Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

MULTIPLE MYELOMA, GAMMOPATHIES

Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data

Leukemia volume 35, pages 2924–2935 (2021)Cite this article

Subjects

Abstract

Multiple myeloma (MM) remains mostly an incurable disease with a heterogeneous clinical evolution. Despite the availability of several prognostic scores, substantial room for improvement still exists. Promising results have been obtained by integrating clinical and biochemical data with gene expression profiling (GEP). In this report, we applied machine learning algorithms to MM clinical and RNAseq data collected by the CoMMpass consortium. We created a 50-variable random forests model (IAC-50) that could predict overall survival with high concordance between both training and validation sets (c-indexes, 0.818 and 0.780). This model included the following covariates: patient age, ISS stage, serum B2-microglobulin, first-line treatment, and the expression of 46 genes. Survival predictions for each patient considering the first line of treatment evidenced that those individuals treated with the best-predicted drug combination were significantly less likely to die than patients treated with other schemes. This was particularly important among patients treated with a triplet combination including bortezomib, an immunomodulatory drug (ImiD), and dexamethasone. Finally, the model showed a trend to retain its predictive value in patients with high-risk cytogenetics. In conclusion, we report a predictive model for MM survival based on the integration of clinical, biochemical, and gene expression data with machine learning tools.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Outline of the research workflow.
Fig. 2: Predicted individual survival curves according to the best random forests model.
Fig. 3: Comparison of optimal vs suboptimal treatment outcomes.

Similar content being viewed by others

Code availability

The code to train and validate IAC-50 of survival can be requested by e-mail to the corresponding author.

References

  1. Hanbali A, Hassanein M, Rasheed W, Aljurf M, Alsharif F. The evolution of prognostic factors in multiple myeloma. Adv Hematol. 2017;2017:4812637.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Greipp PR, San Miguel J, Durie BG, Crowley JJ, Barlogie B, Bladé J, et al. International staging system for multiple myeloma. J Clin Oncol. 2005;23:3412–20.

    Article  PubMed  Google Scholar 

  3. Palumbo A, Avet-Loiseau H, Oliva S, Lokhorst HM, Goldschmidt H, Rosinol L, et al. Revised international staging system for multiple myeloma: a report from International Myeloma Working Group. J Clin Oncol. 2015;33:2863–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Chng WJ, Dispenzieri A, Chim CS, Fonseca R, Goldschmidt H, Lentzsch S, et al. IMWG consensus on risk stratification in multiple myeloma. Leukemia. 2014;28:269–77.

    Article  CAS  PubMed  Google Scholar 

  5. Kastritis E, Terpos E, Roussou M, Gavriatopoulou M, Migkou M, Eleutherakis-Papaiakovou E, et al. Evaluation of the revised international staging system in an independent cohort of unselected patients with multiple myeloma. Haematologica. 2017;102:593–9.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Walker I, Coady A, Neat M, Ladon D, Benjamin R, El-Najjar I, et al. Is the revised International staging system for myeloma valid in a real world population? Br J Haematol. 2018;180:451–4.

    Article  PubMed  Google Scholar 

  7. Harrell FE Jr, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating the yield of medical tests. JAMA 1982;247:2543–6.

    Article  PubMed  Google Scholar 

  8. Zhang Y, Chen XL, Chen WM, Zhou HB. Prognostic nomogram for the overall survival of patients with newly diagnosed multiple myeloma. Biomed Res Int 2019;2019:5652935. Published 2019 Apr 8

    PubMed  PubMed Central  Google Scholar 

  9. Pawlyn C, Davies FE. Toward personalized treatment in multiple myeloma based on molecular characteristics. Blood. 2019;133:660–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Keats JJ, Reiman T, Maxwell CA, Taylor BJ, Larratt LM, Mant MJ, et al. In multiple myeloma, t(4;14)(p16;q32) is an adverse prognostic factor irrespective of FGFR3 expression. Blood. 2003;101:1520–9.

    Article  CAS  PubMed  Google Scholar 

  11. Boyd KD, Ross FM, Chiecchio L, Dagrada GP, Konn ZJ, Tapper WJ, et al. NCRI Haematology Oncology Studies Group. A novel prognostic model in myeloma based on co-segregating adverse FISH lesions and the ISS: analysis of patients treated in the MRC Myeloma IX trial. Leukemia. 2012;26:349–55.

