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<p>Acute myeloid leukemia (AML) remains a highly heterogeneous malignancy in which outcomes are particularly poor for patients classified as having high-risk disease. Traditionally, high-risk AML has been defined by adverse baseline genetic features, including complex cytogenetics, TP53 alterations, and mutations associated with secondary or therapy-related disease. However, this static, genetics-centered definition is increasingly insufficient in the modern therapeutic era. Emerging evidence supports a more dynamic and context-dependent model in which risk is shaped not only by molecular architecture but also by treatment intensity, patient fitness, measurable residual disease (MRD), and evolving resistance mechanisms. Advances in genomic profiling have refined risk stratification frameworks, including ELN 2022 for intensively treated patients and the ELN 2024 classification for those receiving less-intensive therapies. In parallel, MRD has emerged as a powerful biomarker that reclassifies patients during treatment, identifying those with persistent, therapy-resistant disease despite morphologic remission. Biologically, high-risk AML is driven by the interplay of clonal evolution, epigenetic plasticity, leukemic stem cell persistence, and protective microenvironmental and immune interactions, all of which contribute to relapse. Therapeutically, the landscape has expanded to include targeted agents, venetoclax-based combinations, and transplantation strategies, yet outcomes remain limited in key high-risk subsets, particularly TP53-mutated disease and post-venetoclax relapse. Accordingly, current strategies emphasize rational combination therapies, MRD-guided treatment adaptation, and approaches targeting both leukemic cells and their supportive niches. In 2026, high-risk AML is best understood as a dynamic, treatment-context-dependent state. Improving outcomes will require integration of precision diagnostics, biologically informed therapy, and adaptive strategies designed to anticipate and overcome resistance.</p>

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Defining and managing high-risk acute myeloid leukemia (AML) in 2026

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月24日

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  • Mohamed Nazem Alibrahim,
  • Mariem Ahmad Elsayed,
  • Abdullah Nassir Alqahtani,
  • Fahad Saeed Alshehri

摘要

Acute myeloid leukemia (AML) remains a highly heterogeneous malignancy in which outcomes are particularly poor for patients classified as having high-risk disease. Traditionally, high-risk AML has been defined by adverse baseline genetic features, including complex cytogenetics, TP53 alterations, and mutations associated with secondary or therapy-related disease. However, this static, genetics-centered definition is increasingly insufficient in the modern therapeutic era. Emerging evidence supports a more dynamic and context-dependent model in which risk is shaped not only by molecular architecture but also by treatment intensity, patient fitness, measurable residual disease (MRD), and evolving resistance mechanisms. Advances in genomic profiling have refined risk stratification frameworks, including ELN 2022 for intensively treated patients and the ELN 2024 classification for those receiving less-intensive therapies. In parallel, MRD has emerged as a powerful biomarker that reclassifies patients during treatment, identifying those with persistent, therapy-resistant disease despite morphologic remission. Biologically, high-risk AML is driven by the interplay of clonal evolution, epigenetic plasticity, leukemic stem cell persistence, and protective microenvironmental and immune interactions, all of which contribute to relapse. Therapeutically, the landscape has expanded to include targeted agents, venetoclax-based combinations, and transplantation strategies, yet outcomes remain limited in key high-risk subsets, particularly TP53-mutated disease and post-venetoclax relapse. Accordingly, current strategies emphasize rational combination therapies, MRD-guided treatment adaptation, and approaches targeting both leukemic cells and their supportive niches. In 2026, high-risk AML is best understood as a dynamic, treatment-context-dependent state. Improving outcomes will require integration of precision diagnostics, biologically informed therapy, and adaptive strategies designed to anticipate and overcome resistance.