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利用真实世界队列对2022年欧洲白血病网络AML患者不良风险组的拟议优化

Proposed Refinement of 2022 European LeukemiaNet Adverse-Risk Group of AML Patients Using a Real-World Cohort.

作者信息

Wangulu Collins, Zhao Davidson, Zhou Qianghua, Wei Cuihong, Kumar Rajat, Schimmer Aaron, Chang Hong

机构信息

Princess Margaret Cancer Biobank (PMCB), University Health Network, Toronto, ON M5G 2C4, Canada.

Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1V4, Canada.

出版信息

Cancers (Basel). 2025 Apr 23;17(9):1405. doi: 10.3390/cancers17091405.

Abstract

The 2022 European LeukemiaNet (ELN 2022) is a widely used genotypic risk classification tool for the treatment and prognostication of acute myeloid leukemia (AML) patients. Our study evaluates its effectiveness in categorizing adverse-risk AML patients on standard therapy based on their overall survival (OS). : We conducted a retrospective study involving 256 AML patients. : Those in the ELN 2022 adverse-risk group had the shortest OS ( < 0.0001) and were predominantly characterized by myelodysplasia-related () mutations, complex karyotype (CK), monosomal karyotype (MK), and mutation (). Subclassification and analysis of this adverse-risk group based on the status revealed a significantly shorter OS compared to the adverse wild-type () counterparts ( = 0.0036). We propose refining the ELN 2022 adverse-risk group into two categories, adverse and adverse groups, to represent adverse- and ultra-adverse-risk groups, respectively. We used an external validation dataset to confirm our findings. : This refinement allows for a more accurate classification of these adverse-risk patients based on their clinical outcomes.

摘要

2022年欧洲白血病网(ELN 2022)是一种广泛用于急性髓系白血病(AML)患者治疗和预后评估的基因型风险分类工具。我们的研究基于总生存期(OS)评估其对接受标准治疗的高危AML患者进行分类的有效性。:我们进行了一项涉及256例AML患者的回顾性研究。:ELN 2022高危组患者的OS最短(<0.0001),主要特征为骨髓增生异常相关()突变、复杂核型(CK)、单倍体核型(MK)和 突变()。根据 状态对该高危组进行亚分类和分析,发现与野生型()高危组对应患者相比,其OS显著缩短(=0.0036)。我们建议将ELN 2022高危组细分为两类,即高危 和高危 组,分别代表高危和极高危组。我们使用外部验证数据集来证实我们的发现。:这种细化能够根据这些高危患者的临床结局对其进行更准确的分类。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a571/12070929/7bb5ac96b9ff/cancers-17-01405-g0A1a.jpg

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