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Article

Acute Myeloid Leukemia with Normal Cytogenetics and NPM1-Mutation: Impact of Mutation Topography on Outcomes

by
Mingyue Zhao
1,†,
Mingyue Liao
1,†,
Robert Peter Gale
1,2,
Meijie Zhang
3,
Lixin Wu
1,
Nan Yan
1,
Lixia Liu
4,
Jiayue Qin
4,
Shanbo Cao
4,
Yingjun Chang
1,
Qian Jiang
1,
Lanping Xu
1,
Xiaohui Zhang
1,
Xiaojun Huang
1,5,
Hao Jiang
1,* and
Guorui Ruan
1,*
1
Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, National Clinical Research Center for Hematologic Disease, Peking University Institute of Hematology, Peking University People’s Hospital, Beijing 100044, China
2
Centre for Haematology, Department of Immunology and Inflammation, Imperial College of Science, Technology and Medicine, London SW7 2AZ, UK
3
Division of Biostatistics, Medical College of Wisconsin, IBMTR/ABMTR, Milwaukee, WI 53226, USA
4
Acornmed Biotechnology Co., Ltd., Floor 18, Block 5, Yard 18, Kechuang 13 RD, Beijing 100176, China
5
Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100091, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biomedicines 2024, 12(12), 2921; https://doi.org/10.3390/biomedicines12122921
Submission received: 23 November 2024 / Revised: 19 December 2024 / Accepted: 21 December 2024 / Published: 23 December 2024
(This article belongs to the Section Molecular Genetics and Genetic Diseases)
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Abstract

:
Background: About half of adults with acute myeloid leukemia with normal cytogenetics (CN-AML) have NPM1 mutations. There is controversy regarding their prognosis and best therapy. Methods: We studied 150 subjects with these features using targeted regional sequencing. Prognostic stratification was carried out based on risk factors, and we assessed the effects of two post-remission strategies with and without transplant across risk cohorts. Results: In multi-variable analyses, a positive MRD test after the second consolidation cycle (HR = 6.00; 95% CI [3.31, 10.85]; p < 0.001), DNMT3A mutations (HR = 3.01 [1.57, 5.78]; p < 0.001), FLT3-ITD mutation with high variant allele frequency (HR = 4.40 [1.89, 10.24]; p < 0.001) and DDX11 mutations (HR = 4.38 [2.38, 8.04]; p < 0.001) were independently correlated with higher cumulative incidence of relapse (CIR) and worse leukemia-free survival (LFS) (HR = 5.49 [3.01, 10.04]; p < 0.001; HR = 2.99 [1.60, 5.62]; p < 0.001; HR = 4.20 [1.87, 9.40]; p < 0.001; and HR = 4.22, 95% CI [1.99, 8.95], p < 0.001). Subjects with ≥1 high-risk co-variate who received a transplant had a lower CIR and better LFS, whereas others did not. Conclusions: We identified co-variates associated with CIR and LFS in subjects of NPM1-mutated CN-AML.

1. Introduction

Mutations in nucleophosmin 1 (NPM1), FMS-like tyrosine kinase 3 (FLT3) and CCAAT/enhancer-binding protein α (CEBPA) contribute to risk stratification of cytogenetically normal acute myeloid leukemia (CN-AML) [1,2,3]. NPM1 mutation is reported in 25–35% of adults with AML and an even higher percentage of those with normal cytogenetics, 45–64% [4]. AML with NPM1 mutation was first recognized as a distinct entity in the 2008 World Health Organization (WHO) classification system and carried forth in subsequent editions [5].
The distinguishing feature of NPM1 mutation is aberrant cytoplasmic localization [6]. The NPM1 mutation appears to be a late event in leukemia development and is usually associated with co-mutations such as DNA methyltransferase 3 alpha (DNMT3A), Fms-like tyrosine kinase 3 internal tandem duplication (FLT3-ITD) and isocitrate dehydrogenase (NADP(+)1/2) (IDH1/2), which also affect prognosis [2,3].
People with NPM1/NRAS proto-oncogene GTPase (NRAS), NPM1/RAD21 cohesin complex component (RAD21), or NPM1/FLT3-tyrosine kinase point mutation (FLT3-TKD) genotypes have relatively good outcomes, whereas those with NPM1/WT1 transcription factor (WT1)-mutated or NPM1/FLT3-ITD/DNMT3A-mutated genotypes have adverse outcomes [3,7,8,9].
These complexities make it important to consider co-mutations in subjects with NPM1 mutations to predict outcomes. Our aim was to utilize high-depth targeted regional sequencing (TRS) technology to detect more myeloid-cancer-related mutations, combined with clinical data, to estimate the prognosis of adults with CN-AML harboring NPM1 mutations.

