Molecular signatures of multiple myeloma progression through single cell RNA-Seq

Cytogenetic and molecular aberrations at diagnosis and in prognosis of multiple myeloma

Multiple myeloma accounts for ∼10 % of all hematologic malignancies. It is genetically a highly heterogeneous disease. Cytogenetic aberrations can be found in only one-third of myeloma cases by karyotyping, in contrast to >90 % by interphase fluorescence in situ hybridization analysis, especially when performed on CD138-enriched plasma cells. Next generation sequencing has shown that mutations affecting the MAP kinase pathway, including KRAS, NRAS and BRAF are the most frequent driver mutations in myeloma detected in up to 40 % of cases. Biallelic TP53 inactivation is one of the most important high-risk factor that is associated with poor survival, frequency of which increases in relapsed/refractory myeloma and is rare in MGUS and SMM. De novo plasma cell leukemia is associated with frequent MYC translocations and t(11;14) while plasma cell leukemia associated with a pre-existing myeloma (so-called secondary plasma cell leukemia) more commonly harbors del(17p). Early or primary cytogenetic alterations (such as trisomies and/or one of the IGH translocation) lead to formation of a founder clone which over time expands, and evolves by acquiring additional genetic abnormalities such as copy number and epigenetic changes and secondary mutations contributing to intratumoral heterogeneity seen in myeloma. Thus, presence of clonal heterogeneity at baseline, subsequent linear and branching clonal evolution that occurs with disease progression and therapeutic selection of resistant mutant clones underscores the need for a comprehensive cytogenetic and molecular testing over time. This testing allows for a better understanding of disease pathogenesis and can inform therapeutic options for better patient care and outcomes.

Are we maintaining minimal residual disease in myeloma?

Minimal residual disease (MRD) has emerged as an important prognostic maker in patients with multiple myeloma at different stages of their treatment. Moreover, it is being increasingly incorporated as an endpoint in various clinical trials. Since maintenance therapy is an integral part of myeloma treatment, especially in the upfront setting post autologous transplantation, it is imperative to understand the role of MRD testing in the maintenance stetting. This review aims to examine the utility and dynamics of MRD testing in order to elucidate its prognostic role and possible incorporation in clinical decision making processes.

Plasma Cell Enrichment and New Genomic Approaches in Multiple Myeloma: A Scoping Review

BACKGROUND

Multiple myeloma (MM) is a genetically heterogeneous disease where specific genetic abnormalities have a significant impact on a patient's prognosis. Diagnostic and prognostic tools like fluorescence in situ hybridization (FISH), PCR, microarrays, and next-generation sequencing (NGS) have transformed MM management. However, the effectiveness of these techniques is often limited by the low concentration of plasma cells in bone marrow samples, which makes enrichment methods necessary. This review aims to clarify how these techniques enhance the detection of genetic abnormalities, reduce false-negative results, and facilitate more precise risk stratification for MM patients.

CONTENT

Following Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Review (PRISMA-ScR) guidelines, the literature on plasma cell separation methods used in genetic studies of MM was systematically identified and mapped. Searches were conducted in the Medline and Embase databases using a structured strategy, supplemented by manual searches on Google Scholar. Of 399 publications evaluated, 69 met the inclusion criteria; 37% utilized FISH and 19% demonstrated an increasing use of NGS. Plasma cell enrichment significantly improved diagnostic accuracy, increasing the detection rates of genetic abnormalities from 61% in non-enriched samples to 95.5% in enriched samples. While FISH remains the gold standard, emerging technologies such as NGS offer superior sensitivity and the ability to identify critical genetic alterations to refine molecular subtypes.

SUMMARY

Clinically significant genetic alterations are detected more frequently with plasma cell enrichment techniques, contributing to improved prognosis and treatment strategies for MM patients.

Single-Cell Sequencing: Genomic and Transcriptomic Approaches in Cancer Cell Biology

This article reviews the impact of single-cell sequencing (SCS) on cancer biology research. SCS has revolutionized our understanding of cancer and tumor heterogeneity, clonal evolution, and the complex interplay between cancer cells and tumor microenvironment. SCS provides high-resolution profiling of individual cells in genomic, transcriptomic, and epigenomic landscapes, facilitating the detection of rare mutations, the characterization of cellular diversity, and the integration of molecular data with phenotypic traits. The integration of SCS with multi-omics has provided a multidimensional view of cellular states and regulatory mechanisms in cancer, uncovering novel regulatory mechanisms and therapeutic targets. Advances in computational tools, artificial intelligence (AI), and machine learning have been crucial in interpreting the vast amounts of data generated, leading to the identification of new biomarkers and the development of predictive models for patient stratification. Furthermore, there have been emerging technologies such as spatial transcriptomics and in situ sequencing, which promise to further enhance our understanding of tumor microenvironment organization and cellular interactions. As SCS and its related technologies continue to advance, they are expected to drive significant advances in personalized cancer diagnostics, prognosis, and therapy, ultimately improving patient outcomes in the era of precision oncology.

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.

Mechanisms and strategies of immunosenescence effects on non-small cell lung cancer (NSCLC) treatment: A comprehensive analysis and future directions

Non-small cell lung cancer (NSCLC), the most prevalent form of lung cancer, remains a leading cause of cancer-related mortality worldwide, particularly among elderly individuals. The phenomenon of immunosenescence, characterized by the progressive decline in immune cell functionality with aging, plays a pivotal role in NSCLC progression and contributes to the diminished efficacy of therapeutic interventions in older patients. Immunosenescence manifests through impaired immune surveillance, reduced cytotoxic responses, and increased chronic inflammation, collectively fostering a pro-tumorigenic microenvironment. This review provides a comprehensive analysis of the molecular, cellular, and genetic mechanisms of immunosenescence and its impact on immune surveillance and the tumor microenvironment (TME) in NSCLC. We explore how aging affects various immune cells, including T cells, B cells, NK cells, and macrophages, and how these changes compromise the immune system's ability to detect and eliminate tumor cells. Furthermore, we address the challenges posed by immunosenescence to current therapeutic strategies, particularly immunotherapy, which faces significant hurdles in elderly patients due to immune dysfunction. The review highlights emerging technologies, such as single-cell sequencing and CRISPR-Cas9, which offer new insights into immunosenescence and its potential as a therapeutic target. Finally, we outline future research directions, including strategies for rejuvenating the aging immune system and optimizing immunotherapy for older NSCLC patients, with the goal of improving treatment efficacy and survival outcomes. These efforts hold promise for the development of more effective, personalized therapies for elderly patients with NSCLC.

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.

Highlighting roles of autophagy in human diseases: a perspective from single-cell RNA sequencing analyses

Autophagy, the lysosome-driven breakdown of intracellular components, is pivotal in regulating eukaryotic cellular processes and maintaining homeostasis, making it physiologically important even under normal conditions. Cellular mechanisms involving autophagy include the response to nutrient deprivation, intracellular quality control, early development, and cell differentiation. Despite its established health significance, the role of autophagy in cancer and other diseases remains complex and not fully understood. A comprehensive understanding of autophagy is crucial to facilitate the development of novel therapies and drugs that can protect and improve human health. High-throughput technologies, such as single-cell RNA sequencing (scRNA-seq), have enabled researchers to study transcriptional landscapes at single-cell resolution, significantly advancing our knowledge of autophagy pathways across diverse physiological and pathological contexts. This review discusses the latest advances in scRNA-seq for autophagy research and highlights its potential in the molecular characterization of various diseases.

