The oncologist · 2026
Clinical implications of variant allele frequencies of genes in patients with acute myeloid leukemia.
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Zhao Li, Cheng Feng, Wei Jinming, Liu Riming, Zhao Qi, Zhang Yanhong, Li Yulan
Аннотация
PURPOSES: Although next-generation sequencing (NGS) has allowed for the detection of mutations in acute myeloid leukemia (AML), the clinical relevance of variant allele frequency (VAF) for the majority of mutations is unknown. This study aimed to investigate whether the VAFs of AML-associated mutations could improve the prognostic classification in AML.
METHODS: Bone marrow samples from 254 AML patients were included for targeted sequencing. Univariate Cox regression analyses identified mutations affecting survival, and optimal VAF cutoffs were determined by X-tile software. Multivariate Cox regression analyses were used to validate the independent prognostic value of the variables.
RESULTS: The presence of ASXL1, SF3B1, DNMT3A, and TP53 mutations had a significantly adverse effect on survival outcomes. High FLT3-ITD allelic ratio (≥35%) and high mutation VAFs of ASXL1 (≥2.8%), DNMT3A (≥45%), DNMT3A R882 (≥45%), NPM1 (≥38%), NPM1 type A (≥39%), SF3B1 (≥10%), and TP53 (≥10%) genes were the significant risk factors of overall survival (OS). Patients with high VAFs of bZIP in-frame mutated CEBPA (≥2%) had favorable OS. Notably, the prognostic utility of VAF for ASXL1 and CEBPA was limited compared to their binary mutation status due to the relatively low cutoff values. Based on optimal VAF cutoff-based classifications of five genes (DNMT3A, FLT3-ITD, NPM1, SF3B1, and TP53), mutation status-based classifications of two genes (ASXL1 and CEBPA) and cytogenetic stratification according to European LeukemiaNet (ELN) 2022 guidelines, we developed a prognostic model. This model effectively stratified non-transplanted AML patients into low-, intermediate-, and high-risk groups in both the internal and external cohorts (all pairwise comparisons, P <.05). However, the model showed limited utility for prognostic stratification in transplant patients (P >.05).
CONCLUSIONS: Our study revealed that beyond binary mutation status, the relative amount of mutations exerts a significant prognostic impact in AML patients, suggesting its potential integration into risk stratification systems to guide clinical management in AML.
METHODS: Bone marrow samples from 254 AML patients were included for targeted sequencing. Univariate Cox regression analyses identified mutations affecting survival, and optimal VAF cutoffs were determined by X-tile software. Multivariate Cox regression analyses were used to validate the independent prognostic value of the variables.
RESULTS: The presence of ASXL1, SF3B1, DNMT3A, and TP53 mutations had a significantly adverse effect on survival outcomes. High FLT3-ITD allelic ratio (≥35%) and high mutation VAFs of ASXL1 (≥2.8%), DNMT3A (≥45%), DNMT3A R882 (≥45%), NPM1 (≥38%), NPM1 type A (≥39%), SF3B1 (≥10%), and TP53 (≥10%) genes were the significant risk factors of overall survival (OS). Patients with high VAFs of bZIP in-frame mutated CEBPA (≥2%) had favorable OS. Notably, the prognostic utility of VAF for ASXL1 and CEBPA was limited compared to their binary mutation status due to the relatively low cutoff values. Based on optimal VAF cutoff-based classifications of five genes (DNMT3A, FLT3-ITD, NPM1, SF3B1, and TP53), mutation status-based classifications of two genes (ASXL1 and CEBPA) and cytogenetic stratification according to European LeukemiaNet (ELN) 2022 guidelines, we developed a prognostic model. This model effectively stratified non-transplanted AML patients into low-, intermediate-, and high-risk groups in both the internal and external cohorts (all pairwise comparisons, P <.05). However, the model showed limited utility for prognostic stratification in transplant patients (P >.05).
CONCLUSIONS: Our study revealed that beyond binary mutation status, the relative amount of mutations exerts a significant prognostic impact in AML patients, suggesting its potential integration into risk stratification systems to guide clinical management in AML.