Skip to main content

Advertisement

Springer Nature Link
Log in

Navigating the clinical landscape: Update on the diagnostic and prognostic biomarkers in multiple myeloma

  • Review
  • Published:
Molecular Biology Reports Aims and scope Submit manuscript

Abstract

Multiple myeloma, a complex hematologic malignancy, has devastating consequences for patients, including dramatic bone loss, severe bone pain, and pathological fractures that markedly decrease the quality of life and impact the survival of affected patients. This necessitates a refined understanding of biomarkers for accurate diagnosis and prognosis of such severe malignancy. Therefore, this article comprehensively covers current research, elucidating the diverse spectrum of biomarkers employed in clinical settings. From traditional serum markers to advanced molecular profiling techniques, the review provides a thorough examination of their utility and limitations. Through this scoping review, emphasis is placed on the evolving landscape of personalized medicine, where biomarkers play a pivotal role in tailoring therapeutic strategies. The integration of genomic, proteomic, next generation sequencing and flow cytometric data further enriches the discussion, unravelling the molecular intricacies underlying disease progression. The updated criteria allow for the treatment of people who clearly would benefit from therapy and might live longer if treated before significant organ damage occurs. Navigating through the evolving diagnostic and prognostic paradigms in multiple myeloma, this article equips clinicians and researchers with crucial insights for optimizing patient care and advancing future therapeutic approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

No datasets were generated or analysed during the current study.

References

  1. Rajkumar SV (2011) Treatment of multiple myeloma. Nat Reviews Clin Oncol 8(8):479–491. https://doi.org/10.1038/nrclinonc.2011.63

    Article  CAS  Google Scholar 

  2. Rajkumar SV (2018) Multiple myeloma: 2018 update on diagnosis, risk-stratification, and management. Am J Hematol 93(8):1091–1110. https://doi.org/10.1002/ajh.25117

    Article  CAS  Google Scholar 

  3. Cowan AJ, Green DJ, Kwok M, Lee S, Coffey DG, Holmberg LA et al (2022) Diagnosis and management of multiple myeloma: a review. JAMA 327(5):464–477. https://doi.org/10.1001/jama.2022.0003

    Article  CAS  PubMed  Google Scholar 

  4. Grant SJ, Wildes TM, Rosko AE, Silberstein J, Giri S (2023) A real-world data analysis of predictors of early mortality after a diagnosis of multiple myeloma. Cancer 129(13):2023–2034. https://doi.org/10.1002/cncr.34760

    Article  PubMed  Google Scholar 

  5. Nieto MJ, Hedjar A, Locke M, Caro J, Saif MW (2023) Analysis of updates in multiple myeloma treatment and management. J Clin Haematol 4(1):35. https://doi.org/10.33696/haematology.4.055

    Article  PubMed  Google Scholar 

  6. Bangolo AI, Fwelo P, Trivedi C, Sagireddy S, Aljanaahi H, Auda A et al (2023) Interaction between age and gender on survival outcomes in extramedullary multiple myeloma over the past two decades. World J Clin Oncol 14(4):179. https://doi.org/10.5306/wjco.v14.i4.179

    Article  PubMed  Google Scholar 

  7. Ho M, Patel A, Goh CY, Moscvin M, Zhang L, Bianchi G (2020) Changing paradigms in diagnosis and treatment of monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). Leukemia 34(12):3111–3125. https://doi.org/10.1038/s41375-020-01051-x

    Article  PubMed  Google Scholar 

  8. Kyle RA, Rajkumar SV (2013) Monoclonal gammopathy of undetermined significance. Neoplast Dis Blood. https://doi.org/10.1111/j.1365-2141.2006.06235.x. 751 – 85

    Article  Google Scholar 

  9. Van de Donk N, Mutis T, Poddighe P, Lokhorst H, Zweegman S (2016) Diagnosis, risk stratification and management of monoclonal gammopathy of undetermined significance and smoldering multiple myeloma. Int J Lab Hematol 38:110–122. https://doi.org/10.1111/ijlh.12504

    Article  PubMed  Google Scholar 

  10. Clay-Gilmour AI, Kumar S, Rajkumar SV, Rishi A, Kyle RA, Katzmann JA et al (2019) Risk of MGUS in relatives of multiple myeloma cases by clinical and tumor characteristics. Leukemia 33(2):499–507. https://doi.org/10.1038/s41375-018-0246-2

    Article  CAS  PubMed  Google Scholar 

  11. Mateos M-V, Landgren O (2016) MGUS and smoldering multiple myeloma: diagnosis and epidemiology. Plasma Cell Dyscrasias 3–12. https://doi.org/10.1007/978-3-319-40320-5_1

  12. Brigle K, Rogers B (eds) (2017) Pathobiology and diagnosis of multiple myeloma. Seminars in oncology nursing. Elsevier. https://doi.org/10.1016/j.soncn.2017.05.012

