微小残留病
残余物
突变
医学
DNA测序
遗传学
计算生物学
生物
生物信息学
计算机科学
DNA
白血病
基因
算法
作者
Yitian Wu,Shuai Zhang,Ru Feng,Kang-Wen Xiao,Ting Wang,Jiefei Bai,Xiaoyu Zhou,Yuji Wang,Peng Dai,Hui Liu,Lucia R. Wu
标识
DOI:10.1038/s41467-024-54254-6
摘要
Relapse is one of the major challenges in clinical treatment of acute myeloid leukemia (AML). Though minimal residual disease (MRD) monitoring plays a crucial role in quantitative assessment of the disease, molecular MRD analysis has been mainly limited to patients diagnosed with gene fusions and NPM1 mutations. Here, we report a longitudinal ultra-sensitive mutation burden (UMB) monitoring strategy for accurate MRD analysis in AML patients regardless of genetic abnormality types. Using a Quantitative Blocker Displacement Amplification (QBDA) sequencing panel with limit of detection below 0.01% variant allele frequency (VAF), a hazard ratio of 14.8 (p < 0.001) is observed in cumulative incidence of relapse analysis of 20 patients with ≥ 2 samples during complete remission (CR). The ROC area under curve (AUC) is 0.98 when predicting relapse within 30 weeks of CR timepoint 2 (N = 20). Furthermore, we demonstrate quantitating VAF below 0.01% is essential for accurate relapse prediction. Monitoring of minimal residual disease is a key method for detecting potential acute myeloid leukaemia relapse. Here, the authors developed a longitudinal mutation burden monitoring strategy using ultra-sensitive mutation sequencing to quantitate VAF below 0.01% for accurate minimal residual disease assessment and relapse prediction.
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