Single-cell sequencing analysis of multiple myeloma heterogeneity and identification of new theranostic targets

Multiple myeloma (MM) is a heterogeneous and incurable tumor characterized by the malignant proliferation of plasma cells. It is necessary to clarify the heterogeneity of MM and identify new theranostic targets. We constructed a single-cell transcriptome profile of 48,293 bone marrow cells from MM patients and health donors (HDs) annotated with 7 continuous B lymphocyte lineages. Through CellChat, we discovered that the communication among B lymphocyte lineages between MM and HDs was disrupted, and unique signaling molecules were observed. Through pseudotime analysis, it was found that the differences between MM and HDs were mainly reflected in plasma cells. These differences are primarily related to various biological processes involving mitochondria. Then, we identified the key subpopulation associated with the malignant proliferation of plasma cells. This group of cells exhibited strong proliferation ability, high CNV scores, high expression of frequently mutated genes, and strong glucose metabolic activity. Furthermore, we demonstrated the therapeutic potential of WNK1 as a target. Our study provides new insights into the development of B cells and the heterogeneity of plasma cells in MM and suggests that WNK1 is a potential therapeutic target for MM.

Role of minimal residual disease assessment in multiple myeloma

Multiple myeloma (MM) is a hematologic malignancy characterized by clonal proliferation of plasma cells. MM is a heterogeneous disease, featured by various molecular subtypes with different outcomes. With the advent of very efficient therapies including monoclonal antibodies, bispecific T-cell engagers and chimeric antigen receptor T cells (CAR T cells), most MM patients now have a prolonged survival. However, the disease remains incurable, and a subgroup of high-risk patients continue to have early relapse and short survival. Novel and highly sensitive methods have been developed allowing the detection of minimal residual disease (MRD) during or after treatment. Achievement of MRD negativity is a strong and independent prognostic factor in both prospective randomized clinical trials and in the real-world setting. While MRD assessment is now a validated endpoint in clinical trials, its incorporation in clinical practice is not yet established and its potential impact on guiding therapy remains under in-depth evaluation. Here we discuss the different methods available for MRD assessment and the role of MRD evaluation in MM management.

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.

Anchored-fusion enables targeted fusion search in bulk and single-cell RNA sequencing data

Here, we present Anchored-fusion, a highly sensitive fusion gene detection tool. It anchors a gene of interest, which often involves driver fusion events, and recovers non-unique matches of short-read sequences that are typically filtered out by conventional algorithms. In addition, Anchored-fusion contains a module based on a deep learning hierarchical structure that incorporates self-distillation learning (hierarchical view learning and distillation [HVLD]), which effectively filters out false positive chimeric fragments generated during sequencing while maintaining true fusion genes. Anchored-fusion enables highly sensitive detection of fusion genes, thus allowing for application in cases with low sequencing depths. We benchmark Anchored-fusion under various conditions and found it outperformed other tools in detecting fusion events in simulated data, bulk RNA sequencing (bRNA-seq) data, and single-cell RNA sequencing (scRNA-seq) data. Our results demonstrate that Anchored-fusion can be a useful tool for fusion detection tasks in clinically relevant RNA-seq data and can be applied to investigate intratumor heterogeneity in scRNA-seq data.

HDAC Inhibition Induces CD26 Expression on Multiple Myeloma Cells via the c-Myc/Sp1-mediated Promoter Activation

CD26 is ubiquitously and intensely expressed in osteoclasts in patients with multiple myeloma, whereas its expression in plasma cells of patients with multiple myeloma is heterogeneous because of its cellular diversity, immune escape, and disease progression. Decreased expression levels of CD26 in myeloma cells constitute one of the mechanisms underlying resistance to humanized anti-CD26 mAb therapy in multiple myeloma. In the current study, we show that histone deacetylase inhibition (HDACi) with broad or class-specific inhibitors involves the induction of CD26 expression on CD26neg myeloma cells both transcriptionally and translationally. Furthermore, dipeptidyl peptidase Ⅳ (DPPⅣ) enzymatic activity was concomitantly enhanced in myeloma cells. Combined treatment with HDACi plus CD26mAb synergistically facilitated lysis of CD26neg myeloma cells not only by antibody-dependent cellular cytotoxicity but also by the direct effects of mAb. Of note, its combination readily augmented lysis of CD26neg cell populations, refractory to CD26mAb or HDACi alone. Chromatin immunoprecipitation assay revealed that HDACi increased acetylation of histone 3 lysine 27 at the CD26 promoter of myeloma cells. Moreover, in the absence of HDACi, c-Myc was attached to the CD26 promoter via Sp1 on the proximal G-C box of myeloma cells, whereas, in the presence of HDACi, c-Myc was detached from Sp1 with increased acetylation of c-Myc on the promoter, leading to activation of the CD26 promoter and initiation of transcription in myeloma cells. Collectively, these results confirm that HDACi plays crucial roles not only through its anti-myeloma activity but by sensitizing CD26neg myeloma cells to CD26mAb via c-Myc/Sp1-mediated CD26 induction, thereby augmenting its cytotoxicity.

SIGNIFICANCE

There is a desire to induce and sustain CD26 expression on multiple myeloma cells to elicit superior anti-myeloma response by humanized anti-CD26 mAb therapy. HDACi upregulates the expression levels of CD26 on myeloma cells via the increased acetylation of c-MycK323 on the CD26 promoter, leading to initiation of CD26 transcription, thereby synergistically augments the efficacy of CD26mAb against CD26neg myeloma cells.

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.

Construction of multiplexed transcriptome NGS libraries of microdissected tissue samples based on combinational DNA barcode microbeads

The combination of single-cell RNA sequencing and microdissection techniques that preserves positional information has become a major tool for spatial transcriptome analyses. However, high costs and time requirements, especially for experiments at the single cell scale, make it challenging for this approach to meet the demand for increased throughput. Therefore, we proposed combinational DNA barcode (CDB)-seq as a medium-throughput, multiplexed approach combining Smart-3SEQ and CDB magnetic microbeads for transcriptome analyses of microdissected tissue samples. We conducted a comprehensive comparison of conditions for CDB microbead preparation and related factors and then applied CDB-seq to RNA extracts, fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE) mouse brain tissue samples. CDB-seq transcriptomic profiles of tens of microdissected samples could be obtained in a simple, cost-effective way, providing a promising method for future spatial transcriptomics.

