Acute myeloid leukemia patients with high-risk karyotypes benefit from decitabine in combination with modified CAG

Acute myeloid leukemia patients with high-risk karyotypes benefit from decitabine in combination with modified CAG | 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 Acute myeloid leukemia patients with high-risk karyotypes benefit from decitabine in combination with modified CAG Wen-Jie Liu, Qian Sun, Yu Zhu, Xiao-Li Zhao, Jian-Yong Li, Si-Xuan Qian, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3823801/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 This study aims to investigate the cytogenetic and molecular characteristics of patients with Acute Myeloid Leukemia (AML) and determine which patients would benefit most from a low-intensity regimen of decitabine in combination with modified CAG (D-CAG) or intensive chemotherapy. We retrospectively analyzed cytogenetic and molecular data from 331 newly diagnosed AML patients and investigated the relationship between genetic characteristics, risk status, treatments and clinical outcomes. The median followed-up was 45 months (2-120 months). Overall, a single cycle of IA induction resulted in a CR rate of 79.3%, which was superior to the 66.4% observed in the cohort treated with D-CAG (P < .05). However, there was no significant difference in ORR between the two arms. The median OS was reduced in the D-CAG cohort compared to the IA cohort (P < .05). Favorable-risk groups and patients who undergo allo-HSCT treated with IA had longer OS than those in the D-CAG groups (P < .05). While the median OS of the intermediate- and high-risk groups who were not recipients of allo-HSCT was comparable between two regimen. Within the IA group, patients with TET2, NRAS, and biallelic CEBPA gene mutations achieved better OS than those in the D-CAG group (P < .05). While older patients with complex and monosomal karyotypes were tend to have longer median OS compared to younger patients (P < .05). In conclusion, it is crucial to select AML chemotherapy regimens based on karyotypes and genetic characteristics. D-CAG may be a better choice for AML patients with high-risk karyotypes. acute myeloid leukemia D-CAG intensive chemotherapy prognosis mutations karyotypes Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Acute myeloid leukemia (AML) is a heterogeneous group of diseases resulting from clonal transformation of hematopoietic precursors through the acquisition of chromosomal rearrangements and gene mutations [ 1 ]. Over the past few decades, treatment selection has primarily been based on the age and physical condition of AML patients. The standard therapy for eligible patients usually involves intensive chemotherapy with cytarabine and an anthracycline. In recent years, advancements in the diagnosis of acute leukemias have improved accuracy in determining appropriate treatment for individual patients. Cytogenetic and genetic characteristics have emerged as crucial prognostic factors for AML patient [ 2 ]. Additionally, the introduction of hypomethylating agents and venetoclax has posed increasing challenges to the use of intensive chemotherapy [ 3 – 5 ]. In our previous study, we reported encouraging results from a chemotherapy regimen combining decitabine with modified CAG (D-CAG), which demonstrated good tolerability and promising efficacy in elderly AML patients [ 6 ]. Building on these findings, we conducted a retrospective analysis involving cytogenetic and molecular analyses of bone marrow samples from 331 AML patients. The aim was to explore the relationship between genetic characteristics, risk status, treatments, and clinical outcomes, with the ultimate goal of providing more personalized treatment regimens. Materials and methods Study desigh and patients A total of 331 patients with adult-onset AML (excluding those with acute promyelocytic leukemia) who received induction regimes comprising of either an intensive (IA, n = 179) or relatively low-intensive chemotherapy regimen (D-CAG, n = 152) were enrolled in this study. AML was diagnosed based on the World Health Organization (WHO) criteria [ 7 ]. All patients enrolled in this study had pre-treatment bone marrow specimens available for analysis. Histologic, chromosomal, immunophenotypic analyses and NGS were performed for all patients upon diagnosis. Cytogenetic risk groups were defined according to the the Medical Research Council (MRC) classification. This study was performed in accordance with the Declaration of Helsinki and all patients provided written informed consent. All study procedures and informed consent forms were approved by Institutional Review Board.This study was registered at www.chictr.org as ChiCTR-ONC-11001700. Treatments Our study included 179 previously untreated AML patients who received the standard IA regimen as induction therapy (idarubicin 10–12 mg/m 2 on days 1 to 3 and cytarabine 100 mg/m 2 /d on days 1 to 7). A total of 37 patients were recipients of allo-HSCT and 27 patients were recipients of auto-HSCT. Patients who were unsuitable for HSCT were subjected to post-remission therapy consisting of 2–4 courses of intermediate to high dose cytarabine (cytarabine 2–3 g/m 2 twice daily on days 1–3). The D-CAG regimen was given to 152 patients who were aged between 60 to 86 years (decitabine 15 mg/m 2 intravenously on days 1–5, cytarabine 10 mg/m 2 subcutaneous injection twice daily on days 3–9, aclarubicin 8–10 mg/m 2 /d on days 3–6, and G-CSF 300 µg/d for priming until white blood cell count was > 20×10 9 /L). An additional 4–6 cycles of D-CAG were administered to those who achieved CR. Those who failed to obtain CR after two cycles of D-CAG were given the option of palliative care or alternative treatment. None of the patients in this subgroup received allo- or auto-HSCT. None of the individuals involved in this study were exposed to targeted agents such as FLT3 tyrosine kinase, IDH1/2 and BCL-2 inhibitors as these agents had yet to be approved for treating AML in China. Cytogenetic analyses An unstimulated culture was used to source for bone marrow (BM) cells. The improved heat treatment and Giemsa R-banding methods were utilized to band metaphase cells. The karyotyping was based on conventional cytogenetic examination of ≥ 20 metaphases (Supplementary file). Next-generation sequencing A total of 21 genes comprising of PHF6, TP53, CSF3R , KIT , CBL , KRAS , NRAS , FLT3 , ETV6 , CEBPA , NPM1 , RUNX1 , EZH2 , ASXL1 , ZRSR2 , U2AF1 , SRSF2 , IDH2 , IDH1 , TET2 and DNMT3A were subjected to targeted gene sequencing (TGS) panel (Supplementary file). BM aspirate was used to extract genomic DNA (gDNA) with the help of an Autopure extractor (Qiagen, Hilden, Germany). 10ng of gDNA was amplified using the Ion AmpliSeq Library kit 2.0 (Ion Torrent, Thermo Fisher Scientific, USA). The KAPA Hyper Prep kit for Illumina Platforms (Kapa Biosystems, Wilmington, MA, USA) was used to construct amplicon libraries, which were sequenced using the Illumina Miseq platform [ 8 ]. The human reference genome (genome build hg19) was aligned with the sequenced reads using the Burrows-Wheeler Aligner (BWA) [ 9 ]. The Genome Analysis Toolkit (GATK) was used for variant calling [ 10 ]. Sequenced reads and variants were visualized with the Integrative Genomics Viewer (IGV) version 2.3.32 [ 11 ]. Synonymous and non-synonymous variants occurring at a frequency of more than 0.1% in the normal eastern Asian population from the Exome Aggregation Consortium (ExAC; http://exac.broadinstitute.org/ ) or the 1000 Genomes Project database ( http://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/ ) were excluded from further analysis. Definition of outcomes The NCCN AML practice guideline (version 1.2021) was used to assess treatment responses. CR was defined as no residual evidence of extramedullary disease, and peripheral blood counts (absolute neutrophil count > 1.0×10 9 /L and platelet count ≥ 100×10 9 /L), BM aspirate with < 5% blasts with spicules and no blasts with Auer rods and an independence from transfusion. Partial remission (PR) was determined to be the presence of normalized blood counts and a reduction of at least 50% to 5–25% blasts in BM aspirate. All cases which did not meet the CR or PR criterion were deemed to be patients with no remission (NR). ORR calculation took into consideration PR and CR rates. The duration between diagnosis to death or to last follow-up was the OS. Time to stable neutrophil recovery was determined from the end of induction therapy until patients possessed two consecutive ANC measurements on different days which was ≥ 500/µl. A similar means was used to determine time to stable platelet recovery, using a cutoff of ≥ 20,000/µl for a minimum of three consecutive days. The National Cancer Institute (NCI) Common Toxicity Criteria v.5.0 was used to define and grade toxicities [ 12 ]. Statistical analyses The Statistical Package for Social Science (SPSS version 20.0) was used for all data analyses. The t-test and