Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 4856 2023-11-15 06:45:17 |
2 format change + 2 word(s) 4858 2023-11-15 09:41:33 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Sharma, N.S.; Choudhary, B. Immune Landscape in Multiple Myeloma. Encyclopedia. Available online: https://encyclopedia.pub/entry/51580 (accessed on 03 September 2024).
Sharma NS, Choudhary B. Immune Landscape in Multiple Myeloma. Encyclopedia. Available at: https://encyclopedia.pub/entry/51580. Accessed September 03, 2024.
Sharma, Niyati Seshagiri, Bibha Choudhary. "Immune Landscape in Multiple Myeloma" Encyclopedia, https://encyclopedia.pub/entry/51580 (accessed September 03, 2024).
Sharma, N.S., & Choudhary, B. (2023, November 15). Immune Landscape in Multiple Myeloma. In Encyclopedia. https://encyclopedia.pub/entry/51580
Sharma, Niyati Seshagiri and Bibha Choudhary. "Immune Landscape in Multiple Myeloma." Encyclopedia. Web. 15 November, 2023.
Immune Landscape in Multiple Myeloma
Edit

Multiple myeloma (MM) is a dyscrasia of plasma cells (PCs) characterized by abnormal immunoglobulin (Ig) production. The disease remains incurable due to a multitude of mutations and structural abnormalities in MM cells, coupled with a favorable microenvironment and immune suppression that eventually contribute to the development of drug resistance. The bone marrow microenvironment (BMME) is composed of a cellular component comprising stromal cells, endothelial cells, osteoclasts, osteoblasts, and immune cells, and a non-cellular component made of the extracellular matrix (ECM) and the liquid milieu, which contains cytokines, growth factors, and chemokines. The bone marrow stromal cells (BMSCs) are involved in the adhesion of MM cells, promote the growth, proliferation, invasion, and drug resistance of MM cells, and are also crucial in angiogenesis and the formation of lytic bone lesions. Classical immunophenotyping in combination with advanced immune profiling using single-cell sequencing technologies has enabled immune cell-specific gene expression analysis in MM to further elucidate the roles of specific immune cell fractions from peripheral blood and bone marrow (BM) in myelomagenesis and progression, immune evasion and exhaustion mechanisms, and development of drug resistance and relapse. 

multiple myeloma tumor microenvironment hematopoiesis immune profiling immunotherapy

1. Introduction

B-cell development and maturation is a tightly regulated process. Throughout adult life, the bone marrow (BM) serves as the primary hotspot for a finite number of pluripotent hematopoietic stem cells (HSCs) to sequentially give rise to all the blood cell types through a process called hematopoiesis (Figure 1).
Figure 1. Hematopoiesis is the process by which all the blood cell types are produced through differentiation from increasingly lineage-committed precursor cells in the adult BM. Broadly, the cells can be classified as myeloid or lymphoid in origin. The myeloid lineage gives rise to thrombocytes (platelets), erythrocytes, granulocytes (mast cells, basophils, neutrophils, eosinophils), macrophages, and myeloid-derived dendritic cells (DCs). The lymphoid lineage produces T- and B-lymphocytes, natural killer (NK) cells, and lymphoid DCs.
Immature plasma cells (PCs) originate from the lymphoid progenitor lineage [1][2], express a surface B-cell immunoglobulin receptor (BCR), and move to secondary lymphoid organs [3][4]. Here, the B-cells start to mature while remaining non-proliferative and transcriptionally dormant but primed for antigen recognition through the toll-like receptors (TLR) or the BCR [3][5]. B-cell activation can then occur in a T-cell-independent or dependent manner to generate acute or long-term immunity. The latter process involves complex activation by cytokines and affinity maturation in the germinal center of the lymph nodes. During activation and differentiation, B-cells are equipped to express a host of antibodies through the physiological process of class-switch recombination and somatic hypermutation [3][5][6]. However, these processes are highly error-prone and can cause the accumulation of genomic changes such as chromosomal translocations and aneuploidies [3][5]. Such translocations can place proto-oncogenes under the control of powerful enhancers in the Ig gene locus which can create aberrant PCs that can establish multiple myeloma (MM) precursor conditions such as monoclonal gammopathy of uncertain significance (MGUS) and smoldering MM (SMM) [5][7][8][9].
In MM, a clonal subset of these transformed post-germinal-center B-cells undergoes malignant proliferation in the favorable microenvironment of the BM (Figure 2) to produce abnormal levels of Igs that are secreted into the serum and urine and termed as Bence-Jones proteins [9][10]. Monoclonal antibody accumulation in the BM can cause anemia, hypercalcemia and lytic bone lesions with accompanying bone pain and fractures, and impaired kidney function leading to renal failure [6][8][9]. These bone- and organ-damaging events are characteristic features of MM, often abbreviated as “CRAB” which stands for hyperCalcemia, Renal involvement, Anemia, and Bone lesions [8][9].
Figure 2. Normal B-cell development and myelomagenesis. Upon BCR activation by antigenic stimulus, B-cell precursors migrate from the BM to peripheral lymph nodes to elicit a short-term immune response and the remaining cells differentiate into long-lived circulating plasma and memory cells, eventually becoming resident cells in the tissue niches. Some of the post-germinal-center B-cells carrying oncogenic mutations enter the circulation as plasmablasts and memory B-cells that migrate to the BM as pre-malignant cells to establish MGUS. Further advantageous oncogenic mutations and the favorable BMME drive malignancy and MM development after which the cells become niche-independent and re-enter the circulation, resulting in extramedullary plasmacytoma.