Gene interaction network analysis in multiple myeloma detects complex immune dysregulation associated with shorter survival

The plasma cell cancer multiple myeloma (MM) varies significantly in genomic characteristics, response to therapy, and long-term prognosis. To investigate global interactions in MM, we combined a known protein interaction network with a large clinically annotated MM dataset. We hypothesized that an unbiased network analysis method based on large-scale similarities in gene expression, copy number aberration, and protein interactions may provide novel biological insights. Applying a novel measure of network robustness, Ollivier-Ricci Curvature, we examined patterns in the RNA-Seq gene expression and CNA data and how they relate to clinical outcomes. Hierarchical clustering using ORC differentiated high-risk subtypes with low progression free survival. Differential gene expression analysis defined 118 genes with significantly aberrant expression. These genes, while not previously associated with MM, were associated with DNA repair, apoptosis, and the immune system. Univariate analysis identified 8/118 to be prognostic genes; all associated with the immune system. A network topology analysis identified both hub and bridge genes which connect known genes of biological significance of MM. Taken together, gene interaction network analysis in MM uses a novel method of global assessment to demonstrate complex immune dysregulation associated with shorter survival.

Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value

Multiple myeloma (MM) is a malignant neoplasm characterized by clonal proliferation of abnormal plasma cells. In many countries, it ranks as the second most prevalent malignant neoplasm of the hematopoietic system. Although treatment methods for MM have been continuously improved and the survival of patients has been dramatically prolonged, MM remains an incurable disease with a high probability of recurrence. As such, there are still many challenges to be addressed. One promising approach is single-cell RNA sequencing (scRNA-seq), which can elucidate the transcriptome heterogeneity of individual cells and reveal previously unknown cell types or states in complex tissues. In this review, we outlined the experimental workflow of scRNA-seq in MM, listed some commonly used scRNA-seq platforms and analytical tools. In addition, with the advent of scRNA-seq, many studies have made new progress in the key molecular mechanisms during MM clonal evolution, cell interactions and molecular regulation in the microenvironment, and drug resistance mechanisms in target therapy. We summarized the main findings and sequencing platforms for applying scRNA-seq to MM research and proposed broad directions for targeted therapies based on these findings.

Mitochondrial dysfunction and drug targets in multiple myeloma

Multiple myeloma (MM) is the second most common hematological cancer that has no cure. Although currently there are several novel drugs, most MM patients experience drug resistance and disease relapse. The results of previous studies suggest that aberrant mitochondrial function may contribute to tumor progression and drug resistance. Mitochondrial DNA mutations and metabolic reprogramming have been reported in MM patients. Several preclinical and clinical studies have shown encouraging results of mitochondria-targeting therapy in MM patients. In this review, we have summarized our current understanding of mitochondrial biology in MM. More importantly, we have reviewed mitochondrial targeting strategies in MM treatment.

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.

Metastatic Infiltration of Nervous Tissue and Periosteal Nerve Sprouting in Multiple Myeloma-Induced Bone Pain in Mice and Human

Multiple myeloma (MM) is a neoplasia of B plasma cells that often induces bone pain. However, the mechanisms underlying myeloma-induced bone pain (MIBP) are mostly unknown. Using a syngeneic MM mouse model, we show that periosteal nerve sprouting of calcitonin gene-related peptide (CGRP+) and growth associated protein 43 (GAP43+) fibers occurs concurrent to the onset of nociception and its blockade provides transient pain relief. MM patient samples also showed increased periosteal innervation. Mechanistically, we investigated MM induced gene expression changes in the dorsal root ganglia (DRG) innervating the MM-bearing bone of male mice and found alterations in pathways associated with cell cycle, immune response and neuronal signaling. The MM transcriptional signature was consistent with metastatic MM infiltration to the DRG, a never-before described feature of the disease that we further demonstrated histologically. In the DRG, MM cells caused loss of vascularization and neuronal injury, which may contribute to late-stage MIBP. Interestingly, the transcriptional signature of a MM patient was consistent with MM cell infiltration to the DRG. Overall, our results suggest that MM induces a plethora of peripheral nervous system alterations that may contribute to the failure of current analgesics and suggest neuroprotective drugs as appropriate strategies to treat early onset MIBP.SIGNIFICANCE STATEMENT Multiple myeloma (MM) is a painful bone marrow cancer that significantly impairs the quality of life of the patients. Analgesic therapies for myeloma-induced bone pain (MIBP) are limited and often ineffective, and the mechanisms of MIBP remain unknown. In this manuscript, we describe cancer-induced periosteal nerve sprouting in a mouse model of MIBP, where we also encounter metastasis to the dorsal root ganglia (DRG), a never-before described feature of the disease. Concomitant to myeloma infiltration, the lumbar DRGs presented blood vessel damage and transcriptional alterations, which may mediate MIBP. Explorative studies on human tissue support our preclinical findings. Understanding the mechanisms of MIBP is crucial to develop targeted analgesic with better efficacy and fewer side effects for this patient population.

Single cell clonotypic and transcriptional evolution of multiple myeloma precursor disease

Multiple myeloma remains an incurable disease, and the cellular and molecular evolution from precursor conditions, including monoclonal gammopathy of undetermined significance and smoldering multiple myeloma, is incompletely understood. Here, we combine single-cell RNA and B cell receptor sequencing from fifty-two patients with myeloma precursors in comparison with myeloma and normal donors. Our comprehensive analysis reveals early genomic drivers of malignant transformation, distinct transcriptional features, and divergent clonal expansion in hyperdiploid versus non-hyperdiploid samples. Additionally, we observe intra-patient heterogeneity with potential therapeutic implications and identify distinct patterns of evolution from myeloma precursor disease to myeloma. We also demonstrate distinctive characteristics of the microenvironment associated with specific genomic changes in myeloma cells. These findings add to our knowledge about myeloma precursor disease progression, providing valuable insights into patient risk stratification, biomarker discovery, and possible clinical applications.

Applications of single-cell RNA sequencing in drug discovery and development

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.

Transitioning single-cell genomics into the clinic

The use of genomics is firmly established in clinical practice, resulting in innovations across a wide range of disciplines such as genetic screening, rare disease diagnosis and molecularly guided therapy choice. This new field of genomic medicine has led to improvements in patient outcomes. However, most clinical applications of genomics rely on information generated from bulk approaches, which do not directly capture the genomic variation that underlies cellular heterogeneity. With the advent of single-cell technologies, research is rapidly uncovering how genomic data at cellular resolution can be used to understand disease pathology and mechanisms. Both DNA-based and RNA-based single-cell technologies have the potential to improve existing clinical applications and open new application spaces for genomics in clinical practice, with oncology, immunology and haematology poised for initial adoption. However, challenges in translating cellular genomics from research to a clinical setting must first be overcome.

Progression of Hodgkin lymphoma and plasma cell neoplasms: Report from the 2021 SH/EAHP Workshop

OBJECTIVES

To summarize cases submitted to the 2021 Society for Hematopathology/European Association for Haematopathology Workshop under the categories of progression of Hodgkin lymphoma, plasmablastic myeloma, and plasma cell myeloma.

METHODS

The workshop panel reviewed 20 cases covered in this session. In addition, whole-exome sequencing (WES) and whole-genome RNA expression analysis were performed on 10 submitted cases, including 6 Hodgkin lymphoma and 4 plasma neoplasm cases.