2. Materials and Methods

2.1. Subjects and Treatment Protocols

A total of 1004 consecutive newly diagnosed, untreated persons with AML were studied from January 2012 to December 2019 at Peking University People’s Hospital (China). AML was diagnosed using the WHO 2022 criteria [5]. Cytogenetic studies were carried out using standard techniques. Targeted regional sequencing was carried out on a cohort of 150 consecutive subjects with NPM1-mutated CN-AML who presented the following characteristics: (1) age > 15 years; and (2) achieved a histological complete remission with or without normalization of blood cell concentrations (Figure 1).
The protocols for induction and consolidation therapies were reported [10,11,12]. After the second consolidation cycle, the physicians assessed the risks and benefits of the patient receiving an allotransplant versus continuing with consolidation chemotherapy. Allogeneic hematopoietic stem cell transplantation (allo-HSCT) was carried out as reported [10,11,12].

2.2. Response Definitions

Histological complete response (CR) was defined as <5 percent bone marrow blasts. Complete hematological recovery was defined as blood neutrophils > 1.0 × 109/L, platelet concentration > 100 × 109/L without platelet transfusions, and no RBC transfusions. Relapse was defined as >5 percent bone marrow blasts, blasts in the blood or extra-medullary leukemia.

2.3. Measurable Residual Disease Testing

Measurable residual disease (MRD) testing was performed using multi-parameter flow cytometry (MPFC) with a panel of eight antibody combinations that recognize CD7, CD11b, CD13, CD14, CD16, CD19, CD33, CD34, CD38, CD41, CD45, CD56, CD61, CD64, CD71, CD117, CD123, and HLA-DR. After each chemotherapy cycle, bone marrow samples were tested for MRD based on the leukemia-associated immune phenotype (LAIP) identified at diagnosis. An MRD test result ≥ 0.01 percent was scored as positive.

2.4. High-Depth Targeted Regional Sequencing and Analyses

High-depth targeted region sequencing (TRS) analysis was performed on bone marrow samples from subjects, focusing on 236 genes (Supplement Table S1) known or considered to be potential mutation hotspots in hematological cancers. DNA was extracted using DNAzol® kits (Invitrogen, Carlsbad, CA, USA) using the manufacturers’ instructions. Sequencing details were reported [12]. The following criteria were used to filter raw variant results: (1) average effective sequencing depth on target per sample ≥ 1000×; (2) mapping quality ≥ 30; (3) base quality ≥ 30; and (4) variant allele frequency (VAF) ≥ 1% for SNVs and small indels. Burrows–Wheeler alignment (BWA version 0.7.12) was used to align the trimmed reads. The MarkDuplicates tool from Picard was used to mark the PCR duplicates. Indel Realigner and Base Recalibrator from Genome Analysis Toolkit (GATK version 3.8) were applied for re-alignment and re-calibration of the BWA data. Variant calling, including SNVs and small indels, was carried out in Mutect2. ANNOVAR software (V2020) was used to annotate all the variants including 1000 G projects, COSMIC, SIFT and PolyPhen. Typical and atypical NPM1 mutations (type A/B/D) were detected by real-time quantitative polymerase chain reaction (RT-qPCR) and first-generation sequencing [13]. FLT3-ITD mutations were detected by capillary electrophoresis fragment analyses.

