Therapeutic effect of allogeneic stem cell transplantation in acute myeloid leukemia patients with epigenetic modifier gene mutations

Therapeutic effect of allogeneic stem cell transplantation in acute myeloid leukemia patients with epigenetic modifier gene mutations | 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 Research Article Therapeutic effect of allogeneic stem cell transplantation in acute myeloid leukemia patients with epigenetic modifier gene mutations Shu Fang, Sai Huang, Mengzhen Wang, Kun Qian, Zhenyang Gu, Jingjing Yang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3848683/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Epigenetic modifier gene mutations (EMM) have been reported to be associated with poor prognosis in acute myeloid leukemia (AML). Whether allogeneic stem cell transplantation (allo-HSCT) can improve outcomes in this patients remains unknown. Material/Methods: This study retrospectively collected clinical information of 353 AML patients with gene mutations detected by next-generation sequencing (NGS) and analyzed the therapeutic effect of allogeneic stem cell transplantation in acute myeloid leukemia patients with epigenetic modifier gene mutations. Results EMM-positive patients tended to have inferior OS compared with EMM-negative patients (p = 0.065, HR = 1.343, 95%CI: 0.981–1.838), EMM-positive patients had inferior LFS (p = 0.031, HR = 1.385, 95%CI: 1.030–1.863). In EMM-positive patients, multivariate analysis showed that patients who received allo-HSCT had a superior OS (yes vs. no, p < 0.001, HR = 0.213, 95%CI: 0.134–0.339, Table 3) and LFS (yes vs. no, p < 0.001, HR = 0.303, 95%CI: 0.199–0.461, Table 3) compared with patients who did not receive allo-HSCT. A total of 220 patients received allo-HSCT in all patients. Univariate analysis in patients undergoing allo-HSCT showed that EMM was not a risk factor for OS (EMM-positive vs. EMM-negative, p = 0.470, HR = 1.192, 95%CI: 0.740–1.920) and LFS (EMM-positive vs. EMM-negative, p = 0.323, HR = 1.235, 95%CI: 0.813–1.876). Conclusion EMM tended to be a poor risk factor for OS and was a poor risk factor for LFS in our cohort. Allo-HSCT might improve the OS and LFS of EMM-positive patients. mutation Hematopoietic Stem Cell Transplantation prognosis Leukemia Myeloid Acute Figures Figure 1 Figure 2 Background Acute myeloid leukemia is the most common adult acute leukemia of highly heterogeneous. Over the past years, anthracycline-based regimen remained the standard induction therapy, achieving 60%-80% complete remission rates (CRR) in newly diagnosed acute myeloid leukemia (AML) [ 1 , 2 ]. However, the majority of patients still suffer from relapsed or refractory diseases [ 3 – 6 ]. The prognosis of these patients is dismal. Allogeneic stem cell transplantation (allo-HSCT) is one of the most effective therapies in AML. Early use of allo-HSCT for poor-risk patients at the first complete remission (CR) can significantly improve the survival of these patients [ 7 ]. Epigenetic modifier gene mutations (EMM), mainly including DNMT3A [ 8 ], TET2 [ 9 ], IDH1, and IDH2 [ 10 ], have been reported to be associated with poor prognosis in AML. Some studies show that decitabine could benefit EMM-positive AML patients with intermediate-risk karyotypes [ 11 ]. Compared with patients without DNMT3A mutations, patients harboring DNMT3A mutations responded well to decitabine in acute myeloid leukemia [ 12 ]. Others show that allo-HSCT could improve the survival of patients with DNMT3A mutations in cytogenetically normal adult AML and with DNA-methylation regulatory gene mutations in AML [ 13 , 14 ]. Nonetheless, fewer studies explored the therapeutic effect of allo-HSCT in patients with combined mutations of methylation and acetylation regulatory genes. In this article, we aimed to study whether there was an association between the therapeutic effect of allo-HSCT and epigenetic modifier gene mutations (EMM). We then explored whether different induction regimens could influence the survival of EMM-positive patients undergoing allo-HSCT. We also analyzed the prognostic factors before transplant in patients undergoing allo-HSCT. Materials and Methods Patients A total of 353 AML patients were involved in this retrospective study. Patients with acute promyelocytic leukemia were excluded. Bone marrow (BM) or peripheral blood (PB) samples were obtained in patients who received treatment in Chinese PLA General Hospital between August 2008 and November 2020 before treatment. This study was approved by the Ethics Committee of the General Hospital of the Chinese People’s Liberation Army, following the Declaration of Helsinki. The diagnosis and classification of patients were performed according to French-American-British (FAB) and World Health Organization (WHO). NCCN risk stratifications were performed based on NCCN guidelines 2021 [ 15 ]. Treatment In our study, a total of 116 patients received a DCAG regimen as an induction regimen, and 237 patients received a conventional “3 + 7” regimen as an induction regimen. Treatment options were based on the patient's wishes and the physician's evaluation. The DCAG standard regimen included decitabine (DAC) 20 mg/m 2 , intravenous drip (VD) Day 1-5; cytarabine 100 or 200 mg/m 2 every 12 hours, VD Day 1–5; aclarubicin 10 mg/m 2 /day, intravenous injection (IV) Day 1–5, and G-CSF 300 µg/day subcutaneously from Day 0 to neutrophil recovery. DCAG regimen for elderly patients included decitabine 20 mg/m 2 , intravenous drip (VD) Day 1–5; cytarabine 10 or 20 mg/m 2 every 12 hours, VD Day 1–5; aclarubicin 10 mg/m 2 /day, IV Day 1–5, and G-CSF 300 µg/day subcutaneously from Day 0 to neutrophil recovery. The conventional “3 + 7” regimen included one of the anthracyclines (daunorubicin or idarubicin) or mitoxantrone or homoharringtonine for 3 days, and cytarabine for 7 days, as previously described [ 16 , 11 ]. After induction therapy, 220 out of 353 patients received allo-HSCT. The number of patients achieving first CR (CR1), second CR (CR2), partial remission (PR), non-remission (NR), and relapsed before transplantation was 191 (86.8%), 10 (4.6%), 6 (2.7%), 4 (1.8%), and 9 (4.1), respectively (Table 1 ). Table 1 Clinical characteristics of 353 patients. Characteristic No. (%) Age, y, median (range) 42 (10–78) < 60y 307 (87.0) ≥ 60y 46 (13.0) Gender Female 142 (40.2) Male 211 (59.8) Extramedullary infiltration No 331 (93.8) Yes 22 (6.2) Median WBC, ×10 9 /L (range) 10.84 (0.25-405.13) <10×10 9 /L 168 (47.6) ≥10×10 9 /L 185 (52.4) Median Hb, g/L (range) 1 83 (26–161) Median PLT, ×10 9 /L (range) 1 43 (3-924) Median BM blast, % (range) 59.2 (20.0–96.0) <50% 142 (40.2) ≥50% 211 (59.8) Origin of disease De novo AML 323 (91.5) Secondary AML 30 (8.5) FAB classification M0 1 (0.3) M1 7 (2.0) M2 116 (32.9) M4 70 (19.8) M5 81 (22.9) M6 6 (1.7) M7 1 (0.3) undefined 71 (20.1) NCCN risk stratification Favorable 143 (40.5) Intermediate 86 (24.4) Poor 124 (35.1) Types of induction therap 3 + 7 regimen 237 (67.1) DCAG regimen 116 (32.9) Allo-HSCT No 133 (37.7) Yes 220 (62.3) Disease status before transplantation CR1 191 (86.8) CR2 10 (4.6) PR 6 (2.7) NR 4 (1.8) relapse 9 (4.1) EMM + No 192 (54.4) Yes 161 (45.6) WBC, white blood cells; Hb, hemoglobin; PLT, platelet; BM, bone marrow; FAB, French–American–British; NCCN, National Comprehensive Cancer Network; Allo-HSCT, allogeneic hematopoietic stem cell transplantation; CR1, first complete remission; CR2, second complete remission; PR, partial remission; NR, non-remission; EMM +, epigenetic modifier gene mutation-positive. 1 Information of one patient was missing Prognostic endpoints The primary endpoints were overall survival (OS) and leukemia-free survival (LFS). The OS was defined as the time from diagnosis to death from any cause or the last follow-up (July 20th ,2021). The LFS was defined as the time from diagnosis to relapse or death or the last follow-up. LFS of refractory patients was defined as zero. CR, PR, and NR were defined according to NCCN. Overall response rate (ORR) included patients who obtained CR or PR. Three patients were lost during follow-up. The median follow-up was 23.63 months (range: 0.40-145.80 months). Next-generation sequencing Genomic DNA was isolated from bone marrow or peripheral blood samples. Mutation detection was performed using a targeted capture deep sequencing with next-generation sequencing at Acornmed Biotechnology Co. Ltd (Tianjin, China) (Supplementary Table S1 ). NovaSeq instrument (Illumina) was used to sequence multiplex libraries. Then, trimmed reads were aligned by using Burrows-Wheeler Alignment (BWA, version 0.7.12). PCR duplicates were marked by using the MarkDuplicates tool from Picard. The BWA data was then realigned and recalibrated by using IndelRealigner and BaseRecalibrator from Genome Analysis Toolkit (GATK; version 3.8). Variants, including the SNVs and Indels, were called by using mutect2. At last, ANNOVAR software was used to annotate variants. Patients harboring ≥ 1 mutation in genes with epigenetically modified functions, including mutated DNMT3A, IDH1, IDH2, TET2, ASXL1, KMT2C, EZH2, SETD2, CREBBP, EP300, and KDM6A, were defined as epigenetic modifier gene mutation-positive (EMM-positive) group. Patients without the aforementioned mutated genes were assigned to the epigenetic modifier gene mutation-negative (EMM-negative) group. Statistical analysis Statistical analysis was performed by using SPSS 24.0 and GraphPad Prism 8.0.2 software. Mutation frequency was calculated by dividing the number of mutations by sample size. Continuous variables (age, white blood cell [ABC] count, hemoglobin