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RUNX1 mutations contribute to the progression of MDS due to disruption of antitumor cellular defense: a study on patients with lower-risk MDS.

Monika KaisrlikovaJitka VeselaDavid KundratHana VotavovaMichaela Dostalova MerkerovaZdenek KrejcikVladimír DivokýMarek JedlickaJan FricJiri KlemaDana MikulenkovaMarketa Stastna MarkovaMarie LauermannovaJolana MertovaJacqueline Soukupova MaaloufovaAnna JonášováJaroslav CermakMonika Belickova
Published in: Leukemia (2022)
Patients with lower-risk myelodysplastic syndromes (LR-MDS) have a generally favorable prognosis; however, a small proportion of cases progress rapidly. This study aimed to define molecular biomarkers predictive of LR-MDS progression and to uncover cellular pathways contributing to malignant transformation. The mutational landscape was analyzed in 214 LR-MDS patients, and at least one mutation was detected in 137 patients (64%). Mutated RUNX1 was identified as the main molecular predictor of rapid progression by statistics and machine learning. To study the effect of mutated RUNX1 on pathway regulation, the expression profiles of CD34 + cells from LR-MDS patients with RUNX1 mutations were compared to those from patients without RUNX1 mutations. The data suggest that RUNX1-unmutated LR-MDS cells are protected by DNA damage response (DDR) mechanisms and cellular senescence as an antitumor cellular barrier, while RUNX1 mutations may be one of the triggers of malignant transformation. Dysregulated DDR and cellular senescence were also observed at the functional level by detecting γH2AX expression and β-galactosidase activity. Notably, the expression profiles of RUNX1-mutated LR-MDS resembled those of higher-risk MDS at diagnosis. This study demonstrates that incorporating molecular data improves LR-MDS risk stratification and that mutated RUNX1 is associated with a suppressed defense against LR-MDS progression.
Keyphrases
  • transcription factor
  • end stage renal disease
  • machine learning
  • ejection fraction
  • newly diagnosed
  • prognostic factors
  • dna damage
  • endothelial cells
  • poor prognosis
  • deep learning
  • patient reported