RESULTS

The cases of Hodgkin lymphoma included transformed cases to or from various types of B-cell lymphoma with 1 exception, which had T-cell differentiation. The cases of plasma cell neoplasms included cases with plasmablastic progression, progression to plasma cell leukemia, and secondary B-lymphoblastic leukemia. Gene variants identified by WES included some known to be recurrent in Hodgkin lymphoma and plasma cell neoplasm. All submitted Hodgkin lymphoma samples showed 1 or more of these mutations: SOCS1, FGFR2, KMT2D, RIT1, SPEN, STAT6, TET2, TNFAIP3, and ZNF217.

CONCLUSIONS

Better molecular characterization of both of these neoplasms and mechanisms of progression will help us to better understand mechanisms of progression and perhaps develop better prognostic models, as well as identifying novel therapeutic targets.

Lactate trafficking inhibition restores sensitivity to proteasome inhibitors and orchestrates immuno-microenvironment in multiple myeloma

Metabolic changes of malignant plasma cells (PCs) and adaptation to tumour microenvironment represent one of the hallmarks of multiple myeloma (MM). We previously showed that MM mesenchymal stromal cells are more glycolytic and produce more lactate than healthy counterpart. Hence, we aimed to explore the impact of high lactate concentration on metabolism of tumour PCs and its impact on the efficacy of proteasome inhibitors (PIs). Lactate concentration was performed by colorimetric assay on MM patient's sera. The metabolism of MM cell treated with lactate was assessed by seahorse and real time Polymerase Chain Reaction (PCR). Cytometry was used to evaluate mitochondrial reactive oxygen species (mROS), apoptosis and mitochondrial depolarization. Lactate concentration resulted increased in MM patient's sera. Therefore, PCs were treated with lactate and we observed an increase of oxidative phosphorylation-related genes, mROS and oxygen consumption rate. Lactate supplementation exhibited a significant reduction in cell proliferation and less responsive to PIs. These data were confirmed by pharmacological inhibition of monocarboxylate transporter 1 (MCT1) by AZD3965 which was able to overcame metabolic protective effect of lactate against PIs. Consistently, high levels of circulating lactate caused expansion of Treg and monocytic myeloid derived suppressor cells and such effect was significantly reduced by AZD3965. Overall, these findings showed that targeting lactate trafficking in TME inhibits metabolic rewiring of tumour PCs, lactate-dependent immune evasion and thus improving therapy efficacy.

Integrated Single-Cell (Phospho-)Protein and RNA Detection Uncovers Phenotypic Characteristics and Active Signal Transduction of Human Antibody-Secreting Cells

Single-cell technologies are currently widely applied to obtain a deeper understanding of the phenotype of single-cells in heterogenous mixtures. However, integrated multilayer approaches including simultaneous detection of mRNA, protein expression, and intracellular phospho-proteins are still challenging. Here, we combined an adapted method to in vitro-differentiate peripheral B-cells into antibody-secreting cells (ASCs) (i.e., plasmablasts and plasma cells) with integrated multi-omic single-cell sequencing technologies to detect and quantify immunoglobulin subclass-specific surface markers, transcriptional profiles, and signaling transduction pathway components. Using a common set of surface proteins, we integrated two multimodal datasets to combine mRNA, protein expression, and phospho-protein detection in one integrated dataset. Next, we tested whether ASCs that only seem to differ in its ability to secrete different IgM, IgA, or IgG antibodies exhibit other differences that characterize these different ASCs. Our approach detected differential expression of plasmablast and plasma cell markers, homing receptors, and TNF receptors. In addition, differential sensitivity was observed for the different cytokine stimulations that were applied during in vitro differentiation. For example, IgM ASCs were more sensitive to IL-15, while IgG ASC responded more to IL-6 and IFN addition. Furthermore, tonic BCR activity was detected in IgA and IgM ASCs, while IgG ASC exhibited active BCR-independent SYK activity and NF-κB and mTOR signaling. We confirmed these findings using flow cytometry and small molecules inhibitors, demonstrating the importance of SYK, NF-κB, and mTOR activity for plasmablast/plasma cell differentiation/survival and/or IgG secretion. Taken together, our integrated multi-omics approach allowed high-resolution phenotypic characterization of single cells in a heterogenous sample of in vitro-differentiated human ASCs. Our strategy is expected to further our understanding of human ASCs in healthy and diseased samples and provide a valuable tool to identify novel biomarkers and potential drug targets.

Integrative Analysis of Bulk RNA-Seq and Single-Cell RNA-Seq Unveils Novel Prognostic Biomarkers in Multiple Myeloma

Molecular heterogeneity has great significance in the disease biology of multiple myeloma (MM). Thus, the analysis combined single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data were performed to investigate the clonal evolution characteristics and to find novel prognostic targets in MM. The scRNA-seq data were analyzed by the Seurat pipeline and Monocle 2 to identify MM cell branches with different differentiation states. Marker genes in each branch were uploaded to the STRING database to construct the Protein-Protein Interaction (PPI) network, followed by the detection of hub genes by Cytoscape software. Using bulk RNA-seq data, Kaplan-Meier (K-M) survival analysis was then carried out to determine prognostic biomarkers in MM. A total of 342 marker genes in two branches with different differentiation states were identified, and the top 20 marker genes with the highest scores in the network calculated by the MCC algorithm were selected as hub genes in MM. Furthermore, K-M survival analysis revealed that higher NDUFB8, COX6C, NDUFA6, USMG5, and COX5B expression correlated closely with a worse prognosis in MM patients. Moreover, ssGSEA and Pearson analyses showed that their expression had a significant negative correlation with the proportion of Tcm (central memory cell) immune cells. Our findings identified NDUFB8, COX6C, NDUFA6, USMG5, and COX5B as novel prognostic biomarkers in MM, and also revealed the significance of genetic heterogeneity during cell differentiation in MM prognosis.

The Evidence That 25(OH)D3 and VK2 MK-7 Vitamins Influence the Proliferative Potential and Gene Expression Profiles of Multiple Myeloma Cells and the Development of Resistance to Bortezomib

Multiple myeloma (MM) remains an incurable hematological malignancy. Bortezomib (BTZ) is a proteasome inhibitor widely used in MM therapy whose potent activity is often hampered by the development of resistance. The immune system is vital in the pathophysiology of BTZ resistance. Vitamins D (VD) and K (VK) modulate the immune system; therefore, they are potentially beneficial in MM. The aim of the study was to evaluate the effect of BTZ therapy and VD and VK supplementation on the proliferation potential and gene expression profiles of MM cells in terms of the development of BTZ resistance. The U266 MM cell line was incubated three times with BTZ, VD and VK at different timepoints. Then, proliferation assays, RNA sequencing and bioinformatics analysis were performed. We showed BTZ resistance to be mediated by processes related to ATP metabolism and oxidative phosphorylation. The upregulation of genes from the SNORDs family suggests the involvement of epigenetic mechanisms. Supplementation with VD and VK reduced the proliferation of MM cells in both the non-BTZ-resistant and BTZ-resistant phenotypes. VD and VK, by restoring proper metabolism, may have overcome resistance to BTZ in vitro. This observation forms the basis for further clinical trials evaluating VD and VK as potential adjuvant therapies for MM patients.

