Frequency and Impact of Somatic Co-occurring Mutations on Post-Transplant Outcomes in Acute Myeloid Leukemia: A Multicenter Registry Analysis on Behalf of the EBMT ALWP

Frequency and Impact of Somatic Co-occurring Mutations on Post-Transplant Outcomes in Acute Myeloid Leukemia: A Multicenter Registry Analysis on Behalf of the EBMT ALWP | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Frequency and Impact of Somatic Co-occurring Mutations on Post-Transplant Outcomes in Acute Myeloid Leukemia: A Multicenter Registry Analysis on Behalf of the EBMT ALWP Ali Bazarbachi, Jacques-Emmanuel Galimard, iman abou dalle, Myriam Labopin, and 24 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7375987/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Bone Marrow Transplantation → Version 1 posted 11 You are reading this latest preprint version Abstract Acute myeloid leukemia (AML) includes genetically defined subsets. In allogeneic hematopoietic cell transplantation (allo-HCT) setting, the frequency and prognosis of gene-gene interactions may differ from those of patients treated with chemotherapy alone. In this study, adult patients (N = 952) with AML allografted between 2015–2023, with available next generation sequencing (NGS) at diagnosis were included. The most frequent mutations were DNMT3A (24%), FLT3- ITD (21%), NPM1 (21%), RUNX1 (16%), NRAS (16%), TET2 (14%) , and IDH2 (12%). Multiple correspondence analysis identified distinct groups of co-occurring mutations. Outcome analysis was performed on 646 AML patients allografted in first complete remission (CR1). Six non-overlapping groups were constructed according to found groups and well-known clinical impact of NPM1 and TP53 mutations: 1) TP53 mutation; 2) NPM1 mutation; 3) FLT3 -ITD and/or DNMT3A mutation; 4) SRSF2 and/or ASXL1 and/or RUNX1 mutation (SAR group); 5) IDH1 and/or IDH2 and/or TET2 mutation; and 6) all ten genes unmutated. In multivariable analysis, TP53 mutation, adverse karyotype, and age negatively affected leukemia-free survival (LFS) and overall survival (OS). OS was additionally negatively affected when the ten genes were unmutated. Notably, outcomes were excellent for SAR mutations (2-year LFS 76%, OS 84%), indicating allo-HCT in CR1 can overcome their adverse risk at diagnosis. Health sciences/Diseases/Haematological diseases/Haematological cancer/Leukaemia/Acute myeloid leukaemia Biological sciences/Genetics/Cancer genomics gene-gene interaction NGS SRSF2 ASXL1 RUNX1 post-transplant prognosis Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy, characterized by a complex genomic landscape and diverse genetic subsets. 1 , 2 Historically, cytogenetic abnormalities represented a major factor in prognosticating, guiding risk classification into favorable, intermediate and adverse categories based on chromosomal aberrations. 3 More recently, whole-genome sequencing unraveled the complexity of the AML genomic landscape, showing multiple infrequently mutated genes and the presence of co-occurring mutations within the same patient. 2 Moreover, multiple competing clones can be frequently encountered, playing an important role in the evolution of the disease. 4 The genomic classification of AML has evolved substantially with the identification of mutations in key regulatory pathways, including transcription factors, epigenetic modifiers, spliceosome machinery, the cohesin complex, and signaling cascades. 2 This has led to the development of more refined risk stratification models from diagnosis, such as the European LeukemiaNet (ELN) 2022 classification, which integrates specific gene aberrations to improve prognostic accuracy. 5 Among the main genetic mutations considered at diagnosis, NPM1 status is risk-dependent on karyotype and FLT3 -ITD co-occurrence, FLT3 -ITD confers an intermediate-risk category, while bZIP in-frame mutated CEBPA is of favorable risk. Adverse-risk mutations encompass ASXL1, BCOR, EZH2, RUNX1, SF3B1, SRSF2, STAG2, U2AF1, ZRSR2 and TP53 mutations. 5 The selection of patients with AML in first complete remission (CR1) for allogeneic hematopoietic cell transplantation (allo-HCT) depends on relapse risk and estimated non-relapse mortality (NRM). Current guidelines recommend allo-HCT in CR1 for those classified as intermediate- or adverse-risk, based on the ELN 2022 classification. 6 Patients with persistent measurable residual disease (MRD) are also offered allo-HCT, irrespective of genetic risk. 5 However, in the context of allo-HCT, the frequency and prognostic value of different gene-gene interactions have not been studied and may differ from that of patients treated with intensive chemotherapy alone. In this study, leveraging data from the European Society for Blood and Marrow Transplantation (EBMT) database, we aimed to analyze the frequency and interaction of different recurrent somatic mutations in AML patients undergoing allo-HCT, with a particular focus on those transplanted in CR1. Understanding these genetic interactions may provide deeper insights into post-transplant disease biology and inform personalized therapeutic strategies. PATIENTS AND METHODS Study Design and Inclusion Criteria This is a retrospective, registry-based, multicenter study utilizing patient data collected and approved by the Acute Leukemia Working Party (ALWP) of the EBMT. The EBMT is a collaborative network comprising more than 600 transplant centers that are required to report all consecutive HCTs and subsequent follow-ups on an annual basis. Routine audits are performed to ensure data accuracy and completeness. Since January 2003, all participating transplant centers have been required to obtain written informed consent from patients prior to data registration with the EBMT, in accordance with the ethical principles outlined in the Declaration of Helsinki (1975). For this analysis, a data collection of the NGS reports at diagnosis was performed for adult patients (≥ 18 years) with a diagnosis of AML who underwent allo-HCT between 2015 and 2023. Included patients had available next-generation sequencing (NGS) data at the time of diagnosis (with a margin of a maximum of 30 days from diagnosis), including a documented panel of sequenced genes from blood or bone marrow samples. Patients with a history of prior autologous HCT were excluded from the study. In addition to NGS data, the following clinical and transplant-related variables were collected: recipient age and disease status at transplant, recipient and donor gender, karyotype, FLT3 -ITD mutation status at diagnosis (regardless of the method of analysis), de novo versus ( vs.) secondary AML, and time from diagnosis to transplant. Transplant-related variables included the year of transplant, Karnofsky performance status score at transplant, HCT-specific comorbidity index, conditioning regimen, graft-versus-host disease (GVHD) prophylaxis, use of post-transplant cyclophosphamide (PTCy), in vivo T cell depletion, donor type, recipient and donor cytomegalovirus status, and stem cell source. Endpoints and definitions Endpoints included leukemia-free survival (LFS), overall survival (OS), non-relapse mortality (NRM), relapse incidence (RI), and acute and chronic GVHD. All outcomes were measured from the time of allo-HCT. LFS was defined as survival without leukemia relapse or progression; patients alive without leukemia relapse or progression were censored at the time of last follow-up. OS was defined as death from any cause. NRM was defined as death without previous leukemia relapse. Conditioning intensity was measured using the transplant conditioning regimen intensity, myeloablative conditioning/reduced intensity conditioning (RIC) classification, and transplant conditioning intensity score. 7 , 8 Myeloablative conditioning was defined as a regimen containing either total body irradiation with a dose greater than 6 Gy, a total dose of oral busulfan greater than 8 mg/kg, or a total dose of intravenous busulfan greater than 6.4 mg/kg. All other regimens were defined as RIC. The diagnosis and grading of acute and chronic GVHD were performed by transplant centers using standard criteria. 9 Statistical Analysis Quantitative variables are described as median, interquartile range (quartiles 1 and 3), and minimum/maximum values, while qualitative variables are reported as absolute numbers and percentages. Comparison between groups has been done using Wilcoxon tests for quantitative variables and chi-square test for qualitative variables or Fisher exact test in case of non-valid chi square test. To quantify the association of genes two-by-two, the Fisher exact test was performed, allowing to provide the exact probability of the observed contingency table under the hypothesis of independence between the two genes. The frequency of each somatic mutation was determined across the entire cohort of transplanted patients, irrespective of disease status at transplant, and reported as a percentage of patients carrying a given mutation among those with available molecular results (≥ 100 patients tested per mutation). To evaluate associations among the 12 most frequently tested or mutated somatic mutations (defined as those with available mutation status in at least 800 patients and a mutation frequency of at least 5%), a multiple correspondence analysis (MCA) was performed. MCA is a statistical technique that allows the simultaneous assessment of relationships between multiple qualitative variables. It transforms the matrix of patients and categorical variables into a numerical format, capturing the greatest variability in the data within the first few dimensions. Each patient is assigned a set of values, with the first two dimensions accounting for the most significant variability. Using these two dimensions, we created a two-dimensional graphical representation of the patients. Each variable modality (e.g., presence or absence of a specific mutation) is represented by its centroid (barycenter), providing a visual summary of their relationships. The final output is a graphical representation of the different modalities across the first two dimensions of the 12 analyzed variables. This multidimensional approach provides a comprehensive overview of how variables are linked. Interpretation is based on spatial proximity: when two modalities appear close to each other, it indicates they are frequently observed together in the same patients or presenting the same panel of gene mutations. Post-transplant outcomes were assessed for the group of patients allografted in CR1. Follow-up was calculated using the reverse Kaplan-Meier method. The probabilities of OS and LFS were assessed using the Kaplan-Meier method, while cumulative incidence functions estimated RI and NRM in a competing-risk setting, where RI and NRM were mutually exclusive events. Acute and chronic GVHD outcomes were also analyzed using cumulative incidence functions, considering death and relapse as competing risks. Univariate comparisons were performed using the log-rank test for OS and LFS, and Gray’s test for cumulative incidences. Multivariable models were performed using the Cox model to evaluate to impact of the groups of mutations adjusting on age at allo-HCT, de novo or secondary AML, ELN 2022 cytogenetic risk, donor type, myeloablative regimen and year of allo-HCT. Estimations from the models were provided as hazard ratio (HR) and their 95% confidence interval. The type-1 error rate was fixed at 0.05. All analyses were performed using R 4.1.1 (R Development Core Team, Vienna, Austria, URL: https://www.R-project.org/ ). RESULTS Frequency of somatic mutations in allografted AML patients We collected data from 952 AML patients who underwent allo-HCT and had NGS performed at diagnosis. Patient characteristics are detailed in Supplementary Table 1. The majority had de novo AML (77%), with a median age of 55 years (range: 18–78), and 49% were male. Karyotype was normal in 416 patients (44%), abnormal in 490 (51%), and either failed or missing for (5%). Based on the ELN 2022 classification, cytogenetic abnormalities were categorized as favorable in 7%, intermediate in 68% (including 47% with diploid karyotype), and adverse in 25% of patients. At transplant, 76% were in CR1, 10% in CR2, and 12% had active disease. The median number of sequenced genes included in the NGS panel was 40 (interquartile range (IQR) 23–48), with 6% of patients having fewer than 20 genes analyzed and 7% having more than 55. The most frequently detected mutations were DNMT3A (24%), FLT3-ITD (21%), NPM1 (21%), RUNX1 (16%), NRAS (16%), TET2 (14%), IDH2 (12%), ASXL1 (11%), IDH1 (10%), SRSF2 (10%), KRAS (8%), WT1 (8%), NF1 (7%), TP53 (7%), PTPN11 (7%), CEBPA (7%) , STAG2 (7%), FLT3-TKD (6%), BCOR (5%) and BCORL1 (5%) (Fig. 1 A). The median number of somatic mutations per patient was 2 (IQR: 1–4), with 8% having no detected mutation and 28% harboring four or more. Similarly, the median number of mutated genes per patient was 2 (IQR: 1–3), with 23% having four or more mutated genes (Fig. 1 B, Supplementary Table 1). Among the 716 patients allografted in CR1, mutation frequencies were relatively consistent, with the most frequently detected being FLT3- ITD (23%), DNMT3A (23%), NPM1 (20%), RUNX1 (17%), TET2 (14%), IDH2 (13%), NRAS (12%), SRSF2 (11%), ASXL1 (10%), IDH1 (10%), KRAS (7%), WT1 (7%), STAG2 (7%), TP53 (7%), CEBPA (6%) , NF1 (6%), PTPN11 (5%), FLT3-TKD (5%), SF3B1 (5%) and PHF6 (5%) (Supplementary Fig. 1). The median number of somatic mutations per patient remained 2 (IQR: 1–4), with 9% having no detected mutation and 26% carrying four or more, while the median number of mutated genes per patient was also 2 (IQR: 1–3), with 21% having four or more (Supplementary Table 2). Association of Frequently Mutated Genes We analyzed the co-occurrence and interactions of the most frequently mutated genes, present in more than 5% of cases and tested in at least 800 patients. The twelve selected genes included NPM1 , FLT3- ITD, DNMT3A , RUNX1 , ASXL1 , SRSF2 , TET2 , IDH1 , IDH2 , KRAS , NRAS and TP53 in 753 patients. The interactions between these genes are illustrated in Fig. 2 , with a waterfall plot (Fig. 2 A) showing the co-occurrence of mutations in individual patients, a circos plot (Fig. 2 B) depicting the frequency of co-occurrence between mutation pairs, and a diagram (Fig. 2 C) displaying positive (co-occurring) and negative (mutually exclusive) associations. NPM1 mutations were positively associated with FLT3 -ITD and DNMT3A (p < 0.0001 each), and, to a lesser extent, with IDH1 (p = 0.007) and IDH2 (p = 0.037), but negatively associated with RUNX1 (p < 0.0001), ASXL1 (p = 0.002), SRSF2 (p = 0.0003), and TP53 (p = 0.0009). FLT3 -ITD was also positively linked to DNMT3A (p < 0.0001) but negatively linked to ASXL1 (p = 0.0001), SRSF2 (p = 0.0033), NRAS (p = 0.0005), and TP53 (p = 0.0164). DNMT3A was also positively associated with IDH1 (p < 0.0001) and IDH2 (p = 0.0015), yet negatively associated with ASXL1 (p = 0.048) and SRSF2 (p = 0.0005). RUNX1 mutations were also positively associated with ASXL1 (p = 0.0014) and SRSF2 (p < 0.0001) mutations, but negatively associated with KRAS (p = 0.037). ASXL1 mutation was also positively associated with SRSF2 (p < 0.0001) and TET2 (p < 0.0001), while SRSF2 was positively linked to IDH1 (p = 0.021), IDH2 (p < 0.0001), and TET2 (p = 0.0016) mutations, but negatively associated with KRAS (p = 0.034) and TP53 (p = 0.0044). IDH1 was also positively associated with NRAS (p = 0.02) but negatively associated with IDH2 (p = 0.03), while IDH2 was negatively associated with TP53 (p = 0.017). Lastly, NRAS was negatively associated with KRAS (p = 0.0037), and KRAS was negatively associated with TP53 (p = 0.016) (Fig. 2 C). MCA identified three distinct groups of co-occurring mutations: the first group included DNMT3A , NPM1 , and FLT3-ITD ; the second consisted of ASXL1 , SRSF2 , and RUNX1 , and the third comprised IDH1, IDH2, and TET2 (Fig. 2 D). Additionally, TP53 and KRAS mutations occupied the same position due to their common pattern of being predominantly associated with the absence of other somatic mutations; however, they were not found together in the same patients (Fig. 2 C, 2 D). Finally, NRAS did not present a specific co-occurrence pattern. Effect of Individual Somatic Mutations on Post-Transplant Outcomes in CR1 Outcome analysis was performed on a subset of 646 AML patients allografted in CR1 with available follow-up data and molecular results for the ten genes identified through MCA: