Resolving therapy resistance mechanisms in multiple myeloma by multiomics subclone analysis

Aberrant single-cell phenotype and clinical implications of genotypically defined polyclonal plasma cells in myeloma

ABSTRACT

Multiple myeloma (MM) is driven by clonal plasma cell (cPC)-intrinsic factors and changes in the tumor microenvironment (TME). To investigate whether residual polyclonal PCs (pPCs) are disrupted, single-cell (sc) RNA sequencing (scRNA-seq) and sc B-cell receptor analysis were applied in a cohort of 46 samples with PC dyscrasias and 21 healthy donors (HDs). Of 234 789 PCs, 64 432 were genotypically identified as pPCs with frequencies decreasing over different disease stages, from 23.66% in monoclonal gammopathy of undetermined significance to 3.23% in MMs (P = .00012). Both cPCs and pPCs had a comparable expression of typical lineage markers (ie, CD38 and CD138), whereas others were more variable (CD27 and ITGB7). Only cPCs overexpressed oncogenes (eg, CCND1/2 and NSD2), but CCND3 was often expressed in pPCs. B-cell maturation antigen was expressed on both pPCs and cPCs, whereas GPRC5D was mostly upregulated in cPCs with implications for on-target, off-tumor activity of targeted immunotherapies. In comparison with HDs, pPCs from patients showed upregulated autophagy and disrupted interaction with TME. Importantly, interferon-related pathways were significantly enriched in pPCs from patients vs HDs (adjusted P < .05) showing an inflamed phenotype affecting genotypically normal PCs. The function of pPCs was consequently affected and correlated with immunoparesis, driven by disrupted cellular interactions with TME. Leveraging our scRNA-seq data, we derived a "healthy PC signature" that could be applied to bulk transcriptomics from the CoMMpass data set and predicted significantly better progression-free survival and overall survival (log-rank P < .05 for both). Our findings show that genotypic sc identification of pPCs in PC dyscrasias has relevant pathogenic and clinical implications.

Integrative analysis of bulk and single-cell gene expression profiles to identify bone marrow mesenchymal cell heterogeneity and prognostic significance in multiple myeloma

BACKGROUND

Multiple myeloma is a hematologic malignancy characterized by complex interactions within the tumor microenvironment, where mesenchymal stem cells (MSCs) contribute significantly to disease progression, immune suppression, and drug resistance.

METHODS

This study investigated the heterogeneity of MSCs in multiple myeloma using single-cell RNA sequencing (10X) and bulk transcriptomic data. Further analysis was performed by Seurat, SCENIC, CellChat. GSE4581 and GSE136337 were used as training set and validation set to construct a newly described prognostic model through COX and LASSO.

RESULTS

By analyzing bone marrow samples from healthy donors and multiple myeloma patients at different Revised International Staging System (R-ISS) stages, this study identified distinct MSC subpopulations, including osteogenic, angiogenic, immune regulatory, and multipotent clusters, each of which plays unique roles in the tumor microenvironment. Interestingly, we found a unique subclone with upregulated expression of high mobility group proteins, these MSC exert a strong regulatory effect, which was defined as "HMGhMSC".

CONCLUSIONS

Our findings reveal that the proportion of osteogenic MSCs, which are crucial for bone health, decreases as the disease progresses, which is correlated with the bone lysis commonly observed in advanced multiple myeloma. Additionally, immune regulatory MSCs contribute to the formation of an immunosuppressive microenvironment, promoting tumor immune evasion. A prognostic model based on HMGhMSC subpopulations was developed, which demonstrated that these cells have significant potential as therapeutic targets for improving the prognosis and developing treatments for bone disease in multiple myeloma patients.

