Improved risk stratification in myeloma using a microRNA-based classifier

Ping Wu, Luca Agnelli, Brian A Walker, Katia Todoerti, Marta Lionetti, David C Johnson, Martin Kaiser, Fabio Mirabella, Christopher Wardell, Walter M Gregory, Faith E Davies, Daniel Brewer, Antonino Neri, Gareth J Morgan

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)


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
Original languageEnglish
Pages (from-to)348-59
Number of pages12
JournalBritish Journal of Haematology
Issue number3
Publication statusPublished - Aug 2013


  • Aged
  • Chromosome Aberrations
  • Gene Expression Profiling
  • Gene Expression Regulation, Neoplastic
  • Humans
  • In Situ Hybridization, Fluorescence
  • Kaplan-Meier Estimate
  • MicroRNAs
  • Middle Aged
  • Multiple Myeloma
  • Prognosis
  • RNA, Neoplasm
  • Risk Assessment
  • Tumor Markers, Biological

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