Single cell characterization of myeloma and its precursor conditions reveals transcriptional signatures of early tumorigenesis

B-cell intrinsic RANK signaling cooperates with TCL1 to induce lineage-dependent B-cell transformation

B-cell malignancies, such as chronic lymphocytic leukemia (CLL) and multiple myeloma (MM), remain incurable, with MM particularly prone to relapse. Our study introduces a novel mouse model with active RANK signaling and the TCL1 oncogene, displaying both CLL and MM phenotypes. In younger mice, TCL1 and RANK expression expands CLL-like B1-lymphocytes, while MM originates from B2-cells, becoming predominant in later stages and leading to severe disease progression and mortality. The induced MM mimics human disease, exhibiting features like clonal plasma cell expansion, paraproteinemia, anemia, and kidney and bone failure, as well as critical immunosurveillance strategies that promote a tumor-supportive microenvironment. This research elucidates the differential impacts of RANK activation in B1- and B2-cells and underscores the distinct roles of single versus combined oncogenes in B-cell malignancies. We also demonstrate that human MM cells express RANK and that inhibiting RANK signaling can reduce MM progression in a xenotransplantation model. Our study provides a rationale for further investigating the effects of RANK signaling in B-cell transformation and the shaping of a tumor-promoting microenvironment.

How to Identify and Manage High-Risk Smoldering Multiple Myeloma

PURPOSE OF REVIEW

It elucidates advancements in identifying and managing high-risk smoldering multiple myeloma (SMM), moving from observation strategies to intervention approaches. It highlights the significance of differentiating high-risk SMM from its less aggressive counterparts to prevent progression to multiple myeloma (MM).

RECENT FINDINGS

Recent developments have improved SMM risk-stratification, integrating clinical, molecular and biological markers to identify high-risk individuals accurately. The advent of dynamic risk models that incorporate disease evolution and the application of novel diagnostic technologies are enhancing the understanding of SMM. Clinical trials exploring low to high intensity interventions, have shown promise in delaying MM onset and improving patient prognosis. There is a significant change in high-risk SMM management, leaning towards early intervention and precision medicine. The focus now is on refining these approaches, exploring new treatments, and proving the sustained benefits of early interventions to ultimately improve SMM patient care and outcomes.

Inference of genomic lesions from single-cell RNA-seq in myeloma improves functional intraclonal and interclonal analysis

ABSTRACT

Smoldering multiple myeloma (SMM) is an asymptomatic plasma cell (PC) neoplasm that may evolve with variable frequency into multiple myeloma (MM). SMM is initiated by chromosomal translocations involving the immunoglobulin heavy-chain locus or by hyperdiploidy and evolves through acquisition of additional genetic lesions. In this scenario, we aimed at establishing a reliable analysis pipeline to infer genomic lesions from transcriptomic analysis, by combining single-cell RNA sequencing (scRNA-seq) with B-cell receptor sequencing and copy number abnormality (CNA) analysis to identify clonal PCs at the genetic level along their specific transcriptional landscape. We profiled 20 465 bone marrow PCs derived from 5 patients with SMM/MM and unbiasedly identified clonal and polyclonal PCs. Hyperdiploidy, t(11;14), and t(6;14) were identified at the scRNA level by analysis of chimeric reads. Subclone functional analysis was improved by combining transcriptome with CNA analysis. As examples, we illustrate the different functional properties of a light-chain escape subclone in SMM and of different B-cell and PC subclones in a patient affected by Wäldenstrom macroglobulinemia and SMM. Overall, our data provide a proof of principle for inference of clinically relevant genotypic data from scRNA-seq, which in turn will refine functional annotation of the clonal architecture of PC dyscrasias.