High-Risk Genetic Multiple Myeloma: From Molecular Classification to Innovative Treatment with Monoclonal Antibodies and T-Cell Redirecting Therapies

High-risk genetic multiple myeloma (HRMM) remains a major therapeutic challenge, as patients harboring adverse genetic abnormalities, such as del(17p), TP53 mutations, and biallelic del(1p32), continue to experience poor outcomes despite recent therapeutic advancements. This review explores the evolving definition and molecular features of HRMM, focusing on recent updates in risk stratification and treatment strategies. The new genetic classification proposed at the 2025 EMMA meeting offers improved prognostic accuracy and supports more effective, risk-adapted treatment planning. In transplant-eligible patients, intensified induction regimens, tandem autologous stem cell transplantation, and dual-agent maintenance have shown improved outcomes, particularly when sustained minimal residual disease negativity is achieved. Conversely, in the relapsed or refractory setting, novel agents have demonstrated encouraging activity, although their specific efficacy in HRMM is under investigation. Moreover, treatment paradigms are shifting toward earlier integration of immunotherapy, and therapeutic strategies are individualized based on refined molecular risk profiles and clone dynamics. Therefore, a correct definition of HRMM could help in significantly improving both clinical and therapeutic management of a subgroup of patients with an extremely aggressive disease.

Bioinformatics analysis of intrinsic drivers of immune dysregulation in multiple myeloma to elucidate immune phenotypes and discover prognostic gene signatures

Multiple myeloma (MM) progression is driven by immune dysregulation within the tumor microenvironment (TME). However, myeloma-intrinsic mechanisms underlying immune dysfunction remain poorly defined, and current immunotherapies show limited efficacy. Using RNA-seq data from 859 MM patients (MMRF-CoMMpass), we integrated xCELL, CIBERSORT, and ESTIMATE algorithms to deconvolute immune-stromal dynamics. Consensus clustering identified immune subtypes, followed by differential gene analysis and LASSO-Cox regression to construct a prognostic model validated in an independent cohort (GSE19784, N = 328). Immune Subtype Classification: Two subgroups emerged: Multiple myeloma-associated immune-related cluster 1 (N = 482): Immune-dysfunctional TME with Th2 cell enrichment, preadipocyte accumulation, and CXCL family suppression, linked to poor survival (P < 0.001). Multiple myeloma-associated immune-related cluster 2 (N = 377): Immune-active TME with cytotoxic CD8 + T/NK cell infiltration and favorable outcomes. Prognostic Gene Signature: Ten immune-related genes (UBE2T, E2F2, EXO1, SH2D2A, DRP2, WNT9A, SHROOM3, TMC8, CDCA7, and GPR132) predicted survival (The One-year AUC = 0.682 and The Over 5-years AUC = 0.714). We define a myeloma-intrinsic immune classification system and a 10-gene prognostic index, offering a framework for risk-stratified immunotherapy. Integration with flow cytometry could optimize precision treatment in MM.

Advances in tumor subclone formation and mechanisms of growth and invasion

Tumor subclones refer to distinct cell populations within the same tumor that possess different genetic characteristics. They play a crucial role in understanding tumor heterogeneity, evolution, and therapeutic resistance. The formation of tumor subclones is driven by several key mechanisms, including the inherent genetic instability of tumor cells, which facilitates the accumulation of novel mutations; selective pressures from the tumor microenvironment and therapeutic interventions, which promote the expansion of certain subclones; and epigenetic modifications, such as DNA methylation and histone modifications, which alter gene expression patterns. Major methodologies for studying tumor subclones include single-cell sequencing, liquid biopsy, and spatial transcriptomics, which provide insights into clonal architecture and dynamic evolution. Beyond their direct involvement in tumor growth and invasion, subclones significantly contribute to tumor heterogeneity, immune evasion, and treatment resistance. Thus, an in-depth investigation of tumor subclones not only aids in guiding personalized precision therapy, overcoming drug resistance, and identifying novel therapeutic targets, but also enhances our ability to predict recurrence and metastasis risks while elucidating the mechanisms underlying tumor heterogeneity. The integration of artificial intelligence, big data analytics, and multi-omics technologies is expected to further advance research in tumor subclones, paving the way for novel strategies in cancer diagnosis and treatment. This review aims to provide a comprehensive overview of tumor subclone formation mechanisms, evolutionary models, analytical methods, and clinical implications, offering insights into precision oncology and future translational research.

Exosome-transmitted HSPA9 facilitates bortezomib resistance by targeting TRIP13/USP1 signaling in multiple myeloma

BACKGROUND

Resistance to the proteasome inhibitor bortezomib (BTZ) poses a formidable therapeutic challenge in multiple myeloma (MM). Our study aims to analyze the mechanism by which exosomes heat shock 70 kDa protein 9 (HSPA9) secreted by BTZ-resistant MM cells disseminate resistance to BTZ-sensitive MM cells.