level, platelet count, percentage of bone marrow [BM] blast) were exhibited as median (range). Mann-Whitney U test was used to compare these variables. Chi-square test was exerted to compare categorical variables (age, gender, extramedullary infiltration, WBC count, percentage of BM blast, origin of disease, FAB classification, NCCN risk stratification, types of induction therapy, EMM, response rate). For the expected count of an event < 5 or a total number of patients < 40, fisher’s exact test was used. The COX proportional hazard model was used to perform univariate and multivariate analysis of the OS and LFS, a stepwise backward procedure selection model was used for extracting independent factors in multivariate analysis. Parameters with sample size > 2 were included in univariate analysis. Parameters with a p -value < 0.1 were included in the multivariate analysis. Kaplan-Meier (K - M) analysis was used to compare OS and LFS between two groups and among five groups. Log-rank test was used to calculate the p -value. A two-sided p < 0.05 was considered statistically significant. Results Gene mutation profile in AML patients A total of 812 mutations in 59 genes were discovered in 309 of 353 patients. Gene mutation profile in 353 patients were shown in Fig. 1 . Genes with mutation frequency > 10% were biCEBPA (20.1%), NRAS (17.6%), DNMT3A (15.6%), NPM1 (14.7%), FLT3-ITD (14.4%), and TET2 (11.0%) (Supplementary Figure S1 a). All mutated genes were grouped into eight different functional pathways, including EMM-positive group which harbored eleven mutated genes (Fig. 1 ). In EMM-positive patients, 161 (45.6%) cases harbored mutated DNMT3A (34.2%), TET2 (24.2%), ASXL1 (20.5%), IDH2 (18.6%), IDH1 (14.3%), KDM6A (6.2%), EZH2 (5.6%), SETD2 (3.1%), KMT2C (2.5%), EP300 (1.9%), and CREBBP (1.2%) (Supplementary Figure S1 b). In EMM-negative patients, genes with mutation frequency > 10% were biCEBPA (23.4%), NRAS (15.1%), FLT3-ITD (14.1%), and WT1 (10.4%) (Supplementary Figure S1 c). Clinical data of all patients A total of 353 de novo AML patients were included in our study. Clinical characteristics of all patients were listed in Table 1 . Patients with or without EMM were assigned to the EMM-positive group or EMM-negative group, respectively. Patients who were EMM-positive had a higher proportion of elderly patients (19.3% vs. 7.8%, p = 0.001, Supplementary Table S2) and a lower proportion of extramedullary infiltration (3.1% vs. 8.9%, p = 0.026) and elevated WBC counts (46.6% vs. 57.3%, p = 0.045) than patients who were EMM-negative. The proportion of patients receiving the DCAG regimen in EMM-positive group was higher than that in EMM-negative group (41.0% vs. 26.0%, p = 0.003, Supplementary Table S2). There were no differences in the distribution of gender, Hb counts, PLT counts, percentage of BM blast, origin of disease, FAB classification, NCCN risk stratification, and allo-HSCT between the two groups (Supplementary Table S2). In EMM-positive group, there were 51, 8, 13 and 11 patients who received “3 + 7” regimen followed by allo-HSCT, “3 + 7” regimen, DCAG regimen followed by allo-HSCT and DCAG regimen achieving CR, respectively (Supplementary Table S3). In EMM-negative group, there were 81, 12, 11 and 19 patients who received “3 + 7” regimen followed by allo-HSCT, “3 + 7” regimen, DCAG regimen followed by allo-HSCT and DCAG regimen achieving CR, respectively (Supplementary Table S4). Prognostic factors for OS and LFS in 353 patients Univariate analysis of the OS and LFS in all patients showed that EMM was a poor risk factor for OS and LFS. Other risk factors included age ≥ 60y, extramedullary infiltration, NCCN risk stratification (Intermediate vs. Favorable, Poor vs. Favorable), types of induction regimen (DCAG vs. 3 + 7 regimen), mutated KRAS, mutated SF3B1 were poor risk factors of OS (Table 2 ). Allo-HSCT was a favorable risk factor of OS ( p < 0.001, HR = 0.239, 95%CI: 0.175–0.326). Risk factors included age ≥ 60y, extramedullary infiltration, NCCN risk stratification (Intermediate vs. Favorable, Poor vs. Favorable), types of induction regimen (DCAG vs. 3 + 7 regimen), mutated SETBP1, mutated SF3B1 were poor risks factor of LFS (Table 2 ). Mutated KRAS tended to have a poor prognosis in LFS ( p = 0.0550, HR = 1.896, 95%CI: 1.000-3.593). Allo-HSCT was a favorable risk factor of OS ( p < 0.001, HR = 0.324, 95%CI: 0.242–0.434, Table 2 ). Table 2 Univariate and multivariate analysis of 353 patients. Univariate analysis Multivariate analysis p -value HR 95% CI p -value HR 95% CI OS Age (≥ 60y vs. <60y) < 0.001 2.998 2.068–4.347 Extramedullary infiltration 0.006 2.077 1.239–3.481 < 0.001 2.633 1.550–4.474 NCCN < 0.001 < 0.001 Intermediate vs. Favorable 0.008 1.728 1.150–2.597 < 0.001 2.379 1.571–3.603 Poor vs. Favorable < 0.001 2.229 1.560–3.185 < 0.001 2.320 1.619–3.325 Types of induction regimen DCAG vs. 3 + 7 regimen < 0.001 1.824 1.341–2.482 Allo-HSCT < 0.001 0.239 0.175–0.326 < 0.001 0.224 0.162–0.308 Mutated KRAS 0.006 2.445 1.285–4.650 Mutated SF3B1 0.029 3.034 1.118–8.230 EMM (+) vs. EMM (-) 0.023 1.385 1.047–1.833 0.065 1.343 0.981–1.838 LFS Age (≥ 60y vs. <60y) < 0.001 2.769 1.924–3.985 Extramedullary infiltration 0.002 2.174 1.316–3.591 < 0.001 2.563 1.538–4.272 NCCN 0.001 < 0.001 Intermediate vs. Favorable 0.050 1.463 1.000-2.141 0.005 1.756 1.186–2.601 Poor vs. Favorable < 0.001 1.939 1.389–2.707 < 0.001 2.034 1.450–2.853 Types of induction regimen DCAG vs. 3 + 7 regimen 0.001 1.665 1.242–2.232 Allo-HSCT < 0.001 0.324 0.242–0.434 < 0.001 0.313 0.231–0.423 Mutated SETBP1 0.046 2.472 1.015–6.021 0.009 3.418 1.365–8.559 Mutated KRAS 0.050 1.896 1.000-3.593 Mutated SF3B1 0.008 3.897 1.429–10.624 0.057 2.718 0.971–7.606 EMM (+) vs. EMM (-) 0.009 1.468 1.101–1.957 0.031 1.385 1.030–1.863 NCCN, National Comprehensive Cancer Network; Allo-HSCT, allogeneic hematopoietic stem cell transplantation; EMM +, epigenetic modifier gene mutation-positive; EMM −, epigenetic modifier gene mutation-negative. Multivariate analysis showed that EMM-positive patients tended to have inferior OS (EMM-positive vs. EMM-negative, p = 0.065, HR = 1.343, 95%CI: 0.981–1.838, Table 2 ) and had inferior LFS (EMM-positive vs. EMM-negative, p = 0.031, HR = 1.385, 95%CI: 1.030–1.863, Table 2 ) compared with EMM-negative patients. Allo-HSCT was an independent risk factor for OS and LFS (OS: yes vs. no, p < 0.001, HR = 0.224, 95%CI: 0.162–0.308; LFS: yes vs. no, p < 0.001, HR = 0.313, 95%CI: 0.231–0.423). Other independent risk factors for OS were extramedullary infiltration (yes vs. no, p < 0.001, HR = 2.633, 95%CI: 1.550–4.474), NCCN risk stratification (intermediate vs. favorable, p < 0.001, HR = 2.379, 95%CI: 1.571–3.603; poor vs. favorable, p < 0.001, HR = 2.320, 95%CI: 1.619–3.325). Other independent risk factors affecting LFS were extramedullary infiltration (yes vs. no, p < 0.001, HR = 2.563, 95%CI: 1.538–4.272), NCCN risk stratification (intermediate vs. favorable, p = 0.005, HR = 1.756, 95%CI: 1.186–2.601; poor vs. favorable, p < 0.001, HR = 2.034, 95%CI: 1.450–2.853), and mutated SETBP1 (yes vs. no, p = 0.009, HR = 3.418, 95%CI: 1.365–8.559) (Table 2 ). Allo-HSCT can reverse the poor prognosis of patients with EMM regardless of induction regimens In EMM-positive patients, multivariate analysis showed that patients who received allo-HSCT had a superior OS (yes vs. no, p < 0.001, HR = 0.213, 95%CI: 0.134–0.339, Table 3 ) and LFS (yes vs. no, p < 0.001, HR = 0.303, 95%CI: 0.199–0.461, Table 3 ) compared with patients who did not receive allo-HSCT. Other independent risk factors for OS and LFS were origin of disease (secondary vs. de novo, OS: p = 0.002, HR = 3.368, 95%CI: 1.563–7.257; LFS: p = 0.002, HR = 3.288, 95%CI: 1.528–7.072). In patients undergoing allo-HSCT, K-M analysis showed that the two-year OS and LFS rate of patients receiving the DCAG regimen were similar to that of patients receiving the “3 + 7” regimen (the DCAG regimen followed by allo-HSCT group vs. the “3 + 7” regimen followed by allo-HSCT group, two-year OS, 71.43% vs. 72.64%, p = 0.677; two-year LFS, 52.44% vs. 63.43%, p = 0.542, Fig. 2 A-B). Table 3 Univariate and multivariate analysis of patients who were EMM-positive. Univariate analysis Multivariate analysis p -value HR 95% CI p -value HR 95% CI OS Age (≥ 60y vs. <60y) < 0.001 2.988 1.876–4.757 Extramedullary infiltration 0.052 2.722 0.990–7.482 Origin of disease Secondary vs. de novo < 0.001 4.649 2.220–9.733 0.002 3.368 1.563–7.257 NCCN 0.019 0.098 Intermediate vs. Favorable 0.189 1.488 0.822–2.695 0.081 1.731 0.934–3.207 Poor vs. Favorable 0.005 2.020 1.238–3.296 0.049 1.651 1.002–2.721 Types of induction regimen DCAG vs. 3 + 7 regimen 0.002 1.969 1.285–3.017 Allo-HSCT < 0.001 0.214 0.136–0.336 < 0.001 0.213 0.134–0.339 Mutated KRAS 0.081 2.244 0.906–5.560 Mutated JAK2 0.058 3.993 0.955–16.694 Mutated U2AF1 0.037 2.622 1.059–6.489 LFS Age (≥ 60y vs. <60y) < 0.001 2.391 1.515–3.771 Origin of disease Secondary vs. de novo 0.001 3.748 1.778–7.901 0.002 3.288 1.528–7.072 NCCN 0.088 Intermediate vs. Favorable 0.449 1.239 0.712–2.155 Poor vs. Favorable 0.029 1.671 1.055–2.645 Types of induction regimen DCAG vs. 3 + 7 regimen 0.002 1.894 1.264–2.839 Allo-HSCT < 0.001 0.297 0.196–0.450 < 0.001 0.303 0.199–0.461 Mutated SF3B1 0.095 5.477 0.746–40.209 NCCN, National Comprehensive Cancer Network; Allo-HSCT, allogeneic hematopoietic stem cell transplantation. CRR, complete remission rate; ORR, overall remission rate. Prognostic factors before transplant in patients undergoing allo-HSCT Univariate analysis in patients undergoing allo-HSCT showed that EMM was not a risk