Improved risk stratification in myeloma using a micro RNA ‐based classifier

• Published in: British journal of haematology 2013-08, Vol.162 (3), p.348-359
• Main Authors: Wu, Ping, Agnelli, Luca, Walker, Brian A., Todoerti, Katia, Lionetti, Marta, Johnson, David C., Kaiser, Martin, Mirabella, Fabio, Wardell, Christopher, Gregory, Walter M., Davies, Faith E., Brewer, Daniel, Neri, Antonino, Morgan, Gareth J.
• Format: Article
• Language: English
• ISSN: 0007-1048; 1365-2141
• DOI: 10.1111/bjh.12394

Multiple myeloma ( MM ) is a heterogeneous disease. International Staging System/fluorescence hybridization ( ISS / FISH )‐based model and gene expression profiles ( GEP ) are effective approaches to define clinical outcome, although yet to be improved. The discovery of a class of small non‐coding RNA s (micro RNA s, mi RNA s) has revealed a new level of biological complexity underlying the regulation of gene expression. In this work, 163 presenting samples from MM patients were analysed by global mi RNA profiling, and distinct mi RNA expression characteristics in molecular subgroups with prognostic relevance (4p16, MAF and 11q13 translocations) were identified. Furthermore we developed an “outcome classifier”, based on the expression of two mi RNA s ( MIR 17 and MIR 886‐5p), which is able to stratify patients into three risk groups (median OS 19·4, 40·6 and 65·3 months, P  =   0·001). The mi RNA ‐based classifier significantly improved the predictive power of the ISS / FISH approach ( P  =   0·0004), and was independent of GEP ‐derived prognostic signatures ( P  <   0·002). Through integrative genomics analysis, we outlined the potential biological relevance of the mi RNA s included in the classifier and their putative roles in regulating a large number of genes involved in MM biology. This is the first report showing that mi RNA s can be built into molecular diagnostic strategies for risk stratification in MM .