  13. Moore AR, Avery PR (2019) Protein characterization using electrophoresis and immunofixation; a case-based review of dogs and cats. Vet Clin Pathol 48:29–44. https://doi.org/10.1111/vcp.12760

    Article  PubMed  Google Scholar 

  14. Misra A, Mishra J, Chandramohan J, Sharma A, Raina V, Kumar R et al (2016) Old but still relevant: high resolution electrophoresis and immunofixation in multiple myeloma. Indian J Hematol Blood Transfus 32:10–17. https://doi.org/10.1007/s12288-015-0605-3

    Article  PubMed  Google Scholar 

  15. Shragai T, Gatt ME, Shaulov A, Katodritou E, Triantafyllou T, Lavi N et al (2021) Characteristics and outcome of multiple myeloma patients presenting with anaemia only: a retrospective multi-centre study. Leuk Res 101:106498. https://doi.org/10.1016/j.leukres.2020.106498

    Article  PubMed  Google Scholar 

  16. Karim KJ, Hassan AM, Getta HA, Khoshnaw NS, Jalal SD, Mohammed AM et al (2020) Frequency and prognostic significance of hypercalcemia in patients with multiple myeloma. Med J Babylon 17(4):327. https://doi.org/10.4103/MJBL.MJBL_54_20

    Article  Google Scholar 

  17. Szabo AG, Klausen TW, Abildgaard N, Gregersen H, Silkjær T, Pedersen PT et al (2021) Incidence and clinical characteristics of multiple myeloma with low M-protein levels and normal values of hemoglobin, creatinine, calcium, and serum free light chain ratio. Blood cancer J 11(4):70. https://doi.org/10.1038/s41408-021-00460-0

    Article  PubMed  Google Scholar 

  18. Shieh AC, Faraji N, Smith D, Parikh K, Ramaiya NH (2021) Utility and clinical implications of appendicular skeleton magnetic resonance imaging in multiple myeloma: a single-institutional 15-year experience. J Comput Assist Tomogr 45(6):904–911. https://doi.org/10.1097/RCT.0000000000001195

    Article  PubMed  Google Scholar 

  19. Talamo G, Farooq U, Zangari M, Liao J, Dolloff NG, Loughran TP Jr et al (2010) Beyond the CRAB symptoms: a study of presenting clinical manifestations of multiple myeloma. Clin Lymphoma Myeloma Leuk 10(6):464–468. https://doi.org/10.3816/CLML.2010.n.080

    Article  PubMed  Google Scholar 

  20. Ramsenthaler C, Kane P, Gao W, Siegert RJ, Edmonds PM, Schey SA et al (2016) Prevalence of symptoms in patients with multiple myeloma: a systematic review and meta-analysis. Eur J Haematol 97(5):416–429. https://doi.org/10.1111/ejh.12790

    Article  PubMed  Google Scholar 

  21. Heider M, Nickel K, Högner M, Bassermann F (2021) Multiple myeloma: molecular pathogenesis and disease evolution. Oncol Res Treat 44(12):672–681. https://doi.org/10.1159/000520312

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Rustad EH, Yellapantula V, Leongamornlert D, Bolli N, Ledergor G, Nadeu F et al (2020) Timing the initiation of multiple myeloma. Nat Commun 11(1):1917. https://doi.org/10.1038/s41467-020-15740-9

    Article  CAS  PubMed  Google Scholar 

  23. Abdallah N, Rajkumar SV, Greipp P, Kapoor P, Gertz MA, Dispenzieri A et al (2020) Cytogenetic abnormalities in multiple myeloma: association with disease characteristics and treatment response. Blood cancer J 10(8):82. https://doi.org/10.1038/s41408-020-00348-5

    Article  PubMed  Google Scholar 

  24. Zhu S, Li W, Lin M, Li T (2021) Serum protein electrophoresis and immunofixation electrophoresis detection in multiple myeloma. J Coll Physicians Surg Pak 30(7):864–867. https://doi.org/10.29271/jcpsp.2021.07.864

    Article  Google Scholar 

  25. Richard S, Jagannath S, Boccadoro M, Mina R (2020) Should high-risk smouldering multiple myeloma be treated? Lancet Haematol 7(1):e15–e6. https://doi.org/10.1080/10428194.2021.1951723

    Article  Google Scholar 

  26. Durie BG, Salmon SE (1975) A clinical staging system for multiple myeloma correlation of measured myeloma cell mass with presenting clinical features, response to treatment, and survival. Cancer 36(3):842–854

    Article  CAS  PubMed  Google Scholar 

  27. Group IMW (2003) Criteria for the classification of monoclonal gammopathies, multiple myeloma and related disorders: a report of the International Myeloma Working Group. Br J Haematol 121(5):749–757

    Article  Google Scholar 

  28. Greipp PR, Miguel JS, Durie BG, Crowley JJ, Barlogie B, Bladé J et al (2005) International staging system for multiple myeloma. J Clin Oncol 23(15):3412–3420. https://doi.org/10.1200/JCO.2005.04.242

