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.

  • Original Article
  • Published:

Myeloma

A gene expression signature for high-risk multiple myeloma

Leukemia volume 26, pages 2406–2413 (2012)Cite this article

Subjects

A Corrigendum to this article was published on 07 May 2014

Abstract

There is a strong need to better predict the survival of patients with newly diagnosed multiple myeloma (MM). As gene expression profiles (GEPs) reflect the biology of MM in individual patients, we built a prognostic signature based on GEPs. GEPs obtained from newly diagnosed MM patients included in the HOVON65/GMMG-HD4 trial (n=290) were used as training data. Using this set, a prognostic signature of 92 genes (EMC-92-gene signature) was generated by supervised principal component analysis combined with simulated annealing. Performance of the EMC-92-gene signature was confirmed in independent validation sets of newly diagnosed (total therapy (TT)2, n=351; TT3, n=142; MRC-IX, n=247) and relapsed patients (APEX, n=264). In all the sets, patients defined as high-risk by the EMC-92-gene signature show a clearly reduced overall survival (OS) with a hazard ratio (HR) of 3.40 (95% confidence interval (CI): 2.19–5.29) for the TT2 study, 5.23 (95% CI: 2.46–11.13) for the TT3 study, 2.38 (95% CI: 1.65–3.43) for the MRC-IX study and 3.01 (95% CI: 2.06–4.39) for the APEX study (P<0.0001 in all studies). In multivariate analyses this signature was proven to be independent of the currently used prognostic factors. The EMC-92-gene signature is better or comparable to previously published signatures. This signature contributes to risk assessment in clinical trials and could provide a tool for treatment choices in high-risk MM patients.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

Accession codes

Accessions

Gene Expression Omnibus

References

  1. Avet-Loiseau H . Ultra high-risk myeloma. Am Soc Hematol Educ Program 2010, 489–493.

    Article  Google Scholar 

  2. Palumbo A, Anderson K . Multiple myeloma. N Engl J Med 2011; 17: 1046–1060.

    Article  Google Scholar 

  3. Munshi NC, Anderson KC, Bergsagel PL, Shaughnessy J, Palumbo A, Durie B et al. Consensus recommendations for risk stratification in multiple myeloma: report of the International Myeloma Workshop Consensus Panel 2. Blood 2011; 117: 4696–4700.

    Article  CAS  Google Scholar 

  4. Neben K, Jauch A, Bertsch U, Heiss C, Hielscher T, Seckinger A et al. Combining information regarding chromosomal aberrations t(4;14) and del(17p13) with the International Staging System classification allows stratification of myeloma patients undergoing autologous stem cell transplantation. Haematologica 2010; 95: 1150–1157.

    Article  CAS  Google Scholar 

  5. Bergsagel PL, Kuehl WM . Molecular pathogenesis and a consequent classification of multiple myeloma. J Clin Oncol 2005; 23: 6333–6338.

    Article  CAS  Google Scholar 

  6. Zhan F, Huang Y, Colla S, Stewart JP, Hanamura I, Gupta S et al. The molecular classification of multiple myeloma. Blood 2006; 108: 2020–2028.

    Article  CAS  Google Scholar 

  7. Broyl A, Hose D, Lokhorst H, de Knegt Y, Peeters J, Jauch A et al. Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients. Blood 2010; 116: 2543–2553.

    Article  CAS  Google Scholar 

  8. Chng WJ, Kuehl WM, Bergsagel PL, Fonseca R . Translocation t(4;14) retains prognostic significance even in the setting of high-risk molecular signature. Leukemia 2008; 22: 459–461.

    Article  CAS  Google Scholar 

  9. Shaughnessy JD, Zhan F, Burington BE, Huang Y, Colla S, Hanamura I 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: 2276–2284.

    Article  CAS  Google Scholar 

  10. Shaughnessy JD, Qu P, Usmani S, Heuck CJ, Zhang Q, Zhou Y et al. Pharmacogenomics of bortezomib test-dosing identifies hyperexpression of proteasome genes, especially PSMD4, as novel high-risk feature in myeloma treated with total therapy 3. Blood 2011; 118: 3512–3524.

    Article  CAS  Google Scholar 

  11. Mulligan G, Mitsiades C, Bryant B, Zhan F, Chng WJ, Roels S et al. Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib. Blood 2007; 109: 3177–3188.

    Article  CAS  Google Scholar 

  12. Decaux O, Lode L, Magrangeas F, Charbonnel C, Gouraud W, Jezequel P et al. 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 2008; 26: 4798–4805.

    Article  CAS  Google Scholar 

  13. Dickens NJ, Walker BA, Leone PE, Johnson DC, Brito JL, Zeisig A et al. Homozygous deletion mapping in myeloma samples identifies genes and an expression signature relevant to pathogenesis and outcome. Clin Cancer Res 2010; 16: 1856–1864.

