By Kenneth C. Anderson, MD

2009-01-01

Dr. Anderson indicated no relevant conflicts of interest.

Decaux O, Lodé L, Magrangeas F, 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 Myélome. J Clin Oncol. 2008;26:4798-805. 

Multiple attempts have been made to define clinical and laboratory parameters that have prognostic significance in myeloma. The Durie-Salmon Myeloma Staging System¹ has recently been replaced by the International Staging System,² predicated upon serum b2-microglobulin and serum albumin. Assessment of chromosomal abnormalities, such as deletion of 13q and 17p, can further define adverse subgroups.³ Most recently, multiple patient prognostic subgroups have been defined using DNA array comparative genomic hybridization⁴ and RNA profiling.^5-8 Importantly, these prognostic signatures are relevant only in a clinical context. For example, del 13 or t(4;14) do not predict for adverse response to bortezomib.⁹ Additionally, achievement of complete remission after agressive therapy with high-dose chemotherapy, stem cell transplant, and thalidomide (Total 2) predicts survival only in the gene expression profile (GEP)-defined high-risk groups, but not the GEP-defined low-risk groups.¹⁰ Ultimately, genetic profiling will allow for selection of those patients most likely to respond to given therapies and allow for individualized therapies. 

Decaux and colleagues have used GEP to define a gene signature predictive of outcome in 250 patients uniformly treated with high-dose melphalan and autotransplantation therapy protocols of the Intergroupe Francophone du Myélome (IFM). Specifically, 15 genes were used to calculate a risk score to define high risk versus low risk with 47.4 percent versus 90.5 percent survival at three years, respectively.

Importantly, this survival model was validated in a test set of 68 patients and three independent cohorts totaling 853 patients with both newly diagnosed and relapsed myeloma, who were treated with high-dose therapy and autotransplantation, as well as novel therapies including bortezomib.^6-8 This is critical to assure that its value transcends specific treatments or stages of disease. It is also important, as new signatures are identified, to determine their independent prognostic value versus overlap with other published signatures. Interestingly, when compared with the University of Arkansas School of Medical Sciences’ (UAMS) 17-gene prognostic model,⁶ this new 15-gene model did not remain an independent significant prognostic variable for UAMS patients treated with total therapy, but did remain independently prognostic for the other two patient cohorts examined. Conversely, the UAMS model did identify high-risk patients in IFM clinical trials. In this study, serum b2-microglobulin >5.5mg/L and/or t(4:14) identified subsets with distinct survivals within the 15 gene-defined high- and low-risk groups, stressing the potential added value of conventional genetics supplementing GEP-based models. Moreover, gain of 1q and t(4:14) versus hyperdiploidy were associated with the high- versus low-risk groups. Finally, GEP-based prognostic models can yield important discoveries in myeloma biology and pathogenesis. For example, in this study overexpression of regulators of chromosomal segmentation was identified in the high-risk group, consistent with dysfunction of mitosis in myeloma leading to chromosomal instability and aneuploidy. These are the hallmarks of aggressive myeloma and suggest potential utility of anti-mitotic therapies.

Therefore, this study is a harbinger of the future in myeloma, and cancer more generally, where genetic profiling will allow for effective personalized and targeted therapies on the one hand and advance understanding of basic disease pathogenesis on the other.

References

1. Durie BG, Salmon SE. A clinical staging system for multiple myeloma.  Correlation of measured cell mass with presenting clinical features, response to treatment, and survival. Cancer. 1975;36:842-54.

2. Greipp PR, San Miguel J, Durie BG, et al. International staging system for multiple myeloma. J Clin Oncol. 2005;23:3412-20.

3. Avet-Loiseau H, Attal M, Moreau P, et al. Genetic abnormalities in multiple myeloma: the experience of the Intergroupe Francophone du Myélome. Blood. 2007;109:3489-95.

4. Carrasco DR, Tonon G, Huang Y, et al. High-resolution genomic profiles define distinct clinico-pathogenetic subgroups of multiple myeloma patients. Cancer Cell. 2006;9:313-25.

5. Bergsagel PL, Kuehl WM, Zhan F, et al. Cyclin D dysregulation: an early and unifying pathogenic event in multiple myeloma. Blood. 2005;106:296-303.

6. Shaughnessy JD Jr., Zhan F, Burington BE, 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;109:2276-84.

7. Chng WJ, Kumar S, Vanwier S, et al. Molecular dissection of hyperdiploid multiple myeloma by gene expression profiling. Cancer Res. 2007;67:2982-9.

8. Mulligan G, Mitsiades C, Bryant B, et al. Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib. Blood. 2007;109:3177-88.

9. Jagannath S, Richardson PG, Sonneveld P, et al. Bortezomib appears to overcome the poor prognosis conferred by chromosome 13 deletion in phase 2 and 3 trials. Leukemia. 2007;21:151-7.

10. Haessler J, Shaughnessy JD Jr., Zhan F, et al. Benefit of complete response in multiple myeloma limited to high-risk subgroup identified by gene expression profiling. Clin Cancer Res. 2007;13:7073-9.

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