    Article  PubMed  Google Scholar 

  29. Palumbo A, Avet-Loiseau H, Oliva S, Lokhorst HM, Goldschmidt H, Rosinol L et al (2015) Revised international staging system for multiple myeloma: a report from International Myeloma Working Group. J Clin Oncol 33(26):2863. https://doi.org/10.1200/JCO.2015.61.2267

    Article  CAS  PubMed  Google Scholar 

  30. Branagan A, Lei M, Lou U, Raje N (2020) Current treatment strategies for multiple myeloma. JCO Oncol Pract 16(1):5–14. https://doi.org/10.1200/JOP.19.00244

    Article  PubMed  Google Scholar 

  31. Alanazi F, Kwa FA, Burchall G, Jackson DE (2020) New generation drugs for treatment of multiple myeloma. Drug Discovery Today 25(2):367–379. https://doi.org/10.1016/j.drudis.2019.11.008

    Article  CAS  PubMed  Google Scholar 

  32. Das S, Juliana N, Yazit NAA, Azmani S, Abu IF (2022) Multiple myeloma: challenges encountered and future options for better treatment. Int J Mol Sci 23(3):1649. https://doi.org/10.3390/ijms23031649

    Article  CAS  PubMed  Google Scholar 

  33. Cagney DN, Sul J, Huang RY, Ligon KL, Wen PY, Alexander BM (2018) The FDA NIH Biomarkers, EndpointS, and other tools (BEST) resource in neuro-oncology. Neurooncology 20(9):1162–1172. https://doi.org/10.1093/neuonc/nox242

    Article  CAS  Google Scholar 

  34. Laubach J, Garderet L, Mahindra A, Gahrton G, Caers J, Sezer O et al (2016) Management of relapsed multiple myeloma: recommendations of the International Myeloma Working Group. Leukemia 30(5):1005–1017. https://doi.org/10.1038/leu.2015.356

    Article  CAS  PubMed  Google Scholar 

  35. Glavey SV, Leung N (2016) Monoclonal gammopathy: the good, the bad and the ugly. Blood Rev 30(3):223–231. https://doi.org/10.1016/j.blre.2015.12.001

    Article  PubMed  Google Scholar 

  36. Murata K, McCash SI, Carroll B, Lesokhin AM, Hassoun H, Lendvai N et al (2018) Treatment of multiple myeloma with monoclonal antibodies and the dilemma of false positive M-spikes in peripheral blood. Clin Biochem 51:66–71. https://doi.org/10.1016/j.clinbiochem.2016.09.015

    Article  CAS  PubMed  Google Scholar 

  37. Weiss BM, Abadie J, Verma P, Howard RS, Kuehl WM (2009) A monoclonal gammopathy precedes multiple myeloma in most patients. Blood J Am Soc Hematol 113(22):5418–5422. https://doi.org/10.1182/blood-2008-12-195008

    Article  CAS  Google Scholar 

  38. Morrison T, Booth RA, Hauff K, Berardi P, Visram A (2019) Laboratory assessment of multiple myeloma. Adv Clin Chem 89:1–58. https://doi.org/10.1016/bs.acc.2018.12.001

    Article  CAS  PubMed  Google Scholar 

  39. Bradwell AR, Carr-Smith HD, Mead GP, Tang LX, Showell PJ, Drayson MT et al (2001) Highly sensitive, automated immunoassay for immunoglobulin free light chains in serum and urine. Clin Chem 47(4):673–680

    Article  CAS  PubMed  Google Scholar 

  40. Charafeddine KM, Jabbour MN, Kadi RH, Daher RT (2012) Extended use of serum free light chain as a biomarker in lymphoproliferative disorders: a comprehensive review. Am J Clin Pathol 137(6):890–897. https://doi.org/10.1309/AJCP4INKZ6LYAQXW

    Article  CAS  PubMed  Google Scholar 

  41. Scarpa R, Pulvirenti F, Pecoraro A, Vultaggio A, Marasco C, Ria R et al (2020) Serum free light chains in common variable immunodeficiency disorders: role in differential diagnosis and association with clinical phenotype. Front Immunol 11:319. https://doi.org/10.3389/fimmu.2020.00319

    Article  CAS  PubMed  Google Scholar 

  42. Lee WS, Singh G (2019) Serum free light chain assay in monoclonal gammopathic manifestations. Lab Med 50(4):381–389. https://doi.org/10.1093/labmed/lmz007

    Article  CAS  PubMed  Google Scholar 

  43. Barlogie B, Shaughnessy J, Munshi N, Epstein J (2001) Plasma cell myeloma. Williams Hematol 7:1501–1533

    Google Scholar 

  44. Scudla V, Budikova M, Fischerova E, Zadrazil J, Indrak K (1991) Stratification of multiple myeloma according to serum beta 2-microglobulin and serum albumin levels. Acta Universitatis Palackianae Olomucensis Facultatis Medicae 130:201–212