Comprehensive Characterization of the Multiple Myeloma Immune Microenvironment Using Integrated scRNA-seq, CyTOF, and CITE-seq Analysis

As part of the Multiple Myeloma Research Foundation (MMRF) immune atlas pilot project, we compared immune cells of multiple myeloma bone marrow samples from 18 patients assessed by single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to understand the concordance of measurements among single-cell techniques. Cell type abundances are relatively consistent across the three approaches, while variations are observed in T cells, macrophages, and monocytes. Concordance and correlation analysis of cell type marker gene expression across different modalities highlighted the importance of choosing cell type marker genes best suited to particular modalities. By integrating data from these three assays, we found International Staging System stage 3 patients exhibited decreased CD4+ T/CD8+ T cells ratio. Moreover, we observed upregulation of RAC2 and PSMB9, in natural killer cells of fast progressors compared with those of nonprogressors, as revealed by both scRNA-seq and CITE-seq RNA measurement. This detailed examination of the immune microenvironment in multiple myeloma using multiple single-cell technologies revealed markers associated with multiple myeloma rapid progression which will be further characterized by the full-scale immune atlas project.

Significance

scRNA-seq, CyTOF, and CITE-seq are increasingly used for evaluating cellular heterogeneity. Understanding their concordances is of great interest. To date, this study is the most comprehensive examination of the measurement of the immune microenvironment in multiple myeloma using the three techniques. Moreover, we identified markers predicted to be significantly associated with multiple myeloma rapid progression.

Exploring the R-ISS stage-specific regular networks in the progression of multiple myeloma at single-cell resolution

The Revised International Staging System (R-ISS) is a simple and powerful prognostic tool for multiple myeloma (MM). However, heterogeneity in R-ISS stage is still poorly characterised, hampering improvement of treatments. We used single-cell RNA-seq to examine novel cellular heterogeneity and regular networks in nine MM patients stratified by R-ISS. Plasma cells were clustered into nine groups (P1-P9) based on gene expression, where P1-P5 were almost enriched in stage III.PDIA6 was significantly upregulated in P3 and LETM1 was enriched in P1, and they were validated to be upregulated in the MM cell line and in 22 other patients' myeloma cells. Furthermore, in progression, PDIA6 was newly found and verified to be activated by UQCRB through oxidative phosphorylation, while LETM1 was activated by STAT1 via the C-type lectin receptor-signalling pathway. Finally, a subcluster of monocytes was exclusively found in stage III specifically expressed chemokines modulated by ATF3. A few ligand-receptor pairs (CCL3/CCL5/CCL3L1-CCR1) were obviously active in monocyte-plasma communications in stage III. Herein, this study identified novel molecules, networks and crosstalk pairs in different R-ISS stages of MM, providing significant insight for its prognosis and treatment.

Identification of a drug-response gene in multiple myeloma through longitudinal single-cell transcriptome sequencing

Despite recent therapeutic advances for multiple myeloma (MM), relapse is very common. Here, we conducted longitudinal single-cell transcriptome sequencing (scRNA-seq) of MM cells from a patient with relapsed MM, treated with multiple anti-myeloma drugs. We observed five subclusters of MM cells, which appeared and/or disappeared in response to the therapeutic pressure, and identified cluster 3 which emerged during lenalidomide treatment and disappeared after proteasome inhibitor (PI) treatment. Among the differentially expressed genes in cluster 3, we found a candidate drug-response gene; pellino E3 ubiquitin-protein ligase family member 2 (PELI2), which is responsible for PI-induced cell death in in vitro assay. Kaplan-Meier survival analysis of database revealed that higher expression of PELI2 is associated with a better prognosis. Our integrated strategy combining longitudinal scRNA-seq analysis, in vitro functional assay, and database analysis would facilitate the understanding of clonal dynamics of MM in response to anti-myeloma drugs and identification of drug-response genes.

Towards precision medicine in lymphoid malignancies

Careful histopathologic examination remains the cornerstone in the diagnosis of the clinically and biologically heterogeneous group of lymphoid malignancies. However, recent advances in genomic and epigenomic characterization using high-throughput technologies have significantly improved our understanding of these tumors. Although no single genomic alteration is completely specific for a lymphoma entity, some alterations are highly recurrent in certain entities and thus can provide complementary diagnostic information when integrated in the hematopathological diagnostic workup. Moreover, other alterations may provide important information regarding the clinical course, that is, prognostic or risk-stratifying markers, or response to treatment, that is, predictive markers, which may allow tailoring of the patient's treatment based on (epi)genetic characteristics. In this review, we will focus on clinically relevant diagnostic, prognostic, and predictive biomarkers identified in more common types of B-cell malignancies, and discuss how diagnostic assays designed for comprehensive molecular profiling may pave the way for the implementation of precision diagnostics/medicine approaches. We will also discuss future directions in this rapidly evolving field, including the application of single-cell sequencing and other omics technologies, to decipher clonal dynamics and evolution in lymphoid malignancies.

FOXM1 regulates glycolysis and energy production in multiple myeloma

The transcription factor, forkhead box M1 (FOXM1), has been implicated in the natural history and outcome of newly diagnosed high-risk myeloma (HRMM) and relapsed/refractory myeloma (RRMM), but the mechanism with which FOXM1 promotes the growth of neoplastic plasma cells is poorly understood. Here we show that FOXM1 is a positive regulator of myeloma metabolism that greatly impacts the bioenergetic pathways of glycolysis and oxidative phosphorylation (OxPhos). Using FOXM1-deficient myeloma cells as principal experimental model system, we find that FOXM1 increases glucose uptake, lactate output, and oxygen consumption in myeloma. We demonstrate that the novel 1,1-diarylethylene small-compound FOXM1 inhibitor, NB73, suppresses myeloma in cell culture and human-in-mouse xenografts using a mechanism that includes enhanced proteasomal FOXM1 degradation. Consistent with the FOXM1-stabilizing chaperone function of heat shock protein 90 (HSP90), the HSP90 inhibitor, geldanamycin, collaborates with NB73 in slowing down myeloma. These findings define FOXM1 as a key driver of myeloma metabolism and underscore the feasibility of targeting FOXM1 for new approaches to myeloma therapy and prevention.

Understanding the Bioactivity and Prognostic Implication of Commonly Used Surface Antigens in Multiple Myeloma

Multiple myeloma (MM) progression is dependent on its interaction with the bone marrow microenvironment and the immune system and is mediated by key surface antigens. Some antigens promote adhesion to the bone marrow matrix and stromal cells, while others are involved in intercellular interactions that result in differentiation of B-cells to plasma cells (PC). These interactions are also involved in malignant transformation of the normal PC to MM PC as well as disease progression. Here, we review selected surface antigens that are commonly used in the flow cytometry analysis of MM for identification of plasma cells (PC) and the discrimination between normal and malignant PC as well as prognostication. These include the markers: CD38, CD138, CD45, CD19, CD117, CD56, CD81, CD27, and CD28. Furthermore, we will discuss the novel marker CD24 and its involvement in MM. The bioactivity of each antigen is reviewed, as well as its expression on normal vs. malignant PC, prognostic implications, and therapeutic utility. Understanding the role of these specific surface antigens, as well as complex co-expressions of combinations of antigens, may allow for a more personalized prognostic monitoring and treatment of MM patients.