Fisher’s exact test were used to determine differences between continuous variables. Survival probabilities were estimated using the Kaplan–Meier method with curve comparisons performed using the log-rank test. All P values were two-tailed and were considered to be statistically significant when valued at less than 0.05. Results Characteristics of the patients The baseline characteristics of 331 AML patients were summarized in Table 1 . The median age and Eastern Cooperative Oncology Group performance status (ECOG PS) score in the D-CAG cohort were significantly higher than the IA cohort ( P < 0.0001). Based on the 2017 ELN cytogenetic risk classification, a total of 114 patients (34.4%) were in the favorable-risk group, 106 patients (32.0%) were intermediate-risk and 96 patients (29.0%) were poor-risk. According with the previous studies, there were significantly more patients in the favorable-risk category ( P < 0.0001) and less in the poor-risk category ( P = 0.001)in the IA cohort compared with the D-CAG cohort, and older patients in the D-CAG cohort harbored more adverse karyotypes than young patients in IA cohort. The other clinical features of gender, white blood cell (WBC), hemoglobin and platelet count at diagnosis as well as percentage of blasts in bone marrow, were similar between the two cohorts. Table 1 Baseline characteristics of the AML patients. Characteristics D-CAG IA P n 152 179 Age, median (range) 67 (60–86) 36 (14–59) < 0.001 Gender, n (%) 0.509 Male 83 (54.6) 90 (50.3) Female 69 (45.4) 89 (49.7) Prior diagnosis of MDS, n (%) 14 (9.2) 0 < 0.001 WHO 2016 AML classification, n (%) AML with recurrent genetic abnormalities (AML-RGA) 41 (27.0) 102 (57.0) < 0.001 AML with myelodysplasia-related changes (AML-MRC) 32 (21.1) 4 (2.1) < 0.001 Therapy-related myeloid neoplasms (t-AML) 8 (5.3) 1 (0.7) 0.013 AML, not otherwise specified (AML, NOS) 71 (46.7) 72 (40.2) 0.221 ECOG performance status score, n(%) < 0.001 0–1 126 (82.9) 175 (97.8) 2–3 26 (17.1) 4 (2.2) BM blasts (%), median (range) 64.0 (21.6–91.8) 68.0 (22–94) 0.086 Adverse karyotypes*, n (%) 33 (21.7) 16 (8.9) 0.002 Comlpex karyotypes, n (%) 22 (14.5) 9 (5.0) 0.004 Monosomal karyotypes, n (%) 14 (9.2) 5 (2.8) 0.016 17p abnormality, n (%) 5 (3.3) 0 0.002 -5 or del(5q); -7, n (%) 21 (13.8) 12 (6.7) 0.042 Other adverse karyotypes**, n (%) 2 (1.3) 3 (2.2) 0.458 Myelodysplasia-related gene mutations***, n (%) 79 (52.0) 68 (38.0) 0.015 NCCN risk status, n (%) Favorable 29 (19.1) 85 (47.5) < 0.001 Intermediate 57 (37.5) 49 (27.4) 0.059 Poor 58 (38.2) 38 (21.2) 0.001 NA 8 (5.3) 7 (3.9) WBC (×10 9 /L), median (range) 9.8 (1.34–219.6) 12 (0.27–184.7) 0.888 Hemoglobin (g/L), median (range) 80 (42–140) 86 (39–157) 0.166 Platelets (×10 9 /L), median (range) 52.0 (6-257) 37 (2-287) 0.092 Abbreviations: BM: Bone marrow; ECOG PS: Eastern Cooperative Oncology Group performance status; MDS: Myelodysplastic syndrome; WBC: White blood cells count; WHO: World Health Organization; NA: Not available. * Adverse karyotypes: -5 or del(5q); -7; -17/abn(17p); complex karyotype; monosomal karyotype and other adverse karyotypes. ** Other adverse karyotypes: t(6;9)(p23;q34.1); t(v;11q23.3); t(9;22)(q34.1;q11.2); t(8;16)(p11;p13); inv(3)(q21.3q26.2); t(3q26.2;v) *** Myelodysplasia-related gene mutations: defined by mutations in ASXL1, EZH2, RUNX1, SRSF2, U2AF1, or ZRSR2. Mutational analyses Mutations involving any of the 21 genes were detected in 305 (92.1%) patients. Among the entire patient population, 92 patients (27.8%) possessed single gene mutations, 92 (27.8%) possessed mutations in two genes, 70 (21.1%) possessed mutations in three genes, 41 (12.4%) possessed mutations in four genes, and 5 (1.5%) possessed mutations in five and six genes, respectively. ASXL1 (26.0%) was the most frequently encountered gene to harbor mutations. Other genes in a decreasing frequency were: FLT3 , 21.8%; NPM1 , 22.4%; TET2 , 21.8%; CEBPA , 20.8%; NRAS , 17.2%; DNMT3A , 15.4%; KIT , 12.7%; IDH2 , 12.1%; TP53 , 7.2%; IDH1 , 6.3%; and RUNX1 , 5.4%. Mutations in SRSF2 , ETV6 , KRAS , CBL , U2AF1 , PHF6 , ZRSR2 , EZH2 , and CSF3R were rare (< 5.0%) (Fig. 1 ). Mutations in TET2 (27.6% vs . 16.8%, P = 0.0227), SRSF2 (7.2% vs . 1.1%, P = 0.0079), RUNX1 (9.2% vs . 2.2%, P = 0.0037) and TP53 (12.5% vs . 2.8%, P = 0.0010) were more common in older patients in contrast to young patients. Young patients had significantly higher rate of biallelic CEBPA mutations (17.3% vs. 7.2%, P = 0.0075) and KIT mutations (19.0% vs. 5.3%, P = 0.0002) when compared to their older counterparts (Fig. 2 ). The occurrence of Myelodysplasia-related gene mutations such as ASXL1, EZH2, RUNX1, SRSF2, U2AF1 , and ZRSR2 were more common in older patients than young patients (52.0% vs . 30.0%, P = 0.0145; Table 1 ). Association between cytogenetics, gene mutations and clinical outcomes A single cycle of IA induction resulted in CR in 142 young patients (79.3%), which was higher in contrast to the CR rate of 66.4% in elderly patients treated with D-CAG ( P = 0.009). However, no significant difference was observed in the ORR between the two treatment arms (79.6% vs . 85.5%, P = 0.189). When we analyzed the response based on risk stratification, we found that the IA group had a higher CR rate compared to the D-CAG group in both the favorable- and intermediate-risk groups. However, this difference reached statistical significance only in the intermediate-risk group. Additionally, there were no significant differences in ORR among the three risk groups (Table 2 ). Table 2 Clinical responses and outcomes in each treatment arm. D-CAG IA P CR (%) (n) 66.4(101) 79.3 (142) 0.009 Favorable (%) (n) 72.4(21) 84.7(72) 0.168 Intermediate (%) (n) 68.4(39) 85.7(42) 0.042 Poor (%) (n) 62.1(36) 65.8(25) 0.829 ORR (%) (n) 79.6 (121) 85.5 (153) 0.189 Favorable (%) (n) 89.7(26) 90.6(77) 1.000 Intermediate (%) (n) 80.7(46) 93.6(46) 0.082 Poor (%) (n) 74.1(43) 71.1(27) 0.816 Patients were followed-up for a median of 45 months (range: 2-120 months) with a final analysis done on August 31, 2020. The median OS of all the AML patients was 19 months at the final analysis with 1-year and 2-year OS rate of 64.3% and 43.9%, respectively. There were 148 and 159 evaluable cases in the D-CAG and IA arms, respectively. The median OS was significantly reduced in the D-CAG cohort in contrast to the IA cohort (15 months vs. 38 months, P < .0001, Fig. 3 ). Young patients of the favorable-, intermediate- and poor-risk groups possessed notably longer OS in contrast to their older counterparts (Fig. 3 ). Favorable-risk patients treated with IA who did not undergo allo-HSCT still demonstrated increased OS in contrast to older patients. Nevertheless, in the intermediate- and poor-risk subgroups, after excluding patients who received allo-HSCT, the young and old cohorts showed comparable median OS (intermediate-risk: 14 months vs . 18 months, P = 0.2765; poor-risk: 15 months vs . 14 months, P = 0.1827; Fig. 3 ). Based on cytogenetics and gene analysis, the median OS of patients with mutated NPM1 , FLT3 , FLT3-ITD , DNMT3A , IDH1 , IDH2 , TP53 and myelodysplasia-related genes was similar between D-CAG and IA treatment arms (non-allo-HSCT). Patients with TET2 , NRAS and biallelic CEBPA mutations in IA group (non-allo-HSCT) achieved better OS than those in the D-CAG group (Table 3 ). Notably, older patients who possessed complex and monosomal karyotypes were noted to have significantly longer median OS in contrast to young patients with the same cytogenetic abnormalities who did not undergo allo-HSCT (complex: 12 months vs. 4.5 months, P = 0.003; monosomal: 14 months vs. 4 months, P = 0.003; Table 3 , Fig. 4 ). Patients with chromosomes 5 and/or 7 abnormalities (-5/5q- and/or -7/7q-) in D-CAG group have relatively longer median OS than those in IA group, although this difference failed to achieve statistical significance (9 months vs. 5 months, P = 0.147; Table 3 , Fig. 4 ). We did not analyze the survival of patients with other adverse karyotypes given the small numbers of the patients with these cytogenetic abnormalities. Table 3 Overall survival for D-CAG treated patients harboring specific gene mutations and cytogenetics compared with IA treated patients who did not receive allo-HSCT. Gene mutations and cytogenetics D-CAG IA (non allo-HCT) P MUT median OS, months (n) MUT median OS, months (n) TET2 13 (42) 30 (25) 0.006 NPM1 14 (36) 13 (29) 0.844 NPM1+/FLT3-ITD- 19 (23) 120 (13) 0.209 FLT3 12 (33) 11 (39) 0.973 FLT3-ITD 9.5 (22) 10 (29) 0.753 NPM1-/FLT3-ITD+ 20 (9) 13 (15) 0.528 DNMT3A 16 (28) 9 (20) 0.187 NRAS 17 (29) Undefined (21) 0.003 IDH1 13 (13) 14 (5) 0.814 IDH2 19 (18) 12 (13) 0.123 biallelic CEBPA 16 (22) Undefined (41) 0.001 TP53* 12 (18) 8 (5) 0.495 Myelodysplasia-related gene mutations** 16 (78) 14 (44) 0.435 complex karyotypes 12 (22) 4.5 (5) 0.003 -5/5q- and/or -7/7q- 9 (20) 5 (7) 0.147 monosomal karyotypes 14 (13) 4 (2) 0.003 *Including TP53 mutations and deletions. ** Myelodysplasia-related gene mutations: defined by mutations in ASXL1, EZH2, RUNX1, SRSF2, U2AF1, or ZRSR2. Adverse events Early mortality rates (4-week mortality from therapy) of patients who received D-CAG and standard induction were 4.6% and 8.9%, respectively. Infections and myelosuppression were the most commonly encountered adverse effects during induction chemotherapy. All AML patients experienced thrombocytopenia and neutropenia. In the D-CAG group, 93.9% of patients were documented to have grade III-IV hematological toxicity, while all patients in IA group suffered from grade III-IV hematological toxicity. 