MINI REVIEW article

Front. Immunol., 22 August 2022

Sec. Cytokines and Soluble Mediators in Immunity

Volume 13 - 2022 | https://doi.org/10.3389/fimmu.2022.897862

Multiple myeloma metabolism – a treasure trove of therapeutic targets?

  • 1. Department of Immunology and Inflammation, Cancer Cell Protein Metabolism, The Hugh and Josseline Langmuir Centre for Myeloma Research, Centre for Haematology, Imperial College London, London, United Kingdom

  • 2. Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom

  • 3. Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom

Article metrics

View details

22

Citations

6,1k

Views

2,1k

Downloads

Abstract

Multiple myeloma is an incurable cancer of plasma cells that is predominantly located in the bone marrow. Multiple myeloma cells are characterized by distinctive biological features that are intricately linked to their core function, the assembly and secretion of large amounts of antibodies, and their diverse interactions with the bone marrow microenvironment. Here, we provide a concise and introductory discussion of major metabolic hallmarks of plasma cells and myeloma cells, their roles in myeloma development and progression, and how they could be exploited for therapeutic purposes. We review the role of glucose consumption and catabolism, assess the dependency on glutamine to support key metabolic processes, and consider metabolic adaptations in drug-resistant myeloma cells. Finally, we examine the complex metabolic effects of proteasome inhibitors on myeloma cells and the extracellular matrix, and we explore the complex relationship between myeloma cells and bone marrow adipocytes.

Introduction

Multiple myeloma (MM) is a cancer of plasma cells, the terminally differentiated effector cells of the humoral immune system whose main function is the provision of immunoglobulins. To fulfil this task, plasma cells dispose of a well-developed organellar apparatus that enables them to assemble and secrete large amounts of protein (13). Plasma cells, and MM cells, are also defined by their intricate interactions with the bone marrow microenvironment (46). Their highly specialized function and their distinct habitat shape the characteristic metabolic hallmarks of plasma cells and MM cells. Here, we aim to provide a concise introductory review of key metabolic features and adaptations of MM cells, discussing how their metabolic characteristics may contribute to resistance to anti-myeloma therapies, but also how they may be exploited for therapeutic purposes.

Metabolic adaptations in non-malignant plasma cells

When naïve B cells are stimulated with antigen, they undergo a striking metamorphosis. Within days, their cytoplasm expands dramatically to accommodate a rapidly expanding endoplasmic reticulum and Golgi apparatus that push aside an increasingly heterochromatic nucleus (2). These morphologically distinct features reflect the highly specialized function of plasma cells, the synthesis and secretion of immunoglobulins. This complex process is resource-demanding and requires a broad range of metabolic adaptations, which are discussed comprehensively in several recently published reviews (79). Briefly, the bursts of proliferation and organelle biogenesis that occur during B cell activation and in germinal centers are supported by high levels of nutrient uptake, in particular of glucose and glutamine, which serve as major contributors to a diverse range of metabolic processes such as mitochondrial ATP generation, lipid synthesis, and ribonucleoside generation. Once plasma cell differentiation is complete, and the extensive secretory apparatus has been assembled, metabolic pathways are optimized again, and resources are pooled towards high levels of antibody synthesis and secretion, including extensive post-translational modifications. Glucose remains an important nutrient and is used primarily for antibody glycosylation and energy generation. In particular, the provision of pyruvate through glycolysis to enhance the ability to maximize ATP production through mitochondrial respiration and appears to be an important factor in promoting plasma cell longevity (10). While it remains to be established if and how plasma cells fine-tune lipid metabolism, it seems unlikely that this major part of cellular metabolism would not require any adaptations to high-level immunoglobulin production and maintenance of an extensive ER and Golgi network (7, 11). The complex metabolic demands of plasma cells to maintain energy homeostasis while assembling and secreting antibodies are also highlighted by the intricate role of autophagy to sustain long-lived humoral immunity and by their enhanced dependence on mitochondrial energy generation (10, 12, 13).