factor for OS (EMM-positive vs. EMM-negative, p = 0.470, HR = 1.192, 95%CI: 0.740–1.920) and LFS (EMM-positive vs. EMM-negative, p = 0.323, HR = 1.235, 95%CI: 0.813–1.876) (Table 4 , Fig. 2 C-D). Multivariate analysis of OS showed that patients with non-remission status before allo-HSCT was an adverse prognostic factor in patients undergoing allo-HSCT (NR vs. CR1, p = 0.021, HR = 2.168, 95%CI: 1.123–4.188) (Table 4 , Fig. 2 E). Other independent adverse risk factor for OS was mutated KRAS (yes vs. no, p = 0.016, HR = 4.230, 95%CI: 1.606–17.301). Multivariate analysis of LFS showed that patients achieving CR1 before allo-HSCT had superior LFS compared with patients achieving CR2 before allo-HSCT (CR2 vs. CR1, p < 0.001, HR = 4.928, 95%CI: 2.399–10.122) and patients who did not achieve CR (NR vs. CR1, p = 0.001, HR = 2.720, 95%CI: 1.522–4.860) (Table 4 , Fig. 2 F). Other independent adverse risk factor for LFS was mutated BCORL1 (yes vs. no, p < 0.001, HR = 6.374, 95%CI: 2.280-17.821). Table 4 Univariate and multivariate analysis of patients undergoing allo-HSCT. Univariate analysis Multivariate analysis p -value HR 95% CI p -value HR 95% CI OS WBC ≥10×10 9 /L vs. <10×10 9 /L 0.034 0.600 0.375–0.961 Disease status before allo-HSCT 0.030 0.038 CR2 vs. CR1 0.200 1.823 0.727–4.569 0.166 1.919 0.763–4.824 NR vs. CR1 0.014 2.258 1.180–4.320 0.021 2.168 1.123–4.188 Mutated BCORL1 0.096 2.679 0.839–8.553 Mutated KRAS 0.019 4.024 1.257–12.885 0.016 4.230 1.306–13.694 EMM (+) vs. EMM (-) 0.470 1.192 0.740–1.920 LFS Extramedullary infiltration 0.059 2.108 0.971–4.578 Disease status before allo-HSCT < 0.001 < 0.001 CR2 vs. CR1 < 0.001 4.505 2.206-9.200 < 0.001 4.928 2.399–10.122 NR vs. CR1 0.001 2.630 1.477–4.682 0.001 2.720 1.522–4.860 Mutated BCORL1 0.002 4.892 1.772–13.508 < 0.001 6.374 2.280-17.821 Mutated SETBP1 0.012 3.654 1.334–10.007 EMM (+) vs. EMM (-) 0.323 1.235 0.813–1.876 NCCN, National Comprehensive Cancer Network; Allo-HSCT, allogeneic hematopoietic stem cell transplantation. CRR, complete remission rate; ORR, overall remission rate. Discussion Our study aimed to evaluate the prognostic value of EMM and the effective therapeutic method for these patients. We found that EMM was an inferior prognostic factor in the univariate analysis of all patients. Multivariate analysis was used to further analyze the independent prognostic value of EMM in OS and LFS. Our study showed that EMM-positive patients tended to have inferior OS compared with EMM-negative patients and EMM was a poor risk factor for LFS in all patients. In other studies, DNMT3A mutations were reported to be associated with decreased OS [ 8 , 17 ]. Patients with TET2 mutations appeared to have an adverse prognosis in intermediate-risk AML [ 9 ]. ASXL1 mutations were related to poor prognosis in AML patients [ 18 , 19 ]. IDH1 mutations were connected with adverse prognosis in cytogenetically normal acute myeloid leukemia, whereas the prognosis value of IDH2 mutations was controversial [ 10 , 20 ]. Other epigenomic modifier gene mutations, including EZH2 [ 21 ], KMT2C [ 22 ], SETD2 [ 23 ], and KDM6A [ 24 , 25 ], were also reported to be associated with leukemia pathogenesis. Our study showed a similar result by analyzing a mixture of these prognostic mutated genes. Multivariate analysis in EMM-positive patients showed that patients receiving the DCAG regimen had a similar OS and LFS rate as those receiving the “3 + 7” regimen, whereas allo-HSCT could improve the OS and LFS of these patients. Results in our study showed that there were no differences in OS and LFS between the DCAG regimen group and the “3 + 7” regimen group in EMM-positive patients, which was consistent with a previous study of elderly patients treated with induction therapy containing hypomethylating agents [ 26 ]. Nonetheless, allo-HSCT was a favorable risk factor in patients who were EMM-positive. Univariate analysis in patients undergoing allo-HSCT revealed that EMM was no longer a poor prognostic factor in patients undergoing allo-HSCT. These results indicated that allo-HSCT might reverse the poor prognosis of these patients, which was in line with previous studies [ 13 , 14 , 27 ]. Our study also showed that EMM-positive patients receiving the DCAG regimen followed by allo-HSCT had a similar long-term survival compared with those receiving the “3 + 7” regimen followed by allo-HSCT. All these results indicated that allo-HSCT might improve the OS and LFS of EMM-positive patients regardless of the types of induction regimen. Our findings also raised the question of whether epigenetic modifier gene mutations need to be taken into account in addition to high-risk patients when selecting transplant patients. More prospective studies are needed to explore this question. This study also has some limitations. As a singer-center retrospective study, there may be bias in patients, and the results may also be biased. Multicenter prospective studies are still needed to verify these results. Conclusions In conclusion, EMM tended to be a poor risk factor for OS and was a poor risk factor for LFS in our cohort. Allo-HSCT might improve the OS and LFS of EMM-positive patients regardless of the types of induction regimen. Declarations Competing interest None. Funding This work was partially supported by grants from the National Natural Science Foundation of China [grant numbers 820770178, 82170207], the Beijing Natural Science Foundation of China [grant numbers 7222175]. Author Contribution Gao Cj and Dou LP designed the study. Fang S, Huang S, Wang MZ, Wen YN, Wang H, Jiao YF and Wei Y Participated in data collection. Qian K, Gu ZY and Yang JJ performed statistical analysis and data interpretation. Fang S, Huang S and Wang MZ finished manuscript preparation and literature search. 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(2010) IDH1andIDH2Mutations Are Frequent Genetic Alterations in Acute Myeloid Leukemia and Confer Adverse Prognosis in Cytogenetically Normal Acute Myeloid Leukemia WithNPM1Mutation WithoutFLT3Internal Tandem Duplication. Journal of Clinical Oncology 28 (22):3636-3643. https://dx.doi.org/10.1200/jco.2010.28.3762. Xu Q, Li Y, Jing Y, et al. (2020) Epigenetic modifier gene mutations-positive AML patients with intermediate-risk karyotypes benefit from decitabine with CAG regimen. Int J Cancer 146 (5):1457-1467. https://dx.doi.org/10.1002/ijc.32593. Metzeler KH, Walker A, Geyer S, et al. (2012) DNMT3A mutations and response to the hypomethylating agent decitabine in acute myeloid leukemia. Leukemia 26 (5):1106-1107. https://dx.doi.org/10.1038/leu.2011.342. Xu Y, Sun Y, Shen H, et al. (2015) Allogeneic hematopoietic stem cell transplantation could improve survival of cytogenetically normal adult acute myeloid leukemia patients with DNMT3A mutations. Am J Hematol 90 (11):992-997. https://dx.doi.org/10.1002/ajh.24135. Ryotokuji T, Yamaguchi H, Ueki T, et al. (2016) Clinical characteristics and prognosis of acute myeloid leukemia associated with DNA-methylation regulatory gene mutations. Haematologica 101 (9):1074-1081. https://dx.doi.org/10.3324/haematol.2016.143073. Pollyea DA, Bixby D, Perl A, et al. (2021) NCCN Guidelines Insights: Acute Myeloid Leukemia, Version 2.2021. J Natl Compr Canc Netw 19 (1):16-27. https://dx.doi.org/10.6004/jnccn.2021.0002. Dou L, Xu Q, Wang M, et al. (2019) Clinical efficacy of decitabine in combination with standard-dose cytarabine, aclarubicin hydrochloride, and granulocyte colony-stimulating factor in the treatment of young patients with newly diagnosed acute myeloid leukemia. Onco Targets Ther 12:5013-5023. https://dx.doi.org/10.2147/ott.S200005. Thol F, Damm F, Lüdeking A, et al. (2011) Incidence and prognostic influence of DNMT3A mutations in acute myeloid leukemia. J Clin Oncol 29 (21):2889-2896. https://dx.doi.org/10.1200/jco.2011.35.4894. Gelsi-Boyer V, Brecqueville M, Devillier R, et al. (2012) Mutations in ASXL1 are associated with poor prognosis across the spectrum of malignant myeloid diseases. J Hematol Oncol 5:12. https://dx.doi.org/10.1186/1756-8722-5-12. Schnittger S, Eder C, Jeromin S, et al. (2013) ASXL1 exon 12 mutations are frequent in AML with intermediate risk karyotype and are independently associated with an adverse outcome. Leukemia 27 (1):82-91. https://dx.doi.org/10.1038/leu.2012.262. Green CL, Evans CM, Zhao L, et al. (2011) The prognostic significance of IDH2 mutations in AML depends on the location of the mutation. Blood 118 (2):409-412. https://dx.doi.org/10.1182/blood-2010-12-322479. Basheer F, Giotopoulos G, Meduri E, et al. (2019) Contrasting requirements during disease evolution identify EZH2 as a therapeutic target in AML. J Exp Med 216 (4):966-981. https://dx.doi.org/10.1084/jem.20181276. Chen R, Okeyo-Owuor T, Patel RM, et al. (2021) Kmt2c mutations enhance HSC self-renewal capacity and convey a selective advantage after chemotherapy. Cell Rep 34 (7):108751. https://dx.doi.org/10.1016/j.celrep.2021.108751. Licht JD (2017) SETD2: a complex role in blood malignancy. Blood 130 (24):2576-2578. https://dx.doi.org/10.1182/blood-2017-10-811927. Gozdecka M, Meduri E, Mazan M, et al. (2018) UTX-mediated enhancer and chromatin remodeling suppresses myeloid leukemogenesis through noncatalytic inverse regulation of ETS and GATA programs. Nat Genet 50 (6):883-894. https://dx.doi.org/10.1038/s41588-018-0114-z. Stief SM, Hanneforth AL, Weser S, et al. (2020) Loss of KDM6A confers drug resistance in acute myeloid leukemia. Leukemia 34 (1):50-62. https://dx.doi.org/10.1038/s41375-019-0497-6. DiNardo CD, Patel KP, Garcia-Manero G, et al. (2014) Lack of association of IDH1, IDH2 and DNMT3A mutations