Metabolic Disorders in Multiple Myeloma

Metformin and chidamide synergistically suppress multiple myeloma progression and enhance lenalidomide/bortezomib sensitivity

Multiple myeloma (MM) is a common hematological malignancy, and patients with MM are recommended to take immunomodulatory drugs such as lenalidomide along with proteasome inhibitors such as bortezomib to extend survival. However, drug resistance influences the efficacy of treatment for MM. In our study, we found that metformin and chidamide both suppressed MM cell growth in a concentration- and time-dependent way (p < .001). Moreover, combined therapy with metformin and chidamide exhibited enhanced inhibition of the growth of MM cells compared with monotherapy (p < .05). Additionally, the triple-drug combination of metformin and chidamide with lenalidomide or bortezomib was used to stimulate the MM cells, and the results revealed that metformin and chidamide treatment sensitized MM cells to lenalidomide and bortezomib. As a result, the apoptosis (p < .001) together with cell cycle arrest at G0/G1 phase (p < .05) was stimulated by lenalidomide and bortezomib, and showed significant elevation in the triple-drug combination group compared with the lenalidomide or bortezomib treatment alone group (p < .05). Furthermore, the impacts of different drugs on glycolysis in MM cells were examined. We found that metformin and chidamide combined treatment significantly promoted glucose uptake and reduced energy production in MM cells treated with lenalidomide and bortezomib (p < .001), suggesting that metformin and chidamide affected glycolysis in MM cells and enhanced the sensitivity of lenalidomide and bortezomib in MM by regulating glucose metabolism. In conclusion, metformin and chidamide synergistically hindered MM cell growth and sensitized cells to lenalidomide/bortezomib. The findings of this study might provide novel clues to improve MM therapy.

Lipid metabolic vulnerabilities of multiple myeloma

Multiple myeloma (MM) is the second most common hematological malignancy worldwide, characterized by abnormal proliferation of malignant plasma cells within a tumor-permissive bone marrow microenvironment. Metabolic dysfunctions are emerging as key determinants in the pathobiology of MM. In this review, we highlight the metabolic features of MM, showing how alterations in various lipid pathways, mainly involving fatty acids, cholesterol and sphingolipids, affect the growth, survival and drug responsiveness of MM cells, as well as their cross-talk with other cellular components of the tumor microenvironment. These findings will provide a new path to understanding the mechanisms underlying how lipid vulnerabilities may arise and affect the phenotype of malignant plasma cells, highlighting novel druggable pathways with a significant impact on the management of MM.

FOXK2 regulates PFKFB3 in promoting glycolysis and tumorigenesis in multiple myeloma

Forkhead box K2 (FOXK2) is a transcription factor involved in regulating the pathophysiological processes in many types of cancers. Functioning as either an oncogene or tumor suppressor, FOXK2 is involved in cell proliferation, metastasis, DNA damage, metabolism, and autophagy. However, the functions of FOXK2 in multiple myeloma (MM) are still unexplored. Here we show that FOXK2 silencing by small interfering RNA (siRNA) prevented the expression of 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) via dephosphorylation of an AMP-activated protein kinase (AMPK). Consistently, suppression of FOXK2 inhibited glycolysis and cell proliferation in MM cells. Furthermore, the correlation between FOXK2 expression and disease progression in MM was evaluated using the TCGA (The Cancer Genome Atlas) database. Taken together, we identified a novel FOXK2-dependent signaling pathway involved in the regulation of PFKFB3 expression in response to glycolysis, which might serve as a potential therapeutic target in MM.

A Leukemic Target with a Thousand Faces: The Mitochondria

In the era of personalized medicine greatly improved by molecular diagnosis and tailor-made therapies, the survival rate of acute myeloid leukemia (AML) at 5 years remains unfortunately low. Indeed, the high heterogeneity of AML clones with distinct metabolic and molecular profiles allows them to survive the chemotherapy-induced changes, thus leading to resistance, clonal evolution, and relapse. Moreover, leukemic stem cells (LSCs), the quiescent reservoir of residual disease, can persist for a long time and activate the recurrence of disease, supported by significant metabolic differences compared to AML blasts. All these points highlight the relevance to develop combination therapies, including metabolism inhibitors to improve treatment efficacy. In this review, we summarized the metabolic differences in AML blasts and LSCs, the molecular pathways related to mitochondria and metabolism are druggable and targeted in leukemia therapies, with a distinct interest for Venetoclax, which has revolutionized the therapeutic paradigms of several leukemia subtype, unfit for intensive treatment regimens.