METHODS

The serum exosomes were identified by nanoparticle tracking analysis and transmission electron microscopy. Liquid chromatography-mass spectrometry and public databases were performed to screen exosomes HSPA9. Cell counting kit-8, western blotting and colony formation assay were used to detected the role of HSPA9 protein in vitro. Co-immunoprecipitation, immunofluorescence and protein truncation test experiments were used to determine the regulatory network of the HSPA9-USP1-TRIP13 complex. Optical imaging in vivo and xenograft mouse models were performed to investigate that exosomes HSPA9 promoted MM proliferation and BTZ resistance.

RESULTS

We demonstrated that HSPA9 was highly expressed in serum exosomes and BTZ-resistant MM patients. Knockdown of HSPA9 significantly suppressed tumorigenesis and reversed BTZ resistance in vitro. As a downstream molecular of HSPA9, thyroid hormone receptor-interacting protein 13 (TRIP13) was also highly expressed in BTZ-resistant MM patients. Mechanistically, the carboxyl-terminal peptide-binding domain of HSPA9, provides a platform for recruiting the deubiquitinating enzyme ubiquitin-specific peptidase 1 (USP1), which prevents TRIP13 protein degradation. The HSPA9-USP1-TRIP13 complex exhibits stability in the cytoplasm, and its inhibition remarkably enhances BTZ resistance in vito.

CONCLUSION

Our findings propose a pioneering molecular regulatory network in which MM-cell-derived exosomes HSPA9 transmitted BTZ resistance through the USP1/TRIP13 signaling pathway. This research highlights exosomes HSPA9 as a promising target to overcome MM BTZ resistance.

Age- and gender-specific molecular characteristics of diffuse large B-cell lymphoma: Results from clinical trials of the DSHNHL/GLA

Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. Despite a high cure rate, too many patients show refractory (ref) or relapsed (rel) disease. This study examines the frequency of recurring gene mutations and their interplay with well-known biomarkers in female and male patients between 18 and 80 years with ref/rel DLBCL compared to patients with complete remission (CR) to identify biological risk factors associated with treatment response, using cohorts of R-CHOP-like treated DLBCL enrolled in clinical trials of the DSHNHL. The biomarker profile of patients differed between younger and elderly patients with ref/rel DLBCL, with a higher frequency of BCL2 translocations in younger patients, and higher numbers of ABC subtypes and MYC protein expression in the elderly. Amplicon sequencing revealed generally higher mutation frequencies in the younger cohort. Mutations in CREBBP and TNFRSF14 were associated with shorter overall survival (OS) only in younger patients. A higher proportion of GNA13 mutations was detected in female patients of the elderly DLBCL patient cohort, clearly emphasizing the striking differences in biomarker distribution between younger and elderly as well as female and male patients.

Multiple Myeloma Insights from Single-Cell Analysis: Clonal Evolution, the Microenvironment, Therapy Evasion, and Clinical Implications

Multiple myeloma (MM) is a complex and heterogeneous hematologic malignancy characterized by clonal evolution, genetic instability, and interactions with a supportive tumor microenvironment. These factors contribute to treatment resistance, disease progression, and significant variability in clinical outcomes among patients. This review explores the mechanisms underlying MM progression, including the genetic and epigenetic changes that drive clonal evolution, the role of the bone marrow microenvironment in supporting tumor growth and immune evasion, and the impact of genomic instability. We highlight the critical insights gained from single-cell technologies, such as single-cell transcriptomics, genomics, and multiomics, which have enabled a detailed understanding of MM heterogeneity at the cellular level, facilitating the identification of rare cell populations and mechanisms of drug resistance. Despite the promise of advanced technologies, MM remains an incurable disease and challenges remain in their clinical application, including high costs, data complexity, and the need for standardized bioinformatics and ethical considerations. This review emphasizes the importance of continued research and collaboration to address these challenges, ultimately aiming to enhance personalized treatment strategies and improve patient outcomes in MM.

Tendomodulin in pan-cancer analysis: exploring its impact on immune modulation and uncovering functional insights in colorectal cancer

BACKGROUND

Tendomodulin (TNMD) is pivotal in various malignancies, including colorectal cancer (CRC). However, its comprehensive impact across cancers, particularly its immunomodulatory function in CRC, remains underexplored. This study explored the role of TNMD in CRC by focusing on its immunomodulatory functions through comprehensive molecular and clinical analyses.