    Article  CAS  Google Scholar 

  14. Hose D, Reme T, Hielscher T, Moreaux J, Messner T, Seckinger A et al. Proliferation is a central independent prognostic factor and target for personalized and risk-adapted treatment in multiple myeloma. Haematologica 2011; 96: 87–95.

    Article  Google Scholar 

  15. Dispenzieri A, Rajkumar SV, Gertz MA, Fonseca R, Lacy MQ, Bergsagel PL et al. Treatment of newly diagnosed multiple myeloma based on Mayo Stratification of Myeloma and Risk-adapted Therapy (mSMART): consensus statement. Mayo Clin Proc 2007; 82: 323–341.

    Article  CAS  Google Scholar 

  16. Sonneveld P, Schmidt-Wolf I, van der Holt B, Jarari Le, Bertsch U, Salwender H et al. HOVON-65/GMMG-HD4 randomized phase III trial comparing bortezomib, doxorubicin, dexamethasone (PAD) vs VAD followed by high-dose melphalan (HDM) and maintenance with bortezomib or thalidomide in patients with newly diagnosed multiple myeloma (MM). Blood 2010; 116: 40.

    Google Scholar 

  17. Barlogie B, Pineda-Roman M, van Rhee F, Haessler J, Anaissie E, Hollmig K et al. Thalidomide arm of Total Therapy 2 improves complete remission duration and survival in myeloma patients with metaphase cytogenetic abnormalities. Blood 2008; 112: 3115–3121.

    Article  CAS  Google Scholar 

  18. Pineda-Roman M, Zangari M, Haessler J, Anaissie E, Tricot G, van Rhee F et al. Sustained complete remissions in multiple myeloma linked to bortezomib in total therapy 3: comparison with total therapy 2. Br J Haematol 2008; 140: 625–634.

    Article  CAS  Google Scholar 

  19. Morgan GJ, Davies FE, Gregory WM, Bell SE, Szubert AJ, Navarro-Coy N et al. Thalidomide maintenance significantly improves progression-free survival (PFS) and overall survival (OS) of myeloma patients when effective relapse treatments are used: MRC myeloma IX results. Blood 2010; 116: 623–623.

    Google Scholar 

  20. Morgan GJ, Davies FE, Owen RG, Rawstron AC, Bell S, Cocks K et al. Thalidomide Combinations improve response rates; results from the MRC IX study. Blood 2007; 110: 3593–3593.

    Google Scholar 

  21. Jagannath S, Barlogie B, Berenson J, Siegel D, Irwin D, Richardson PG et al. A phase 2 study of two doses of bortezomib in relapsed or refractory myeloma. Br J Haematol 2004; 127: 165–172.

    Article  CAS  Google Scholar 

  22. Richardson PG, Sonneveld P, Schuster MW, Irwin D, Stadtmauer EA, Facon T et al. Bortezomib or high-dose dexamethasone for relapsed multiple myeloma. N Engl J Med 2005; 352: 2487–2498.

    Article  CAS  Google Scholar 

  23. Richardson PG, Barlogie B, Berenson J, Singhal S, Jagannath S, Irwin D et al. A phase 2 study of bortezomib in relapsed, refractory myeloma. N Engl J Med 2003; 348: 2609–2617.

    Article  CAS  Google Scholar 

  24. Gentleman R, Carey V, Bates D . Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol 2004; 5: R80.

    Article  Google Scholar 

  25. Johnson WE, Li C, Rabinovic A . Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007; 8: 118–127.

    Article  Google Scholar 

  26. Therneau T, Lumley T . Survival: survival analysis, including penalised likelihood. R package version 236-2. 2010.

  27. Bair E, Hastie T, Paul D, Tibshirani R . Prediction by supervised principal components. J Amer Statistical Assoc 2006; 101: 119–137.

    Article  CAS  Google Scholar 

  28. Benjamini Y, Hochberg Y . Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B Met 1995; 57: 289–300.

    Google Scholar 

  29. Arulampalam V, Kolosenko I, Hjortsberg L, Bjorklund AC, Grander D, Tamm KP . Activation of STAT1 is required for interferon-alpha-mediated cell death. Exp Cell Res 2011; 317: 9–19.

    Article  CAS  Google Scholar 

  30. Chesi M, Nardini E, Brents LA, Schrock E, Ried T, Kuehl WM et al. Frequent translocation t(4;14)(p16.3;q32.3) in multiple myeloma is associated with increased expression and activating mutations of fibroblast growth factor receptor 3. Nat Genet 1997; 16: 260–264.

    Article  CAS  Google Scholar 

  31. Hideshima T, Catley L, Raje N, Chauhan D, Podar K, Mitsiades C et al. Inhibition of Akt induces significant downregulation of survivin and cytotoxicity in human multiple myeloma cells. Br J Haematol 2007; 138: 783–791.

