Superior Response and Survival of Intensive Chemotherapy Over Venetoclax Plus Azacitidine in Newly Diagnosed KIT-Mutated Acute Myeloid Leukemia

Superior Response and Survival of Intensive Chemotherapy Over Venetoclax Plus Azacitidine in Newly Diagnosed KIT-Mutated Acute Myeloid Leukemia | 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 Superior Response and Survival of Intensive Chemotherapy Over Venetoclax Plus Azacitidine in Newly Diagnosed KIT-Mutated Acute Myeloid Leukemia Qingli Ji, Xinwen Jiang, Xiaoqing Li, Chen Cao, Xinrui Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8030948/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Feb, 2026 Read the published version in Annals of Hematology → Version 1 posted 9 You are reading this latest preprint version Abstract Although KIT mutations hold significant prognostic value in acute myeloid leukemia (AML), their impact on selecting first-line treatment remains unclear. This retrospective study of 222 newly diagnosed AML patients therefore compared the efficacy of venetoclax plus azacitidine (VA) versus intensive chemotherapy (IC) in KIT-mutated AML, while also exploring the prognostic implications of KIT mutation subtypes and their role in predicting VA response. Among patients with KIT mutations, IC was superior to VA, yielding significantly longer median event-free survival (EFS) (14.5 vs. 2.4 months, p = 0.011) and overall survival (OS) (not reached vs. 9.8 months, p < 0.0001), and a higher complete remission (CR) rate (80.0% vs. 17.6%, p < 0.001). Exon 17 mutations were associated with significantly shorter EFS relative to other KIT mutations (7.3 vs. 18.8 months; p = 0.046). Moreover, among all VA-treated patients, KIT mutation was an independent adverse prognostic factor for both EFS (HR = 3.25, p < 0.001) and OS (HR = 3.31, p = 0.001). This study establishes the superiority of IC over VA in KIT-mutated AML and identifies KIT mutation as an important biomarker of resistance to venetoclax-based therapy, providing valuable guidance for first-line treatment decisions. Acute Myeloid Leukemia KIT Intensive Chemotherapy Venetoclax Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Acute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy. Molecular genetic abnormalities play a pivotal role in guiding disease classification, risk stratification, and treatment selection. KIT gene mutations represent an important driver mutation in AML. Although their overall frequency in AML is relatively low, KIT mutations occur in 20–40% of core-binding factor AML (CBF-AML) and are associated with an increased risk of relapse and poorer prognosis [ 1 – 3 ]. Traditionally, for patients considered medically fit, typically characterized by younger age, good performance status, and limited comorbidities, intensive chemotherapy (IC) was the standard first-line treatment. In recent years, low-intensity regimens such as venetoclax combined with azacitidine (VA) have demonstrated remarkable efficacy in untreated AML patients unfit for intensive induction therapy, establishing a cornerstone of first-line therapy for this population [ 4 – 6 ]. However, the differential responses of KIT -mutated AML patients to VA versus IC, and the underlying molecular mechanisms, remain inadequately defined. Notably, KIT mutations exhibit significant heterogeneity; exon 17 mutations (particularly D816V), the most common subtype, may confer distinct biological properties and clinical implications [ 7 – 10 ]. Consequently, elucidating the impact of KIT mutation status and its subtypes on the efficacy of first-line treatment regimens has emerged as a critical, unresolved clinical question. This study aims to conduct a retrospective cohort analysis to systematically compare the efficacy and safety of the VA versus IC as first-line therapy in KIT -mutated AML patients. Furthermore, it seeks to investigate the potential influence of different KIT mutation subtypes, especially exon 17 mutations, on treatment response. By integrating genetic mutational profiles, treatment responses, survival outcomes, clonal dynamic monitoring, and multivariate analyses, we endeavor to provide novel evidence-based insights for personalizing treatment strategies in KIT -mutated AML patients. Methods Study Design and Patient Cohorts This retrospective study enrolled consecutive newly diagnosed AML patients at Qilu Hospital of Shandong University from January 2017 to January 2025. Inclusion criteria were: age ≥ 18 years, newly diagnosed AML, available KIT gene testing data, and complete clinical data. Exclusion criteria were: acute promyelocytic leukemia, secondary AML, not receiving first-line IC or VA treatment, missing key data, and loss to follow-up. A total of 222 patients were included. For the core analysis, patients were stratified into three cohorts based on KIT mutation status and first-line treatment: KIT -mutated/VA (Cohort A, n = 17), KIT -mutated/IC (Cohort B, n = 50), and KIT -wild-type/VA (Cohort C, n = 155). Furthermore, to analyze the impact of mutation subtypes, all KIT -mutated patients (n = 67) were categorized into exon 17 mutation (Cohort D, n = 53) and non-exon 17 mutation (Cohort E, n = 14) subgroups. The study protocol was performed in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Qilu Hospital of Shandong University. Data Collection and Molecular Profiling Baseline clinical and laboratory data, including cytogenetic and molecular genetic features, were collected from the hospital's electronic medical record system. KIT and other gene mutations were detected by next-generation sequencing (NGS). KIT sequencing covered all exons, with deep sequencing of exon 17 hotspots (including D816V, N822K, etc.) at 1% sensitivity. A targeted NGS panel covered common AML-associated genes (such as DNMT3A , NPM1 , FLT3-ITD/TKD , TP53 , etc.) with a 5% variant allele frequency threshold. Treatment and Risk Stratification All treatment regimens were determined based on patient age, performance status, comorbidities, and physician discretion. IC was primarily the standard "7 + 3" protocol, involving an anthracycline (e.g., idarubicin or daunorubicin) combined with cytarabine. VA consisted of azacitidine administered for 7 days per cycle with a standard venetoclax dose ramp-up to the target dose. A small number of KIT -mutated patients subsequently received the KIT inhibitor avapritinib. All patients were risk-stratified according to the European LeukemiaNet (ELN) 2022 recommendations. Efficacy and Safety Assessments Treatment responses were evaluated per ELN 2022 criteria. Complete remission (CR) required < 5% marrow blasts, no extramedullary disease, and count recovery; CR with incomplete hematologic recovery (CRi) required all CR criteria except cytopenias; morphologic leukemia-free state (MLFS) required < 5% blasts without count recovery. Overall response rate (ORR) included CR + CRi + MLFS. Minimal residual disease (MRD) negativity was defined as < 0.1% leukemic cells by flow cytometry post-induction. Overall survival (OS) was from diagnosis to death from any cause or last follow-up; event-free survival (EFS) was from treatment start to treatment failure, relapse, or death from any cause. Early mortality included deaths within 2 months; adverse events were graded by Common Terminology Criteria for Adverse Events (CTCAE) v5.0. Additional evaluations included KIT mutation clearance kinetics (at 3, 6, and 12 months), the impact of allogeneic hematopoietic stem cell transplantation (allo-HSCT) on prognosis, and co-mutation patterns on survival. Statistical Analysis All statistical analyses were performed using R software (version 4.4.2). Categorical variables were compared using Chi-square or Fisher's exact tests; continuous variables using Mann-Whitney U or Kruskal-Wallis tests. Survival was analyzed by Kaplan-Meier method with log-rank test. Univariate Cox regression (P < 0.15) identified variables for multivariate Cox models. Results are expressed as hazard ratios (HR) with 95% confidence intervals (CI). Propensity score matching (PSM) (1:1, caliper = 0.2) controlled confounding. Transplantation impact was assessed via time-dependent Cox regression. Mutation profiles were visualized using maftools. All tests were two-sided; P < 0.05 was considered significant. Results Patient Baseline Characteristics As detailed in Fig. 1 , a total of 222 newly diagnosed AML patients were included in the final analysis. Baseline characteristics are summarized in Table 1 . Compared with Cohort C ( KIT -wild-type/VA), patients in Cohort A ( KIT -mutated/VA) had a significantly higher proportion of age < 65 years (94.1% vs 51.0%), Eastern Cooperative Oncology Group (ECOG) performance status ≥ 2 (58.8% vs 20.6%), and French-American-British (FAB) M2 subtype (64.7% vs 24.5%). Other baseline features were largely balanced across comparison groups. In KIT -mutated AML patients, the co-mutation landscape (Fig. 2 ) identified NRAS (25%), FLT3-ITD (15%), and ASXL1 (13%) as the most frequent co-occurring mutations. KIT mutations were primarily missense variants, overwhelmingly localized to exon 17 (79.10%) and dominated by the D816V allele (66.04%); other recurrent exon 17 mutations included N822K, D816Y, and D816H ( Table S1 , Online Resource 1) . A total of 222 newly diagnosed AML patients were included in the final analysis and categorized into three primary cohorts(A-C) based on KIT mutation status and first-line treatment. Furthermore, all KIT-mutated patients were stratified into two subgroups (D and E) by mutation site (exon 17 vs. non-exon 17) for subsequent analysis. AML, acute myeloid leukemia; APL, acute promyelocytic leukemia; IC, intensive chemotherapy; VA, venetoclax plus azacitidine. Table 1 Baseline characteristics of the study cohorts Characteristics KIT mut / VA (Cohort A, n = 17) KIT mut / IC (Cohort B, n = 50) KIT wt / VA (Cohort C, n = 155) p (A vs B) p (A vs C) Exon17 (Cohort D, n = 53) Non-exon17 (Cohort D, n = 14) p (D vs E) Age 1.000 0.002** 1.000 <65 years 16 (94.1%) 48 (96%) 79 (51%) 50 (94.3%) 14 (100%) ≥65 years 1 (5.9%) 2 (4%) 76 (49%) 3 (5.7%) 0 (0%) Gender 0.917 0.674 0.751 Male 11 (64.7%) 35 (70%) 87 (56.1%) 37 (69.8%) 9 (64.3%) Female 6 (35.3%) 15 (30%) 68 (43.9%) 16 (30.2%) 5 (35.7%) ECOG PS 0.928 0.001** 1.000 0–1 7 (41.2%) 18 (36%) 123 (79.4%) 20 (37.7%) 5 (35.7%) ≥2 10 (58.8%) 32 (64%) 32 (20.6%) 33 (62.3%) 9 (64.3%) FAB 0.019* 0.004** 0.479 M2 11 (64.7%) 14 (28%) 38 (24.5%) 20 (37.7%) 5 (35.7%) M5 5 (29.4%) 20 (40%) 92 (59.4%) 18 (34%) 7 (50%) Other 1 (5.9%) 16 (32%) 25 (16.1%) 15 (28.3%) 2 (14.3%) ELN 2022 risk 0.330 0.098 < 0.001*** Favorable 4 (23.5%) 17 (34%) 34 (21.9%) 9 (17%) 12 (85.7%) Intermediate 9 (52.9%) 28 (56%) 47 (30.3%) 36 (67.9%) 1 (7.1%) Adverse 4 (23.5%) 5 (10%) 74 (47.7%) 8 (15.1%) 1 (7.1%) WBC (×10⁹/L) 10.9 (4.6–39.5) 18.1 (4.5–37.9) 7 (2.4–28.5) 0.676 0.307 13.4 (5-38.2) 15 (3.5–40.5) 0.994 HGB (g/L) 68 (56–97) 82 (65-103.8) 75 (65–87) 0.210 0.417 80 (65–105) 65.5 (53.8–84.8) 0.081 PLT (×10⁹/L) 19 (14–34) 36.5 (24–63) 38 (24–77) 0.015* 0.002** 32 (19–60) 30.5 (20-52.2) 0.926 BM blast 0.682 0.404 0.672 <40% 3 (17.6%) 6 (12%) 45 (29%) 8 (15.1%) 1 (7.1%) ≥40% 14 (82.4%) 44 (88%) 110 (71%) 45 (84.9%) 13 (92.9%) Fusion gene 0.931 < 0.001*** 0.021* RUNX1-RUNX1T1 10 (58.8%) 26 (52%) 2 (1.3%) 32 (60.4%) 4 (28.6%) CBFB-MYH11 3 (17.6%) 12 (24%) 4 (2.6%) 8 (15.1%) 7 (50%) Negative/Other 4 (23.5%) 12 (24%) 149 (96.1%) 13 (24.5%) 3 (21.4%) Complex karyotype 0 (0%) 2 (4%) 29 (18.7%) 1.000 0.080 2 (3.8%) 0 (0%) 1.000 DNMT3A 3 (17.6%) 3 (6%) 50 (32.3%) 0.166 0.336 6 (11.3%) 0 (0%) 0.330 NPM1 0 (0%) 2 (4%) 38 (24.5%) 1.000 0.015* 2 (3.8%) 0 (0%) 1.000 TET2 0 (0%) 3 (6%) 35 (22.6%) 0.565 0.025* 3 (5.7%) 0 (0%) 1.000 NRAS 3 (17.6%) 14 (28%) 34 (21.9%) 0.527 1.000 10 (18.9%) 7 (50%) 0.034* FLT3-ITD 1 (5.9%) 9 (18%) 33 (21.3%) 0.432 0.200 10 (18.9%) 0 (0%) 0.106 TP53 0 (0%) 0 (0%) 11 (7.1%) < 0.001*** 0.604 0 (0%) 0 (0%) < 0.001*** Allo-HSCT 1 (5.9%) 12 (24%) 10 (6.5%) 0.158 1.000 9 (17%) 4 (28.6%) 0.447 1. Abbreviations: ECOG PS, Eastern Cooperative Oncology Group Performance Status; FAB, French-American-British classification; ELN, European LeukemiaNet; WBC, white blood cell count; HGB, hemoglobin; PLT, platelet count; BM, bone marrow; Allo-HSCT, allogeneic hematopoietic stem cell transplantation; KIT mut , KIT-mutated; KIT wt , KIT wild-type; VA, venetoclax plus azacitidine; IC, intensive chemotherapy. 2. Data presentation: Categorical, n (%); continuous, median (IQR). 3. Significance: *p < 0.05, **p < 0.01, ***p < 0.001. Oncoprint displaying the spectrum of frequent gene mutations in 67 KIT-mutated AML patients. The profile shows that KIT mutations are predominantly missense variants. The most frequently co-occurring mutations were in NRAS (25%), FLT3-ITD (15%), and ASXL1 (13%). Efficacy and Survival Comparison of IC versus VA in KIT -Mutated Patients We first compared the survival outcomes of KIT -mutated AML patients receiving first-line IC versus those receiving VA. The IC cohort demonstrated significantly longer median EFS (14.5 vs 2.4 months, p = 0.011; Fig. 3 a) and OS (not reached vs 9.8 months, p < 0.0001; Fig. 3 b) compared to the VA cohort. To control for potential confounding bias, PSM was performed, yielding 12 matched pairs. After matching, the IC cohort maintained numerical advantages in median EFS (11.0 vs 3.0 months, p = 0.360; Figure S1 a, Online Resource 1) and median OS (not reached vs 11.8 months, p = 0.071; Figure S1 b, Online Resource 1), although these differences were not statistically significant. We next compared treatment responses and safety between the IC and VA cohorts (Fig. 5 a). The IC cohort achieved significantly higher CR (80.0% vs 17.6%, p < 0.0001), ORR (82.0% vs 41.2%, p < 0.01), and MRD-negative (76.0% vs 35.3%, p < 0.01) rates compared to the VA cohort. Regarding safety, the VA cohort showed higher incidences of grade ≥ 3 adverse events (82.4% vs 72.0%, p = 0.527) and early death (5.9% vs 2.0%, p = 0.446), though these differences were not statistically significant. (a) Event-free survival (EFS)and (b) overall survival (OS) in KIT-mutated patients receiving first-line intensive chemotherapy (IC) versus venetoclax plus azacitidine (VA). The IC cohort demonstrated significantly longer median EFS (14.5 vs. 2.4 months; p = 0.011) and OS (not reached vs. 9.8 months; p < 0.0001). EFS, event-free survival; OS, overall survival; IC, intensive chemotherapy; VA, venetoclax plus azacitidine; NR, not reached. (a) Event-free survival (EFS)and (b) overall survival (OS) in VA-treated patients with KIT mutation versus KIT wild-type. Patients with KIT mutations had significantly shorter median EFS (2.4 vs. 10.6 months; p = 0.005) and OS (9.8 vs. 18.6 months; p = 0.036). EFS, event-free survival; OS, overall survival; KIT mut , KIT-mutated; KIT wt , KIT wild-type; NR, not reached. Impact of KIT Mutation Status on Efficacy and Prognosis in VA-Treated Patients We then assessed the prognostic impact of KIT mutations in the VA-treated population. Patients with KIT mutations had significantly shorter median EFS (2.4 vs 10.6 months, p = 0.005; Fig. 4 a) and OS (9.8 vs 18.6 months, p = 0.036; Fig. 4 b) than wild-type patients. PSM created 28 balanced pairs, and subsequent analysis showed that differences in EFS (14.5 vs 13.4 months, p = 0.500; Figure S2a, Online Resource 1) and OS (39.3 months vs not reached, p = 0.890; Figure S2b, Online Resource 1) were no longer significant. Regarding treatment response and safety (Fig. 5 b), KIT -wild-type patients demonstrated significantly higher rates of CR (81.9% vs 17.6%; p < 0.0001), ORR (90.3% vs 41.2%; p < 0.0001), and MRD negativity (77.4% vs 35.3%; p < 0.001) compared to KIT -mutated patients. Early death rates were similar between groups (6.5% vs 5.9%; p = 1.000), but grade ≥ 3 adverse events occurred more frequently in the mutated cohort (82.4% vs 55.5%; p < 0.050). (a) Bar graph showing rates of complete remission (CR), overall response rate (ORR), minimal residual disease (MRD) negativity, early death (< 2 months), and grade ≥ 3 adverse events (AEs) in KIT-mutated patients receiving first-line IC versus VA. The IC cohort achieved significantly higher CR (80.0% vs. 17.6%; p < 0.0001), ORR (82.0% vs. 41.2%; p < 0.01), and MRD-negative rates (76.0% vs. 35.3%; p < 0.01). Incidences of early death (2.0% vs. 5.9%; p = 0.446) and grade ≥ 3 AEs (72.0% vs. 82.4%; p = 0.527) were not significantly different. (b) Corresponding outcomes in VA-treated patients with KIT mutation versus KIT wild-type. The KIT-wild-type group had significantly higher CR (81.9% vs. 17.6%; p < 0.0001), ORR (90.3% vs. 41.2%; p < 0.0001), and MRD-negative rates (77.4% vs. 35.3%; p < 0.001), while also exhibiting a lower incidence of grade ≥ 3 AEs (55.5% vs. 82.4%; p < 0.05). Early death rates were similar (6.5% vs. 5.9%; p = 1.000). IC, intensive chemotherapy; VA, venetoclax plus azacitidine; ORR, overall response rate; CR, complete remission; MRD, minimal residual disease; AEs, adverse events; KIT mut , KIT-mutated; KIT wt , KIT wild-type. Prognostic Impact of KIT Mutation Subtypes and Specific Co-mutations Among KIT -mutated patients, the exon 17 mutation group had significantly shorter median EFS than the non-exon 17 mutation group (7.3 vs 18.8 months, p = 0.046; Fig. 6 a). However, the difference in OS (25.1 months vs not reached, p = 0.450; Fig. 6 b) was not statistically significant. (a) Event-free survival (EFS) and (b) overall survival (OS) of KIT-mutated patients stratified by mutation site (exon 17 vs. non-exon 17). Patients harboring exon 17 mutations had significantly inferior median EFS compared to those with KIT mutations outside of exon 17 (7.3 vs. 18.8 months; p = 0.046). The difference in median OS (25.1 months vs. not reached; p = 0.450) was not statistically significant. EFS, event-free survival; OS, overall survival; NR, not reached. Co-mutation analysis revealed that KIT -mutated patients with concurrent NRAS mutations had significantly longer EFS (not reached vs 7.6 months, p = 0.003; Figure S3a, Online Resource 1) and OS (not reached vs 20.1 months, p = 0.004; Figure S3b, Online Resource 1) compared to NRAS -wild-type patients. In contrast, those with FLT3-ITD/TKD mutations showed lower median EFS (7.3 vs 16.5 months, p = 0.220; Figure S4a, Online Resource 1) and median OS (22.5 vs 39.3 months, p = 0.470; Figure S4b, Online Resource 1) compared to FLT3 -wild-type patients, but these differences were not significant. Multivariate Analysis of Prognostic Factors in VA-Treated Patients Multivariate Cox analysis confirmed KIT mutation as an independent adverse prognostic factor for both EFS (HR = 3.25, 95% CI: 1.75–6.03; p < 0.001; Fig. 7 a) and OS (HR = 3.31, 95% CI: 1.59–6.87; p = 0.001; Fig. 7 b) in VA-treated patients. Furthermore, the analysis showed that age ≥ 65 years was a risk factor for OS (HR = 2.20, 95% CI: 1.37–3.53; p < 0.001), whereas the FAB-M2 subtype predicted better EFS (HR = 0.47, 95% CI: 0.23–0.98; p = 0.044) and OS (HR = 0.39, 95% CI: 0.18–0.83; p = 0.015). (a) Factors associated with event-free survival (EFS). (b) Factors associated with overall survival (OS). Forest plots showing factors independently associated with(a) event-free survival (EFS) and (b) overall survival (OS) in patients treated with venetoclax plus azacitidine (VA). KIT mutation was an independent adverse prognostic factor for both EFS (HR = 3.25, 95% CI 1.75–6.03; p < 0.001) and OS (HR = 3.31, 95% CI 1.59–6.87; p = 0.001). Furthermore, age ≥ 65 years was a risk factor for inferior OS (HR = 2.20, 95% CI 1.37–3.53; p < 0.001), whereas the FAB-M2 subtype predicted better EFS (HR = 0.47, 95% CI 0.23–0.98; p = 0.044) and OS (HR = 0.39, 95% CI 0.18–0.83; p = 0.015). EFS, event-free survival; OS, overall survival; VA, venetoclax plus azacitidine; HR, hazard ratio; CI, confidence interval; ELN, European LeukemiaNet; FAB, French-American-British classification; ECOG, Eastern Cooperative Oncology Group. Allo-HSCT Outcomes and Exploratory KIT Inhibition To evaluate the impact of allo-HSCT on survival, we performed a time-dependent Cox analysis to account for immortal time bias, which revealed that allo-HSCT was associated with significantly improved EFS (HR = 0.09, 95% CI: 0.01–0.62; p = 0.015) and OS (HR = 0.11, 95% CI: 0.02–0.77; p = 0.027) (Table S2, Online Resource 1). Concurrently, we conducted an exploratory analysis of avapritinib in nine patients, which showed a median OS of 12.6 months and EFS of 2.1 months. Despite these limited overall outcomes, two patients achieved rapid CR with concurrent MRD and KIT mutation negativity after only one cycle of avapritinib combined with VA. Furthermore, three with refractory AML attained CRi following avapritinib and successfully proceeded to allo-HSCT, subsequently receiving avapritinib maintenance with favorable tolerability. KIT Mutation Clearance and Relapse The KIT mutation clearance rate at 12 months was similar between the exon 17 and non-exon 17 mutation groups (60.0% vs 57.1%; Figure S5, Online Resource 1). However, molecular relapse occurred in 51.9% (27/52) of patients who achieved initial remission. Notably, exon 17 mutations dominated both cases of molecular relapse and persistent positivity, comprising 84.6% (11/13) of the latter. Discussion This study establishes the prognostic significance of KIT mutations in shaping first-line treatment outcomes in AML. Our analysis reveals that IC is superior to VA in KIT -mutated patients; that KIT mutation is an independent adverse prognostic factor for resistance to VA; and that exon 17 mutations define a subgroup with particularly poor outcomes. Furthermore, we identify allo-HSCT as an effective strategy to overcome the poor prognosis associated with KIT mutations in VA-treated patients. These findings position KIT mutation status as a valuable biomarker for guiding initial therapy selection in AML. Although KIT mutations are well-established as a high-risk factor in CBF-AML [ 2 , 7 , 8 , 11 ], their prognostic significance in non-CBF-AML—particularly in the context of the VA regimen—has remained unclear. This study now establishes KIT mutation as an important biomarker of primary resistance to first-line VA therapy. Our multivariate analysis confirmed it as an independent risk factor for both EFS and OS, thereby extending its adverse prognostic impact to the broader population of AML patients receiving low-intensity chemotherapy. Although survival differences were not statistically significant after PSM, likely due to limited sample size, the robust results from the multivariate analysis, which fully adjusted for confounding variables, strongly support the independent predictive value of KIT mutations. The inferior efficacy of the VA regimen in KIT -mutated AML is likely mediated by the constitutive activation of the KIT signaling pathway, which enhances cell proliferation and survival [ 12 , 13 ]. Specifically, KIT receptor mutations (notably D816V) cause ligand-independent activation of its tyrosine kinase domain, leading to persistent signaling through downstream pathways such as STAT5, PI3K/AKT/mTOR, and RAS/MAPK [ 9 , 14 , 15 ]. These pathways are key upstream regulators of MCL-1 expression [ 16 , 17 ]. Given that MCL-1 overexpression is a well-established core mechanism of resistance to venetoclax [ 18 , 19 ], its upregulation becomes critical: MCL-1 functionally complements BCL-2, so that even when venetoclax inhibits BCL-2, high levels of MCL-1 can bind and sequester pro-apoptotic proteins like BIM. This prevents the activation of BAX/BAK, maintains mitochondrial membrane integrity, and ultimately inhibits apoptosis [ 20 – 24 ]. Therefore, KIT mutation likely confers resistance to the VA regimen by driving MCL-1 expression [ 25 ]. This mechanism also explains the superior efficacy of IC observed in our study, as IC can rapidly clear leukemic clones independent of this specific anti-apoptotic pathway. These findings have immediate clinical implications, suggesting that for medically fit patients with KIT -mutated AML, traditional intensive chemotherapy, which enables profound cytoreduction, should be prioritized as first-line treatment. Within KIT -mutated AML, molecular heterogeneity was linked to distinct clinical outcomes. Patients with exon 17 mutations exhibited inferior EFS. Structurally, exon 17 encodes the activation loop, and mutations in this region (e.g., D816V) induce conformational changes that lead to potent constitutive KIT activation [ 26 – 30 ], providing a molecular basis for the enhanced survival advantage of the leukemic cells and the consequent aggressive clinical behavior of this subtype. KIT clearance kinetics further revealed unique behavior: although the 12-month clearance rate was similar to that of the non-exon 17 group, over half (51.9%) of all patients achieving molecular remission experienced KIT mutation reemergence, which was predominantly driven by exon 17-mutated clones. This suggests that exon 17 mutations not only contribute to initial treatment resistance but may also enhance clonal regenerative capacity and molecular relapse risk. Thus, dynamic molecular monitoring and effective maintenance strategies are particularly important for these patients. Co-mutation analysis identified that concurrent NRAS mutations were associated with improved survival in KIT -mutated patients—a finding that contrasts with the traditional adverse prognostic role of NRAS in AML. This may be explained by the genetic context of our cohort, which included a substantial proportion of CBF-AML, a subtype where NRAS mutations have been previously reported to lack adverse or even exhibit neutral prognostic effects[ 31 , 32 ]. In contrast, FLT3-ITD/TKD co-mutations showed a trend toward poorer outcomes. This is consistent with the known biology of FLT3 mutations, which drive leukemogenesis through the constitutive activation of multiple downstream