2.5. Endpoints and Statistical Methods

The last follow-up was 31 December 2020. The primary endpoint was cumulative incidence of relapse (CIR) measured from CR. The secondary endpoint was leukemia-free survival (LFS) defined as the survival period with continuous CR from CR1. Subjects receiving a transplant were censored at the time of transplantation when evaluating co-variates for CIR and LFS.
Categorical variables were expressed in percentages and analyzed by Pearson’s Chi-square test. Normally distributed continuous variables were described as mean (standard deviation) values and analyzed by independent-sample t-test. Non-normally distributed continuous variables were described as median (range) values and analyzed by the Mann–Whitney U test. Homogeneity of variances between groups was assessed using the Levene test. CIR was calculated using competing risks with non-relapse mortality (NRM), and the Fine–Gray test was used for univariate analysis. The LFS rate was calculated using the Kaplan–Meier method with the log-rank test. Competing risks model and multi-variate Cox regression analyses were performed for the co-variates selected by the univariate analyses with a criterion of p < 0.10. The variance inflation factor (VIF) was estimated to check for multi-collinearity amongst co-variates included in the multi-variate analyses. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the mutational genes was carried out by using the R ClusterProfiler package. Analyses were performed using SPSS software version 25.0 (Chicago, IL, USA) and R software version 4.1.0. (http://www.Rproject.org). p < 0.05 was considered significant.

2.6. Trial Registration

This trial is registered at Clinicaltrials.gov (NCT01455272, NCT02185261) and in chictr.org (ChiCTR-OCH-10000940).

3. Results

3.1. Subject Baseline Co-Variates and CIR or LFS

Subject co-variates are displayed in Table 1. For post-remission therapy, 99 did not received a transplant and 51 received a transplant. The transplant cohort was younger, with medians of 33 years (Interquartile Range, IQR, 18 years) versus 53 years (IQR, 19 years; p < 0.001). Other co-variates were similar. Five-year CIR and LFS in the no-transplant cohort were 60% and 36%, compared to 28% and 60% (HR 0.44, 95% CI [0.24, 0.82], p = 0.01; HR 0.59, 95% CI [0.35, 1.01], p = 0.05) in the transplant cohort. (Supplement Figure S1).

3.2. Genomic Analyses and Mutation Topography

One subject had a mis-sense and two in-frame insertion mutations. Eight had >1 type of NPM1 mutation referred to multi-hit (Figure 2). A total of 771 other mutations (not all 236 genes in the panel were found to have mutations) were detected, with a median of five (Range: 1–12) per subject, and >5 mutations were detected in 56 subjects. Mis-sense mutations were the most common (N = 564) followed by frame-shift insertions (N = 182) and in-frame insertions (N = 64). Mutations in ≥10 subjects were divided into eight subgroups: (1) signaling pathway (FLT3, FLT3-ITD, PTPN11, NARS, KRAS, LILRB3, MACF1 and NF1); (2) DNA methylation (DNMT3A, TET2, IDH1 and IDH2); (3) histone methylation (KMT2A and KMT2D); (4) cohesion complex (DDX11 and RAD21); (5) chromatin modifiers (SRCAP); (6) transcription factors (GATA2); (7) tumor suppressors (WT1); (8) and others (CCDC168, PCLO, DNAH2).
DNMT3A (N = 74) was the most frequently mutated gene followed by FLT3-ITD (N = 52, 34.67%), FLT3 (FLT3 mutation types other than FLT3-ITD, N = 51, 34%), and IDH2 (N = 44, 29.33%). Median variable allele frequency (VAF) was 0.43 (Range, 0.01–1.00). The VAFs of mutations in the DNA methylation pathways and histone methylation were significantly higher compared with those of mutations in the activated signaling pathways (0.43 [0.01, 0.92] versus 0.17 [0.01, 0.78]; p < 0.001; 0.44 [0.01, 0.60] versus 0.17 [0.01, 0.78], p < 0.01; Supplement Figure S2).
We identified 10 pairs of co-occurring genes and 5 pairs of mutually exclusive mutations, which were statistically significant. RAD21, NRAS, and PTPN11 were co-mutations, whereas IDH2 and TET2 mutations, and PTPN11 and FLT3-ITD mutations were mutually exclusive mutations (Supplement Figure S3A). Using KEGG pathway enrichment analysis, we found that co-mutations in CN-AML with NPM1 mutations were related to cancer and metabolism (Supplement Table S2, Supplement Figure S3B). Associations between mutations and subject-related co-variates at diagnosis are shown in Table 2. DNMT3A mutations were associated with increased age and high WBC and platelet concentrations at diagnosis. FLT3-ITD mutations were associated with a higher percentage of bone marrow blasts.