DNMT3A, NPM1, FLT3- ITD, ASXL1, SRSF2, RUNX1, IDH1, IDH2, TET2, and TP53 . Patient characteristics are described in Table 1 . Most patients had de novo AML (75%), with a median age of 55 years (range: 19–75 years). The majority received primarily reduced intensity conditioning (57%) and peripheral blood stem cells (95%) from matched sibling (32%), matched unrelated (35%), and haploidentical (18%) donors. Based on the ELN 2022 classification, 4% had favorable-risk cytogenetics, 70% intermediate-risk, and 26% adverse-risk. At the time of transplant, MRD was positive in 129 patients (35%), and negative in 238 (65%), while 279 patients had no MRD data available. After a median follow-up of 3.1 years, the 2-year RI, NRM, LFS and OS for the whole cohort were 21%, 13%, 66%, and 73%, respectively. Table 1 Patient characteristics of the cohort of AML patients allografted in CR1 with available data on follow-up and for the ten genes derived from the MCA results ( DNMT3A, NPM1, FLT3- ITD, ASXL1, SRSF2, RUNX1, IDH1, IDH2, TET2 and TP53) . N = 646 patients. Variables Modalities All patients Decuple wild type IDH/ TET2 RUNX1 ASXL1 SRSF2 FLT3ITD DNMT3A NPM1 TP53 P N = 646 167 43 132 128 129 47 Age at HCT median (range), years 55 (19–75) 47 (19–75) 55 (20–74) 60 (20–74) 57 (19–73) 53 (20–75) 60 (22–72) < 0.001 Year of HCT median (range) 2019 (2013-23) 2019 (2013-23) 2019 (2016-23) 2019 (2015-23) 2019 (2015-23) 2019 (2015-22) 2020 (2015-22) 0.1 Patient Sex Male, N(%) 318 (49) 93 (56) 20 (47) 77 (58) 56 (44) 52 (40) 20 (43) Time to HCT median (range), mo. 4.7 (1–27) 4.5 (2–23) 5 (3–14) 4.4 (2–23) 4.5 (1–9) 5.1 (3–27) 5 (3–17) 0.001 MRD Positive, N(%) 129 (35) 26 (35) 3 (16) 19 (30) 27 (34) 47 (43) 7 (30) 0.21 missing 279 93 24 69 49 20 24 AML type de novo, N(%) 486 (75) 125 (75) 38 (88) 85 (64) 104 (81) 111 (86) 23 (49) < 0.001 Sec AML 160 (25) 42 (25) 5 (12) 47 (36) 24 (19) 18 (14) 24 (51) Cytogenetic ELN2022 Favorable 25 (4) 17 (11) 0 (0) 6 (5) 1 (1) 1 (1) 0 (0) Not done Intermediate 421 (70) 84 (53) 31 (78) 88 (70) 94 (79) 108 (92) 16 (36) Adverse 160 (26) 58 (36) 9 (22) 31 (25) 24 (20) 9 (7) 29 (64) missing 40 8 3 7 9 11 2 Number of genes tested median (range) 40 (8-141) 39 (21–141) 40 (21–96) 40 (15–141) 40 (11–96) 36 (8–78) 40 (12–55) 0.12 Number of mutations median (range) 2 (0–12) 1 (0–7) 2 (1–8) 3 (1–10) 3 (0–9) 3 (1–10) 2 (1–12) < 0.001 Donor type MSD 223 (34) 57 (34) 15 (35) 44 (34) 49 (38) 39 (31) 19 (40) Not done Haplo 119 (18) 31 (19) 7 (16) 27 (21) 22 (17) 23 (18) 9 (19) MUD 223 (35) 56 (33) 16 (37) 46 (35) 39 (30) 53 (41) 13 (28) Others 80 (13) 23 (14) 5 (12) 14 (10) 18 (15) 14 (10) 6 (13) Source of cells PB 611 (95) 156 (93) 40 (93) 129 (98) 120 (94) 122 (95) 44 (94) Not done BM 30 (4) 8 (5) 3 (7) 2 (2) 8 (6) 5 (4) 3 (6) Others 5 (1) 3 (2) 0 (0) 0 (0) 0 (0) 1 (1) 0 (0) Myeloablativeconditioning Yes 271 (43) 85 (53) 15 (39) 51 (39) 53 (42) 55 (43) 12 (26) missing 13 5 4 2 1 1 0 Abbreviations: HCT: Hematopoietic cell transplantation; mo.: months; MRD: measurable residual disease; AML: Acute myeloid leukemia; sec: secondary; ELN: European LeukemiaNet; MSD: Matched related donor; Haplo: haploidentical donor; MUD; matched unrelated donor; PB: peripheral blood; BM: bone marrow. We then evaluated the impact of individual somatic mutations on 2-year post-transplant outcomes (Table 2 ). NPM1 mutation was associated with improved outcomes, showing higher 2-year LFS (74% vs 63%; p = 0.02) and OS (82% vs 71%; p = 0.009). Conversely, TP53 mutation was linked to higher 2-year RI (42 vs 20%; p < 0.001) and lower 2-year LFS (35% vs 68%; p < 0.001) and reduced 2-year OS (47% vs 76%; p < 0.001). FLT3 -ITD mutation was associated with lower NRM (10% vs 15%; p = 0.04) and improved OS (80% vs 71%; p = 0.01). SRSF2 mutation was associated with a slight increase in 2-year RI (26% vs 21%; p = 0.05). Meanwhile, IDH2 mutation was linked to lower 2-year RI (13% vs 23%; p = 0.02) and improved LFS (76% vs 64%; p = 0.02) and OS (83% vs 72%; p = 0.02). The effects of other individual mutations on post-transplant outcomes are detailed in Table 2 . Table 2 Impact of Individual Somatic Mutations on 2-Year Post-Transplant Outcomes for AML patients allografted in CR1 Variable Modality 2-year OS % [95% CI] 2-year LFS % [95% CI] 2-year RI % [95% CI] 2-year NRM % [95% CI] NPM1 Negative 71 [66.6–74.9] 63.2 [58.7–67.4] 22.3 [18.6–26.1] 14.5 [11.5–17.8] Positive 82.4 [74.5–88.1] 73.9 [65.3–80.7] 17.6 [11.4–24.8] 8.5 [4.5–14.2] P value 0.009 0.02 0.15 0.14 TP53 Negative 75.5 [71.7–78.9] 68 [63.9–71.7] 19.6 [16.3–23] 12.4 [9.9–15.3] Positive 46.8 [31.8–60.5] 34.6 [21.3–48.4] 41.5 [26.9–55.5] 23.8 [12.6–37] P value < 0.0001 < 0.0001 0.0002 0.09 DNMT3A Negative 74.1 [69.8–77.9] 65.3 [60.7–69.5] 22 [18.3–26] 12.6 [9.8–15.8] Positive 70.9 [62.4–77.8] 66 [57.4–73.2] 18.6 [12.6–25.6] 15.4 [10-21.8] P value 0.99 0.82 0.33 0.46 FLT3-ITD Negative 71.4 [66.9–75.4] 63.9 [59.3–68.2] 21.5 [17.8–25.5] 14.5 [11.5–17.9] Positive 79.8 [72.2–85.5] 70.2 [61.9–77] 20.3 [14.1–27.3] 9.5 [5.5–15] Missing N (%) 4 (1%) 4 (1%) 4 (1%) 4 (1%) P value 0.01 0.06 0.69 0.04 RUNX1 Negative 73.3 [69.1–77] 65.1 [60.7–69.2] 21.8 [18.2–25.6] 13.1 [10.3–16.2] Positive 73.8 [64.2–81.1] 66.8 [57-74.9] 18.8 [12-26.8] 14.4 [8.6–21.6] Missing N (%) 1 (0%) 1 (0%) 1 (0%) 1 (0%) P value 0.95 0.96 0.61 0.59 ASXL1 Negative 72.3 [68.3–75.9] 64.8 [60.6–68.7] 21.7 [18.3–25.3] 13.5 [10.8–16.5] Positive 83.5 [71.5–90.8] 71.1 [57.5–81] 17.6 [9-28.7] 11.3 [4.9–20.6] P value 0.39 0.82 0.78 0.9 SRSF2 Negative 73.3 [69.2–76.9] 65.8 [61.5–69.7] 21.6 [18.1–25.2] 12.6 [10-15.6] Positive 77 [64.1–85.7] 64.8 [51.1–75.5] 17.5 [8.9–28.5] 17.7 [9.4–28.2] Missing N (%) 16 (2%) 16 (2%) 16 (2%) 16 (2%) P value 0.84 0.69 0.43 0.17 IDH2 Negative 71.9 [67.7–75.6] 63.9 [59.6–67.8] 22.5 [19-26.2] 13.6 [10.9–16.7] Positive 83.2 [72.8–89.9] 75.7 [64.4–83.8] 13.2 [6.7–21.9] 11.2 [5.4–19.2] Missing N (%) 1 (0%) 1 (0%) 1 (0%) 1 (0%) P value 0.02 0.02 0.02 0.65 IDH1 Negative 74.3 [70.3–77.8] 66 [61.8–69.8] 20.7 [17.4–24.3] 13.3 [10.6–16.2] Positive 65.4 [51-76.5] 60.3 [45.9–72] 25.6 [14.7–37.9] 14.1 [6.5–24.6] Missing N (%) 2 (0%) 2 (0%) 2 (0%) 2 (0%) P value 0.11 0.28 0.72 0.27 TET2 Negative 72.7 [68.6–76.3] 65.4 [61.1–69.4] 21.6 [18.1–25.2] 13 [10.3–16.1] Positive 77.1 [66.3–84.8] 64.4 [52.7–73.8] 20.4 [12.2–30.1] 15.2 [8.5–23.7] Missing N (%) 3 (0%) 3 (0%) 3 (0%) 3 (0%) P value 0.86 0.94 0.41 0.38 Abbreviations : OS: overall survival; LFS: leukemia-free survival; RI: Relapse Incidence; NRM: non-relapse mortality. Effect of groups of somatic mutations on post-transplant outcomes in CR1 As stated before, the MCA identified three distinct groups of co-occurring mutations: NPM1/FLT3- ITD /DNMT3A , SRSF2/ASXL1/RUNX1 , and IDH1/IDH2/TET2 (Fig. 2 D). Because of the known impact of NPM1 , the first group was split into two subgroups according to the presence or absence of NPM1 mutation. In addition, we added a group of TP53 mutation for its known negative impact and because in the MCA, TP53 mutations were predominantly associated with the absence of other somatic mutations. Therefore, six non-overlapping groups were constructed: Group 1 comprised patients with TP53 mutation regardless of other co-mutations (N = 47; 7%); Group 2 comprised patients with NPM1 mutation and wild type TP53 regardless of other co-mutations (N = 129; 20%); Group 3 comprised patients with FLT3 -ITD and/or DNMT3A mutation, with wild type NPM1 and TP53 , regardless of other co-mutations (N = 128; 20%); Group 4 comprised patients with RUNX1 and/or ASXL1 and/or SRSF2 mutation (SAR group) without FLT3- ITD and with wild type NPM1 , DNMT3A and TP53 and regardless of other co-mutations (N = 132; 20%); Group 5 comprised patients with IDH1 and/or IDH2 and/or TET2 mutation without FLT3- ITD and with wild type NPM1 , DNMT3A, RUNX1, ASXL1 , SRSF2 and TP53 and regardless of other co-mutations (N = 43; 7%); and Group 6 comprised patients with all ten genes unmutated (N = 167; 26%). These groups differed significantly in median age (60, 53, 57, 59, 55 and 47 years respectively; p < 0.001), the frequency of secondary AML (51%, 14%, 19%, 36%, 12% and 25% respectively; p < 0.001), and the frequency of adverse cytogenetics (64%, 8%, 20%, 25%, 23% and 37% respectively) (Table 1 ). Moreover, the groups of somatic mutations significantly impacted the 2-year RI (42%, 17%, 21%, 18%, 13% and 24% respectively; p = 0.005), LFS (35%, 74%, 68%, 71%, 71% and 61% respectively; p < 0.001) and OS (47%, 82%, 74%, 81%, 76% and 67% respectively; p < 0.001) whereas NRM was not significantly affected (Fig. 3 ). Multivariable analysis In the multivariable analysis (Table 3 ), compared to the Group 2 ( NPM1 mutation and wild-type TP53 ), RI, LFS, and OS were negatively affected by Group1 ( TP53 mutation; hazard ratio [HR] 2.6, 2.9, and 3.05, respectively, all p < 0.001) and OS was additionally negatively affected by Group 6 (all ten genes unmutated; HR 1.73, p = 0.02). NRM, LFS and OS were negatively affected by older age (HR 1.1, p < 0.001; 1.04, p = 0.02 and 1.05; p = 0.02, respectively). Finally, adverse karyotype negatively affected RI, LFS, and OS (HR 1.93, 1.64 and 1.64, respectively, all p < 0.001). Table 3 Multivariable Analysis Variable Modality LFS OS RI NRM HR (95% CI) p value HR (95% CI) p value HR (95% CI) p value HR (95% CI) p value Group of mutations NPM1 1 1 1 1 SRSF2/ASXL1/RUNX1 1.16 (0.7–1.8) 0.51 1.13 (0.7–1.9) 0.62 1.11 (0.6-2) 0.72 1.17 (0.6–2.3) 0.66 FLT3-ITD /DNMT3A 1.2 (0.8–1.9) 0.42 1.2 (0.7-2) 0.46 1.24 (0.7–2.2) 0.44 1.06 (0.5–2.2) 0.88 IDH/TET2 1.05 (0.6-2) 0.87 1.17 (0.6–2.3) 0.66 0.8 (0.3-2) 0.63 1.48 (0.6–3.7) 0.4 Decuple negative 1.49 (1-2.3) 0.06 1.73 (1.1–2.7) 0.02 1.29 (0.8–2.2) 0.35 1.8 (0.9–3.4) 0.08 TP53 2.6 (1.6–4.3) < 0.001 2.90 (1.7-5) < 0.001 3.05 (1.6–5.8) < 0.001 2.07 (0.9–4.8) 0.09 Age at HCT (3y increment) 1.04 (1-1.08) 0.02 1.05 (1-1.09) 0.02 1 (0.96-1) 0.88 1.11 (1.1–1.9) < 0.001 Type of AML de novo 1 1 1 Sec AML 0.88 (0.7–1.2) 0.42 0.92 (0.7–1.3) 0.62 0.78 (0.5–1.2) 0.23 1.02 (0.6–1.6) 0.92 ELN 2022 cytogenetic Fav/Int 1 1 1 Adverse 1.64 (1.2–2.2) < 0.001 1.64 (1.2–2.2) < 0.001 1.93 (1.4–2.8) < 0.001 1.28 (0.8-2) 0.29 Donor type Matched relative 1 1 1 Mismatched relative 0.9 (0.6–1.3) 0.6 0.88 (0.6–1.3) 0.55 0.8 (0.5–1.3) 0.39 0.98 (0.6–1.7) 0.95 Unrelated 0.96 (0.7–1.3) 0.76 0.9 (0.7–1.2) 0.51 1 (0.7–1.5) 1 0.87 (0.6–1.4) 0.54 Myelo-ablative regimen No 1 1 1 Yes 1.03 (0.8–1.4) 0.85 0.99 (0.7–1.4) 0.97 1.03 (0.7–1.5) 0.87 1 (0.6–1.6) 1 Year of HCT (by 5y increment) 0.98 (0.7–1.4) 0.93 1.13 (0.7–1.7) 0.54 0.93 (0.6–1.5) 0.77 0.98 (0.6–1.7) 0.94 Abbreviations : LFS: leukemia-free survival; OS: overall survival; RI: Relapse Incidence; NRM: non-relapse mortality; HR: Hazard ratio; CI: Confidence interval; HCT: hematopoietic cell transplantation; y: year; AML: acute myeloid leukemia; Sec: secondary; Fav: favorable; Int: intermediate. Characteristics and post-transplant outcomes of patients with NPM1 mutation We then analyzed the characteristics, distribution and impact of karyotype, FLT3 -ITD, and MRD status in 129 NPM1 mutated AML patients allografted in CR1, excluding those with TP53 mutations (N = 4). The patients’ characteristics and associated mutations are described in Supplementary Table 3. Karyotype was intermediate in 108 (92%) patients, predominantly normal (96 (74%) patients), and adverse in 9 (8%) patients. MRD was positive in 47 (43%) patients, negative in 62 (57%) patients and missing in 20 patients. FLT3 -ITD was present in 79 (61%) patients and negative in 48 (37%) patients. Patients with FLT3 -ITD were transplanted earlier and were less likely to be MRD positive at transplant (33% versus 61%). In univariate analysis (Supplementary Table 4), karyotype did not significantly affect post-transplant survival. We compared survival outcomes based on pre-transplant MRD positivity (2-year LFS: 67% vs. 79%; 2-year OS: 75% vs. 84%) and FLT3 -ITD status (2-year LFS: 70% vs. 78%; 2-year OS: 81% vs. 82%), though these differences did not reach statistical significance. Effect of SRSF2 and/or ASXL1 and/or RUNX1 mutation on post-transplant outcomes Surprisingly, in addition to the well-established favorable prognosis of NPM1 mutations, the post-transplant outcomes were non different for patients in the SAR Group, with a 2-year LFS of 71% and OS of 81%, despite an older median age and a high frequency of secondary AML (36%) and adverse karyotype (25%). This group consisted of 132 patients carrying at least one of the ELN 2022 adverse-risk mutations - SRSF2 , ASXL1 , and/or RUNX1 (SAR mutations) - after excluding FLT3- ITD, NPM1 , DNMT3A , and TP53 mutations. Given these unexpectedly favorable outcomes, we further analyzed the characteristics, distribution and impact of SAR mutations in the whole cohort of allografted AML patients in CR1, including those with FLT3- ITD, NPM1 , DNMT3A , and TP53 mutations. The patients’ characteristics and associated mutations are described in Supplementary Table 5. According to ELN 2022, karyotype was favorable in 7 (4%) patients, intermediate in 131 (73%, normal in 47%) and adverse in 41 (23%) patients. The number of SAR mutations was 1 for 125 (66%) patients, 2 in 46 (24%) patients and 3 in 18 (10%) patients. Pre-transplant MRD was positive in 28 (28%) patients and negative in 72 (72%) patients (89 missing). The 2-year LFS and OS were 69% and 78% for all 189 SAR patients, and were non different to those of the 132 SAR patients belonging to Group 4. The remaining 57 SAR patients included six patients with concomitant TP53 mutations (Group 1), 12 with concomitant NPM1 mutations (Group 2), and 39 with concomitant FLT3 -ITD and/or DNMT3A mutations (Group 3). In univariate analysis (Supplementary Table 6), post-transplant outcomes were not significantly affected by karyotype, pre-transplant MRD status and the number of SAR mutations. These findings suggest that in the allogeneic transplant setting, SAR mutations are associated with excellent post-transplant outcomes, challenging their traditionally adverse-risk classification at diagnosis. Characteristics and post-transplant outcomes of patients with TP53 mutation The 2-yr LFS of 35% and 2-yr OS of 47% in TP53 mutant patients are quite encouraging. We therefore analyzed the characteristics, distribution and impact of karyotype, type of AML, and TP53 variant allele frequency (VAF) in 47 TP53 mutated AML patients (23 de novo and 24 secondary) allografted in CR1. The patients’ characteristics and associated mutations are described in Supplementary Table 7. According to ELN2022, karyotype was intermediate in 16 (36%) patients and adverse in 29 (64%) patients including 17 (38%) patients with chromosome 17 abnormalities. Median VAF was 45.5 (IQR 31.2–50.8). In univariate analysis (Supplementary Table 8), outcomes of secondary vs de novo AML (2-year LFS: 31% vs. 39%; 2-year OS: 34% vs. 60%), were not significantly different. Conversely, adverse karyotype negatively affected post-transplant survival compared to intermediate karyotype (2-year LFS 22% vs 49%, p = 0.03; 2-year OS 34% vs 61%, p = 0.03). Finally, TP53 mutation VAF above median (45–94) significantly increased the incidence of relapse (2-year RI 55% vs 24%, p = 0.02) but did not significantly affect post-transplant survival (2-year LFS 21% vs 47%; 2-year OS 36% vs 52%) in this small cohort. It is important to note that for 21 patients with TP53 mutation VAF below median, karyotype was intermediate in 10 patients and adverse in 10 patients whereas for 21 patients with TP53 mutation VAF above median, karyotype was intermediate in 4 patients and adverse in 16 patients. These results suggest excellent post-transplant outcomes for AML patients with TP53 mutation in the absence of adverse karyotype or high TP53 VAF. Characteristics and post-transplant outcomes of patients lacking mutations in the 10 most frequently mutated genes Finally, patients in the decuple-negative group, those lacking mutations in the ten most frequently mutated genes, exhibited inferior survival compared to other groups without TP53 mutations, with a 2-year OS of 67%. This finding is notable given the absence of well-characterized driver mutations. We therefore analyzed the characteristics, distribution and impact of karyotype, type of AML, and pre-transplant MRD status in 167 decuple-negative AML patients (125 (75%) de novo and 42 (25%) secondary) allografted in CR1. The patients’ characteristics and associated mutations are described in Supplementary Table 9. According to ELN2022, karyotype was favorable in 17 (11%) patients, intermediate in 84 (53%) and adverse in 58 (36%) patients. Pre-transplant MRD was positive in 26 (35%) patients, negative in 48 (65%) patients and missing in 93 patients. In univariate analysis (Supplementary Table 10), age negatively affected NRM, whereas pre-transplant MRD positivity numerically decreased survival. Adverse karyotype significantly increased relapse (2-year RI 35% compared to 12% for favorable and 18% for intermediate, p = 0.02) and accordingly numerically reduced survival. Surprisingly, in that group, post-transplant outcomes were better in secondary AML (2-year LFS 77% vs 56%, p = 0.03; 2-year OS 78% vs 64%, p = 0.02). DISCUSSION In this large, transplant-focused cohort of 952 AML patients with available diagnostic NGS data, we were able to delineate distinct groups of mutations with different prognostic implications, challenging existing risk assumptions mainly within the ELN 2022 classification. 