Molecular Profiling and Minimal Residual Disease Monitoring in Multiple Myeloma Patients: A Literature Review

AUTOIMMUNE THROMBOCYTOPENIA

AV Semyanikhina1,2, EE Tolstykh1

1 NN Blokhin National Medical Cancer Research Center, 24 Kashirskoye sh., Moscow, Russian Federation, 115478

2 NP Bochkov Research Centre for Medical Genetics, 1 Moskvorech’e str., Moscow, Russian Federation, 115522

For correspondence: Aleksandra Vladimirovna Semyanikhina, MD, PhD, 23 Kashirskoye sh., Moscow, Russian Federation, Российская Федерация, 115478; Tel.: +7(926)371-21-56; e-mail: alexandra_silina@mail.ru

For citation: Semyanikhina AV, Tolstykh EE. Molecular Profiling and Minimal Residual Disease Monitoring in Multiple Myeloma Patients: A Literature Review. Clinical oncohematology. 2021;14(4):436–43. (In Russ).

DOI: 10.21320/2500-2139-2021-14-4-436-443

ABSTRACT

A personalized approach is a promising tool for malignant neoplasm (MN) treatment. Gaining success and benefit assessment of this approach were considerably facilitated by the implementation of the new generation sequencing techniques which allow to obtain comprehensive information on the tumor genome and transcriptome state with identifying potential biomarkers and targets for directed drug action. Despite the exponential growth in the number of sequenced tumor genomes, some of them are not subject of active clinical studies, although obviously and increasingly require optimization of current treatment regimens. One of these pathologies is multiple myeloma (MM). Considerable advances in its diagnosis and treatment have substantially increased survival rates. However, MM cannot be removed from the list of fatal diseases, yet. It is a neoplasm which needs to be further studied and explored for implementation of new treatment strategies, most of which would be based on pheno- and genotypic characteristics of tumor cells. The present review deals with the state of the art in the study of the MM molecular genetic profile, minimal residual disease (MRD) monitoring as well as potentials of the new generation sequencing for MRD diagnosis, prognosis, estimation, and search for predictors aimed at chemotherapy optimization.

Keywords: multiple myeloma, new generation sequencing, minimal residual disease.