    CAS  PubMed  Google Scholar 

  45. Dorina P, Oltean G, Smaranda D, Marcela C, Macarie I (2011) Beta-2 microglobulin as prognostic marker in multiple myeloma. Age 63:38–88

    Google Scholar 

  46. Gupta N, Sharma A, Sharma A (2020) Emerging biomarkers in multiple myeloma: a review. Clin Chim Acta 503:45–53. https://doi.org/10.1016/j.cca.2019.12.026

    Article  CAS  PubMed  Google Scholar 

  47. Munshi NC, Anderson KC, Bergsagel PL, Shaughnessy J, Palumbo A, Durie B et al (2011) Consensus recommendations for risk stratification in multiple myeloma: report of the International Myeloma Workshop Consensus Panel 2. Blood. J Am Soc Hematol 117(18):4696–4700. https://doi.org/10.1182/blood-2010-10-300970

    Article  CAS  Google Scholar 

  48. Kumar SK, Harrison SJ, Cavo M, de la Rubia J, Popat R, Gasparetto C et al (2020) Venetoclax or placebo in combination with bortezomib and dexamethasone in patients with relapsed or refractory multiple myeloma (BELLINI): a randomised, double-blind, multicentre, phase 3 trial. Lancet Oncol 21(12):1630–1642. https://doi.org/10.1016/S1470-2045(20)30525-8

    Article  CAS  PubMed  Google Scholar 

  49. Merz M, Hielscher T, Seckinger A, Hose D, Mai EK, Raab MS, Goldschmidt H, Jauch A, Hillengass J (2016) Baseline characteristics, chromosomal alterations, and treatment affecting prognosis of deletion 17p in newly diagnosed myeloma. Am J Hematol 91(11):E473–E477. https://doi.org/10.1002/ajh.24533

    Article  CAS  PubMed  Google Scholar 

  50. Boyd KD, Ross FM, Walker BA, Wardell CP, Tapper WJ, Chiecchio L et al (2011) Mapping of chromosome 1p deletions in myeloma identifies FAM46C at 1p12 and CDKN2C at 1p32. 3 as being genes in regions associated with adverse survival. Clin Cancer Res 17(24):7776–7784. https://doi.org/10.1158/1078-0432.CCR-11-1791

    Article  CAS  PubMed  Google Scholar 

  51. Weh HJ, Gutensohn K, Selbach J, Kruse R, Wacker-Backhaus G, Seeger D et al (1993) Karyotype in multiple myeloma and plasma cell leukaemia. Eur J Cancer 29(9):1269–1273. https://doi.org/10.1016/0959-8049(93)90071-m

    Article  Google Scholar 

  52. Barilà G, Bonaldi L, Grassi A, Martines A, Liço A, Macrì N et al (2020) Identification of the true hyperdiploid multiple myeloma subset by combining conventional karyotyping and FISH analysis. Blood cancer J 10(2):18. https://doi.org/10.1038/s41408-020-0285-6

    Article  PubMed  Google Scholar 

  53. Schmidt TM, Barwick BG, Joseph N, Heffner LT, Hofmeister CC, Bernal L, Dhodapkar MV, Gupta VA, Jaye DL, Wu J, Goyal S (2019) Gain of chromosome 1q is associated with early progression in multiple myeloma patients treated with lenalidomide, bortezomib, and dexamethasone. Blood Cancer J 9(12):94. https://doi.org/10.1038/s41408-019-0254-0

    Article  PubMed  Google Scholar 

  54. Kumar S, Fonseca R, Ketterling RP, Dispenzieri A, Lacy MQ, Gertz MA et al (2012) Trisomies in multiple myeloma: impact on survival in patients with high-risk cytogenetics. Blood J Am Soc Hematol 119(9):2100–2105. https://doi.org/10.1182/blood-2011-11-390658

    Article  CAS  Google Scholar 

  55. Zhan F, Hardin J, Kordsmeier B, Bumm K, Zheng M, Tian E et al (2002) Global gene expression profiling of multiple myeloma, monoclonal gammopathy of undetermined significance, and normal bone marrow plasma cells. Blood J Am Soc Hematol 99(5):1745–1757. https://doi.org/10.1182/blood.v99.5.1745

    Article  CAS  Google Scholar 

  56. Shaughnessy JD Jr, Zhan F, Burington BE, Huang Y, Colla S, Hanamura I et al (2007) A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1. Blood 109(6):2276–2284. https://doi.org/10.1182/blood-2006-07-038430

    Article  CAS  PubMed  Google Scholar 

  57. Decaux O, Lodé L, Magrangeas F, Charbonnel C, Gouraud W, Jézéquel P et al (2008) Prediction of survival in multiple myeloma based on gene expression profiles reveals cell cycle and chromosomal instability signatures in high-risk patients and hyperdiploid signatures in low-risk patients: a study of the Intergroupe Francophone Du Myelome. J Clin Oncol 26(29):4798–4805. https://doi.org/10.1200/JCO.2007.13.8545

