Stable Expressed DNMT3A Mutants Predict a Poor Prognosis in Acute Myeloid Leukemia Patients Without Receiving Hematopoietic Stem Cell Transplantation

IF 10.7 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
MedComm Pub Date : 2025-03-27 DOI:10.1002/mco2.70151
Xiang Zhang, Lixia Liu, Jiayue Qin, Xiong Ni, Jie Jin
{"title":"Stable Expressed DNMT3A Mutants Predict a Poor Prognosis in Acute Myeloid Leukemia Patients Without Receiving Hematopoietic Stem Cell Transplantation","authors":"Xiang Zhang,&nbsp;Lixia Liu,&nbsp;Jiayue Qin,&nbsp;Xiong Ni,&nbsp;Jie Jin","doi":"10.1002/mco2.70151","DOIUrl":null,"url":null,"abstract":"<p>Dear Editor,</p><p>DNA methyltransferase 3A (<i>DNMT3A</i>) is wildly recognized as a tumor suppressor gene. Its deficiency leads to expanded hematopoietic stem cells (HSCs) pool, blocked HSCs differentiation, genomic instability, and a risk of malignant transformation in clonal hematopoiesis [<span>1</span>]. <i>DNMT3A</i> mutation (<i>DNMT3A</i><sup>Mut</sup>) is prevalent in adults, particularly in monocytic, and cytogenetically normal cases, affecting 25% of acute myeloid leukemia (AML) patients [<span>1</span>]. Although the distribution pattern of <i>DNMT3A</i><sup>Mut</sup>s has been well characterized in AML, its prognostic significance remains controversial.</p><p>To better understand the reasons behind <i>DNMT3A</i><sup>Mut</sup> prognostic heterogeneity, we conducted a retrospective study, as detailed in two previous studies [<span>2, 3</span>]. Our findings show that <i>DNMT3A</i><sup>Mut</sup>s with stable expressed mutants are associated with poor prognosis in AML patients without receiving hematopoietic stem cell transplantation (HSCT). In this study, we enrolled 485 adult <i>de novo</i> AML patients, of whom 98 (20.2%) were found to have <i>DNMT3A</i><sup>Mut</sup>s. Our results indicate the distribution pattern of <i>DNMT3A</i><sup>Mut</sup>s and clinical characteristics of AML patients with these mutations were similar to previous reports. In our cohort, patients with <i>DNMT3A</i><sup>Mut</sup>s showed a relatively shorter overall survival (OS), relapse-free survival (RFS) and disease-free survival (DFS) (Figure 1A).</p><p>We analyzed how different therapeutic strategies, with or without HSCT, affected the prognosis of various subgroups of <i>DNMT3A</i><sup>Mut</sup> patients. In the HSCT group, <i>DNMT3A</i><sup>Mut</sup> patients exhibited comparable OS, RFS, and DFS with <i>DNMT3A</i> wild-type (<i>DNMT3A</i><sup>WT</sup>) patients (Figure 1A). Conversely, <i>DNMT3A</i><sup>Mut</sup> patients showed poorer OS, RFS, and DFS in the non-HSCT group, and the disparity between <i>DNMT3A</i><sup>Mut</sup> and <i>DNMT3A</i><sup>WT</sup> patients was more pronounced in the non-HSCT group compared to the overall cohort (Figure 1A). Therefore, HSCT overcame the poor prognosis of <i>DNMT3A</i><sup>Mut</sup>, indicating <i>DNMT3A</i><sup>Mut</sup> patients without receiving HSCT were primarily responsible for the unfavorable outcomes.</p><p>To further explore key factors contributing to poor prognosis of <i>DNMT3A</i><sup>Mut</sup> patients, we focused on <i>DNMT3A</i><sup>Mut</sup> types in the non-HSCT group. Near 50% of <i>DNMT3A</i><sup>Mut</sup> variants in AML are heterozygous <i>DNMT3A</i><sup>R882</sup>, the hotspot mutation [<span>1</span>]. As reported, <i>DNMT3A</i><sup>R882</sup> played a dominant-negative role against <i>DNMT3A</i><sup>WT</sup> via formatting dimers, leading to genome-wide hypomethylation [<span>1</span>]. However, <i>DNMT3A</i><sup>non-R882</sup> has been infrequently studied. Recently, Yung-Hsin Huang et al. systematically studied protein stability of 253 disease-associated <i>DNMT3A</i><sup>Mut</sup> variants and their methyltransferase activity, and provided a comprehensive view on the implications of various <i>DNMT3A</i><sup>Mut</sup> variants [<span>4</span>]. In our cohort, we identified 