Clinical implications of residual normal plasma cells within bone marrow across various disease stages in multiple myeloma

Residual normal plasma cells (NPCs), which compete with tumor plasma cells, play an important role in multiple myeloma. However, large-scale cohort studies investigating residual NPCs, especially at the minimal residual disease (MRD) phase, are currently lacking. In this study, we conducted a comprehensive investigation into the clinical significance of residual NPCs throughout the entire disease course in 1363 myeloma patients from the NICHE cohort (NCT04645199). Our results revealed that myeloma patients with high baseline NPCs ratio (≥5%) exhibited distinct indolent features, characterized by lower tumor burden, reduced frequencies of cytopenia, immunoparesis, and high-risk cytogenetics. Importantly, high residual NPCs ratio at diagnosis or relapse was independently associated with favorable survival. High absolute percentages of NPCs at undetectable MRD were related with superior clinical benefit and immune reconstitution. At MRD-positive phases, grouping based on NPCs ratio (<50%, 50-90%, ≥90%) demonstrated better risk stratification compared to residual tumor log levels. Based on the time-dependent NPCs ratio trend, we developed a dynamic MRD model that classifies patients into three groups with diverse longitudinal trends, leading to distinct prognoses. Collectively, residual NPCs serves not only as a valuable complementary biomarker for risk stratification but also provides valuable insights on reclassifications and kinetics of MRD.

Functional cure and long-term survival in multiple myeloma: how to challenge the previously impossible

Multiple myeloma (MM) is a heterogeneous disease with survival ranging from months to decades. The goal of 'cure' remains elusive for most patients, but has been shown to be possible, with durable remission and a transition to a plateau phase (analogous to monoclonal gammopathy of uncertain significance/smoldering myeloma). In this review, two representative cases set the stage to illustrate how this might be possible and what still needs to be determined to achieve functional disease control over a prolonged period. Several developments have emerged, such as improved diagnostics including the definitions and use of SLiM-CRAB criteria and measurable residual disease (MRD) with whole-genome/single-cell sequencing as well as other correlates to better understand disease biology. These advances enable earlier detection, more accurate risk stratification and improved personalized treatment strategies by facilitating analysis of genetic alterations and clonal heterogeneity. Whole-genome sequencing may also identify driver mutations and modes of resistance to immunotherapies as well as other targeted therapies. Today, induction with a CD38 antibody, proteasome inhibitor, immunomodulatory drug, and dexamethasone, potentially followed by autologous stem cell transplantation and lenalidomide maintenance, can be considered standard of care for transplant-eligible (TE) patients with newly diagnosed MM (NDMM). That prolonged disease control and functional cure can be achieved in non-transplant-eligible (NTE) patients is currently emerging as a distinct possibility: data from phase III trials that incorporate a CD38 antibody into the treatment of NTE NDMM patients demonstrate impressive MRD negativity rates that appear sustained over several years. While the long-term durability of chimeric antigen receptor T cells, bi-specific antibodies and other immunotherapies are being evaluated, several clinical trials are now investigating their role in frontline treatment for TE and NTE patients. These trials will address whether chimeric antigen receptor T-cell therapy will replace autologous stem cell transplantation and whether such immunotherapies will represent a truly curative option. We conclude that while cure remains elusive, the concept of operational or functional cure provides a new benchmark to strive for and is an emerging area of active and potentially achievable clinical research for MM.

1q amplification and PHF19 expressing high-risk cells are associated with relapsed/refractory multiple myeloma

Multiple Myeloma is an incurable plasma cell malignancy with a poor survival rate that is usually treated with immunomodulatory drugs (iMiDs) and proteosome inhibitors (PIs). The malignant plasma cells quickly become resistant to these agents causing relapse and uncontrolled growth of resistant clones. From whole genome sequencing (WGS) and RNA sequencing (RNA-seq) studies, different high-risk translocation, copy number, mutational, and transcriptional markers can be identified. One of these markers, PHF19, epigenetically regulates cell cycle and other processes and is already studied using RNA-seq. In this study, we generate a large (325,025 cells and 49 patients) single cell multi-omic dataset and jointly quantify ATAC- and RNA-seq for each cell and matched genomic profiles for each patient. We identify an association between one plasma cell subtype with myeloma progression that we call relapsed/refractory plasma cells (RRPCs). These cells are associated with chromosome 1q alterations, TP53 mutations, and higher expression of PHF19. We also identify downstream regulation of cell cycle inhibitors in these cells, possible regulation by the transcription factor (TF) PBX1 on chromosome 1q, and determine that PHF19 may be acting primarily through this subset of cells.

