Institute of Pathology, Hannover Medical School, Hannover, Germany.
Cancer Med. 2019 Feb;8(2):742-750. doi: 10.1002/cam4.1946. Epub 2019 Jan 11.
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.
非典型慢性髓系白血病（aCML）和慢性髓单核细胞白血病（CMML）代表两种组织学和临床表现重叠的骨髓增生异常/骨髓增殖性肿瘤。这两种实体的突变景观也具有一致性。我们通过下一代测序 25 个基因和 nCounter 方法分析比较了 aCML 队列（n=26）和 CMML 队列（n=59），以检测 107 个基因的差异表达。在 TET2、SETBP1 和 CSF3R 的突变频率方面发现了显著差异。骨髓中原始细胞含量与 aCML 中 SETBP1 的突变状态和 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，将 19/26（73%）例 aCML 和 54/59（92%）例 CMML 正确分类。经过多次校正后，在一组基因中可以检测到两个队列之间的差异 mRNA 表达（FLT3、CSF3R 和 SETBP1 显示出最强的相关性）。然而，由于 mRNA 表达的高变异性，其在临床上的应用潜力有限。我们得出结论，中等大小的 NGS 面板为正确分类 aCML 和 CMML 提供了有价值的帮助。