5 Unlike most prior studies that evaluate the impact of individual mutations in isolation, our analysis uniquely assesses the effect of grouped mutational profiles, offering a more integrated view of molecular risk stratification, specifically in the context of allo-HCT. 10 – 14 Despite a highly selected population undergoing allo-HCT, where the majority of patients had de novo AML with mainly diploid intermediate-risk cytogenetics, and transplanted in first remission, we observed that the most frequently mutated genes included DNMT3A , FLT3 -ITD, and NPM1 , consistent with prior genomic studies that analyzed mutation frequency at diagnosis. 2 , 15 Notably, 23% of patients harbored four or more somatic mutations, with a median of two mutations per patient, highlighting the complex clonal architecture at diagnosis, even among those achieving remission prior to allo-HCT. This genetic heterogeneity was further explored through co-mutation analysis of the 12 commonly altered genes, revealing distinct co-occurrence and exclusivity patterns which are biologically plausible: for example, NPM1 was strongly associated with FLT3 -ITD and DNMT3A , a well-established mutational triad, and was less likely to occur with adverse-risk genes like TP53 , RUNX1 , ASXL1 and SRSF2 . In contrast, mutations in epigenetic and splicing regulators ( ASXL1 , SRSF2 , RUNX1 ) formed a separate cluster, frequently co-mutating with one another but largely excluding FLT3 -ITD and NPM1 . 16 – 18 This multidimensional clustering by MCA supports the presence of biologically coherent subgroups that may inform prognostic modeling. Six genetically defined subgroups were constructed based on recurrent co-mutation patterns and the established prognostic roles of TP53 and NPM1 . These subgroups had different clinical and biological features, including age, secondary AML, cytogenetics, and MRD status. Importantly, they showed significantly different post-transplant survival outcomes. Basically, there were three prognostic categories according to somatic mutations: TP53 mutation (poor risk), no mutation (intermediate risk), and others (good risk). As expected, TP53 -mutated patients (Group 1) had the worst outcomes among all groups, with a 2-year RI of 42% and OS of 47%, but notably superior to previously reported benchmarks. 19 – 23 A meta-analysis reported a pooled 2-year OS of 30% for 297 TP5 3-mutated AML patients undergoing allo-HCT. 24 The relatively improved outcomes in our TP53 cohort may reflect the fact that only 45% of patients with mutant TP53 harbored either 17p chromosomal abnormalities or complex karyotype, both of which are strongly linked to poor outcomes. In a recent large cohort from the EBMT, TP53 -mutated AML patients lacking both 17p abnormalities and complex karyotype had a markedly improved 2-year OS of 65.2%. 25 Overall, our results suggest excellent post-transplant outcomes for AML patients with TP53 mutation in the absence of adverse karyotype or high TP53 VAF. Conversely, NPM1 -mutated patients with TP53 wild-type status (Group 2) had excellent outcomes, with a 2-year OS and LFS of 82% and 74%, respectively, in line with prior data demonstrating sensitivity of this subgroup to intensive chemotherapy and allo-HCT. 26 , 27 In this group, outcomes were not significantly affected by karyotype, pre-transplant MRD status or the presence of FLT3 -ITD. Notably, FLT3 -ITD mutation was paradoxically linked to reduced NRM and improved 2-year OS (80%) in our cohort, suggesting that FLT3 -ITD mutant AML may be transitioning toward a more favorable-risk category when managed with a total therapy approach, incorporating allo-HCT in CR1 along with pre- and post-transplant FLT3 inhibitor therapies. 28 – 34 Interestingly, beyond the negative prognostic role of TP53 mutation, there was no significant difference in post-transplant outcomes in CR1 between the other groups of somatic mutations. Notably, patients classified as group 4, harboring SRSF2 , ASXL1 , and/or RUNX1 mutations (SAR mutations), in the absence of TP53 , FLT3 -ITD, DNMT3A , or NPM1 , had unexpectedly favorable post-transplant outcomes, with a 2-year OS of 81% and LFS of 71%. These results persisted despite the group's older median age, higher frequency of secondary AML, and enrichment for adverse cytogenetics. Historically, SAR mutations are considered adverse-risk in the ELN classification, 5 , 35 based on their poor response to chemotherapy and association with clonal hematopoiesis and secondary AML. 16 – 18 , 36 However, our data suggest that in the transplant setting, their adverse impact may be abrogated, possibly through enhanced sensitivity to graft-versus-leukemia effects. When extended to the entire CR1 cohort with SAR mutations, regardless of additional co-mutations, we confirmed a similarly favorable outcome profile (2-year OS 78%, LFS 70%). One possible explanation for the favorable outcomes observed in this group is that patients with these mutations typically exhibit low response rates to conventional chemotherapy, often less than 50%. 16–18 Therefore, those who achieved CR1 and proceeded to allo-HCT may represent a biologically selected group with inherently better disease control or chemosensitivity, translating into superior post-transplant outcomes. Additionally, the lack of an adverse prognostic impact of our SAR group may be dominated by the RUNX1 -associated risk, as a RUNX1 -only mutation was present in 38% of patients in this subgroup. The existing literature shows that patients harboring isolated RUNX1 mutations without co-occurring mutations in the four most frequently mutated splicing genes ( SRSF2 , SF3B1 , U2AF1 , and ZRSR2 ) have outcomes comparable to the intermediate-risk group, with a reported 5-year OS of 44%. 37 In contrast, the co-occurrence of RUNX1 and SRSF2 or ASXL1 and SRSF2 mutations, both of which have been independently associated with significantly inferior survival in prior studies, accounted for only 8% and 11% of our cohort, respectively. 2 , 16 , 37 This finding may partially explain the unexpectedly favorable outcomes observed in our SAR group. Finally, patients in the decuple-negative group, those lacking mutations in the ten most frequently mutated genes, exhibited inferior survival compared to other groups without TP53 mutations, with a 2-year OS of 67%. This finding is notable given the absence of well-characterized driver mutations. Interestingly, a substantial proportion (40%) of these patients harbored either KRAS , NRAS , or PTPN11 mutations, genes often associated with proliferation signaling, chemoresistance, and suboptimal post-transplant outcomes. 11 , 38 – 40 Furthermore, 37% of them had adverse cytogenetics, a rate significantly higher than that observed in the other molecular subgroups (8%-25%) excluding those with TP53 mutations. These findings may have contributed to their poorer prognosis post-transplant. This study has several limitations due to its retrospective nature. The absence of centralized NGS testing across centers likely resulted in variations in NGS platforms, library preparation protocols, and data analysis pipelines. Moreover, there were some missing genetic data, as many genes were not systematically tested across the cohort. VAF data were also unavailable for the majority of the genes tested, limiting the ability to assess the clonal burden at diagnosis and its potential prognostic value. Most importantly, it is critical to mention that included patients were primarily from centers performing NGS at diagnosis, many of which were high-expertise or high-volume centers. MRD data pre-transplant were also missing in 43% of the overall cohort. However, this was not uniform as in cases with NPM1 mutations, MRD data were missing in only 16%, while in patients with decuple-negative, MRD was missing in 55% of cases, suggesting that MRD data availability was dependent on the mutational profile where MRD techniques actually exist. Finally, the lack of data on the induction and consolidation treatments received before transplant, and the lack of a control group of non-transplanted patients, preclude the direct assessment of the benefit of allo-HCT versus chemotherapy alone for the different groups of mutational profiles. CONCLUSION NGS at diagnosis can be extremely useful in risk stratification of AML patients undergoing allo-HCT, potentially allowing adequate post-transplant interventions. Surprisingly, despite their older age and higher frequency of secondary AML and adverse cytogenetics, the excellent outcomes (2-year LFS 71%, OS 81%) were observed for patients harboring SRSF2 and/or ASXL1 and/or RUNX1 in the absence of FLT3- ITD or NPM1 , DNMT3A and TP53 mutation, indicating that allo-HCT in CR1 can overcome the adverse-risk associated with these somatic mutations at diagnosis. Together, these results argue for a revised transplant-specific risk model with better post-transplant prognostication. In particular, the data challenge the assumption that FLT3 -ITD and SAR mutations uniformly confer adverse prognosis, and raise the possibility that certain mutational contexts, historically deemed high-risk, may derive substantial benefit from allo-HCT. Prospective studies are warranted to validate these observations and explore the mechanistic basis of transplant sensitivity in these subgroups. Declarations Ethical approval and informed consent statement: This is a retrospective, registry-based, multicenter study utilizing patient data collected and approved by the Acute Leukemia Working Party (ALWP) of the EBMT. The EBMT is a collaborative network comprising more than 600 transplant centers that are required to report all consecutive HCTs and subsequent follow-ups on an annual basis. Routine audits are performed to ensure data accuracy and completeness. Since January 2003, all participating transplant centers have been required to obtain written informed consent from patients prior to data registration with the EBMT, in accordance with the ethical principles outlined in the Declaration of Helsinki (1975). Data Availability Statement : The data analyzed in this study were provided and approved by the ALWP of the EBMT. All relevant data supporting the findings of this study are available within the Article and the Supplementary Material. Requests for access to EBMT study data from qualified external researchers will be reviewed by the relevant working party in accordance with EBMT’s data-sharing policies. This process ensures the protection of patient privacy, maintains data security and integrity, and promotes scientific and medical innovation. Additional details on data-sharing criteria and the request process can be obtained by contacting [email protected] . Individual patient-level data will not be shared. Conflict of Interest: All authors have no conflicts of interest to declare. Funding: This project was not funded. Authors Contribution: A.Ba. proposed the study, interpreted the data and wrote the manuscript. JEG and M.Mo. participated in study design, interpreted the data, and edited the manuscript. JEG. was responsible for statistical analysis. All other authors reported updated patient data and read and commented on the manuscript. All authors proofread the manuscript and agreed on the data presented. Acknowledgments: Patients and caregivers, EBMT participating centers, the ALWP statistical and data management team, particularly Aleksandra (Sasha) Gavrilina, as well as the leadership team. This was presented in part during the American Society of Hematology meeting in San Diego, December 2023. References DiNardo CD, Erba HP, Freeman SD, Wei AH. Acute myeloid leukaemia. 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Sorafenib Maintenance After Allogeneic Hematopoietic Stem Cell Transplantation for Acute Myeloid Leukemia With FLT3-Internal Tandem Duplication Mutation (SORMAIN). J Clin Oncol 2020; 38(26): 2993–3002. e-pub ahead of print 2020/07/17; doi: 10.1200/jco.19.03345 Abou Dalle I, El Cheikh J, Bazarbachi A. Pharmacologic Strategies for Post-Transplant Maintenance in Acute Myeloid Leukemia: It Is Time to Consider! Cancers (Basel) 2022; 14(6). e-pub ahead of print 2022/03/26; doi: 10.3390/cancers14061490 Stone RM, Yin J, Mandrekar SJ, Benner A, Saadati M, Galinsky IA et al. 10 Year Follow-up of CALGB 10603/Ratify: Midostaurin Versus Placebo Plus Intensive Chemotherapy in Newly Diagnosed FLT3 Mutant Acute Myeloid Leukemia Patients Aged 18–60 Years. Blood 2024; 144(Supplement 1): 218–218. doi: 10.1182/blood-2024-201058 Bazarbachi A, Labopin M, Gedde-Dahl T, Remenyi P, Forcade E, Kröger N et al. 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Blood Adv 2020; 4(9): 1942–1949. e-pub ahead of print 2020/05/08; doi: 10.1182/bloodadvances.2019001349 Lindsley RC, Mar BG, Mazzola E, Grauman PV, Shareef S, Allen SL et al. Acute myeloid leukemia ontogeny is defined by distinct somatic mutations. Blood 2015; 125(9): 1367–1376. e-pub ahead of print 2015/01/01; doi: 10.1182/blood-2014-11-610543 van der Werf I, Wojtuszkiewicz A, Meggendorfer M, Hutter S, Baer C, Heymans M et al. Splicing factor gene mutations in acute myeloid leukemia offer additive value if incorporated in current risk classification. Blood Adv 2021; 5(17): 3254–3265. e-pub ahead of print 2021/08/28; doi: 10.1182/bloodadvances.2021004556 Alawieh D, Cysique-Foinlan L, Willekens C, Renneville A. RAS mutations in myeloid malignancies: revisiting old questions with novel insights and therapeutic perspectives. Blood Cancer J 2024; 14(1): 72. e-pub ahead of print 2024/04/25; doi: 10.1038/s41408-024-01054-2 Fobare S, Kohlschmidt J, Ozer HG, Mrózek K, Nicolet D, Mims AS et al. Molecular, clinical, and prognostic implications of PTPN11 mutations in acute myeloid leukemia. Blood Adv 2022; 6(5): 1371–1380. e-pub ahead of print 2021/12/01; doi: 10.1182/bloodadvances.2021006242 Heuser M, Gabdoulline R, Löffeld P, Dobbernack V, Kreimeyer H, Pankratz M et al. Individual outcome prediction for myelodysplastic syndrome (MDS) and secondary acute myeloid leukemia from MDS after allogeneic hematopoietic cell transplantation. Ann Hematol 2017; 96(8): 1361–1372. e-pub ahead of print 2017/06/15; doi: 10.1007/s00277-017-3027-5 Additional Declarations The authors have declared there is NO conflict of interest to disclose. Supplementary Files SupplementaryMaterialNGS14Aug.docx Supplementary Material Cite Share Download PDF Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Bone Marrow Transplantation → Version 1 posted Editorial decision: revise 16 Sep, 2025 Review # 2 received at journal 15 Sep, 2025 Review # 3 received at journal 07 Sep, 2025 Review # 1 received at journal 04 Sep, 2025 Reviewer # 3 agreed at journal 23 Aug, 2025 Reviewer # 2 agreed at journal 22 Aug, 2025 Reviewer # 1 agreed at journal 22 Aug, 2025 Reviewers invited by journal 22 Aug, 2025 Submission checks completed at journal 15 Aug, 2025 Editor assigned by journal 14 Aug, 2025 First submitted to journal 14 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7375987","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":504268257,"identity":"0d0b9c5d-dbe7-4fc2-b6ec-548ca5ef2008","order_by":0,"name":"Ali Bazarbachi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACAxDB2MDAwM/AwAZkMjMwsPMQqUWyAaaFmVgtBgeI1WLOfvzh58oddvLGx8+YPWCosE5sYOY9gFeLZU9CsuTZM8mG287kmBswnEkHauFLwO+wAwkHJBvbmBPMDuSYSTC2HQZq4THAr+X8w+afjW31Ccb9b4Ba/hGj5UYyG9CWwwkGEiBbGojQYjnjGZtl45njhjNuPCs3SDiWbtxGyC/m/OmPbzbuqJbn70/e9uBDjbVsP3vvAbxaUAHIeDYS1I+CUTAKRsEowAEAg0VD/QXyuDcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-7171-4997","institution":"American University of Beirut Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Bazarbachi","suffix":""},{"id":504268258,"identity":"ba983e33-085a-4a8c-98fb-ccb93a53298b","order_by":1,"name":"Jacques-Emmanuel Galimard","email":"","orcid":"https://orcid.org/0000-0001-9102-4427","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jacques-Emmanuel","middleName":"","lastName":"Galimard","suffix":""},{"id":504268259,"identity":"cf8f13bd-3571-4d3c-8e71-11969db556a8","order_by":2,"name":"iman abou dalle","email":"","orcid":"https://orcid.org/0000-0002-6520-9134","institution":"American University of Beirut","correspondingAuthor":false,"prefix":"","firstName":"iman","middleName":"abou","lastName":"dalle","suffix":""},{"id":504268260,"identity":"fe8a9978-a8e9-4ad8-8cbf-3eabf0b2118b","order_by":3,"name":"Myriam Labopin","email":"","orcid":"https://orcid.org/0000-0003-4514-4748","institution":"Hôpital Saint-Antoine","correspondingAuthor":false,"prefix":"","firstName":"Myriam","middleName":"","lastName":"Labopin","suffix":""},{"id":504268261,"identity":"d38447d0-0d80-4308-81c3-d903db54af16","order_by":4,"name":"Jaime Sanz","email":"","orcid":"https://orcid.org/0000-0001-6934-4619","institution":"Hospital Universitari i Politècnic la Fe","correspondingAuthor":false,"prefix":"","firstName":"Jaime","middleName":"","lastName":"Sanz","suffix":""},{"id":504268262,"identity":"9104000e-578c-4cfe-85c7-eaefdd93fbe6","order_by":5,"name":"He Huang","email":"","orcid":"","institution":"The First Affiliated Hospital, College of Medicine, Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Huang","suffix":""},{"id":504268263,"identity":"ecfd437d-4921-4489-844b-ebb2d37641f6","order_by":6,"name":"Jiri Mayer","email":"","orcid":"","institution":"University Hospital Brno","correspondingAuthor":false,"prefix":"","firstName":"Jiri","middleName":"","lastName":"Mayer","suffix":""},{"id":504268264,"identity":"ff03264a-2a2f-4bf0-ad7e-1409ef29747c","order_by":7,"name":"Carlos Solano","email":"","orcid":"","institution":"Hospital Clínico Universitario de Valencia. 