From spear to trident: Upgrading arsenal of CAR-T cells in the treatment of multiple myeloma

Multiple myeloma (MM), marked by abnormal proliferation of plasma cells and production of monoclonal immunoglobulin heavy or light chains in the majority of patients, has traditionally been associated with poor survival, despite improvements achieved in median survival in all age groups since the introduction of novel agents. Survival has significantly improved with the development of new drugs and new treatment options, such as chimeric antigen receptor T-cell therapy (CAR-T), which have shown promise and given new hope in MM therapy. CARs are now classified as first-, second-, and third-generation CARs based on the number of monovalent to trivalent co-stimulatory molecules incorporated into their design. The scope of this review was relatively narrow because it was mainly about a comparison of the literature on the clinical application of CAR-T therapy in MM. Thus, our goal is to provide an overview of the new advances of CAR-T cells in the cure of MM, so in this review we looked at the progress of the clinical use of CAR-T cells in MM to try to provide a reference for their clinical use when managing MM.

Single-cell multiomic dissection of response and resistance to chimeric antigen receptor T cells against BCMA in relapsed multiple myeloma

Markers that predict response and resistance to chimeric antigen receptor (CAR) T cells in relapsed/refractory multiple myeloma are currently missing. We subjected mononuclear cells isolated from peripheral blood and bone marrow before and after the application of approved B cell maturation antigen-directed CAR T cells to single-cell multiomic analyses to identify markers associated with resistance and early relapse. Differences between responders and nonresponders were identified at the time of leukapheresis. Nonresponders showed an immunosuppressive microenvironment characterized by increased numbers of monocytes expressing the immune checkpoint molecule CD39 and suppressed CD8+ T cell and natural killer cell function. Analysis of CAR T cells showed cytotoxic and exhausted phenotypes in hyperexpanded clones compared to low/intermediate expanded clones. We identified potential immunotherapy targets on CAR T cells, like PD1, to improve their functionality and durability. Our work provides evidence that an immunosuppressive microenvironment causes resistance to CAR T cell therapies in multiple myeloma.

Exploring natural killer cell-related biomarkers in multiple myeloma: a novel nature killer cell-related model predicting prognosis and immunotherapy response using single-cell study

BACKGROUND

Natural killer cells (NKs) may be involved in multiple myeloma (MM) progression. The present study elucidated the correlation between NKs and the progression of MM using single-cell binding transcriptome probes to identify NK cell-related biomarkers.

METHODS

Single-cell analysis was performed including cell and subtype annotation, cell communication, and pseudotime analysis. Hallmark pathway enrichment analysis of NKs and NKs-related differentially expressed genes (DEGs) were conducted using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction (PPI) networks. Then, a risk model was structured based on biomarkers identified through univariate Cox regression analysis and least absolute shrinkage and selection operator regression analysis and subsequently validated. Additionally, correlation of clinical characteristics, gene set enrichment analysis, immune analysis, regulatory network, and drug forecasting were explored.

RESULTS

A total of 13 cell clusters were obtained and annotated, including 8 cell populations that consisted of NKs. Utilizing 123 PPI network node genes, 8 NK-related DEGs were selected to construct a prognostic model. Immune cell infiltration results suggested that 11 immune cells exhibited marked differences in the high and low-risk groups. Finally, the model was used to screen potential drug targets to enhance immunotherapy efficacy.

CONCLUSION

A new prognostic model for MM associated with NKs was constructed and validated. This model provides a fresh perspective for predicting patient outcomes, immunotherapeutic response, and candidate drugs.

Immune-Pathogenesis of Myeloma

This research indicates that monoclonal gammopathy of undetermined significance (MGUS) and myeloma may stem from chronic immune activation and inflammation, causing immune dysfunction and spatial immune exclusion. As the conditions progress, a shift toward myeloma involves ongoing immune impairment, affecting both innate and adaptive immunity. Intriguingly, even in advanced myeloma stages, susceptibility to immune effector cells persists. This insight highlights the intricate interplay between immune responses and the development of these conditions, paving the way for potential therapeutic interventions targeting immune modulation in the management of MGUS and myeloma.

