PF544 THE DIFFERENT MUTATIONAL LANDSCAPES OF A PANEL GENES BY NEXT GENERATION SEQUENCING IN 967 CASES OF MDS AND AML
Authors: Y. Liu, L. Liao, S. Wang, C. Wang, T. Li, Y. Li, Y. Chang, Q. Hao, W. Wang, J. Ma, S. Chen, Z. Jiang
Journal: HemaSphere (2019)

Background: Myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) are myeloid disease with dysplasia characterized by multiple gene mutations in most cases. With advances in Next-generation sequencing techniques, the molecular landscapes in MDS or AML have been reported with further understanding the disease pathogenesis. As well known, MDS may end up in evolving into AML in most cases, especially the subtypes of MDS with excessive blasts. The driver mutation in MDS developing to AML is still open to study. The comparison of mutational landscapes between MDS and AML will provide evidence to better understand the underlying molecular mechanisms of the myeloid dysplasia evolution. Aims: To analyze the different mutational status of 22 genes commonly observed in MDS and AML. Methods: The bone marrow samples were collected from patients with AML (749 cases) and MDS (218 cases) with written consent from June 2017 to January 2019. Next generation sequencing of a panel of 22 genes was used to evaluate the mutations in MDS/AML patients. Results: 719/ 967(74.4%) cases with MDS/AML were detected at least one gene mutation. There were 274 gene mutations in 142/ 218 (65.1%) cases with MDS and 1224 gene mutations in 577/749(77.0%) cases with AML. Both the median mutations and the frequency of gene mutation between MDS and AML were significantly difference (p < 0.05). Compared to MDS, the mutation rate of CEBPA (11.10% vs 1.40%, p < 0.01), NRAS (14.00% vs 6.40%, p < 0.01), KIT (7.30% vs 0.00%,p < 0.01), NPM1 (17.80% vs 5.00%, p < 0.01), FLT3-ITD (15.40% vs 0.90%, p < 0.01), DNMT3A (10.90% vs 4.10%, p < 0.01), IDH2 (9.5% vs 2.8%, p < 0.01), and IDH1 (6.00% vs 1.40%, p < 0.01) genes were significantly higher in AML. While the mutation rate of ASXL1 (5.30% vs 14.7%, p < 0.01), U2AF1 (5.90% vs 22.00%, p < 0.01), RUNX1 (6.30% vs 10.60%, p < 0.05), SRSF2 (2.30% vs 6.00%, p < 0.05), EZH2 (0.50% vs 3.20%, p < 0.01), SETBP1 (0.70% vs 5.00%, p < 0.01), SF3B1 (2.50% vs 7.80%, p < 0.01), and CBL (0.80% vs 3.20%, p < 0.05) genes were significantly higher in MDS. There were no significant difference of the incidence in TET2, FLT3-TKD, JAK2, ETV6, TP53, PHF6 and ZRSR2 gene mutations. Considering the gene mutation increasing with age, the cases were divided into the young (age < 60 years old) and the old (age≥60 years old). In the young cases, the gene mutation profile was similar to the cohort with incidences of CEBPA, NRAS, KIT, NPM1, FLT3-ITD, DNMT3A, IDH1, plus TET2 gene mutations were significantly higher in AML (p < 0.05). The incidences of ASXL1, U2AF1, TP53, EZH2, SETBP1, and CBL gene mutations were significantly higher in MDS (p < 0.05), and the incidences of FLT3-TKD, JAK2, RUNX1, IDH2, ETV6, PHF6, SRSF2, SF3B1 and ZRSR2 gene mutations were not significantly different between AML and MDS cases (p>0.05). The old cohort showed a different gene mutation profile as compared to the young cohort with the incidences of CEBPA, NPM1, FLT3-ITD, and IDH2 gene mutations were significantly higher in AML (p < 0.01), and the incidences of U2AF1 and SF3B1 gene mutations were significantly higher in MDS. There were no significant differences of all the other gene mutations between AML and MDS cases in the old cohort. Summary/Conclusion: There are typical gene mutation patterns in MDS and AML cases. Some genes mutate in MDS and AML cases without differences and others mutate mainly in AML or MDS. The roles and significances of these gene mutation profiles warranted further investigations to understand the pathogenesis underlying MDS/AML.