3.3. Prognostic Co-Variates

Clinical variables and gene mutations occurring in ≥10 patients were included in the univariate analyses and six variables with p < 0.10 were eligible for subsequent analyses (Supplement Table S3). Considering the interaction between MRD test results after the first and second consolidation cycles and referring to previous reports [14], we used a backward elimination process and ultimately included the MRD test results after the second consolidation cycle as a co-variate.
Eventually, four co-variates were significantly correlated with CIR in multi-variable analyses: (1) positive MRD test after the second consolidation cycle (HR = 6.00 [3.31, 10.85]; p < 0.001), (2) DNMT3A mutation (HR = 3.01 [1.57, 5.78], p < 0.001); (3) FLT3-ITD mutation with high VAF (≥0.5) (HR = 4.40 [1.89, 10.24]; p < 0.001) and (4) DEAD/H-box helicase 11 (DDX11) mutation (HR = 4.38 [2.38, 8.04]; p < 0.001) and LFS (HR = 5.49 [3.01, 10.04]; p < 0.001; HR = 2.99 [1.60, 5.62]; p < 0.001; HR = 4.20 [1.87, 9.40]; p < 0.001; and HR = 4.22, 95% CI [1.99, 8.95], p < 0.001). (Table 3). The VIFs of the co-variates included in the multi-variate analyses were below five, suggesting no significant multi-collinearity among the co-variates.
For the newly discovered DDX11 mutations, we found that, compared to wild-type subjects, those with the DDX11 mutations had a significantly higher CIR and a notably shorter LFS (Supplement Figure S4).

3.4. Impact of Transplants

We assigned subjects with ≥1 adverse risk co-variates to a high-risk cohort (N = 104) and others to a low-risk cohort (N = 46) with corresponding 5-year CIRs of 73% [58, 88%] and 37% [16, 57%]; (HR = 3.85 [1.98, 7.50]; p < 0.001) and 5-year LFSs of 24% [14, 43%] and 58% [41, 84%] (HR = 3.77 [1.90, 7.48]; p < 0.001) (Figure 3).
Transplant recipients in the high-risk cohort (N = 37) had a lower 5-year CIR, 33% [16, 49%] versus 74% [60, 89%] (HR = 0.37 [0.19, 0.74]; p < 0.01), and a better 5-year LFS, 58% [43, 78%] versus 23% [13, 41%] (HR = 0.45 [0.25, 0.83]; p = 0.01), compared with those in the no-transplant cohort (N = 67). In the low-risk cohort, 14 subjects received a transplant and 32 did not receive a transplant. Five-year CIRs were 17% [0, 40%] versus 32% [11, 54%] (HR = 0.63 [0.13, 3.05]; p = 0.57), and five-year LFSs were 66% [44, 100%] versus 62% [44, 88%] (HR = 1.22 [0.37, 4.06]; p = 0.75) (Figure 4).