Epidemiology, Diagnosis, and Cytogenetics in Multiple Myeloma

As of 2020, 176,404 new cases of MM were reported globally, accounting for close to 1% of all newly diagnosed cancer cases and 1.2% of all cancer-related deaths [11]. MM is often labelled as a neoplasm of ageing, with the age at diagnosis often being ≥ 65 years [9][12][13]. Very rarely, MM has been reported in individuals younger than 30 years of age at frequencies of 0.02% to 0.3% of all diagnosed cases [14]. In 1979, the Durie-Salmon staging system was developed for evaluating MM based on the presence of anemia and bone disease [15][16]. The International Myeloma Working Group defines the International and Revised International Staging systems (ISS and R-ISS) for MM based on either clinical and laboratory parameters only (ISS) or in combination with cytogenetic abnormalities (R-ISS) [17][18]. Chromosomal aneuploidy is a complex yet distinguishing feature in MM, with the disease often presenting as hyperdiploid or non-hyperdiploid, involving translocations with the IgH locus [19][20]. Fluorescent in situ hybridization (FISH) is most commonly employed to detect cytogenetic abnormalities in MM [19][20][21]. Hyperdiploidy is observed in nearly half of MM cases and is associated with a good prognosis [5][22][23]. Primary reciprocal translocations specific to B-cell chromosomal rearrangements, observed in nearly half the MGUS and MM cases, are early events in myeloma pathogenesis and are considered high-risk (HR) abnormalities as they bring oncogenes, mainly cyclins, fibroblast growth factor receptor (FGFR) 3, multiple myeloma SET domain (MMSET), and musculoaponeurotic fibrosarcoma (c-MAF), under the control of powerful IgH enhancers [5][22][24][25][26]. Secondary genetic alterations indicate disease progression, generally do not involve the IgH locus, and may or may not be clonal. Further, these changes are seen in a non-hyperdiploid background with reduced dependence on the BMME and often indicate a poor prognosis (Figure 3) [5][25]. Detailed classification and staging based on clinical and cytogenetic parameters are listed in Table 1.
Figure 3. Cytogenetic abnormalities and involvement of the BMME in myeloma initiation and progression. Abnormal PCs in the BM harbor chromosomal abnormalities such as translocations and aneuploidy which could establish the precursor condition of monoclonal gammopathy of uncertain significance (MGUS). BM involvement and modification of the immune landscape by myeloma cells sets off myeloma genesis, first as smoldering MM (SMM), and with the accumulation of secondary genetic abnormalities such as CNVs and epigenetic changes as well as oncogenic driver mutations, active MM is established which eventually progresses to aggressive extramedullary plasmacytoma upon bone lysis and egress from the marrow to peripheral blood.
Table 1. Staging of myeloma based on myeloma-defining events, cytogenetic abnormalities, and prognosis [27][28][29][30][31][32][33][34][35][36][37][38]. * CRAB criteria defined by the International Myeloma Working Group (IMWG) refer to organ involvement based on hyperCalcemia (serum calcium > 10.5 mg/dL), Renal impairment (serum creatinine > 2 mg/dL), Anemia (hemoglobin < 10 g/dL), and Bone lesions (osteolysis or osteoporosis on any skeletal examination). LR = Low Risk, SR = Standard Risk, IR = Intermediate Risk, HR = High Risk, IS = Improved Survival, AP = Adverse Prognosis for progression-free/overall survival.

Myeloma Stage

Clinical Parameters

Cytogenetic Abnormalities

Prognosis

MGUS

Serum M-protein ≤ 30 mg/l, <10% BM clonal PC, no organ damage evidence (CRAB *)

Hyperdiploidy (HD) (chromosomes 3, 5, 7, 9, 11, 15, 17)

SR

IgH translocations (IgHT)—t(11;14), t(4;14), t(6;14), t(14;16) and t(14;20) and involved partner genes—4p16: FGFR3/MMSET, 11q13: CCND1, 16q23: MAF, 6p21: CCND3, 20q11: MAFB

HR, AP

monosomy 13/del(13q)

SR/IR

SMM

Serum M-protein ≥ 30 mg/l (IgG or IgA), >10% BM clonal PC, no organ damage evidence (CRAB)

MGUS abnormalities

SR

MUGUS + secondary abnormalities—monosomy17/del(17p) (gene P53), gain 1q21

HR

MM

Serum M-protein ≥ 30 mg/l (IgG or IgA), >10% BM clonal PC, and organ damage evidence (CRAB)

HD, HD (chromosomes 3, 5) + gain 5q31

IS

Hypoploidy, IgHT + non-HD

HR, AP

del(17p), gain 1q21 (gene CSK1B), MYC translocations, del(1p)

AP

Extramedullary MM (not including solitary/bone plasmacytoma)

MM criteria and involvement of skeleton or soft tissue or lymph node(s), BM-independence, drug resistance

del(17p13), del(13q14), MYC-over-expression, t(4;14)

AP

Mutations in TP53, RB1, KRAS, FAK

HR

del(17p) + non-HD

AP

RRMM

Reappearance of M-protein, ≥5% BMPCs, new lytic bone lesions and/or soft tissue plasmacytomas, increase in size of residual bone lesions, and/or development of hypercalcemia > 11.5 mg/dL not attributable to another cause

del(17p), t(4;14) or t(14;16)

HR, AP

gain 1q21

AP

t(4;14): overexpression of FGFR3, t(14:16): overexpression of c-maf, t(14:20): overexpression of c-maf, del(17p): deletion of p53

