional pharmacodynamic profiling of functional and transcriptomic changes in pt samples by combining TDB treatment with scRNA-seq. Consistent with the expected mechanism of action and preliminary biomarker analysis reported for cevostamab (Nakamura et al. ASH 2020), FcRH5 TDB led to increased expression of genes in T cells associated with infiltration, activation and effector function. It also induced transcriptional changes in MM cells regardless of the ex vivo response status, including upregulation of genes associated with T and NK cell infiltration and antigen presentation. For certain genes, a greater magnitude of these changes was observed in Rs. These identified genes, and enriched clusters will be examined in clinical RNAseq data from cevostamab-treated pts. It remains to be determined whether our results reflect treatment effects that are applicable to other targeted bispecifics in MM. Overall, our assay setup can serve as a platform to generate novel hypotheses, corroborate clinical observations, identify pt-specific features driving improved or dampened response, and test novel combinations for improved killing. Figure 1 : UMAP (Uniform Manifold Approximation and Projection) plot of all (A) T cells and (B) tumor cells shows distinct clustering of cells by treatment type for all pts (red: FcRH5 TDB; blue: non-targeted TDB)

Delivery of Therapeutic RNA to the Bone Marrow in Multiple Myeloma Using CD38‐Targeted Lipid Nanoparticles (Adv. Sci. 21/2023)

Delivery of Therapeutic RNA to the Bone Marrow in Multiple Myeloma Using CD38‐Targeted Lipid Nanoparticles (Adv. Sci. 21/2023)

Expression of NAMPT in Patients with Multiple Myeloma and Its Correlation with Clinical Manifestation

To analyze the expression level of nicotinamide phosphoribosyltransferase (NAMPT ) in bone marrow of multiple myeloma (MM) patients and its correlation with clinicopathological features, clinical efficacy and prognosis. RT-qPCR and Western blot were used to detect the expression of NAMPT mRNA and protein in bone marrow mononuclear cells from 85 newly diagnosed MM patients (including 17 relapsed MM patients) and 15 healthy donors, and explore the correlation of the expression of NAMPT gene with clinicopathological features and efficacy. Kaplan-Meier method was used to analyze the effects of NAMPT on progression-free survival (PFS) and overall survival (OS), and univariate and multivariate survival analysis were performed. The median expression level of NAMPT mRNA in bone marrow of newly diagnosed and relapsed MM patients was significantly higher than that of healthy donors (P <0.001). The expression of NAMPT mRNA in relapsed MM patients was significantly higher than that in newly diagnosed MM patients (P <0.001), which was consistent with the expression of NAMPT protein. ISS staging, lactate dehydrogenase and C-reactive protein levels, p53 deletion and the proportion of myeloma cells were increased in high NAMPT expression group compared with low NAMPT expression group (P <0.001). Compared with complete remission group, NAMPT mRNA expression was significantly up-regulated in partial remission group, progression group and relapsed group (P <0.001). The median OS and PFS of patients in high NAMPT expression group was 27.3 and 14.9 months, respectively, which was significantly shorter than 39.1 and 27 months in low NAMPT expression group (P =0.048, P <0.001). Both univariate and multivariate analysis showed that NAMPT expression was correlated with PFS and OS. The expression level of NAMPT in newly diagnosed and relapsed MM patients is significantly higher than that in normal controls, and its up-regulation is related to the adverse clinical characteristics, efficacy and prognosis of MM patients. NAMPT is an independent prognostic risk factor of MM.

KAPPA MYELOMA ANTIGEN (KMA) AND LAMBDA MYELOMA ANTIGEN (LMA) ARE EXPRESSED ON B CELL MALIGNANCIES AND ARE PROMISING NOVEL TARGETS ON MALIGNANT PLASMA CELLS

Introduction: The kappa myeloma antigen (KMA) and the lambda myeloma antigen (LMA) are found on the surface of malignant plasma cells (PCs) in multiple myeloma (MM), other plasma cell dyscrasia patient bone marrow (BM) samples, human myeloma cell lines and in patient BM samples from plasmacytomas, lymphoplasmacytoid and B cell lymphoproliferative disorders (BLPD). KMA and LMA are not present on normal B cells in BM but are found on occasional mononuclear cells in tonsillar tissue and secondary mucosal lymphoid tissue. KappaMab is a therapeutic antibody that specifically targets KMA and a phase IIb study in combination with lenalidomide and dexamethasone has recently been completed. In early clinical trials, KappaMab was shown to have an excellent safety profile, with low-grade infusion-related reactions and no haematological toxicities. Two human therapeutic LambdaMabs (10B3 and 7F11) bind specifically to LMA expressed only on malignant cells in lambda restricted B cell diseases. Here we compare the expression of KMA and LMA to that of BCMA in MGUS, newly diagnosed MM and relapsed, refractory (RRMM) myeloma BM samples. We describe the difference in phenotype between KMA and LMA expressing malignant PCs and their enrichment in RRMM compared to early disease stages. Methods: Patient BM samples (κ = 70 and λ = 40) were analysed using multiparametric FCM immunophenotyping with APC labelled LambdaMab (10B3) and KappaMab Fab’2 fragments, CD38, CD138, CD269 (BCMA), CD319 (SLAM F7), CD56 and CD45 monoclonal antibodies. PCs were identified by initial gating using CD38 and CD138. LMA and KMA expression was determined and compared with the other cell markers. Results: KMA was expressed on the majority of kappa restricted BM PCs from MGUS through to RRMM, on lymphoplasmacytoid cells in 2 out of 3 BM samples and in all 4 plasmacytoma BM samples. LMA was expressed on the surface of BM PCs in lambda restricted MGUS through to RRMM and in all 4 AL amyloidosis BM samples. KMA and LMA antigen densities were both higher than BCMA in the RRMM population: KMA mean 1899 (range 468–7943) versus BCMA mean 1426 (range 350-2630) and LMA mean 2439 (range 263–6664) versus BCMA mean 817 (range 537–1065) molecules of PE/cell. Conclusions: Although there are smaller numbers of samples from other B cell malignancies such as lymphoplasmacytoid, amyloidosis and BLPDs, we have demonstrated that KMA or LMA are expressed on malignant BM PCs. In the myeloma cases, the antigen density of both KMA and LMA was increased compared to BCMA on RRMM BM PCs, which suggests a clonal advantage of KMA and LMA expressing plasma cells that are resistant to standard of care treatments. These data suggest that KappaMab and LambdaMabs have therapeutic potential in RRMM where the combination of high antigen density and restriction of the target antigen to the malignant subpopulation of PCs may confer clinical benefit by retaining normal plasma cells following treatment. Keywords: Immunotherapy, Multiple Myeloma Conflicts of interests pertinent to the abstract D. Gottlieb Consultant or advisory role David sits on the Scientific Advisory Board for HaemaLogiX Ltd. R. Dunn Employment or leadership position: Employee at HaemaLogiX Ltd. Stock ownership: Yes