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mohamad","middleName":"","lastName":"Mohty","suffix":""}],"badges":[],"createdAt":"2025-08-14 17:20:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7375987/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7375987/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41409-025-02770-4","type":"published","date":"2025-12-05T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90380906,"identity":"9e46e92d-5863-4060-bc08-dbc13340642f","added_by":"auto","created_at":"2025-09-02 06:45:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":122078,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e \u003cstrong\u003eFrequency of somatic mutations in the entire cohort of 952 AML patients undergoing allo-HCT. \u003c/strong\u003eThe bars represent the percentage of patients harboring each mutation among those with available molecular data for the respective gene (between parenthesis).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Distribution of the number of detected somatic mutations per patient in the entire cohort. \u003c/strong\u003eEach column represents a single patient, with the blue color indicating the presence of specific mutations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7375987/v1/9b7ee734cb7aaf64dff66722.png"},{"id":90380907,"identity":"5748efe8-dbfb-4647-b07b-3a6da550cf22","added_by":"auto","created_at":"2025-09-02 06:45:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103058,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Waterfall plot illustrating the distribution of co-occurring mutations in the most frequently mutated genes (more than 5% positivity and selected genes tested for at least 800 patients) across individual patients (n=753). \u003c/strong\u003eEach column represents a single patient, with the blue color indicating the presence of specific mutations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Circos plot showing the pairwise co-occurrence frequency of the most frequent somatic mutations.\u003c/strong\u003e The width of connecting lines reflects the frequency of co-occurrence between each mutation pair.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. Network diagram of positive and negative interactions among the most frequent somatic mutations. \u003c/strong\u003eBlue shading indicates frequent co-occurrence, red shading indicates mutual exclusivity, and gold shading reflects the statistical significance (p value of the exact Fisher test) of the interaction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD. Multiple Correspondence Analysis. Associations between the 12 most frequent somatic mutations\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7375987/v1/aaeec92669480ddf523edce7.png"},{"id":90382643,"identity":"57622e17-125a-474e-afd7-7ea4c80349ad","added_by":"auto","created_at":"2025-09-02 06:53:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69924,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of somatic mutation groups on post-transplant outcomes for patients allografted in CR1: \u003c/strong\u003eKaplan-Meier curves illustrating the effect of the six somatic mutation groups on post-transplant outcomes. \u003cstrong\u003eA:\u003c/strong\u003eLeukemia-free survival (LFS), \u003cstrong\u003eB:\u003c/strong\u003e Overall survival (OS), \u003cstrong\u003eC:\u003c/strong\u003eRelapse incidence (RI) at 2 years, \u003cstrong\u003eD:\u003c/strong\u003e Non-relapse mortality (NRM).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7375987/v1/eb65df04b2aa6cedb452d630.png"},{"id":97505279,"identity":"aa591e40-8e31-4aa1-82d2-bf014c9e51dd","added_by":"auto","created_at":"2025-12-05 08:06:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2512611,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7375987/v1/34a1acc1-17c7-4ce6-b056-5948e2f7db2b.pdf"},{"id":90380920,"identity":"c7443382-fe9b-4019-8ffd-1fdee78348ce","added_by":"auto","created_at":"2025-09-02 06:45:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2958742,"visible":true,"origin":"","legend":"Supplementary Material","description":"","filename":"SupplementaryMaterialNGS14Aug.docx","url":"https://assets-eu.researchsquare.com/files/rs-7375987/v1/8d4510c1e8dec77adc8ac2e4.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Frequency and Impact of Somatic Co-occurring Mutations on Post-Transplant Outcomes in Acute Myeloid Leukemia: A Multicenter Registry Analysis on Behalf of the EBMT ALWP","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAcute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy, characterized by a complex genomic landscape and diverse genetic subsets.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Historically, cytogenetic abnormalities represented a major factor in prognosticating, guiding risk classification into favorable, intermediate and adverse categories based on chromosomal aberrations.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e More recently, whole-genome sequencing unraveled the complexity of the AML genomic landscape, showing multiple infrequently mutated genes and the presence of co-occurring mutations within the same patient.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Moreover, multiple competing clones can be frequently encountered, playing an important role in the evolution of the disease.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe genomic classification of AML has evolved substantially with the identification of mutations in key regulatory pathways, including transcription factors, epigenetic modifiers, spliceosome machinery, the cohesin complex, and signaling cascades.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e This has led to the development of more refined risk stratification models from diagnosis, such as the European LeukemiaNet (ELN) 2022 classification, which integrates specific gene aberrations to improve prognostic accuracy.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Among the main genetic mutations considered at diagnosis, \u003cem\u003eNPM1\u003c/em\u003e status is risk-dependent on karyotype and \u003cem\u003eFLT3\u003c/em\u003e-ITD co-occurrence, \u003cem\u003eFLT3\u003c/em\u003e-ITD confers an intermediate-risk category, while bZIP in-frame mutated \u003cem\u003eCEBPA\u003c/em\u003e is of favorable risk. Adverse-risk mutations encompass \u003cem\u003eASXL1, BCOR, EZH2, RUNX1, SF3B1, SRSF2, STAG2, U2AF1, ZRSR2\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e mutations.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe selection of patients with AML in first complete remission (CR1) for allogeneic hematopoietic cell transplantation (allo-HCT) depends on relapse risk and estimated non-relapse mortality (NRM). Current guidelines recommend allo-HCT in CR1 for those classified as intermediate- or adverse-risk, based on the ELN 2022 classification.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Patients with persistent measurable residual disease (MRD) are also offered allo-HCT, irrespective of genetic risk.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e However, in the context of allo-HCT, the frequency and prognostic value of different gene-gene interactions have not been studied and may differ from that of patients treated with intensive chemotherapy alone.\u003c/p\u003e\u003cp\u003eIn this study, leveraging data from the European Society for Blood and Marrow Transplantation (EBMT) database, we aimed to analyze the frequency and interaction of different recurrent somatic mutations in AML patients undergoing allo-HCT, with a particular focus on those transplanted in CR1. Understanding these genetic interactions may provide deeper insights into post-transplant disease biology and inform personalized therapeutic strategies.\u003c/p\u003e"},{"header":"PATIENTS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Inclusion Criteria\u003c/h2\u003e\u003cp\u003eThis is a retrospective, registry-based, multicenter study utilizing patient data collected and approved by the Acute Leukemia Working Party (ALWP) of the EBMT. The EBMT is a collaborative network comprising more than 600 transplant centers that are required to report all consecutive HCTs and subsequent follow-ups on an annual basis. Routine audits are performed to ensure data accuracy and completeness. Since January 2003, all participating transplant centers have been required to obtain written informed consent from patients prior to data registration with the EBMT, in accordance with the ethical principles outlined in the Declaration of Helsinki (1975).\u003c/p\u003e\u003cp\u003eFor this analysis, a data collection of the NGS reports at diagnosis was performed for adult patients (\u0026ge;\u0026thinsp;18 years) with a diagnosis of AML who underwent allo-HCT between 2015 and 2023. Included patients had available next-generation sequencing (NGS) data at the time of diagnosis (with a margin of a maximum of 30 days from diagnosis), including a documented panel of sequenced genes from blood or bone marrow samples. Patients with a history of prior autologous HCT were excluded from the study.\u003c/p\u003e\u003cp\u003eIn addition to NGS data, the following clinical and transplant-related variables were collected: recipient age and disease status at transplant, recipient and donor gender, karyotype, \u003cem\u003eFLT3\u003c/em\u003e-ITD mutation status at diagnosis (regardless of the method of analysis), \u003cem\u003ede novo\u003c/em\u003e versus \u003cem\u003e(\u003c/em\u003evs.) secondary AML, and time from diagnosis to transplant. Transplant-related variables included the year of transplant, Karnofsky performance status score at transplant, HCT-specific comorbidity index, conditioning regimen, graft-versus-host disease (GVHD) prophylaxis, use of post-transplant cyclophosphamide (PTCy), \u003cem\u003ein vivo\u003c/em\u003e T cell depletion, donor type, recipient and donor cytomegalovirus status, and stem cell source.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEndpoints and definitions\u003c/h3\u003e\n\u003cp\u003eEndpoints included leukemia-free survival (LFS), overall survival (OS), non-relapse mortality (NRM), relapse incidence (RI), and acute and chronic GVHD. All outcomes were measured from the time of allo-HCT. LFS was defined as survival without leukemia relapse or progression; patients alive without leukemia relapse or progression were censored at the time of last follow-up. OS was defined as death from any cause. NRM was defined as death without previous leukemia relapse. Conditioning intensity was measured using the transplant conditioning regimen intensity, myeloablative conditioning/reduced intensity conditioning (RIC) classification, and transplant conditioning intensity score.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Myeloablative conditioning was defined as a regimen containing either total body irradiation with a dose greater than 6 Gy, a total dose of oral busulfan greater than 8 mg/kg, or a total dose of intravenous busulfan greater than 6.4 mg/kg. All other regimens were defined as RIC. The diagnosis and grading of acute and chronic GVHD were performed by transplant centers using standard criteria.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eQuantitative variables are described as median, interquartile range (quartiles 1 and 3), and minimum/maximum values, while qualitative variables are reported as absolute numbers and percentages. Comparison between groups has been done using Wilcoxon tests for quantitative variables and chi-square test for qualitative variables or Fisher exact test in case of non-valid chi square test. To quantify the association of genes two-by-two, the Fisher exact test was performed, allowing to provide the exact probability of the observed contingency table under the hypothesis of independence between the two genes. The frequency of each somatic mutation was determined across the entire cohort of transplanted patients, irrespective of disease status at transplant, and reported as a percentage of patients carrying a given mutation among those with available molecular results (\u0026ge;\u0026thinsp;100 patients tested per mutation).\u003c/p\u003e\u003cp\u003eTo evaluate associations among the 12 most frequently tested or mutated somatic mutations (defined as those with available mutation status in at least 800 patients and a mutation frequency of at least 5%), a multiple correspondence analysis (MCA) was performed. MCA is a statistical technique that allows the simultaneous assessment of relationships between multiple qualitative variables. It transforms the matrix of patients and categorical variables into a numerical format, capturing the greatest variability in the data within the first few dimensions. Each patient is assigned a set of values, with the first two dimensions accounting for the most significant variability. Using these two dimensions, we created a two-dimensional graphical representation of the patients. Each variable modality (e.g., presence or absence of a specific mutation) is represented by its centroid (barycenter), providing a visual summary of their relationships. The final output is a graphical representation of the different modalities across the first two dimensions of the 12 analyzed variables. This multidimensional approach provides a comprehensive overview of how variables are linked. Interpretation is based on spatial proximity: when two modalities appear close to each other, it indicates they are frequently observed together in the same patients or presenting the same panel of gene mutations.\u003c/p\u003e\u003cp\u003ePost-transplant outcomes were assessed for the group of patients allografted in CR1. Follow-up was calculated using the reverse Kaplan-Meier method. The probabilities of OS and LFS were assessed using the Kaplan-Meier method, while cumulative incidence functions estimated RI and NRM in a competing-risk setting, where RI and NRM were mutually exclusive events. Acute and chronic GVHD outcomes were also analyzed using cumulative incidence functions, considering death and relapse as competing risks. Univariate comparisons were performed using the log-rank test for OS and LFS, and Gray\u0026rsquo;s test for cumulative incidences. Multivariable models were performed using the Cox model to evaluate to impact of the groups of mutations adjusting on age at allo-HCT, \u003cem\u003ede novo\u003c/em\u003e or secondary AML, ELN 2022 cytogenetic risk, donor type, myeloablative regimen and year of allo-HCT. Estimations from the models were provided as hazard ratio (HR) and their 95% confidence interval. The type-1 error rate was fixed at 0.05. All analyses were performed using R 4.1.1 (R Development Core Team, Vienna, Austria, URL:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eFrequency of somatic mutations in allografted AML patients\u003c/h2\u003e\u003cp\u003eWe collected data from 952 AML patients who underwent allo-HCT and had NGS performed at diagnosis. Patient characteristics are detailed in Supplementary Table\u0026nbsp;1. The majority had \u003cem\u003ede novo\u003c/em\u003e AML (77%), with a median age of 55 years (range: 18\u0026ndash;78), and 49% were male. Karyotype was normal in 416 patients (44%), abnormal in 490 (51%), and either failed or missing for (5%). Based on the ELN 2022 classification, cytogenetic abnormalities were categorized as favorable in 7%, intermediate in 68% (including 47% with diploid karyotype), and adverse in 25% of patients. At transplant, 76% were in CR1, 10% in CR2, and 12% had active disease.