with outcome in older patients with acute myeloid leukemia treated with hypomethylating agents. Leuk Lymphoma 55 (8):1925-1929. https://dx.doi.org/10.3109/10428194.2013.855309. Antherieu G, Bidet A, Huet S, et al. (2021) Allogenic Stem Cell Transplantation Abrogates Negative Impact on Outcome of AML Patients with KMT2A Partial Tandem Duplication. Cancers (Basel) 13 (9). https://dx.doi.org/10.3390/cancers13092272. Additional Declarations No competing interests reported. Supplementary Files Supplementarymateria.docx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-3848683","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266274236,"identity":"717d94da-b9ae-42cf-bef5-b22088682947","order_by":0,"name":"Shu Fang","email":"","orcid":"","institution":"Beijing Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shu","middleName":"","lastName":"Fang","suffix":""},{"id":266274237,"identity":"d9ee8bcd-a308-41b6-8be6-2d0aca45a238","order_by":1,"name":"Sai Huang","email":"","orcid":"","institution":"the Fifth Medical Center of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sai","middleName":"","lastName":"Huang","suffix":""},{"id":266274238,"identity":"eb191288-b2de-468b-9e82-0dbfbeb9cf40","order_by":2,"name":"Mengzhen Wang","email":"","orcid":"","institution":"Beijing Jishuitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mengzhen","middleName":"","lastName":"Wang","suffix":""},{"id":266274239,"identity":"939feb2e-b6d6-48c1-817c-39d08398c0dd","order_by":3,"name":"Kun Qian","email":"","orcid":"","institution":"the Fifth Medical Center of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Qian","suffix":""},{"id":266274240,"identity":"a917fd22-f4d2-431d-a68f-63fe8a83e34e","order_by":4,"name":"Zhenyang Gu","email":"","orcid":"","institution":"the Fifth Medical Center of Chinese PLA General 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunji","middleName":"","lastName":"Gao","suffix":""},{"id":266274247,"identity":"36e07498-db61-462f-8bb3-a73687a9f547","order_by":11,"name":"Liping Dou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACPmYGhgMgBhsQH/hgYGNHUAsbspaDMwrSkglrQeYw83w4xNhAUAs778HDBb/uJfZJnzE8bGNwgJmB/fDRDfgdxpdweGZfcWIbX47B4RyDO3wMPGlpN/Br4TE4zNuTkNjGwwPS8oyZQYLHjAQtFgaHGRuI0sLzA6qFgTgtQL/wNiQYt/GwFRzsMUhLZiPkF37+s4c/8/xJkJ3fw7z5w48/Nnb87IeP4dXCwMDDwMDYhmwvfuVQLQx/CCsbBaNgFIyCEQwAaSdEsYTzqs4AAAAASUVORK5CYII=","orcid":"","institution":"the Fifth Medical Center of Chinese PLA General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Liping","middleName":"","lastName":"Dou","suffix":""}],"badges":[],"createdAt":"2024-01-09 15:44:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3848683/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3848683/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49491943,"identity":"fe899318-f357-434b-a885-b4d8eb67fe34","added_by":"auto","created_at":"2024-01-11 18:24:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103088,"visible":true,"origin":"","legend":"\u003cp\u003eThe genomic landscape of 353 AML patients. Mutations were divided into eight functional types, epigenetic modification, signal transduction, transcription, RNA splicing, spliceosome, cell cycle regulation, cohesion, and others.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3848683/v1/b68259a0ed8e6658833480fa.png"},{"id":49491944,"identity":"11c80ba1-c85e-4698-bfba-7b23f8b9ce39","added_by":"auto","created_at":"2024-01-11 18:24:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":261020,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis of patients. (A) OS in patients who were EMM-positive; (B) LFS in patients who were EMM-positive; (C) OS between EMM-positive and EMM-negative patients undergoing allo-HSCT; (D) LFS between EMM-positive and EMM-negative patients undergoing allo-HSCT; (E) OS among patients achieving CR1, CR2, PR, NR and relapse before transplantation in patients undergoing allo-HSCT; (F) LFS among patients achieving CR1, CR2, PR, NR and relapse before transplantation in patients undergoing allo-HSCT. OS, overall survival; LFS, leukemia-free survival; EMM, epigenetic modifier gene mutation; allo-HSCT, allogeneic stem cell transplantation; CR1, first complete remission; CR2, second complete remission; PR, partial remission; NR, non-remission.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3848683/v1/0c1d2e99788e4e54494be8e1.png"},{"id":50665696,"identity":"fd85ed80-b355-4f69-a4fa-0392515314bb","added_by":"auto","created_at":"2024-02-05 13:13:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":894252,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3848683/v1/a23814ee-967d-42e7-a382-a41342ebfbda.pdf"},{"id":49491945,"identity":"1c68b28a-b2e1-4d29-9bab-103ad2ce7fc5","added_by":"auto","created_at":"2024-01-11 18:24:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":372053,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymateria.docx","url":"https://assets-eu.researchsquare.com/files/rs-3848683/v1/93d0aa4ff3fb25586f28fb25.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Therapeutic effect of allogeneic stem cell transplantation in acute myeloid leukemia patients with epigenetic modifier gene mutations","fulltext":[{"header":"Background","content":"\u003cp\u003eAcute myeloid leukemia is the most common adult acute leukemia of highly heterogeneous. Over the past years, anthracycline-based regimen remained the standard induction therapy, achieving 60%-80% complete remission rates (CRR) in newly diagnosed acute myeloid leukemia (AML) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the majority of patients still suffer from relapsed or refractory diseases [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The prognosis of these patients is dismal. Allogeneic stem cell transplantation (allo-HSCT) is one of the most effective therapies in AML. Early use of allo-HSCT for poor-risk patients at the first complete remission (CR) can significantly improve the survival of these patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEpigenetic modifier gene mutations (EMM), mainly including DNMT3A [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], TET2 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], IDH1, and IDH2 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], have been reported to be associated with poor prognosis in AML. Some studies show that decitabine could benefit EMM-positive AML patients with intermediate-risk karyotypes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Compared with patients without DNMT3A mutations, patients harboring DNMT3A mutations responded well to decitabine in acute myeloid leukemia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Others show that allo-HSCT could improve the survival of patients with DNMT3A mutations in cytogenetically normal adult AML and with DNA-methylation regulatory gene mutations in AML [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Nonetheless, fewer studies explored the therapeutic effect of allo-HSCT in patients with combined mutations of methylation and acetylation regulatory genes. In this article, we aimed to study whether there was an association between the therapeutic effect of allo-HSCT and epigenetic modifier gene mutations (EMM). We then explored whether different induction regimens could influence the survival of EMM-positive patients undergoing allo-HSCT. We also analyzed the prognostic factors before transplant in patients undergoing allo-HSCT.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eA total of 353 AML patients were involved in this retrospective study. Patients with acute promyelocytic leukemia were excluded. Bone marrow (BM) or peripheral blood (PB) samples were obtained in patients who received treatment in Chinese PLA General Hospital between August 2008 and November 2020 before treatment. This study was approved by the Ethics Committee of the General Hospital of the Chinese People\u0026rsquo;s Liberation Army, following the Declaration of Helsinki. The diagnosis and classification of patients were performed according to French-American-British (FAB) and World Health Organization (WHO). NCCN risk stratifications were performed based on NCCN guidelines 2021 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTreatment\u003c/h2\u003e \u003cp\u003eIn our study, a total of 116 patients received a DCAG regimen as an induction regimen, and 237 patients received a conventional \u0026ldquo;3\u0026thinsp;+\u0026thinsp;7\u0026rdquo; regimen as an induction regimen. Treatment options were based on the patient's wishes and the physician's evaluation. The DCAG standard regimen included decitabine (DAC) 20 mg/m\u003csup\u003e2\u003c/sup\u003e, intravenous drip (VD) Day 1-5; cytarabine 100 or 200 mg/m\u003csup\u003e2\u003c/sup\u003e every 12 hours, VD Day 1\u0026ndash;5; aclarubicin 10 mg/m\u003csup\u003e2\u003c/sup\u003e/day, intravenous injection (IV) Day 1\u0026ndash;5, and G-CSF 300 \u0026micro;g/day subcutaneously from Day 0 to neutrophil recovery. DCAG regimen for elderly patients included decitabine 20 mg/m\u003csup\u003e2\u003c/sup\u003e, intravenous drip (VD) Day 1\u0026ndash;5; cytarabine 10 or 20 mg/m\u003csup\u003e2\u003c/sup\u003e every 12 hours, VD Day 1\u0026ndash;5; aclarubicin 10 mg/m\u003csup\u003e2\u003c/sup\u003e/day, IV Day 1\u0026ndash;5, and G-CSF 300 \u0026micro;g/day subcutaneously from Day 0 to neutrophil recovery. The conventional \u0026ldquo;3\u0026thinsp;+\u0026thinsp;7\u0026rdquo; regimen included one of the anthracyclines (daunorubicin or idarubicin) or mitoxantrone or homoharringtonine for 3 days, and cytarabine for 7 days, as previously described [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. After induction therapy, 220 out of 353 patients received allo-HSCT. The number of patients achieving first CR (CR1), second CR (CR2), partial remission (PR), non-remission (NR), and relapsed before transplantation was 191 (86.8%), 10 (4.6%), 6 (2.7%), 4 (1.8%), and 9 (4.1), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eClinical characteristics of 353 patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y, median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (10\u0026ndash;78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e307 (87.