A number of patient-specific and leukemia-associated factors are related to the poor outcome in older patients with acute myeloid leukemia (AML). However, comprehensive studies regarding the impact of genetic alterations in this group of patients are limited. In this study, we compared relevant mutations in 21 genes between AML patients aged 60 years or older and those younger and exposed their prognostic implications. Compared with the younger patients, the elderly had significantly higher incidences of PTPN11, NPM1, RUNX1, ASXL1, TET2, DNMT3A and TP53 mutations but a lower frequency of WT1 mutations. The older patients more frequently harbored one or more adverse genetic alterations. Multivariate analysis showed that DNMT3A and TP53 mutations were independent poor prognostic factors among the elderly, while NPM1 mutation in the absence of FLT3/ITD was an independent favorable prognostic factor. Furthermore, the status of mutations could well stratify older patients with intermediate-risk cytogenetics into three risk groups. In conclusion, older AML patients showed distinct genetic alterations from the younger group. Integration of cytogenetics and molecular mutations can better risk-stratify older AML patients. Development of novel therapies is needed to improve the outcome of older patients with poor prognosis under current treatment modalities.

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Gene mutations have not yet been included in the 2016 WHO classification and revised International Prognostic Scoring System (IPSS-R), which are now widely utilized to discriminate myelodysplastic syndrome (MDS) patients regarding risk of leukemia evolution and overall survival (OS). In this study, we aimed to investigate whether integration of gene mutations with other risk factors could further improve the stratification of MDS patients. Mutational analyses of 25 genes relevant to myeloid malignancies in 426 primary MDS patients showed that mutations of CBL, IDH2, ASXL1, DNMT3A, and TP53 were independently associated with shorter survival. Patients within each IPSS-R or 2016 WHO classification-defined risk group could be stratified into two risk subgroups based on the mutational status of these five genes; patients with these poor-risk mutations had an OS shorter than others in the same risk group, but similar to those with the next higher risk category. A scoring system incorporating age, IPSS-R and five poor-risk mutations could divide the MDS patients into four risk groups (P < 0.001 for both OS and leukemia-free survival). In conclusion, integration of gene mutations in current IPSS-R improves the prognostication of MDS patients and may help identify high-risk patients for more aggressive treatment in IPSS-R lower risk group.

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Mutations in splicing factor (SF) genes are frequently detected in myelodysplastic syndrome, but the prognostic relevance of these genes mutations in acute myeloid leukemia (AML) remains unclear. In this study, we investigated mutations of three SF genes, SF3B1, U2AF1 and SRSF2, by Sanger sequencing in 500 patients with de novo AML and analysed their clinical relevance. SF mutations were identified in 10.8% of total cohort and 13.2% of those with intermediate-risk cytogenetics. SF mutations were closely associated with RUNX1, ASXL1, IDH2 and TET2 mutations. SF-mutated AML patients had a significantly lower complete remission rate and shorter disease-free survival (DFS) and overall survival (OS) than those without the mutation. Multivariate analysis demonstrated that SFmutation was an independent poor prognostic factor for DFS and OS. A scoring system incorporating SF mutation and ten other prognostic factors was proved very useful to risk-stratify AML patients. Sequential study of paired samples showed that SF mutations were stable during AML evolution. In conclusion, SF mutations are associated with distinct clinic-biological features and poor prognosis in de novo AML patients and are rather stable during disease progression. These mutations may be potential targets for novel treatment and biomarkers for disease monitoring in AML.

Next-generation sequencing (NGS) has been applied to measurable/minimal residual disease (MRD) monitoring after induction chemotherapy in patients with acute myeloid leukemia (AML), but the optimal time point for the test remains unclear. In this study, we aimed to investigate the clinical significance of NGS MRD at 2 different time points. We performed targeted NGS of 54 genes in bone marrow cells serially obtained at diagnosis, first complete remission (first time point), and after the first consolidation chemotherapy (second time point) from 335 de novo AML patients. Excluding DNMT3A, TET2, and ASXL1 mutations, which are commonly present in individuals with clonal hematopoiesis of indeterminate potential, MRD could be detected in 46.4% of patients at the first time point (MRD1st), and 28.9% at the second time point (MRD2nd). The patients with detectable NGS MRD at either time point had a significantly higher cumulative incidence of relapse and shorter relapse-free survival and overall survival. In multivariate analysis, MRD1st and MRD2nd were both independent poor prognostic factors. However, the patients with positive MRD1st but negative MRD2nd had a similar good prognosis as those with negative MRD at both time points. The incorporation of multiparameter flow cytometry and NGS MRD revealed that the presence of NGS MRD predicted poorer prognosis among the patients without detectable MRD by multiparameter flow cytometry at the second time point but not the first time point. In conclusion, the presence of NGS MRD, especially after the first consolidation therapy, can help predict the clinical outcome of AML patients.

Mutations of the GATA binding protein 2 (GATA2) gene in myeloid malignancies usually cluster in the zinc finger 1 (ZF1) and the ZF2 domains. Mutations in different locations of GATA2 may have distinct impact on clinico-biological features and outcomes in AML patients, but little is known in this aspect. In this study, we explored GATA2 mutations in 693 de novo non-M3 AML patients and identified 44 GATA2 mutations in 43 (6.2%) patients, including 31 in ZF1, 10 in ZF2, and three outside the two domains. Different from GATA2 ZF2 mutations, ZF1 mutations were closely associated with French-American-British (FAB) M1 subtype, CEBPA double mutations (CEBPAdouble-mut), but inversely correlated with FAB M4 subtype, NPM1 mutations, and FLT3-ITD. ZF1-mutated AML patients had a significantly longer overall survival (OS) than GATA2-wild patients and ZF2-mutated patients in total cohort as well as in those with intermediate-risk cytogenetics and normal karyotype. ZF1 mutations also predicted better disease-free survival and a trend of better OS in CEBPAdouble-mut patients. Sequential analysis showed GATA2 mutations could be acquired at relapse. In conclusion, GATA2 ZF1 mutations are associated with distinct clinico-biological features and predict better prognosis, different from ZF2 mutations, in AML patients.

BackgroundMulticolor flow cytometry (MFC) analysis is widely used to identify minimal residual disease (MRD) after treatment for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). However, current manual interpretation suffers from drawbacks of time consuming and interpreter idiosyncrasy. Artificial intelligence (AI), with the expertise in assisting repetitive or complex analysis, represents a potential solution for these drawbacks.MethodsFrom 2009 to 2016, 5333 MFC data from 1742 AML or MDS patients were collected. The 287 MFC data at post-induction were selected as the outcome set for clinical outcome validation. The rest were 4:1 randomized into the training set (n = 4039) and the validation set (n = 1007). AI algorithm learned a multi-dimensional MFC phenotype from the training set and input it to support vector machine (SVM) classifier after Gaussian mixture model (GMM) modeling, and the performance was evaluated in The validation set.FindingsPromising accuracies (84·6% to 92·4%) and AUCs (0·921–0·950) were achieved by the developed algorithms. Interestingly, the algorithm from even one testing tube achieved similar performance. The clinical significance was validated in the outcome set, and normal MFC interpreted by the AI predicted better progression-free survival (10·9 vs 4·9 months, p < 0·0001) and overall survival (13·6 vs 6·5 months, p < 0·0001) for AML.InterpretationThrough large-scaled clinical validation, we showed that AI algorithms can produce efficient and clinically-relevant MFC analysis. This approach also possesses a great advantage of the ability to integrate other clinical tests.FundThis work was supported by the Ministry of Science and Technology (107-2634-F-007-006 and 103–2314-B-002-185-MY2) of Taiwan.