87.6% of all patients were documented to have febrile neutropenia, with 83.1% and 91.8% in the D-CAG and IA treatment arms, respectively. Non-hematological adverse events were often mild or moderately severe. Older patients who achieved CR demonstrated median times for stable neutrophil (0.5×10 9 neutrophils/L) and platelet (20×10 9 /L) recovery of 14 and 12 days, respectively. Those who received IA induction demonstrated median times to stable neutrophil recovery to 0.5×10 9 /L and platelet to 20×10 9 /L of 13 and 16 days, respectively. Discussion The effective treatment of AML remains a fundamental challenge, as a uniform approach to this condition has been proven ineffective given the heterogenous nature of this disease. Current strategies involve increasingly individualized treatment regimens [ 13 ]. Previous investigations by our group and others have been reported that the response rates of low-intensity regimens show similarity to those of standard induction regimens with the additional benefit of lower toxicities, offering safer treatment options to different subgroups of AML patients [ 6 , 14 , 15 ]. In this study, we attempted to distinguish amongst AML patients the most suitable candidates for either the D-CAG or intensive chemotherapy regime. Our findings highlight that an intensive regimen might not be the only and the best optimal option for all the AML patients especially in a new era of precision target treatment, with the development of several novel treatment options to cater for a wider range of patients with AML [ 16 – 21 ]. Importantly, our study suggests that the D-CAG regimen may be the better choice for AML patients with high-risk karyotypes. There is a stark difference in the cytogenetics and gene mutations between AML patients at both ends of the age spectrum, which has been linked to AML treatment response [ 3 ]. For instance, young patients more often harbored biallelic CEBPA and KIT mutations compared to older patients. Approximately 10–15% of all patients with AML have been found to harbor NRAS, which has been reported to cooperate with antecedent molecular lesions [ 22 ]. This study found that patients with NRAS and biallelic CEBPA mutations appeared to be more likely to benefit from intensive regimens. While the elderly are more likely to possesses mutations in the DNMT3A, ASXL1, IDH1/2, and TET2 genes that encode for epigenetic modification [ 2 , 23 ]. Qingyu Xu et al. reported that epigenetic modifier gene mutations to be potentially predictive biomarkers of better response to the D-CAG regimen in cytogenetically intermediate-risk AML [ 24 ]. Another study found that younger patients with non-DNMT3A-R882 mutations and older patients with DNMT3A-R882 mutations were more likely to encounter poorer prognoses [ 25 ]. In the intermediate-risk AML groups, Ley et al. highlighted the presence of DNMT3A mutations to function as an independent predictor of poor survival [ 26 ]. Consistent with previous reports, a higher percentage of elderly AML patients in this study demonstrated TET2, SRSF2, RUNX1 and TP53 mutations, which are related to poorer outcomes. We found that the median OS to be comparable between D-CAG treated patients with or without FLT3-ITD, DNMT3A, IDH2 mutations, as well as DNA methylation associated genes mutations, whereas patients treated with IA who harbored these mutations demonstrated markedly reduced median OS in contrast to those harboring wild type genes. These results illustrated that D-CAG might offset the adverse effects of these mutations. Approximately 5–8% of all AML patients possess TP53 mutations, a feature associated with dismal outcomes [ 27 ]. Dohner H et al. reported that elderly AML patients with TP53 mutations who received either azacitidine or conventional therapy possessed a median OS of 7.2 and 2.4 months, respectively [ 28 ]. Previous reports showed that elderly patients possessing TP53 mutations who were treated with standard chemotherapy had a median OS ranging from 4 to 6 months [ 5 , 28 , 29 ]. In the D-CAG group, 19 patients (12.5%) possessing TP53 mutations or deletions demonstrated a median OS of 12 months. Interestingly, IA-treated patients harboring TP53 mutations had even poorer median OS compared to those treated with D-CAG, suggesting that D-CAG may offer a survival benefit for patients with TP53 abnormalities. We found the CR and ORR of the high-risk groups was comparable between both older and young patient. Notably, older patients with complex or monosomal karyotypes even showed significantly increased median OS in contrast to young patients who were not recipients of allo-HSCT. This suggests that even intensive chemotherapy may not be optimal in improving the prognosis of intermediate- and high-risk groups in the absence of allo-HSCT compared to D-CAG. In conclusion, we speculated that intensive chemotherapy could confer a significant survival benefit for the patients with favorable-risk status, NRAS and biallelic CEBPA mutations, while it might not improve the prognosis of the intermediate- or high-risk patients who were not recipients of allo-HSCT. Patients with high-risk cytogenetics and certain mutations such as TP53, FLT3-ITD and DNA methylation associated mutations might benefit from the D-CAG induction regimen rather than an intensive regimen. Allo-HSCT should be considered for eligible patients to prolong survival following achievement of CR. Given the clear benefits of treating AML based on individual genetic profiles, one approach may be to await cytogenetic and molecular results prior to choosing more personalized treatment, on the condition that these delays do not confer additional harm to the patients. This study is limited by its retrospective design, the relatively small sample size, and lack of analysis for age-matched cases. Nevertheless, our findings support the clinical benefits of implementing cytogenetics and mutation screening in stratifying AML patient risk and treatment. Further prospective studies in larger patient cohorts are necessary to validate our findings. Abbreviations acute myeloid leukemia AML allogeneic hematopoietic stem cell transplantation allo-HSCT next-generation sequencing NGS complete remission CR objective response rate ORR partial remission PR no remission NR overall survival OS decitabine in combination with modified CAG D-CAG World Health Organization WHO Medical Research Council MRC bone marrow BM targeted gene sequencing TGS genomic DNA gDNA Burrows-Wheeler Aligner BWA Genome Analysis Toolkit GATK Integrative Genomics Viewer IGV Eastern Cooperative Oncology Group performance status ECOG PS white blood cell WBC variant allele frequency VAF Declarations Ethics approval and consent to participate The study protocol and all amendments were approved by the ethics committee of the First Affiliated Hospital of Nanjing Medical University and were conducted per the principles expressed in the Declaration of Helsinki. All patients provided written informed consent to participate. Consent for publication Not applicable. Availability of data and material Due to the policy of China, the data of next-generation sequencing can not been uploaded to the international database. If necessary, we can upload the data to the GS (https://ngdc.cncb.ac.cn/gsa/). Competing interests The authors declare no conflict of interest. Funding This study was supported by the National Natural Science Foundation of China (No. 81870119, 82170153, 81700114, 81720108002), National Science and Technology Major Project of China (2018ZX09734-007), Jiangsu Province’s Medical Elite Program (ZDRCA2016022), and Jiangsu Provincial Special Program of Medical Science (BE2017751). Authors' contributions M.H. and S.X.Q. designed the research study. W.J.L., Q.S., Y.Z. and H.Z. performed the research. M.H., W.J.L. and Q.S. analyzed the data. W.J.L., Q.S. and M.H. wrote the paper. J.Y.L., S.X.Q. and M.H revised the manuscript and finalized the last version of the article. All authors checked and approved the submitted version. 