signaling pathways, including STAT5, PI3K/AKT, and RAS/MAPK, leading to synergistic effects on cell proliferation, survival, and inhibition of apoptosis [ 33 – 35 ]. Given the poor response to VA and high risk of molecular relapse observed in KIT -mutated patients, the finding that allo-HSCT emerged as an independent protective factor in this context strongly supports its consideration as a key consolidation strategy for this high-risk subgroup. Although our exploratory analysis of nine patients receiving the KIT inhibitor avapritinib [ 36 – 38 ] did not demonstrate a significant survival benefit, it enabled rapid treatment responses and successfully bridged refractory patients to transplant. These clinical observations support its further evaluation in larger cohorts. This study has several limitations. Firstly, its retrospective, single-center design may introduce selection bias. Secondly, the relatively limited sample size in some analytical groups may have reduced statistical power. Additionally, although treatment strategies followed clinical standards, variations in specific drug dosing and treatment cycles existed; such real-world heterogeneity might introduce unmeasured confounding factors. Future multicenter, prospective studies are essential to validate these findings, explore targeted and combination therapies for KIT -mutated AML, and further elucidate the molecular mechanisms of treatment resistance in this patient subset. Conclusion This study elucidates the dual guiding value of KIT mutation status in first-line treatment for newly diagnosed AML. Our findings confirm that KIT mutations, particularly the exon 17 subtype, are associated with poor response to the VA regimen and a high risk of molecular relapse, thereby supporting the prioritization of IC as the preferred treatment option. Collectively, KIT mutation status provides a crucial basis for individualized treatment decision-making in AML. Declarations Funding: Not applicable. Competing interests: The authors declare no competing interests. Ethics approval: This study was approved by the Ethics Committee of Qilu Hospital of Shandong University (KYLL-202502-054), with a waiver for informed consent. Consent for publication: Not applicable. Data availability: Data are available from the corresponding author upon reasonable request. Authors' contributions: Qingli Ji: Designed the study, collected and analyzed data, and wrote the manuscript. Xinwen Jiang, Xiaoqing Li, Chen Cao, Xinrui Zhang: Collected data and performed patient follow-up. Minran Zhou and Sai Ma: Reviewed and edited the manuscript. Chunyan Chen: Supervised the research and critically reviewed the manuscript. References Döhner H, Wei AH, Appelbaum FR et al (2022) Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood 140:1345–1377. https://doi.org/10.1182/blood.2022016867 Paschka P, Schlenk RF, Weber D et al (2018) Adding dasatinib to intensive treatment in core-binding factor acute myeloid leukemia—results of the AMLSG 11 – 08 trial. 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Supplementary Files ESM1.pdf Cite Share Download PDF Status: Published Journal Publication published 16 Feb, 2026 Read the published version in Annals of Hematology → Version 1 posted Editorial decision: Revision requested 03 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviews received at journal 29 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers invited by journal 10 Nov, 2025 Editor assigned by journal 10 Nov, 2025 Submission checks completed at journal 10 Nov, 2025 First submitted to journal 04 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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08:25:18","extension":"html","order_by":41,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":164679,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8030948/v1/9aba208a1f5043f9630820c3.html"},{"id":96357307,"identity":"12cdfa2e-0ebb-406b-b54d-3670c10544ea","added_by":"auto","created_at":"2025-11-20 08:25:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":513640,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eStudy Flow Diagram of Patient Selection and Cohort Stratification.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 222 newly diagnosed AML patients were included in the final analysis and categorized into three primary cohorts(A-C) based on KIT mutation status and first-line treatment. Furthermore, all KIT-mutated patients were stratified into two subgroups (D and E) by mutation site (exon 17 vs. non-exon 17) for subsequent analysis.\u003c/p\u003e\n\u003cp\u003eAML, acute myeloid leukemia; APL, acute promyelocytic leukemia; IC, intensive chemotherapy; VA, venetoclax plus azacitidine.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8030948/v1/1c546b09d82e0398a48aa14c.png"},{"id":96367606,"identity":"7d698c30-8b6d-42d8-82ef-16dfce88dc66","added_by":"auto","created_at":"2025-11-20 10:13:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":672242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGenetic Landscape of KIT-Mutated AML.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOncoprint displaying the spectrum of frequent gene mutations in 67 KIT-mutated AML patients. The profile shows that KIT mutations are predominantly missense variants. The most frequently co-occurring mutations were in NRAS (25%), FLT3-ITD (15%), and ASXL1 (13%).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8030948/v1/16b4ee9250e327a3c987feb4.png"},{"id":96357310,"identity":"8f66065e-7c33-4a90-8dd6-7c7fa208df0f","added_by":"auto","created_at":"2025-11-20 08:25:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":265575,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSurvival Outcomes of First-Line Therapy in KIT-Mutated AML.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eEvent-free survival (EFS)and \u003cstrong\u003e(b)\u003c/strong\u003e overall survival (OS) in KIT-mutated patients receiving first-line intensive chemotherapy (IC) versus venetoclax plus azacitidine (VA). The IC cohort demonstrated significantly longer median EFS (14.5 vs. 2.4 months; p=0.011) and OS (not reached vs. 9.8 months; p\u0026lt;0.0001).\u003c/p\u003e\n\u003cp\u003eEFS, event-free survival; OS, overall survival; IC, intensive chemotherapy; VA, venetoclax plus azacitidine; NR, not reached.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8030948/v1/e97021b55fb2efc007016e03.png"},{"id":96367351,"identity":"c5284f3b-a7ce-4932-be43-660be0ad3449","added_by":"auto","created_at":"2025-11-20 10:12:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":395824,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePrognostic Impact of KIT Mutation in VA-Treated Patients.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Event-free survival (EFS)and\u003cstrong\u003e (b) \u003c/strong\u003eoverall survival (OS) in VA-treated patients with KIT mutation versus KIT wild-type. Patients with KIT mutations had significantly shorter median EFS (2.4 vs. 10.6 months; p=0.005) and OS (9.8 vs. 18.6 months; p=0.036).\u003c/p\u003e\n\u003cp\u003eEFS, event-free survival; OS, overall survival; KIT\u003csup\u003emut\u003c/sup\u003e, KIT-mutated; KIT\u003csup\u003ewt\u003c/sup\u003e, KIT wild-type; NR, not reached.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8030948/v1/bc34ac6dbcb374c62762ba46.png"},{"id":96367529,"identity":"9961934f-d533-4905-a2d0-d9aecd3ed091","added_by":"auto","created_at":"2025-11-20 10:13:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":399110,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eComparison of Efficacy and Safety.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eBar graph showing rates of complete remission (CR), overall response rate (ORR), minimal residual disease (MRD) negativity, early death (\u0026lt; 2 months), and grade ≥3 adverse events (AEs) in KIT-mutated patients receiving first-line IC versus VA. The IC cohort achieved significantly higher CR (80.0% vs. 17.6%; p\u0026lt;0.0001), ORR (82.0% vs. 41.2%; p\u0026lt;0.01), and MRD-negative rates (76.0% vs. 35.3%; p\u0026lt;0.01). Incidences of early death (2.0% vs. 5.9%; p=0.446) and grade ≥3 AEs (72.0% vs. 82.4%; p=0.527) were not significantly different.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8030948/v1/43048b2dc3a20181bbe9cf67.png"},{"id":96357313,"identity":"ecdec44b-730a-43f9-b213-a83410036c7d","added_by":"auto","created_at":"2025-11-20 08:25:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":551125,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eImpact of KIT mutation subtypes on survival.