    Article  CAS  Google Scholar 

  32. Mah N, Thelin A, Lu T, Nikolaus S, Kuhbacher T, Gurbuz Y et al. A comparison of oligonucleotide and cDNA-based microarray systems. Physiol Genomics 2004; 16: 361–370.

    Article  CAS  Google Scholar 

  33. Trudel S, Ely S, Farooqi Y, Affer M, Robbiani DF, Chesi M et al. Inhibition of fibroblast growth factor receptor 3 induces differentiation and apoptosis in t(4;14) myeloma. Blood 2004; 103: 3521–3528.

    Article  CAS  Google Scholar 

  34. Inoue J, Otsuki T, Hirasawa A, Imoto I, Matsuo Y, Shimizu S et al. Overexpression of PDZK1 within the 1q12-q22 amplicon is likely to be associated with drug-resistance phenotype in multiple myeloma. The Am J Pathol 2004; 165: 71–81.

    Article  CAS  Google Scholar 

  35. Kumar SK, Mikhael JR, Buadi FK, Dingli D, Dispenzieri A, Fonseca R et al. Management of newly diagnosed symptomatic multiple myeloma: updated Mayo Stratification of Myeloma and Risk-Adapted Therapy (mSMART) consensus guidelines. Mayo Clin Proc 2009; 84: 1095–1110.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We would like to thank John Shaughnessy for kindly providing the data. Peter Valk and Mathijs Sanders are acknowledged for insightful discussions. Financial support: This work was supported by the Center for Translational Molecular Medicine (BioCHIP), a clinical research grant from the European Hematology Association and an EMCR Translational Research Grant from Skyline Diagnostics, Janssen and MSCNET.

Author contributions

RK, AB, MVD and PS designed the research. AB, YDK performed the research and collected the data. RK developed the classifier, AB, RK, MVD analyzed and interpreted the data and wrote the manuscript. MVV, EVB analyzed and interpreted the data and critically reviewed the paper. BVDH and LEJ performed data management and statistical analyses. GM provided the CEL files from the APEX data set and critically reviewed the paper. GM and WG provided CEL files and clinical data from the MRC-IX data set and critically reviewed the paper. HG is principal investigator of the performed research in the German part of the H65/GMMG-HD4 and critically reviewed the paper. HL organized the trial and critically reviewed the paper. PS organized the trial, is principal investigator of the performed research in the Dutch part of the H65/GMMG-HD4 and critically reviewed the paper.

Author information

Author notes

  1. R Kuiper and A Broyl: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands

    R Kuiper, A Broyl, Y de Knegt, M van Duin & P Sonneveld

  2. Skyline Diagnostics, Erasmus Medical Center, Rotterdam, The Netherlands

    M H van Vliet & E H van Beers

  3. HOVON Data Center, Erasmus MC-Daniel den Hoed, Rotterdam, The Netherlands

    B van der Holt & L el Jarari

  4. Millennium Pharmaceuticals, Cambridge, MA, USA

    G Mulligan

  5. Clinical Trials Research Unit, University of Leeds, Leeds, UK

    W Gregory

  6. Haemato-oncology Unit, Royal Marsden Hospital, London, UK

    G Morgan

  7. Department of Internal Medicine V, University of Heidelberg, Heidelberg, Germany

    H Goldschmidt

  8. Department of Hematology, University Medical Center Utrecht, Utrecht, The Netherlands

    H M Lokhorst

Authors
  1. R Kuiper
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A Broyl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Y de Knegt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M H van Vliet
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. E H van Beers
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. B van der Holt
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. L el Jarari
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. G Mulligan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. W Gregory
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. G Morgan
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. H Goldschmidt
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. H M Lokhorst
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. M van Duin
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. P Sonneveld
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P Sonneveld.

Ethics declarations

Competing interests

GM has declared a financial interest in Millennium Pharmaceuticals Inc., whose product was an object of study in the HOVON 65/ GMMG-HD4. GM is currently employed by Millennium Pharmaceuticals Inc. HG has served on the advisory boards of Johnson & Johnson. HL is on the advisory Boards of Celgene and Genmab. EVB and MVV are employees of Skyline Diagnostics. PS is on the advisory boards of Skyline Diagnostics, Janssen and Celgene. The other authors declare no conflict of interest.

Additional information

Supplementary Information accompanies the paper on the Leukemia website

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kuiper, R., Broyl, A., de Knegt, Y. et al. A gene expression signature for high-risk multiple myeloma. Leukemia 26, 2406–2413 (2012). https://doi.org/10.1038/leu.2012.127

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/leu.2012.127

Keywords

This article is cited by

Search

Advanced search

Quick links