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Blood 124(26):3947–3955 Jaiswal S, Fontanillas P, Flannick J et al (2014) Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med 371(26):2488–2498 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 Metzeler KH, Herold T, Rothenberg-Thurley M et al (2016) Spectrum and prognostic relevance of driver gene mutations in acute myeloid leukemia. Blood 128(5):686–698 Ley TJ, Ding L, Walter MJ et al (2010) DNMT3A mutations in acute myeloid leukemia. N Engl J Med 363(25):2424–2433 Rucker FG, Schlenk RF, Bullinger L et al (2012) TP53 alterations in acute myeloid leukemia with complex karyotype correlate with specific copy number alterations, monosomal karyotype, and dismal outcome. Blood 119(9):2114–2121 Hou HA, Chou WC, Kuo YY et al (2015) TP53 mutations in de novo acute myeloid leukemia patients: longitudinal follow-ups show the mutation is stable during disease evolution. Blood Cancer J 5:e331 Welch JS, Petti AA, Miller CA et al (2016) TP53 and Decitabine in Acute Myeloid Leukemia and Myelodysplastic Syndromes. N Engl J Med 375(21):2023–2036. Additional Declarations No competing interests reported. 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. We do this by developing innovative software and high quality services for the global research community. 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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-3823801","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265507818,"identity":"d9b765ee-82ce-4937-9a44-6bc401d6c2fc","order_by":0,"name":"Wen-Jie Liu","email":"","orcid":"","institution":"the First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wen-Jie","middleName":"","lastName":"Liu","suffix":""},{"id":265507819,"identity":"74a62697-8f90-4bc3-9f0c-a553b1193334","order_by":1,"name":"Qian Sun","email":"","orcid":"","institution":"the First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Sun","suffix":""},{"id":265507820,"identity":"85a2d82c-441e-412d-9dac-3729b63eb2d3","order_by":2,"name":"Yu Zhu","email":"","orcid":"","institution":"the First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhu","suffix":""},{"id":265507821,"identity":"a42bb9a0-adad-4095-91cb-29f25f2a5cd7","order_by":3,"name":"Xiao-Li Zhao","email":"","orcid":"","institution":"the First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Li","middleName":"","lastName":"Zhao","suffix":""},{"id":265507822,"identity":"90a936ae-c243-40dc-8584-c13216437655","order_by":4,"name":"Jian-Yong Li","email":"","orcid":"","institution":"the First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jian-Yong","middleName":"","lastName":"Li","suffix":""},{"id":265507823,"identity":"5f3b155d-22a1-45a0-bad6-429335bf4b7e","order_by":5,"name":"Si-Xuan Qian","email":"","orcid":"","institution":"the First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Si-Xuan","middleName":"","lastName":"Qian","suffix":""},{"id":265507824,"identity":"10fe3e78-b85b-4230-8190-0c2199a18796","order_by":6,"name":"Ming Hong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDACZiBOYDjAwMbefPBBQkUNCVr4eI4lGzw4c4xouw4wyEnkmEk+bGEmrFa3nffYgwcVd6LZGBLMKhIb2Bj427sT8GoxO8yXbpBw5lluG8OBtBuJO2QYJM6c3UBAC4+ZRGLb4dw2xoZjNxLPsDEYSOQSo+UfUAszY1tBYhszsVoagFrYmNkYiNeScAyohYeNWSLhzDEewn45f8ZM8kfN4dz5899//PijokaOv70XvxYMwEOa8lEwCkbBKBgFWAEAbilLiohamNIAAAAASUVORK5CYII=","orcid":"","institution":"the First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ming","middleName":"","lastName":"Hong","suffix":""}],"badges":[],"createdAt":"2023-12-30 11:00:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3823801/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3823801/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49332339,"identity":"4536ece2-ee51-417e-99ae-d01435c70e62","added_by":"auto","created_at":"2024-01-08 19:29:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60073,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap depicting gene mutations in 331 patients. Each column represents one patient.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3823801/v1/0a34f61664083f3bca39f241.png"},{"id":49332341,"identity":"c58fc4ce-3dea-4faf-bcf5-ee9447f2a7e4","added_by":"auto","created_at":"2024-01-08 19:29:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":207911,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram showing the frequency of driven gene mutations in D-CAG and IA treatment arm.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3823801/v1/b55cf35e7c5b0a088aab8e60.png"},{"id":49332994,"identity":"97ce129d-532e-40e6-9a11-594d3906e7d6","added_by":"auto","created_at":"2024-01-08 19:37:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":411566,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival (OS) of the AML patients in each treatment arm. (A) OS for D-CAG treated patients in contrast to IA treated patients or after excluding cases who received allo-HSCT. (B) OS for favorable-risk patients treated with D-CAG and IA or after excluding cases who received allo-HSCT. (C) OS for intermediate-risk patients treated with D-CAG and IA or after excluding cases who received allo-HSCT. (D) OS for poor-risk patients treated with D-CAG and IA or after excluding cases who received allo-HSCT.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3823801/v1/5e4e04e7dd14a670b2e38cef.png"},{"id":49332340,"identity":"1dfcbcc8-89a5-4801-a29f-ede32e2be543","added_by":"auto","created_at":"2024-01-08 19:29:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":146429,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival (OS) of the AML patients in each treatment arm according to adverse, monosomal, complex, and -5/5q or -7/7q- karyotypes. (A) OS for adverse karyotypes patients treated with D-CAG and IA who did not receive allo-HSCT. (B) OS for monosomal karyotypes patients treated with D-CAG and IA who did not receive allo-HSCT. (C) OS for complex karyotypes patients treated with D-CAG and IA who did not receive allo-HSCT. (D) OS for -5/5q or -7/7q- karyotypes patients treated with D-CAG and IA who did not receive allo-HSCT.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3823801/v1/5820d59c8182f58e1466cfdb.png"},{"id":51351589,"identity":"a6592e04-3b85-4461-9e6e-5a54f88fcc0e","added_by":"auto","created_at":"2024-02-20 05:29:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":978803,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3823801/v1/691cd4d5-e806-435b-804b-eb99912c8ffa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Acute myeloid leukemia patients with high-risk karyotypes benefit from decitabine in combination with modified CAG","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute myeloid leukemia (AML) is a heterogeneous group of diseases resulting from clonal transformation of hematopoietic precursors through the acquisition of chromosomal rearrangements and gene mutations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Over the past few decades, treatment selection has primarily been based on the age and physical condition of AML patients. The standard therapy for eligible patients usually involves intensive chemotherapy with cytarabine and an anthracycline. In recent years, advancements in the diagnosis of acute leukemias have improved accuracy in determining appropriate treatment for individual patients. Cytogenetic and genetic characteristics have emerged as crucial prognostic factors for AML patient [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Additionally, the introduction of hypomethylating agents and venetoclax has posed increasing challenges to the use of intensive chemotherapy [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In our previous study, we reported encouraging results from a chemotherapy regimen combining decitabine with modified CAG (D-CAG), which demonstrated good tolerability and promising efficacy in elderly AML patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Building on these findings, we conducted a retrospective analysis involving cytogenetic and molecular analyses of bone marrow samples from 331 AML patients. The aim was to explore the relationship between genetic characteristics, risk status, treatments, and clinical outcomes, with the ultimate goal of providing more personalized treatment regimens.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy desigh and patients\u003c/h2\u003e \u003cp\u003eA total of 331 patients with adult-onset AML (excluding those with acute promyelocytic leukemia) who received induction regimes comprising of either an intensive (IA, n\u0026thinsp;=\u0026thinsp;179) or relatively low-intensive chemotherapy regimen (D-CAG, n\u0026thinsp;=\u0026thinsp;152) were enrolled in this study. AML was diagnosed based on the World Health Organization (WHO) criteria [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. All patients enrolled in this study had pre-treatment bone marrow specimens available for analysis. Histologic, chromosomal, immunophenotypic analyses and NGS were performed for all patients upon diagnosis. Cytogenetic risk groups were defined according to the the Medical Research Council (MRC) classification. This study was performed in accordance with the Declaration of Helsinki and all patients provided written informed consent. All study procedures and informed consent forms were approved by Institutional Review Board.This study was registered at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.chictr.org\" target=\"_blank\"\u003ewww.chictr.