112 <i>DNMT3A</i><sup>Mut</sup> variants. We first categorized <i>DNMT3A</i><sup>Mut</sup> patients into two groups: <i>DNMT3A</i><sup>R882</sup> and <i>DNMT3A</i><sup>non-R882</sup>. Except for OS, both of <i>DNMT3A</i><sup>R882</sup> and <i>DNMT3A</i><sup>non-R882</sup> patients a had worse RFS and DFS compared to <i>DNMT3A</i><sup>WT</sup> patients, indicating that <i>DNMT3A</i><sup>R882</sup> and <i>DNMT3A</i><sup>non-R882</sup> did not effectively distinguish between prognostic outcomes (Figure 1A). Accurately, nearly half of <i>DNMT3A</i><sup>non-R882</sup> variants were missense mutations, displaying similar mutant stability and diminished methyltransferase activity with <i>DNMT3A</i><sup>R882</sup>, so we further divided <i>DNMT3A</i><sup>Mut</sup> patients into stable and instable <i>DNMT3A</i><sup>Mut</sup> groups (Supplementary Materials and Methods). Notably, stable but not instable <i>DNMT3A</i><sup>Mut</sup> patients exhibited worse OS, RFS and DFS compared to <i>DNMT3A</i><sup>WT</sup> patients (Figure 1A). Additionally, we observed similar results when comparing stable <i>DNMT3A</i><sup>Mut</sup> to nonstable <i>DNMT3A</i><sup>Mut</sup> patients (nonstable <i>DNMT3A</i><sup>Mut</sup> included both instable <i>DNMT3A</i><sup>Mut</sup> and <i>DNMT3A</i><sup>WT</sup>) (Figure 1A). Thus, stable <i>DNMT3A</i><sup>Mut</sup> was responsible for poor prognosis of <i>DNMT3A</i><sup>Mut</sup> patients in the non-HSCT group.</p><p>As stable <i>DNMT3A</i><sup>Mut</sup> serves as a strong prognostic factor for AML patients, we were interested in exploring whether differences existed in distribution patterns and clinical characteristics between stable and instable <i>DNMT3A</i><sup>Mut</sup>. In our cohort, we identified stable <i>DNMT3A</i><sup>Mut</sup> in 51 patients and instable <i>DNMT3A</i><sup>Mut</sup> in 25 patients, while 22 <i>DNMT3A</i><sup>Mut</sup> cases were undefined. Apart from a relatively higher frequency of <i>NPM1</i> mutation in stable <i>DNMT3A</i><sup>Mut</sup> patients, the distribution pattern of stable <i>DNMT3A</i><sup>Mut</sup> was similar to that of instable <i>DNMT3A</i><sup>Mut</sup>. Furthermore, compared to 412 nonstable <i>DNMT3A</i><sup>Mut</sup> patients, 51 stable <i>DNMT3A</i><sup>Mut</sup> patients presented a distribution pattern similar with <i>DNMT3A</i><sup>Mut</sup> compared to <i>DNMT3A</i><sup>WT</sup> patients. Definitely, stable <i>DNMT3A</i><sup>Mut</sup> did not exhibit a specific distribution pattern. We also compared the baseline clinical characteristics of stable <i>DNMT3A</i><sup>Mut</sup> with those of instable <i>DNMT3A</i><sup>Mut</sup> or nonstable <i>DNMT3A</i><sup>Mut</sup> patients. However, stable <i>DNMT3A</i><sup>Mut</sup> patients did not obviously exhibit distinct clinical features. Although there were no significant statistical differences, the stable <i>DNMT3A</i><sup>Mut</sup> group showed a relatively lower complete remission rate and a higher relapse rate compared to both instable <i>DNMT3A</i><sup>Mut</sup> and nonstable <i>DNMT3A</i><sup>Mut</sup> groups, regardless of whether all patients or non-HSCT patients were considered, which possibly contributed to poor prognosis of stable <i>DNMT3A</i><sup>Mut</sup> patients.</p><p>To determine whether stable <i>DNMT3A</i><sup>Mut</sup> was an independent risk factor for poor prognosis in non-HSCT patients, we divided patients into stable <i>DNMT3A</i><sup>Mut</sup> group and nonstable <i>DNMT3A</i><sup>Mut</sup> group, and displayed univariate analysis for OS, RFS, or DFS, respectively. Notably, stable <i>DNMT3A</i><sup>Mut</sup> exhibited an adverse effect on OS, RFS and DFS. Following this, we conducted multivariate analyses, confirming that stable <i>DNMT3A</i><sup>Mut</sup> was an independent adverse risk factor for OS, RFS or DFS in the non-HSCT AML patients (Table S1).