Received: May 21, 2021

Accepted: August 29, 2021

Read in PDF

Статистика Plumx английский

REFERENCES

  1. Swerdlow SH, Campo E, Harris NL, et al. WHO classification of tumours of haematopoietic and lymphoid tissues. Revised 4th edition. Lyon: IARC Press; 2017. 592 p.
  2. Brigle K, Rogers B. Pathobiology and Diagnosis of Multiple Myeloma. Semin Oncol Nurs. 2017;33(3):225–36. doi: 10.1016/j.soncn.2017.05.012.
  3. Naymagon L, Abdul-Hay M. Novel agents in the treatment of multiple myeloma: a review about the future. J Hematol Oncol. 2016;9(1):52. doi: 10.1186/s13045-016-0282-1.
  4. Castaneda O, Baz R. Multiple Myeloma Genomics – A Concise Review. Acta Med Acad. 2019;48(1):57–67. doi: 10.5644/ama2006-124.242.
  5. Kumar SK, Rajkumar V, Kyle RA, et al. Multiple myeloma. Nat Rev Dis Primers. 2017;3(1):17046. doi: 10.1038/nrdp.2017.46.
  6. Поддубная И.В., Савченко В.Г., Каприн А.Д. Клинические рекомендации. Множественная миелома. М., 2020. 222 с.
    [Poddubnaya IV, Savchenko VG, Kaprin AD. Klinicheskie rekomendatsii. Mnozhestvennaya mieloma. (Clinical guidelines. Multiple myeloma.) Moscow; 2020. 222 p. (In Russ)]
  7. Bolli N, Genuardi E, Ziccheddu B, et al. Next-Generation Sequencing for Clinical Management of Multiple Myeloma: Ready for Prime Time? Front Oncol. 2020;25(10):a189. doi: 10.3389/fonc.2020.00189.
  8. Chng WJ, Van Wier SA, Ahmann GJ, et al. A validated FISH trisomy index demonstrates the hyperdiploid and nonhyperdiploid dichotomy in MGUS. Blood. 2005;106(6):2156–61. doi: 10.1182/blood-2005-02-0761.
  9. Lai JL, Zandecki M, Mary JY, et al. Improved cytogenetics in multiple myeloma: a study of 151 patients including 117 patients at diagnosis. Blood. 1995;85(9):2490–7. doi: 10.1182/blood.v85.9.2490.bloodjournal8592490.
  10. Morgan GJ, Walker BA, Davies FE. The genetic architecture of multiple myeloma. Nat Rev Cancer. 2012;12(5):335–48. doi: 10.1038/nrc3257.
  11. Kumar S, Fonseca R, Ketterling RP, et al. Trisomies in multiple myeloma: impact on survival in patients with high-risk cytogenetics. Blood. 2012;119(9):2100–5. doi: 10.1182/blood-2011-11-390658.
  12. Kumar SK, Rajkumar SV. The multiple myelomas – current concepts in cytogenetic classification and therapy. Nat Rev Clin Oncol. 2018;15(7):409–21. doi: 10.1038/s41571-018-0018-y.
  13. Binder M, Rajkumar SV, Ketterling RP, et al. Prognostic implications of abnormalities of chromosome 13 and the presence of multiple cytogenetic high-risk abnormalities in newly diagnosed multiple myeloma. Blood Cancer J. 2017;7(9):e600. doi: 10.1038/bcj.2017.83.
  14. Fonseca R, Bergsagel PL, Drach J, et al. International Myeloma Working Group molecular classification of multiple myeloma: spotlight review. Leukemia. 2009;23(12):2210–21. doi: 10.1038/leu.2009.174.
  15. Bergsagel PL, Kuehl WM, Zhan F, et al. Cyclin D dysregulation: an early and unifying pathogenic event in multiple myeloma. Blood. 2005;106(1):296–303. doi: 10.1182/blood-2005-01-0034.
  16. Kuiper R, van Duin M, van Vliet MH, et al. Prediction of high- and low-risk multiple myeloma based on gene expression and the International Staging System. Blood. 2015;126(17):1996–2004. doi: 10.1182/blood-2015-05-644039.
  17. Shaughnessy JD Jr, Zhan F, Burington BE, et al. A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1. Blood. 2007;109(6):2276–84. doi: 10.1182/blood-2006-07-038430.
  18. Chapman MA, Lawrence MS, Keats JJ, et al. Initial genome sequencing and analysis of multiple myeloma. Nature. 2011;471(7339):467–72. doi: 10.1038/nature09837.
  19. Treon SP, Xu L, Yang G, et al. MYD88 L265P somatic mutation in Waldenstrom’s macroglobulinemia. N Engl J Med. 2012;367(9):826–33. doi: 10.1056/NEJMoa1200710.