\u003c/p\u003e\u003cp\u003eThe median number of sequenced genes included in the NGS panel was 40 (interquartile range (IQR) 23\u0026ndash;48), with 6% of patients having fewer than 20 genes analyzed and 7% having more than 55. The most frequently detected mutations were \u003cem\u003eDNMT3A (24%), FLT3-ITD (21%), NPM1 (21%), RUNX1 (16%), NRAS (16%), TET2 (14%), IDH2 (12%), ASXL1 (11%), IDH1 (10%), SRSF2 (10%), KRAS (8%), WT1 (8%), NF1 (7%), TP53 (7%), PTPN11\u003c/em\u003e (7%), \u003cem\u003eCEBPA (7%)\u003c/em\u003e, \u003cem\u003eSTAG2 (7%), FLT3-TKD (6%), BCOR (5%) and BCORL1 (5%)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The median number of somatic mutations per patient was 2 (IQR: 1\u0026ndash;4), with 8% having no detected mutation and 28% harboring four or more. Similarly, the median number of mutated genes per patient was 2 (IQR: 1\u0026ndash;3), with 23% having four or more mutated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Supplementary Table\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eAmong the 716 patients allografted in CR1, mutation frequencies were relatively consistent, with the most frequently detected being \u003cem\u003eFLT3-\u003c/em\u003eITD \u003cem\u003e(23%), DNMT3A (23%), NPM1 (20%), RUNX1 (17%), TET2 (14%), IDH2 (13%), NRAS (12%), SRSF2 (11%), ASXL1 (10%), IDH1 (10%), KRAS (7%), WT1 (7%), STAG2 (7%), TP53 (7%), CEBPA (6%)\u003c/em\u003e, \u003cem\u003eNF1 (6%), PTPN11\u003c/em\u003e (5%), \u003cem\u003eFLT3-TKD (5%), SF3B1 (5%) and PHF6 (5%)\u003c/em\u003e (Supplementary Fig.\u0026nbsp;1). The median number of somatic mutations per patient remained 2 (IQR: 1\u0026ndash;4), with 9% having no detected mutation and 26% carrying four or more, while the median number of mutated genes per patient was also 2 (IQR: 1\u0026ndash;3), with 21% having four or more (Supplementary Table\u0026nbsp;2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eAssociation of Frequently Mutated Genes\u003c/h2\u003e\u003cp\u003eWe analyzed the co-occurrence and interactions of the most frequently mutated genes, present in more than 5% of cases and tested in at least 800 patients. The twelve selected genes included \u003cem\u003eNPM1\u003c/em\u003e, \u003cem\u003eFLT3-\u003c/em\u003eITD, \u003cem\u003eDNMT3A\u003c/em\u003e, \u003cem\u003eRUNX1\u003c/em\u003e, \u003cem\u003eASXL1\u003c/em\u003e, \u003cem\u003eSRSF2\u003c/em\u003e, \u003cem\u003eTET2\u003c/em\u003e, \u003cem\u003eIDH1\u003c/em\u003e, \u003cem\u003eIDH2\u003c/em\u003e, \u003cem\u003eKRAS\u003c/em\u003e, \u003cem\u003eNRAS\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e in 753 patients. The interactions between these genes are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003e, with a waterfall plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) showing the co-occurrence of mutations in individual patients, a circos plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) depicting the frequency of co-occurrence between mutation pairs, and a diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) displaying positive (co-occurring) and negative (mutually exclusive) associations. \u003cem\u003eNPM1\u003c/em\u003e mutations were positively associated with \u003cem\u003eFLT3\u003c/em\u003e-ITD and \u003cem\u003eDNMT3A\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 each), and, to a lesser extent, with \u003cem\u003eIDH1\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.007) and \u003cem\u003eIDH2\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.037), but negatively associated with \u003cem\u003eRUNX1\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), \u003cem\u003eASXL1\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.002), \u003cem\u003eSRSF2\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0003), and \u003cem\u003eTP53\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0009). \u003cem\u003eFLT3\u003c/em\u003e-ITD was also positively linked to \u003cem\u003eDNMT3A\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) but negatively linked to \u003cem\u003eASXL1\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0001), \u003cem\u003eSRSF2\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0033), \u003cem\u003eNRAS\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0005), and \u003cem\u003eTP53\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0164). \u003cem\u003eDNMT3A\u003c/em\u003e was also positively associated with \u003cem\u003eIDH1\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and \u003cem\u003eIDH2\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0015), yet negatively associated with \u003cem\u003eASXL1\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.048) and \u003cem\u003eSRSF2\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0005). \u003cem\u003eRUNX1\u003c/em\u003e mutations were also positively associated with \u003cem\u003eASXL1\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0014) and \u003cem\u003eSRSF2\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) mutations, but negatively associated with \u003cem\u003eKRAS\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.037). \u003cem\u003eASXL1\u003c/em\u003e mutation was also positively associated with \u003cem\u003eSRSF2\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and \u003cem\u003eTET2\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), while \u003cem\u003eSRSF2\u003c/em\u003e was positively linked to \u003cem\u003eIDH1\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.021), \u003cem\u003eIDH2\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and \u003cem\u003eTET2\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0016) mutations, but negatively associated with \u003cem\u003eKRAS\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.034) and \u003cem\u003eTP53\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0044). \u003cem\u003eIDH1\u003c/em\u003e was also positively associated with \u003cem\u003eNRAS\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.02) but negatively associated with \u003cem\u003eIDH2\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.03), while \u003cem\u003eIDH2\u003c/em\u003e was negatively associated with \u003cem\u003eTP53\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.017). Lastly, \u003cem\u003eNRAS\u003c/em\u003e was negatively associated with \u003cem\u003eKRAS\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0037), and \u003cem\u003eKRAS\u003c/em\u003e was negatively associated with \u003cem\u003eTP53\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.016) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eMCA identified three distinct groups of co-occurring mutations: the first group included \u003cem\u003eDNMT3A\u003c/em\u003e, \u003cem\u003eNPM1\u003c/em\u003e, and \u003cem\u003eFLT3-ITD\u003c/em\u003e; the second consisted of \u003cem\u003eASXL1\u003c/em\u003e, \u003cem\u003eSRSF2\u003c/em\u003e, and \u003cem\u003eRUNX1\u003c/em\u003e, and the third comprised \u003cem\u003eIDH1, IDH2, and TET2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Additionally, \u003cem\u003eTP53\u003c/em\u003e and \u003cem\u003eKRAS\u003c/em\u003e mutations occupied the same position due to their common pattern of being predominantly associated with the absence of other somatic mutations; however, they were not found together in the same patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Finally, \u003cem\u003eNRAS\u003c/em\u003e did not present a specific co-occurrence pattern.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEffect of Individual Somatic Mutations on Post-Transplant Outcomes in CR1\u003c/h3\u003e\n\u003cp\u003eOutcome analysis was performed on a subset of 646 AML patients allografted in CR1 with available follow-up data and molecular results for the ten genes identified through MCA: \u003cem\u003eDNMT3A, NPM1, FLT3-\u003c/em\u003eITD, \u003cem\u003eASXL1, SRSF2, RUNX1, IDH1, IDH2, TET2, and TP53\u003c/em\u003e. Patient characteristics are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Most patients had \u003cem\u003ede novo\u003c/em\u003e AML (75%), with a median age of 55 years (range: 19\u0026ndash;75 years). The majority received primarily reduced intensity conditioning (57%) and peripheral blood stem cells (95%) from matched sibling (32%), matched unrelated (35%), and haploidentical (18%) donors. Based on the ELN 2022 classification, 4% had favorable-risk cytogenetics, 70% intermediate-risk, and 26% adverse-risk. At the time of transplant, MRD was positive in 129 patients (35%), and negative in 238 (65%), while 279 patients had no MRD data available. After a median follow-up of 3.1 years, the 2-year RI, NRM, LFS and OS for the whole cohort were 21%, 13%, 66%, and 73%, respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003ePatient characteristics of the cohort of AML patients allografted in CR1\u003c/b\u003e with available data on follow-up and for the ten genes derived from the MCA results (\u003cem\u003eDNMT3A, NPM1, FLT3-\u003c/em\u003eITD, \u003cem\u003eASXL1, SRSF2, RUNX1, IDH1, IDH2, TET2 and TP53)\u003c/em\u003e. N\u0026thinsp;=\u0026thinsp;646 patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModalities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAll patients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDecuple wild type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIDH/\u003c/p\u003e\u003cp\u003eTET2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRUNX1\u003c/p\u003e\u003cp\u003eASXL1\u003c/p\u003e\u003cp\u003eSRSF2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFLT3ITD\u003c/p\u003e\u003cp\u003eDNMT3A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNPM1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTP53\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;646\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e167\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at HCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emedian\u003c/p\u003e\u003cp\u003e(range), years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003cp\u003e(19\u0026ndash;75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47\u003c/p\u003e\u003cp\u003e(19\u0026ndash;75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55\u003c/p\u003e\u003cp\u003e(20\u0026ndash;74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u003c/p\u003e\u003cp\u003e(20\u0026ndash;74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57\u003c/p\u003e\u003cp\u003e(19\u0026ndash;73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e53\u003c/p\u003e\u003cp\u003e(20\u0026ndash;75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e60\u003c/p\u003e\u003cp\u003e(22\u0026ndash;72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear of HCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emedian\u003c/p\u003e\u003cp\u003e(range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003cp\u003e(2013-23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003cp\u003e(2013-23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003cp\u003e(2016-23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003cp\u003e(2015-23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003cp\u003e(2015-23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003cp\u003e(2015-22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003cp\u003e(2015-22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient Sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale, N(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e318 (49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93 (56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77 (58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e56 (44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e52 (40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20 (43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime to HCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emedian\u003c/p\u003e\u003cp\u003e(range), mo.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003cp\u003e(1\u0026ndash;27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003cp\u003e(2\u0026ndash;23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e(3\u0026ndash;14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003cp\u003e(2\u0026ndash;23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003cp\u003e(1\u0026ndash;9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003cp\u003e(3\u0026ndash;27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e(3\u0026ndash;17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMRD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive, N(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129 (35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19 (30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27 (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e47 (43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7 (30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAML type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ede novo, N(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e486 (75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125 (75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38 (88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e85 (64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e104 (81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e111 (86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e23 (49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSec AML\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160 (25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42 (25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47 (36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24 (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e24 (51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eCytogenetic ELN2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFavorable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNot done\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e421 (70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84 (53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88 (70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e94 (79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e108 (92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e16 (36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160 (26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 (36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9 (22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31 (25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24 (20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e29 (64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of genes tested\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emedian\u003c/p\u003e\u003cp\u003e(range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003cp\u003e(8-141)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39\u003c/p\u003e\u003cp\u003e(21\u0026ndash;141)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40\u003c/p\u003e\u003cp\u003e(21\u0026ndash;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40\u003c/p\u003e\u003cp\u003e(15\u0026ndash;141)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40\u003c/p\u003e\u003cp\u003e(11\u0026ndash;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e36\u003c/p\u003e\u003cp\u003e(8\u0026ndash;78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e40\u003c/p\u003e\u003cp\u003e(12\u0026ndash;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of