4. Discussion

We found that in subjects with CN-AML and NPM1 mutation, those with ≥1 high-risk co-variate, including positive MRD test after the second consolidation cycle, DNMT3A mutation, FLT3-ITD mutation with high VAF and DDX11 mutation, had a higher CIR and worse LFS compared to subjects without these co-variates. We also found a lower CIR and better LFS in subjects in the high-risk cohort who received a transplant, which was not observed in the low-risk cohort.
Compared to the recent study published by Yao et al. [15], our research revealed that the mutation landscape of NPM1 mutations in CN-AML subjects was similar to their findings. We also identified DNMT3A and FLT3-ITD mutations as poor prognostic factors. However, their study did not emphasize that the subjects were cytogenetically normal AML patients. Furthermore, we discovered a novel DDX11 mutation that adversely affects prognosis. In addition, we considered the impact of MRD as a clinical factor on prognosis.
DNMT3A, a protein responsible for DNA methylation, is mutated in approximately 20% of all AML cases. It commonly co-mutates with NPM1 in 60% of cases, and in about 30% of cases, both NPM1 and FLT3 mutations are present [16,17,18,19,20]. Compared to wild-type DNMT3A subjects, those with DNMT3A mutations tend to be older, have higher WBC concentration, and more frequently have normal karyotype at diagnosis [16,18,19,21,22]. Although several studies reported that DNMT3A mutations were independently associated with poor outcomes in AML, especially in CN-AML [18,21,23,24], the impact of DNMT3A mutations in clinical decision-making remains debatable [19,20,25]. In our study, DNMT3A mutations were the most frequently co-occurring event in NPM1-mutated CN-AML subjects (49.33%). These subjects were older and had a higher WBC concentration, which was consistent with previous studies [16,17,18,19,21,22]. We further identified DNMT3A mutations as one of the poor prognostic factors, and subjects harboring DNMT3A mutations were classified into high-risk cohort, for whom allo-HSCT could improve survival.
FLT3-ITD has consistently been associated with high WBC and BM blast concentration, an increased risk for relapse, and inferior survival [26,27,28]. In the 2022 ELN guidelines [29], the FLT3-ITD allelic ratio is no longer considered in risk classification. This change reflects the impact of FLT3 inhibitor use [30] on the natural course of AML with FLT3-ITD mutations and the increasing importance of MRD in guiding treatment decisions [29]. In our study, subjects with FLT3-ITD mutations with high VAF were classified into the high-risk cohort and may benefit from allo-HSCT; this may be related to our data, where among the 19 subjects with high VAF of FLT3-ITD mutations, 12 were found to have concurrent DNMT3A mutations. Several studies have reported NPM1/FLT3-ITD/DNMT3A, the most common triple mutation pattern in NPM1-mutated patients, with extremely poor prognosis [9,15,16,17,18], and allo-HSCT has been shown to significantly improve the survival of this subgroup [15].
We further identified that DDX11 mutations had prognostic significance in our study. DDX11 encodes an iron–sulfur cluster DNA helicase required for development, mutation and over-expression in cancers [31]. DDX11 has been shown to be associated with the progression of multiple cancers, including melanoma [32], lung adenocarcinoma [33], and hepatocellular carcinoma [34]. In multivariate analyses conducted in a CIBMTR study, it was revealed that DDX11 mutations affect survival by increasing the risk of both relapse and transplant-related mortality in patients with myelodysplastic syndrome [35]. Encouragingly, this study further validates the recent findings published by us, which indicated that CN-AML patients with DDX11 mutations have a significantly higher CIR and poorer LFS [36], and DDX11 dysfunctions were linked to AML via promoting cell proliferation [37]. Other than that, DDX11 mutations have not been reported in NPM1-mutated CN-AML, and the mutation frequency was 8% (N = 12) in our study; nine of these subjects relapsed.
In addition to baseline genetic characterization, the 2022 ELN guidelines also emphasize the importance of response to initial therapy and early assessment of MRD in individual risk assignment [29]. In clinical practice, MRD can serve as an important biomarker for prognosis, prediction, monitoring, and efficacy evaluation [38]. However, MRD-negative patients may still relapse in some cases, while some MRD-positive patients may remain in CR. Additionally, MRD positivity might be more common in genetically adverse AML cases compared to genetically favorable ones, suggesting that MRD is influenced to some extent by genetic factors [39]. Therefore, integrating MRD monitoring results with additional genetic data during the treatment process for risk stratification may further enhance prognostic prediction.
Our study has important limitations. First, it was retrospective study. Second, the subjects received diverse therapies and were not randomly assigned to receive a transplant. Third, our censoring of transplant recipients was imperfect. Fourth, there were few subjects with DDX11 gene mutations. Given these limitations, our conclusions need validation. Our data, if validated, could assist physicians in predicting the prognostic risk stratification for subjects with CN-AML and NPM1 mutations, and could potentially be incorporated into clinical decision-making.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedicines12122921/s1, Figure S1: Cumulative incidence of relapse (A) and leukemia-free survival (B) of post-remission therapies; Figure S2: Variant allele frequencies (VAFs) of different driver gene mutations; Figure S3: Genomic analysis. (A) Pair-wise association between genes mutated in ≥10 subjects. (B) KEGG enrichment analysis; Figure S4: Cumulative incidence of relapse (A) and leukemia- free survival (B) in subjects with DDX11 mutations versus wild type; Table S1: The 236-gene panel for TRS; Table S2: KEGG pathway enrichment analysis; Table S3: Univariate analyses of CIR and LFS.