Novel Insights into the Initiation, Evolution, and Progression of Multiple Myeloma by Multi-Omics Investigation

The evolutionary history of multiple myeloma (MM) includes malignant transformation, followed by progression to pre-malignant stages and overt malignancy, ultimately leading to more aggressive and resistant forms. Over the past decade, large effort has been made to identify the potential therapeutic targets in MM. However, MM remains largely incurable. Most patients experience multiple relapses and inevitably become refractory to treatment. Tumor-initiating cell populations are the postulated population, leading to the recurrent relapses in many hematological malignancies. Clonal evolution of tumor cells in MM has been identified along with the disease progression. As a consequence of different responses to the treatment of heterogeneous MM cell clones, the more aggressive populations survive and evolve. In addition, the tumor microenvironment is a complex ecosystem which plays multifaceted roles in supporting tumor cell evolution. Emerging multi-omics research at single-cell resolution permits an integrative and comprehensive profiling of the tumor cells and microenvironment, deepening the understanding of biological features of MM. In this review, we intend to discuss the novel insights into tumor cell initiation, clonal evolution, drug resistance, and tumor microenvironment in MM, as revealed by emerging multi-omics investigations. These data suggest a promising strategy to unravel the pivotal mechanisms of MM progression and enable the improvement in treatment, both holistically and precisely.

Single-cell sequencing analysis within biologically relevant dimensions

The currently predominant approach to transcriptomic and epigenomic single-cell analysis depends on a rigid perspective constrained by reduced dimensions and algorithmically derived and annotated clusters. Here, we developed Seqtometry (sequencing-to-measurement), a single-cell analytical strategy based on biologically relevant dimensions enabled by advanced scoring with multiple gene sets (signatures) for examination of gene expression and accessibility across various organ systems. By utilizing information only in the form of specific signatures, Seqtometry bypasses unsupervised clustering and individual annotations of clusters. Instead, Seqtometry combines qualitative and quantitative cell-type identification with specific characterization of diverse biological processes under experimental or disease conditions. Comprehensive analysis by Seqtometry of various immune cells as well as other cells from different organs and disease-induced states, including multiple myeloma and Alzheimer's disease, surpasses corresponding cluster-based analytical output. We propose Seqtometry as a single-cell sequencing analysis approach applicable for both basic and clinical research.

A digital twin model for evidence-based clinical decision support in multiple myeloma treatment

The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine.

HAPLN1 matrikine: a bone marrow homing factor linked to poor outcomes in patients with MM

The bone marrow (BM) microenvironment is critical for dissemination, growth, and survival of multiple myeloma (MM) cells. Homing of myeloma cells to the BM niche is a crucial step in MM dissemination, but the mechanisms involved are incompletely understood. In particular, any role of matrikines, neofunctional peptides derived from extracellular matrix proteins, remains unknown. Here, we report that a matrikine derived from hyaluronan and proteoglycan link protein 1 (HAPLN1) induces MM cell adhesion to the BM stromal components, such as fibronectin, endothelial cells, and stromal cells and, furthermore, induces their chemotactic and chemokinetic migration. In a mouse xenograft model, we show that MM cells preferentially home to HAPLN1 matrikine-conditioned BM. The transcription factor STAT1 is activated by HAPLN1 matrikine and is necessary to induce MM cell adhesion, migration, migration-related genes, and BM homing. STAT1 activation is mediated by interferon beta (IFN-β), which is induced by NF-κB after stimulation by HAPLN1 matrikine. Finally, we also provide evidence that higher levels of HAPLN1 in BM samples correlate with poorer progression-free survival of patients with newly diagnosed MM. These data reveal that a matrikine present in the BM microenvironment acts as a chemoattractant, plays an important role in BM homing of MM cells via NF-κB-IFN-β-STAT1 signaling, and may help identify patients with poor outcomes. This study also provides a mechanistic rationale for targeting HAPLN1 matrikine in MM therapy.