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eEvent-free survival (EFS) and\u003cstrong\u003e (b)\u003c/strong\u003e overall survival (OS) of KIT-mutated patients stratified by mutation site (exon 17 vs. non-exon 17). Patients harboring exon 17 mutations had significantly inferior median EFS compared to those with KIT mutations outside of exon 17 (7.3 vs. 18.8 months; p=0.046). The difference in median OS (25.1 months vs. not reached; p=0.450) was not statistically significant.\u003c/p\u003e\n\u003cp\u003eEFS, event-free survival; OS, overall survival; NR, not reached.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8030948/v1/3dfdc4dca11b5726c6a6083c.png"},{"id":96367446,"identity":"174fcf7a-36f2-4375-844b-4e3a346ed610","added_by":"auto","created_at":"2025-11-20 10:12:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":614260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMultivariate Analysis of Prognostic Factors in VA-Treated Patients.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eFactors associated with event-free survival (EFS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b)\u003c/strong\u003eFactors associated with overall survival (OS).\u003c/p\u003e\n\u003cp\u003eForest plots showing factors independently associated with(a) event-free survival (EFS) and (b) overall survival (OS) in patients treated with venetoclax plus azacitidine (VA). KIT mutation was an independent adverse prognostic factor for both EFS (HR = 3.25, 95% CI 1.75–6.03; p\u0026lt;0.001) and OS (HR = 3.31, 95% CI 1.59–6.87; p=0.001). Furthermore, age ≥65 years was a risk factor for inferior OS (HR = 2.20, 95% CI 1.37–3.53; p\u0026lt;0.001), whereas the FAB-M2 subtype predicted better EFS (HR = 0.47, 95% CI 0.23–0.98; p=0.044) and OS (HR = 0.39, 95% CI 0.18–0.83; p=0.015).\u003c/p\u003e\n\u003cp\u003eEFS, event-free survival; OS, overall survival; VA, venetoclax plus azacitidine; HR, hazard ratio; CI, confidence interval; ELN, European LeukemiaNet; FAB, French-American-British classification; ECOG, Eastern Cooperative Oncology Group.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8030948/v1/ad6ec0af4ad82916fffb692a.png"},{"id":103251502,"identity":"cdeebf40-0ca6-43c1-bf8e-5735be670818","added_by":"auto","created_at":"2026-02-23 16:09:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4682152,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8030948/v1/f21860a7-b006-40df-adaf-bf4a26891a1f.pdf"},{"id":96357309,"identity":"da75b730-9de6-43f9-a15e-85967c67b285","added_by":"auto","created_at":"2025-11-20 08:25:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":469268,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8030948/v1/0faef08ae9ddf43890408ed9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Superior Response and Survival of Intensive Chemotherapy Over Venetoclax Plus Azacitidine in Newly Diagnosed KIT-Mutated Acute Myeloid Leukemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy. Molecular genetic abnormalities play a pivotal role in guiding disease classification, risk stratification, and treatment selection. \u003cem\u003eKIT\u003c/em\u003e gene mutations represent an important driver mutation in AML. Although their overall frequency in AML is relatively low, \u003cem\u003eKIT\u003c/em\u003e mutations occur in 20\u0026ndash;40% of core-binding factor AML (CBF-AML) and are associated with an increased risk of relapse and poorer prognosis [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Traditionally, for patients considered medically fit, typically characterized by younger age, good performance status, and limited comorbidities, intensive chemotherapy (IC) was the standard first-line treatment.\u003c/p\u003e\u003cp\u003eIn recent years, low-intensity regimens such as venetoclax combined with azacitidine (VA) have demonstrated remarkable efficacy in untreated AML patients unfit for intensive induction therapy, establishing a cornerstone of first-line therapy for this population [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, the differential responses of \u003cem\u003eKIT\u003c/em\u003e-mutated AML patients to VA versus IC, and the underlying molecular mechanisms, remain inadequately defined. Notably, \u003cem\u003eKIT\u003c/em\u003e mutations exhibit significant heterogeneity; exon 17 mutations (particularly D816V), the most common subtype, may confer distinct biological properties and clinical implications [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Consequently, elucidating the impact of \u003cem\u003eKIT\u003c/em\u003e mutation status and its subtypes on the efficacy of first-line treatment regimens has emerged as a critical, unresolved clinical question.\u003c/p\u003e\u003cp\u003eThis study aims to conduct a retrospective cohort analysis to systematically compare the efficacy and safety of the VA versus IC as first-line therapy in \u003cem\u003eKIT\u003c/em\u003e-mutated AML patients. Furthermore, it seeks to investigate the potential influence of different \u003cem\u003eKIT\u003c/em\u003e mutation subtypes, especially exon 17 mutations, on treatment response. By integrating genetic mutational profiles, treatment responses, survival outcomes, clonal dynamic monitoring, and multivariate analyses, we endeavor to provide novel evidence-based insights for personalizing treatment strategies in \u003cem\u003eKIT\u003c/em\u003e-mutated AML patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Patient Cohorts\u003c/h2\u003e\u003cp\u003eThis retrospective study enrolled consecutive newly diagnosed AML patients at Qilu Hospital of Shandong University from January 2017 to January 2025. Inclusion criteria were: age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, newly diagnosed AML, available \u003cem\u003eKIT\u003c/em\u003e gene testing data, and complete clinical data. Exclusion criteria were: acute promyelocytic leukemia, secondary AML, not receiving first-line IC or VA treatment, missing key data, and loss to follow-up.\u003c/p\u003e\u003cp\u003eA total of 222 patients were included. For the core analysis, patients were stratified into three cohorts based on \u003cem\u003eKIT\u003c/em\u003e mutation status and first-line treatment: \u003cem\u003eKIT\u003c/em\u003e-mutated/VA (Cohort A, n\u0026thinsp;=\u0026thinsp;17), \u003cem\u003eKIT\u003c/em\u003e-mutated/IC (Cohort B, n\u0026thinsp;=\u0026thinsp;50), and \u003cem\u003eKIT\u003c/em\u003e-wild-type/VA (Cohort C, n\u0026thinsp;=\u0026thinsp;155). Furthermore, to analyze the impact of mutation subtypes, all \u003cem\u003eKIT\u003c/em\u003e-mutated patients (n\u0026thinsp;=\u0026thinsp;67) were categorized into exon 17 mutation (Cohort D, n\u0026thinsp;=\u0026thinsp;53) and non-exon 17 mutation (Cohort E, n\u0026thinsp;=\u0026thinsp;14) subgroups.\u003c/p\u003e\u003cp\u003e The study protocol was performed in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Qilu Hospital of Shandong University.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Collection and Molecular Profiling\u003c/h3\u003e\n\u003cp\u003eBaseline clinical and laboratory data, including cytogenetic and molecular genetic features, were collected from the hospital's electronic medical record system. \u003cem\u003eKIT\u003c/em\u003e and other gene mutations were detected by next-generation sequencing (NGS). \u003cem\u003eKIT\u003c/em\u003e sequencing covered all exons, with deep sequencing of exon 17 hotspots (including D816V, N822K, etc.) at 1% sensitivity. A targeted NGS panel covered common AML-associated genes (such as \u003cem\u003eDNMT3A\u003c/em\u003e, \u003cem\u003eNPM1\u003c/em\u003e, \u003cem\u003eFLT3-ITD/TKD\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, etc.) with a 5% variant allele frequency threshold.\u003c/p\u003e\n\u003ch3\u003eTreatment and Risk Stratification\u003c/h3\u003e\n\u003cp\u003eAll treatment regimens were determined based on patient age, performance status, comorbidities, and physician discretion. IC was primarily the standard \"7\u0026thinsp;+\u0026thinsp;3\" protocol, involving an anthracycline (e.g., idarubicin or daunorubicin) combined with cytarabine. VA consisted of azacitidine administered for 7 days per cycle with a standard venetoclax dose ramp-up to the target dose. A small number of \u003cem\u003eKIT\u003c/em\u003e-mutated patients subsequently received the \u003cem\u003eKIT\u003c/em\u003e inhibitor avapritinib. All patients were risk-stratified according to the European LeukemiaNet (ELN) 2022 recommendations.