    Article  CAS  PubMed  Google Scholar 

  58. Ria R, Todoerti K, Berardi S, Coluccia AML, De Luisi A, Mattioli M et al (2009) Gene expression profiling of bone marrow endothelial cells in patients with multiple myeloma. Clin Cancer Res 15(17):5369–5378. https://doi.org/10.1158/1078-0432.CCR-09-0040

    Article  CAS  PubMed  Google Scholar 

  59. van Beers EH, van Vliet MH, Kuiper R, de Best L, Anderson KC, Chari A et al (2017) Prognostic validation of SKY92 and its combination with ISS in an independent cohort of patients with multiple myeloma. Clin Lymphoma Myeloma Leuk 17(9):555–562. https://doi.org/10.1016/j.clml.2017.06.020

    Article  PubMed  Google Scholar 

  60. Zamagni E, Cavo M (2012) The role of imaging techniques in the management of multiple myeloma. Br J Haematol 159(5):499–513. https://doi.org/10.1111/bjh.12007

    Article  PubMed  Google Scholar 

  61. Xu L, Wu S (2023) New diagnostic strategy for multiple myeloma: a review. Medicine 102(52):e36660. https://doi.org/10.1097/MD.0000000000036660

    Article  PubMed  Google Scholar 

  62. Bartel TB, Haessler J, Brown TL, Shaughnessy JD Jr, van Rhee F, Anaissie E et al (2009) F18-fluorodeoxyglucose positron emission tomography in the context of other imaging techniques and prognostic factors in multiple myeloma. Blood J Am Soc Hematol 114(10):2068–2076. https://doi.org/10.1182/blood-2009-03-213280

    Article  CAS  Google Scholar 

  63. Lecouvet FE, Boyadzhiev D, Collette L, Berckmans M, Michoux N, Triqueneaux P et al (2020) MRI versus 18 F-FDG-PET/CT for detecting bone marrow involvement in multiple myeloma: diagnostic performance and clinical relevance. Eur Radiol 30:1927–1937. https://doi.org/10.1007/s00330-019-06469-1

    Article  PubMed  Google Scholar 

  64. Dimopoulos MA, Hillengass J, Usmani S, Zamagni E, Lentzsch S, Davies FE et al (2015) Role of magnetic resonance imaging in the management of patients with multiple myeloma: a consensus statement. J Clin Oncol 33(6):657–664. https://doi.org/10.1200/JCO.2014.57.9961

    Article  PubMed  Google Scholar 

  65. Cavo M, Terpos E, Nanni C, Moreau P, Lentzsch S, Zweegman S et al (2017) Role of 18F-FDG PET/CT in the diagnosis and management of multiple myeloma and other plasma cell disorders: a consensus statement by the International Myeloma Working Group. Lancet Oncol 18(4):e206–e17. https://doi.org/10.1016/S1470-2045(17)30189-4

    Article  PubMed  Google Scholar 

  66. Gozzetti A, Raspadori D, Pacelli P, Antonioli E, Bestoso E, Ciofini S et al (2020) Minimal residual disease (MRD) at 10 – 6 measured by Next Generation Flow (NGF) during daratumumab consolidation therapy: analysis at 18 months follow up of DART4MM Study (Daratumumab Treatment for multiple myeloma eradication). Blood 136:7–8. https://doi.org/10.3390/jpm10030120

    Article  Google Scholar 

  67. Oliva S, Genuardi E, Petrucci MT, D’Agostino M, Auclair D, Spadano A et al (2020) Impact of minimal residual disease (MRD) by multiparameter flow cytometry (MFC) and next-generation sequencing (NGS) on outcome: results of newly diagnosed transplant-eligible multiple myeloma (MM) patients enrolled in the forte trial. Blood 136:44–45

    Article  Google Scholar 

  68. Kumar S, Paiva B, Anderson KC, Durie B, Landgren O, Moreau P et al (2016) International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol 17(8):e328–e46. https://doi.org/10.1016/S1470-2045(16)30206-6

    Article  PubMed  Google Scholar 

  69. Kvorning SL, Nielsen MC, Andersen NF, Hokland M, Andersen MN, Møller HJ (2020) Circulating extracellular vesicle-associated CD163 and CD206 in multiple myeloma. Eur J Haematol 104(5):409–419. https://doi.org/10.1111/ejh.13371

    Article  CAS  PubMed  Google Scholar 

  70. Zhang L, Lei Q, Wang H, Xu C, Liu T, Kong F et al (2019) Tumor-derived extracellular vesicles inhibit osteogenesis and exacerbate myeloma bone disease. Theranostics 9(1):196. https://doi.org/10.7150/thno.27550