METHODS

Multiple bioinformatics databases and analytical tools were utilized for the TNMD in pan-cancer analysis. To validate the role of TNMD in CRC, we performed experiments, including immunofluorescence (IF), immunohistochemistry (IHC), real-time quantitative reverse transcription PCR (qPCR), western blotting, and cell migration assays.

RESULTS

TNMD expression and gene mutation vary across cancers and offer high diagnostic value. Survival analysis found that TNMD is associated with prognosis in multiple cancers. Notably, in patients with high microsatellite instability (MSI-H) CRC, TNMD expression correlated positively with various immune cells, particularly natural killer (NK) cells, whereas it was inversely correlated with regulatory T cells (Tregs). Crucially, in patients with microsatellite stability (MSS) CRC, high TNMD expression was associated with better immunotherapy outcomes, indicating its potential as a biomarker for patient stratification and tailored treatment approaches. Furthermore, single-cell sequencing data revealed stronger interactions between TNMD-positive tumor cells and fibroblasts or macrophages in the tumor microenvironment. Finally, TNMD was overexpressed in CRC tumor tissues and cell lines, thereby promoting invasion and metastasis.

CONCLUSIONS

Our findings reveal a critical immunomodulatory role of TNMD in CRC, particularly in influencing tumor-immune interactions. Beyond its potential diagnostic and prognostic biomarker, TNMD promotes CRC metastasis and invasion, thus emerging as a promising therapeutic target. These findings highlight TNMD's significance in CRC and potentially other malignancies.

COMMD3 Regulates Copper Metabolism via the ATOX1-ATP7A-LOX Axis to Promote Multiple Myeloma Progression

BACKGROUND

Multiple myeloma (MM) is a hematologic malignancy characterized by the clonal proliferation of plasma cells, with extramedullary myeloma (EMM) being an aggressive form involving malignant infiltration beyond the bone marrow. Copper metabolism is essential for tumor proliferation and metastasis, with copper metabolism MURR1 domain (COMMD) proteins regulating these processes and maintaining copper homeostasis. Dysregulated copper homeostasis contributes to cancer progression, including MM, with elevated copper levels linked to disease aggressiveness and poor prognosis. This study investigates the role of the COMMD3 in mediating MM cell progression, particularly its influence on copper metabolism.

METHODS

Comprehensive bioinformatics analyses were conducted on bone marrow and extramedullary samples to determine the expression of COMMD3, which was validated through in vitro and in vivo functional assays. The MM cell lines RPMI8226 and MM1S underwent lentiviral transfection for COMMD3 overexpression and knockdown. RNA sequencing was conducted on COMMD3 knockdown cells to identify differentially expressed genes. Functional assays measured cell proliferation, migration, apoptosis, and copper metabolism, with a non-obese diabetic severe combined immune-deficiency gamma (NSG) mouse xenograft model providing in vivo validation.

RESULTS

Elevated COMMD3 expression was correlated with extramedullary myeloma and poor prognosis in MM patients. COMMD3 promoted MM cell proliferation and migration, modulating intracellular copper levels, likely through the ATOX1-ATP7A-LOX copper-metabolism-related pathway. High ATOX1 expression was correlated with worse outcomes, and ATOX1 inhibition abolished COMMD3's effects.

CONCLUSIONS

This study highlights the pivotal role of COMMD3 in MM progression, particularly via the ATOX1-ATP7A-LOX axis. These findings provide insights into EMM mechanisms and position COMMD3 as a potential therapeutic target. Future research is needed to validate these findings in larger clinical cohorts and to unravel the precise molecular interactions between COMMD3 and copper metabolism proteins.

The role of 1q abnormalities in multiple myeloma: Genomic insights, clinical implications, and therapeutic challenges

Chromosome 1q copy number variations, collectively termed +1q, are 1 of the most common cytogenetic abnormalities in multiple myeloma. 1q abnormalities are associated with overexpression of a high-risk gene signature promoting cell proliferation, apoptosis resistance, genomic instability, and treatment resistance, and acquisition or expansion of +1q subclones mediate disease development and relapse. While there remains significant controversy as to whether the presence of +1q is itself an independent driver of poor prognosis or is simply a marker of other high-risk features, +1q has recently been incorporated into multiple prognostic scoring models as a new high-risk cytogenetic abnormality. In this review, we present possible underlying genetic mechanisms of high-risk disease in +1q myeloma, implications for subclonal development, its role in modifying the tumor microenvironment, current evidence for clinical significance in newly-diagnosed and relapsed patients, and current controversies in +1q classification and prognostication.