Multiple myeloma dependency on glucose and glutamine

MM cells retain many of the metabolic features of plasma cells and further adapt them to meet the increased demands of malignant cells (14). This includes the heavy reliance of MM cells on glucose and glycolysis for energy generation (1518). Clinically, the increase in glucose uptake underlies the use of 18F-Fluorodeoxyglucose (FDG)-positron emission tomography (PET) scanning to quantify and localize MM load in patients (19). MM cells also highly depend on glutamine, the most abundant amino acid in humans, not only as a protein building block but also as an anaplerotic substrate to replenish tricarboxylic acid (TCA) cycle components Glutamine depletion and inhibition of glutamine metabolism have already been shown to inhibit MM cell growth and to enhance sensitivity to anti-MM drugs (2023). MM cells show high expression levels of glutaminase but not glutamine synthetase, a characteristic that renders the cells particularly dependent on extracellular glutamine. This dependency points to glutamine transporters as potentially useful therapeutic targets. While the expression of glutamine transporters such as LAT1, SNAT1 and ASCT2 increases with MM progression, the evidence so far indicates that MM cells mainly rely on ASCT2 for glutamine uptake. In particular, experiments combining the proteasome inhibitor, carfilzomib, with ASCT2 inhibitors have shown a synergic induction of proteotoxic stress and ROS generation suggesting a possible new therapeutic strategy (24, 25). Moreover, MM cells highly depend on mitochondrial energy generation (2628). Thus, while the metabolic networks that support MM cell survival and proliferation and promote resistance to approved and investigational treatment approaches are only beginning to be understood, it is becoming increasingly clear that they may provide a plethora of novel opportunities for therapeutic interventions.

Are proteasome inhibitors metabolic drugs?

Among the many anti-MM drugs that now form the therapeutic armamentarium that has dramatically improved outcomes for MM patients over the past 2 decades, proteasome inhibitors are a particularly intriguing class of drugs. Developed in the 1990 as experimental tools to better understand the function of the ubiquitin-proteasome system (UPS) in cell biology, they rapidly became the backbone of multiple regimens and are now widely used for the treatment of newly diagnosed and relapsed MM, in combination with essentially all other drug classes (2931). Initially, their highly selective toxicity for MM cells was attributed to the high dependency of plasma cells on the UPS to clear so-called misfolded proteins (3236), the toxic by-products of the complex and error-prone post-translational modification processes that secreted proteins undergo in the endoplasmic reticulum (ER). It has been widely assumed that proteasome inhibitors block the degradation of misfolded proteins, leading to their accumulation in the ER and overwhelming ER stress, and that this effect is particularly pronounced in MM cells, because their secretory load is exceptionally high (37). While ER stress may indeed represent one mechanism of action of proteasome inhibitors, it does not take into account the downstream effects of UPS inhibition on protein breakdown, namely the potentially lethal drop in intracellular amino acid availability and the wider metabolic consequences of such as effect (3840). Not surprisingly, resistance to proteasome inhibition has been linked to hyperactivation of amino acid synthetic pathways. MM cell lines resistant to the proteasome inhibitor bortezomib show higher serine synthesis pathway (SSP) activity than bortezomib-sensitive cells. Phosphoglycerate dehydrogenase (PHGDH), the first rate-limiting enzyme in the SSP, was found to be significantly elevated in CD138+ cells derived from patients with relapsed MM, and high PHGDH expression conferred inferior survival. Mechanistically, PHGDH promotes proliferation and bortezomib resistance through increasing glutathione synthesis, thereby decreasing bortezomib-induced ROS generation, increasing effective protein folding, and promoting MM cell survival (41, 42). Metabolic reprogramming also supports regeneration of NAD(P)H and TCA cycle components to promote oxidative phosphorylation, and proteasome inhibitor-resistant cells contain structurally adapted mitochondria and have a different lipid content. These observations support the notion that proteasome inhibitors are complex metabolic drugs, and that targeting metabolic processes might offer novel therapeutic approaches to overcome proteasome inhibitor resistance (43, 44). To better understand the metabolic responses of MM cells to proteasome inhibition, we used a time-resolved integrated systems-level approach based on an in vitro model of proteasome inhibitor stress build-up and recovery that closely replicates typical clinical pharmacokinetics and antitumor responses in patients (45). These multi-omics time course studies allowed us to define the transcriptional, proteomic, and metabolic changes that occur in dying MM cells, and, perhaps more importantly, in MM cells that recovered from proteasome inhibition. These cells are relevant because they provide a faithful model for clinically proteasome inhibitor-resistant MM cells that ultimately cause relapse or progression. What we found was that surviving and resolving proteasome inhibitor-induced stress is a surprisingly protracted process during which MM cells undergo complex and dynamic changes of multiple metabolic pathways. These include a decrease in glucose uptake, a persistent drop in mitochondrial energy generation, enhanced lipid catabolism, and reduced intracellular levels of amino acids such as glutamine throughout and beyond immediate stress resolution. Importantly, the temporal patterns and functional connections of these waves of biological processes support a model in which at least some of the challenges that arise in surviving cells are directly linked to the mechanisms of stress resolution rather than the initial insult. As such, the cellular recovery processes generate potential therapeutic Achilles’ heels in the sense of dynamic trade-offs - temporarily increased vulnerabilities that are triggered specifically by the processes of stress resolution (46, 47). One of those vulnerabilities, in line with the proteasome inhibitor-induced prolonged depletion of intracellular amino acids, is that some MM cells depended on GCN2, the only kinase known to be activated by low amino acid levels (4850), to recover from proteasome inhibition. Moreover, and highlighting the complex layers of metabolic regulation in MM cells in the wake of proteasome inhibition, we found GCN2 to be a major regulator of lipid metabolism during stress resolution. More recently, MM cell dependency on GCN2 has been linked to oncogenic MYC signaling (51), and observations that amino acid depletion sensitizes MM cells to proteasome inhibition by inducing compound mitochondria damage (52) provide further evidence for complex metabolic processes linked to proteasome inhibitors. We also found that drugs that target different aspects of mitochondrial function, such as the electron transport chain or mitochondrial translation, largely triggered a greater reduction in cell viability in recovering cells than in acutely stressed cells. These observations also raise the question if and how proteasome inhibitors or other drugs targeting cellular protein homeostasis might affect the myeloma microenvironment, in particular the formation of the extracellular matrix (ECM). We therefore applied a combination of materials science characterization techniques to an in vitro model of bone-like tissue formation by human bone-marrow derived mesenchymal stromal cells (hMSC). Intermittent and low-level inhibition of key UPS components, which triggered only very mild stress in the differentiating hMSCs, had surprisingly distinct effects on the bone-like material they formed, altering its stiffness and the amount of protein and crystalline mineral it contains, and affecting its micro- and ultra-structural organization (53). It remains to be established if such effects occur in vivo, and how they may affect MM cell behavior. However, the well-established crosstalk between physical tissue traits and cancer biology, for example the often-observed correlation of tissue stiffness with cancer aggressiveness and treatment resistance (54), raises the possibility that proteasome inhibitors might have effects on physical ECM characteristics that in turn alter MM cell responses to anti-cancer agents.