mutations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emedian\u003c/p\u003e\u003cp\u003e(range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e(0\u0026ndash;12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e(0\u0026ndash;7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e(1\u0026ndash;8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(1\u0026ndash;10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(0\u0026ndash;9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(1\u0026ndash;10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e(1\u0026ndash;12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eDonor type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e223 (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57 (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44 (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e49 (38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e39 (31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e19 (40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNot done\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHaplo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27 (21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22 (17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9 (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMUD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e223 (35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46 (35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e39 (30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e53 (41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e13 (28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSource of cells\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e611 (95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e156 (93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40 (93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e129 (98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e120 (94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e122 (95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e44 (94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNot done\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyeloablativeconditioning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e271 (43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85 (53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51 (39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e53 (42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e55 (43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12 (26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eHCT: Hematopoietic cell transplantation; mo.: months; MRD: measurable residual disease; AML: Acute myeloid leukemia; sec: secondary; ELN: European LeukemiaNet; MSD: Matched related donor; Haplo: haploidentical donor; MUD; matched unrelated donor; PB: peripheral blood; BM: bone marrow.\u0026nbsp;\u003c/p\u003e\u003cp\u003eWe then evaluated the impact of individual somatic mutations on 2-year post-transplant outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). \u003cem\u003eNPM1\u003c/em\u003e mutation was associated with improved outcomes, showing higher 2-year LFS (74% vs 63%; p\u0026thinsp;=\u0026thinsp;0.02) and OS (82% vs 71%; p\u0026thinsp;=\u0026thinsp;0.009). Conversely, \u003cem\u003eTP53\u003c/em\u003e mutation was linked to higher 2-year RI (42 vs 20%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and lower 2-year LFS (35% vs 68%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and reduced 2-year OS (47% vs 76%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u003cem\u003eFLT3\u003c/em\u003e-ITD mutation was associated with lower NRM (10% vs 15%; p\u0026thinsp;=\u0026thinsp;0.04) and improved OS (80% vs 71%; p\u0026thinsp;=\u0026thinsp;0.01). \u003cem\u003eSRSF2\u003c/em\u003e mutation was associated with a slight increase in 2-year RI (26% vs 21%; p\u0026thinsp;=\u0026thinsp;0.05). Meanwhile, \u003cem\u003eIDH2\u003c/em\u003e mutation was linked to lower 2-year RI (13% vs 23%; p\u0026thinsp;=\u0026thinsp;0.02) and improved LFS (76% vs 64%; p\u0026thinsp;=\u0026thinsp;0.02) and OS (83% vs 72%; p\u0026thinsp;=\u0026thinsp;0.02). The effects of other individual mutations on post-transplant outcomes are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eImpact of Individual Somatic Mutations on 2-Year Post-Transplant Outcomes for AML patients allografted in CR1\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2-year OS\u003c/p\u003e\u003cp\u003e% [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2-year LFS\u003c/p\u003e\u003cp\u003e% [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e2-year RI\u003c/p\u003e\u003cp\u003e% [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e2-year NRM\u003c/p\u003e\u003cp\u003e% [95% CI]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eNPM1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71 [66.6\u0026ndash;74.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.2 [58.7\u0026ndash;67.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e22.3 [18.6\u0026ndash;26.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e14.5 [11.5\u0026ndash;17.8]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.4 [74.5\u0026ndash;88.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73.9 [65.3\u0026ndash;80.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e17.6 [11.4\u0026ndash;24.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e8.5 [4.5\u0026ndash;14.2]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eTP53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.5 [71.7\u0026ndash;78.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 [63.9\u0026ndash;71.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e19.6 [16.3\u0026ndash;23]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e12.4 [9.9\u0026ndash;15.3]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.8 [31.8\u0026ndash;60.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.6 [21.3\u0026ndash;48.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e41.5 [26.9\u0026ndash;55.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e23.8 [12.6\u0026ndash;37]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eDNMT3A\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.1 [69.8\u0026ndash;77.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.3 [60.7\u0026ndash;69.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e22 [18.3\u0026ndash;26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e12.6 [9.8\u0026ndash;15.8]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.9 [62.4\u0026ndash;77.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66 [57.4\u0026ndash;73.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e18.6 [12.6\u0026ndash;25.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e15.4 [10-21.8]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eFLT3-ITD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.4 [66.9\u0026ndash;75.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.9 [59.3\u0026ndash;68.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e21.5 [17.8\u0026ndash;25.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e14.5 [11.5\u0026ndash;17.9]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.8 [72.2\u0026ndash;85.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.2 [61.9\u0026ndash;77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e20.3 [14.1\u0026ndash;27.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e9.5 [5.5\u0026ndash;15]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissing N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e4 (1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e4 (1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eRUNX1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.3 [69.1\u0026ndash;77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.1 [60.7\u0026ndash;69.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e21.8 [18.2\u0026ndash;25.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e13.1 [10.3\u0026ndash;16.2]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.8 [64.2\u0026ndash;81.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.8 [57-74.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e18.8 [12-26.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e14.4 [8.6\u0026ndash;21.6]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissing N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e1 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eASXL1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.3 [68.3\u0026ndash;75.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.8 [60.6\u0026ndash;68.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e21.7 [18.3\u0026ndash;25.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e13.5 [10.8\u0026ndash;16.5]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.5 [71.5\u0026ndash;90.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.1 [57.5\u0026ndash;81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e17.6 [9-28.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e11.3 [4.9\u0026ndash;20.6]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eSRSF2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.3 [69.2\u0026ndash;76.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.8 [61.5\u0026ndash;69.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e21.6 [18.1\u0026ndash;25.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e12.6 [10-15.6]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 [64.1\u0026ndash;85.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.8 [51.1\u0026ndash;75.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e17.5 [8.9\u0026ndash;28.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e17.7 [9.4\u0026ndash;28.2]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissing N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e16 (2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e16 (2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eIDH2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.9 [67.7\u0026ndash;75.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.9 [59.6\u0026ndash;67.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e22.5 [19-26.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e13.6 [10.9\u0026ndash;16.7]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.2 [72.8\u0026ndash;89.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.7 [64.4\u0026ndash;83.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e13.2 [6.7\u0026ndash;21.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e11.2 [5.4\u0026ndash;19.2]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissing N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eIDH1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.3 [70.3\u0026ndash;77.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e66 [61.8\u0026ndash;69.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e20.7 [17.4\u0026ndash;24.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.3 [10.6\u0026ndash;16.2]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.4 [51-76.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e60.3 [45.9\u0026ndash;72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e25.6 [14.7\u0026ndash;37.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.1 [6.5\u0026ndash;24.6]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissing N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e2 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eTET2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.7 [68.6\u0026ndash;76.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e65.4 [61.1\u0026ndash;69.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e21.6 [18.1\u0026ndash;25.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13 [10.3\u0026ndash;16.1]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77.1 [66.3\u0026ndash;84.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e64.4 [52.7\u0026ndash;73.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e20.4 [12.2\u0026ndash;30.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.2 [8.5\u0026ndash;23.7]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissing N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e3 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: OS: overall survival; LFS: leukemia-free survival; RI: Relapse Incidence; NRM: non-relapse mortality.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eEffect of groups of somatic mutations on post-transplant outcomes in CR1\u003c/h3\u003e\n\u003cp\u003eAs stated before, the MCA identified three distinct groups of co-occurring mutations: \u003cem\u003eNPM1/FLT3-\u003c/em\u003eITD\u003cem\u003e/DNMT3A\u003c/em\u003e, \u003cem\u003eSRSF2/ASXL1/RUNX1\u003c/em\u003e, and \u003cem\u003eIDH1/IDH2/TET2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Because of the known impact of \u003cem\u003eNPM1\u003c/em\u003e, the first group was split into two subgroups according to the presence or absence of \u003cem\u003eNPM1\u003c/em\u003e mutation. In addition, we added a group of \u003cem\u003eTP53\u003c/em\u003e mutation for its known negative impact and because in the MCA, \u003cem\u003eTP53\u003c/em\u003e mutations were predominantly associated with the absence of other somatic mutations. Therefore, six non-overlapping groups were constructed: Group 1 comprised patients with \u003cem\u003eTP53\u003c/em\u003e mutation regardless of other co-mutations (N\u0026thinsp;=\u0026thinsp;47; 7%); Group 2 comprised patients with \u003cem\u003eNPM1\u003c/em\u003e mutation and wild type \u003cem\u003eTP53\u003c/em\u003e regardless of other co-mutations (N\u0026thinsp;=\u0026thinsp;129; 20%); Group 3 comprised patients with \u003cem\u003eFLT3\u003c/em\u003e-ITD and/or \u003cem\u003eDNMT3A\u003c/em\u003e mutation, with wild type \u003cem\u003eNPM1 and TP53\u003c/em\u003e, regardless of other co-mutations (N\u0026thinsp;=\u0026thinsp;128; 20%); Group 4 comprised patients with \u003cem\u003eRUNX1\u003c/em\u003e and/or \u003cem\u003eASXL1\u003c/em\u003e and/or \u003cem\u003eSRSF2\u003c/em\u003e mutation (SAR group) without \u003cem\u003eFLT3-\u003c/em\u003eITD and with wild type \u003cem\u003eNPM1\u003c/em\u003e, \u003cem\u003eDNMT3A\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e and regardless of other co-mutations (N\u0026thinsp;=\u0026thinsp;132; 20%); Group 5 comprised patients with \u003cem\u003eIDH1\u003c/em\u003e and/or \u003cem\u003eIDH2\u003c/em\u003e and/or \u003cem\u003eTET2\u003c/em\u003e mutation without \u003cem\u003eFLT3-\u003c/em\u003eITD and with wild type \u003cem\u003eNPM1\u003c/em\u003e, \u003cem\u003eDNMT3A, RUNX1, ASXL1\u003c/em\u003e, \u003cem\u003eSRSF2\u003c/em\u003e and \u003cem\u003eTP53 and\u003c/em\u003e regardless of other co-mutations (N\u0026thinsp;=\u0026thinsp;43; 7%); and Group 6 comprised patients with all ten genes unmutated (N\u0026thinsp;=\u0026thinsp;167; 26%). These groups differed significantly in median age (60, 53, 57, 59, 55 and 47 years respectively; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the frequency of secondary AML (51%, 14%, 19%, 36%, 12% and 25% respectively; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the frequency of adverse cytogenetics (64%, 8%, 20%, 25%, 23% and 37% respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMoreover, the groups of somatic mutations significantly impacted the 2-year RI (42%, 17%, 21%, 18%, 13% and 24% respectively; p\u0026thinsp;=\u0026thinsp;0.005), LFS (35%, 74%, 68%, 71%, 71% and 61% respectively; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and OS (47%, 82%, 74%, 81%, 76% and 67% respectively; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) whereas NRM was not significantly affected (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMultivariable analysis\u003c/h2\u003e\u003cp\u003eIn the multivariable analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), compared to the Group 2 (\u003cem\u003eNPM1\u003c/em\u003e mutation and wild-type \u003cem\u003eTP53\u003c/em\u003e), RI, LFS, and OS were negatively affected by Group1 (\u003cem\u003eTP53\u003c/em\u003e mutation; hazard ratio [HR] 2.6, 2.9, and 3.05, respectively, all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and OS was additionally negatively affected by Group 6 (all ten genes unmutated; HR 1.73, p\u0026thinsp;=\u0026thinsp;0.02). NRM, LFS and OS were negatively affected by older age (HR 1.