</p><p>Given its strong prognostic role, we sought to validate the clinical significances of stable <i>DNMT3A</i><sup>Mut</sup> in external cohorts. Herein, we analyzed data from The Cancer Genome Atlas TCGA (TCGA) database, and found that stable <i>DNMT3A</i><sup>Mut</sup> consistently indicates poor prognosis in the non-HSCT patients (Figure 1B).</p><p>Although most evidences supported that <i>DNMT3A</i><sup>Mut</sup> was a biomarker for poor prognosis in AML, the role of <i>DNMT3A</i><sup>Mut</sup> in AML has not been widely accepted. In our cohort, <i>DNMT3A</i><sup>Mut</sup> patients exhibited a shorter OS, RFS, and DFS in the entire cohort, consistent with results from most other reported cohorts. Most stable <i>DNMT3A</i><sup>Mut</sup> variants are distributed across the methyltransferase domain, which is known to act as a dominant negative, resulting in decreased DNA methylation activity and a poor prognosis. HSCT eliminates leukemia cells, improves the hematopoietic function of the bone marrow, and enhances patient prognosis. A study has shown that HSCT improves outcomes of <i>DNMT3A</i><sup>Mut</sup> AML patients [<span>5</span>], and our findings also indicate that HSCT reduces the prognostic gap between <i>DNMT3A</i><sup>Mut</sup> and <i>DNMT3A</i><sup>WT</sup> patients. In contrast, the prognostic disparity existed between <i>DNMT3A</i><sup>Mut</sup> and <i>DNMT3A</i><sup>WT</sup> patients in the non-HSCT group. <i>DNMT3A</i><sup>R882</sup>, the hotpot mutation in <i>DNMT3A</i><sup>Mut</sup> variants, was recognized as the primary factor contributing to the poor prognosis associated with <i>DNMT3A</i><sup>Mut</sup> [<span>1</span>]. Nearly 50% of <i>DNMT3A</i><sup>Mut</sup> variants are attributed to <i>DNMT3A</i><sup>R882</sup>, but significant heterogeneity also exits in <i>DNMT3A</i><sup>non-R882</sup> [<span>1</span>]. A recent study indicated that certain missense <i>DNMT3A</i><sup>non-R882</sup> variants also shared similar protein stability and methyltransferase activity features with <i>DNMT3A</i><sup>R882</sup>, thus, classifying <i>DNMT3A</i><sup>Mut</sup> into <i>DNMT3A</i><sup>R882</sup> and <i>DNMT3A</i><sup>non-R882</sup> was not accurate [<span>4</span>]. Herein, we classified <i>DNMT3A</i><sup>Mut</sup> as stable or instable <i>DNMT3A</i><sup>Mut</sup> according to mutant stability, and found that stable <i>DNMT3A</i><sup>Mut</sup>, rather than instable <i>DNMT3A</i><sup>Mut</sup>, was an independent predictor for relatively shorter OS, RFS and DFS. Furthermore, stable <i>DNMT3A</i><sup>Mut</sup> primarily worsened the prognosis for AML genetic subtypes, indicating stable <i>DNMT3A</i><sup>Mut</sup> was a dominant contributor for poor prognosis in the non-HSCT patients. In the future, we aim to demonstrate the prognostic value of stable <i>DNMT3A</i><sup>Mut</sup> through prospective multicenter clinical studies.</p><p>Collectively, stable <i>DNMT3A</i><sup>Mut</sup>, but not instable <i>DNMT3A</i><sup>Mut</sup>, should be regarded as a strong predictor of poor prognosis in AML patients without receiving HSCT, thereby providing insights into the mechanism underlying AML pathogenesis.</p><p>Xiang Zhang designed this study. Xiang Zhang and Xiong Ni collected clinical data and updated follow-up. Xiong Ni and Jin Jie guided clinical managements for patients. Xiang Zhang, Lixia Liu, and Jiayue Qin displayed data analysis. Xiang Zhang and Jiayue Qin wrote the manuscript. Jie Jin provided critical comments on this study. Xiong Ni and Jie Jin revised the manuscript. All authors have read and approved the final manuscript.</p><p>This study was approved by the ethical review committees of the First Affiliated Hospital of Zhejiang University School of Medicine (IIT20220659A) and Changhai Hospital (B2022-035). All procedures in studies involving human participants were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments. 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引用次数: 0