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.chictr.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e as ChiCTR-ONC-11001700.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTreatments\u003c/h2\u003e \u003cp\u003eOur study included 179 previously untreated AML patients who received the standard IA regimen as induction therapy (idarubicin 10\u0026ndash;12 mg/m\u003csup\u003e2\u003c/sup\u003e on days 1 to 3 and cytarabine 100 mg/m\u003csup\u003e2\u003c/sup\u003e/d on days 1 to 7). A total of 37 patients were recipients of allo-HSCT and 27 patients were recipients of auto-HSCT. Patients who were unsuitable for HSCT were subjected to post-remission therapy consisting of 2\u0026ndash;4 courses of intermediate to high dose cytarabine (cytarabine 2\u0026ndash;3 g/m\u003csup\u003e2\u003c/sup\u003e twice daily on days 1\u0026ndash;3).\u003c/p\u003e \u003cp\u003eThe D-CAG regimen was given to 152 patients who were aged between 60 to 86 years (decitabine 15 mg/m\u003csup\u003e2\u003c/sup\u003e intravenously on days 1\u0026ndash;5, cytarabine 10 mg/m\u003csup\u003e2\u003c/sup\u003e subcutaneous injection twice daily on days 3\u0026ndash;9, aclarubicin 8\u0026ndash;10 mg/m\u003csup\u003e2\u003c/sup\u003e/d on days 3\u0026ndash;6, and G-CSF 300 \u0026micro;g/d for priming until white blood cell count was \u0026gt;\u0026thinsp;20\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L). An additional 4\u0026ndash;6 cycles of D-CAG were administered to those who achieved CR. Those who failed to obtain CR after two cycles of D-CAG were given the option of palliative care or alternative treatment. None of the patients in this subgroup received allo- or auto-HSCT.\u003c/p\u003e \u003cp\u003eNone of the individuals involved in this study were exposed to targeted agents such as \u003cem\u003eFLT3\u003c/em\u003e tyrosine kinase, \u003cem\u003eIDH1/2\u003c/em\u003e and BCL-2 inhibitors as these agents had yet to be approved for treating AML in China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCytogenetic analyses\u003c/h2\u003e \u003cp\u003eAn unstimulated culture was used to source for bone marrow (BM) cells. The improved heat treatment and Giemsa R-banding methods were utilized to band metaphase cells. The karyotyping was based on conventional cytogenetic examination of \u0026ge;\u0026thinsp;20 metaphases (Supplementary file).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eNext-generation sequencing\u003c/h2\u003e \u003cp\u003eA total of 21 genes comprising of \u003cem\u003ePHF6, TP53, CSF3R\u003c/em\u003e, \u003cem\u003eKIT\u003c/em\u003e, \u003cem\u003eCBL\u003c/em\u003e, \u003cem\u003eKRAS\u003c/em\u003e, \u003cem\u003eNRAS\u003c/em\u003e, \u003cem\u003eFLT3\u003c/em\u003e, \u003cem\u003eETV6\u003c/em\u003e, \u003cem\u003eCEBPA\u003c/em\u003e, \u003cem\u003eNPM1\u003c/em\u003e, \u003cem\u003eRUNX1\u003c/em\u003e, \u003cem\u003eEZH2\u003c/em\u003e, \u003cem\u003eASXL1\u003c/em\u003e, \u003cem\u003eZRSR2\u003c/em\u003e, \u003cem\u003eU2AF1\u003c/em\u003e, \u003cem\u003eSRSF2\u003c/em\u003e, \u003cem\u003eIDH2\u003c/em\u003e, \u003cem\u003eIDH1\u003c/em\u003e, \u003cem\u003eTET2\u003c/em\u003e and \u003cem\u003eDNMT3A\u003c/em\u003e were subjected to targeted gene sequencing (TGS) panel (Supplementary file). BM aspirate was used to extract genomic DNA (gDNA) with the help of an Autopure extractor (Qiagen, Hilden, Germany). 10ng of gDNA was amplified using the Ion AmpliSeq Library kit 2.0 (Ion Torrent, Thermo Fisher Scientific, USA). The KAPA Hyper Prep kit for Illumina Platforms (Kapa Biosystems, Wilmington, MA, USA) was used to construct amplicon libraries, which were sequenced using the Illumina Miseq platform [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The human reference genome (genome build hg19) was aligned with the sequenced reads using the Burrows-Wheeler Aligner (BWA) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The Genome Analysis Toolkit (GATK) was used for variant calling [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Sequenced reads and variants were visualized with the Integrative Genomics Viewer (IGV) version 2.3.32 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Synonymous and non-synonymous variants occurring at a frequency of more than 0.1% in the normal eastern Asian population from the Exome Aggregation Consortium (ExAC; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://exac.broadinstitute.org/\u003c/span\u003e\u003cspan address=\"http://exac.broadinstitute.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e or the 1000 Genomes Project database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e were excluded from further analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of outcomes\u003c/h2\u003e \u003cp\u003e The NCCN AML practice guideline (version 1.2021) was used to assess treatment responses. CR was defined as no residual evidence of extramedullary disease, and peripheral blood counts (absolute neutrophil count\u0026thinsp;\u0026gt;\u0026thinsp;1.0\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L and platelet count\u0026thinsp;\u0026ge;\u0026thinsp;100\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L), BM aspirate with \u0026lt;\u0026thinsp;5% blasts with spicules and no blasts with Auer rods and an independence from transfusion. Partial remission (PR) was determined to be the presence of normalized blood counts and a reduction of at least 50% to 5\u0026ndash;25% blasts in BM aspirate. All cases which did not meet the CR or PR criterion were deemed to be patients with no remission (NR). ORR calculation took into consideration PR and CR rates. The duration between diagnosis to death or to last follow-up was the OS. Time to stable neutrophil recovery was determined from the end of induction therapy until patients possessed two consecutive ANC measurements on different days which was \u0026ge;\u0026thinsp;500/\u0026micro;l. A similar means was used to determine time to stable platelet recovery, using a cutoff of \u0026ge;\u0026thinsp;20,000/\u0026micro;l for a minimum of three consecutive days. The National Cancer Institute (NCI) Common Toxicity Criteria v.5.0 was used to define and grade toxicities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eThe Statistical Package for Social Science (SPSS version 20.0) was used for all data analyses. The t-test and Fisher\u0026rsquo;s exact test were used to determine differences between continuous variables. Survival probabilities were estimated using the Kaplan\u0026ndash;Meier method with curve comparisons performed using the log-rank test. All \u003cem\u003eP\u003c/em\u003e values were two-tailed and were considered to be statistically significant when valued at less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the patients\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of 331 AML patients were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age and Eastern Cooperative Oncology Group performance status (ECOG PS) score in the D-CAG cohort were significantly higher than the IA cohort (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Based on the 2017 ELN cytogenetic risk classification, a total of 114 patients (34.4%) were in the favorable-risk group, 106 patients (32.0%) were intermediate-risk and 96 patients (29.0%) were poor-risk. According with the previous studies, there were significantly more patients in the favorable-risk category (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and less in the poor-risk category (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.001)in the IA cohort compared with the D-CAG cohort, and older patients in the D-CAG cohort harbored more adverse karyotypes than young patients in IA cohort. The other clinical features of gender, white blood cell (WBC), hemoglobin and platelet count at diagnosis as well as percentage of blasts in bone marrow, were similar between the two cohorts.\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\u003eBaseline characteristics of the AML patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD-CAG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (60\u0026ndash;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (14\u0026ndash;59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\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 \u003cp\u003e0.509\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\u003e83 (54.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003e69 (45.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior diagnosis of MDS, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO 2016 AML classification, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML with recurrent genetic abnormalities (AML-RGA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML with myelodysplasia-related changes (AML-MRC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTherapy-related myeloid neoplasms (t-AML)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML, not otherwise specified (AML, NOS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG performance status score, n(%)\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 \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (82.