Single-cell sequencing reveals the mechanisms of multiple myeloma progression: clarity or confusion?

Multiple myeloma (MM), the second most common hematologic malignancy, is characterized by the clonal expansion of myeloma cells and accumulation of genetic lesions. MM progression is accompanied by increased aggressiveness and drug resistance. Even the goal of "cure" remains hard to reach for most patients, advances in diagnosis and treatment have allowed some to achieve durable remissions and transition to plateau phase. Single-cell sequencing, with its powerful ability to analyze cellular heterogeneity and molecular patterns at ground-breaking resolution, is informative for deciphering tumors and their microenvironment. In this review, we summarize the new insights of studies facilitated by emerging single-cell sequencing into clonal evolution, myeloma-supported microenvironment transformation, epigenetic changes, and novel prognostic and therapeutic strategies for MM, revealing the key mechanisms underlying MM progression and the direction of future efforts. With the continuous expansion of the research scope and optimization of related technologies, single-cell sequencing is expected to revolutionize our understanding of the biology and evolutionary dynamics of MM and contribute to the radical and precise improvement of treatment.

From Biology to Clinical Practice: The Bone Marrow Microenvironment in Multiple Myeloma

Multiple Myeloma (MM) is a complex hematological malignancy characterized by the clonal proliferation of malignant plasma cells within bone marrow (BM) [...].

A systematic literature review on clonal evolution events preceding relapse in multiple myeloma

Despite considerable treatment advances, multiple myeloma (MM) remains an incurable hematological cancer due to treatment resistance. A systematic literature search was conducted to identify determinants for clonal evolution driving relapse and drug resistance in MM. A total of 631 non-duplicate publications were screened of which 28 articles were included for data extraction. Genetic alterations, mutational signatures, evolutionary trajectories, and non-genetic determinants were identified as key topics to characterize clonal evolution in relapsed MM. A variety of factors led to clonal diversification and increased tumor mutation burden, such as MAPK-Ras mutations and incremental changes related to chromosomal bands 1 and 17, while mutational signature analyses revealed that APOBEC activity and melphalan treatment leave a distinct impact on the clonal composition in MM genomes. To capture and dissect tumor heterogeneity, our review suggests combining methods or using technical approaches with high resolution to assess the impact of clonal evolution.

Epigenomic heterogeneity as a source of tumour evolution

In the past decade, remarkable progress in cancer medicine has been achieved by the development of treatments that target DNA sequence variants. However, a purely genetic approach to treatment selection is hampered by the fact that diverse cell states can emerge from the same genotype. In multicellular organisms, cell-state heterogeneity is driven by epigenetic processes that regulate DNA-based functions such as transcription; disruption of these processes is a hallmark of cancer that enables the emergence of defective cell states. Advances in single-cell technologies have unlocked our ability to quantify the epigenomic heterogeneity of tumours and understand its mechanisms, thereby transforming our appreciation of how epigenomic changes drive cancer evolution. This Review explores the idea that epigenomic heterogeneity and plasticity act as a reservoir of cell states and therefore as a source of tumour evolution. Best practices to quantify epigenomic heterogeneity and explore its various causes and consequences are discussed, including epigenomic reprogramming, stochastic changes and lasting memory. The design of new therapeutic approaches to restrict epigenomic heterogeneity, with the long-term objective of limiting cancer development and progression, is also addressed.

scBlood: A comprehensive single-cell accessible chromatin database of blood cells

