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Comprehensive mutation profiling and mRNA expression analysis in atypical chronic myeloid leukemia in comparison with chronic myelomonocytic leukemia

机译:与慢性粒细胞性白血病相比,非典型慢性粒细胞白血病的综合突变谱分析和mRNA表达分析

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Atypical chronic myeloid leukemia (aCML) and chronic myelomonocytic leukemia (CMML) represent two histologically and clinically overlapping myelodysplastic/myeloproliferative neoplasms. Also the mutational landscapes of both entities show congruencies. We analyzed and compared an aCML cohort (n?=?26) and a CMML cohort (n?=?59) by next‐generation sequencing of 25 genes and by an nCounter approach for differential expression in 107 genes. Significant differences were found with regard to the mutation frequency of TET2 , SETBP1 , and CSF3R . Blast content of the bone marrow revealed an inverse correlation with the mutation status of SETBP1 in aCML and TET2 in CMML, respectively. By linear discriminant analysis, a mutation‐based machine learning algorithm was generated which placed 19/26 aCML cases (73%) and 54/59 (92%) CMML cases into the correct category. After multiple correction, differential mRNA expression could be detected between both cohorts in a subset of genes ( FLT3 , CSF3R , and SETBP1 showed the strongest correlation). However, due to high variances in the mRNA expression, the potential utility for the clinic is limited. We conclude that a medium‐sized NGS panel provides a valuable assistance for the correct classification of aCML and CMML.
机译:非典型慢性粒细胞白血病(aCML)和慢性粒细胞单核细胞白血病(CMML)代表了两个组织学和临床上重叠的骨髓增生异常/骨髓增生性肿瘤。两个实体的突变景观也显示出一致性。我们通过25个基因的下一代测序和nCounter方法对107个基因的差异表达进行了分析和比较,其中aCML队列(n?=?26)和CMML队列(n?=?59)。发现在TET2,SETBP1和CSF3R的突变频率上有显着差异。骨髓的爆炸含量分别与aCML中SETBP1和CMML中TET2的突变状态呈负相关。通过线性判别分析,生成了基于突变的机器学习算法,该算法将19/26 aCML案例(73%)和54/59(92%)CMML案例归为正确类别。经过多次校正后,可以在一个基因子集(FLT3,CSF3R和SETBP1显示最强的相关性)的两个队列之间检测到差异的mRNA表达。然而,由于mRNA表达的高度差异,在临床上的潜在用途受到限制。我们得出的结论是,中型NGS面板为正确分类aCML和CMML提供了宝贵的帮助。

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