Additional evidence of the metabolic consequences of proteasome inhibitors is also provided by their interaction with mTOR, an integrator of environmental signals to regulate protein synthesis, autophagy, cell proliferation, growth and survival that plays a key role in MM and other cancers (55, 56). One of the signals that mTOR responds to is the availability of amino acids but, opposite to GCN2, mTOR activation by adequate amino acid availability promotes protein synthesis. Under conditions of amino acid deprivation, GCN2 has been shown to negatively regulate mTOR (57), indicating a functional interaction between both signaling pathways that is supported by observations that the discordant regulation of GCN2 and mTORC1 can adversely affect cell viability (58). It is therefore not surprising that experimental observations suggest that mTOR inhibition and proteosome inhibition may be synergistically toxic to MM cells (59, 60). However, clinical investigations into combining mTOR and proteasome inhibition have not resulted in major breakthroughs (61).

Bone marrow adipocytes and myeloma

The bone marrow has a unique cellular composition, with cells such as bone marrow adipocytes, bone marrow stromal cells and immune cells ideally placed to interact with MM cells and modify their metabolic activity. Bone marrow stromal cells have been found to be the source of intercellular mitochondrial transfer, facilitated through tumor-derived tunneling nanotubes connecting stromal cells to MM cells (62). The consequence of this mitochondrial transfer was the promotion of oxidative phosphorylation, demonstrating MM metabolic plasticity driven by the bone microenvironment. There is increasing evidence to support the dysregulation of bone marrow adipocytes in MM, with reciprocal cross-talk between bone marrow adipocytes and MM cells not only promoting MM growth, chemoresistance and bone disease (6367), but also inducing metabolic plasticity in both tumor cells and adipocytes. Bone marrow adipocytes are a major source of adiponectin, a key adipokine which we have previously shown to be decreased in MM, contributing to disease pathogenesis (68). More recently, we have shown that MM cells can downregulate adiponectin in bone marrow adipocytes, at least partly through TNFα signaling, providing a mechanism driving the hypoadiponectinemia associated with MM progression (69). MM cells have also been found to damage the mitochondria of adipocytes and preadipocytes, driving abnormal cytokine production, a senescence-like phenotype and disease progression (70, 71). While the role of lipid-uptake by MM cells and any functional consequences is unclear, recent studies demonstrate that myeloma-associated bone marrow adipocytes undergo lipolysis, associated with the uptake of secreted free fatty acids by adjacent MM cells (69, 70, 72). Obesity is one of the major risk factors for MM, with increased adiposity associated with the development of MM in preclinical models (73). The increase in adiposity has been associated with metabolic changes in MM cells, such as an increase in the metabolite acetyl-coA synthetase 2 (ACSS2) in MM cells, induced by adipocyte-secreted angiotensin II and driving MM growth in vitro and in vivo at least partly by enhancing the stability of the myeloma oncoprotein IRF4 (74). An increased understanding of the symbiotic relationship between tumor cells and bone cells can be gained by turning to solid tumor metastases such as breast and prostate cancer bone metastases, where commonalities in key mechanisms have led to similar approaches in combating the associated bone disease. There is increasing evidence to support the metabolic plasticity of tumor cells within the bone microenvironment (75). Our own recent observations used metabolomic profiling to identify elevation of the pentose phosphate pathway in prostate cancer cells, driven by interactions with bone cells. The rate-limiting enzyme of the pentose phosphate pathway, G6PD, was found to contribute to prostate cancer growth within the skeleton, representing a potential metabolic target for the treatment of bone metastasis (76).