Author Contributions

G.R., H.J. and R.P.G. designed the project and revised the typescript. M.Z. (Mingyue Zhao) and M.L. contributed to the data analyses and wrote the typescript. M.Z. (Meijie Zhang) provided statistical guidance. L.W., N.Y., L.L., J.Q. and S.C. collected the clinical data and performed the experiments. Y.C., Q.J., L.X., X.Z. and X.H. provided clinical data. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported, in part, by grants from the National Natural Science Foundation of China [Grant 81770156], the National Natural Science Foundation of China [Grant 81930004], the Major Program of the National Natural Science Foundation of China [Grant 82293630], the National Key Research and Development Program of China [No. 2022YFA1103300] and the Beijing Municipal Natural Science Foundation [Grant 7192213].

Institutional Review Board Statement

This study involving human participants was reviewed and approved by the Ethics Committee of Peking University People′s Hospital (protocol code: 2023PHB028-001, approval date: 17 January 2023).

Informed Consent Statement

Written informed consent to participate in this study was provided by the participants′ legal guardian/next of kin.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding authors.

Acknowledgments

We thank colleagues at the Institute of Hematology, Peking University, for help obtaining samples. R.P.G. acknowledges support from the UK National Institute of Health Research (NIHR) Biomedical Research Centre.

Conflicts of Interest

R.P.G. is a consultant to Antengene Biotech LLC; Medical Director, FFF Enterprises Inc.; A speaker for Janssen Pharma and Hengrui Pharma; Board of Directors: Russian Foundation for Cancer Research Support and Scientific Advisory Board, StemRad Ltd. L.L., J.Q. and S.C. are employed by the company Acornmed Biotechnology Co., Ltd. The Acornmed Biotechnology Co., Ltd. had no role in the design of the study, interpretation of data, in the writing of the manuscript, and in the decision to publish the results, they have no conflicts of interest.