    Article  CAS  PubMed  Google Scholar 

  12. Narita T, Inagaki A, Kobayashi T, Kuroda Y, Fukushima T, Nezu M, et al. t(14;16)-positive multiple myeloma shows negativity for CD56 expression and unfavorable outcome even in the era of novel drugs. Blood Cancer J. 2015;5:e285.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Teoh PJ, Chung TH, Sebastian S, Choo SN, Yan J, Ng SB, et al. p53 haploinsufficiency and functional abnormalities in multiple myeloma. Leukemia 2014;28:2066–74.

    Article  CAS  PubMed  Google Scholar 

  14. Fonseca R, Van Wier SA, Chng WJ, Ketterling R, Lacy MQ, Dispenzieri A, et al. Prognostic value of chromosome 1q21 gain by fluorescent in situ hybridization and increase CKS1B expression in myeloma. Leukemia. 2006;20:2034–40.

    Article  CAS  PubMed  Google Scholar 

  15. Hebraud B, Leleu X, Lauwers-Cances V, Roussel M, Caillot D, Marit G, et al. Deletion of the 1p32 region is a major independent prognostic factor in young patients with myeloma: the IFM experience on 1195 patients. Leukemia. 2014;28:675–9.

    Article  CAS  PubMed  Google Scholar 

  16. Walker BA, Mavrommatis K, Wardell CP, Ashby TC, Bauer M, Davies F, et al. A high-risk, double-hit, group of newly diagnosed myeloma identified by genomic analysis. Leukemia. 2019;33:159–70.

    Article  CAS  PubMed  Google Scholar 

  17. Walker BA, Boyle EM, Wardell CP, Murison A, Begum DB, Dahir NM, et al. Mutational spectrum, copy number changes, and outcome: results of a sequencing study of patients with newly diagnosed myeloma. J Clin Oncol. 2015;33:3911–20.

    Article  CAS  PubMed  Google Scholar 

  18. Joseph NS, Kaufman JL, Dhodapkar MV, Hofmeister CC, Almaula DK, Heffner LT, et al. Long-term follow-up results of lenalidomide, bortezomib, and dexamethasone induction therapy and risk-adapted maintenance approach in newly diagnosed multiple myeloma. J Clin Oncol. 2020;38(Jun):1928–37. Epub 2020 Apr 16. Erratum in: J Clin Oncol. 2020 Aug 10;38(23):2702. PMID: 32298201; PMCID: PMC7587409

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zimmerman T, Raje NS, Vij R, Reece D, Berdeja JG, Stephens LA, et al. Final results of a phase 2 trial of extended treatment (tx) with carfilzomib (CFZ), lenalidomide (LEN), and dexamethasone (carfilzomib-based triplets) plus autologous stem cell transplantation (ASCT) in newly diagnosed multiple myeloma (NDMM). Blood. 2016;128:675.

    Article  Google Scholar 

  20. Stewart AK, Rajkumar SV, Dimopoulos MA, Masszi T, Špička I, Oriol A, et al. ASPIRE Investigators. Carfilzomib, lenalidomide, and dexamethasone for relapsed multiple myeloma. N Engl J Med. 2015;372:142–52.

    Article  PubMed  CAS  Google Scholar 

  21. Sidiqi MH, Aljama MA, Bin Riaz I, Dispenzieri A, Muchtar E, Buadi FK, et al. Bortezomib, lenalidomide, and dexamethasone (VRd) followed by autologous stem cell transplant for multiple myeloma. Blood Cancer J. 2018;8:106. Published 2018 Nov 8

    Article  PubMed  PubMed Central  Google Scholar 

  22. Avet-Loiseau H, Bahlis NJ, Chng WJ, Masszi T, Viterbo L, Pour L, et al. Ixazomib significantly prolongs progression-free survival in high-risk relapsed/refractory myeloma patients. Blood. 2017;130(Dec):2610–8. Epub 2017 Oct. PMID: 29054911

    Article  CAS  PubMed  Google Scholar 

  23. Kumar S, Jacobus SJ, Cohen AD, Weiss M, Callander NS, Singh AA et al. Carfilzomib, lenalidomide, and dexamethasone (carfilzomib-based triplets) versus bortezomib, lenalidomide, and dexamethasone (VRd) for initial therapy of newly diagnosed multiple myeloma (NDMM): results of ENDURANCE (E1A11) phase III trial. J Clin Oncol. 2020 38:18_suppl, LBA3-LBA3

