医学
髓系白血病
乐观 主义
地平线
重症监护医学
白血病
肿瘤科
内科学
心理治疗师
心理学
物理
天文
作者
Jayastu Senapati,Tapan M. Kadia,Naval Daver,Courtney D. DiNardo,Gautam Borthakur,Farhad Ravandi,Hagop M. Kantarjian
出处
期刊:Cancer
[Wiley]
日期:2025-03-19
卷期号:131 (7)
被引量:4
摘要
Focused research in acute myeloid leukemia (AML) biology and treatment has led to the identification of new therapeutic targets and several new drug approvals over the last decade. Progressive improvements in response and survival have mirrored these improvements in treatment options. Traditionally adverse subtypes such as FLT3-internal tandem duplication-positive AML now have better outcomes with potent FLT3 inhibitors, and menin inhibitors in KMT2A-rearranged and other MEIS/HOX-dependent leukemias hold promise toward improving outcomes. More patients with AML are now able to undergo a consolidative allogeneic hematopoietic stem cell transplantation (HSCT), and the rates of nonrelapse mortality with or without HSCT have also decreased. Comprehensive genomic interrogation of AML has elucidated mechanisms of response and resistance to treatments, which has enabled more precise decision algorithms and better prognostication. Deep levels of measurable residual disease assessment in some AML subsets hold the potential to dynamically modify treatment on the basis of these responses. Improving frontline intensive and low-intensity therapies, by incorporating venetoclax and other targeted agents, is the most important intervention to improve AML outcomes. Despite these developments, a sizeable percentage of AML, such as AML with TP53 or MECOM aberrations, postmyeloproliferative neoplasm AML, and so forth, remains as subsets without significant improvement in outcomes and no targeted options. Evolving strategies with natural killer cell-based approaches, novel antibody-drug conjugates, bispecific T-cell engagers, and engineered chimeric antigen receptor T-cell therapies are being evaluated, and may fill the therapeutic vacuum for some of the high-risk AML subtypes.
科研通智能强力驱动
Strongly Powered by AbleSci AI