Abstract

Dear Editor,

DNA methyltransferase 3A (DNMT3A) is wildly recognized as a tumor suppressor gene. Its deficiency leads to expanded hematopoietic stem cells (HSCs) pool, blocked HSCs differentiation, genomic instability, and a risk of malignant transformation in clonal hematopoiesis [1]. DNMT3A mutation (DNMT3AMut) is prevalent in adults, particularly in monocytic, and cytogenetically normal cases, affecting 25% of acute myeloid leukemia (AML) patients [1]. Although the distribution pattern of DNMT3AMuts has been well characterized in AML, its prognostic significance remains controversial.

To better understand the reasons behind DNMT3AMut prognostic heterogeneity, we conducted a retrospective study, as detailed in two previous studies [2, 3]. Our findings show that DNMT3AMuts with stable expressed mutants are associated with poor prognosis in AML patients without receiving hematopoietic stem cell transplantation (HSCT). In this study, we enrolled 485 adult de novo AML patients, of whom 98 (20.2%) were found to have DNMT3AMuts. Our results indicate the distribution pattern of DNMT3AMuts and clinical characteristics of AML patients with these mutations were similar to previous reports. In our cohort, patients with DNMT3AMuts showed a relatively shorter overall survival (OS), relapse-free survival (RFS) and disease-free survival (DFS) (Figure 1A).

We analyzed how different therapeutic strategies, with or without HSCT, affected the prognosis of various subgroups of DNMT3AMut patients. In the HSCT group, DNMT3AMut patients exhibited comparable OS, RFS, and DFS with DNMT3A wild-type (DNMT3AWT) patients (Figure 1A). Conversely, DNMT3AMut patients showed poorer OS, RFS, and DFS in the non-HSCT group, and the disparity between DNMT3AMut and DNMT3AWT patients was more pronounced in the non-HSCT group compared to the overall cohort (Figure 1A). Therefore, HSCT overcame the poor prognosis of DNMT3AMut, indicating DNMT3AMut patients without receiving HSCT were primarily responsible for the unfavorable outcomes.

