Original Article

Leukemia (2014) 28, 2229–2234; doi:10.1038/leu.2014.140; published online 24 June 2014

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

S B Amin1,2,3, W-K Yip4, S Minvielle5,6, A Broyl7, Y Li8, B Hanlon9, D Swanson4, P K Shah3,4, P Moreau5,6, B van der Holt7, M van Duin7, F Magrangeas5,6, P Pieter Sonneveld5,6, K C Anderson1, C Li3,4, H Avet-Loiseau5,6 and N C Munshi1,2

  1. 1Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
  2. 2Department of Hematology/Oncology, Boston VA Healthcare System, Harvard Medical School, Boston, MA, USA
  3. 3Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
  4. 4Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
  5. 5Hematology Department, Hopital de Nantes, 9, Quai Moncousu, Nantes, France
  6. 6Department of Hematology, Inserm U892, University of Nantes, Nantes, France
  7. 7Department of Hematology and HOVON Data Center, Erasmus Medical Center and University, Rotterdam, The Netherlands
  8. 8Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
  9. 9Department of Statistics, University of Wisconsin, Madison, WI, USA

Correspondence: Dr NC Munshi, Jerome Lipper Multiple Myeloma Center, Department of Adult Oncology, Dana-Farber Cancer Institute, 44 Binney Street, Boston 02115, MA, USA. E-mail: nikhil_munshi@dfci.harvard.edu

Received 14 January 2014; Revised 9 March 2014; Accepted 21 March 2014
Accepted article preview online 15 April 2014; Advance online publication 24 June 2014



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.