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175 (97.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBM blasts (%), median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.0 (21.6\u0026ndash;91.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.0 (22\u0026ndash;94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdverse karyotypes*, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComlpex karyotypes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonosomal karyotypes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17p abnormality, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-5 or del(5q); -7, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther adverse karyotypes**, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyelodysplasia-related gene mutations***, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCCN risk status, n (%)\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 \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\u003e29 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003e57 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.059\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\u003e58 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L), median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.8 (1.34\u0026ndash;219.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (0.27\u0026ndash;184.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L), median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (42\u0026ndash;140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (39\u0026ndash;157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L), median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.0 (6-257)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (2-287)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: BM: Bone marrow; ECOG PS: Eastern Cooperative Oncology Group performance status; MDS: Myelodysplastic syndrome; WBC: White blood cells count; WHO: World Health Organization; NA: Not available.\u003c/p\u003e \u003cp\u003e* Adverse karyotypes: -5 or del(5q); -7; -17/abn(17p); complex karyotype; monosomal karyotype and other adverse karyotypes.\u003c/p\u003e \u003cp\u003e** Other adverse karyotypes: t(6;9)(p23;q34.1); t(v;11q23.3); t(9;22)(q34.1;q11.2); t(8;16)(p11;p13); inv(3)(q21.3q26.2); t(3q26.2;v)\u003c/p\u003e \u003cp\u003e*** Myelodysplasia-related gene mutations: defined by mutations in ASXL1, EZH2, RUNX1, SRSF2, U2AF1, or ZRSR2.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMutational analyses\u003c/h2\u003e \u003cp\u003eMutations involving any of the 21 genes were detected in 305 (92.1%) patients. Among the entire patient population, 92 patients (27.8%) possessed single gene mutations, 92 (27.8%) possessed mutations in two genes, 70 (21.1%) possessed mutations in three genes, 41 (12.4%) possessed mutations in four genes, and 5 (1.5%) possessed mutations in five and six genes, respectively. \u003cem\u003eASXL1\u003c/em\u003e (26.0%) was the most frequently encountered gene to harbor mutations. Other genes in a decreasing frequency were: \u003cem\u003eFLT3\u003c/em\u003e, 21.8%; \u003cem\u003eNPM1\u003c/em\u003e, 22.4%; \u003cem\u003eTET2\u003c/em\u003e, 21.8%; \u003cem\u003eCEBPA\u003c/em\u003e, 20.8%; \u003cem\u003eNRAS\u003c/em\u003e, 17.2%; \u003cem\u003eDNMT3A\u003c/em\u003e, 15.4%; \u003cem\u003eKIT\u003c/em\u003e, 12.7%; \u003cem\u003eIDH2\u003c/em\u003e, 12.1%; \u003cem\u003eTP53\u003c/em\u003e, 7.2%; \u003cem\u003eIDH1\u003c/em\u003e, 6.3%; and \u003cem\u003eRUNX1\u003c/em\u003e, 5.4%. Mutations in \u003cem\u003eSRSF2\u003c/em\u003e, \u003cem\u003eETV6\u003c/em\u003e, \u003cem\u003eKRAS\u003c/em\u003e, \u003cem\u003eCBL\u003c/em\u003e, \u003cem\u003eU2AF1\u003c/em\u003e, \u003cem\u003ePHF6\u003c/em\u003e, \u003cem\u003eZRSR2\u003c/em\u003e, \u003cem\u003eEZH2\u003c/em\u003e, and \u003cem\u003eCSF3R\u003c/em\u003e were rare (\u0026lt;\u0026thinsp;5.0%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Mutations in \u003cem\u003eTET2\u003c/em\u003e (27.6% \u003cem\u003evs\u003c/em\u003e. 16.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0227), \u003cem\u003eSRSF2\u003c/em\u003e (7.2% \u003cem\u003evs\u003c/em\u003e. 1.1%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0079), \u003cem\u003eRUNX1\u003c/em\u003e (9.2% \u003cem\u003evs\u003c/em\u003e. 2.2%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0037) and \u003cem\u003eTP53\u003c/em\u003e (12.5% \u003cem\u003evs\u003c/em\u003e. 2.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0010) were more common in older patients in contrast to young patients. Young patients had significantly higher rate of biallelic \u003cem\u003eCEBPA\u003c/em\u003e mutations (17.3% vs. 7.2%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0075) and \u003cem\u003eKIT\u003c/em\u003e mutations (19.0% vs. 5.3%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0002) when compared to their older counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The occurrence of Myelodysplasia-related gene mutations such as \u003cem\u003eASXL1, EZH2, RUNX1, SRSF2, U2AF1\u003c/em\u003e, and \u003cem\u003eZRSR2\u003c/em\u003e were more common in older patients than young patients (52.0% \u003cem\u003evs\u003c/em\u003e. 30.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0145; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between cytogenetics, gene mutations and clinical outcomes\u003c/h2\u003e \u003cp\u003eA single cycle of IA induction resulted in CR in 142 young patients (79.3%), which was higher in contrast to the CR rate of 66.4% in elderly patients treated with D-CAG (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). However, no significant difference was observed in the ORR between the two treatment arms (79.6% \u003cem\u003evs\u003c/em\u003e. 85.5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.189). When we analyzed the response based on risk stratification, we found that the IA group had a higher CR rate compared to the D-CAG group in both the favorable- and intermediate-risk groups. However, this difference reached statistical significance only in the intermediate-risk group. Additionally, there were no significant differences in ORR among the three risk groups (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\u003eClinical responses and outcomes in each treatment arm.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD-CAG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR (%) (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.4(101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.3 (142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFavorable (%) (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.4(21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.7(72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate (%) (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.4(39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.7(42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor (%) (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.1(36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.8(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORR (%) (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.6 (121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.5 (153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFavorable (%) (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.7(26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.6(77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate (%) (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.7(46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.6(46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor (%) (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.1(43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.1(27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePatients were followed-up for a median of 45 months (range: 2-120 months) with a final analysis done on August 31, 2020. The median OS of all the AML patients was 19 months at the final analysis with 1-year and 2-year OS rate of 64.3% and 43.9%, respectively. There were 148 and 159 evaluable cases in the D-CAG and IA arms, respectively. The median OS was significantly reduced in the D-CAG cohort in contrast to the IA cohort (15 months vs. 38 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Young patients of the favorable-, intermediate- and poor-risk groups possessed notably longer OS in contrast to their older counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Favorable-risk patients treated with IA who did not undergo allo-HSCT still demonstrated increased OS in contrast to older patients. Nevertheless, in the