AP

2. Marrow, Microenvironment, and Multiple Myeloma

The BMME comprises an extracellular matrix (ECM), blood vessels, cells from the hematopoietic lineage, supporting cells required for the regulation of hematopoiesis, and cells from the osteo-lineage [39][40][41]. Normal hematopoiesis occurs in two distinct niches in the BM: the central niche which is in the core of the BM and the endosteal niche which is close to the surface of the bone and the terminal epiphysis containing the trabecular marrow (Figure 4) [40][42].
Figure 4. Anatomy of the BMME and niche interactions of BM-resident cells during hematopoiesis. Two major niches can be described in the bone marrow: vascular and endosteal. The vascular niche is at the core of the bone marrow comprising both arteriolar and sinusoidal vessels and is associated with LepR+ or nestin+ BMSCs and CXAR cells, macrophages, megakaryocytes, sympathetic nerve fibers, Schwann cells, endothelial cells, and adipocytes. The endosteal niche is closer to terminal bone and comprises osteolineage cells—osteoblasts and osteoclasts—involved in bone formation and recycling. Secreted growth factors, cytokines, CAMs, and ECM molecules participate in an intricate signaling network to regulate HSC quiescence and differentiation/proliferation programs. The major factors involved are CXCL-12, TGF-β, G-CSF, GM-CSF, MMPs, collagens, and integrins. Particularly, the Schwann cells and stromal cells associated with the vasculature help in the regulation of HSC migration and differentiation while cells at the endosteal surface promote HSC quiescence and maintenance of their self-renewal capacity. ★ Activation of hematopoiesis. ▲ proliferation. retention in the BM mobilization ♻ recycling.
Hematopoiesis starts in the embryonic stage, primarily to produce red blood cells from transitory, non-renewing erythroid progenitor cells that carry oxygen to tissues during growth and development. This is the “primitive” phase of hematopoiesis [43][44]. As embryonic development progresses, the primitive wave allows definitive hematopoiesis to begin in various fetal niches, giving rise to erythroid-myeloid progenitor cells [43][44]. HSCs progressively move into the fetal liver and then the BM, which is the primary site for adult hematopoiesis [1][44]. The HSCs generate increasingly lineage-committed progenitor cells that give rise to all the blood cells (Figure 1). The first line of cells are the common lymphoid progenitors (CLP) and the common myeloid progenitors (CMP) [45]. Subsequent differentiation of CLP gives rise to B and T lymphocytes, which are integral for antigen-specific adaptive immunity, and to natural killer (NK) [46] cells, which are part of the innate immune system. CMPs differentiate into erythroid/platelet progenitors which produce the red blood cells (erythrocytes) and platelets (thrombocytes), and granulocyte-macrophage progenitors which produce granulocytes (neutrophils, eosinophils, basophils, and mast cells), dendritic cells (DCs), and monocytes, which differentiate into macrophages [45][47].
Disruptions to genome integrity, transcription efficiency, and cell-specific protein expression capacity affect several cellular processes including hematopoiesis [48]. Such events can drive the malignant transformation of BM cells and alter the environment in a manner disruptive to normal cells but favorable for their own survival and proliferation [49]. A double-edged hypothesis is suggested by several studies whereby both the microenvironment and the hematopoietic cells themselves are suggested to initiate or promote malignancy [42][50][51][52]. Cells of the hematological lineage and the BMME can co-evolve to promote tumorigenesis, cancer progression, and the development of drug resistance. In MM, the BMME exhibits distinct, stage-specific profiles (Figure 5).
Figure 5. Immune cell function in myeloma BM is altered by secreted factors from the MMPCs and the cells lose their normal immune surveillance capacity. An immunosuppressive state is then established, helping MMPCs evade immune system detection. The immune cells show features of exhaustion and anergy. Myeloma cells primarily use secreted IL-6 and TGF-β to disrupt normal immune system activation and express cell adhesion molecules (CAMs) such as PSGL-1, ICAM-1, and VLA-4 which helps in attachment to various immune cells as well as the BMSCs or ECM. PD-L1 is another surface-expressed ligand on MMPCs that can bind NK- and T-cell programmed cell death protein (PD)-1 to suppress their cytotoxic functions and cause immune exhaustion. MMPCs also express CXCL-12 which results in macrophage polarization to M2 or the tumor-associated macrophage phenotype. MMPC-derived cytokines including IL-6 and IL-10, along with secreted M-CSF and VEGF, affect DC maturation and proliferation in the BM. PC = plasma cell, TC = T-cell, NKT = natural killer-like T-cell, Nφ = neutrophil, NK = natural killer cell, MMPC = multiple myeloma plasma cell, Mφ = macrophage, MKφ = megakaryocyte, TAMφ = tumor-associated macrophage, tMKφ = transformed megakaryocyte, DC = dendritic cell, mDC = monocytic DC, pDC = plasmacytoid DC, ExNK = exhausted NK, AnNφ = anergic Nφ, AnTC = anergic TC.

3. Immune Profiling Using Single-Cell Transcriptome Sequencing in Multiple Myeloma

In hematological malignancies, immune system dysfunction is well-established, and a deeper understanding of the immune landscape can inform precision diagnostics, drug response prediction, and targeted treatment. Recent technological advancement has seen an influx of several non- and minimally invasive approaches for individualized immune profiling from limited samples to obtain gene/protein expression information from bulk immune cells and single cells [53]. In this section, the researchers attempt to review the application of one of the most promising technologies in this area—single-cell transcriptome sequencing (SCTS)—in immune profiling of hematological malignancies, focusing on MM. Table 2 provides a detailed list of recent single-cell sequencing studies in MM focusing on the immune microenvironment.
Table 2. Recent single-cell sequencing studies in MM exploring the role of immune cells and the BMME in myelomagenesis, progression, and drug response. NA = not available.
SCTS is an emerging technology used to resolve gene expression differences between cell populations of a heterogeneous tumor sample, and it has been employed in hematological malignancies to understand aberrant immune cell frequency and function. Many SCTS studies have been carried out for MM in recent years, focusing on the BM tumor microenvironment, the immune landscape and immune evasion, and the development of drug resistance and relapse.
Single-cell sequencing of around 75,000 cells from 14 MM patients by Liu et al. provided insights into the malleability of the tumor microenvironment and its role in tumor heterogeneity and MM progression, as demonstrated by co-clustering patient immune and PCs with similar genetic backgrounds [57]. The authors also traced the journey of PCs from SMM to active myeloma and relapse using somatic alteration, cell type-specific marker expression, and gene expression data in relation to the tumor microenvironment. Unlike the malignant cells, the clustering of non-malignant cells was independent of tumor stage or origin. However, patient-specific differences in expression were observed, further underscoring the involvement of the tumor microenvironment’s interaction with the genetic background [57]. BM cells from different stages of MM progression and healthy donors were used for SCTS, revealing that NK cell abundance increases early in the disease along with altered chemokine receptor expression. In this study, the authors found that granzyme K+ cytotoxic T-memory cells were lost as early as SMM and could play a critical role in MM immunosurveillance in mouse models. Additionally, major histocompatibility complex (MHC)-class II dysregulation in CD14+ monocytes leads to T-cell suppression in vitro [56]. A compromised tumor microenvironment was also demonstrated in a study combining SCTS with validation using bulk RNA sequencing (RNAseq), flow cytometry, and functional experiments [60]. Results showed enrichment of exhausted NK cells and CD8+ T cells, macrophage reprogramming to a mixed M1-M2 phenotype, and two TAM clusters present only in the MM stage with increased M2 scores. Interaction analysis showed enrichment for two ligand–receptor pairs between macrophages and malignant PCs involving phagocytosis suppression and the macrophage inhibitory factor, which alters macrophage phenotype, and these alterations correlated with disease progression and adverse outcomes. In a multi-omics study that included SCTS as part of the MMRF immune atlas pilot project, 18 MM patient BM samples were used to compare immune cell profiles using scRNA-seq, cytometry by time-of-flight (CyTOF), and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq). The techniques exhibited differences in identifying T-cell, macrophage, and monocyte numbers. Overall, ISS stage 3 patients had decreased CD4+ T/CD8+ T-cell ratios, and ras-related C3 botulinum toxin substrate 2 (RAC2) and proteasome 20S subunit beta 9 (PSMB9) expression was upregulated in NK cells of progressive versus non-progressive MM [58]. SCTS of 10 MM individuals pre and post two cycles of a bortezomib-cyclophosphamide-dexamethasone (VCD) regimen revealed increased immune-reactive and stress-associated pathways while unfolded-protein response (UPR) and metabolic-related programs were reduced. Low immune-reactive gene expression indicated poor survival and non-responsiveness to drugs, likely by reduced MHC-class I-mediated antigen presentation capacity (APC) and immune surveillance, and upregulation of immune escape genes. The authors also found a connection between the tumor-intrinsic immune reactive program and the immunosuppressive microenvironment arising from immune cell exhaustion and checkpoint molecule expression in T-cells, NK cells, and monocytes [59]. Another study focusing specifically on SCTS of DCs and monocytes from 10 MM patients found five distinct clusters for each cell type [61]. Using trajectory analysis, one subset—monocyte-derived DCs (mono-DCs)—was shown to be generated from intermediate monocytes. Compared to healthy controls, conventional DC2 (cDC2), mono-DC, and intermediate monocytes of MM patients exhibited impaired APC. Additionally, the regulatory function of interferon regulatory factor 1 (IRF1) was shown to be decreased in the cDC2, mono-DC, and intermediate monocytes of MM patients.