  20. Bolli N, Biancon G, Moarii M, et al. Analysis of the genomic landscape of multiple myeloma highlights novel prognostic markers and disease subgroups. Leukemia. 2018;32(12):2604–16. doi: 10.1038/s41375-018-0037-9.
  21. Raab MS, Lehners N, Xu J, et al. Spatially divergent clonal evolution in multiple myeloma: overcoming resistance to BRAF inhibition. Blood. 2016;127(17):2155–7. doi: 10.1182/blood-2015-12-686782.
  22. Keats JJ, Chesi M, Egan JB, et al. Clonal competition with alternating dominance in multiple myeloma. Blood. 2012;120(5):1067–76. doi: 10.1182/blood-2012-01-405985.
  23. Zhao S, Choi M, Heuck C, et al. Serial exome analysis of disease progression in premalignant gammopathies. Leukemia. 2014;28(7):1548–52. doi: 10.1038/leu.2014.59.
  24. Walker BA, Wardell CP, Melchor L, et al. Intraclonal heterogeneity is a critical early event in the development of myeloma and precedes the development of clinical symptoms. Leukemia. 2014;28(2):384–90. doi: 10.1038/leu.2013.199.
  25. Miller A, Asmann Y, Cattaneo L, et al. High somatic mutation and neoantigen burden are correlated with decreased progression-free survival in multiple myeloma. Blood Cancer J. 2017;7(9):e612. doi: 10.1038/bcj.2017.94.
  26. Benson DM Jr. Checkpoint inhibition in myeloma. Hematology Am Soc Hematol Educ Program. 2016;2016(1):528–33. doi: 10.1182/asheducation-2016.1.528.
  27. Walker BA, Boyle EM, Wardell CP, et al. Mutational Spectrum, Copy Number Changes, and Outcome: Results of a Sequencing Study of Patients With Newly Diagnosed Myeloma. J Clin Oncol. 2015;33(33):3911–20. doi: 10.1200/JCO.2014.59.1503.
  28. Mailankody S, Kazandjian D, Korde N, et al. Baseline mutational patterns and sustained MRD negativity in patients with high-risk smoldering myeloma. Blood Adv. 2017;1(22):1911–8. doi: 10.1182/bloodadvances.2017005934.
  29. Manier S, Sacco A, Leleu X, et al. Bone marrow microenvironment in multiple myeloma progression. J Biomed Biotechnol. 2012;2012:1–5. doi: 10.1155/2012/157496.
  30. Misund K, Keane N, Stein CK, et al. MYC dysregulation in the progression of multiple myeloma. Leukemia. 2020;34(1):322–6. doi: 10.1038/s41375-019-0543-4.
  31. Sive JI, Feber A, Smith D, et al. Global hypomethylation in myeloma is associated with poor prognosis. Br J Haematol. 2016;172(3):473–5. doi: 10.1111/bjh.13506.
  32. Bollati V, Fabris S, Pegoraro V, et al. Differential repetitive DNA methylation in multiple myeloma molecular subgroups. Carcinogenesis. 2009;30(8):1330–5. doi: 10.1093/carcin/bgp149.
  33. Esquela-Kerscher A, Slack FJ. Oncomirs – microRNAs with a role in cancer. Nat Rev Cancer. 2006;6(4):259–69. doi: 10.1038/nrc1840.
  34. Van Beers EH, van Vliet MH, Kuiper R, et al. Prognostic Validation of SKY92 and Its Combination With ISS in an Independent Cohort of Patients With Multiple Myeloma. Clin Lymphoma Myel Leuk. 2017;17(9):555–62. doi: 10.1016/j.clml.2017.06.020.
  35. Paiva B, Vidriales MB, Cervero J, et al. Multiparameter flow cytometric remission is the most relevant prognostic factor for multiple myeloma patients who undergo autologous stem cell transplantation. 2008;112(10):4017–23. doi: 10.1182/blood-2008-05-159624.
  36. Paiva B, Martinez-Lopez J, Vidriales MB, et al. Comparison of immunofixation, serum free light chain, and immunophenotyping for response evaluation and prognostication in multiple myeloma. J Clin Oncol. 2011;29(12):1627–33. doi: 10.1200/JCO.2010.33.1967.
  37. Paiva B, Gutierrez NC, Rosinol L, et al. High-risk cytogenetics and persistent minimal residual disease by multiparameter flow cytometry predict unsustained complete response after autologous stem cell transplantation in multiple myeloma. 2012;119(3):687–91. doi: 10.1182/blood-2011-07-370460.
