Gene Mutation Distribution Characteristics And Prognosis Survival Analysis In Patients With Acute Myeloid Leukemia

Objective:To analyze the distribution characteristics of 25 gene mutations commonly found in myeloid malignancies in patients with newly diagnosed acute myeloid leukemia retrospectively.Methods:428 patients including 198 females and 230 males with median age of 54 years old were newly diagnosed as de novo non-M3 AML and were performed with targeted NGS of marrow samples from First Affiliated Hospital of Nanjing Medical University from January 2010 to May 2018.According to the 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia,189 cases were AML with recurring genetic abnormalities(AML-RGA),55 cases were AML with myelodysplasia-related changes(AML-MRC),and 171 cases were AML not otherwise specified(AML-NOS);14 cases were treatment-related AML(tAML).The distribution characteristics of gene mutations were reviewed retrospectively.Results: According to the first edition of NCCN Clinical Practice Guidelines in Oncology: Acute myeloid leukemia.V2.2018,62 cases(14.7%)were in the low-risk group and 150 cases were in the intermediate-risk group(34.7%),213 patients(49.9%)were in the high-risk group,and 3 patients(0.7%)who could not be stratified because the karyotype analysis failed to obtain a valid result.Among them,411 patients(95.8%)had at least one gene mutation,324 patients(75.5%)had 2 or more gene mutations,and 4 patients had up to 6 gene mutations at the same time(1.3%).The most common gene mutation was TET2(287 cases,66.9%),followed by FLT3(108 cases,25.2%),ASXL1(101 cases,23.5%),NPM1(88 cases,20.5%),CEBPA(80 cases,18.6)%),DNMT3A(67 cases,15.6%),NRAS(61 cases,14.2%),IDH2(50 cases,11.7%),KIT(49 cases,11.4%),TP53(33 cases,7.7%),IDH1(26 cases,6.1%),RUNX1(20 cases 4.7%);the detection rates of other gene mutations were all lower than 5%,the lowest was SETBP1,and the detection rate was 0.7%.Comparing gene mutations with chromosomal karyotypes,20 patients(20/20,100%)with RUNX1 mutations were all in the high-risk group,33 patients(33/33,100%)with TP53 mutations were all in the high-risk group,and 99(99/101,98%)ASXL1 mutation patients were in the high-risk group,and 9(9/11,81.8%)SRSF2 mutation patients were in the high-risk group,suggesting that these four gene mutations are associated with poor prognosis.Among people aged ≥60 years(N=171),96 cases were in the high-risk group(56.1%),61 cases were in the intermediate-risk group(35.7%),12 cases were in the low-risk group(7%),and 2 cases could not be stratified due to invalid karyotype analysis.In the elderly group(≥60 years old),an average of 2.66 gene mutations could be detected per patient,and there was no significant difference compared with the low-risk group(2.31 gene mutations).The detection rate of the following genes in the elderly group was significantly higher than that in the non-elderly group,and the difference was statistically significant: DNMT3A(21.6% vs 11.7%),IDH2(15.7% vs 8.9%),TP53(13.5% vs 3.9%),SF3B1(4.1% vs 0.8%),SRSF2(5.3% vs 0.8%),RUNX1(7.6% vs 5.7%).The detection rate of the following gene mutations in the elderly group was significantly lower than that in the non-elderly group,and the difference was statistically significant: KIT(4.7% vs 15.9%),CEBPA(15.6% vs 21.3%),EZH2(0.6% vs 1.9%),ZRSR2(0% vs 2.3%),CBL(0.6% vs 3.5%),KRAS(0% vs 1.9%).The following gene mutations have no significant differences in age groups: FLT3,NPM1,IDH1,TET2,AML1,ASXL1,PHF6,U2AF1,NRAS,SETBP1,ETV6,JAK2,GATA2.Conclusion: Using next-generation sequencing to analyze gene mutation profiles can help us further understand the differences in disease evolution of patients with different risk stratifications,thereby providing ideas for new targeted therapies.Objectives: This study aimed to understand genome diversification and complexity that developed in Acute myeloid leukemia(AML).Methods: Next-generation sequencing(NGS)was used to identify the genetic profiles of 22 genes relevant to hematological malignancy in 204 patients with de novo non-APL AML.Results: At time of initial diagnosis,at least one mutation was identified in 80.9% of patients(165/204).The most commonly mutated gene was NPM1(22.1%),followed by ASXL1(18.1%),TET2(18.1%),IDH2(15.7%),CEBPA(14.7%),FLT3-ITD(13.2%)and DNMT3A(11.8%).Mutations landscape analysis indicated several patterns of co-occurring and mutual exclusive gene mutations.Some correlation was observed between gene mutations and clinical hematological features.Multivariate analysis showed that age ≥60 years,karyotypes,IDH2 and KIT mutations were the independent unfavorable prognostic factors for OS;NPM1-mut/ FLT3-ITD-wt was independently correlated with prolonged OS;whereas the independent poor risk factors for RFS were karyotypes,high WBC and RUNX1 mutation.According to different genotype demonstrated by multivariate analysis,163 patients with intermediate risk cytogenetics were classified into three subgroups: patients with NPM1-mut/ FLT3-ITD-wt or biallelic CEBPA mutation as favorable risk,patients with KIT,IDH2,TP53 or NRAS mutations as unfavorable risk,and the remaining was the intermediate risk.We also obtain information of clonal evolution during leukemia progression by observing 5 patients who underwent repeat NGS at relapse in our cohort.Conclusion: NGS technology is a useful tool for discovering gene mutations and clonal evolution of AML.Based on NGS technology,physicians can to better prognostic risk stratification of AML and use targeted therapy precisely.