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Updated: Mar 17, 2026

Use of Hematopoietic Stem Cell Transplantation to Assess the Origin of Myelodysplastic Syndrome
Published on: October 3, 2018
Somatic mutations predict outcomes of hypomethylating therapy in patients with myelodysplastic syndrome
Seung-Hyun Jung1, Yoo-Jin Kim2, Seon-Hee Yim1
1Integrated Research Center for Genome Polymorphism, Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Abstract:
Although hypomethylating therapy (HMT) is the first line therapy in higher-risk myelodysplastic syndromes (MDS), predicting response to HMT remains an unresolved issue. We aimed to identify mutations associated with response to HMT and survival in MDS. A total of 107 Korean patients with MDS who underwent HMT (57 responders and 50 non-responders) were enrolled. Targeted deep sequencing (median depth of coverage 1,623X) was performed for 26 candidate MDS genes. In multivariate analysis, no mutation was significantly associated with response to HMT, but a lower hemoglobin level (<10g/dL, OR 3.56, 95% CI 1.22-10.33) and low platelet count (<50,000/μL, OR 2.49, 95% CI 1.05-5.93) were independent markers of poor response to HMT. In the subgroup analysis by type of HMT agents, U2AF1 mutation was significantly associated with non-response to azacitidine, which was consistent in multivariate analysis (OR 14.96, 95% CI 1.67-134.18). Regarding overall survival, mutations in DNMT1 (P=0.031), DNMT3A (P=0.006), RAS (P=0.043), and TP53 (P=0.008), and two clinical variables (male-gender, P=0.002; IPSS-R H/VH, P=0.026) were independent predicting factors of poor prognosis. For AML-free survival, mutations in DNMT3A (P<0.001), RAS (P=0.001), and TP53 (P=0.047), and two clinical variables (male-gender, P=0.024; IPSS-R H/VH, P=0.005) were independent predicting factors of poor prognosis. By combining these mutations and clinical predictors, we developed a quantitative scoring model for response to azacitidine, overall- and AML-free survival. Response to azacitidine and survival rates became worse significantly with increasing risk-scores. This scoring model can make prognosis prediction more reliable and clinically applicable.
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