    Article  CAS  PubMed  Google Scholar 

  71. Caivano A, Laurenzana I, De Luca L, La Rocca F, Simeon V, Trino S et al (2015) High serum levels of extracellular vesicles expressing malignancy-related markers are released in patients with various types of hematological neoplastic disorders. Tumor Biology 36:9739–9752. https://doi.org/10.1007/s13277-015-3741-3

    Article  CAS  PubMed  Google Scholar 

  72. Gao S-S, Wang Y-J, Zhang G-X, Zhang W-T (2020) Potential diagnostic value of circulating miRNA for multiple myeloma: a meta-analysis. J Bone Oncol 25:100327. https://doi.org/10.1016/j.jbo.2020.100327

    Article  PubMed  Google Scholar 

  73. Yang N, Chen J, Zhang H, Wang X, Yao H, Peng Y et al (2017) LncRNA OIP5-AS1 loss-induced microRNA-410 accumulation regulates cell proliferation and apoptosis by targeting KLF10 via activating PTEN/PI3K/AKT pathway in multiple myeloma. Cell Death Dis 8(8):e2975–e. https://doi.org/10.1038/cddis.2017.358

    Article  CAS  PubMed  Google Scholar 

  74. Wang N, Liang X, Yu W, Zhou S, Fang M (2018) Differential expression of microRNA-19b promotes proliferation of cancer stem cells by regulating the TSC1/mTOR signaling pathway in multiple myeloma. Cell Physiol Biochem 50(5):1804–1814. https://doi.org/10.1159/000494821

    Article  CAS  PubMed  Google Scholar 

  75. Kubiczkova L, Kryukov F, Slaby O, Dementyeva E, Jarkovsky J, Nekvindova J et al (2014) Circulating serum microRNAs as novel diagnostic and prognostic biomarkers for multiple myeloma and monoclonal gammopathy of undetermined significance. Haematologica 99(3):511. https://doi.org/10.3324/haematol.2013.093500

    Article  CAS  PubMed  Google Scholar 

  76. Wu P, Agnelli L, Walker BA, Todoerti K, Lionetti M, Johnson DC et al (2013) Improved risk stratification in myeloma using a micro RNA-based classifier. Br J Haematol 162(3):348–359. https://doi.org/10.1111/bjh.12394

    Article  CAS  PubMed  Google Scholar 

  77. Hao M, Zang M, Wendlandt E, Xu Y, An G, Gong D et al (2015) Low serum Mi R-19a expression as a novel poor prognostic indicator in multiple myeloma. Int J Cancer 136(8):1835–1844. https://doi.org/10.1002/ijc.29199

    Article  CAS  PubMed  Google Scholar 

  78. Lin D, Shen L, Luo M, Zhang K, Li J, Yang Q et al (2021) Circulating tumor cells: Biology and clinical significance. Signal Transduct Target Therapy 6(1):404. https://doi.org/10.1038/s41392-021-00817-8

    Article  CAS  Google Scholar 

  79. Ignatiadis M, Lee M, Jeffrey SS (2015) Circulating tumor cells and circulating tumor DNA: challenges and opportunities on the path to clinical utility. Clin Cancer Res 21(21):4786–4800. https://doi.org/10.1038/s41392-021-00817-8

    Article  CAS  PubMed  Google Scholar 

  80. Garcés J-J, Cedena M-T, Puig N, Burgos L, Perez JJ, Cordon L et al (2022) Circulating tumor cells for the staging of patients with newly diagnosed transplant-eligible multiple myeloma. J Clin Oncol 40(27):3151–3161. https://doi.org/10.1200/JCO.21.01365

    Article  CAS  PubMed  Google Scholar 

  81. Garcés J-J, Bretones G, Burgos L, Valdes-Mas R, Puig N, Cedena M-T et al (2020) Circulating tumor cells for comprehensive and multiregional non-invasive genetic characterization of multiple myeloma. Leukemia 34(11):3007–3018. https://doi.org/10.1038/s41375-020-0883-0

    Article  CAS  PubMed  Google Scholar 

  82. Mishima Y, Paiva B, Shi J, Park J, Manier S, Takagi S et al (2017) The mutational landscape of circulating tumor cells in multiple myeloma. Cell Rep 19(1):218–224. https://doi.org/10.1016/j.celrep.2017.03.025

    Article  CAS  PubMed  Google Scholar 

  83. Huhn S, Weinhold N, Nickel J, Pritsch M, Hielscher T, Hummel M et al (2017) Circulating tumor cells as a biomarker for response to therapy in multiple myeloma patients treated within the GMMG-MM5 trial. Bone Marrow Transplant 52(8):1194–1198. https://doi.org/10.1038/bmt.2017.91

    Article  CAS  PubMed  Google Scholar 

  84. Lohr JG, Kim S, Gould J, Knoechel B, Drier Y, Cotton MJ et al (2016) Genetic interrogation of circulating multiple myeloma cells at single-cell resolution. Sci Transl Med 8(363):363ra147–363ra147. https://doi.org/10.1126/scitranslmed.aac7037