Delivery of Therapeutic RNA to the Bone Marrow in Multiple Myeloma Using CD38-Targeted Lipid Nanoparticles.

Multiple myeloma (MM) is a cancer of differentiated plasma cells that occurs in the bone marrow (BM). Despite the recent advancements in drug development, most patients with MM eventually relapse and the disease remains incurable. RNA therapy delivered via lipid nanoparticles (LNPs) has the potential to be a promising cancer treatment, however, its clinical implementation is limited due to inefficient delivery to non-hepatic tissues. Here, targeted (t)LNPs designed for delivery of RNA payload to MM cells are presented. The tLNPs consist of a novel ionizable lipid and are coated with an anti-CD38 antibody (αCD38-tLNPs). To explore their therapeutic potential, it is demonstrated that LNPs encapsulating small interference RNA (siRNA) against cytoskeleton-associated protein 5 (CKAP5) lead to a ≈90% decrease in cell viability of MM cells in vitro. Next, a new xenograft MM mouse model is employed, which clinically resembles the human disease and demonstrates efficient homing of MM cells to the BM. Specific delivery of αCD38-tLNPs to BM-residing and disseminated MM cells and the improvement in therapeutic outcome of MM-bearing mice treated with αCD38-tLNPs-siRNA-CKAP5 are shown. These results underscore the potential of RNA therapeutics for treatment of MM and the importance of developing effective targeted delivery systems and reliable preclinical models.

Uptake-Dependent and -Independent Effects of Fibroblasts-Derived Extracellular Vesicles on Bone Marrow Endothelial Cells from Patients with Multiple Myeloma: Therapeutic and Clinical Implications.

Extracellular vesicles (EVs) have emerged as important players in cell-to-cell communication within the bone marrow (BM) of multiple myeloma (MM) patients, where they mediate several tumor-associated processes. Here, we investigate the contribution of fibroblasts-derived EVs (FBEVs) in supporting BM angiogenesis. We demonstrate that FBEVs' cargo contains several angiogenic cytokines (i.e., VEGF, HGF, and ANG-1) that promote an early over-angiogenic effect independent from EVs uptake. Interestingly, co-culture of endothelial cells from MM patients (MMECs) with FBEVs for 1 or 6 h activates the VEGF/VEGFR2, HGF/HGFR, and ANG-1/Tie2 axis, as well as the mTORC2 and Wnt/β-catenin pathways, suggesting that the early over-angiogenic effect is a cytokine-mediated process. FBEVs internalization occurs after longer exposure of MMECs to FBEVs (24 h) and induces a late over-angiogenic effect by increasing MMECs migration, chemotaxis, metalloproteases release, and capillarogenesis. FBEVs uptake activates mTORC1, MAPK, SRC, and STAT pathways that promote the release of pro-angiogenic cytokines, further supporting the pro-angiogenic milieu. Overall, our results demonstrate that FBEVs foster MM angiogenesis through dual time-related uptake-independent and uptake-dependent mechanisms that activate different intracellular pathways and transcriptional programs, providing the rationale for designing novel anti-angiogenic strategies.

Latest Findings on the Mechanism of the Interaction Between Multiple Myeloma Cells and Bone Marrow Microenvironment

Multiple myeloma (MM) is a hematologic malignancy of terminally differentiated plasma cells. The mechanisms of the pathogenesis and progression of MM include genetic abnormalities of the MM cells and the interaction between MM cells and bone marrow microenvironment (BMME). MM cells start malignant proliferation in BMME and contribute to the pathogenesis and progression of MM through direct or indirect interactions between cells and the extracellular matrix. Exploring the mechanism of interaction between MM cells and the microenvironment is crucial to improving our understanding of the pathogenesis and progression of MM and early diagnosis and treatment. In addition, the metabolic reprogramming of tumors is one of the key issues of oncology research. Herein, we summarized published findings on the the altered metabolic reprogramming of MM and the characteristics of MM metabolic-microbial interactions in order to gain an in-depth understanding of MM pathogenesis and progression and drug resistance mechanisms, and ultimately to explore for new strategies for MM treatment.