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142 (40.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211 (59.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtramedullary infiltration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e331 (93.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian WBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.84 (0.25-405.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;10\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168 (47.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;10\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185 (52.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian Hb, g/L (range) \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (26\u0026ndash;161)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian PLT, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L (range) \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (3-924)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian BM blast, % (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.2 (20.0\u0026ndash;96.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142 (40.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211 (59.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrigin of disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDe novo AML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e323 (91.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary AML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAB classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116 (32.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (19.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (22.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eundefined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (20.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCCN risk stratification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (40.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (24.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (35.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes of induction therap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026thinsp;+\u0026thinsp;7 regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e237 (67.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCAG regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116 (32.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllo-HSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (37.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220 (62.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease status before transplantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191 (86.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erelapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (4.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMM +\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192 (54.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161 (45.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eWBC, white blood cells; Hb, hemoglobin; PLT, platelet; BM, bone marrow; FAB, French\u0026ndash;American\u0026ndash;British; NCCN, National Comprehensive Cancer Network; Allo-HSCT, allogeneic hematopoietic stem cell transplantation; CR1, first complete remission; CR2, second complete remission; PR, partial remission; NR, non-remission; EMM +, epigenetic modifier gene mutation-positive.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003e1\u003c/sup\u003e Information of one patient was missing\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic endpoints\u003c/h2\u003e \u003cp\u003eThe primary endpoints were overall survival (OS) and leukemia-free survival (LFS). The OS was defined as the time from diagnosis to death from any cause or the last follow-up (July 20th ,2021). The LFS was defined as the time from diagnosis to relapse or death or the last follow-up. LFS of refractory patients was defined as zero. CR, PR, and NR were defined according to NCCN. Overall response rate (ORR) included patients who obtained CR or PR. Three patients were lost during follow-up. The median follow-up was 23.63 months (range: 0.40-145.80 months).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNext-generation sequencing\u003c/h2\u003e \u003cp\u003eGenomic DNA was isolated from bone marrow or peripheral blood samples. Mutation detection was performed using a targeted capture deep sequencing with next-generation sequencing at Acornmed Biotechnology Co. Ltd (Tianjin, China) (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). NovaSeq instrument (Illumina) was used to sequence multiplex libraries. Then, trimmed reads were aligned by using Burrows-Wheeler Alignment (BWA, version 0.7.12). PCR duplicates were marked by using the MarkDuplicates tool from Picard. The BWA data was then realigned and recalibrated by using IndelRealigner and BaseRecalibrator from Genome Analysis Toolkit (GATK; version 3.8). Variants, including the SNVs and Indels, were called by using mutect2. At last, ANNOVAR software was used to annotate variants. Patients harboring\u0026thinsp;\u0026ge;\u0026thinsp;1 mutation in genes with epigenetically modified functions, including mutated DNMT3A, IDH1, IDH2, TET2, ASXL1, KMT2C, EZH2, SETD2, CREBBP, EP300, and KDM6A, were defined as epigenetic modifier gene mutation-positive (EMM-positive) group. Patients without the aforementioned mutated genes were assigned to the epigenetic modifier gene mutation-negative (EMM-negative) group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed by using SPSS 24.0 and GraphPad Prism 8.0.2 software. Mutation frequency was calculated by dividing the number of mutations by sample size. Continuous variables (age, white blood cell [ABC] count, hemoglobin level, platelet count, percentage of bone marrow [BM] blast) were exhibited as median (range). Mann-Whitney U test was used to compare these variables. Chi-square test was exerted to compare categorical variables (age, gender, extramedullary infiltration, WBC count, percentage of BM blast, origin of disease, FAB classification, NCCN risk stratification, types of induction therapy, EMM, response rate). For the expected count of an event\u0026thinsp;\u0026lt;\u0026thinsp;5 or a total number of patients\u0026thinsp;\u0026lt;\u0026thinsp;40, fisher\u0026rsquo;s exact test was used. The COX proportional hazard model was used to perform univariate and multivariate analysis of the OS and LFS, a stepwise backward procedure selection model was used for extracting independent factors in multivariate analysis. Parameters with sample size\u0026thinsp;\u0026gt;\u0026thinsp;2 were included in univariate analysis. Parameters with a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were included in the multivariate analysis. Kaplan-Meier (K\u003cb\u003e-\u003c/b\u003eM) analysis was used to compare OS and LFS between two groups and among five groups. Log-rank test was used to calculate the \u003cem\u003ep\u003c/em\u003e-value. A two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGene mutation profile in AML patients\u003c/h2\u003e \u003cp\u003eA total of 812 mutations in 59 genes were discovered in 309 of 353 patients. Gene mutation profile in 353 patients were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Genes with mutation frequency\u0026thinsp;\u0026gt;\u0026thinsp;10% were biCEBPA (20.1%), NRAS (17.6%), DNMT3A (15.6%), NPM1 (14.7%), FLT3-ITD (14.4%), and TET2 (11.0%) (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). All mutated genes were grouped into eight different functional pathways, including EMM-positive group which harbored eleven mutated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In EMM-positive patients, 161 (45.6%) cases harbored mutated DNMT3A (34.2%), TET2 (24.2%), ASXL1 (20.5%), IDH2 (18.6%), IDH1 (14.3%), KDM6A (6.2%), EZH2 (5.6%), SETD2 (3.1%), KMT2C (2.5%), EP300 (1.9%), and CREBBP (1.2%) (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb). In EMM-negative patients, genes with mutation frequency\u0026thinsp;\u0026gt;\u0026thinsp;10% were biCEBPA (23.4%), NRAS (15.1%), FLT3-ITD (14.1%), and WT1 (10.4%) (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eClinical data of all patients\u003c/h2\u003e \u003cp\u003eA total of 353 de novo AML patients were included in our study. Clinical characteristics of all patients were listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients with or without EMM were assigned to the EMM-positive group or EMM-negative group, respectively. Patients who were EMM-positive had a higher proportion of elderly patients (19.3% \u003cem\u003evs.\u003c/em\u003e 7.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, Supplementary Table S2) and a lower proportion of extramedullary infiltration (3.1% \u003cem\u003evs.\u003c/em\u003e 8.9%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) and elevated WBC counts (46.6% \u003cem\u003evs.\u003c/em\u003e 57.