\u003c/p\u003e\n\u003ch3\u003eEfficacy and Safety Assessments\u003c/h3\u003e\n\u003cp\u003eTreatment responses were evaluated per ELN 2022 criteria. Complete remission (CR) required\u0026thinsp;\u0026lt;\u0026thinsp;5% marrow blasts, no extramedullary disease, and count recovery; CR with incomplete hematologic recovery (CRi) required all CR criteria except cytopenias; morphologic leukemia-free state (MLFS) required\u0026thinsp;\u0026lt;\u0026thinsp;5% blasts without count recovery. Overall response rate (ORR) included CR\u0026thinsp;+\u0026thinsp;CRi\u0026thinsp;+\u0026thinsp;MLFS. Minimal residual disease (MRD) negativity was defined as \u0026lt;\u0026thinsp;0.1% leukemic cells by flow cytometry post-induction. Overall survival (OS) was from diagnosis to death from any cause or last follow-up; event-free survival (EFS) was from treatment start to treatment failure, relapse, or death from any cause. Early mortality included deaths within 2 months; adverse events were graded by Common Terminology Criteria for Adverse Events (CTCAE) v5.0. Additional evaluations included \u003cem\u003eKIT\u003c/em\u003e mutation clearance kinetics (at 3, 6, and 12 months), the impact of allogeneic hematopoietic stem cell transplantation (allo-HSCT) on prognosis, and co-mutation patterns on survival.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using R software (version 4.4.2). Categorical variables were compared using Chi-square or Fisher's exact tests; continuous variables using Mann-Whitney U or Kruskal-Wallis tests. Survival was analyzed by Kaplan-Meier method with log-rank test. Univariate Cox regression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.15) identified variables for multivariate Cox models. Results are expressed as hazard ratios (HR) with 95% confidence intervals (CI). Propensity score matching (PSM) (1:1, caliper\u0026thinsp;=\u0026thinsp;0.2) controlled confounding. Transplantation impact was assessed via time-dependent Cox regression. Mutation profiles were visualized using maftools. All tests were two-sided; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003ePatient Baseline Characteristics\u003c/h2\u003e\u003cp\u003eAs detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 222 newly diagnosed AML patients were included in the final analysis. Baseline characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Compared with Cohort C (\u003cem\u003eKIT\u003c/em\u003e-wild-type/VA), patients in Cohort A (\u003cem\u003eKIT\u003c/em\u003e-mutated/VA) had a significantly higher proportion of age\u0026thinsp;\u0026lt;\u0026thinsp;65 years (94.1% vs 51.0%), Eastern Cooperative Oncology Group (ECOG) performance status\u0026thinsp;\u0026ge;\u0026thinsp;2 (58.8% vs 20.6%), and French-American-British (FAB) M2 subtype (64.7% vs 24.5%). Other baseline features were largely balanced across comparison groups. In \u003cem\u003eKIT\u003c/em\u003e-mutated AML patients, the co-mutation landscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) identified NRAS (25%), FLT3-ITD (15%), and ASXL1 (13%) as the most frequent co-occurring mutations. \u003cem\u003eKIT\u003c/em\u003e mutations were primarily missense variants, overwhelmingly localized to exon 17 (79.10%) and dominated by the D816V allele (66.04%); other recurrent exon 17 mutations included N822K, D816Y, and D816H (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Online Resource 1)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA total of 222 newly diagnosed AML patients were included in the final analysis and categorized into three primary cohorts(A-C) based on KIT mutation status and first-line treatment. Furthermore, all KIT-mutated patients were stratified into two subgroups (D and E) by mutation site (exon 17 vs. non-exon 17) for subsequent analysis.\u003c/p\u003e\u003cp\u003eAML, acute myeloid leukemia; APL, acute promyelocytic leukemia; IC, intensive chemotherapy; VA, venetoclax plus azacitidine.\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 study cohorts\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\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\u003eKIT \u003csup\u003emut\u003c/sup\u003e / VA\u003c/p\u003e\u003cp\u003e(Cohort A, n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKIT \u003csup\u003emut\u003c/sup\u003e / IC\u003c/p\u003e\u003cp\u003e(Cohort B, n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKIT \u003csup\u003ewt\u003c/sup\u003e / VA\u003c/p\u003e\u003cp\u003e(Cohort C, n\u0026thinsp;=\u0026thinsp;155)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep (A vs B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep (A vs C)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eExon17\u003c/p\u003e\u003cp\u003e(Cohort D, n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eNon-exon17\u003c/p\u003e\u003cp\u003e(Cohort D, n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003ep (D vs E)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;65 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (94.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79 (51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e50 (94.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;65 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76 (49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\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\u003e11 (64.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87 (56.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e37 (69.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9 (64.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\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\u003e6 (35.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 (43.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16 (30.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5 (35.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECOG PS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\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\u003e7 (41.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123 (79.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20 (37.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5 (35.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (58.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (20.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33 (62.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9 (64.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (64.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20 (37.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5 (35.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (29.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92 (59.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18 (34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7 (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (16.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15 (28.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2 (14.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eELN 2022 risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\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\u003e4 (23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (21.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9 (17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12 (85.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\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\u003e9 (52.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (30.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e36 (67.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74 (47.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8 (15.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC (\u0026times;10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.9 (4.6\u0026ndash;39.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.1 (4.5\u0026ndash;37.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (2.4\u0026ndash;28.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13.4 (5-38.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15 (3.5\u0026ndash;40.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHGB (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (56\u0026ndash;97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (65-103.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75 (65\u0026ndash;87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e80 (65\u0026ndash;105)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e65.5 (53.8\u0026ndash;84.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT (\u0026times;10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (14\u0026ndash;34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.5 (24\u0026ndash;63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (24\u0026ndash;77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32 (19\u0026ndash;60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30.5 (20-52.