intermediate- and poor-risk subgroups, after excluding patients who received allo-HSCT, the young and old cohorts showed comparable median OS (intermediate-risk: 14 months \u003cem\u003evs\u003c/em\u003e. 18 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2765; poor-risk: 15 months \u003cem\u003evs\u003c/em\u003e. 14 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1827; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on cytogenetics and gene analysis, the median OS of patients with mutated \u003cem\u003eNPM1\u003c/em\u003e, \u003cem\u003eFLT3\u003c/em\u003e, \u003cem\u003eFLT3-ITD\u003c/em\u003e, \u003cem\u003eDNMT3A\u003c/em\u003e, \u003cem\u003eIDH1\u003c/em\u003e, \u003cem\u003eIDH2\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e and myelodysplasia-related genes was similar between D-CAG and IA treatment arms (non-allo-HSCT). Patients with \u003cem\u003eTET2\u003c/em\u003e, \u003cem\u003eNRAS\u003c/em\u003e and biallelic \u003cem\u003eCEBPA\u003c/em\u003e mutations in IA group (non-allo-HSCT) achieved better OS than those in the D-CAG group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, older patients who possessed complex and monosomal karyotypes were noted to have significantly longer median OS in contrast to young patients with the same cytogenetic abnormalities who did not undergo allo-HSCT (complex: 12 months vs. 4.5 months, P\u0026thinsp;=\u0026thinsp;0.003; monosomal: 14 months vs. 4 months, P\u0026thinsp;=\u0026thinsp;0.003; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Patients with chromosomes 5 and/or 7 abnormalities (-5/5q- and/or -7/7q-) in D-CAG group have relatively longer median OS than those in IA group, although this difference failed to achieve statistical significance (9 months vs. 5 months, P\u0026thinsp;=\u0026thinsp;0.147; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). We did not analyze the survival of patients with other adverse karyotypes given the small numbers of the patients with these cytogenetic abnormalities.\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\u003eOverall survival for D-CAG treated patients harboring specific gene mutations and cytogenetics compared with IA treated patients who did not receive allo-HSCT.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGene mutations and cytogenetics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD-CAG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIA (non allo-HCT)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMUT median OS, months (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMUT median OS, months (n)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTET2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNPM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNPM1+/FLT3-ITD-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFLT3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFLT3-ITD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.5 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNPM1-/FLT3-ITD+\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDNMT3A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNRAS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUndefined (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIDH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIDH2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebiallelic \u003cem\u003eCEBPA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUndefined (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTP53*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyelodysplasia-related gene mutations**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecomplex karyotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.5 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-5/5q- and/or -7/7q-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emonosomal karyotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e*Including \u003cem\u003eTP53\u003c/em\u003e mutations and deletions. ** Myelodysplasia-related gene mutations: defined by mutations in ASXL1, EZH2, RUNX1, SRSF2, U2AF1, or ZRSR2.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\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\u003eAdverse events\u003c/h2\u003e \u003cp\u003eEarly mortality rates (4-week mortality from therapy) of patients who received D-CAG and standard induction were 4.6% and 8.9%, respectively. Infections and myelosuppression were the most commonly encountered adverse effects during induction chemotherapy. All AML patients experienced thrombocytopenia and neutropenia. In the D-CAG group, 93.9% of patients were documented to have grade III-IV hematological toxicity, while all patients in IA group suffered from grade III-IV hematological toxicity. 87.6% of all patients were documented to have febrile neutropenia, with 83.1% and 91.8% in the D-CAG and IA treatment arms, respectively. Non-hematological adverse events were often mild or moderately severe. Older patients who achieved CR demonstrated median times for stable neutrophil (0.5\u0026times;10\u003csup\u003e9\u003c/sup\u003e neutrophils/L) and platelet (20\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L) recovery of 14 and 12 days, respectively. Those who received IA induction demonstrated median times to stable neutrophil recovery to 0.5\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L and platelet to 20\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L of 13 and 16 days, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe effective treatment of AML remains a fundamental challenge, as a uniform approach to this condition has been proven ineffective given the heterogenous nature of this disease. Current strategies involve increasingly individualized treatment regimens [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Previous investigations by our group and others have been reported that the response rates of low-intensity regimens show similarity to those of standard induction regimens with the additional benefit of lower toxicities, offering safer treatment options to different subgroups of AML patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In this study, we attempted to distinguish amongst AML patients the most suitable candidates for either the D-CAG or intensive chemotherapy regime. Our findings highlight that an intensive regimen might not be the only and the best optimal option for all the AML patients especially in a new era of precision target treatment, with the development of several novel treatment options to cater for a wider range of patients with AML [\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Importantly, our study suggests that the D-CAG regimen may be the better choice for AML patients with high-risk karyotypes.\u003c/p\u003e \u003cp\u003eThere is a stark difference in the cytogenetics and gene mutations between AML patients at both ends of the age spectrum, which has been linked to AML treatment response [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For instance, young patients more often harbored biallelic CEBPA and KIT mutations compared to older patients. Approximately 10\u0026ndash;15% of all patients with AML have been found to harbor NRAS, which has been reported to cooperate with antecedent molecular lesions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This study found that patients with NRAS and biallelic CEBPA mutations appeared to be more likely to benefit from intensive regimens.\u003c/p\u003e \u003cp\u003eWhile the elderly are more likely to possesses mutations in the DNMT3A, ASXL1, IDH1/2, and TET2 genes that encode for epigenetic modification [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Qingyu Xu et al. reported that epigenetic modifier gene mutations to be potentially predictive biomarkers of better response to the D-CAG regimen in cytogenetically intermediate-risk AML [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Another study found that younger patients with non-DNMT3A-R882 mutations and older patients with DNMT3A-R882 mutations were more likely to encounter poorer prognoses [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In the intermediate-risk AML groups, Ley et al. highlighted the presence of DNMT3A mutations to function as an independent predictor of poor survival [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Consistent with previous reports, a higher percentage of elderly AML patients in this study demonstrated TET2, SRSF2, RUNX1 and TP53 mutations, which are related to poorer outcomes. We found that the median OS to be comparable between D-CAG treated patients with or without FLT3-ITD, DNMT3A, IDH2 mutations, as well as DNA methylation associated genes mutations, whereas patients treated with IA who harbored these mutations demonstrated markedly reduced median OS in contrast to those harboring wild type genes. These results illustrated that D-CAG might offset the adverse effects of these mutations.