Single-cell RNA-seq reveals clonal diversity and prognostic genes of relapsed multiple myeloma

BACKGROUND

Multiple myeloma (MM) is a clinically and biologically heterogeneous plasma-cell malignancy. Despite extensive research, disease heterogeneity and relapse remain a big challenge in MM therapeutics. We tried to dissect this disease and identify novel biomarkers for patient stratification and treatment outcome prediction by applying single-cell technology.

METHODS

We performed single-cell RNA sequencing (scRNA-seq) and variable-diversity-joining regions-targeted sequencing (scVDJ-seq) concurrently on bone marrow samples from a cohort of 18 patients with newly diagnosed MM (NDMM; n = 12) or refractory/relapsed MM (RRMM; n = 6). We analysed the malignant clonotypes using scVDJ-seq data and conducted data integration and cell-type annotation through the CCA algorithm based on gene expression profiling. Furthermore, we identified disease status-specific genes and modules by comparison of NDMM and RRMM datasets and explored the findings in a larger MM cohort from the MMRF CoMMpass study.

RESULTS

We found that all the myeloma cells in either diagnosed or relapsed samples were dominated by a major clone, with a few subclones in several samples (n = 5). Next, we investigated the universal transcriptional features of myeloma cells and identified eight meta-programs correlated with this disease, especially meta-programs 1 and 8 (M1 and M8), which were the most significant and related to cell cycle and stress response, respectively. Furthermore, we classified the malignant plasma cells into eight clusters and found that the cell numbers in clusters 2/6/7 were exclusively higher in relapsed samples. Besides, we identified several attractive candidates for biomarkers (e.g. SMAD1 and STMN1) associated with disease progression and relapse in our dataset and related to overall survival in the CoMMpass dataset.

CONCLUSIONS

Our data provide insights into the heterogeneity of MM as well as highlight the relevance of intra-tumour heterogeneity and discover novel biomarkers that might be a potent therapy.

Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease

We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information "impressions," which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single-cell and patient bulk tissue transcriptomic datasets from glioblastoma multiforme (GBM), Alzheimer's disease (AD), and multiple myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on myeloma single-cell transcriptomics identified PHF19high myeloma cells associated with progression. Availability: https://github.com/tsteelejohnson91/DEGAS .

Single-Cell Sequencing: Current Applications in Precision Onco-Genomics and Cancer Therapeutics

Simple Summary

Single-cell sequencing technologies are growing, advancing, and supporting new opportunities to better understand cancer. A variety of technologies are available that analyze the human transcriptome, genome, epigenome, and proteome, enabling integrated datasets. As a result, these integrated datasets contribute to new mechanistic insights and areas with therapeutic potential. This review summarizes the various single-cell sequencing techniques and provides examples of recent high-impact findings from the utilization of these technologies. Additionally, the translational relevance of these technologies and their use in clinical trials is described, along with the future potential for novel findings using these innovative methods.

Abstract

Single-cell sequencing encompasses a variety of technologies that evaluate cells at the genomic, transcriptomic, epigenomic, and proteomic levels. Each of these levels can be split into additional techniques that enable specific and optimized sequencing for a specialized purpose. At the transcriptomic level, single-cell sequencing has been used to understand immune-malignant cell networks, as well as differences between primary versus metastatic tumors. At the genomic and epigenomic levels, single-cell sequencing technology has been used to study genetic mutations involved in tumor evolution or the reprogramming of regulatory elements present in metastasized disease, respectively. Lastly, at the proteomic level, single-cell sequencing has been used to identify biomarkers important for predicting patient prognosis, as well as biomarkers essential for evaluating optimal treatment strategies. Integrated databases and atlases, as a result of large sequencing experiments, provide a vast array of information that can be applied to various studies and accessed by researchers to further answer scientific questions. This review summarizes recent, high-impact literature covering these aspects, as well as single-cell sequencing in the translational setting. Specifically, we review the potential that single-cell sequencing has in the clinic and its implementation in current clinical studies.

Single-cell profiling of tumour evolution in multiple myeloma - opportunities for precision medicine

Multiple myeloma (MM) is a haematological malignancy of plasma cells characterized by substantial intraclonal genetic heterogeneity. Although therapeutic advances made in the past few years have led to improved outcomes and longer survival, MM remains largely incurable. Over the past decade, genomic analyses of patient samples have demonstrated that MM is not a single disease but rather a spectrum of haematological entities that all share similar clinical symptoms. Moreover, analyses of samples from monoclonal gammopathy of undetermined significance and smouldering MM have also shown the existence of genetic heterogeneity in precursor stages, in some cases remarkably similar to that of MM. This heterogeneity highlights the need for a greater dissection of underlying disease biology, especially the clonal diversity and molecular events underpinning MM at each stage to enable the stratification of individuals with a high risk of progression. Emerging single-cell sequencing technologies present a superlative solution to delineate the complexity of monoclonal gammopathy of undetermined significance, smouldering MM and MM. In this Review, we discuss how genomics has revealed novel insights into clonal evolution patterns of MM and provide examples from single-cell studies that are beginning to unravel the mutational and phenotypic characteristics of individual cells within the bone marrow tumour, immune microenvironment and peripheral blood. We also address future perspectives on clinical application, proposing that multi-omics single-cell profiling can guide early patient diagnosis, risk stratification and treatment strategies.

Using MMRFBiolinks R-Package for Discovering Prognostic Markers in Multiple Myeloma

Multiple myeloma (MM) is the second most frequent hematological malignancy in the world although the related pathogenesis remains unclear. Gene profiling studies, commonly carried out through next-generation sequencing (NGS) and Microarrays technologies, represent powerful tools for discovering prognostic markers in MM. NGS technologies have made great leaps forward both economically and technically gaining in popularity. As NGS techniques becomes simpler and cheaper, researchers choose NGS over microarrays for more of their genomic applications. However, Microarrays still provide significant benefits with respect to NGS. For instance, RNA-Seq requires more complex bioinformatic analysis with respect to Microarray as well as it lacks of standardized protocols for analysis. Therefore, a synergy between the two technologies may be well expected in the future. In order to take up this challenge, a valid tool for integrative analysis of MM data retrieved through NGS techniques is MMRFBiolinks, a new R package for integrating and analyzing datasets from the Multiple Myeloma Research Foundation (MMRF) CoMMpass (Clinical Outcomes in MM to Personal Assessment of Genetic Profile) study, available at MMRF Researcher Gateway (MMRF-RG), and at the National Cancer Institute Genomic Data Commons (NCI-GDC) Data Portal. Instead of developing a completely new package from scratch, we decided to leverage TC-GABiolinks, an R/Bioconductor package, because it provides some useful methods to access and analyze MMRF-CoMMpass data. An integrative analysis workflow based on the usage of MMRFBiolinks is illustrated.In particular, it leads towards a comparative analysis of RNA-Seq data stored at GDC Data Portal that allows to carry out a Kaplan Meier (KM ) Survival Analysis and an enrichment analysis for a Differential Gene Expression (DGE) gene set.Furthermore, it deals with MMRF-RG data for analyzing the correlation between canonical variants and treatment outcome as well as treatment class. In order to show the potential of the workflow, we present two case studies. The former deals with data of MM Bone Marrow sample types available at GDC Data Portal. The latter deals with MMRF-RG data for analyzing the correlation between canonical variants in a gene set obtained from the case study 1 and the treatment outcome as well as the treatment class.