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Biomedicines 12 02921 g001
Figure 1. Subject recruitment and cohort assignment. AML, acute myeloid leukemia; CN, cytogenetically normal; CR, complete response.
Figure 1. Subject recruitment and cohort assignment. AML, acute myeloid leukemia; CN, cytogenetically normal; CR, complete response.
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Figure 2. Genomic landscape. Genes mutated in ≥10 subjects are shown. Each column indicates data of one sample; each row represents a gene. Mutated genes were color-coded for mis-sense (green), non-sense (blue), in-frame ins (red), splice-site (orange), frameshift del (pink), in-frame del (yellow), frame-shift ins (purple) and multi-hit (black). The top bar indicates mutation load (mutation/Mb DNA), and the right bar indicates mutation frequency.
Figure 2. Genomic landscape. Genes mutated in ≥10 subjects are shown. Each column indicates data of one sample; each row represents a gene. Mutated genes were color-coded for mis-sense (green), non-sense (blue), in-frame ins (red), splice-site (orange), frameshift del (pink), in-frame del (yellow), frame-shift ins (purple) and multi-hit (black). The top bar indicates mutation load (mutation/Mb DNA), and the right bar indicates mutation frequency.
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Figure 3. Cumulative incidence of relapse (A) and leukemia-free survival (B) in the high-risk and low-risk cohort.
Figure 3. Cumulative incidence of relapse (A) and leukemia-free survival (B) in the high-risk and low-risk cohort.
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Figure 4. Cumulative incidence of relapse and leukemia-free survival in the transplant and no-transplant subjects of high-risk cohort (A,B) and low-risk cohort (C,D).
Figure 4. Cumulative incidence of relapse and leukemia-free survival in the transplant and no-transplant subjects of high-risk cohort (A,B) and low-risk cohort (C,D).
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Table 1. Subject co-variates and post-remission therapy.
Table 1. Subject co-variates and post-remission therapy.
No Transplant (N = 99)Transplant (N = 51)p-Value
Gender (male/female)48/5130/210.30
Age (median, range)53 (16–76)33 (17–61)<0.001
WBC (×109/L) (median, range)29.8 (0.3–224.4)35.1 (0.9–266.0)0.69
Hemoglobin (g/L) (mean, SD)87 (22)88 (25)0.86
Platelets (×109/L) (median, range)68 (6–806)59 (10–535)0.61
Bone marrow blasts (%) (median, range)72 (26–99)70 (21–96)0.31
CR after first induction (n, %)78 (79%)44 (86%)0.28
MRD-positive (n, %)50 (51%)23 (45%)0.61
Abbreviations: CR, complete remission; MRD, measurable residual disease.
Table 2. Associations of mutations with subject-related variables.
Table 2. Associations of mutations with subject-related variables.
VariableDNMT3AFLT3-ITDIDH2PTPN11
WT
(N = 76)		MUT
(N = 74)		WT
(N = 98)		MUT
(N = 52)		WT
(N = 106)		MUT
(N = 44)		WT
(N = 121)		MUT
(N = 29)
Gender, male/female35/4143/3148/5030/2253/5325/1962/5916/13
Age (median, range)41 (17–73)50 (22–76) *49 (17–76)47 (16–68)48 (16–76)48 (17–65)47 (16–76)48 (17–67)
WBC (×109/L) (median, range)21 (0–228)37 (2–226) *29 (0–266)38 (1–185)32 (1–266)25 (0–192)31 (0–266)30 (3–224)
Hemoglobin (g/L) (mean, SD)85 (24)91(23)85 (23)92 (24)86 (24)91 (22)88 (24)86 (19)
Platelets (×109/L) (median, range)48 (6–267)76(11–806) *69 (6–535)56(10–806)59 (6–806)78 (7–535)64 (6–806)73 (9–211)
Bone marrow blasts (%) (median, range)70 (23–96)72 (21–99)65 (21–99)76 (27–96) *70 (21–94)73 (31–99)71 (24–99)57 (21–91)
WT, wild type; MUT, mutation; SD, standard deviation. * p < 0.05 when compared with wild type.
Table 3. Multivariate analyses of CIR and LFS.
Table 3. Multivariate analyses of CIR and LFS.
VariablesMultivariate Analysis of CIRMultivariate Analysis of LFS
HR (95% CI)p-ValueHR (95% CI)p-Value
MRD test (+/−)6.00 (3.31–10.85)<0.0015.49 (3.01–10.04)<0.001
DNMT3A (Mutated vs. WT)3.01 (1.57–5.78)<0.0012.99 (1.60–5.62)<0.001
FLT3-ITD (Mutated with VAF ≥ 0.5 vs. WT or mutated with VAF < 0.5)4.40 (1.89–10.24)<0.0014.20 (1.87–9.40)<0.001
DDX11 (Mutated vs. WT)4.38 (2.38–8.04)<0.0014.22 (1.99–8.95)<0.001
MRD, measurable residual disease; WT, wild type; VAF, variant allele frequency; CIR, cumulative incidence of relapse; HR, hazard ratio; CI, confidence interval; LFS, leukemia-free survival.
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Zhao, M.; Liao, M.; Gale, R.P.; Zhang, M.; Wu, L.; Yan, N.; Liu, L.; Qin, J.; Cao, S.; Chang, Y.; et al. Acute Myeloid Leukemia with Normal Cytogenetics and NPM1-Mutation: Impact of Mutation Topography on Outcomes. Biomedicines 2024, 12, 2921. https://doi.org/10.3390/biomedicines12122921

AMA Style

Zhao M, Liao M, Gale RP, Zhang M, Wu L, Yan N, Liu L, Qin J, Cao S, Chang Y, et al. Acute Myeloid Leukemia with Normal Cytogenetics and NPM1-Mutation: Impact of Mutation Topography on Outcomes. Biomedicines. 2024; 12(12):2921. https://doi.org/10.3390/biomedicines12122921

Chicago/Turabian Style

Zhao, Mingyue, Mingyue Liao, Robert Peter Gale, Meijie Zhang, Lixin Wu, Nan Yan, Lixia Liu, Jiayue Qin, Shanbo Cao, Yingjun Chang, and et al. 2024. "Acute Myeloid Leukemia with Normal Cytogenetics and NPM1-Mutation: Impact of Mutation Topography on Outcomes" Biomedicines 12, no. 12: 2921. https://doi.org/10.3390/biomedicines12122921

APA Style

Zhao, M., Liao, M., Gale, R. P., Zhang, M., Wu, L., Yan, N., Liu, L., Qin, J., Cao, S., Chang, Y., Jiang, Q., Xu, L., Zhang, X., Huang, X., Jiang, H., & Ruan, G. (2024). Acute Myeloid Leukemia with Normal Cytogenetics and NPM1-Mutation: Impact of Mutation Topography on Outcomes. Biomedicines, 12(12), 2921. https://doi.org/10.3390/biomedicines12122921

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