Repurposing Metformin in hematologic tumor: State of art

Metformin is an ancient drug for the treatment of type 2 diabetes, and many studies now suggested that metformin can be used as an adjuvant drug in the treatment of many types of tumors. The mechanism of action of metformin for tumor treatment mainly involves: 1. activation of AMPK signaling pathway 2. inhibition of DNA damage repair in tumor cells 3. downregulation of IGF-1 expression 4. inhibition of chemoresistance and enhancement of chemotherapy sensitivity in tumor cells 5. enhancement of antitumor immunity 6. inhibition of oxidative phosphorylation (OXPHOS). Metformin also plays an important role in the treatment of hematologic tumors, especially in leukemia, lymphoma, and multiple myeloma (MM). The combination of metformin and chemotherapy enhances the efficacy of chemotherapy, and metformin reduces the progression of monoclonal gammopathy of undetermined significance (MGUS) to MM. The purpose of this review is to summarize the anticancer mechanism of metformin and the role and mechanism of action of metformin in hematologic tumors. We mainly summarize the studies related to metformin in hematologic tumors, including cellular experiments and animal experiments, as well as controlled clinical studies and clinical trials. In addition, we also focus on the possible side effects of metformin. Although a large number of preclinical and clinical studies have been performed and the role of metformin in preventing the progression of MGUS to MM has been demonstrated, metformin has not been approved for the treatment of hematologic tumors, which is related to the adverse effects of its high-dose application. Low-dose metformin reduces adverse effects and has been shown to alter the tumor microenvironment and enhance antitumor immune response, which is one of the main directions for future research.

Exosome miRNAs profiling in serum and prognostic evaluation in patients with multiple myeloma

MicroRNAs (MiRNAs) carried by exosomes play pivotal roles in the crosstalk between cell components in the tumor microenvironment. Our study aimed at identifying the expression profile of exosomal miRNAs (exo-miRNAs) in the serum of multiple myeloma (MM) patients and investigating the regulation networks and their potential functions by integrated bioinformatics analysis. Exosomes in serum from 19 newly diagnosed MM patients and 9 healthy donors were isolated and the miRNA profile was investigated by small RNA sequencing. Differential expression of exo-miRNAs was calculated and target genes of miRNAs were predicted. CytoHubba was applied to identify the hub miRNAs and core target genes. The LASSO Cox regression model was used to develop the prognostic model, and the ESTIMATE immune score was calculated to investigate the correlation between the model and immune status in MM patients. The top six hub differentially expressed serum exo-miRNAs were identified. 513 target genes of the six hub exo-miRNAs were confirmed to be differentially expressed in MM cells in the Zhan Myeloma microarray dataset. Functional enrichment analysis indicated that these target genes were mainly involved in mRNA splicing, cellular response to stress, and deubiquitination. 13 core exo-miRNA target genes were applied to create a novel prognostic signature to provide risk stratification for MM patients, which is associated with the immune microenvironment of MM patients. Our study comprehensively investigated the exo-miRNA profiles in MM patients. A novel prognostic signature was constructed to facilitate the risk stratification of MM patients with distinct outcomes.

Head-to-head comparison of [68Ga]Ga-PSMA-11 and [18F]FDG PET/CT in multiple myeloma

PURPOSE

The aim of this study was to compare [18F]FDG and [68Ga]Ga-PSMA-11 PET/CT image findings in patients with multiple myeloma (MM).

METHODS

Twenty consecutive patients with symptomatic biopsy-proven MM were submitted to whole body [18F]FDG and [68Ga]Ga-PSMA-11 PET/CT with a time interval of 1-8 days between procedures. All lesions were counted and had their maximum SUV (SUVmax) measured. Intra-class correlation (ICC) was used to assess the agreement between [18F]FDG and [68Ga]Ga-PSMA-11 PET/CT findings.