To further explore key factors contributing to poor prognosis of DNMT3AMut patients, we focused on DNMT3AMut types in the non-HSCT group. Near 50% of DNMT3AMut variants in AML are heterozygous DNMT3AR882, the hotspot mutation [1]. As reported, DNMT3AR882 played a dominant-negative role against DNMT3AWT via formatting dimers, leading to genome-wide hypomethylation [1]. However, DNMT3Anon-R882 has been infrequently studied. Recently, Yung-Hsin Huang et al. systematically studied protein stability of 253 disease-associated DNMT3AMut variants and their methyltransferase activity, and provided a comprehensive view on the implications of various DNMT3AMut variants [4]. In our cohort, we identified 112 DNMT3AMut variants. We first categorized DNMT3AMut patients into two groups: DNMT3AR882 and DNMT3Anon-R882. Except for OS, both of DNMT3AR882 and DNMT3Anon-R882 patients a had worse RFS and DFS compared to DNMT3AWT patients, indicating that DNMT3AR882 and DNMT3Anon-R882 did not effectively distinguish between prognostic outcomes (Figure 1A). Accurately, nearly half of DNMT3Anon-R882 variants were missense mutations, displaying similar mutant stability and diminished methyltransferase activity with DNMT3AR882, so we further divided DNMT3AMut patients into stable and instable DNMT3AMut groups (Supplementary Materials and Methods). Notably, stable but not instable DNMT3AMut patients exhibited worse OS, RFS and DFS compared to DNMT3AWT patients (Figure 1A). Additionally, we observed similar results when comparing stable DNMT3AMut to nonstable DNMT3AMut patients (nonstable DNMT3AMut included both instable DNMT3AMut and DNMT3AWT) (Figure 1A). Thus, stable DNMT3AMut was responsible for poor prognosis of DNMT3AMut patients in the non-HSCT group.

As stable DNMT3AMut serves as a strong prognostic factor for AML patients, we were interested in exploring whether differences existed in distribution patterns and clinical characteristics between stable and instable DNMT3AMut. In our cohort, we identified stable DNMT3AMut in 51 patients and instable DNMT3AMut in 25 patients, while 22 DNMT3AMut cases were undefined. Apart from a relatively higher frequency of NPM1 mutation in stable DNMT3AMut patients, the distribution pattern of stable DNMT3AMut was similar to that of instable DNMT3AMut. Furthermore, compared to 412 nonstable DNMT3AMut patients, 51 stable DNMT3AMut patients presented a distribution pattern similar with DNMT3AMut compared to DNMT3AWT patients. Definitely, stable DNMT3AMut did not exhibit a specific distribution pattern. We also compared the baseline clinical characteristics of stable DNMT3AMut with those of instable DNMT3AMut or nonstable DNMT3AMut patients. However, stable DNMT3AMut patients did not obviously exhibit distinct clinical features. Although there were no significant statistical differences, the stable DNMT3AMut group showed a relatively lower complete remission rate and a higher relapse rate compared to both instable DNMT3AMut and nonstable DNMT3AMut groups, regardless of whether all patients or non-HSCT patients were considered, which possibly contributed to poor prognosis of stable DNMT3AMut patients.

To determine whether stable DNMT3AMut was an independent risk factor for poor prognosis in non-HSCT patients, we divided patients into stable DNMT3AMut group and nonstable DNMT3AMut group, and displayed univariate analysis for OS, RFS, or DFS, respectively. Notably, stable DNMT3AMut exhibited an adverse effect on OS, RFS and DFS. Following this, we conducted multivariate analyses, confirming that stable DNMT3AMut was an independent adverse risk factor for OS, RFS or DFS in the non-HSCT AML patients (Table S1).

