International Journal of Hematology

DOI: 10.1007/s12185-011-0948-y Pages: 321-333

The use of molecular-based risk stratification and pharmacogenomics for outcome prediction and personalized therapeutic management of multiple myeloma

1. University of Arkansas for Medical Sciences, Department of Pathology

2. University of Arkansas for Medical Sciences, Myeloma Institute for Research and Therapy

3. Signal Genetics LLC

4. Cancer Research and Biostatistics

5. University of Arkansas for Medical Sciences, Donna D and Donald M Lambert Laboratory for Myeloma Genetics

6. University of Arkansas for Medical Sciences, Department of Biostatistics

Correspondence to:
John D. Shaughnessy
Tel: +1-501-2961503
Fax: +1-501-6866442
Email: shaughnessyjohn@uams.edu

Close

Abstract

Despite improvement in therapeutic efficacy, multiple myeloma (MM) remains incurable with a median survival of approximately 10 years. Gene-expression profiling (GEP) can be used to elucidate the molecular basis for resistance to chemotherapy through global assessment of molecular alterations that exist at diagnosis, after therapeutic treatment and that evolve during tumor progression. Unique GEP signatures associated with recurrent chromosomal translocations and ploidy changes have defined molecular classes with differing clinical features and outcomes. When compared to other stratification systems the GEP70 test remained a significant predictor of outcome, reduced the number of patients classified with a poor prognosis, and identified patients at increased risk of relapse despite their standard clinico-pathologic and genetic findings. GEP studies of serial samples showed that risk increases over time, with relapsed disease showing GEP shifts toward a signature of poor outcomes. GEP signatures of myeloma cells after therapy were prognostic for event-free and overall survival and thus may be used to identify novel strategies for overcoming drug resistance. This brief review will focus on the use of GEP of MM to define high-risk myeloma, and elucidate underlying mechanisms that are beginning to change clinical decision-making and inform drug design.

To access the full text, please Sign in

If you have institutional access, please click here

Share the Knowledge