RESULTS

A total of 266 lesions were detected in 19/20 patients. [18F]FDG detected 223/266 (84%) lesions in 17 patients and [68Ga]Ga-PSMA-11 190/266 (71%) lesions in 19 patients. Both procedures did not identify any active lesion in 1 patient. Forty-three (16%) lesions were detected only by [68Ga]Ga-PSMA-11 and 76 (29%) only by [18F]FDG. Both tracers identified 147 (55%) lesions. Intralesional mismatch of FDG-PSMA uptake was identified in 25 of these 147 lesions, found in 8 different patients. Different lesions with uptake of only [18F]FDG or [68Ga]Ga-PSMA-11 in the same patient were found in 4 patients. The highest SUVmax of [18F]FDG and [68Ga]Ga-PSMA-11 had a median (min-max) SUVmax of 6.5 (2.0-37.8) and 5.5 (1.7-51.3), respectively. [18F]FDG and [68Ga]Ga-PSMA-11 respectively identified 18 and 19 soft tissue lesions. False-positive [18F]FDG findings had minimal or no uptake of [68Ga]Ga-PSMA-11. Good reliability (ICC ≥ 0.75) was found for number of lesions, number of soft tissue lesions and highest SUVmax in each patient.

CONCLUSION

[18F]FDG or [68Ga]Ga-PSMA-11 alone can detect most MM lesions. Almost half of the lesions take up only one of the tracers, reflecting increased glycolysis or angiogenesis in specific lesions, and suggesting their possible complementary role in MM. The marked [68Ga]Ga-PSMA-11 uptake in some cases raises the possibility of a theranostic approach in selected patients.

Shutting off the fuel supply to target metabolic vulnerabilities in multiple myeloma

Pathways that govern cellular bioenergetics are deregulated in tumor cells and represent a hallmark of cancer. Tumor cells have the capacity to reprogram pathways that control nutrient acquisition, anabolism and catabolism to enhance their growth and survival. Tumorigenesis requires the autonomous reprogramming of key metabolic pathways that obtain, generate and produce metabolites from a nutrient-deprived tumor microenvironment to meet the increased bioenergetic demands of cancer cells. Intra- and extracellular factors also have a profound effect on gene expression to drive metabolic pathway reprogramming in not only cancer cells but also surrounding cell types that contribute to anti-tumor immunity. Despite a vast amount of genetic and histologic heterogeneity within and between cancer types, a finite set of pathways are commonly deregulated to support anabolism, catabolism and redox balance. Multiple myeloma (MM) is the second most common hematologic malignancy in adults and remains incurable in the vast majority of patients. Genetic events and the hypoxic bone marrow milieu deregulate glycolysis, glutaminolysis and fatty acid synthesis in MM cells to promote their proliferation, survival, metastasis, drug resistance and evasion of immunosurveillance. Here, we discuss mechanisms that disrupt metabolic pathways in MM cells to support the development of therapeutic resistance and thwart the effects of anti-myeloma immunity. A better understanding of the events that reprogram metabolism in myeloma and immune cells may reveal unforeseen vulnerabilities and advance the rational design of drug cocktails that improve patient survival.

Single-cell RNA sequencing reveals XBP1-SLC38A2 axis as a metabolic regulator in cytotoxic T lymphocytes in multiple myeloma

The mechanisms underlying the functional impairment and metabolic reprogramming of T lymphocytes in multiple myeloma (MM) have not been fully elucidated. In this study, single-cell RNA sequencing was used to compare gene expression profiles in T cells in bone marrow and peripheral blood of 10 newly diagnosed MM patients versus 3 healthy donors. Unbiased bioinformatics analysis revealed 9 cytotoxic T cell clusters. All 9 clusters in MM had higher expression of senescence markers (e.g., KLRG1 and CTSW) than the healthy control; some had higher expression of exhaustion-related markers (e.g., LAG3 and TNFRSF14). Pathway enrichment analyses showed downregulated amino acid metabolism and upregulated unfolded protein response (UPR) pathways, along with absent expression of glutamine transporter SLC38A2 and increased expression of UPR hallmark XBP1 in cytotoxic T cells in MM. In vitro studies revealed that XBP1 inhibited SLC38A2 by directly binding to its promoter, and silencing SLC38A2 resulted in decreased glutamine uptake and immune dysfunction of T cells. This study provided a landscape description of the immunosuppressive and metabolic features in T lymphocytes in MM, and suggested an important role of XBP1-SLC38A2 axis in T cell function.

