High mutation burden in the checkpoint and micro-RNA processing genes in myelodysplastic syndrome

PLoS One. 2021 Mar 17;16(3):e0248430. doi: 10.1371/journal.pone.0248430. eCollection 2021.

Abstract

A number of sequencing studies identified the prognostic impact of somatic mutations in myelodysplastic syndrome (MDS). However the majority of them focused on methylation regulation, apoptosis and proliferation genes. Despite the number of experimental studies published on the role of micro-RNA processing and checkpoint genes in the development of MDS, the clinical data about mutational landscape in these genes is limited. We performed a pilot study which evaluated mutational burden in these genes and their association with common MDS mutations. High prevalence of mutations was observed in the genes studied: 54% had mutations in DICER1, 46% had mutations in LAG3, 20% in CTLA4, 23% in B7-H3, 17% in DROSHA, 14% in PD-1 and 3% in PD-1L. Cluster analysis that included these mutations along with mutations in ASXL1, DNMT3A, EZH2, IDH1, RUNX1, SF3B1, SRSF2, TET2 and TP53 effectively predicted overall survival in the study group (HR 4.2, 95%CI 1.3-13.6, p = 0.016). The study results create the rational for incorporating micro-RNA processing and checkpoint genes in the sequencing panels for MDS and evaluate their role in the multicenter studies.

Publication types

  • Observational Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • DNA Mutational Analysis / statistics & numerical data
  • Disease Progression
  • Female
  • Hematopoietic Stem Cell Transplantation
  • Humans
  • Immune Checkpoint Proteins / genetics*
  • Kaplan-Meier Estimate
  • Male
  • MicroRNAs / metabolism
  • Middle Aged
  • Mutation
  • Myelodysplastic Syndromes / genetics*
  • Myelodysplastic Syndromes / mortality
  • Myelodysplastic Syndromes / therapy
  • Pilot Projects
  • RNA Processing, Post-Transcriptional / genetics
  • Risk Assessment / methods
  • Risk Assessment / statistics & numerical data
  • Young Adult

Substances

  • Immune Checkpoint Proteins
  • MicroRNAs

Grants and funding

This work was supported by Russian Science Foundation grant № 17-75-20145.