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Updated: Aug 27, 2026

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
Published on: September 20, 2016
Mutation Patterns Define Clinical Heterogeneity in Acute Myeloid Leukemia
Zhengrong Xu1,2, Ying Zheng1,2, Yi Zheng1,2
1Fujian Institute of Hematology, Fujian Provincial Key Laboratory on Hematology, Department of Hematology, Fujian Medical University Union Hospital, Fuzhou 350001, PR China.
Background:
Acute myeloid leukemia (AML) is a molecularly heterogeneous malignancy where next-generation sequencing (NGS) has revolutionized risk stratification and treatment paradigms. However, the interplay between mutation cooperativity, clinical phenotypes, and biochemical markers of organ dysfunction remains poorly characterized. This study investigates how co-mutational patterns influence hematological/biochemical parameters and survival outcomes in AML.
Methods:
In this single-center retrospective study (2017-2024), 1,416 non-M3 AML patients with NGS-confirmed somatic mutations were analyzed. Clinical parameters included hematologic parameters, biochemical markers, and survival outcomes. Multivariate Cox models, Kaplan-Meier analysis, propensity score matching (1:4), and Cohen's d effect sizes were employed to assess mutation-clinical correlations.
Results:
A higher mutational burden was associated with older age, elevated platelets, and renal impairment, with DNMT3A mutation identified as an independent factor for renal impairment. Multivariable analysis identified higher mutational burden, age, renal impairment, and high white blood cell counts as independent predictors of inferior survival. We identified novel co-occurring (e.g., KRAS and PTPN11; OR = 3.19; 95% CI, 2.97-3.42) or mutually exclusive (e.g., CEBPA-bZIP and NPM1; OR = 0.03; 95% CI, 0.01-0.17) gene pairs (all adjusted P <0.001). The NPM1/IDH1 co-mutation elevated blast percentages in bone marrow and peripheral blood compared to either single mutation alone. Three novel triple-mutations were significantly associated with higher mortality: IDH2: FLT3-ITD: NPM1 (HR = 4.68), IDH2: SRSF2: ASXL1 (HR = 3.31), and RUNX1: TET2: NRAS (HR = 3.08) (all adjusted P <0.05).
Conclusions:
These findings support integrating genomic and biochemical profiling for AML management, as co-mutation patterns, particularly those affecting renal function, refine risk stratification and highlight the need for tailored monitoring and organ support.
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