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045) than patients who were EMM-negative. The proportion of patients receiving the DCAG regimen in EMM-positive group was higher than that in EMM-negative group (41.0% \u003cem\u003evs.\u003c/em\u003e 26.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, Supplementary Table S2). There were no differences in the distribution of gender, Hb counts, PLT counts, percentage of BM blast, origin of disease, FAB classification, NCCN risk stratification, and allo-HSCT between the two groups (Supplementary Table S2). In EMM-positive group, there were 51, 8, 13 and 11 patients who received \u0026ldquo;3\u0026thinsp;+\u0026thinsp;7\u0026rdquo; regimen followed by allo-HSCT, \u0026ldquo;3\u0026thinsp;+\u0026thinsp;7\u0026rdquo; regimen, DCAG regimen followed by allo-HSCT and DCAG regimen achieving CR, respectively (Supplementary Table S3). In EMM-negative group, there were 81, 12, 11 and 19 patients who received \u0026ldquo;3\u0026thinsp;+\u0026thinsp;7\u0026rdquo; regimen followed by allo-HSCT, \u0026ldquo;3\u0026thinsp;+\u0026thinsp;7\u0026rdquo; regimen, DCAG regimen followed by allo-HSCT and DCAG regimen achieving CR, respectively (Supplementary Table S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic factors for OS and LFS in 353 patients\u003c/h2\u003e \u003cp\u003eUnivariate analysis of the OS and LFS in all patients showed that EMM was a poor risk factor for OS and LFS. Other risk factors included age\u0026thinsp;\u0026ge;\u0026thinsp;60y, extramedullary infiltration, NCCN risk stratification (Intermediate vs. Favorable, Poor vs. Favorable), types of induction regimen (DCAG vs. 3\u0026thinsp;+\u0026thinsp;7 regimen), mutated KRAS, mutated SF3B1 were poor risk factors of OS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Allo-HSCT was a favorable risk factor of OS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;0.239, 95%CI: 0.175\u0026ndash;0.326). Risk factors included age\u0026thinsp;\u0026ge;\u0026thinsp;60y, extramedullary infiltration, NCCN risk stratification (Intermediate vs. Favorable, Poor vs. Favorable), types of induction regimen (DCAG vs. 3\u0026thinsp;+\u0026thinsp;7 regimen), mutated SETBP1, mutated SF3B1 were poor risks factor of LFS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Mutated KRAS tended to have a poor prognosis in LFS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0550, HR\u0026thinsp;=\u0026thinsp;1.896, 95%CI: 1.000-3.593). Allo-HSCT was a favorable risk factor of OS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;0.324, 95%CI: 0.242\u0026ndash;0.434, 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\u003eUnivariate and multivariate analysis of 353 patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (\u0026ge;\u0026thinsp;60y vs. \u0026lt;60y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.068\u0026ndash;4.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtramedullary infiltration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.239\u0026ndash;3.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.550\u0026ndash;4.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate vs. Favorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.150\u0026ndash;2.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.571\u0026ndash;3.603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor vs. Favorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.560\u0026ndash;3.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.619\u0026ndash;3.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes of induction regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCAG vs. 3\u0026thinsp;+\u0026thinsp;7 regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.341\u0026ndash;2.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllo-HSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.175\u0026ndash;0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.162\u0026ndash;0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutated KRAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.285\u0026ndash;4.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutated SF3B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.118\u0026ndash;8.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMM (+) vs. EMM (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.047\u0026ndash;1.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.981\u0026ndash;1.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (\u0026ge;\u0026thinsp;60y vs. \u0026lt;60y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.924\u0026ndash;3.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtramedullary infiltration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.316\u0026ndash;3.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.538\u0026ndash;4.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate vs. Favorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000-2.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.186\u0026ndash;2.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor vs. Favorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.389\u0026ndash;2.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.450\u0026ndash;2.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes of induction regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCAG vs. 3\u0026thinsp;+\u0026thinsp;7 regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.242\u0026ndash;2.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllo-HSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.242\u0026ndash;0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.231\u0026ndash;0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutated SETBP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.015\u0026ndash;6.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.365\u0026ndash;8.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutated KRAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000-3.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutated SF3B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.429\u0026ndash;10.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.971\u0026ndash;7.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMM (+) vs. EMM (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.101\u0026ndash;1.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.030\u0026ndash;1.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNCCN, National Comprehensive Cancer Network; Allo-HSCT, allogeneic hematopoietic stem cell transplantation; EMM +, epigenetic modifier gene mutation-positive; EMM \u0026minus;, epigenetic modifier gene mutation-negative.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariate analysis showed that EMM-positive patients tended to have inferior OS (EMM-positive \u003cem\u003evs.\u003c/em\u003e EMM-negative, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.065, HR\u0026thinsp;=\u0026thinsp;1.343, 95%CI: 0.981\u0026ndash;1.838, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and had inferior LFS (EMM-positive \u003cem\u003evs.\u003c/em\u003e EMM-negative, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031, HR\u0026thinsp;=\u0026thinsp;1.385, 95%CI: 1.030\u0026ndash;1.863, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) compared with EMM-negative patients. Allo-HSCT was an independent risk factor for OS and LFS (OS: yes \u003cem\u003evs.\u003c/em\u003e no, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;0.224, 95%CI: 0.162\u0026ndash;0.308; LFS: yes \u003cem\u003evs.\u003c/em\u003e no, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;0.313, 95%CI: 0.231\u0026ndash;0.423). Other independent risk factors for OS were extramedullary infiltration (yes \u003cem\u003evs.\u003c/em\u003e no, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;2.633, 95%CI: 1.550\u0026ndash;4.474), NCCN risk stratification (intermediate \u003cem\u003evs.\u003c/em\u003e favorable, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;2.379, 95%CI: 1.571\u0026ndash;3.603; poor \u003cem\u003evs.\u003c/em\u003e favorable, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;2.320, 95%CI: 1.619\u0026ndash;3.325). Other independent risk factors affecting LFS were extramedullary infiltration (yes \u003cem\u003evs.\u003c/em\u003e no, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;2.563, 95%CI: 1.538\u0026ndash;4.272), NCCN risk stratification (intermediate \u003cem\u003evs.\u003c/em\u003e favorable, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, HR\u0026thinsp;=\u0026thinsp;1.756, 95%CI: 1.186\u0026ndash;2.601; poor \u003cem\u003evs.\u003c/em\u003e favorable, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;2.034, 95%CI: 1.450\u0026ndash;2.853), and mutated SETBP1 (yes \u003cem\u003evs.\u003c/em\u003e no, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009, HR\u0026thinsp;=\u0026thinsp;3.418, 95%CI: 1.365\u0026ndash;8.559) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAllo-HSCT can reverse the poor prognosis of patients with EMM regardless of induction regimens\u003c/h2\u003e \u003cp\u003eIn EMM-positive patients, multivariate analysis showed that patients who received allo-HSCT had a superior OS (yes \u003cem\u003evs.\u003c/em\u003e no, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;0.213, 95%CI: 0.134\u0026ndash;0.339, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and LFS (yes \u003cem\u003evs.