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 1.04, p\u0026thinsp;=\u0026thinsp;0.02 and 1.05; p\u0026thinsp;=\u0026thinsp;0.02, respectively). Finally, adverse karyotype negatively affected RI, LFS, and OS (HR 1.93, 1.64 and 1.64, respectively, all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eLFS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eRI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eNRM\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eGroup of mutations\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNPM1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eSRSF2/ASXL1/RUNX1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.16 (0.7\u0026ndash;1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.13 (0.7\u0026ndash;1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.11 (0.6-2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.17 (0.6\u0026ndash;2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eFLT3-ITD /DNMT3A\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003cp\u003e(0.8\u0026ndash;1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003cp\u003e(0.7-2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.24 (0.7\u0026ndash;2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.06 (0.5\u0026ndash;2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eIDH/TET2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05 (0.6-2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.17 (0.6\u0026ndash;2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003cp\u003e(0.3-2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.48 (0.6\u0026ndash;3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eDecuple negative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003cp\u003e(1-2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.73 (1.1\u0026ndash;2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.29 (0.8\u0026ndash;2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003cp\u003e(0.9\u0026ndash;3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTP53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003cp\u003e(1.6\u0026ndash;4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.90 (1.7-5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.05 (1.6\u0026ndash;5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.07 (0.9\u0026ndash;4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at HCT (3y increment)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003cp\u003e(1-1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003cp\u003e(1-1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e(0.96-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003cp\u003e(1.1\u0026ndash;1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eType of AML\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ede novo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eSec AML\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88 (0.7\u0026ndash;1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.92 (0.7\u0026ndash;1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.78 (0.5\u0026ndash;1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.02 (0.6\u0026ndash;1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eELN 2022 cytogenetic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eFav/Int\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAdverse\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.64 (1.2\u0026ndash;2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.64 (1.2\u0026ndash;2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.93 (1.4\u0026ndash;2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.28 (0.8-2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eDonor type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMatched relative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMismatched relative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003cp\u003e(0.6\u0026ndash;1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.88 (0.6\u0026ndash;1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003cp\u003e(0.5\u0026ndash;1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.98 (0.6\u0026ndash;1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eUnrelated\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96 (0.7\u0026ndash;1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003cp\u003e(0.7\u0026ndash;1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e(0.7\u0026ndash;1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.87 (0.6\u0026ndash;1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eMyelo-ablative regimen\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.03 (0.8\u0026ndash;1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99 (0.7\u0026ndash;1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.03 (0.7\u0026ndash;1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e(0.6\u0026ndash;1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYear of HCT\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(by 5y increment)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98 (0.7\u0026ndash;1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.13 (0.7\u0026ndash;1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.93 (0.6\u0026ndash;1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.98 (0.6\u0026ndash;1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: LFS: leukemia-free survival; OS: overall survival; RI: Relapse Incidence; NRM: non-relapse mortality; HR: Hazard ratio; CI: Confidence interval; HCT: hematopoietic cell transplantation; y: year; AML: acute myeloid leukemia; Sec: secondary; Fav: favorable; Int: intermediate.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCharacteristics and post-transplant outcomes of patients with\u003c/b\u003e \u003cb\u003eNPM1\u003c/b\u003e \u003cb\u003emutation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe then analyzed the characteristics, distribution and impact of karyotype, \u003cem\u003eFLT3\u003c/em\u003e-ITD, and MRD status in 129 \u003cem\u003eNPM1\u003c/em\u003e mutated AML patients allografted in CR1, excluding those with \u003cem\u003eTP53\u003c/em\u003e mutations (N\u0026thinsp;=\u0026thinsp;4). The patients\u0026rsquo; characteristics and associated mutations are described in Supplementary Table\u0026nbsp;3. Karyotype was intermediate in 108 (92%) patients, predominantly normal (96 (74%) patients), and adverse in 9 (8%) patients. MRD was positive in 47 (43%) patients, negative in 62 (57%) patients and missing in 20 patients. \u003cem\u003eFLT3\u003c/em\u003e-ITD was present in 79 (61%) patients and negative in 48 (37%) patients. Patients with \u003cem\u003eFLT3\u003c/em\u003e-ITD were transplanted earlier and were less likely to be MRD positive at transplant (33% versus 61%). In univariate analysis (Supplementary Table\u0026nbsp;4), karyotype did not significantly affect post-transplant survival. We compared survival outcomes based on pre-transplant MRD positivity (2-year LFS: 67% vs. 79%; 2-year OS: 75% vs. 84%) and \u003cem\u003eFLT3\u003c/em\u003e-ITD status (2-year LFS: 70% vs. 78%; 2-year OS: 81% vs. 82%), though these differences did not reach statistical significance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEffect of\u003c/b\u003e \u003cb\u003eSRSF2\u003c/b\u003e \u003cb\u003eand/or\u003c/b\u003e \u003cb\u003eASXL1\u003c/b\u003e \u003cb\u003eand/or\u003c/b\u003e \u003cb\u003eRUNX1\u003c/b\u003e \u003cb\u003emutation on post-transplant outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSurprisingly, in addition to the well-established favorable prognosis of \u003cem\u003eNPM1\u003c/em\u003e mutations, the post-transplant outcomes were non different for patients in the SAR Group, with a 2-year LFS of 71% and OS of 81%, despite an older median age and a high frequency of secondary AML (36%) and adverse karyotype (25%). This group consisted of 132 patients carrying at least one of the ELN 2022 adverse-risk mutations - \u003cem\u003eSRSF2\u003c/em\u003e, \u003cem\u003eASXL1\u003c/em\u003e, and/or \u003cem\u003eRUNX1\u003c/em\u003e (SAR mutations) - after excluding \u003cem\u003eFLT3-\u003c/em\u003eITD, \u003cem\u003eNPM1\u003c/em\u003e, \u003cem\u003eDNMT3A\u003c/em\u003e, and \u003cem\u003eTP53\u003c/em\u003e mutations. Given these unexpectedly favorable outcomes, we further analyzed the characteristics, distribution and impact of SAR mutations in the whole cohort of allografted AML patients in CR1, including those with \u003cem\u003eFLT3-\u003c/em\u003eITD, \u003cem\u003eNPM1\u003c/em\u003e, \u003cem\u003eDNMT3A\u003c/em\u003e, and \u003cem\u003eTP53\u003c/em\u003e mutations. The patients\u0026rsquo; characteristics and associated mutations are described in Supplementary Table\u0026nbsp;5. According to ELN 2022, karyotype was favorable in 7 (4%) patients, intermediate in 131 (73%, normal in 47%) and adverse in 41 (23%) patients. The number of SAR mutations was 1 for 125 (66%) patients, 2 in 46 (24%) patients and 3 in 18 (10%) patients. Pre-transplant MRD was positive in 28 (28%) patients and negative in 72 (72%) patients (89 missing). The 2-year LFS and OS were 69% and 78% for all 189 SAR patients, and were non different to those of the 132 SAR patients belonging to Group 4. The remaining 57 SAR patients included six patients with concomitant \u003cem\u003eTP53\u003c/em\u003e mutations (Group 1), 12 with concomitant \u003cem\u003eNPM1\u003c/em\u003e mutations (Group 2), and 39 with concomitant \u003cem\u003eFLT3\u003c/em\u003e-ITD and/or \u003cem\u003eDNMT3A\u003c/em\u003e mutations (Group 3). In univariate analysis (Supplementary Table\u0026nbsp;6), post-transplant outcomes were not significantly affected by karyotype, pre-transplant MRD status and the number of SAR mutations. These findings suggest that in the allogeneic transplant setting, SAR mutations are associated with excellent post-transplant outcomes, challenging their traditionally adverse-risk classification at diagnosis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCharacteristics and post-transplant outcomes of patients with\u003c/b\u003e \u003cb\u003eTP53\u003c/b\u003e \u003cb\u003emutation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 2-yr LFS of 35% and 2-yr OS of 47% in \u003cem\u003eTP53\u003c/em\u003e mutant patients are quite encouraging. We therefore analyzed the characteristics, distribution and impact of karyotype, type of AML, and \u003cem\u003eTP53\u003c/em\u003e variant allele frequency (VAF) in 47 \u003cem\u003eTP53\u003c/em\u003e mutated AML patients (23 \u003cem\u003ede novo\u003c/em\u003e and 24 secondary) allografted in CR1. The patients\u0026rsquo; characteristics and associated mutations are described in Supplementary Table\u0026nbsp;7. According to ELN2022, karyotype was intermediate in 16 (36%) patients and adverse in 29 (64%) patients including 17 (38%) patients with chromosome 17 abnormalities. Median VAF was 45.5 (IQR 31.2\u0026ndash;50.8). In univariate analysis (Supplementary Table\u0026nbsp;8), outcomes of secondary vs \u003cem\u003ede novo\u003c/em\u003e AML (2-year LFS: 31% vs. 39%; 2-year OS: 34% vs. 60%), were not significantly different. Conversely, adverse karyotype negatively affected post-transplant survival compared to intermediate karyotype (2-year LFS 22% vs 49%, p\u0026thinsp;=\u0026thinsp;0.03; 2-year OS 34% vs 61%, p\u0026thinsp;=\u0026thinsp;0.03). Finally, \u003cem\u003eTP53\u003c/em\u003e mutation VAF above median (45\u0026ndash;94) significantly increased the incidence of relapse (2-year RI 55% vs 24%, p\u0026thinsp;=\u0026thinsp;0.02) but did not significantly affect post-transplant survival (2-year LFS 21% vs 47%; 2-year OS 36% vs 52%) in this small cohort. It is important to note that for 21 patients with \u003cem\u003eTP53\u003c/em\u003e mutation VAF below median, karyotype was intermediate in 10 patients and adverse in 10 patients whereas for 21 patients with \u003cem\u003eTP53\u003c/em\u003e mutation VAF above median, karyotype was intermediate in 4 patients and adverse in 16 patients. These results suggest excellent post-transplant outcomes for AML patients with \u003cem\u003eTP53\u003c/em\u003e mutation in the absence of adverse karyotype or high \u003cem\u003eTP53\u003c/em\u003e VAF.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics and post-transplant outcomes of patients lacking mutations in the 10 most frequently mutated genes\u003c/h2\u003e\u003cp\u003eFinally, patients in the decuple-negative group, those lacking mutations in the ten most frequently mutated genes, exhibited inferior survival compared to other groups without \u003cem\u003eTP53\u003c/em\u003e mutations, with a 2-year OS of 67%. This finding is notable given the absence of well-characterized driver mutations. We therefore analyzed the characteristics, distribution and impact of karyotype, type of AML, and pre-transplant MRD status in 167 decuple-negative AML patients (125 (75%) \u003cem\u003ede novo\u003c/em\u003e and 42 (25%) secondary) allografted in CR1. The patients\u0026rsquo; characteristics and associated mutations are described in Supplementary Table\u0026nbsp;9. According to ELN2022, karyotype was favorable in 17 (11%) patients, intermediate in 84 (53%) and adverse in 58 (36%) patients. Pre-transplant MRD was positive in 26 (35%) patients, negative in 48 (65%) patients and missing in 93 patients. In univariate analysis (Supplementary Table\u0026nbsp;10), age negatively affected NRM, whereas pre-transplant MRD positivity numerically decreased survival. Adverse karyotype significantly increased relapse (2-year RI 35% compared to 12% for favorable and 18% for intermediate, p\u0026thinsp;=\u0026thinsp;0.02) and accordingly numerically reduced survival. Surprisingly, in that group, post-transplant outcomes were better in secondary AML (2-year LFS 77% vs 56%, p\u0026thinsp;=\u0026thinsp;0.03; 2-year OS 78% vs 64%, p\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this large, transplant-focused cohort of 952 AML patients with available diagnostic NGS data, we were able to delineate distinct groups of mutations with different prognostic implications, challenging existing risk assumptions mainly within the ELN 2022 classification.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Unlike most prior studies that evaluate the impact of individual mutations in isolation, our analysis uniquely assesses the effect of grouped mutational profiles, offering a more integrated view of molecular risk stratification, specifically in the context of allo-HCT.\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Despite a highly selected population undergoing allo-HCT, where the majority of patients had de novo AML with mainly diploid intermediate-risk cytogenetics, and transplanted in first remission, we observed that the most frequently mutated genes included \u003cem\u003eDNMT3A\u003c/em\u003e, \u003cem\u003eFLT3\u003c/em\u003e-ITD, and \u003cem\u003eNPM1\u003c/em\u003e, consistent with prior genomic studies that analyzed mutation frequency at diagnosis.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Notably, 23% of patients harbored four or more somatic mutations, with a median of two mutations per patient, highlighting the complex clonal architecture at diagnosis, even among those achieving remission prior to allo-HCT. This genetic heterogeneity was further explored through co-mutation analysis of the 12 commonly altered genes, revealing distinct co-occurrence and exclusivity patterns which are biologically plausible: for example, \u003cem\u003eNPM1\u003c/em\u003e was strongly associated with \u003cem\u003eFLT3\u003c/em\u003e-ITD and \u003cem\u003eDNMT3A\u003c/em\u003e, a well-established mutational triad, and was less likely to occur with adverse-risk genes like \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eRUNX1\u003c/em\u003e, ASXL1 and \u003cem\u003eSRSF2\u003c/em\u003e. In contrast, mutations in epigenetic and splicing regulators (\u003cem\u003eASXL1\u003c/em\u003e, \u003cem\u003eSRSF2\u003c/em\u003e, \u003cem\u003eRUNX1\u003c/em\u003e) formed a separate cluster, frequently co-mutating with one another but largely excluding \u003cem\u003eFLT3\u003c/em\u003e-ITD and \u003cem\u003eNPM1\u003c/em\u003e.