High NEK2 expression in myeloid progenitors suppresses T cell immunity in multiple myeloma

Multiple myeloma (MM) growth is supported by an immune-tolerant bone marrow microenvironment. Here, we find that loss of Never in mitosis gene A (NIMA)-related kinase 2 (NEK2) in tumor microenvironmental cells is associated with MM growth suppression. The absence of NEK2 leads to both fewer tumor-associated macrophages (TAMs) and inhibitory T cells. NEK2 expression in myeloid progenitor cells promotes the generation of functional TAMs when stimulated with MM conditional medium. Clinically, high NEK2 expression in MM cells is associated with increased CD8+ T effector memory cells, while low NEK2 is associated with an IFN-γ gene signature and activated T cell response. Inhibition of NEK2 upregulates PD-L1 expression in MM cells and myeloid cells. In a mouse model, the combination of NEK2 inhibitor INH154 with PD-L1 blockade effectively eliminates MM cells and prolongs survival. Our results provide strong evidence that NEK2 inhibition may overcome tumor immune escape and support its further clinical development.

Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations

Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. We introduce DeepScena, a novel single-cell hierarchical clustering tool that fully incorporates nonlinear dimension reduction, negative binomial-based convolutional autoencoder for data fitting, and a self-supervision model for cell similarity enhancement. In comprehensive evaluation using multiple large-scale scRNA-seq datasets, DeepScena consistently outperformed seven popular clustering tools in terms of accuracy. Notably, DeepScena exhibits high proficiency in identifying rare cell populations within large datasets that contain large numbers of clusters. When applied to scRNA-seq data of multiple myeloma cells, DeepScena successfully identified not only previously labeled large cell types but also subpopulations in CD14 monocytes, T cells and natural killer cells, respectively.

Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods

Gastric cancer (GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development; therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.

Identifying 1q amplification and PHF19 expressing high-risk cells associated with relapsed/refractory multiple myeloma

Multiple Myeloma is an incurable plasma cell malignancy with a poor survival rate that is usually treated with immunomodulatory drugs (iMiDs) and proteosome inhibitors (PIs). The malignant plasma cells quickly become resistant to these agents causing relapse and uncontrolled growth of resistant clones. From whole genome sequencing (WGS) and RNA sequencing (RNA-seq) studies, different high-risk translocation, copy number, mutational, and transcriptional markers have been identified. One of these markers, PHF19, epigenetically regulates cell cycle and other processes and has already been studied using RNA-seq. In this study a massive (325,025 cells and 49 patients) single cell multiomic dataset was generated with jointly quantified ATAC- and RNA-seq for each cell and matched genomic profiles for each patient. We identified an association between one plasma cell subtype with myeloma progression that we have called relapsed/refractory plasma cells (RRPCs). These cells are associated with 1q alterations, TP53 mutations, and higher expression of PHF19. We also identified downstream regulation of cell cycle inhibitors in these cells, possible regulation of the transcription factor (TF) PBX1 on 1q, and determined that PHF19 may be acting primarily through this subset of cells.

Systematic characterization of therapeutic vulnerabilities in Multiple Myeloma with Amp1q reveals increased sensitivity to the combination of MCL1 and PI3K inhibitors

The development of targeted therapy for patients with Multiple Myeloma (MM) is hampered by the low frequency of actionable genetic abnormalities. Gain or amplification of chr1q (Amp1q) is the most frequent arm-level copy number gain in patients with MM, and it is associated with higher risk of progression and death despite recent advances in therapeutics. Thus, developing targeted therapy for patients with MM and Amp1q stands to benefit a large portion of patients in need of more effective management. Here, we employed large-scale dependency screens and drug screens to systematically characterize the therapeutic vulnerabilities of MM with Amp1q and showed increased sensitivity to the combination of MCL1 and PI3K inhibitors. Using single-cell RNA sequencing, we compared subclones with and without Amp1q within the same patient tumors and showed that Amp1q is associated with higher levels of MCL1 and the PI3K pathway. Furthermore, by isolating isogenic clones with different copy number for part of the chr1q arm, we showed increased sensitivity to MCL1 and PI3K inhibitors with arm-level gain. Lastly, we demonstrated synergy between MCL1 and PI3K inhibitors and dissected their mechanism of action in MM with Amp1q.