\u003c/em\u003e no, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;0.303, 95%CI: 0.199\u0026ndash;0.461, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) compared with patients who did not receive allo-HSCT. Other independent risk factors for OS and LFS were origin of disease (secondary vs. de novo, OS: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, HR\u0026thinsp;=\u0026thinsp;3.368, 95%CI: 1.563\u0026ndash;7.257; LFS: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, HR\u0026thinsp;=\u0026thinsp;3.288, 95%CI: 1.528\u0026ndash;7.072). In patients undergoing allo-HSCT, K-M analysis showed that the two-year OS and LFS rate of patients receiving the DCAG regimen were similar to that of patients receiving the \u0026ldquo;3\u0026thinsp;+\u0026thinsp;7\u0026rdquo; regimen (the DCAG regimen followed by allo-HSCT group \u003cem\u003evs.\u003c/em\u003e the \u0026ldquo;3\u0026thinsp;+\u0026thinsp;7\u0026rdquo; regimen followed by allo-HSCT group, two-year OS, 71.43% \u003cem\u003evs.\u003c/em\u003e 72.64%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.677; two-year LFS, 52.44% \u003cem\u003evs.\u003c/em\u003e 63.43%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.542, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B).\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\u003eUnivariate and multivariate analysis of patients who were EMM-positive.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (\u0026ge;\u0026thinsp;60y vs. \u0026lt;60y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.876\u0026ndash;4.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtramedullary infiltration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.990\u0026ndash;7.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrigin of disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary vs. de novo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.220\u0026ndash;9.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.563\u0026ndash;7.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate vs. Favorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.822\u0026ndash;2.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.934\u0026ndash;3.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor vs. Favorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.238\u0026ndash;3.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.002\u0026ndash;2.721\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes of induction regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCAG vs. 3\u0026thinsp;+\u0026thinsp;7 regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.285\u0026ndash;3.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllo-HSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.136\u0026ndash;0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.134\u0026ndash;0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutated KRAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.906\u0026ndash;5.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutated JAK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.955\u0026ndash;16.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutated U2AF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.059\u0026ndash;6.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (\u0026ge;\u0026thinsp;60y vs. \u0026lt;60y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.515\u0026ndash;3.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrigin of disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary vs. de novo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.778\u0026ndash;7.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.528\u0026ndash;7.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate vs. Favorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.712\u0026ndash;2.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor vs. Favorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.055\u0026ndash;2.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes of induction regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCAG vs. 3\u0026thinsp;+\u0026thinsp;7 regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.264\u0026ndash;2.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllo-HSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.196\u0026ndash;0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.199\u0026ndash;0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutated SF3B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.746\u0026ndash;40.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNCCN, National Comprehensive Cancer Network; Allo-HSCT, allogeneic hematopoietic stem cell transplantation. CRR, complete remission rate; ORR, overall remission rate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic factors before transplant in patients undergoing allo-HSCT\u003c/h2\u003e \u003cp\u003eUnivariate analysis in patients undergoing allo-HSCT showed that EMM was not a risk factor for OS (EMM-positive vs. EMM-negative, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.470, HR\u0026thinsp;=\u0026thinsp;1.192, 95%CI: 0.740\u0026ndash;1.920) and LFS (EMM-positive vs. EMM-negative, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.323, HR\u0026thinsp;=\u0026thinsp;1.235, 95%CI: 0.813\u0026ndash;1.876) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D). Multivariate analysis of OS showed that patients with non-remission status before allo-HSCT was an adverse prognostic factor in patients undergoing allo-HSCT (NR vs. CR1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021, HR\u0026thinsp;=\u0026thinsp;2.168, 95%CI: 1.123\u0026ndash;4.188) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Other independent adverse risk factor for OS was mutated KRAS (yes \u003cem\u003evs.\u003c/em\u003e no, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016, HR\u0026thinsp;=\u0026thinsp;4.230, 95%CI: 1.606\u0026ndash;17.301). Multivariate analysis of LFS showed that patients achieving CR1 before allo-HSCT had superior LFS compared with patients achieving CR2 before allo-HSCT (CR2 vs. CR1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;4.928, 95%CI: 2.399\u0026ndash;10.122) and patients who did not achieve CR (NR vs. CR1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;2.720, 95%CI: 1.522\u0026ndash;4.860) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Other independent adverse risk factor for LFS was mutated BCORL1 (yes \u003cem\u003evs.\u003c/em\u003e no, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;6.374, 95%CI: 2.280-17.821).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis of patients undergoing allo-HSCT.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;10\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L vs. \u0026lt;10\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.375\u0026ndash;0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease status before allo-HSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR2 vs. CR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.727\u0026ndash;4.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.763\u0026ndash;4.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR vs. CR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.180\u0026ndash;4.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.123\u0026ndash;4.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutated BCORL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.839\u0026ndash;8.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutated KRAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.257\u0026ndash;12.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.306\u0026ndash;13.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMM (+) vs. EMM (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.740\u0026ndash;1.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtramedullary infiltration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.971\u0026ndash;4.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease status before allo-HSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR2 vs. CR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.206-9.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.399\u0026ndash;10.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR vs. CR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.477\u0026ndash;4.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.522\u0026ndash;4.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutated BCORL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.772\u0026ndash;13.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.280-17.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutated SETBP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.334\u0026ndash;10.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMM (+) vs. EMM (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.813\u0026ndash;1.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNCCN, National Comprehensive Cancer Network; Allo-HSCT, allogeneic hematopoietic stem cell transplantation. CRR, complete remission rate; ORR, overall remission rate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study aimed to evaluate the prognostic value of EMM and the effective therapeutic method for these patients. We found that EMM was an inferior prognostic factor in the univariate analysis of all patients. Multivariate analysis was used to further analyze the independent prognostic value of EMM in OS and LFS. Our study showed that EMM-positive patients tended to have inferior OS compared with EMM-negative patients and EMM was a poor risk factor for LFS in all patients. In other studies, DNMT3A mutations were reported to be associated with decreased OS [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Patients with TET2 mutations appeared to have an adverse prognosis in intermediate-risk AML [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. ASXL1 mutations were related to poor prognosis in AML patients [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. IDH1 mutations were connected with adverse prognosis in cytogenetically normal acute myeloid leukemia, whereas the prognosis value of IDH2 mutations was controversial [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Other epigenomic modifier gene mutations, including EZH2 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], KMT2C [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], SETD2 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and KDM6A [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], were also reported to be associated with leukemia pathogenesis. Our study showed a similar result by analyzing a mixture of these prognostic mutated genes.