    Article  CAS  PubMed  Google Scholar 

  85. Kis O, Kaedbey R, Chow S, Danesh A, Dowar M, Li T et al (2017) Circulating tumour DNA sequence analysis as an alternative to multiple myeloma bone marrow aspirates. Nat Commun 8(1):15086. https://doi.org/10.1038/ncomms15086

    Article  PubMed  Google Scholar 

  86. Nijhof IS, Casneuf T, Van Velzen J, van Kessel B, Axel AE, Syed K et al (2016) CD38 expression and complement inhibitors affect response and resistance to daratumumab therapy in myeloma. Blood J Am Soc Hematol 128(7):959–970. https://doi.org/10.1182/blood-2016-03-703439

    Article  CAS  Google Scholar 

  87. Romano A, Parrinello N, Simeon V, Puglisi F, La Cava P, Bellofiore C et al (2020) High-density neutrophils in MGUS and multiple myeloma are dysfunctional and immune-suppressive due to increased STAT3 downstream signaling. Sci Rep 10(1):1983. https://doi.org/10.1038/s41598-020-58859-x

    Article  CAS  PubMed  Google Scholar 

  88. Gupta N, Khan R, Kumar R, Kumar L, Sharma A (2015) Versican and its associated molecules: potential diagnostic markers for multiple myeloma. Clin Chim Acta 442:119–124. https://doi.org/10.1016/j.cca.2015.01.012

    Article  CAS  PubMed  Google Scholar 

  89. Schumacher TN, Schreiber RD (2015) Neoantigens in cancer immunotherapy. Science 348(6230):69–74. https://doi.org/10.1126/science.aaa4971

    Article  CAS  PubMed  Google Scholar 

  90. Siwakoti A, Khadka S, Grimshaw AA, Giri S (2024 Jul) Association between Immunoparesis and treatment outcomes of patients with newly diagnosed multiple myeloma: a systematic review and Meta-analysis. Clin Lymphoma Myeloma Leuk 19. https://doi.org/10.1016/j.jaci.2022.12.248

  91. Sørrig R, Klausen TW, Salomo M, Vangsted AJ, Frølund UC, Andersen KT, Klostergaard A, Helleberg C, Pedersen RS, Pedersen PT, Helm-Petersen S (2017) Immunoparesis in newly diagnosed multiple myeloma patients: effects on overall survival and progression free survival in the Danish population. PLoS ONE 12(12):e0188988. https://doi.org/10.1371/journal.pone.0188988

    Article  CAS  PubMed  Google Scholar 

  92. Heaney JL, Campbell JP, Iqbal G, Cairns D, Richter A, Child JA, Gregory W, Jackson G, Kaiser M, Owen R, Davies F (2018) Characterisation of immunoparesis in newly diagnosed myeloma and its impact on progression-free and overall survival in both old and recent myeloma trials. Leukemia 32(8):1727–1738. https://doi.org/10.1038/s41375-018-0163-4

    Article  PubMed  Google Scholar 

  93. Girmenia C, Cavo M, Offidani M, Scaglione F, Corso A, Di Raimondo F, Musto P, Petrucci MT, Barosi G (2019) Management of infectious complications in multiple myeloma patients: Expert panel consensus-based recommendations. Blood Rev 34:84–94. https://doi.org/10.1016/j.blre.2019.01.001

    Article  PubMed  Google Scholar 

  94. Shah N, Chari A, Scott E, Mezzi K, Usmani SZ (2020) B-cell maturation antigen (BCMA) in multiple myeloma: rationale for targeting and current therapeutic approaches. Leukemia 34(4):985–1005. https://doi.org/10.1038/s41375-020-0734-z

    Article  PubMed  Google Scholar 

  95. Gavriatopoulou M, Ntanasis-Stathopoulos I, Dimopoulos MA, Terpos E (2019) Anti-BCMA antibodies in the future management of multiple myeloma. Expert Rev Anticancer Ther 19(4):319–326. https://doi.org/10.1080/14737140.2019.1586539

    Article  CAS  PubMed  Google Scholar 

  96. Thomas NE, Busam KJ, From L, Kricker A, Armstrong BK, Anton-Culver H et al (2013) Tumor-infiltrating lymphocyte grade in primary melanomas is independently associated with melanoma-specific survival in the population-based genes, environment and melanoma study. J Clin Oncol 31(33):4252. https://doi.org/10.1200/JCO.2013.51.3002

    Article  PubMed  Google Scholar 

  97. Puchades-Carrasco L, Lecumberri R, Martínez-López J, Lahuerta J-J, Mateos M-V, Prósper F et al (2013) Multiple myeloma patients have a specific serum metabolomic profile that changes after achieving complete remission. Clin Cancer Res 19(17):4770–4779. https://doi.org/10.1158/1078-0432.CCR-12-2917