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBM blast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (17.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45 (29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8 (15.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (82.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110 (71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e45 (84.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13 (92.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFusion gene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.021*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRUNX1-RUNX1T1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (58.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32 (60.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4 (28.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCBFB-MYH11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (17.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8 (15.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7 (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative/Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e149 (96.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3 (21.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplex karyotype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29 (18.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDNMT3A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (17.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (32.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6 (11.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.015*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTET2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (22.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.025*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNRAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (17.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (21.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10 (18.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7 (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.034*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFLT3-ITD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33 (21.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10 (18.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTP53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAllo-HSCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (6.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9 (17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4 (28.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e0.447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e1. Abbreviations: ECOG PS, Eastern Cooperative Oncology Group Performance Status; FAB, French-American-British classification; ELN, European LeukemiaNet; WBC, white blood cell count; HGB, hemoglobin; PLT, platelet count; BM, bone marrow; Allo-HSCT, allogeneic hematopoietic stem cell transplantation; KIT\u003csup\u003emut\u003c/sup\u003e, KIT-mutated; KIT\u003csup\u003ewt\u003c/sup\u003e, KIT wild-type; VA, venetoclax plus azacitidine; IC, intensive chemotherapy.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003e2. Data presentation: Categorical, n (%); continuous, median (IQR).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003e3. Significance: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOncoprint displaying the spectrum of frequent gene mutations in 67 KIT-mutated AML patients. The profile shows that KIT mutations are predominantly missense variants. The most frequently co-occurring mutations were in NRAS (25%), FLT3-ITD (15%), and ASXL1 (13%).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEfficacy and Survival Comparison of IC versus VA in\u003c/b\u003e \u003cb\u003eKIT\u003c/b\u003e\u003cb\u003e-Mutated Patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe first compared the survival outcomes of \u003cem\u003eKIT\u003c/em\u003e-mutated AML patients receiving first-line IC versus those receiving VA. The IC cohort demonstrated significantly longer median EFS (14.5 vs 2.4 months, p\u0026thinsp;=\u0026thinsp;0.011; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) and OS (not reached vs 9.8 months, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) compared to the VA cohort. To control for potential confounding bias, PSM was performed, yielding 12 matched pairs. After matching, the IC cohort maintained numerical advantages in median EFS (11.0 vs 3.0 months, p\u0026thinsp;=\u0026thinsp;0.360; Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea, Online Resource 1) and median OS (not reached vs 11.8 months, p\u0026thinsp;=\u0026thinsp;0.071; Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb, Online Resource 1), although these differences were not statistically significant.\u003c/p\u003e\u003cp\u003eWe next compared treatment responses and safety between the IC and VA cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The IC cohort achieved significantly higher CR (80.0% vs 17.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), ORR (82.0% vs 41.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and MRD-negative (76.0% vs 35.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) rates compared to the VA cohort. Regarding safety, the VA cohort showed higher incidences of grade\u0026thinsp;\u0026ge;\u0026thinsp;3 adverse events (82.4% vs 72.0%, p\u0026thinsp;=\u0026thinsp;0.527) and early death (5.9% vs 2.0%, p\u0026thinsp;=\u0026thinsp;0.446), though these differences were not statistically significant.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(a)\u003c/b\u003e Event-free survival (EFS)and \u003cb\u003e(b)\u003c/b\u003e overall survival (OS) in KIT-mutated patients receiving first-line intensive chemotherapy (IC) versus venetoclax plus azacitidine (VA). The IC cohort demonstrated significantly longer median EFS (14.5 vs. 2.4 months; p\u0026thinsp;=\u0026thinsp;0.011) and OS (not reached vs. 9.8 months; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\u003cp\u003eEFS, event-free survival; OS, overall survival; IC, intensive chemotherapy; VA, venetoclax plus azacitidine; NR, not reached.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(a)\u003c/b\u003e Event-free survival (EFS)and \u003cb\u003e(b)\u003c/b\u003e overall survival (OS) in VA-treated patients with KIT mutation versus KIT wild-type. Patients with KIT mutations had significantly shorter median EFS (2.4 vs. 10.6 months; p\u0026thinsp;=\u0026thinsp;0.005) and OS (9.8 vs. 18.6 months; p\u0026thinsp;=\u0026thinsp;0.036).\u003c/p\u003e\u003cp\u003eEFS, event-free survival; OS, overall survival; KIT\u003csup\u003emut\u003c/sup\u003e, KIT-mutated; KIT\u003csup\u003ewt\u003c/sup\u003e, KIT wild-type; NR, not reached.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImpact of\u003c/b\u003e \u003cb\u003eKIT\u003c/b\u003e \u003cb\u003eMutation Status on Efficacy and Prognosis in VA-Treated Patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe then assessed the prognostic impact of \u003cem\u003eKIT\u003c/em\u003e mutations in the VA-treated population. Patients with \u003cem\u003eKIT\u003c/em\u003e mutations had significantly shorter median EFS (2.4 vs 10.6 months, p\u0026thinsp;=\u0026thinsp;0.005; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) and OS (9.8 vs 18.6 months, p\u0026thinsp;=\u0026thinsp;0.036; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) than wild-type patients. PSM created 28 balanced pairs, and subsequent analysis showed that differences in EFS (14.5 vs 13.4 months, p\u0026thinsp;=\u0026thinsp;0.500; Figure S2a, Online Resource 1) and OS (39.3 months vs not reached, p\u0026thinsp;=\u0026thinsp;0.890; Figure S2b, Online Resource 1) were no longer significant.\u003c/p\u003e\u003cp\u003eRegarding treatment response and safety (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), \u003cem\u003eKIT\u003c/em\u003e-wild-type patients demonstrated significantly higher rates of CR (81.9% vs 17.6%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), ORR (90.3% vs 41.2%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and MRD negativity (77.4% vs 35.3%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to \u003cem\u003eKIT\u003c/em\u003e-mutated patients. Early death rates were similar between groups (6.5% vs 5.9%; p\u0026thinsp;=\u0026thinsp;1.000), but grade\u0026thinsp;\u0026ge;\u0026thinsp;3 adverse events occurred more frequently in the mutated cohort (82.4% vs 55.5%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.050).