| Literature DB >> 29097382 |
Yusuke Shiozawa1,2, Luca Malcovati3,4, Anna Gallì4, Andrea Pellagatti5, Mohsen Karimi6, Aiko Sato-Otsubo2, Yusuke Sato2,7, Hiromichi Suzuki2, Tetsuichi Yoshizato2, Kenichi Yoshida2, Yuichi Shiraishi8, Kenichi Chiba8, Hideki Makishima2, Jacqueline Boultwood5, Eva Hellström-Lindberg6, Satoru Miyano8,9, Mario Cazzola3,4, Seishi Ogawa2.
Abstract
Myelodysplastic syndromes (MDSs) 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 2 subgroups. The first subtype was characterized by increased expression of genes related to erythroid/megakaryocytic (EMK) lineages, whereas the second subtype showed upregulation of genes related to immature progenitor (IMP) cells. Compared with the first so-called EMK subtype, the IMP subtype showed upregulation of many signaling pathways and downregulation 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 = .002) and multivariate analysis (HR, 4.9; 95% CI, 1.3-19; P = .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. The 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.Entities:
Mesh:
Year: 2017 PMID: 29097382 DOI: 10.1182/blood-2017-05-783050
Source DB: PubMed Journal: Blood ISSN: 0006-4971 Impact factor: 22.113