The advent of single cell transposase-accessible chromatin sequencing (scATAC-seq) technology enables us to explore the genomic characteristics and chromatin accessibility of blood cells at the single-cell level. To fully make sense of the roles and regulatory complexities of blood cells, it is critical to collect and analyze these rapidly accumulating scATAC-seq datasets at a system level. Here, we present scBlood (https://bio.liclab.net/scBlood/), a comprehensive single-cell accessible chromatin database of blood cells. The current version of scBlood catalogs 770,907 blood cells and 452,247 non-blood cells from ∼400 high-quality scATAC-seq samples covering 30 tissues and 21 disease types. All data hosted on scBlood have undergone preprocessing from raw fastq files and multiple standards of quality control. Furthermore, we conducted comprehensive downstream analyses, including multi-sample integration analysis, cell clustering and annotation, differential chromatin accessibility analysis, functional enrichment analysis, co-accessibility analysis, gene activity score calculation, and transcription factor (TF) enrichment analysis. In summary, scBlood provides a user-friendly interface for searching, browsing, analyzing, visualizing, and downloading scATAC-seq data of interest. This platform facilitates insights into the functions and regulatory mechanisms of blood cells, as well as their involvement in blood-related diseases.

Tumour evolution and microenvironment interactions in 2D and 3D space

To study the spatial interactions among cancer and non-cancer cells1, we here examined a cohort of 131 tumour sections from 78 cases across 6 cancer types by Visium spatial transcriptomics (ST). This was combined with 48 matched single-nucleus RNA sequencing samples and 22 matched co-detection by indexing (CODEX) samples. To describe tumour structures and habitats, we defined 'tumour microregions' as spatially distinct cancer cell clusters separated by stromal components. They varied in size and density among cancer types, with the largest microregions observed in metastatic samples. We further grouped microregions with shared genetic alterations into 'spatial subclones'. Thirty five tumour sections exhibited subclonal structures. Spatial subclones with distinct copy number variations and mutations displayed differential oncogenic activities. We identified increased metabolic activity at the centre and increased antigen presentation along the leading edges of microregions. We also observed variable T cell infiltrations within microregions and macrophages predominantly residing at tumour boundaries. We reconstructed 3D tumour structures by co-registering 48 serial ST sections from 16 samples, which provided insights into the spatial organization and heterogeneity of tumours. Additionally, using an unsupervised deep-learning algorithm and integrating ST and CODEX data, we identified both immune hot and cold neighbourhoods and enhanced immune exhaustion markers surrounding the 3D subclones. These findings contribute to the understanding of spatial tumour evolution through interactions with the local microenvironment in 2D and 3D space, providing valuable insights into tumour biology.

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.

The biological and clinical impact of deletions before and after large chromosomal gains in multiple myeloma

ABSTRACT

Acquisition of a hyperdiploid (HY) karyotype or immunoglobulin heavy chain (IgH) translocations are considered key initiating events in multiple myeloma (MM). To explore if other genomic events can precede these events, we analyzed whole-genome sequencing data from 1173 MM samples. By integrating molecular time and structural variants within early chromosomal duplications, we indeed identified pregain deletions in 9.4% of patients with an HY karyotype without IgH translocations, challenging acquisition of an HY karyotype as the earliest somatic event. Remarkably, these deletions affected tumor suppressor genes (TSGs) and/or oncogenes in 2.4% of patients with an HY karyotype without IgH translocations, supporting their role in MM pathogenesis. Furthermore, our study points to postgain deletions as novel driver mechanisms in MM. Using multiomics approaches to investigate their biologic impact, we found associations with poor clinical outcome in newly diagnosed patients and profound effects on both the oncogene and TSG activity despite the diploid gene status. Overall, this study provides novel insights into the temporal dynamics of genomic alterations in MM.

Mitochondrial genetics through the lens of single-cell multi-omics

Mitochondria carry their own genetic information encoding for a subset of protein-coding genes and translational machinery essential for cellular respiration and metabolism. Despite its small size, the mitochondrial genome, its natural genetic variation and molecular phenotypes have been challenging to study using bulk sequencing approaches, due to its variation in cellular copy number, non-Mendelian modes of inheritance and propensity for mutations. Here we highlight emerging strategies designed to capture mitochondrial genetic variation across individual cells for lineage tracing and studying mitochondrial genetics in primary human cells and clinical specimens. We review recent advances surrounding single-cell mitochondrial genome sequencing and its integration with functional genomic readouts, including leveraging somatic mitochondrial DNA mutations as clonal markers that can resolve cellular population dynamics in complex human tissues. Finally, we discuss how single-cell whole mitochondrial genome sequencing approaches can be utilized to investigate mitochondrial genetics and its contribution to cellular heterogeneity and disease.