\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e This multidimensional clustering by MCA supports the presence of biologically coherent subgroups that may inform prognostic modeling. Six genetically defined subgroups were constructed based on recurrent co-mutation patterns and the established prognostic roles of \u003cem\u003eTP53\u003c/em\u003e and \u003cem\u003eNPM1\u003c/em\u003e. These subgroups had different clinical and biological features, including age, secondary AML, cytogenetics, and MRD status. Importantly, they showed significantly different post-transplant survival outcomes. Basically, there were three prognostic categories according to somatic mutations: \u003cem\u003eTP53\u003c/em\u003e mutation (poor risk), no mutation (intermediate risk), and others (good risk). As expected, \u003cem\u003eTP53\u003c/em\u003e-mutated patients (Group 1) had the worst outcomes among all groups, with a 2-year RI of 42% and OS of 47%, but notably superior to previously reported benchmarks.\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e A meta-analysis reported a pooled 2-year OS of 30% for 297 \u003cem\u003eTP5\u003c/em\u003e3-mutated AML patients undergoing allo-HCT.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e The relatively improved outcomes in our \u003cem\u003eTP53\u003c/em\u003e cohort may reflect the fact that only 45% of patients with mutant \u003cem\u003eTP53\u003c/em\u003e harbored either 17p chromosomal abnormalities or complex karyotype, both of which are strongly linked to poor outcomes. In a recent large cohort from the EBMT, \u003cem\u003eTP53\u003c/em\u003e-mutated AML patients lacking both 17p abnormalities and complex karyotype had a markedly improved 2-year OS of 65.2%.\u003csup\u003e25\u003c/sup\u003e Overall, our results suggest excellent post-transplant outcomes for AML patients with TP53 mutation in the absence of adverse karyotype or high \u003cem\u003eTP53\u003c/em\u003e VAF.\u003c/p\u003e\u003cp\u003eConversely, \u003cem\u003eNPM1\u003c/em\u003e-mutated patients with \u003cem\u003eTP53\u003c/em\u003e wild-type status (Group 2) had excellent outcomes, with a 2-year OS and LFS of 82% and 74%, respectively, in line with prior data demonstrating sensitivity of this subgroup to intensive chemotherapy and allo-HCT.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e In this group, outcomes were not significantly affected by karyotype, pre-transplant MRD status or the presence of \u003cem\u003eFLT3\u003c/em\u003e-ITD.\u003c/p\u003e\u003cp\u003eNotably, \u003cem\u003eFLT3\u003c/em\u003e-ITD mutation was paradoxically linked to reduced NRM and improved 2-year OS (80%) in our cohort, suggesting that \u003cem\u003eFLT3\u003c/em\u003e-ITD mutant AML may be transitioning toward a more favorable-risk category when managed with a total therapy approach, incorporating allo-HCT in CR1 along with pre- and post-transplant FLT3 inhibitor therapies.\u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32 CR33\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eInterestingly, beyond the negative prognostic role of \u003cem\u003eTP53\u003c/em\u003e mutation, there was no significant difference in post-transplant outcomes in CR1 between the other groups of somatic mutations. Notably, patients classified as group 4, harboring \u003cem\u003eSRSF2\u003c/em\u003e, \u003cem\u003eASXL1\u003c/em\u003e, and/or \u003cem\u003eRUNX1\u003c/em\u003e mutations (SAR mutations), in the absence of \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eFLT3\u003c/em\u003e-ITD, \u003cem\u003eDNMT3A\u003c/em\u003e, or \u003cem\u003eNPM1\u003c/em\u003e, had unexpectedly favorable post-transplant outcomes, with a 2-year OS of 81% and LFS of 71%. These results persisted despite the group's older median age, higher frequency of secondary AML, and enrichment for adverse cytogenetics. Historically, SAR mutations are considered adverse-risk in the ELN classification, \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e based on their poor response to chemotherapy and association with clonal hematopoiesis and secondary AML.\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e However, our data suggest that in the transplant setting, their adverse impact may be abrogated, possibly through enhanced sensitivity to graft-versus-leukemia effects. When extended to the entire CR1 cohort with SAR mutations, regardless of additional co-mutations, we confirmed a similarly favorable outcome profile (2-year OS 78%, LFS 70%). One possible explanation for the favorable outcomes observed in this group is that patients with these mutations typically exhibit low response rates to conventional chemotherapy, often less than 50%.\u003csup\u003e16\u0026ndash;18\u003c/sup\u003e Therefore, those who achieved CR1 and proceeded to allo-HCT may represent a biologically selected group with inherently better disease control or chemosensitivity, translating into superior post-transplant outcomes. Additionally, the lack of an adverse prognostic impact of our SAR group may be dominated by the \u003cem\u003eRUNX1\u003c/em\u003e-associated risk, as a \u003cem\u003eRUNX1\u003c/em\u003e-only mutation was present in 38% of patients in this subgroup. The existing literature shows that patients harboring isolated \u003cem\u003eRUNX1\u003c/em\u003e mutations without co-occurring mutations in the four most frequently mutated splicing genes (\u003cem\u003eSRSF2\u003c/em\u003e, \u003cem\u003eSF3B1\u003c/em\u003e, \u003cem\u003eU2AF1\u003c/em\u003e, and \u003cem\u003eZRSR2\u003c/em\u003e) have outcomes comparable to the intermediate-risk group, with a reported 5-year OS of 44%.\u003csup\u003e37\u003c/sup\u003e In contrast, the co-occurrence of \u003cem\u003eRUNX1\u003c/em\u003e and \u003cem\u003eSRSF2\u003c/em\u003e or \u003cem\u003eASXL1\u003c/em\u003e and \u003cem\u003eSRSF2\u003c/em\u003e mutations, both of which have been independently associated with significantly inferior survival in prior studies, accounted for only 8% and 11% of our cohort, respectively.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e This finding may partially explain the unexpectedly favorable outcomes observed in our SAR group.\u003c/p\u003e\u003cp\u003eFinally, patients in the decuple-negative group, those lacking mutations in the ten most frequently mutated genes, exhibited inferior survival compared to other groups without \u003cem\u003eTP53\u003c/em\u003e mutations, with a 2-year OS of 67%. This finding is notable given the absence of well-characterized driver mutations. Interestingly, a substantial proportion (40%) of these patients harbored either \u003cem\u003eKRAS\u003c/em\u003e, \u003cem\u003eNRAS\u003c/em\u003e, or \u003cem\u003ePTPN11\u003c/em\u003e mutations, genes often associated with proliferation signaling, chemoresistance, and suboptimal post-transplant outcomes. \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e Furthermore, 37% of them had adverse cytogenetics, a rate significantly higher than that observed in the other molecular subgroups (8%-25%) excluding those with \u003cem\u003eTP53\u003c/em\u003e mutations. These findings may have contributed to their poorer prognosis post-transplant.\u003c/p\u003e\u003cp\u003eThis study has several limitations due to its retrospective nature. The absence of centralized NGS testing across centers likely resulted in variations in NGS platforms, library preparation protocols, and data analysis pipelines. Moreover, there were some missing genetic data, as many genes were not systematically tested across the cohort. VAF data were also unavailable for the majority of the genes tested, limiting the ability to assess the clonal burden at diagnosis and its potential prognostic value. Most importantly, it is critical to mention that included patients were primarily from centers performing NGS at diagnosis, many of which were high-expertise or high-volume centers. MRD data pre-transplant were also missing in 43% of the overall cohort. However, this was not uniform as in cases with \u003cem\u003eNPM1\u003c/em\u003e mutations, MRD data were missing in only 16%, while in patients with decuple-negative, MRD was missing in 55% of cases, suggesting that MRD data availability was dependent on the mutational profile where MRD techniques actually exist. Finally, the lack of data on the induction and consolidation treatments received before transplant, and the lack of a control group of non-transplanted patients, preclude the direct assessment of the benefit of allo-HCT versus chemotherapy alone for the different groups of mutational profiles.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eNGS at diagnosis can be extremely useful in risk stratification of AML patients undergoing allo-HCT, potentially allowing adequate post-transplant interventions. Surprisingly, despite their older age and higher frequency of secondary AML and adverse cytogenetics, the excellent outcomes (2-year LFS 71%, OS 81%) were observed for patients harboring \u003cem\u003eSRSF2\u003c/em\u003e and/or \u003cem\u003eASXL1\u003c/em\u003e and/or \u003cem\u003eRUNX1\u003c/em\u003e in the absence of \u003cem\u003eFLT3-\u003c/em\u003eITD or \u003cem\u003eNPM1\u003c/em\u003e, \u003cem\u003eDNMT3A\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e mutation, indicating that allo-HCT in CR1 can overcome the adverse-risk associated with these somatic mutations at diagnosis. Together, these results argue for a revised transplant-specific risk model with better post-transplant prognostication. In particular, the data challenge the assumption that \u003cem\u003eFLT3\u003c/em\u003e-ITD and SAR mutations uniformly confer adverse prognosis, and raise the possibility that certain mutational contexts, historically deemed high-risk, may derive substantial benefit from allo-HCT. Prospective studies are warranted to validate these observations and explore the mechanistic basis of transplant sensitivity in these subgroups.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and informed consent statement:\u0026nbsp;\u003c/strong\u003eThis is a retrospective, registry-based, multicenter study utilizing patient data collected and approved by the Acute Leukemia Working Party (ALWP) of the EBMT. The EBMT is a collaborative network comprising more than 600 transplant centers that are required to report all consecutive HCTs and subsequent follow-ups on an annual basis. Routine audits are performed to ensure data accuracy and completeness. Since January 2003, all participating transplant centers have been required to obtain written informed consent from patients prior to data registration with the EBMT, in accordance with the ethical principles outlined in the Declaration of Helsinki (1975).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe data analyzed in this study were provided and approved by the ALWP of the EBMT. All relevant data supporting the findings of this study are available within the Article and the Supplementary Material. Requests for access to EBMT study data from qualified external researchers will be reviewed by the relevant working party in accordance with EBMT\u0026rsquo;s data-sharing policies. This process ensures the protection of patient privacy, maintains data security and integrity, and promotes scientific and medical innovation. Additional details on data-sharing criteria and the request process can be obtained by contacting [email protected]. Individual patient-level data will not be shared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e All authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis project was not funded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contribution:\u0026nbsp;\u003c/strong\u003eA.Ba. proposed the study, interpreted the data and wrote the manuscript. JEG and M.Mo. participated in study design, interpreted the data, and edited the manuscript. JEG. was responsible for statistical analysis. All other authors reported updated patient data and read and commented on the manuscript. All authors proofread the manuscript and agreed on the data presented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003ePatients and caregivers, EBMT participating centers, the ALWP statistical and data management team, particularly Aleksandra (Sasha) Gavrilina, as well as the leadership team.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis was presented in part during the American Society of Hematology meeting in San Diego, December 2023. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDiNardo CD, Erba HP, Freeman SD, Wei AH. 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In allogeneic hematopoietic cell transplantation (allo-HCT) setting, the frequency and prognosis of gene-gene interactions may differ from those of patients treated with chemotherapy alone. In this study, adult patients (N\u0026thinsp;=\u0026thinsp;952) with AML allografted between 2015\u0026ndash;2023, with available next generation sequencing (NGS) at diagnosis were included. The most frequent mutations were \u003cem\u003eDNMT3A (24%), FLT3-\u003c/em\u003eITD \u003cem\u003e(21%), NPM1 (21%), RUNX1 (16%), NRAS (16%), TET2 (14%)\u003c/em\u003e, and \u003cem\u003eIDH2 (12%).\u003c/em\u003e Multiple correspondence analysis identified distinct groups of co-occurring mutations. Outcome analysis was performed on 646 AML patients allografted in first complete remission (CR1). Six non-overlapping groups were constructed according to found groups and well-known clinical impact of \u003cem\u003eNPM1\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e mutations: 1) \u003cem\u003eTP53\u003c/em\u003e mutation; 2) \u003cem\u003eNPM1\u003c/em\u003e mutation; 3) \u003cem\u003eFLT3\u003c/em\u003e-ITD and/or \u003cem\u003eDNMT3A\u003c/em\u003e mutation; 4) \u003cem\u003eSRSF2\u003c/em\u003e and/or \u003cem\u003eASXL1\u003c/em\u003e and/or \u003cem\u003eRUNX1\u003c/em\u003e mutation (SAR group); 5) \u003cem\u003eIDH1\u003c/em\u003e and/or \u003cem\u003eIDH2\u003c/em\u003e and/or \u003cem\u003eTET2\u003c/em\u003e mutation; and 6) all ten genes unmutated. In multivariable analysis, \u003cem\u003eTP53\u003c/em\u003e mutation, adverse karyotype, and age negatively affected leukemia-free survival (LFS) and overall survival (OS). OS was additionally negatively affected when the ten genes were unmutated. Notably, outcomes were excellent for SAR mutations (2-year LFS 76%, OS 84%), indicating allo-HCT in CR1 can overcome their adverse risk at diagnosis.\u003c/p\u003e","manuscriptTitle":"Frequency and Impact of Somatic Co-occurring Mutations on Post-Transplant Outcomes in Acute Myeloid Leukemia: A Multicenter Registry Analysis on Behalf of the EBMT ALWP","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 06:45:29","doi":"10.21203/rs.3.rs-7375987/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-09-16T15:21:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-15T23:39:29+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-07T06:52:27+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-04T22:09:32+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-08-23T10:55:06+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-08-22T18:36:42+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-08-22T14:28:27+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-08-22T14:21:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-15T09:12:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-14T17:16:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bone Marrow Transplantation","date":"2025-08-14T17:16:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bone-marrow-transplantation","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"bmt","sideBox":"Learn more about [Bone Marrow Transplantation](http://www.nature.com/bmt/)","snPcode":"41409","submissionUrl":"https://mts-bmt.nature.com/cgi-bin/main.plex","title":"Bone Marrow Transplantation","twitterHandle":"@bmtjournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2e5924f0-117c-4292-b3cd-49cca7c6706f","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53578461,"name":"Health sciences/Diseases/Haematological diseases/Haematological cancer/Leukaemia/Acute myeloid leukaemia"},{"id":53578462,"name":"Biological sciences/Genetics/Cancer genomics"}],"tags":[],"updatedAt":"2025-12-05T08:06:16+00:00","versionOfRecord":{"articleIdentity":"rs-7375987","link":"https://doi.org/10.1038/s41409-025-02770-4","journal":{"identity":"bone-marrow-transplantation","isVorOnly":false,"title":"Bone Marrow Transplantation"},"publishedOn":"2025-12-05 05:00:00","publishedOnDateReadable":"December 5th, 2025"},"versionCreatedAt":"2025-09-02 06:45:29","video":"","vorDoi":"10.1038/s41409-025-02770-4","vorDoiUrl":"https://doi.org/10.1038/s41409-025-02770-4","workflowStages":[]},"version":"v1","identity":"rs-7375987","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7375987","identity":"rs-7375987","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}