Original Article

Gene expression profile alone is inadequate in predicting complete response in multiple myeloma

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Abstract

With advent of several treatment options in multiple myeloma (MM), a selection of effective regimen has become an important issue. Use of gene expression profile (GEP) is considered an important tool in predicting outcome; however, it is unclear whether such genomic analysis alone can adequately predict therapeutic response. We evaluated the ability of GEP to predict complete response (CR) in MM. GEP from pretreatment MM cells from 136 uniformly treated MM patients with response data on an IFM, France led study were analyzed. To evaluate variability in predictive power due to microarray platform or treatment types, additional data sets from three different studies (n=511) were analyzed using same methods. We used several machine learning methods to derive a prediction model using training and test subsets of the original four data sets. Among all methods employed for GEP-based CR predictive capability, we got accuracy range of 56–78% in test data sets and no significant difference with regard to GEP platforms, treatment regimens or in newly diagnosed or relapsed patients. Importantly, permuted P-value showed no statistically significant CR predictive information in GEP data. This analysis suggests that GEP-based signature has limited power to predict CR in MM, highlighting the need to develop comprehensive predictive model using integrated genomics approach.

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Acknowledgements

This work was supported in part by grants from the Department of Veterans Affairs Merit Review Awards I01-BX001584 and from the National Institutes of Health Grants RO1–124929 to NCM., RO1-050947 to KCA, and P50-100007 and PO1-78378 to NCM and KCA, PO1-155258 to NCM, KCA, HAL, SM, CL and PM and R01GM077122 to CL. KCA is an American Cancer Society Clinical Research Professor.

Author information

Affiliations

  1. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    • S B Amin
    • , K C Anderson
    •  & N C Munshi
  2. Department of Hematology/Oncology, Boston VA Healthcare System, Harvard Medical School, Boston, MA, USA

    • S B Amin
    •  & N C Munshi
  3. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA

    • S B Amin
    • , P K Shah
    •  & C Li
  4. Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

    • W-K Yip
    • , D Swanson
    • , P K Shah
    •  & C Li
  5. Hematology Department, Hopital de Nantes, 9, Quai Moncousu, Nantes, France

    • S Minvielle
    • , P Moreau
    • , F Magrangeas
    • , P Pieter Sonneveld
    •  & H Avet-Loiseau
  6. Department of Hematology, Inserm U892, University of Nantes, Nantes, France

    • S Minvielle
    • , P Moreau
    • , F Magrangeas
    • , P Pieter Sonneveld
    •  & H Avet-Loiseau
  7. Department of Hematology and HOVON Data Center, Erasmus Medical Center and University, Rotterdam, The Netherlands

    • A Broyl
    • , B van der Holt
    •  & M van Duin
  8. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

    • Y Li
  9. Department of Statistics, University of Wisconsin, Madison, WI, USA

    • B Hanlon

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Competing interests

The authors declare no conflict of interest.

Corresponding author

Correspondence to N C Munshi.

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