Metformin attenuates multiple myeloma cell proliferation and encourages apoptosis by suppressing METTL3-mediated m6A methylation of THRAP3, RBM25, and USP4

Based on the results of epidemiological and preclinical studies, metformin can improve the prognosis of patients with malignant tumors. Studies have confirmed that metformin inhibits multiple myeloma (MM) cell proliferation and promotes apoptosis. Nevertheless, the specific mechanism remains to be elucidated. MM cells were intervened with different doses of metformin to detect cell proliferation and apoptosis. Western blotting and RT-qPCR were employed to assess the expression of METTL3, METTL14, WTAP, FTO, and ALKBH5 after metformin intervention. The microarray dataset GSE29023 was retrieved from the Gene Expression Omnibus (GEO) database and calculated using the R language (limma package) to authenticate differentially expressed genes (DEGs). The database for annotation, visualization, and integrated discovery (David) was applied for GO annotation analysis of DEGs. Subsequently, the string database and Cytoscape software were applied to construct protein-protein interaction (PPI) and DEM hub gene networks. Bioinformatics analysis and MeRIP were applied to predict and test METTL3-mediated m6A levels on mRNA of THRAP3, RBM25, and USP4 in METTL3 knocked-down cells. Then rescue experiments were performed to explore effects of METTL3 and THRAP3, RBM25, or USP4 on cell proliferation and apoptosis. The effect on MM cell xenograft tumor growth was observed by injection of metformin or/and overexpression of METTL3 in in vivo experiments. Metformin decreased cell proliferation and encouraged cell apoptosis in a dose-dependent manner. Global m6A modification was elevated in MM cells compared to normal cells, which was counteracted by metformin treatment. Furthermore, THRAP3, RBM25, and USP4 were identified as possible candidate genes for metformin treatment by GSE29023 data mining. METTL3 interference impaired m6A modification on mRNA of THRAP3, RBM25, and USP4 as well as expression levels. The mRNA stability and expression of THRAP3, RBM25, and USP4 was decreased after metformin treatment, which was reversed by METTL3 overexpression. THRAP3, RBM25 or USP4 knockdown reversed the assistance of METTL3 overexpression on the malignant behavior of MM cells. Finally, upregulation of METTL3 was shown to exert facilitative effects on xenograft tumor growth by blocking metformin injection. The present study demonstrates that metformin can repress the expression of THRAP3, RBM25, and USP4 by inhibiting METTL3-mediated m6A modification, which in turn hamper cell proliferation and promotes cell apoptosis.Abbreviations: multiple myeloma (MM), Gene Expression Omnibus (GEO), differentially expressed genes (DEGs), database for annotation, visualization and integrated discovery (David), protein-protein interaction (PPI), epithelial‑mesenchymal transition (EMT), methyltransferase like 3 (METTL3), methyltransferase like 14 (METTL14), wilms tumor 1-associated protein (WTAP), methyltransferase like 16 (METTL16), acute myeloid leukemia (AML), non-small lung cancer (NSCLC), glioma stem cells (GSCs), normal bone marrow-derived plasma cells (nPCs), false discovery rate (FDR), biological process (BP), optical density (OD), horseradish peroxidase (HRP), M6A RNA immunoprecipitation assay (MeRIP).

The immune suppressive tumor microenvironment in multiple myeloma: The contribution of myeloid-derived suppressor cells

Myeloid derived suppressors cells (MDSC) play major roles in regulating immune homeostasis and immune responses in many conditions, including cancer. MDSC interact with cancer cells within the tumor microenvironment (TME) with direct and indirect mechanisms: production of soluble factors and cytokines, expression of surface inhibitory molecules, metabolic rewiring and exosome release. The two-way relationship between MDSC and tumor cells results in immune evasion and cancer outgrowth. In multiple myeloma (MM), MDSC play a major role in creating protumoral TME conditions. In this minireview, we will discuss the interplay between MDSC and MM TME and the possible strategies to target MDSC.