  38. Munshi NC, Avet-Loiseau H, Rawstron AC, et al. Association of Minimal Residual Disease With Superior Survival Outcomes in Patients With Multiple Myeloma: A Meta-analysis. JAMA Oncol. 2017;3(1):28–35. doi: 10.1001/jamaoncol.2016.3160.
  39. Gambella M, Omede P, Spada S, et al. Minimal residual disease by flow cytometry and allelic-specific oligonucleotide real-time quantitative polymerase chain reaction in patients with myeloma receiving lenalidomide maintenance: A pooled analysis. Cancer. 2019;125(5):750–60. doi: 10.1002/cncr.31854.
  40. Perrot A, Lauwers-Cances V, Corre J, et al. Minimal residual disease negativity using deep sequencing is a major prognostic factor in multiple myeloma. Blood. 2018;132(23):2456–64. doi: 10.1182/blood-2018-06-858613.
  41. Mateos MV, Dimopoulos MA, Cavo M, et al. Daratumumab plus Bortezomib, Melphalan, and Prednisone for Untreated Myeloma. N Engl J Med. 2018;378(6):518–28. doi: 10.1056/NEJMoa1714678.
  42. Langerak AW, Groenen PJ, Bruggemann M, et al. EuroClonality/BIOMED-2 guidelines for interpretation and reporting of Ig/TCR clonality testing in suspected lymphoproliferations. Leukemia. 2012;26(10):2159–71. doi: 10.1038/leu.2012.246.
  43. Van der Velden VH, Cazzaniga G, Schrauder A, et al. Analysis of minimal residual disease by Ig/TCR gene rearrangements: guidelines for interpretation of real-time quantitative PCR data. Leukemia. 2007;21(4):604–11. doi: 10.1038/sj.leu.2404586.
  44. Corradini P, Voena C, Tarella C, et al. Molecular and clinical remissions in multiple myeloma: role of autologous and allogeneic transplantation of hematopoietic cells. J Clin Oncol. 1999;17(1):208–15. doi: 10.1200/JCO.1999.17.1.208.
  45. Sarasquete ME, Garcia-Sanz R, Gonzalez D, et al. Minimal residual disease monitoring in multiple myeloma: a comparison between allelic-specific oligonucleotide real-time quantitative polymerase chain reaction and flow cytometry. Haematologica. 2005;90(10):1365–72.
  46. Martinez-Lopez J, Lahuerta JJ, Pepin F, et al. Prognostic value of deep sequencing method for minimal residual disease detection in multiple myeloma. Blood. 2014;123(20):3073–9. doi: 10.1182/blood-2014-01-550020.
  47. Kumar S, Paiva B, Anderson KC, et al. International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol. 2016;17(8):e328–e346. doi: 10.1016/S1470-2045(16)30206-6.
  48. Lohr JG, Kim S, Gould J, et al. Genetic interrogation of circulating multiple myeloma cells at single-cell resolution. Sci Transl Med. 2016;8(363):363ra147. doi: 10.1126/scitranslmed.aac7037.
  49. Mishima Y, Paiva B, Shi J, et al. The Mutational Landscape of Circulating Tumor Cells in Multiple Myeloma. Cell Rep. 2017;19(1):218–24. doi: 10.1016/j.celrep.2017.03.025.
  50. Manier S, Park J, Capelletti M, et al. Whole-exome sequencing of cell-free DNA and circulating tumor cells in multiple myeloma. Nat Commun. 2018;9(1):1691. doi: 10.1038/s41467-018-04001-5.
  51. Zamagni E, Nanni C, Mancuso K, et al. PET/CT Improves the Definition of Complete Response and Allows to Detect Otherwise Unidentifiable Skeletal Progression in Multiple Myeloma. Clin Cancer Res. 2015;21(19):4384–90. doi: 10.1158/1078-0432.CCR-15-0396.
  52. Паива Б., Видриалес М.Б., Алмейда Х. и др. Оценка эффекта лечения при множественной миеломе: клиническое значение мониторинга МОЗ. Иммунология гемопоэза. 2012;10(1):34–77.
    [Paiva B, Vidriales MB, Almeida J, et al. Treatment response assessment in multiple myeloma: clinical significance of MRD monitoring. Immunologiya gemopoeza. 2012;10(1):34–77. (In Russ)]
  53. Kumar SK. Targeted Management Strategies in Multiple Myeloma. Cancer J. 2019;25(1):59–64. doi: 10.1097/PPO.0000000000000353.
  54. Multiple Myeloma Research Consortium. Myeloma-Developing Regimens Using Genomics (MyDRUG). Available from: https://clinicaltrials.gov/ct2/show/NCT03732703 (accessed 2.06.2021).