Given its strong prognostic role, we sought to validate the clinical significances of stable DNMT3AMut in external cohorts. Herein, we analyzed data from The Cancer Genome Atlas TCGA (TCGA) database, and found that stable DNMT3AMut consistently indicates poor prognosis in the non-HSCT patients (Figure 1B).

Although most evidences supported that DNMT3AMut was a biomarker for poor prognosis in AML, the role of DNMT3AMut in AML has not been widely accepted. In our cohort, DNMT3AMut patients exhibited a shorter OS, RFS, and DFS in the entire cohort, consistent with results from most other reported cohorts. Most stable DNMT3AMut variants are distributed across the methyltransferase domain, which is known to act as a dominant negative, resulting in decreased DNA methylation activity and a poor prognosis. HSCT eliminates leukemia cells, improves the hematopoietic function of the bone marrow, and enhances patient prognosis. A study has shown that HSCT improves outcomes of DNMT3AMut AML patients [5], and our findings also indicate that HSCT reduces the prognostic gap between DNMT3AMut and DNMT3AWT patients. In contrast, the prognostic disparity existed between DNMT3AMut and DNMT3AWT patients in the non-HSCT group. DNMT3AR882, the hotpot mutation in DNMT3AMut variants, was recognized as the primary factor contributing to the poor prognosis associated with DNMT3AMut [1]. Nearly 50% of DNMT3AMut variants are attributed to DNMT3AR882, but significant heterogeneity also exits in DNMT3Anon-R882 [1]. A recent study indicated that certain missense DNMT3Anon-R882 variants also shared similar protein stability and methyltransferase activity features with DNMT3AR882, thus, classifying DNMT3AMut into DNMT3AR882 and DNMT3Anon-R882 was not accurate [4]. Herein, we classified DNMT3AMut as stable or instable DNMT3AMut according to mutant stability, and found that stable DNMT3AMut, rather than instable DNMT3AMut, was an independent predictor for relatively shorter OS, RFS and DFS. Furthermore, stable DNMT3AMut primarily worsened the prognosis for AML genetic subtypes, indicating stable DNMT3AMut was a dominant contributor for poor prognosis in the non-HSCT patients. In the future, we aim to demonstrate the prognostic value of stable DNMT3AMut through prospective multicenter clinical studies.

Collectively, stable DNMT3AMut, but not instable DNMT3AMut, should be regarded as a strong predictor of poor prognosis in AML patients without receiving HSCT, thereby providing insights into the mechanism underlying AML pathogenesis.

Xiang Zhang designed this study. Xiang Zhang and Xiong Ni collected clinical data and updated follow-up. Xiong Ni and Jin Jie guided clinical managements for patients. Xiang Zhang, Lixia Liu, and Jiayue Qin displayed data analysis. Xiang Zhang and Jiayue Qin wrote the manuscript. Jie Jin provided critical comments on this study. Xiong Ni and Jie Jin revised the manuscript. All authors have read and approved the final manuscript.

This study was approved by the ethical review committees of the First Affiliated Hospital of Zhejiang University School of Medicine (IIT20220659A) and Changhai Hospital (B2022-035). All procedures in studies involving human participants were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments. Written informed consent was obtained from these patients.

The authors declare no conflicts of interest.

