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Gene expression and risk of leukemic transformation in myelodysplasia

Yusuke Shiozawa, Luca Malcovati, Anna Gallì, Andrea Pellagatti, Mohsen Karimi, Aiko Sato-Otsubo, Yusuke Sato, Hiromichi Suzuki, Tetsuichi Yoshizato, Kenichi Yoshida, Yuichi Shiraishi, Kenichi Chiba, Hideki Makishima, Jacqueline Boultwood, Eva Hellström-Lindberg, Satoru Miyano, Mario Cazzola and Seishi Ogawa

Key points

  • Through a comprehensive transcriptomic analysis, we discovered two major subgroups of myelodysplasia defined by gene expression profiles.

  • The gene expression-based subgroups had independent prognostic value, which was validated in an external cohort.

Abstract

Myelodysplastic syndromes (MDS) are a heterogeneous group of clonal hematopoietic disorders with a highly variable prognosis. To identify a gene expression-based classification of myelodysplasia with biological and clinical relevance, we performed a comprehensive transcriptomic analysis of myeloid neoplasms with dysplasia using transcriptome sequencing. Unsupervised clustering of gene expression data of bone marrow CD34+ cells from 100 patients identified two subgroups. The first subtype was characterized by increased expression of genes related to erythroid/megakaryocytic (EMK) lineages, whereas the second subtype showed up-regulation of genes related to immature progenitor (IMP) cells. Compared to the first, so-called EMK subtype, the IMP subtype showed up-regulation of many signaling pathways and down-regulation of several pathways related to metabolism and DNA repair. The IMP subgroup was associated with a significantly shorter survival in both univariate (hazard ratio [HR] 5.0 [95% confidence interval (CI), 1.8–14], P = 0.002) and multivariate analysis (HR 4.9 [95% CI, 1.3–19], P = 0.02). Leukemic transformation was limited to the IMP subgroup. The prognostic significance of our classification was validated in an independent cohort of 183 patients. We also constructed a model to predict the subgroups using gene expression profiles of unfractionated bone marrow mononuclear cells (BMMNCs). The model successfully predicted clinical outcomes in a test set of 114 patients with BMMNC samples. Addition of our classification to the clinical model improved prediction of patient outcomes. These results indicated biological and clinical relevance of our gene expression-based classification, which will improve risk prediction and treatment stratification of MDS.

  • Submitted May 4, 2017.
  • Accepted October 11, 2017.