4. Immunotherapy in Multiple Myeloma

Immunotherapy has come a long way from the Nobel Prize-winning first documented use of allogeneic bone marrow hematopoietic cell transplantation in 1968 by Edward Donnall Thomas for leukemia treatment [62]. The primary immunotherapies for hematological cancers are checkpoint inhibitors, vaccines, cell-based antibodies, and oncolytic viruses [63]. Here, the researchers will review the antibody- and cell-based methods. In cancer, immunotherapy refers to either activating or rescuing an anti-tumor immunogenic response or suppressing an undesirable immune dysfunction state to destroy existing cancer cells or control the progression. This is often achieved by identifying and targeting specific proteins usually expressed only by malignant cells using antibody- or cell-based methods. Monoclonal or bispecific antibodies are designed against the malignant cell proteins to destroy cancer cells while leaving healthy cells untouched. Similarly, healthy immune cells (mainly T- or NK-cells) derived from the patient are engineered in the laboratory to express specific antibodies or peptides against cancer cell-specific proteins and termed chimeric antigen receptor (CAR) cells, which can home onto cancer cells and kill them via cell-mediated cytotoxic responses. The patient’s immune function can also be rescued using specific recombinant cytokines and growth factors. Immunotherapy can be used as a monotherapy or in combination with a cytotoxic/chemotherapeutic drug to eliminate cancer cells while minimizing off-target effects.
Bispecific T-cell engagers (BiTEs) have two specific variable region chains—one for the immune cell receptor and one for the cancer-specific target antigen—to improve specificity and reduce off-target action.
MM’s standard-of-care treatment has remained relatively the same since autologous stem cell transplantation (ASCT) was developed in the 1980s and, later, PIs in the 2000s (Figure 6). MM remains an incurable disease with high relapse and drug resistance rates [64][65][66][67][68]. Cell- and antibody-based immunotherapy are emerging as invaluable tools in MM treatment, with clinical trials showing great promise. B-cell maturation antigen (BCMA) and SLAMF7 are the two novel targets in MM immunotherapy. BCMA is an important member of the TNFR superfamily expressed by all malignant MMPCs and required for survival, proliferation, and myeloma progression. At the same time, SLAMF7 is thought to play a role In BM stromal interactions with MMPCs to promote survival and is also highly expressed by all stages of MMPCs [69][70]. The landmark advancement in MM therapy was the development and approval of the monoclonal antibodies elotuzumab (anti-SLAMF7) and daratumumab (anti-CD38) in 2015 for both monotherapy and combination forms, particularly in the case of RRMM [71][72][73][74][75][76]. The first anti-BCMA CAR-T cells were made by lentiviral vector-mediated transfection in 2013 using a single-chain variable fragment from mouse anti-BCMA antibody combined with hinge and transmembrane regions of human CD8α, CD3ζ T-cell activation domain, and a costimulatory molecule (CD28), and the first clinical trial took place in 2016 to show potent cytotoxicity in refractory MM [77][78]. Since then, a wide array of immunotherapies have been developed, including chimeric antigen receptor-T (CAR-T) cells, bispecific antibodies, antibody-drug conjugates, and immune checkpoint inhibitors [64][65][79]. A combination of the PI3K inhibitor with BCMA was used to develop the Bb2121 CAR T-cell therapy, designed to improve the memory phenotype for RRMM. In clinical trials, this showed minimal residual disease (MRD) negativity and median progression-free survival (PFS) of 17.7 months [80]. LCAR-B38M, a dual epitope anti-BCMA-targeting CAR-T therapy, underwent a Phase 1 clinical trial and showed a median duration of response (DOR) of 16 months, median PFS of 15 months, and median PFS for patients achieving CR of 24 months [81]. The CARAMBA Phase 1/2A clinical trial employs CAR-T cells targeting SLAMF7, utilizing a novel virus-free transient mRNA expression system to minimize the incidence of cytokine release syndrome (CRS) in the first European virus-free CAR-T clinical trial [82]. CAR-NK cell therapy has also been developed against SLAMF7, which was shown to eliminate human MM cells in a mouse xenograft model and is currently under phase 1 clinical trial evaluation (NCT03710421) [83]. A majority of MMPCs express CD56—required for MM cell peripheral blood egress and setup of extramedullary disease—which is also an emerging CAR cell therapy target showing positive results in a mouse model and currently in clinical trials (NCT03473496, NCT03271632) [84][85]. Another exciting target is CD19, which is rarely expressed by MMPCs but could potentially be expressed by “stem” clones in some patients. It has also been tested in two clinical trials with no conclusive results [84]. Other important targets in CAR cell therapy are CD138, κ-light chain, G-protein coupled receptor (GPRC) 5D, NKG2D, and New York Esophageal Squamous Cell Carcinoma (NY-ESO)-1, also being investigated in several clinical trials [84]. Table 3 lists successful ongoing and completed clinical trials for immunotherapy in MM at various stages of the disease. An anti-BCMA antibody, erlanatamab, is currently in a Phase 1 dose escalation clinical trial (NCT03269136) for RRMM post-PI or anti-CD38 treatment over an 8.1 month follow-up, with 67% of participants showing Grade 1 or 2 CRS, 31% complete remission (CR), 64% overall response rate (ORR), and 91% probability of event-free status at 6 months [86]. Several other anti-BCMA BiTEs and BsAbs are in various stages of clinical trials, and more targets are being explored, including CD138, CD38, CD19, SLAMF7, Fc receptor-like 5 (FcRL5), CS1-NKG2D, GPRC5D, and NY-ESO-1 [87].
Figure 6. Timeline of therapeutic developments in MM. Historically, corticosteroids were the first drugs to be used in MM treatment, and in the late 1970s, BM stem cell transplantation became one of the most promising treatments in eligible patients. Discovery of the proteasome inhibitor (PI) bortezomib was a significant advancement in MM treatment and remains the standard first-line therapy in most MM regimens today. The last decade has witnessed significant research into targeting the dysregulated immune landscape as well as myeloma cell-specific markers through development of antibody- and cell-based immunotherapeutic approaches.
Table 3. Ongoing immunotherapy clinical trials for MM {adapted from the NCBI Clinical Trials Registry. Source: [88]}.

