ArticleNature communications2024
Longitudinal ultra-sensitive mutation burden sequencing for precise minimal residual disease assessment in AML.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Benchmarking Q40 sequencing for sensitive and efficient detection of rare genomic variants.Genome biology · 2026Article
- Article
- Immune monitoring for relapse of acute myeloid leukemia after allogeneic stem cell transplantation in clinical laboratories.Frontiers in immunology · 2026Review
- Leukemia stem cells for relapse prediction in AML patients receiving allografts: long-term follow-up of a prospective study.Bone marrow transplantation · 2025Article
- High-Throughput Sequencing and Estimated Relapse Risk in Patients With Sézary Syndrome.JAMA dermatology · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
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.
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