Somatic mutations predict outcomes of hypomethylating therapy in patients with myelodysplastic syndrome.
Somatic mutations predict outcomes of hypomethylating therapy in patients with myelodysplastic syndrome.
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DOI：
10.18632/oncotarget.10526
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发表时间：
2016-08-23
期刊：
影响因子：
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通讯作者：
Lee JH
中科院分区：
文献类型：
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作者：
Jung SH;Kim YJ;Yim SH;Kim HJ;Kwon YR;Hur EH;Goo BK;Choi YS;Lee SH;Chung YJ;Lee JH
尽管低甲基化治疗(HMT)是高危骨髓增生异常综合征(MDS)的一线治疗方法，但预测HMT的疗效仍然是一个未解决的问题。我们的目标是确定与HMT反应和MDS存活率相关的突变。共有107名接受HMT的韩国MDS患者(57名应答者和50名无应答者)入选。对26个候选MDS基因进行靶向深度测序(覆盖深度中位数为1,623倍)。在多因素分析中，未发现突变与HMT疗效相关，但低血红蛋白水平(10g/dL，OR3.56，95%CI1.22~10.33)和低血小板计数(<；50,000/μL，OR2.49，95%CI1.05~5.93)是HMT不良反应的独立标志。在HMT用药类型的亚组分析中，U2AF1突变与阿扎替丁治疗无效显著相关，多因素分析结果一致(OR14.96，95%CI1.67~134.18)。在总体生存方面，DNMT1(P=0.031)、DNMT3A(P=0.006)、RAS(P=0.043)和TP53(P=0.008)突变和两个临床变量(男性，P=0.002；IPSS-R H/VH，P=0.026)是预后不良的独立预测因素。DNMT3A(P<；0.001)、RAS(P=0.001)、TP53(P=0.047)突变和两个临床变量(男性，P=0.024；IPSS-R H/VH，P=0.005)是预后不良的独立预测因素。通过结合这些突变和临床预测因素，我们开发了一个量化评分模型，用于评估阿扎替丁的疗效、总体和非急性髓细胞白血病的存活率。随着危险评分的增加，对阿扎替丁的反应和存活率显著变差。该评分模型可使预后预测更可靠，更具临床应用价值。
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.