References

  1. Rieger, M.A.; Schroeder, T. Hematopoiesis. Cold Spring Harb. Perspect. Biol. 2012, 4, a008250.
  2. Elsaid, R.; Soares-Da-Silva, F.; Peixoto, M.; Amiri, D.; Mackowski, N.; Pereira, P.; Bandeira, A.; Cumano, A. Hematopoiesis: A Layered Organization Across Chordate Species. Front. Cell Dev. Biol. 2020, 8, 606642.
  3. González, D.; van der Burg, M.; García-Sanz, R.; Fenton, J.A.; Langerak, A.W.; González, M.; van Dongen, J.J.M.; Miguel, J.F.S.; Morgan, G.J. Immunoglobulin gene rearrangements and the pathogenesis of multiple myeloma. Blood 2007, 110, 3112–3121.
  4. Godin, I.; Cumano, A. Hematopoietic Stem Cell Development; Springer Science & Business Media: Berlin, Germany, 2010; 178p.
  5. Barwick, B.G.; Gupta, V.A.; Vertino, P.M.; Boise, L.H. Cell of Origin and Genetic Alterations in the Pathogenesis of Multiple Myeloma. Front. Immunol. 2019, 10, 1121.
  6. Cowan, A.J.; Green, D.J.; Kwok, M.; Lee, S.; Coffey, D.G.; Holmberg, L.A.; Tuazon, S.; Gopal, A.K.; Libby, E.N. Diagnosis and Management of Multiple Myeloma: A Review. JAMA 2022, 327, 464–477.
  7. Rustad, E.H.; Yellapantula, V.; Leongamornlert, D.; Bolli, N.; Ledergor, G.; Nadeu, F.; Angelopoulos, N.; Dawson, K.J.; Mitchell, T.J.; Osborne, R.J.; et al. Timing the initiation of multiple myeloma. Nat. Commun. 2020, 11, 1917.
  8. Morgan, G.J.; Walker, B.A.; Davies, F.E. The genetic architecture of multiple myeloma. Nat. Rev. Cancer 2012, 12, 335–348.
  9. Padala, S.A.; Barsouk, A.; Barsouk, A.; Rawla, P.; Vakiti, A.; Kolhe, R.; Kota, V.; Ajebo, G.H. Epidemiology, Staging, and Management of Multiple Myeloma. Med. Sci. 2021, 9, 3.
  10. Röllig, C.; Knop, S.; Bornhäuser, M. Multiple myeloma. Lancet 2015, 385, 2197–2208.
  11. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249.
  12. Zhou, L.; Yu, Q.; Wei, G.; Wang, L.; Huang, Y.; Hu, K.; Hu, Y.; Huang, H. Measuring the global, regional, and national burden of multiple myeloma from 1990 to 2019. BMC Cancer 2021, 21, 606.
  13. Turesson, I.; Bjorkholm, M.; Blimark, C.H.; Kristinsson, S.; Velez, R.; Landgren, O. Rapidly changing myeloma epidemiology in the general population: Increased incidence, older patients, and longer survival. Eur. J. Haematol. 2018, 101, 237–244.
  14. Kazandjian, D. Multiple myeloma epidemiology and survival: A unique malignancy. Semin. Oncol. 2016, 43, 676–681.
  15. International Myeloma Foundation. Durie-Salmon Staging System. Available online: https://www.myeloma.org/ (accessed on 8 October 2022).
  16. Filonzi, G.; Mancuso, K.; Zamagni, E.; Nanni, C.; Spinnato, P.; Cavo, M.; Fanti, S.; Salizzoni, E.; Bazzocchi, A. A Comparison of Different Staging Systems for Multiple Myeloma: Can the MRI Pattern Play a Prognostic Role? AJR Am. J. Roentgenol. 2017, 209, 152–158.
  17. International Myeloma Foundation. International Staging System (ISS) and Revised ISS (R-ISS). Available online: https://www.myeloma.org/ (accessed on 8 October 2022).
  18. International Myeloma Foundation. International Myeloma Working Group (IMWG) criteria for the diagnosis of multiple myeloma. Available online: https://www.myeloma.org/ (accessed on 8 October 2022).
  19. Sawyer, J.R. The prognostic significance of cytogenetics and molecular profiling in multiple myeloma. Cancer Genet. 2011, 204, 3–12.
  20. Liebisch, P.; Viardot, A.; Baßermann, N.; Wendl, C.; Roth, K.; Goldschmidt, H.; Einsele, H.; Straka, C.; Stilgenbauer, S.; Döhner, H.; et al. Value of comparative genomic hybridization and fluorescence in situ hybridization for molecular diagnostics in multiple myeloma. Br. J. Haematol. 2003, 122, 193–201.
  21. Tassone, P.; Tagliaferri, P.; Rossi, M.; Gaspari, M.; Terracciano, R.; Venuta, S. Genetics and molecular profiling of multiple myeloma: Novel tools for clinical management? Eur. J. Cancer 2006, 42, 1530–1538.
  22. Bergsagel, P.L.; Kuehl, W.M. Molecular Pathogenesis and a Consequent Classification of Multiple Myeloma. J. Clin. Oncol. 2005, 23, 6333–6338.
  23. Hideshima, T.; Mitsiades, C.; Tonon, G.; Richardson, P.G.; Anderson, K.C. Understanding multiple myeloma pathogenesis in the bone marrow to identify new therapeutic targets. Nat. Rev. Cancer 2007, 7, 585–598.
  24. Fairfield, H.; Falank, C.; Avery, L.; Reagan, M.R. Multiple myeloma in the marrow: Pathogenesis and treatments. Ann. N. Y. Acad. Sci. 2016, 1364, 32–51.
  25. Kuehl, W.M.; Bergsagel, P.L. Multiple myeloma: Evolving genetic events and host interactions. Nat. Rev. Cancer 2002, 2, 175–187.