Abstract Image

稳定表达的DNMT3A突变体预测未接受造血干细胞移植的急性髓系白血病患者预后不良
DNA甲基转移酶3A (DNMT3A)被广泛认为是一种肿瘤抑制基因。它的缺乏导致造血干细胞(hsc)池扩大,阻碍hsc分化,基因组不稳定,并在克隆造血bb0中存在恶性转化的风险。DNMT3A突变(DNMT3AMut)在成人中普遍存在,特别是在单核细胞和细胞遗传学正常的病例中,影响25%的急性髓性白血病(AML)患者。尽管DNMT3AMuts在AML中的分布模式已被很好地表征,但其预后意义仍存在争议。为了更好地了解DNMT3AMut预后异质性背后的原因,我们进行了一项回顾性研究,详见之前的两项研究[2,3]。我们的研究结果表明,具有稳定表达突变的DNMT3AMuts与未接受造血干细胞移植(HSCT)的AML患者预后不良相关。在这项研究中,我们招募了485名成年新发AML患者,其中98名(20.2%)被发现有DNMT3AMuts。我们的研究结果表明,DNMT3AMuts的分布模式和具有这些突变的AML患者的临床特征与之前的报道相似。在我们的队列中,DNMT3AMuts患者的总生存期(OS)、无复发生存期(RFS)和无病生存期(DFS)相对较短(图1A)。我们分析了采用或不采用HSCT的不同治疗策略如何影响DNMT3AMut患者不同亚组的预后。在HSCT组中,DNMT3AMut患者表现出与DNMT3A野生型(DNMT3AWT)患者相当的OS、RFS和DFS(图1A)。相反,DNMT3AMut患者在非hsct组中表现出较差的OS、RFS和DFS,与整体队列相比,非hsct组中DNMT3AMut和DNMT3AWT患者之间的差异更为明显(图1A)。因此,HSCT克服了DNMT3AMut的不良预后,表明未接受HSCT的DNMT3AMut患者是造成不良结果的主要原因。为了进一步探讨DNMT3AMut患者预后不良的关键因素,我们将重点放在非hsct组的DNMT3AMut类型上。AML中近50%的DNMT3AMut变异是杂合子DNMT3AR882,即热点突变[1]。据报道,DNMT3AR882通过形成二聚体对DNMT3AWT起显性负向作用,导致全基因组低甲基化[1]。然而,对dnmt3non - r882的研究却很少。最近,Yung-Hsin Huang等系统地研究了253个疾病相关DNMT3AMut变异的蛋白质稳定性及其甲基转移酶活性,并对各种DNMT3AMut变异[4]的影响提供了全面的看法。在我们的队列中,我们确定了112个DNMT3AMut变体。我们首先将DNMT3AMut患者分为两组:DNMT3AR882和DNMT3Anon-R882。除OS外,DNMT3AR882和DNMT3Anon-R882患者的RFS和DFS均较DNMT3AWT患者差,表明DNMT3AR882和DNMT3Anon-R882不能有效区分预后结果(图1A)。准确地说,近一半的DNMT3Anon-R882变异是错义突变,与DNMT3AR882表现出相似的突变稳定性和甲基转移酶活性降低,因此我们进一步将DNMT3AMut患者分为稳定组和不稳定组(补充材料和方法)。值得注意的是,与DNMT3AWT患者相比,稳定而非不稳定的DNMT3AMut患者表现出更差的OS、RFS和DFS(图1A)。此外,在比较稳定的DNMT3AMut和不稳定的DNMT3AMut患者时,我们观察到类似的结果(不稳定的DNMT3AMut包括不稳定的DNMT3AMut和DNMT3AWT)(图1A)。因此,稳定的DNMT3AMut是导致非hsct组DNMT3AMut患者预后不良的原因。由于稳定的DNMT3AMut是AML患者的一个重要预后因素,我们有兴趣探索稳定和不稳定的DNMT3AMut在分布模式和临床特征上是否存在差异。在我们的队列中,我们在51例患者中发现了稳定的DNMT3AMut,在25例患者中发现了不稳定的DNMT3AMut,而22例DNMT3AMut未定义。除了稳定型DNMT3AMut患者NPM1突变频率相对较高外,稳定型DNMT3AMut的分布模式与不稳定型DNMT3AMut相似。此外,与412例非稳定型DNMT3AMut患者相比,51例稳定型DNMT3AMut患者的分布模式与DNMT3AWT患者相似。