\u003c/p\u003e \u003cp\u003eApproximately 5\u0026ndash;8% of all AML patients possess TP53 mutations, a feature associated with dismal outcomes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Dohner H et al. reported that elderly AML patients with TP53 mutations who received either azacitidine or conventional therapy possessed a median OS of 7.2 and 2.4 months, respectively [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Previous reports showed that elderly patients possessing TP53 mutations who were treated with standard chemotherapy had a median OS ranging from 4 to 6 months [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In the D-CAG group, 19 patients (12.5%) possessing TP53 mutations or deletions demonstrated a median OS of 12 months. Interestingly, IA-treated patients harboring TP53 mutations had even poorer median OS compared to those treated with D-CAG, suggesting that D-CAG may offer a survival benefit for patients with TP53 abnormalities.\u003c/p\u003e \u003cp\u003eWe found the CR and ORR of the high-risk groups was comparable between both older and young patient. Notably, older patients with complex or monosomal karyotypes even showed significantly increased median OS in contrast to young patients who were not recipients of allo-HSCT. This suggests that even intensive chemotherapy may not be optimal in improving the prognosis of intermediate- and high-risk groups in the absence of allo-HSCT compared to D-CAG.\u003c/p\u003e \u003cp\u003eIn conclusion, we speculated that intensive chemotherapy could confer a significant survival benefit for the patients with favorable-risk status, NRAS and biallelic CEBPA mutations, while it might not improve the prognosis of the intermediate- or high-risk patients who were not recipients of allo-HSCT. Patients with high-risk cytogenetics and certain mutations such as TP53, FLT3-ITD and DNA methylation associated mutations might benefit from the D-CAG induction regimen rather than an intensive regimen. Allo-HSCT should be considered for eligible patients to prolong survival following achievement of CR. Given the clear benefits of treating AML based on individual genetic profiles, one approach may be to await cytogenetic and molecular results prior to choosing more personalized treatment, on the condition that these delays do not confer additional harm to the patients.\u003c/p\u003e \u003cp\u003eThis study is limited by its retrospective design, the relatively small sample size, and lack of analysis for age-matched cases. Nevertheless, our findings support the clinical benefits of implementing cytogenetics and mutation screening in stratifying AML patient risk and treatment. Further prospective studies in larger patient cohorts are necessary to validate our findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eacute\u0026nbsp;myeloid leukemia \u0026nbsp;AML\u003c/p\u003e\n\u003cp\u003eallogeneic hematopoietic stem cell transplantation \u0026nbsp; allo-HSCT\u0026nbsp;\u003c/p\u003e\n\u003cp\u003enext-generation sequencing \u0026nbsp;NGS\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ecomplete remission \u0026nbsp;CR\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eobjective response rate \u0026nbsp;ORR\u003c/p\u003e\n\u003cp\u003epartial remission \u0026nbsp; PR\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eno remission \u0026nbsp;NR\u003c/p\u003e\n\u003cp\u003eoverall survival \u0026nbsp;OS\u0026nbsp;\u003c/p\u003e\n\u003cp\u003edecitabine in combination with modified CAG\u0026nbsp; D-CAG\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWorld Health Organization \u0026nbsp;WHO\u003c/p\u003e\n\u003cp\u003eMedical Research Council \u0026nbsp;MRC\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ebone marrow \u0026nbsp;BM\u003c/p\u003e\n\u003cp\u003etargeted gene sequencing \u0026nbsp;TGS\u0026nbsp;\u003c/p\u003e\n\u003cp\u003egenomic DNA \u0026nbsp;gDNA\u003c/p\u003e\n\u003cp\u003eBurrows-Wheeler Aligner \u0026nbsp;BWA\u003c/p\u003e\n\u003cp\u003eGenome Analysis Toolkit \u0026nbsp;GATK\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIntegrative Genomics Viewer \u0026nbsp;IGV\u003c/p\u003e\n\u003cp\u003eEastern Cooperative Oncology Group performance status \u0026nbsp;ECOG PS\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ewhite blood cell \u0026nbsp;WBC\u003c/p\u003e\n\u003cp\u003evariant allele frequency \u0026nbsp;VAF\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol and all amendments were approved by the ethics committee of the First Affiliated Hospital of Nanjing Medical University and were conducted per the principles expressed in the Declaration of Helsinki. All patients provided written informed consent to participate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the policy of China, the data of next-generation sequencing can not been uploaded to the international database. If necessary, we can upload the data to the GS (https://ngdc.cncb.ac.cn/gsa/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (No. 81870119, 82170153, 81700114, 81720108002),\u0026nbsp;National Science and Technology Major Project of China\u0026nbsp;(2018ZX09734-007), Jiangsu Province\u0026rsquo;s Medical Elite Program (ZDRCA2016022), and Jiangsu Provincial Special Program of Medical Science (BE2017751). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.H. and S.X.Q. designed the research study. W.J.L., Q.S., Y.Z. and H.Z. performed the research. M.H., W.J.L. and Q.S. analyzed the data. W.J.L., Q.S. and M.H. wrote the paper. J.Y.L., S.X.Q. and M.H revised the manuscript and finalized the last version of the article. All authors checked and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDohner H, Weisdorf DJ, Bloomfield CD (2015) Acute Myeloid Leukemia. N Engl J Med 373(12):1136\u0026ndash;1152\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapaemmanuil E, Gerstung M, Bullinger L et al (2016) Genomic Classification and Prognosis in Acute Myeloid Leukemia. N Engl J Med 374(23):2209\u0026ndash;2221\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDohner H, Dolnik A, Tang L et al (2018) Cytogenetics and gene mutations influence survival in older patients with acute myeloid leukemia treated with azacitidine or conventional care. 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N Engl J Med 375(21):2023\u0026ndash;2036.\u003c/span\u003e\u003c/li\u003e \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":"acute myeloid leukemia, D-CAG, intensive chemotherapy, prognosis, mutations, karyotypes","lastPublishedDoi":"10.21203/rs.3.rs-3823801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3823801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to investigate the cytogenetic and molecular characteristics of patients with Acute Myeloid Leukemia (AML) and determine which patients would benefit most from a low-intensity regimen of decitabine in combination with modified CAG (D-CAG) or intensive chemotherapy. We retrospectively analyzed cytogenetic and molecular data from 331 newly diagnosed AML patients and investigated the relationship between genetic characteristics, risk status, treatments and clinical outcomes. The median followed-up was 45 months (2-120 months). Overall, a single cycle of IA induction resulted in a CR rate of 79.3%, which was superior to the 66.4% observed in the cohort treated with D-CAG (P\u0026thinsp;\u0026lt;\u0026thinsp;.05). However, there was no significant difference in ORR between the two arms. The median OS was reduced in the D-CAG cohort compared to the IA cohort (P\u0026thinsp;\u0026lt;\u0026thinsp;.05). Favorable-risk groups and patients who undergo allo-HSCT treated with IA had longer OS than those in the D-CAG groups (P\u0026thinsp;\u0026lt;\u0026thinsp;.05). While the median OS of the intermediate- and high-risk groups who were not recipients of allo-HSCT was comparable between two regimen. Within the IA group, patients with TET2, NRAS, and biallelic CEBPA gene mutations achieved better OS than those in the D-CAG group (P\u0026thinsp;\u0026lt;\u0026thinsp;.05). While older patients with complex and monosomal karyotypes were tend to have longer median OS compared to younger patients (P\u0026thinsp;\u0026lt;\u0026thinsp;.05). In conclusion, it is crucial to select AML chemotherapy regimens based on karyotypes and genetic characteristics. D-CAG may be a better choice for AML patients with high-risk karyotypes.\u003c/p\u003e","manuscriptTitle":"Acute myeloid leukemia patients with high-risk karyotypes benefit from decitabine in combination with modified CAG","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 19:29:45","doi":"10.21203/rs.3.rs-3823801/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":"9ab2816a-6a45-4312-8e37-4117bab82ce9","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-20T05:29:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-08 19:29:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3823801","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3823801","identity":"rs-3823801","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}