  26. Bergsagel, P.L.; Kuehl, W.M. Chromosome translocations in multiple myeloma. Oncogene 2001, 20, 5611–5622.
  27. Rajan, A.M.; Rajkumar, S.V. Interpretation of cytogenetic results in multiple myeloma for clinical practice. Blood Cancer J. 2015, 5, e365.
  28. Hillengass, J.; Moehler, T.; Hundemer, M. Monoclonal gammopathy and smoldering multiple myeloma: Diagnosis, staging, prognosis, management. Recent Results Cancer Res. 2011, 183, 113–131.
  29. Korde, N.; Kristinsson, S.Y.; Landgren, O. Monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM): Novel biological insights and development of early treatment strategies. Blood 2011, 117, 5573–5581.
  30. Seong, C.; Delasalle, K.; Hayes, K.; Weber, D.; Dimopoulos, M.; Swantkowski, J.; Huh, Y.; Glassman, A.; Champlin, R.; Alexanian, R. Prognostic value of cytogenetics in multiple myeloma. Br. J. Haematol. 1998, 101, 189–194.
  31. Kumar, S.; Fonseca, R.; Ketterling, R.P.; Dispenzieri, A.; Lacy, M.Q.; Gertz, M.A.; Hayman, S.R.; Buadi, F.K.; Dingli, D.; Knudson, R.A.; et al. Trisomies in multiple myeloma: Impact on survival in patients with high-risk cytogenetics. Blood 2012, 119, 2100–2105.
  32. Sonneveld, P.; Avet-Loiseau, H.; Lonial, S.; Usmani, S.; Siegel, D.; Anderson, K.C.; Chng, W.-J.; Moreau, P.; Attal, M.; Kyle, R.A.; et al. Treatment of multiple myeloma with high-risk cytogenetics: A consensus of the International Myeloma Working Group. Blood 2016, 127, 2955–2962.
  33. Billecke, L.; Penas, E.M.M.; May, A.M.; Engelhardt, M.; Nagler, A.; Leiba, M.; Schiby, G.; Kröger, N.; Zustin, J.; Marx, A.; et al. Cytogenetics of extramedullary manifestations in multiple myeloma. Br. J. Haematol. 2013, 161, 87–94.
  34. Besse, L.; Sedlarikova, L.; Greslikova, H.; Kupska, R.; Almasi, M.; Penka, M.; Jelinek, T.; Pour, L.; Adam, Z.; Kuglik, P.; et al. Cytogenetics in multiple myeloma patients progressing into extramedullary disease. Eur. J. Haematol. 2016, 97, 93–100.
  35. Harrison, S.J.; Perrot, A.; Alegre, A.; Simpson, D.; Wang, M.C.; Spencer, A.; Delimpasi, S.; Hulin, C.; Sunami, K.; Facon, T.; et al. Subgroup analysis of ICARIA-MM study in relapsed/refractory multiple myeloma patients with high-risk cytogenetics. Br. J. Haematol. 2021, 194, 120–131.
  36. Dimopoulos, M.A.; Kastritis, E.; Christoulas, D.; Migkou, M.; Gavriatopoulou, M.; Gkotzamanidou, M.; Iakovaki, M.; Matsouka, C.; Mparmparoussi, D.; Roussou, M.; et al. Treatment of patients with relapsed/refractory multiple myeloma with lenalidomide and dexamethasone with or without bortezomib: Prospective evaluation of the impact of cytogenetic abnormalities and of previous therapies. Leukemia 2010, 24, 1769–1778.
  37. Bhutani, M.; Foureau, D.M.; Atrash, S.; Voorhees, P.M.; Usmani, S.Z. Extramedullary multiple myeloma. Leukemia 2019, 34, 1–20.
  38. Lonial, S.; Mitsiades, C.S.; Richardson, P.G. Treatment Options for Relapsed and Refractory Multiple Myeloma. Clin. Cancer Res. 2011, 17, 1264–1277.
  39. Shafat, M.S.; Gnaneswaran, B.; Bowles, K.M.; Rushworth, S.A. The bone marrow microenvironment—Home of the leukemic blasts. Blood Rev. 2017, 31, 277–286.
  40. Morrison, S.J.; Scadden, D.T. The bone marrow niche for haematopoietic stem cells. Nature 2014, 505, 327–334.
  41. Yu, V.W.; Scadden, D.T. Heterogeneity of the bone marrow niche. Curr. Opin. Hematol. 2016, 23, 331–338.
  42. Méndez-Ferrer, S.; Bonnet, D.; Steensma, D.P.; Hasserjian, R.P.; Ghobrial, I.M.; Gribben, J.G.; Andreeff, M.; Krause, D.S. Bone marrow niches in haematological malignancies. Nat. Rev. Cancer 2020, 20, 285–298.
  43. Orkin, S.H.; Zon, L.I. Hematopoiesis: An Evolving Paradigm for Stem Cell Biology. Cell 2008, 132, 631–644.
  44. Jagannathan-Bogdan, M.; Zon, L.I. Hematopoiesis. Development 2013, 140, 2463–2467.
  45. Pucella, J.N.; Upadhaya, S.; Reizis, B. The Source and Dynamics of Adult Hematopoiesis: Insights from Lineage Tracing. Annu. Rev. Cell Dev. Biol. 2020, 36, 529–550.
  46. Yokota, T.; Oritani, K.; Mitsui, H.; Aoyama, K.; Ishikawa, J.; Sugahara, H.; Matsumura, I.; Tsai, S.; Tomiyama, Y.; Kanakura, Y.; et al. Growth-supporting activities of fibronectin on hematopoietic stem/progenitor cells in vitro and in vivo: Structural requirement for fibronectin activities of CS1 and cell-binding domains. Blood 1998, 91, 3263–3272.
  47. Smith, C. Hematopoietic Stem Cells and Hematopoiesis. Cancer Control. 2003, 10, 9–16.
  48. Waterstrat, A.; Van Zant, G. Effects of aging on hematopoietic stem and progenitor cells. Curr. Opin. Immunol. 2009, 21, 408–413.