显然,稳定的DNMT3AMut并没有表现出特定的分布模式。我们还比较了稳定的DNMT3AMut与不稳定的DNMT3AMut或不稳定的DNMT3AMut患者的基线临床特征。而稳定型DNMT3AMut患者没有明显的临床特征。 虽然没有显著的统计学差异,但无论是否考虑所有患者或非hsct患者,稳定型DNMT3AMut组与不稳定型DNMT3AMut组相比,完全缓解率相对较低,复发率较高,这可能是稳定型DNMT3AMut患者预后较差的原因。为了确定稳定型DNMT3AMut是否是非hsct患者预后不良的独立危险因素,我们将患者分为稳定型DNMT3AMut组和不稳定型DNMT3AMut组,并分别对OS、RFS和DFS进行单因素分析。值得注意的是,稳定的DNMT3AMut对OS、RFS和DFS均有不利影响。在此之后,我们进行了多变量分析,证实稳定的DNMT3AMut是非hsct AML患者OS、RFS或DFS的独立不良危险因素(表S1)。鉴于其强大的预后作用,我们试图在外部队列中验证稳定的DNMT3AMut的临床意义。在此,我们分析了来自The Cancer Genome Atlas TCGA (TCGA)数据库的数据,发现稳定的DNMT3AMut一致表明非hsct患者预后较差(图1B)。虽然大多数证据支持DNMT3AMut是AML预后不良的生物标志物,但DNMT3AMut在AML中的作用尚未被广泛接受。在我们的队列中,DNMT3AMut患者在整个队列中表现出较短的OS、RFS和DFS,与大多数其他报道的队列结果一致。大多数稳定的DNMT3AMut变异分布在甲基转移酶结构域,已知其作为显性阴性,导致DNA甲基化活性降低和预后不良。造血干细胞移植消除白血病细胞,改善骨髓造血功能,改善患者预后。一项研究表明,HSCT改善了DNMT3AMut AML患者的预后,我们的研究结果还表明,HSCT缩小了DNMT3AMut和DNMT3AWT患者之间的预后差距。相比之下,在非hsct组中,DNMT3AMut和DNMT3AWT患者的预后存在差异。DNMT3AMut变异体中的“火锅突变”DNMT3AR882被认为是导致DNMT3AMut bb0相关不良预后的主要因素。近50%的DNMT3AMut变异归因于DNMT3AR882,但dnmt3non - r882 bbb中也存在显著的异质性。最近的一项研究表明,某些错义DNMT3Anon-R882变体也与DNMT3AR882具有相似的蛋白质稳定性和甲基转移酶活性特征,因此将DNMT3AMut分为DNMT3AR882和DNMT3Anon-R882并不准确[1]。本文根据突变体稳定性将DNMT3AMut分为稳定型和不稳定型DNMT3AMut,发现稳定型DNMT3AMut是相对较短OS、RFS和DFS的独立预测因子,而非不稳定型DNMT3AMut。此外,稳定的DNMT3AMut主要恶化AML遗传亚型的预后,表明稳定的DNMT3AMut是非hsct患者预后不良的主要因素。在未来,我们的目标是通过前瞻性多中心临床研究来证明稳定的DNMT3AMut的预后价值。总的来说,稳定的DNMT3AMut,而不是不稳定的DNMT3AMut,应该被视为未接受HSCT的AML患者预后不良的一个强有力的预测因素,从而为AML发病机制提供了新的见解。张翔设计了这项研究。张翔、倪雄收集临床资料并更新随访。熊妮、金杰指导患者的临床管理。张翔、刘丽霞和秦嘉月展示了数据分析。张翔和秦嘉月撰写了手稿。金杰对这项研究提出了批评意见。熊妮和金杰对原稿进行了修改。所有作者都阅读并批准了最终稿件。本研究经浙江大学医学院第一附属医院(编号:IIT20220659A)和长海医院(编号:B2022-035)伦理审查委员会批准。涉及人类参与者的所有研究程序均按照机构研究委员会的道德标准和1964年《赫尔辛基宣言》及其后来的修正案进行。获得了这些患者的书面知情同意。作者声明无利益冲突。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.70
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