As such, dissecting the metabolic cross-talk between MM cells and their surrounding microenvironment may reveal new therapeutic opportunities to disrupt the symbiotic relationship. Moreover, it will be fascinating, albeit far from straightforward, to explore if dietary interventions can have an impact on MM progression or the response to anti MM drugs.

In summary, a plethora of metabolic adaptations that occur in MM cells and in the myeloma microenvironment (Figure 1) may offer various approaches to delay the development or progression of MM, and to optimize treatment outcomes.

Figure 1

Figure 1

Schematic representation of selected key metabolic pathways in multiple myeloma cells and their interaction with bone marrow adipocytes. 3PG, 3-phosphoglycerate; ACSS2, acetyl-CoA synthetase 2; αKG, α-ketoglutarate; Ang II, angiotensin II; ASCT2, sodium-dependent neutral amino acid transporter type 2; AT1, angiotensin II receptor type 1; FFA, free fatty acids; GLUT1/4, glucose transporter 1/4; GSH, glutathione; PHGDH, phosphoglycerate dehydrogenase; ROS, reactive oxygen species; SLC1A4, solute carrier family 1 member 4; SSP, serine synthesis pathway; TCA cycle, tricarboxylic acid cycle; TNFα, tumor necrosis factor alpha. Created in BioRender.

Funding

MR-T and HA are funded by a Cancer Research UK Advanced Clinician Scientist Fellowship (C41494/A29035). CE is supported by Blood Cancer UK.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Statements

Author contributions

MR-T, HA, and CE contributed to the conception, literature evaluation and writing of the review article. All authors contributed to the article revision, read, and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  • 1

    ShafferALShapuro-ShelefMIwakoshiNNLeeAHQianSBZhaoHet al. XBP1, downstream of blimp-1, expands the secretory apparatus and other organelles, and increases protein synthesis in plasma cell differentiation. Immunity (2004) 21:8193. doi: 10.1016/j.immuni.2004.06.010

  • 2

    KirkSJCliffJMThomasJAWardTH. Biogenesis of secretory organelles during b cell differentiation. J Leukoc Biol (2010) 87:245–55. doi: 10.1189/jlb.1208774

  • 3

    van AnkenERomijnEPMaggioniCMezghraniASitiaRBraakmanIet al. Sequential waves of functionally related proteins are expressed when b cells prepare for antibody secretion. Immunity (2003) 18:243–53. doi: 10.1016/S1074-7613(03)00024-4

  • 4

    MimuraNHideshimaTAndersonKC. Novel therapeutic strategies for multiple myeloma. Exp Hematol (2015) 43:732–41. doi: 10.1016/j.exphem.2015.04.010

  • 5

    FairfieldHFalankCAveryLReaganMR. Multiple myeloma in the marrow: pathogenesis and treatments. Ann N Y Acad Sci (2016) 1364:3251. doi: 10.1111/nyas.13038

  • 6

    GoodingSEdwardsCM. New approaches to targeting the bone marrow microenvironment in multiple myeloma. Curr Opin Pharmacol (2016) 28:43–9. doi: 10.1016/j.coph.2016.02.013

  • 7

    D'SouzaLBhattacharyaD. Plasma cells: You are what you eat. Immunol Rev (2019) 288:161–77. doi: 10.1111/imr.12732

  • 8

    RippergerTJBhattacharyaD. Transcriptional and metabolic control of memory b cells and plasma cells. Annu Rev Immunol (2021) 39:345–68. doi: 10.1146/annurev-immunol-093019-125603

  • 9

    BoothbyMRickertRC. Metabolic regulation of the immune humoral response. Immunity (2017) 46:743–55. doi: 10.1016/j.immuni.2017.04.009

  • 10

    LamWYBeckerAMKennerlyKMWongRCurtisJDLlufrioEMet al. Mitochondrial pyruvate import promotes long-term survival of antibody-secreting plasma cells. Immunity (2016) 45:6073. doi: 10.1016/j.immuni.2016.06.011

  • 11

    BoothbyMRBrookensSKRaybuckALChoSH. Supplying the trip to antibody production-nutrients, signaling, and the programming of cellular metabolism in the mature b lineage. Cell Mol Immunol (2022) 19:352–69. doi: 10.1038/s41423-021-00782-w