\u003c/p\u003e \u003cp\u003eMultivariate analysis in EMM-positive patients showed that patients receiving the DCAG regimen had a similar OS and LFS rate as those receiving the \u0026ldquo;3\u0026thinsp;+\u0026thinsp;7\u0026rdquo; regimen, whereas allo-HSCT could improve the OS and LFS of these patients. Results in our study showed that there were no differences in OS and LFS between the DCAG regimen group and the \u0026ldquo;3\u0026thinsp;+\u0026thinsp;7\u0026rdquo; regimen group in EMM-positive patients, which was consistent with a previous study of elderly patients treated with induction therapy containing hypomethylating agents [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Nonetheless, allo-HSCT was a favorable risk factor in patients who were EMM-positive. Univariate analysis in patients undergoing allo-HSCT revealed that EMM was no longer a poor prognostic factor in patients undergoing allo-HSCT. These results indicated that allo-HSCT might reverse the poor prognosis of these patients, which was in line with previous studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our study also showed that EMM-positive patients receiving the DCAG regimen followed by allo-HSCT had a similar long-term survival compared with those receiving the \u0026ldquo;3\u0026thinsp;+\u0026thinsp;7\u0026rdquo; regimen followed by allo-HSCT. All these results indicated that allo-HSCT might improve the OS and LFS of EMM-positive patients regardless of the types of induction regimen. Our findings also raised the question of whether epigenetic modifier gene mutations need to be taken into account in addition to high-risk patients when selecting transplant patients. More prospective studies are needed to explore this question.\u003c/p\u003e \u003cp\u003eThis study also has some limitations. As a singer-center retrospective study, there may be bias in patients, and the results may also be biased. Multicenter prospective studies are still needed to verify these results.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, EMM tended to be a poor risk factor for OS and was a poor risk factor for LFS in our cohort. Allo-HSCT might improve the OS and LFS of EMM-positive patients regardless of the types of induction regimen.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partially supported by grants from the National Natural Science Foundation of China [grant numbers 820770178, 82170207], the Beijing Natural Science Foundation of China [grant numbers 7222175].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGao Cj and Dou LP designed the study. Fang S, Huang S, Wang MZ, Wen YN, Wang H, Jiao YF and Wei Y Participated in data collection. Qian K, Gu ZY and Yang JJ performed statistical analysis and data interpretation. Fang S, Huang S and Wang MZ finished manuscript preparation and literature search. Gao Cj and Dou LP provided funds Collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFreireich EJ (1998) Four decades of therapy for AML. Leukemia 12 Suppl 1:S54-56.\u003c/li\u003e\n\u003cli\u003eZhu X, Ma Y and Liu D (2010) Novel agents and regimens for acute myeloid leukemia: 2009 ASH annual meeting highlights. J Hematol Oncol 3:17. https://dx.doi.org/10.1186/1756-8722-3-17.\u003c/li\u003e\n\u003cli\u003eDombret H and Gardin C (2016) An update of current treatments for adult acute myeloid leukemia. Blood 127 (1):53-61. https://dx.doi.org/10.1182/blood-2015-08-604520.\u003c/li\u003e\n\u003cli\u003eD\u0026ouml;hner H, Estey E, Grimwade D, et al. (2017) Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood 129 (4):424-447. https://dx.doi.org/10.1182/blood-2016-08-733196.\u003c/li\u003e\n\u003cli\u003eSchlenk RF, Frech P, Weber D, et al. (2017) Impact of pretreatment characteristics and salvage strategy on outcome in patients with relapsed acute myeloid leukemia. Leukemia 31 (5):1217-1220. https://dx.doi.org/10.1038/leu.2017.22.\u003c/li\u003e\n\u003cli\u003eEstey EH (2018) Acute myeloid leukemia: 2019 update on risk-stratification and management. Am J Hematol 93 (10):1267-1291. https://dx.doi.org/10.1002/ajh.25214.\u003c/li\u003e\n\u003cli\u003eCornelissen JJ and Blaise D (2016) Hematopoietic stem cell transplantation for patients with AML in first complete remission. Blood 127 (1):62-70. https://dx.doi.org/10.1182/blood-2015-07-604546.\u003c/li\u003e\n\u003cli\u003eLey TJ, Ding L, Walter MJ, et al. (2010) DNMT3A mutations in acute myeloid leukemia. N Engl J Med 363 (25):2424-2433. https://dx.doi.org/10.1056/NEJMoa1005143.\u003c/li\u003e\n\u003cli\u003eChou WC, Chou SC, Liu CY, et al. (2011) TET2 mutation is an unfavorable prognostic factor in acute myeloid leukemia patients with intermediate-risk cytogenetics. Blood 118 (14):3803-3810. https://dx.doi.org/10.1182/blood-2011-02-339747.\u003c/li\u003e\n\u003cli\u003ePaschka P, Schlenk RF, Gaidzik VI, et al. (2010) IDH1andIDH2Mutations Are Frequent Genetic Alterations in Acute Myeloid Leukemia and Confer Adverse Prognosis in Cytogenetically Normal Acute Myeloid Leukemia WithNPM1Mutation WithoutFLT3Internal Tandem Duplication. Journal of Clinical Oncology 28 (22):3636-3643. https://dx.doi.org/10.1200/jco.2010.28.3762.\u003c/li\u003e\n\u003cli\u003eXu Q, Li Y, Jing Y, et al. (2020) Epigenetic modifier gene mutations-positive AML patients with intermediate-risk karyotypes benefit from decitabine with CAG regimen. Int J Cancer 146 (5):1457-1467. https://dx.doi.org/10.1002/ijc.32593.\u003c/li\u003e\n\u003cli\u003eMetzeler KH, Walker A, Geyer S, et al. (2012) DNMT3A mutations and response to the hypomethylating agent decitabine in acute myeloid leukemia. 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Cancers (Basel) 13 (9). https://dx.doi.org/10.3390/cancers13092272.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"mutation, Hematopoietic Stem Cell Transplantation, prognosis, Leukemia, Myeloid, Acute","lastPublishedDoi":"10.21203/rs.3.rs-3848683/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3848683/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eEpigenetic modifier gene mutations (EMM) have been reported to be associated with poor prognosis in acute myeloid leukemia (AML). Whether allogeneic stem cell transplantation (allo-HSCT) can improve outcomes in this patients remains unknown.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMaterial/Methods:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study retrospectively collected clinical information of 353 AML patients with gene mutations detected by next-generation sequencing (NGS) and analyzed the therapeutic effect of allogeneic stem cell transplantation in acute myeloid leukemia patients with epigenetic modifier gene mutations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eEMM-positive patients tended to have inferior OS compared with EMM-negative patients (p\u0026thinsp;=\u0026thinsp;0.065, HR\u0026thinsp;=\u0026thinsp;1.343, 95%CI: 0.981\u0026ndash;1.838), EMM-positive patients had inferior LFS (p\u0026thinsp;=\u0026thinsp;0.031, HR\u0026thinsp;=\u0026thinsp;1.385, 95%CI: 1.030\u0026ndash;1.863). In EMM-positive patients, multivariate analysis showed that patients who received allo-HSCT had a superior OS (yes vs. no, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;0.213, 95%CI: 0.134\u0026ndash;0.339, Table\u0026nbsp;3) and LFS (yes vs. no, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;0.303, 95%CI: 0.199\u0026ndash;0.461, Table\u0026nbsp;3) compared with patients who did not receive allo-HSCT. A total of 220 patients received allo-HSCT in all patients. Univariate analysis in patients undergoing allo-HSCT showed that EMM was not a risk factor for OS (EMM-positive vs. EMM-negative, p\u0026thinsp;=\u0026thinsp;0.470, HR\u0026thinsp;=\u0026thinsp;1.192, 95%CI: 0.740\u0026ndash;1.920) and LFS (EMM-positive vs. EMM-negative, p\u0026thinsp;=\u0026thinsp;0.323, HR\u0026thinsp;=\u0026thinsp;1.235, 95%CI: 0.813\u0026ndash;1.876).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eEMM tended to be a poor risk factor for OS and was a poor risk factor for LFS in our cohort. Allo-HSCT might improve the OS and LFS of EMM-positive patients.\u003c/p\u003e","manuscriptTitle":"Therapeutic effect of allogeneic stem cell transplantation in acute myeloid leukemia patients with epigenetic modifier gene mutations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-11 18:24:00","doi":"10.21203/rs.3.rs-3848683/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90e7ed3a-17f7-41f1-bb1c-3eba2b43f6e2","owner":[],"postedDate":"January 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-05T13:05:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-11 18:24:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3848683","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3848683","identity":"rs-3848683","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}