  24. Gay F, Cerrato C, Petrucci MT, Zambello L, Gamberi B, Ballanti S et al. Efficacy of carfilzomib lenalidomide dexamethasone (carfilzomib-based triplets) with or without transplantation in newly diagnosed myeloma according to risk status: results from the FORTE trial. J Clin Oncol. 2019 37_suppl, 8002-8002

  25. Rosiñol L, Oriol A, Rios R, Sureda A, Blanchard MJ, Hernández MT, et al. Bortezomib, lenalidomide, and dexamethasone as induction therapy prior to autologous transplant in multiple myeloma. Blood. 2019;134:1337–45.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. van Rhee F, Giralt S, Barlogie B. The future of autologous stem cell transplantation in myeloma. Blood. 2014;124:328–33.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. MMRF CoMMpass study: an update. Multiple myeloma research foundation. 2020. July 9, 2020. bit.ly/2BPiUH3.

  28. Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Stat. 2008;2:841–60. https://projecteuclid.org/euclid.aoas/1223908043

    Article  Google Scholar 

  29. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks [published correction appears in Nat Protoc. 2014 Oct;9(10):2513. Nat Protoc. 2012;7:562–78. Published 2012 Mar 1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Scrucca L, Fop M, Murphy TB, Raftery AE. mclust 5: clustering, classification and density estimation using gaussian finite mixture models. R J. 2016;8:289–317.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Wang J, Vasaikar S, Shi Z, Greer M, Zhang B. WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res. 2017;45:W130–W137.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Lub S, Maes K, Menu E, De Bruyne E, Vanderkerken K, Van Valckenborgh E. Novel strategies to target the ubiquitin proteasome system in multiple myeloma. Oncotarget. 2016;7:6521–37.

    Article  PubMed  Google Scholar 

  33. Szalat R, Avet-Loiseau H, Munshi NC. Gene expression profiles in myeloma: ready for the real world? Clin Cancer Res. 2016;22:5434–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kuiper R, van Duin M, van Vliet MH, Broijl A, van der Holt B, El Jarari L, et al. Prediction of high- and low-risk multiple myeloma based on gene expression and the international staging system. Blood. 2015;126:1996–2004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hummel M, Hielscher T, Salwender HJ, Scheid C, Martin H, Bertsch U, et al. Quantitative integrative prediction of survival probability in multiple myeloma using molecular and clinical prognostic factors in 657 patients treated with bortezomib-based induction, high-dose therapy and autologous stem cell transplantation. Blood. 2018;132(Supplement 1):403.

    Article  Google Scholar 

  36. Perrot A, Lauwers-Cances V, Tournay E, Hulin C, Chretien ML, Royer B, et al. Development and validation of a cytogenetic prognostic index predicting survival in multiple myeloma. J Clin Oncol. 2019;37:1657–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Terebelo HR, Abonour R, Gasparetto CJ, Toomey K, Durie BGM, Hardin JW, et al. Development of a prognostic model for overall survival in multiple myeloma using the Connect® MM Patient Registry. Br J Haematol. 2019;187:602–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Amin SB, Yip WK, Minvielle S, Broyl A, Li Y, Hanlon B, et al. Gene expression profile alone is inadequate in predicting complete response in multiple myeloma. Leukemia. 2014;28:2229–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Chapman MA, Sive J, Ambrose J, Roddie C, Counsell N, Lach A, et al. RNA-seq of newly diagnosed patients in the PADIMAC study leads to a bortezomib/lenalidomide decision signature. Blood. 2018;132:2154–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hari P, Lentzsch S, DiCapua Siegel DS, Usmani SZ, Dhakal B, Rossi AC et al. Prospective study to measure the impact of MMprofiler on treatment intention in newly diagnosed multiple myeloma patients (PROMMIS). J Clin Oncol. 2019;37_suppl:8030–8030

  41. Boccadoro M. Validation of a personalised medicine tool for multiple myeloma that predicts treatment effectiveness in patients (MMpredict). NCT03409692. Retrieved from https://clinicaltrials.gov/ct2/show/NCT03409692. 24 Jan 2008.

  42. Vangsted AJ, Helm-Petersen S, Cowland JB, Jensen PB, Gimsing P, Barlogie B, et al. Drug response prediction in high-risk multiple myeloma. Gene. 2018;644:80–86.