Association between metabolic obesity phenotypes and multiple myeloma hospitalization burden: A national retrospective study

Background & purpose

Obesity and metabolic disorders were associated with increased risk of MM, a disease characterized by high risk of relapsing and require frequent hospitalizations. In this study, we conducted a retrospective cohort study to explore the association of metabolic obesity phenotypes with the readmission risk of MM.

Patients & methods

We analyzed 34,852 patients diagnosed with MM from the Nationwide Readmissions Database (NRD), a nationally representative database from US. Hospitalization diagnosis of patients were obtained using ICD-10 diagnosis codes. According to obesity and metabolic status, the population was divided into four phenotypes: metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). The patients with different phenotypes were observed for hospital readmission at days 30-day, 60-day, 90-day and 180-day. Multivariate cox regression model was used to estimate the relationship between obesity metabolic phenotypes and readmissions risk.

Results

There were 5,400 (15.5%), 7,255 (22.4%), 8,025 (27.0%) and 7,839 (35.6%) unplanned readmissions within 30-day, 60-day, 90-day and 180-day follow-up, respectively. For 90-day and 180-day follow-up, compared with patients with the MHNO phenotype, those with metabolic unhealthy phenotypes MUNO (90-day: P = 0.004; 180-day: P = < 0.001) and MUO (90-day: P = 0.049; 180-day: P = 0.004) showed higher risk of readmission, while patients with only obesity phenotypes MHO (90-day: P = 0.170; 180-day: P = 0.090) experienced no higher risk. However, similar associations were not observed for 30-day and 60-day. Further analysis in 90-day follow-up revealed that, readmission risk elevated with the increase of the combined factor numbers, with aHR of 1.068 (CI: 1.002-1.137, P = 0.043, with one metabolic risk factor), 1.109 (CI: 1.038-1.184, P = 0.002, with two metabolic risk factors) and 1.125 (95% CI: 1.04-1.216, P = 0.003, with three metabolic risk factors), respectively.

Conclusion

Metabolic disorders, rather than obesity, were independently associated with higher readmission risk in patients with MM, whereas the risk elevated with the increase of the number of combined metabolic factors. However, the effect of metabolic disorders on MM readmission seems to be time-dependent. For MM patient combined with metabolic disorders, more attention should be paid to advance directives to reduce readmission rate and hospitalization burden.

Generation of the human pluripotent stem cell lines KUMi005-A from a patients with multiple myeloma

Multiple myeloma (MM) progresses with abnormal monoclonal proliferation and accumulation of malignant plasma cells in the bone marrow. We established human induced pluripotent stem cells (iPSCs), KUMi005-A, from bone marrow samples of a patient with MM. This reprogrammed cell line has similar characteristics to human embryonic stem cells, such as proliferation properties and pluripotency. KUMi005-A iPSCs may be applicable in MM disease modeling and cell-based therapies.

Patient-Derived Multiple Myeloma 3D Models for Personalized Medicine-Are We There Yet?

Despite the wide variety of existing therapies, multiple myeloma (MM) remains a disease with dismal prognosis. Choosing the right treatment for each patient remains one of the major challenges. A new approach being explored is the use of ex vivo models for personalized medicine. Two-dimensional culture or animal models often fail to predict clinical outcomes. Three-dimensional ex vivo models using patients' bone marrow (BM) cells may better reproduce the complexity and heterogeneity of the BM microenvironment. Here, we review the strengths and limitations of currently existing patient-derived ex vivo three-dimensional MM models. We analyze their biochemical and biophysical properties, molecular and cellular characteristics, as well as their potential for drug testing and identification of disease biomarkers. Furthermore, we discuss the remaining challenges and give some insight on how to achieve a more biomimetic and accurate MM BM model. Overall, there is still a need for standardized culture methods and refined readout techniques. Including both myeloma and other cells of the BM microenvironment in a simple and reproducible three-dimensional scaffold is the key to faithfully mapping and examining the relationship between these players in MM. This will allow a patient-personalized profile, providing a powerful tool for clinical and research applications.