  • 12

    PengoNScolariMOlivaLMilanEMainoldiFRaimondiAet al. Plasma cells require autophagy for sustainable immunoglobulin production. Nat Immunol (2013) 14:298305. doi: 10.1038/ni.2524

  • 13

    PengoNCenciS. The role of autophagy in plasma cell ontogenesis. Autophagy (2013) 9:942–4. doi: 10.4161/auto.24399

  • 14

    HanahanD. Hallmarks of cancer: New dimensions. Cancer Discovery (2022) 12:3146. doi: 10.1158/2159-8290.CD-21-1059

  • 15

    XuSZhouTDohHMTrinhRCatapangALeeJTet al. An HK2 antisense oligonucleotide induces synthetic lethality in HK1(-)HK2(+) multiple myeloma. Cancer Res (2019) 79:2748–60. doi: 10.1158/0008-5472.CAN-18-2799

  • 16

    GuZXiaJXuHFrenchITricotGZhanFl. NEK2 promotes aerobic glycolysis in multiple myeloma through regulating splicing of pyruvate kinase. J Hematol Oncol (2017) 10:17. doi: 10.1186/s13045-017-0392-4

  • 17

    WaltersDKArendtBKJelinekDF. CD147 regulates the expression of MCT1 and lactate export in multiple myeloma cells. Cell Cycle (2013) 12:3175–83. doi: 10.4161/cc.26193

  • 18

    McBrayerSKChengJCSinghalSKrettNRosenSTShanmugamM. Multiple myeloma exhibits novel dependence on GLUT4, GLUT8, and GLUT11: implications for glucose transporter-directed therapy. Blood (2012) 119:4686–97. doi: 10.1182/blood-2011-09-377846

  • 19

    ZamagniETacchettiPCavoM. Imaging in multiple myeloma: How? When? Blood (2019) 133:644–51. doi: 10.1182/blood-2018-08-825356

  • 20

    BajpaiRMatulisSMWeiCNookaAKvon HollenHELonialSet al. Targeting glutamine metabolism in multiple myeloma enhances BIM binding to BCL-2 eliciting synthetic lethality to venetoclax. Oncogene (2016) 35:3955–64. doi: 10.1038/onc.2015.464

  • 21

    ThompsonRMDytfeldDReyesLRobinsonRMSmithBManevichYet al. Glutaminase inhibitor CB-839 synergizes with carfilzomib in resistant multiple myeloma cells. Oncotarget (2017) 8:35863–76. doi: 10.18632/oncotarget.16262

  • 22

    GonsalvesWIRamakrishnanVHitosugiTGhoshTJevremovicDDuttaTet al. Glutamine-derived 2-hydroxyglutarate is associated with disease progression in plasma cell malignancies. JCI Insight (2018) 3(1):e94543. doi: 10.1172/jci.insight.94543

  • 23

    GonsalvesWIJangJSJessenEHitosugiTEvansLAJevremovicDet al. In vivo assessment of glutamine anaplerosis into the TCA cycle in human pre-malignant and malignant clonal plasma cells. Cancer Metab (2020) 8:29. doi: 10.1186/s40170-020-00235-4

  • 24

    BolzoniMChiuMAccardiFVescoviniRAiroldiIStortiPet al. Dependence on glutamine uptake and glutamine addiction characterize myeloma cells: a new attractive target. Blood (2016) 128:667–79. doi: 10.1182/blood-2016-01-690743

  • 25

    PrelowskaMKMehlichDUgurluMTKedzierskaHCwiekAKosnikAet al. Inhibition of the l-glutamine transporter ASCT2 sensitizes plasma cell myeloma cells to proteasome inhibitors. Cancer Lett (2021) 507:1325. doi: 10.1016/j.canlet.2021.02.020

  • 26

    Ortiz-RuizARuiz-HerediaYMoralesMLAgilar-GarridoPGarcía-OrtizAValeriAet al. Myc-related mitochondrial activity as a novel target for multiple myeloma. Cancers (Basel) (2021) 13(7):1662. doi: 10.3390/cancers13071662

  • 27

    XiangYFangBLiuYYanSCaoDMeiHet al. SR18292 exerts potent antitumor effects in multiple myeloma via inhibition of oxidative phosphorylation. Life Sci (2020) 256:117971. doi: 10.1016/j.lfs.2020.117971

  • 28

    CohenYCZadaMWangSYBornsteinCDavidEMosheAet al. Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single-cell sequencing. Nat Med (2021) 27:491503. doi: 10.1038/s41591-021-01232-w

  • 29

    Saavedra-GarciaPMartiniFAunerHW. Proteasome inhibition in multiple myeloma: lessons for other cancers. Am J Physiol Cell Physiol (2020) 318:C451–62. doi: 10.1152/ajpcell.00286.2019