Genome Instability in Multiple Myeloma: Facts and Factors

Multiple myeloma (MM) is a malignant neoplasm of terminally differentiated immunoglobulin-producing B lymphocytes called plasma cells. MM is the second most common hematologic malignancy, and it poses a heavy economic and social burden because it remains incurable and confers a profound disability to patients. Despite current progress in MM treatment, the disease invariably recurs, even after the transplantation of autologous hematopoietic stem cells (ASCT). Biological processes leading to a pathological myeloma clone and the mechanisms of further evolution of the disease are far from complete understanding. Genetically, MM is a complex disease that demonstrates a high level of heterogeneity. Myeloma genomes carry numerous genetic changes, including structural genome variations and chromosomal gains and losses, and these changes occur in combinations with point mutations affecting various cellular pathways, including genome maintenance. MM genome instability in its extreme is manifested in mutation kataegis and complex genomic rearrangements: chromothripsis, templated insertions, and chromoplexy. Chemotherapeutic agents used to treat MM add another level of complexity because many of them exacerbate genome instability. Genome abnormalities are driver events and deciphering their mechanisms will help understand the causes of MM and play a pivotal role in developing new therapies.

Minimal Residual Disease in Myeloma: Application for Clinical Care and New Drug Registration

The development of novel agents has transformed the treatment paradigm for multiple myeloma, with minimal residual disease (MRD) negativity now achievable across the entire disease spectrum. Bone marrow-based technologies to assess MRD, including approaches using next-generation flow and next-generation sequencing, have provided real-time clinical tools for the sensitive detection and monitoring of MRD in patients with multiple myeloma. Complementary liquid biopsy-based assays are now quickly progressing with some, such as mass spectrometry methods, being very close to clinical use, while others utilizing nucleic acid-based technologies are still developing and will prove important to further our understanding of the biology of MRD. On the regulatory front, multiple retrospective individual patient and clinical trial level meta-analyses have already shown and will continue to assess the potential of MRD as a surrogate for patient outcome. Given all this progress, it is not surprising that a number of clinicians are now considering using MRD to inform real-world clinical care of patients across the spectrum from smoldering myeloma to relapsed refractory multiple myeloma, with each disease setting presenting key challenges and questions that will need to be addressed through clinical trials. The pace of advances in targeted and immune therapies in multiple myeloma is unprecedented, and novel MRD-driven biomarker strategies are essential to accelerate innovative clinical trials leading to regulatory approval of novel treatments and continued improvement in patient outcomes.

Cancer cell states and emergent properties of the dynamic tumor system

Phenotypic heterogeneity within malignant cells of a tumor is emerging as a key property of tumorigenesis. Recent work using single-cell transcriptomics has led to the identification of distinct cancer cell states across a range of cancer types, but their functional relevance and the advantage that they provide to the tumor as a system remain elusive. We present here a definition of cancer cell states in terms of coherently and differentially expressed gene modules and review the origins, dynamics, and impact of states on the tumor system as a whole. The spectrum of cell states taken on by a malignant population may depend on cellular lineage, epigenetic history, genetic mutations, or environmental cues, which has implications for the relative stability or plasticity of individual states. Finally, evidence has emerged that malignant cells in different states may cooperate or compete within a tumor niche, thereby providing an evolutionary advantage to the tumor through increased immune evasion, drug resistance, or invasiveness. Uncovering the mechanisms that govern the origin and dynamics of cancer cell states in tumorigenesis may shed light on how heterogeneity contributes to tumor fitness and highlight vulnerabilities that can be exploited for therapy.

RNA-Sequencing-Based Transcriptomic Score with Prognostic and Theranostic Values in Multiple Myeloma

Multiple myeloma (MM) is the second most frequent hematological cancer and is characterized by the clonal proliferation of malignant plasma cells. Genome-wide expression profiling (GEP) analysis with DNA microarrays has emerged as a powerful tool for biomedical research, generating a huge amount of data. Microarray analyses have improved our understanding of MM disease and have led to important clinical applications. In MM, GEP has been used to stratify patients, define risk, identify therapeutic targets, predict treatment response, and understand drug resistance. In this study, we built a gene risk score for 267 genes using RNA-seq data that demonstrated a prognostic value in two independent cohorts (n = 674 and n = 76) of newly diagnosed MM patients treated with high-dose Melphalan and autologous stem cell transplantation. High-risk patients were associated with the expression of genes involved in several major pathways implicated in MM pathophysiology, including interferon response, cell proliferation, hypoxia, IL-6 signaling pathway, stem cell genes, MYC, and epigenetic deregulation. The RNA-seq-based risk score was correlated with specific MM somatic mutation profiles and responses to targeted treatment including EZH2, MELK, TOPK/PBK, and Aurora kinase inhibitors, outlining potential utility for precision medicine strategies in MM.

Single cell RNA-seq data and bulk gene profiles reveal a novel signature of disease progression in multiple myeloma

Background

The development of multiple myeloma (MM) is considered to involve a multistep transformation process, but the role of cytogenetic abnormalities and molecular alterations in determining the cell fate of multiple myeloma (MM) remains unclear. Here, we have analyzed single cell RNA-seq data and bulk gene profiles to reveal a novel signature associated with MM development.

Methods

The scRNA-seq data from GSE118900 was used to profile the transcriptomes of cells from MM patients at different stages. Pseudotemporal ordering of the single cells was performed using Monocle package to feature distinct transcriptomic states of the developing MM cells. The bulk microarray profiles from GSE24080 and GSE9782 were applied to identify a signature associated with MM development.

Results

The 597 cells were divided into 7 clusters according to different risk levels. They were initiated mainly from monoclonal gammopathy of undetermined significance (MGUS), newly diagnosed MM (NDMM), or relapsed and/or refractory myeloma (RRMM) with cytogenetically favorable t(11;14), moved towards the cells from smoldering MM (SMM) or NDMM without t(11;14) or t(4;14), and then finally to cells from SMM or RRMM with t(4;14). Based on the markers identified in the late stage, the bulk data was used to develop a 20-gene signature stratifying patients into high and low-risk groups (GSE24080: HR = 3.759, 95% CI 2.746–5.145; GSE9782: HR = 2.612, 95% CI 1.894–3.603), which was better than the previously published gene signatures (EMC92, UAMS70, and UAMS17) and International Staging System. This signature also succeeded in predicting the clinical outcome of patients treated with bortezomib (HR = 2.884, 95% CI 1.994–4.172, P = 1.89e−8). The 20 genes were further verified by quantitative real-time polymerase chain reaction using samples obtained from the patients with MM.