Single-cell genomics-based immune and disease monitoring in blood malignancies

Achieving long-term disease control using therapeutic immunomodulation is a long-standing concept with a strong tradition in blood malignancies. Besides allogeneic hematopoietic stem cell transplantation that continues to provide potentially curative treatment for otherwise challenging diagnoses, recent years have seen impressive progress in immunotherapies for leukemias and lymphomas with immune checkpoint blockade, bispecific monoclonal antibodies, and CAR T cell therapies. Despite their success, non-response, relapse, and immune toxicities remain frequent, thus prioritizing the elucidation of the underlying mechanisms and identifying predictive biomarkers. The increasing availability of single-cell genomic tools now provides a system's immunology view to resolve the molecular and cellular mechanisms of immunotherapies at unprecedented resolution. Here, we review recent studies that leverage these technological advancements for tracking immune responses, the emergence of immune resistance, and toxicities. As single-cell immune monitoring tools evolve and become more accessible, we expect their wide adoption for routine clinical applications to catalyze more precise therapeutic steering of personal immune responses.

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.

MD3F: Multivariate Distance Drift Diffusion Framework for High-Dimensional Datasets

High-dimensional biomedical datasets have become easier to collect in the last two decades with the advent of multi-omic and single-cell experiments. These can generate over 1000 measurements per sample or per cell. More recently, focus has been drawn toward the need for longitudinal datasets, with the appreciation that important dynamic changes occur along transitions between health and disease. Analysis of longitudinal omics data comes with many challenges, including type I error inflation and corresponding loss in power when thousands of hypothesis tests are needed. Multivariate analysis can yield approaches with higher statistical power; however, multivariate methods for longitudinal data are currently limited. We propose a multivariate distance-based drift-diffusion framework (MD3F) to tackle the need for a multivariate approach to longitudinal, high-throughput datasets. We show that MD3F can result in surprisingly simple yet valid and powerful hypothesis testing and estimation approaches using generalized linear models. Through simulation and application studies, we show that MD3F is robust and can offer a broadly applicable method for assessing multivariate dynamics in omics data.

Impact of Clonal Heterogeneity in Multiple Myeloma

Multiple myeloma is characterized by a highly heterogeneous disease distribution within the bone marrow-containing skeletal system. In this review, we introduce the molecular mechanisms underlying clonal heterogeneity and the spatio-temporal evolution of myeloma. We discuss the clinical impact of clonal heterogeneity, which is thought to be one of the biggest obstacles to overcome therapy resistance and to achieve cure.

Integration of single-cell transcriptome and chromatin accessibility and its application on tumor investigation

The advent of single-cell sequencing techniques has not only revolutionized the investigation of biological processes but also significantly contributed to unraveling cellular heterogeneity at unprecedented levels. Among the various methods, single-cell transcriptome sequencing stands out as the best established, and has been employed in exploring many physiological and pathological activities. The recently developed single-cell epigenetic sequencing techniques, especially chromatin accessibility sequencing, have further deepened our understanding of gene regulatory networks. In this review, we summarize the recent breakthroughs in single-cell transcriptome and chromatin accessibility sequencing methodologies. Additionally, we describe current bioinformatic strategies to integrate data obtained through these single-cell sequencing methods and highlight the application of this analysis strategy on a deeper understanding of tumorigenesis and tumor progression. Finally, we also discuss the challenges and anticipated developments in this field.

Resolving the spatial architecture of myeloma and its microenvironment at the single-cell level

In multiple myeloma spatial differences in the subclonal architecture, molecular signatures and composition of the microenvironment remain poorly characterized. To address this shortcoming, we perform multi-region sequencing on paired random bone marrow and focal lesion samples from 17 newly diagnosed patients. Using single-cell RNA- and ATAC-seq we find a median of 6 tumor subclones per patient and unique subclones in focal lesions. Genetically identical subclones display different levels of spatial transcriptional plasticity, including nearly identical profiles and pronounced heterogeneity at different sites, which can include differential expression of immunotherapy targets, such as CD20 and CD38. Macrophages are significantly depleted in the microenvironment of focal lesions. We observe proportional changes in the T-cell repertoire but no site-specific expansion of T-cell clones in intramedullary lesions. In conclusion, our results demonstrate the relevance of considering spatial heterogeneity in multiple myeloma with potential implications for models of cell-cell interactions and disease progression.