  49. Fröbel, J.; Landspersky, T.; Percin, G.; Schreck, C.; Rahmig, S.; Ori, A.; Nowak, D.; Essers, M.; Waskow, C.; Oostendorp, R.A.J. The Hematopoietic Bone Marrow Niche Ecosystem. Front. Cell Dev. Biol. 2021, 9, 705410.
  50. Krause, D.S.; Scadden, D.T. A hostel for the hostile: The bone marrow niche in hematologic neoplasms. Haematologica 2015, 100, 1376–1387.
  51. Kumar, R.; Godavarthy, P.S.; Krause, D.S. The bone marrow microenvironment in health and disease at a glance. J. Cell Sci. 2018, 131, jcs201707.
  52. Duarte, D.; Hawkins, E.D.; Celso, C.L. The interplay of leukemia cells and the bone marrow microenvironment. Blood 2018, 131, 1507–1511.
  53. Lyons, Y.A.; Wu, S.Y.; Overwijk, W.W.; Baggerly, K.A.; Sood, A.K. Immune cell profiling in cancer: Molecular approaches to cell-specific identification. Npj Precis. Oncol. 2017, 1, 26.
  54. Ledergor, G.; Weiner, A.; Zada, M.; Wang, S.-Y.; Cohen, Y.C.; Gatt, M.E.; Snir, N.; Magen, H.; Koren-Michowitz, M.; Herzog-Tzarfati, K.; et al. Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma. Nat. Med. 2018, 24, 1867–1876.
  55. Khoo, W.H.; Ledergor, G.; Weiner, A.; Roden, D.L.; Terry, R.L.; McDonald, M.M.; Chai, R.C.; De Veirman, K.; Owen, K.L.; Opperman, K.S.; et al. A niche-dependent myeloid transcriptome signature defines dormant myeloma cells. Blood 2019, 134, 30–43.
  56. Zavidij, O.; Haradhvala, N.J.; Mouhieddine, T.H.; Sklavenitis-Pistofidis, R.; Cai, S.; Reidy, M.; Rahmat, M.; Flaifel, A.; Ferland, B.; Su, N.K.; et al. Single-cell RNA sequencing reveals compromised immune microenvironment in precursor stages of multiple myeloma. Nat. Cancer 2020, 1, 493–506.
  57. Liu, R.; Gao, Q.; Foltz, S.M.; Fowles, J.S.; Yao, L.; Wang, J.T.; Cao, S.; Sun, H.; Wendl, M.C.; Sethuraman, S.; et al. Co-evolution of tumor and immune cells during progression of multiple myeloma. Nat. Commun. 2021, 12, 282.
  58. Yao, L.; Jayasinghe, R.G.; Lee, B.H.; Bhasin, S.S.; Pilcher, W.; Doxie, D.B.; Gonzalez-Kozlova, E.; Dasari, S.; Fiala, M.A.; Pita-Juarez, Y.; et al. Comprehensive Characterization of the Multiple Myeloma Immune Microenvironment Using Integrated scRNA-seq, CyTOF, and CITE-seq Analysis. Cancer Res. Commun. 2022, 2, 1255–1265.
  59. Chen, M.; Wan, Y.; Li, X.; Xiang, J.; Chen, X.; Jiang, J.; Han, X.; Zhong, L.; Xiao, F.; Liu, J.; et al. Dynamic single-cell RNA-seq analysis reveals distinct tumor program associated with microenvironmental remodeling and drug sensitivity in multiple myeloma. Cell Biosci. 2023, 13, 19.
  60. Li, J.; Yang, Y.; Wang, W.; Xu, J.; Sun, Y.; Jiang, J.; Tan, H.; Ren, L.; Wang, Y.; Ren, Y.; et al. Single-cell atlas of the immune microenvironment reveals macrophage reprogramming and the potential dual macrophage-targeted strategy in multiple myeloma. Br. J. Haematol. 2023, 201, 917–934.
  61. Jiang, J.; Xiang, J.; Chen, M.; Wan, Y.; Zhong, L.; Han, X.; Chen, X.; Wang, J.; Xiao, F.; Liu, J.; et al. Distinct mechanisms of dysfunctional antigen-presenting DCs and monocytes by single-cell sequencing in multiple myeloma. Cancer Sci. 2023, 114, 2750–2760.
  62. Donnall Thomas, E. A history of haemopoietic cell transplantation. Br. J. Haematol. 1999, 105, 330–339.
  63. Lanier, O.L.; Pérez-Herrero, E.; Andrea, A.P.D.; Bahrami, K.; Lee, E.; Ward, D.M.; Ayala-Suárez, N.; Rodríguez-Méndez, S.M.; Peppas, N.A. Immunotherapy approaches for hematological cancers. iScience 2022, 25, 105326.
  64. Holthof, L.C.; Mutis, T. Challenges for Immunotherapy in Multiple Myeloma: Bone Marrow Microenvironment-Mediated Immune Suppression and Immune Resistance. Cancers 2020, 12, 988.
  65. A Shah, U.; Mailankody, S. Emerging immunotherapies in multiple myeloma. BMJ 2020, 370, m3176.
  66. Ntanasis-Stathopoulos, I.; Gavriatopoulou, M.; Kastritis, E.; Terpos, E.; Dimopoulos, M.A. Multiple myeloma: Role of autologous transplantation. Cancer Treat. Rev. 2020, 82, 101929.
  67. Mateos, M.-V.; Ludwig, H.; Bazarbachi, A.; Beksac, M.; Bladé, J.; Boccadoro, M.; Cavo, M.; Delforge, M.; Dimopoulos, M.A.; Facon, T.; et al. Insights on Multiple Myeloma Treatment Strategies. HemaSphere 2019, 3, e163.
  68. Gandolfi, S.; Laubach, J.P.; Hideshima, T.; Chauhan, D.; Anderson, K.C.; Richardson, P.G. The proteasome and proteasome inhibitors in multiple myeloma. Cancer Metastasis Rev. 2017, 36, 561–584.
  69. Palma, B.D.; Marchica, V.; Catarozzo, M.T.; Giuliani, N.; Accardi, F. Monoclonal and Bispecific Anti-BCMA Antibodies in Multiple Myeloma. J. Clin. Med. 2020, 9, 3022.