Single-cell profiling in multiple myeloma: insights, problems, and promises

In a short time, single-cell platforms have become the norm in many fields of research, including multiple myeloma (MM). In fact, the large amount of cellular heterogeneity in MM makes single-cell platforms particularly attractive because bulk assessments can miss valuable information about cellular subpopulations and cell-to-cell interactions. The decreasing cost and increasing accessibility of single-cell platform, combined with breakthroughs in obtaining multiomics data for the same cell and innovative computational programs for analyzing data, have allowed single-cell studies to make important insights into MM pathogenesis; yet, there is still much to be done. In this review, we will first focus on the types of single-cell profiling and the considerations for designing a single-cell profiling experiment. Then, we will discuss what have learned from single-cell profiling about myeloma clonal evolution, transcriptional reprogramming, and drug resistance, and about the MM microenvironment during precursor and advanced disease.

Monoclonal B-cell lymphocytosis, monoclonal gammopathy of undetermined significance, and T-cell clones of uncertain significance: are these premalignant conditions sharing a common identity?

Monoclonal B-cell lymphocytosis, monoclonal gammopathy of undetermined significance, and T-cell clones of uncertain significance are three premalignant conditions characterised by the presence of small clonal cell expansions in individuals without symptoms or signs that distinguish the related overt malignancies (chronic lymphocytic leukaemia, multiple myeloma, and T-cell large granular lymphocytic leukaemia). As most individuals with these precursor states never progress to malignancies, considerable interest has arisen in comprehending the steps involved in the progression to malignancy, providing more accurate models to investigate potential mechanisms of early blood cancer identification, prevention, and, possibly, intervention. Single-cell technologies and recent progress in high-throughput sequencing and multiomics approaches have contributed to a better definition of the pathophysiological mechanisms of these premalignant conditions, moving our knowledge in the field forward. In this Viewpoint, we analyse the seemingly shared biological trajectories in these precursor haematological malignancies in search of common pathogenetic events. In particular, we address the issue of interactions between expanding clones and their immune ecosystem, offering new clues that might prompt innovative ideas and inspire further investigations to understand the cellular and molecular dynamics entailing progression into overt malignant disease. The relationships between the non-leukaemic microenvironmental cells and the leukaemic counterpart, and the primary drivers of their initial clonal expansion, represent shared biologies that suggest a common identity among the premalignant conditions considered in this Viewpoint.

Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications

Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Although therapeutic advances have been made to improve clinical outcomes and to prolong patients' survival in the past two decades, MM remains largely incurable. Single-cell sequencing (SCS) is a powerful method to dissect the cellular and molecular landscape at single-cell resolution, instead of providing averaged results. The application of single-cell technologies promises to address outstanding questions in myeloma biology and has revolutionized our understanding of the inter- and intra-tumor heterogeneity, tumor microenvironment, and mechanisms of therapeutic resistance in MM. In this review, we summarize the recently developed SCS methodologies and latest MM research progress achieved by single-cell profiling, including information regarding the cancer and immune cell landscapes, tumor heterogeneities, underlying mechanisms and biomarkers associated with therapeutic response and resistance. We also discuss future directions of applying transformative SCS approaches with contribution to clinical translation.

Ferritin Metabolism Reflects Multiple Myeloma Microenvironment and Predicts Patient Outcome

Multiple myeloma (MM) is a hematologic malignancy with a multistep evolutionary pattern, in which the pro-inflammatory and immunosuppressive microenvironment and genomic instability drive tumor evolution. MM microenvironment is rich in iron, released by pro-inflammatory cells from ferritin macromolecules, which contributes to ROS production and cellular damage. In this study, we showed that ferritin increases from indolent to active gammopathies and that patients with low serum ferritin had longer first line PFS (42.6 vs. 20.7 months and, p = 0.047, respectively) and OS (NR vs. 75.1 months and p = 0.029, respectively). Moreover, ferritin levels correlated with systemic inflammation markers and with the presence of a specific bone marrow cell microenvironment (including increased MM cell infiltration). Finally, we verified by bioinformatic approaches in large transcriptomic and single cell datasets that a gene expression signature associated with ferritin biosynthesis correlated with worse outcome, MM cell proliferation, and specific immune cell profiles. Overall, we provide evidence of the role of ferritin as a predictive/prognostic factor in MM, setting the stage for future translational studies investigating ferritin and iron chelation as new targets for improving MM patient outcome.