Department of Internal Medicine III, University Hospital of Ulm, Ulm, Germany.
Saadati Solutions, Ladenburg, Germany.
Leukemia. 2023 Nov;37(11):2187-2196. doi: 10.1038/s41375-023-01999-6. Epub 2023 Aug 17.
To characterize the genomic landscape and leukemogenic pathways of older, newly diagnosed, non-intensively treated patients with AML and to study the clinical implications, comprehensive genetics analyses were performed including targeted DNA sequencing of 263 genes in 604 patients treated in a prospective Phase III clinical trial. Leukemic trajectories were delineated using oncogenetic tree modeling and hierarchical clustering, and prognostic groups were derived from multivariable Cox regression models. Clonal hematopoiesis-related genes (ASXL1, TET2, SRSF2, DNMT3A) were most frequently mutated. The oncogenetic modeling algorithm produced a tree with five branches with ASXL1, DDX41, DNMT3A, TET2, and TP53 emanating from the root suggesting leukemia-initiating events which gave rise to further subbranches with distinct subclones. Unsupervised clustering mirrored the genetic groups identified by the tree model. Multivariable analysis identified FLT3 internal tandem duplications (ITD), SRSF2, and TP53 mutations as poor prognostic factors, while DDX41 mutations exerted an exceptionally favorable effect. Subsequent backwards elimination based on the Akaike information criterion delineated three genetic risk groups: DDX41 mutations (favorable-risk), DDX41/FLT3-ITD/TP53 (intermediate-risk), and FLT3-ITD or TP53 mutations (high-risk). Our data identified distinct trajectories of leukemia development in older AML patients and provide a basis for a clinically meaningful genetic outcome stratification for patients receiving less intensive therapies.
为了描述新诊断的、未经强化治疗的老年 AML 患者的基因组景观和白血病发生途径,并研究其临床意义,对 604 例接受前瞻性 III 期临床试验治疗的患者进行了包括 263 个基因的靶向 DNA 测序在内的综合遗传学分析。使用oncogenetic 树建模和层次聚类来描绘白血病轨迹,并从多变量 Cox 回归模型中得出预后组。克隆性造血相关基因(ASXL1、TET2、SRSF2、DNMT3A)最常发生突变。oncogenetic 建模算法生成了一棵树,根部分支为 ASXL1、DDX41、DNMT3A、TET2 和 TP53,提示存在引发进一步分支的白血病起始事件,这些分支又具有不同的亚克隆。无监督聚类反映了树模型识别的遗传组。多变量分析确定 FLT3 内部串联重复(ITD)、SRSF2 和 TP53 突变是不良预后因素,而 DDX41 突变则产生了极好的效果。随后基于 Akaike 信息准则的向后消除确定了三个遗传风险组:DDX41 突变(有利风险)、DDX41/FLT3-ITD/TP53(中等风险)和 FLT3-ITD 或 TP53 突变(高风险)。我们的数据确定了老年 AML 患者白血病发展的不同轨迹,并为接受较少强化治疗的患者提供了基于遗传学的有意义的预后分层基础。