Prognostic miRNA classifier in early-stage mycosis fungoides: development and validation in a Danish nationwide study

Lise M. Lindahl, Søren Besenbacher, Anne H. Rittig, Pamela Celis, Andreas Willerslev-Olsen, Lise M.R. Gjerdrum, Thorbjørn Krejsgaard, Claus Johansen, Thomas Litman, Anders Woetmann, Niels Odum and Lars Iversen

Key points

  • A validated three-miRNA classifier can effectively predict progression from early to advanced stage MF and survival at time of diagnosis.

  • This classifier outperforms existing clinical prognostic factors and paves the way for implementation of personalised treatment in MF.


Mycosis fungoides (MF) is the most frequent form of cutaneous T-cell lymphoma. The disease often takes an indolent course, but in approximately one third of the patients, the disease progresses to an aggressive malignancy with a poor prognosis. At the time of diagnosis, it is impossible to predict which patients develop severe disease and are in need of aggressive treatment. Accordingly, we investigated the prognostic potential of microRNAs (miRNAs) at the time of diagnosis in MF. Using a qRT-PCR platform, we analysed miRNA expression in diagnostic skin biopsies from 154 Danish patients with early-stage MF. The patients were subdivided into a discovery cohort (n=82) and an independent validation cohort (n=72). The miRNA classifier was built using a LASSO Cox regression to predict progression-free survival (PFS). We developed a three-miRNA classifier, based on miR-106b-5p, miR-148a-3p and miR-338-3p, which successfully separated patients into high-risk and low-risk groups of disease progression. PFS was significantly different between these groups both in the discovery cohort and in the validation cohort. The classifier was stronger than existing clinical prognostic factors and remained a strong independent prognostic tool after stratification and adjustment for these factors. Importantly, patients in the high-risk group had a significantly reduced overall survival. The three-miRNA classifier is an effective tool to predict disease progression of early-stage MF at the time of diagnosis. The classifier adds significant prognostic value to existing clinical prognostic factors and may facilitate more individualised treatment of these patients.

  • Submitted June 6, 2017.
  • Accepted November 19, 2017.