  • 30

    MoreauPRichardsonPGCavoMOrlowskiRZSan MiguelJFPalumboAet al. Proteasome inhibitors in multiple myeloma: 10 years later. Blood (2012) 120:947–59. doi: 10.1182/blood-2012-04-403733

  • 31

    DimopoulosMAMoreauPTerposEMateosMVZweegmanSCookGet al. Multiple myeloma: EHA-ESMO clinical practice guidelines for diagnosis, treatment and follow-up(dagger). Ann Oncol (2021) 32:309–22. doi: 10.1016/j.annonc.2020.11.014

  • 32

    MitsiadesNMitsiadesCSPoulakiVChauhanDFanourakisGGuXet al. Molecular sequelae of proteasome inhibition in human multiple myeloma cells. Proc Natl Acad Sci USA (2002) 99:14374–9.

  • 33

    MitsiadesCSMitsiadesNHideshimaTRichardsonPGAndersonKC. Proteasome inhibition as a new therapeutic principle in hematological malignancies. Curr Drug Targets (2006) 7:1341–7. doi: 10.2174/138945006778559247

  • 34

    ObengEACarlsonLMGutmanDMHarringtonWJLeeKPBoiseLH. Proteasome inhibitors induce a terminal unfolded protein response in multiple myeloma cells. Blood (2006) 107:4907–16. doi: 10.1182/blood-2005-08-3531

  • 35

    LeeAHIwakoshiNNAndersonKCGlimcherLH. Proteasome inhibitors disrupt the unfolded protein response in myeloma cells. Proc Natl Acad Sci USA (2003) 100:9946–51.

  • 36

    MeisterSSchubertUNeubertKHerrmannKBurgerRGramatzkiMet al. Extensive immunoglobulin production sensitizes myeloma cells for proteasome inhibition. Cancer Res (2007) 67:1783–92. doi: 10.1158/0008-5472.CAN-06-2258

  • 37

    AunerHWCenciS. Recent advances and future directions in targeting the secretory apparatus in multiple myeloma. Br J Haematol (2015) 168:1425. doi: 10.1111/bjh.13172

  • 38

    SuraweeraAMunchCHanssumABertolottiA. Failure of amino acid homeostasis causes cell death following proteasome inhibition. Mol Cell (2012) 48:242–53. doi: 10.1016/j.molcel.2012.08.003

  • 39

    ParzychKChinnTMChenZLoaizaSPorschFValbuenaGN. Inadequate fine-tuning of protein synthesis and failure of amino acid homeostasis following inhibition of the ATPase VCP/p97. Cell Death Dis (2015) 6:e2031. doi: 10.1038/cddis.2015.373

  • 40

    ParzychKSaavedra-GarcíaPValbuenaGNAl-SadahHARobinsonMEPenfoldL. The coordinated action of VCP/p97 and GCN2 regulates cancer cell metabolism and proteostasis during nutrient limitation. Oncogene (2019) 38:3216–31. doi: 10.1038/s41388-018-0651-z

  • 41

    ZaalEAWuWJansenGZweegmanSCloosJBerkersCR. Bortezomib resistance in multiple myeloma is associated with increased serine synthesis. Cancer Metab (2017) 5:7. doi: 10.1186/s40170-017-0169-9

  • 42

    WuXXiaJZhangJZhuYWuYGuoJ. Phosphoglycerate dehydrogenase promotes proliferation and bortezomib resistance through increasing reduced glutathione synthesis in multiple myeloma. Br J Haematol (2020) 190:5266. doi: 10.1111/bjh.16503

  • 43

    SorianoGPBesseLLiNKraussMBesseAMeeuwenoordNet al. Proteasome inhibitor-adapted myeloma cells are largely independent from proteasome activity and show complex proteomic changes, in particular in redox and energy metabolism. Leukemia (2016) 30:2198–207. doi: 10.1038/leu.2016.102

  • 44

    BesseLBesseAMendez-LopezMVasickovaKSedlackovaMVanharaP. A metabolic switch in proteasome inhibitor-resistant multiple myeloma ensures higher mitochondrial metabolism, protein folding and sphingomyelin synthesis. Haematologica (2019) 104:e415–9. doi: 10.3324/haematol.2018.207704

  • 45

    Saavedra-GarciaPRomán-TruferoMAl-SadahHABligheKLópez-JiménezEChristoforouM. Systems level profiling of chemotherapy-induced stress resolution in cancer cells reveals druggable trade-offs. Proc Natl Acad Sci USA (2021) 118(17):e2018229118'. doi: 10.1073/pnas.2018229118

  • 46

    GarlandTJr. Trade-offs. Curr Biol (2014) 24:R60–61. doi: 10.1016/j.cub.2013.11.036

