Improved risk stratification in myeloma using a microRNA-based classifier.

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 RNAs (micro RNAs, miRNAs) 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 miRNA profiling, and distinct miRNA 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 miRNAs (MIR17 and MIR886-5p), which is able to stratify patients into three risk groups (median OS 19.4, 40.6 and 65.3 months, P = 0.001). The miRNA-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 miRNAs 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 miRNAs can be built into molecular diagnostic strategies for risk stratification in MM.