    Article  CAS  PubMed  Google Scholar 

  98. Fei F, Ma T, Zhou X, Zheng M, Cao B, Li J (2021) Metabolic markers for diagnosis and risk-prediction of multiple myeloma. Life Sci 265:118852. https://doi.org/10.1016/j.lfs.2020.118852

    Article  CAS  PubMed  Google Scholar 

  99. Yue L, Zeng P, Li Y, Chai Y, Wu C, Gao B (2022) Nontargeted and targeted metabolomics approaches reveal the key amino acid alterations involved in multiple myeloma. PeerJ 10:e12918. https://doi.org/10.7717/peerj.12918

    Article  CAS  PubMed  Google Scholar 

  100. Mohamed A, Collins J, Jiang H, Molendijk J, Stoll T, Torta F et al (2020) Concurrent lipidomics and proteomics on malignant plasma cells from multiple myeloma patients: probing the lipid metabolome. PLoS ONE 15(1):e0227455. https://doi.org/10.1371/journal.pone.0227455

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Dunphy K, Bazou D, Henry M, Meleady P, Miettinen JJ, Heckman CA et al (2023) Proteomic and metabolomic analysis of bone marrow and plasma from patients with extramedullary multiple myeloma identifies distinct protein and metabolite signatures. Cancers 15(15):3764. https://doi.org/10.3390/cancers15153764

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Chakravarti D, LaBella KA, DePinho RA (2021) Telomeres: history, health, and hallmarks of aging. Cell 184(2):306–322. https://doi.org/10.1016/j.cell.2020.12.028

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Fernandes SG, Dsouza R, Pandya G, Kirtonia A, Tergaonkar V, Lee SY, Garg M, Khattar E (2020) Role of telomeres and telomeric proteins in human malignancies and their therapeutic potential. Cancers 12(7):1901. https://doi.org/10.3390/cancers12071901

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Campa D, Martino A, Varkonyi J, Lesueur F, Jamroziak K, Landi S, Jurczyszyn A, Marques H, Andersen V, Jurado M, Brenner H (2015) Risk of multiple myeloma is associated with polymorphisms within telomerase genes and telomere length. Int J Cancer 136(5):E351–E358. https://doi.org/10.1002/ijc.29101

    Article  CAS  PubMed  Google Scholar 

  105. Hyatt S Examining telomere dysfunction in multiple myeloma (Doctoral dissertation, Cardiff University)

  106. Kumar R, Khan R, Gupta N, Seth T, Sharma A, Kalaivani M, Sharma A (2018) Identifying the biomarker potential of telomerase activity and shelterin complex molecule, telomeric repeat binding factor 2 (TERF2), in multiple myeloma. Leuk Lymphoma 59(7):1677–1689. https://doi.org/10.1080/10428194.2017.1387915

    Article  CAS  PubMed  Google Scholar 

  107. Romero M, Mosquera Orgueira A, Mejía Saldarriaga M (2024) How artificial intelligence revolutionizes the world of multiple myeloma. Front Hematol 3:1331109. https://doi.org/10.3389/frhem.2024.1331109

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank Dr. Pushkal Sinduvadi Ramesh, University of Pennsylvania for his helpful comments and for proofreading the manuscript.

Funding

No funding was received in the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Biochemistry, JSS Medical College, JSS Academy of Higher Education & Research, Mysuru, 570015, India

    Sahana Kabbathi Raghunathachar, D. Abhijith, Akila Prashant & Arpitha Maraliga Ramesh

  2. Department of Oncology, JSS Medical College, JSS Academy of Higher Education & Research, Mysuru, 570015, India

    Kiran Pura Krishnamurthy

  3. Mysore Medical College and Research Institute, Mysuru, 570015, India

    Lokesh Maragowdanahalli Gopalaiah

  4. Medgenome labs, Bengaluru, 560100, India

    S. R. Parichay

Authors
  1. Sahana Kabbathi Raghunathachar
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Kiran Pura Krishnamurthy
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Lokesh Maragowdanahalli Gopalaiah
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. D. Abhijith
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Akila Prashant
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. S. R. Parichay
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Arpitha Maraliga Ramesh
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

S.K., A.M.R., and K.P.K.- conceptualization, writing the initial draft, and revision, L.M.G., and A.D. - writing and providing critical inputs, A.P. - writing, providing essential inputs and supervision, P.S.R. - writing, creating figures and revision. All authors - read and approved the final version of the manuscript.

Corresponding author

Correspondence to Arpitha Maraliga Ramesh.

Ethics declarations

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publish

Not applicable.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Raghunathachar, S.K., Krishnamurthy, K.P., Gopalaiah, L.M. et al. Navigating the clinical landscape: Update on the diagnostic and prognostic biomarkers in multiple myeloma. Mol Biol Rep 51, 972 (2024). https://doi.org/10.1007/s11033-024-09892-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11033-024-09892-w

Keywords