Mitochondrial metabolic determinants of multiple myeloma growth, survival, and therapy efficacy

Multiple myeloma (MM) is a plasma cell dyscrasia characterized by the clonal proliferation of antibody producing plasma cells. Despite the use of next generation proteasome inhibitors (PI), immunomodulatory agents (IMiDs) and immunotherapy, the development of therapy refractory disease is common, with approximately 20% of MM patients succumbing to aggressive treatment-refractory disease within 2 years of diagnosis. A large emphasis is placed on understanding inter/intra-tumoral genetic, epigenetic and transcriptomic changes contributing to relapsed/refractory disease, however, the contribution of cellular metabolism and intrinsic/extrinsic metabolites to therapy sensitivity and resistance mechanisms is less well understood. Cancer cells depend on specific metabolites for bioenergetics, duplication of biomass and redox homeostasis for growth, proliferation, and survival. Cancer therapy, importantly, largely relies on targeting cellular growth, proliferation, and survival. Thus, understanding the metabolic changes intersecting with a drug's mechanism of action can inform us of methods to elicit deeper responses and prevent acquired resistance. Knowledge of the Warburg effect and elevated aerobic glycolysis in cancer cells, including MM, has allowed us to capitalize on this phenomenon for diagnostics and prognostics. The demonstration that mitochondria play critical roles in cancer development, progression, and therapy sensitivity despite the inherent preference of cancer cells to engage aerobic glycolysis has re-invigorated deeper inquiry into how mitochondrial metabolism regulates tumor biology and therapy efficacy. Mitochondria are the sole source for coupled respiration mediated ATP synthesis and a key source for the anabolic synthesis of amino acids and reducing equivalents. Beyond their core metabolic activities, mitochondria facilitate apoptotic cell death, impact the activation of the cytosolic integrated response to stress, and through nuclear and cytosolic retrograde crosstalk maintain cell fitness and survival. Here, we hope to shed light on key mitochondrial functions that shape MM development and therapy sensitivity.

11C-Methionine PET/CT in Assessment of Multiple Myeloma Patients: Comparison to 18F-FDG PET/CT and Prognostic Value

Multiple myeloma (MM) is the second most common haematological malignancy and remains incurable despite therapeutic advances. ¹⁸F-FDG (FDG) PET/CT is a relevant tool MM for staging and it is the reference imaging technique for treatment evaluation. However, it has limitations, and investigation of other PET tracers is required. Preliminary results with L-methyl-[¹¹C]- methionine (MET), suggest higher sensitivity than ¹⁸F-FDG. This study aimed to compare the diagnostic accuracy and prognostic value of ¹FDG and MET in MM patients. We prospectively compared FDG and MET PET/CT for assessment of bone disease and extramedullary disease (EMD) in a series of 52 consecutive patients (8 smoldering MM, 18 newly diagnosed MM and 26 relapsed MM patients). Bone marrow (BM) uptake patterns and the detection of focal lesions (FLs) and EMD were compared. Furthermore, FDG PET parameters with known MM prognostic value were explored for both tracers, as well as total lesion MET uptake (TLMU). Median patient age was 61 years (range, 37–83 years), 54% were male, 13% of them were in stage ISS (International Staging System) III, and 31% had high-risk cytogenetics. FDG PET/CT did not detect active disease in 6 patients, while they were shown to be positive by MET PET/CT. Additionally, MET PET/CT identified a higher number of FLs than FDG in more than half of the patients (63%). For prognostication we focussed on the relapsed cohort, due to the low number of progressions in the two other cohorts. Upon using FDG PET/CT in relapsed patients, the presence of more than 3 FLs (HR 4.61, p = 0.056), more than 10 FLs (HR 5.65, p = 0.013), total metabolic tumor volume (TMTV) p50 (HR 4.91, p = 0.049) or TMTV p75 (HR 5.32, p = 0.016) were associated with adverse prognosis. In MET PET/CT analysis, TMTV p50 (HR 4.71, p = 0.056), TMTV p75 (HR 6.27, p = 0.007), TLMU p50 (HR 8.8, p = 0.04) and TLMU p75 (HR 6.3, p = 0.007) adversely affected PFS. This study confirmed the diagnostic and prognostic value of FDG in MM. In addition, it highlights that MET has higher sensitivity than FDG PET/CT for detection of myeloma lesions, including FLs. Moreover, we show, for the first time, the prognostic value of TMTV and TLMU MET PET/CT in the imaging evaluation of MM patients.