  • 47

    JacquelineCBiroPABeckmannCMollerAPRenaudFSorciG. Cancer: A disease at the crossroads of trade-offs. Evol Appl (2017) 10:215–25. doi: 10.1111/eva.12444

  • 48

    ZhangPMcGrathBReinertJOlsenDASLeiLGillS. The GCN2 eIF2alpha kinase is required for adaptation to amino acid deprivation in mice. Mol Cell Biol (2002) 22:6681–8. doi: 10.1128/MCB.22.19.6681-6688.2002

  • 49

    CastilhoBAShanmugamRSilvaRCRameshRHimmeBMSattleggerE. Keeping the eIF2 alpha kinase Gcn2 in check. Biochim Biophys Acta (2014) 1843:1948–68. doi: 10.1016/j.bbamcr.2014.04.006

  • 50

    WekSAZhuSWekRC. The histidyl-tRNA synthetase-related sequence in the eIF-2 alpha protein kinase GCN2 interacts with tRNA and is required for activation in response to starvation for different amino acids. Mol Cell Biol (1995) 15:4497–506. doi: 10.1128/MCB.15.8.4497

  • 51

    CroucherDCRichardsLMTsofackSWallerDLiZWeiEN. Longitudinal single-cell analysis of a myeloma mouse model identifies subclonal molecular programs associated with progression. Nat Commun (2021) 12:6322. doi: 10.1038/s41467-021-26598-w

  • 52

    SonciniDMinettoPMartinuzziCBecheriniPFenuVGuoloF. Amino acid depletion triggered by l-asparaginase sensitizes MM cells to carfilzomib by inducing mitochondria ROS-mediated cell death. Blood Adv (2020) 4:4312–26. doi: 10.1182/bloodadvances.2020001639

  • 53

    LoaizaSFerreiraSAChinnTMKirbyATsolakiEDondiCet al. An engineered, quantifiable in vitro model for analysing the effect of proteostasis-targeting drugs on tissue physical properties. Biomaterials (2018) 183:102–13. doi: 10.1016/j.biomaterials.2018.08.041

  • 54

    NiaHTMunnLLJainRK. Physical traits of cancer. Sci 370 (2020) 370(6516):eaaz0868. doi: 10.1126/science.aaz0868

  • 55

    EfeyanACombWCSabatiniDM. Nutrient-sensing mechanisms and pathways. Nature (2015) 517:302–10. doi: 10.1038/nature14190

  • 56

    JohnLKrauthMTPodarKRaabMS. Pathway-directed therapy in multiple myeloma. Cancers (Basel) 13 (2021) 13(7):1668. doi: 10.3390/cancers13071668

  • 57

    AverousJLambert-LanglaisSMesclonFCarraroVParryLJousseC. GCN2 contributes to mTORC1 inhibition by leucine deprivation through an ATF4 independent mechanism. Sci Rep (2016) 6:27698. doi: 10.1038/srep27698

  • 58

    MisraJHolmesMJMirekETLangevinMKimHGCarlsonKR. Discordant regulation of eIF2 kinase GCN2 and mTORC1 during nutrient stress. Nucleic Acids Res (2021) 49:5726–42. doi: 10.1093/nar/gkab362

  • 59

    BaumannPMandl-WeberSOduncuFSchmidmaierR. The novel orally bioavailable inhibitor of phosphoinositol-3-kinase and mammalian target of rapamycin, NVP-BEZ235, inhibits growth and proliferation in multiple myeloma. Exp Cell Res (2009) 315:485–97. doi: 10.1016/j.yexcr.2008.11.007

  • 60

    EichnerRFernandez-SaizVTargoszBSBassermannF. Cross talk networks of mammalian target of rapamycin signaling with the ubiquitin proteasome system and their clinical implications in multiple myeloma. Int Rev Cell Mol Biol (2019) 343:219–97. doi: 10.1016/bs.ircmb.2018.06.001

  • 61

    GhobrialIMWellerEVijRMunshiNCBanwaitRBagshawM. Weekly bortezomib in combination with temsirolimus in relapsed or relapsed and refractory multiple myeloma: a multicentre, phase 1/2, open-label, dose-escalation study. Lancet Oncol (2011) 12:263–72. doi: 10.1016/S1470-2045(11)70028-6

  • 62

    MarleinCRPiddockREMistryJJZaitsevaLHellmichCHortonRH. CD38-driven mitochondrial trafficking promotes bioenergetic plasticity in multiple myeloma. Cancer Res (2019) 79:2285–97. doi: 10.1158/0008-5472.CAN-18-0773

  • 63

    CaersJDeleuSBelaidZDe RaeveHVan ValkenborghEDe BruyneE. Neighboring adipocytes participate in the bone marrow microenvironment of multiple myeloma cells. Leukemia (2007) 21:1580–4. doi: 10.1038/sj.leu.2404658

