Gene signature combinations improve prognostic stratification of multiple myeloma patients
Multiple myeloma (MM) is a plasma cell neoplasm with significant molecular heterogeneity. Gene expression profiling (GEP) has contributed significantly to our understanding of the underlying biology and has led to several prognostic gene signatures. However, the best way to apply these GEP signature...
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| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
15 January 2016
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| In: |
Leukemia
Year: 2016, Jahrgang: 30, Heft: 5, Pages: 1071-1078 |
| ISSN: | 1476-5551 |
| DOI: | 10.1038/leu.2015.341 |
| Online-Zugang: | Verlag, Volltext: https://doi.org/10.1038/leu.2015.341 Verlag, Volltext: https://www.nature.com/articles/leu2015341 
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| Verfasserangaben: | W.J. Chng, T.-H. Chung, S. Kumar, S. Usmani, N. Munshi, H. Avet-Loiseau, H. Goldschmidt, B. Durie, and P. Sonneveld on behalf of the International Myeloma Working Group |
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| 520 | |a Multiple myeloma (MM) is a plasma cell neoplasm with significant molecular heterogeneity. Gene expression profiling (GEP) has contributed significantly to our understanding of the underlying biology and has led to several prognostic gene signatures. However, the best way to apply these GEP signatures in clinical practice is unclear. In this study, we investigated the integration of proven prognostic signatures for improved patient risk stratification. Three publicly available MM GEP data sets that encompass newly diagnosed as well as relapsed patients were analyzed using standardized estimation of nine prognostic MM signature indices and simulations of signature index combinations. Cox regression analysis was used to assess the performance of simulated combination indices. Taking the average of multiple GEP signature indices was a simple but highly effective way of integrating multiple GEP signatures. Furthermore, although adding more signatures in general improved performance substantially, we identified a core signature combination, EMC92+HZDCD, as the top-performing prognostic signature combination across all data sets. In this study, we provided a rationale for gene signature integration and a practical strategy to choose an optimal risk score estimation in the presence of multiple prognostic signatures. | ||
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