  70. Gogishvili, T.; Danhof, S.; Prommersberger, S.; Rydzek, J.; Schreder, M.; Brede, C.; Einsele, H.; Hudecek, M. SLAMF7-CAR T cells eliminate myeloma and confer selective fratricide of SLAMF7+ normal lymphocytes. Blood 2017, 130, 2838–2847.
  71. Nishida, H.; Yamada, T. Monoclonal Antibody Therapies in Multiple Myeloma: A Challenge to Develop Novel Targets. J. Oncol. 2019, 2019, 6084012.
  72. Bapatla, A.; Kaul, A.; Dhalla, P.S.; Armenta-Quiroga, A.S.; Khalid, R.; Garcia, J.; Khan, S. Role of Daratumumab in Combination With Standard Therapies in Patients With Relapsed and Refractory Multiple Myeloma. Cureus 2021, 13, e15440.
  73. Trudel, S.; Moreau, P.; Touzeau, C. Update on elotuzumab for the treatment of relapsed/refractory multiple myeloma: Patients’ selection and perspective. OncoTargets Ther. 2019, 12, 5813–5822.
  74. Abodunrin, F.O.; Tauseef, A.; Silberstein, P. Role of Daratumumab in Relapsed and Refractory Multiple Myeloma Patients: Meta-Analysis and Literature Review. Blood 2021, 138, 4734.
  75. Bruzzese, A.; Martino, E.A.; Vigna, E.; Iaccino, E.; Mendicino, F.; Lucia, E.; Olivito, V.; Filippelli, G.; Neri, A.; Morabito, F.; et al. Elotuzumab in multiple myeloma. Expert Opin. Biol. Ther. 2022, 23, 7–10.
  76. Grosicki, S.; Bednarczyk, M.; Barchnicka, A.; Grosicka, O. Elotuzumab in the treatment of relapsed and refractory multiple myeloma. Futur. Oncol. 2021, 17, 1581–1591.
  77. Carpenter, R.O.; Evbuomwan, M.O.; Pittaluga, S.; Rose, J.J.; Raffeld, M.; Yang, S.; Gress, R.E.; Hakim, F.T.; Kochenderfer, J.N. B-cell maturation antigen is a promising target for adoptive T-cell therapy of multiple myeloma. Clin. Cancer Res. 2013, 19, 2048–2060.
  78. Ali, S.A.; Shi, V.; Maric, I.; Wang, M.; Stroncek, D.F.; Rose, J.J.; Brudno, J.N.; Stetler-Stevenson, M.; Feldman, S.A.; Hansen, B.G.; et al. T cells expressing an anti–B-cell maturation antigen chimeric antigen receptor cause remissions of multiple myeloma. Blood 2016, 128, 1688–1700.
  79. Melchers, F. Checkpoints that control B cell development. J. Clin. Investig. 2015, 125, 2203–2210.
  80. Raje, N.S.; Berdeja, J.G.; Lin, Y.; Munshi, N.C.; Siegel, D.S.D.; Liedtke, M.; Jagannath, S.; Madduri, D.; Rosenblatt, J.; Maus, M.V.; et al. bb2121 anti-BCMA CAR T-cell therapy in patients with relapsed/refractory multiple myeloma: Updated results from a multicenter phase I study. J. Clin. Oncol. 2018, 36 (Suppl. 15), 8007.
  81. Zhao, W.H.; Liu, J.; Wang, B.-Y.; Chen, Y.-X.; Cao, X.-M.; Yang, Y.; Zhang, Y.-L.; Wang, F.-X.; Zhang, P.-Y.; Lei, B.; et al. A phase 1, open-label study of LCAR-B38M, a chimeric antigen receptor T cell therapy directed against B cell maturation antigen, in patients with relapsed or refractory multiple myeloma. J. Hematol. Oncol. 2018, 11, 141.
  82. Prommersberger, S.; Reiser, M.; Beckmann, J.; Danhof, S.; Amberger, M.; Quade-Lyssy, P.; Einsele, H.; Hudecek, M.; Bonig, H.; Ivics, Z. CARAMBA: A first-in-human clinical trial with SLAMF7 CAR-T cells prepared by virus-free Sleeping Beauty gene transfer to treat multiple myeloma. Gene Ther. 2021, 28, 560–571.
  83. Chu, J.; Deng, Y.; Benson, D.M.; He, S.; Hughes, T.; Zhang, J.; Peng, Y.; Mao, H.; Yi, L.; Ghoshal, K.; et al. CS1-specific chimeric antigen receptor (CAR)-engineered natural killer cells enhance in vitro and in vivo antitumor activity against human multiple myeloma. Leukemia 2013, 28, 917–927.
  84. Wang, X.; Pan, B.; Huang, H.; Xu, K. Non-BCMA targeted CAR-T cell therapies for multiple myeloma. ImmunoMedicine 2021, 1, e1030.
  85. Gagelmann, N.; Riecken, K.; Wolschke, C.; Berger, C.; Ayuk, F.A.; Fehse, B.; Kröger, N. Development of CAR-T cell therapies for multiple myeloma. Leukemia 2020, 34, 2317–2332.
  86. Bahlis, N.J.; Costello, C.L.; Raje, N.S.; Levy, M.Y.; Dholaria, B.; Solh, M.; Tomasson, M.H.; Damore, M.A.; Jiang, S.; Basu, C.; et al. Elranatamab in relapsed or refractory multiple myeloma: The MagnetisMM-1 phase 1 trial. Nat. Med. 2023, 29, 2023.
  87. Caraccio, C.; Krishna, S.; Phillips, D.J.; Schürch, C.M. Bispecific Antibodies for Multiple Myeloma: A Review of Targets, Drugs, Clinical Trials, and Future Directions. Front. Immunol. 2020, 11, 501.
  88. CTG Labs—NCBI. Available online: https://clinicaltrials.gov/search?cond=multiple%20myeloma&viewType=Table&limit=50&aggFilters=phase:1%202%203%204,results:with,status:com%20act%20not%20rec&intr=Immunotherapy (accessed on 23 September 2023).
More
Information
Subjects: Immunology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : ,
View Times: 464
Revisions: 2 times (View History)
Update Date: 15 Nov 2023
1000/1000
ScholarVision Creations