  • 64

    LiuHHeJKohSPZhongYLiuZWangZ. Reprogrammed marrow adipocytes contribute to myeloma-induced bone disease. Sci Transl Med (2019) 11. doi: 10.1126/scitranslmed.aau9087

  • 65

    LiuZXuJHeJLiuHLinPWanX. Mature adipocytes in bone marrow protect myeloma cells against chemotherapy through autophagy activation. Oncotarget (2015) 6:34329–41. doi: 10.18632/oncotarget.6020

  • 66

    MorrisEVSuchackiKJHockingJCartwrightRSowmanAGamezB. Myeloma cells down-regulate adiponectin in bone marrow adipocytes Via TNF-alpha. J Bone Miner Res (2019) 35(5):942–55. doi: 10.1002/jbmr.3951

  • 67

    TrotterTNGibsonJTSherpaTLGowdaPSPekerDYangY. Adipocyte-lineage cells support growth and dissemination of multiple myeloma in bone. Am J Pathol (2016) 186:3054–63. doi: 10.1016/j.ajpath.2016.07.012

  • 68

    FowlerJALwinSTDrakeMTEdwardsJRKyleRAMundyGR. Host-derived adiponectin is tumor-suppressive and a novel therapeutic target for multiple myeloma and the associated bone disease. Blood (2011) 118:5872–82. doi: 10.1182/blood-2011-01-330407

  • 69

    MorrisEVSuchackiKJHockingJCartwrightRSowmanAGamezB. Myeloma cells down-regulate adiponectin in bone marrow adipocytes Via TNF-alpha. J Bone Miner Res (2020) 35:942–55. doi: 10.1002/jbmr.3951

  • 70

    FairfieldHDudakovicAKhatibCMFarrelMCostaSFalankC. Myeloma-modified adipocytes exhibit metabolic dysfunction and a senescence-associated secretory phenotype. Cancer Res (2021) 81:634–47. doi: 10.1158/0008-5472.CAN-20-1088

  • 71

    FairfieldHCostaSFalankCFarrelMMurphyCSD’AmicoAet al. Multiple myeloma cells alter adipogenesis, increase senescence-related and inflammatory gene transcript expression, and alter metabolism in preadipocytes. Front Oncol (2020) 10:584683. doi: 10.3389/fonc.2020.584683

  • 72

    PanaroniCFulzeleKMoriTSiuKTOnyewadumeCMaebiusAet al. Multiple myeloma cells induce lipolysis in adipocytes and uptake fatty acids through fatty acid transporter proteins. Blood (2022) 139:876–88. doi: 10.1182/blood.2021013832

  • 73

    LwinSTOlechnowiczSWFowlerJAEdwardsCM. Diet-induced obesity promotes a myeloma-like condition in vivo. Leukemia (2015) 29:507–10. doi: 10.1038/leu.2014.295

  • 74

    LiZLiuHHeJWangZYinZYouGet al. Acetyl-CoA synthetase 2: A critical linkage in obesity-induced tumorigenesis in myeloma. Cell Metab (2021) 33:7893 e77. doi: 10.1016/j.cmet.2020.12.011

  • 75

    WhitburnJEdwardsCM. Metabolism in the tumour-bone microenvironment. Curr osteoporosis Rep (2021) 19:494–9. doi: 10.1007/s11914-021-00695-7

  • 76

    WhitburnJRaoSRMorrisEVTabataSHirayamaASogaT. Metabolic profiling of prostate cancer in skeletal microenvironments identifies G6PD as a key mediator of growth and survival. Sci Adv (2022) 8:eabf9096. doi: 10.1126/sciadv.abf9096

Summary

Keywords

plasma cell, multiple myeloma, metabolism, proteasome, cancer biology, proteostasis, bone marrow (BM) adipocytes

Citation

Roman-Trufero M, Auner HW and Edwards CM (2022) Multiple myeloma metabolism – a treasure trove of therapeutic targets?. Front. Immunol. 13:897862. doi: 10.3389/fimmu.2022.897862

Received

16 March 2022

Accepted

18 July 2022

Published

22 August 2022

Volume

13 - 2022

Edited by

Kamil R. Kranc, Queen Mary University of London, United Kingdom

Reviewed by

Paolo Gallipoli, Queen Mary University of London, United Kingdom; Andy Finch, Queen Mary University of London, United Kingdom

Updates

Copyright

*Correspondence: Holger W. Auner,

This article was submitted to Cytokines and Soluble Mediators in Immunity, a section of the journal Frontiers in Immunology

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Outline

Figures

Cite article

Copy to clipboard


Export citation file


Share article

Article metrics