Format

Send to

Choose Destination
See comment in PubMed Commons below
Leukemia. 2014 Nov;28(11):2229-34. doi: 10.1038/leu.2014.140. Epub 2014 Apr 15.

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

Author information

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

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.

PMID:
24732597
PMCID:
PMC4198516
DOI:
10.1038/leu.2014.140
[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Nature Publishing Group Icon for PubMed Central
    Loading ...
    Support Center