Conclusion

Our comprehensive analyses offered new insights in MM development, and established a 20-gene signature as an independent biomarker for MM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02190-6.

YAP and TAZ are transcriptional co-activators of AP-1 proteins and STAT3 during breast cellular transformation

The YAP and TAZ paralogs are transcriptional co-activators recruited to target sites by TEAD proteins. Here, we show that YAP and TAZ are also recruited by JUNB (a member of the AP-1 family) and STAT3, key transcription factors that mediate an epigenetic switch linking inflammation to cellular transformation. YAP and TAZ directly interact with JUNB and STAT3 via a WW domain important for transformation, and they stimulate transcriptional activation by AP-1 proteins. JUNB, STAT3, and TEAD co-localize at virtually all YAP/TAZ target sites, yet many target sites only contain individual AP-1, TEAD, or STAT3 motifs. This observation and differences in relative crosslinking efficiencies of JUNB, TEAD, and STAT3 at YAP/TAZ target sites suggest that YAP/TAZ is recruited by different forms of an AP-1/STAT3/TEAD complex depending on the recruiting motif. The different classes of YAP/TAZ target sites are associated with largely non-overlapping genes with distinct functions. A small minority of target sites are YAP- or TAZ-specific, and they are associated with different sequence motifs and gene classes from shared YAP/TAZ target sites. Genes containing either the AP-1 or TEAD class of YAP/TAZ sites are associated with poor survival of breast cancer patients with the triple-negative form of the disease.

Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis

Background

Multiple myeloma (MM), the second most hematological malignancy, has high incidence and remains incurable till now. The pathogenesis of MM is poorly understood. This study aimed to identify novel prognostic model for MM on gene expression profiles.

Methods

Gene expression datas of MM (GSE6477, GSE136337) were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in GSE6477 between case samples and normal control samples were screened by the limma package. Meanwhile, enrichment analysis was conducted, and a protein-protein interaction (PPI) network of these DEGs was established by STRING and cytoscape software. Co-expression modules of genes were built by Weighted Correlation Network Analysis (WGCNA). Key genes were identified both from hub genes and the DEGs. Univariate and multivariate Cox congression were performed to screen independent prognostic genes to construct a predictive model. The predictive power of the model was evaluated by Kaplan–Meier curve and time-dependent receiver operating characteristic (ROC) curves. Finally, univariate and multivariate Cox regression analyse were used to investigate whether the prognostic model could be independent of other clinical parameters.

Results

GSE6477, including 101 case and 15 normal control, were screened as the datasets. A total of 178 DEGs were identified, including 59 up-regulated and 119 down-regulated genes. In WGCNA analysis, module black and module purple were the most relevant modules with cancer traits, and 92 hub genes in these two modules were selected for further analysis. Next, 47 genes were chosen both from the DEGs and hub genes as key genes. Three genes (LYVE1, RNASE1, and RNASE2) were finally screened by univariate and multivariate Cox regression analyses and used to construct a risk model. In addition, the three-gene prognostic model revealed independent and accurate prognostic capacity in relation to other clinical parameters for MM patients.

Conclusion

In summary, we identified and constructed a three-gene-based prognostic model that could be used to predict overall survival of MM patients.

Applications of single-cell sequencing in cancer research: progress and perspectives

Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics sequencing, is a powerful tool to decipher the cellular and molecular landscape at a single-cell resolution, unlike bulk sequencing, which provides averaged data. The use of single-cell sequencing in cancer research has revolutionized our understanding of the biological characteristics and dynamics within cancer lesions. In this review, we summarize emerging single-cell sequencing technologies and recent cancer research progress obtained by single-cell sequencing, including information related to the landscapes of malignant cells and immune cells, tumor heterogeneity, circulating tumor cells and the underlying mechanisms of tumor biological behaviors. Overall, the prospects of single-cell sequencing in facilitating diagnosis, targeted therapy and prognostic prediction among a spectrum of tumors are bright. In the near future, advances in single-cell sequencing will undoubtedly improve our understanding of the biological characteristics of tumors and highlight potential precise therapeutic targets for patients.

Single-cell technologies and analyses in hematopoiesis and hematological malignancies

In recent years, single-cell technologies have emerged as breakthrough techniques that enable the characterization of hematopoietic cell populations of normal and malignant tissue samples and will be combined in the near future with bulk technologies, currently used in clinical practice, to improve diagnosis, prognosis, and the search for novel molecular targets. These single-cell methods have the advantage of not masking cell-to-cell variation features and involve the study of genetic, epigenetic, transcriptional, and proteomic landscapes from a single-cell perspective. Latest advances in this field have enabled the development of novel strategies that significantly increase both sensitivity and high throughput. In this review, we emphasize emerging techniques aimed at assessing individual or multiomic parameters at single-cell resolution and analyze how these technologies have helped us understand hematopoietic variability and identify unknown and/or rare subpopulations. We also summarize the impact of these single-cell profiling strategies on the characterization of cell diversity within the tumor and the clonal evolution of multiple hematological malignancies in samples from untreated and treated patients, which provide valuable information for diagnosis, prognosis, and future treatments and explain why current therapies may fail. However, despite these improvements, new challenges lie ahead.

Co-evolution of tumor and immune cells during progression of multiple myeloma

Multiple myeloma (MM) is characterized by the uncontrolled proliferation of plasma cells. Despite recent treatment advances, it is still incurable as disease progression is not fully understood. To investigate MM and its immune environment, we apply single cell RNA and linked-read whole genome sequencing to profile 29 longitudinal samples at different disease stages from 14 patients. Here, we collect 17,267 plasma cells and 57,719 immune cells, discovering patient-specific plasma cell profiles and immune cell expression changes. Patients with the same genetic alterations tend to have both plasma cells and immune cells clustered together. By integrating bulk genomics and single cell mapping, we track plasma cell subpopulations across disease stages and find three patterns: stability (from precancer to diagnosis), and gain or loss (from diagnosis to relapse). In multiple patients, we detect “B cell-featured” plasma cell subpopulations that cluster closely with B cells, implicating their cell of origin. We validate AP-1 complex differential expression (JUN and FOS) in plasma cell subpopulations using CyTOF-based protein assays, and integrated analysis of single-cell RNA and CyTOF data reveals AP-1 downstream targets (IL6 and IL1B) potentially leading to inflammation regulation. Our work represents a longitudinal investigation for tumor and microenvironment during MM progression and paves the way for expanding treatment options.

Clonal evolution in multiple myeloma (MM) needs to be understood in both the tumor and its microenvironment. Here the authors perform single-cell multi-omics profiling of samples from MM patients at different stages, finding transitions in the immune cell composition throughout progression.