    Article  CAS  PubMed  Google Scholar 

  43. Ubels J, Sonneveld P, van Beers EH, Broijl A, van Vliet MH, de Ridder J. Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects. Nat Commun. 2018;9:2943. Published 2018 Jul

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Facon T, Kumar S, Plesner T, Orlowski RZ, Moreau P, Bahlis N, et al. Daratumumab plus lenalidomide and dexamethasone for untreated myeloma. N. Engl J Med. 2019;380:2104–15.

    Article  CAS  PubMed  Google Scholar 

  45. Moreau P, Attal M, Hulin C, Arnulf B, Belhadj K, Benboubker L, et al. Bortezomib, thalidomide, and dexamethasone with or without daratumumab before and after autologous stem-cell transplantation for newly diagnosed multiple myeloma (CASSIOPEIA): a randomised, open-label, phase 3 study [published correction appears in Lancet. 2019 Jun 14. Lancet 2019;394:29–38.

    Article  CAS  PubMed  Google Scholar 

  46. Attal M, Lauwers-Cances V, Hulin C, Leleu X, Caillot D, Escoffre M, et al. Lenalidomide, bortezomib, and dexamethasone with transplantation for myeloma. N Engl J Med. 2017;376:1311–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors wish to thank the Supercomputing Center of Galicia (CESGA) and the Multiple Myeloma Research Foundation (MMRF) for technical support and facilitating access to the data from the CoMMpass consortium, respectively.

Author information

Authors and Affiliations

  1. Health Research Institute of Santiago de Compostela (IDIS), Compostela, Spain

    Adrián Mosquera Orgueira, Marta Sonia González Pérez, José Ángel Díaz Arias, Beatriz Antelo Rodríguez, Natalia Alonso Vence, Ángeles Bendaña López, Aitor Abuín Blanco, Laura Bao Pérez, Andrés Peleteiro Raíndo, Miguel Cid López, Manuel Mateo Pérez Encinas & José Luis Bello López

  2. Department of Hematology, Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), SERGAS, Compostela, Spain

    Adrián Mosquera Orgueira, Marta Sonia González Pérez, José Ángel Díaz Arias, Beatriz Antelo Rodríguez, Natalia Alonso Vence, Ángeles Bendaña López, Aitor Abuín Blanco, Laura Bao Pérez, Andrés Peleteiro Raíndo, Miguel Cid López, Manuel Mateo Pérez Encinas & José Luis Bello López

  3. University of Santiago de Compostela, Compostela, Spain

    Adrián Mosquera Orgueira, José Ángel Díaz Arias, Beatriz Antelo Rodríguez, Manuel Mateo Pérez Encinas & José Luis Bello López

  4. Hospital Universitario de Salamanca, Instituto de Investigación Biomédica de Salamanca (IBSAL), Centro de Investigación del Cancer (IBMCC-USAL, CSIC), Salamanca, Spain

    Maria Victoria Mateos Manteca

Authors
  1. Adrián Mosquera Orgueira
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marta Sonia González Pérez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Ángel Díaz Arias
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Beatriz Antelo Rodríguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Natalia Alonso Vence
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ángeles Bendaña López
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Aitor Abuín Blanco
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Laura Bao Pérez
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Andrés Peleteiro Raíndo
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Miguel Cid López
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Manuel Mateo Pérez Encinas
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. José Luis Bello López
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Maria Victoria Mateos Manteca
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maria Victoria Mateos Manteca.

Ethics declarations

Conflict of interest

M-VM has received honoraria for lectures and participation in advisory boards from Janssen, Celgene-BMS, Amgen, Takeda, Abbvie, GSK, Adaptive, Roche, Seatle Genetics, Pfizer, and Regeneron. AMO has received honoraria for lectures and participation in advisory boards from Janssen and AstraZeneca. AMO has received research grants from Roche and Celgene-BMS. MSGP has received honoraria for lectures and participation in advisory boards from Janssen, Amgen, Celgene-BMS, Takeda, Sanofi, and GSK.

Additional information

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

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mosquera Orgueira, A., González Pérez, M.S., Díaz Arias, J.Á. et al. Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data. Leukemia 35, 2924–2935 (2021). https://doi.org/10.1038/s41375-021-01286-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41375-021-01286-2

This article is cited by

Search

Advanced search

Quick links