Abstract

We aimed to retrospectively discern the heterogeneity of outcomes from clinicopathological characteristics and next-generation sequencing (NGS) data in adult patients with NPM1-mutated (NPM1mut ) acute myeloid leukemia (AML) induced with standard-dose (SD, 100-200 mg/m2 ) and intermediate-dose (ID, 1000-2000 mg/m2 ) cytarabine arabinose (Ara-C). In the entire cohort and FLT3-ITD subgroups, multivariate Logistic and Cox regression analyses were used to analyze the comprehensive complete remission (cCR) rate after one or two induction cycles, event-free survival (EFS), and overall survival (OS). Among a total of 203 NPM1mut patients evaluable for clinical outcome, 144 (70.9%) received a first SD-Ara-C induction and 59 (29.1%) received ID-Ara-C induction. Early death was recorded in seven (3.4%) after one or two cycles of induction. Focusing analysis on the NPM1mut /FLT3-ITD(-) subgroup, independent factors showing inferior outcome were presence of TET2 mutation [cCR rate, OR=12.82 (95%CI 1.93-85.28), p=0.008; EFS, HR=2.92 (95%CI 1.46-5.86), p=0.003], increasing age [EFS, HR=1.49 (95%CI 1.10-2.02), p=0.012 by every 10-years elevation], white blood cell count ≥60 × 109 /L [EFS, HR=3.30 (95%CI 1.63-6.70), p=0.001], and ≥4 mutated genes at initial diagnosis [OS, HR=5.54 (95%CI 1.77-17.33), p=0.003]. In contrast, when focusing on the NPM1mut /FLT3-ITD(+) subgroup, factors showing superior outcome were ID-Ara-C induction [cCR rate, OR=0.20 (95%CI 0.05-0.81), p=0.025; EFS, HR=0.27 (95%CI 0.13-0.60), p=0.001] and allo-transplantation [OS, HR=0.45 (95%CI 0.21-0.94), p=0.033]. Factors showing inferior outcome included CD34(+) [cCR rate, OR=6.22 (95%CI 1.86-20.77), p=0.003; EFS, HR=2.01 (95%CI 1.12-3.61), p=0.020] and ≥5 mutated genes [OS, HR=2.85 (95%CI 1.33-6.10), p=0.007]. We conclude that TET2(+) , age, and white blood cell count convey an outcome risk modulation for AML with NPM1mut /FLT3-ITD(-) , as does CD34 and ID-Ara-C induction for NPM1mut /FLT3-ITD(+) . The findings permit re-stratification of NPM1mut AML into distinct prognostic subsets to guide risk-adapted individualized treatment.

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