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(a)\u003c/b\u003eBar graph showing rates of complete remission (CR), overall response rate (ORR), minimal residual disease (MRD) negativity, early death (\u0026lt;\u0026thinsp;2 months), and grade\u0026thinsp;\u0026ge;\u0026thinsp;3 adverse events (AEs) in KIT-mutated patients receiving first-line IC versus VA. The IC cohort achieved significantly higher CR (80.0% vs. 17.6%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), ORR (82.0% vs. 41.2%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and MRD-negative rates (76.0% vs. 35.3%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Incidences of early death (2.0% vs. 5.9%; p\u0026thinsp;=\u0026thinsp;0.446) and grade\u0026thinsp;\u0026ge;\u0026thinsp;3 AEs (72.0% vs. 82.4%; p\u0026thinsp;=\u0026thinsp;0.527) were not significantly different.\u003c/p\u003e\u003cp\u003e\u003cb\u003e(b)\u003c/b\u003e Corresponding outcomes in VA-treated patients with KIT mutation versus KIT wild-type. The KIT-wild-type group had significantly higher CR (81.9% vs. 17.6%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), ORR (90.3% vs. 41.2%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and MRD-negative rates (77.4% vs. 35.3%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while also exhibiting a lower incidence of grade\u0026thinsp;\u0026ge;\u0026thinsp;3 AEs (55.5% vs. 82.4%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Early death rates were similar (6.5% vs. 5.9%; p\u0026thinsp;=\u0026thinsp;1.000).\u003c/p\u003e\u003cp\u003eIC, intensive chemotherapy; VA, venetoclax plus azacitidine; ORR, overall response rate; CR, complete remission; MRD, minimal residual disease; AEs, adverse events; KIT\u003csup\u003emut\u003c/sup\u003e, KIT-mutated; KIT\u003csup\u003ewt\u003c/sup\u003e, KIT wild-type.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrognostic Impact of\u003c/b\u003e \u003cb\u003eKIT\u003c/b\u003e \u003cb\u003eMutation Subtypes and Specific Co-mutations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong \u003cem\u003eKIT\u003c/em\u003e-mutated patients, the exon 17 mutation group had significantly shorter median EFS than the non-exon 17 mutation group (7.3 vs 18.8 months, p\u0026thinsp;=\u0026thinsp;0.046; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). However, the difference in OS (25.1 months vs not reached, p\u0026thinsp;=\u0026thinsp;0.450; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) was not statistically significant.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(a)\u003c/b\u003eEvent-free survival (EFS) and \u003cb\u003e(b)\u003c/b\u003e overall survival (OS) of KIT-mutated patients stratified by mutation site (exon 17 vs. non-exon 17). Patients harboring exon 17 mutations had significantly inferior median EFS compared to those with KIT mutations outside of exon 17 (7.3 vs. 18.8 months; p\u0026thinsp;=\u0026thinsp;0.046). The difference in median OS (25.1 months vs. not reached; p\u0026thinsp;=\u0026thinsp;0.450) was not statistically significant.\u003c/p\u003e\u003cp\u003eEFS, event-free survival; OS, overall survival; NR, not reached.\u003c/p\u003e\u003cp\u003eCo-mutation analysis revealed that \u003cem\u003eKIT\u003c/em\u003e-mutated patients with concurrent \u003cem\u003eNRAS\u003c/em\u003e mutations had significantly longer EFS (not reached vs 7.6 months, p\u0026thinsp;=\u0026thinsp;0.003; Figure S3a, Online Resource 1) and OS (not reached vs 20.1 months, p\u0026thinsp;=\u0026thinsp;0.004; Figure S3b, Online Resource 1) compared to \u003cem\u003eNRAS\u003c/em\u003e-wild-type patients. In contrast, those with \u003cem\u003eFLT3-ITD/TKD\u003c/em\u003e mutations showed lower median EFS (7.3 vs 16.5 months, p\u0026thinsp;=\u0026thinsp;0.220; Figure S4a, Online Resource 1) and median OS (22.5 vs 39.3 months, p\u0026thinsp;=\u0026thinsp;0.470; Figure S4b, Online Resource 1) compared to \u003cem\u003eFLT3\u003c/em\u003e-wild-type patients, but these differences were not significant.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMultivariate Analysis of Prognostic Factors in VA-Treated Patients\u003c/h3\u003e\n\u003cp\u003eMultivariate Cox analysis confirmed \u003cem\u003eKIT\u003c/em\u003e mutation as an independent adverse prognostic factor for both EFS (HR\u0026thinsp;=\u0026thinsp;3.25, 95% CI: 1.75\u0026ndash;6.03; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea) and OS (HR\u0026thinsp;=\u0026thinsp;3.31, 95% CI: 1.59\u0026ndash;6.87; p\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb) in VA-treated patients. Furthermore, the analysis showed that age\u0026thinsp;\u0026ge;\u0026thinsp;65 years was a risk factor for OS (HR\u0026thinsp;=\u0026thinsp;2.20, 95% CI: 1.37\u0026ndash;3.53; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas the FAB-M2 subtype predicted better EFS (HR\u0026thinsp;=\u0026thinsp;0.47, 95% CI: 0.23\u0026ndash;0.98; p\u0026thinsp;=\u0026thinsp;0.044) and OS (HR\u0026thinsp;=\u0026thinsp;0.39, 95% CI: 0.18\u0026ndash;0.83; p\u0026thinsp;=\u0026thinsp;0.015).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003e(a)\u003c/b\u003e Factors associated with event-free survival (EFS).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003e(b)\u003c/b\u003e Factors associated with overall survival (OS).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eForest plots showing factors independently associated with(a) event-free survival (EFS) and (b) overall survival (OS) in patients treated with venetoclax plus azacitidine (VA). KIT mutation was an independent adverse prognostic factor for both EFS (HR\u0026thinsp;=\u0026thinsp;3.25, 95% CI 1.75\u0026ndash;6.03; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and OS (HR\u0026thinsp;=\u0026thinsp;3.31, 95% CI 1.59\u0026ndash;6.87; p\u0026thinsp;=\u0026thinsp;0.001). Furthermore, age\u0026thinsp;\u0026ge;\u0026thinsp;65 years was a risk factor for inferior OS (HR\u0026thinsp;=\u0026thinsp;2.20, 95% CI 1.37\u0026ndash;3.53; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas the FAB-M2 subtype predicted better EFS (HR\u0026thinsp;=\u0026thinsp;0.47, 95% CI 0.23\u0026ndash;0.98; p\u0026thinsp;=\u0026thinsp;0.044) and OS (HR\u0026thinsp;=\u0026thinsp;0.39, 95% CI 0.18\u0026ndash;0.83; p\u0026thinsp;=\u0026thinsp;0.015).\u003c/p\u003e\u003cp\u003eEFS, event-free survival; OS, overall survival; VA, venetoclax plus azacitidine; HR, hazard ratio; CI, confidence interval; ELN, European LeukemiaNet; FAB, French-American-British classification; ECOG, Eastern Cooperative Oncology Group.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAllo-HSCT Outcomes and Exploratory\u003c/b\u003e \u003cb\u003eKIT\u003c/b\u003e \u003cb\u003eInhibition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the impact of allo-HSCT on survival, we performed a time-dependent Cox analysis to account for immortal time bias, which revealed that allo-HSCT was associated with significantly improved EFS (HR\u0026thinsp;=\u0026thinsp;0.09, 95% CI: 0.01\u0026ndash;0.62; p\u0026thinsp;=\u0026thinsp;0.015) and OS (HR\u0026thinsp;=\u0026thinsp;0.11, 95% CI: 0.02\u0026ndash;0.77; p\u0026thinsp;=\u0026thinsp;0.027) (Table S2, Online Resource 1). Concurrently, we conducted an exploratory analysis of avapritinib in nine patients, which showed a median OS of 12.6 months and EFS of 2.1 months. Despite these limited overall outcomes, two patients achieved rapid CR with concurrent MRD and \u003cem\u003eKIT\u003c/em\u003e mutation negativity after only one cycle of avapritinib combined with VA. Furthermore, three with refractory AML attained CRi following avapritinib and successfully proceeded to allo-HSCT, subsequently receiving avapritinib maintenance with favorable tolerability.\u003c/p\u003e\u003cp\u003e\u003cb\u003eKIT\u003c/b\u003e \u003cb\u003eMutation Clearance and Relapse\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eKIT\u003c/em\u003e mutation clearance rate at 12 months was similar between the exon 17 and non-exon 17 mutation groups (60.0% vs 57.1%; Figure S5, Online Resource 1). However, molecular relapse occurred in 51.9% (27/52) of patients who achieved initial remission. Notably, exon 17 mutations dominated both cases of molecular relapse and persistent positivity, comprising 84.6% (11/13) of the latter.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study establishes the prognostic significance of \u003cem\u003eKIT\u003c/em\u003e mutations in shaping first-line treatment outcomes in AML. Our analysis reveals that IC is superior to VA in \u003cem\u003eKIT\u003c/em\u003e-mutated patients; that \u003cem\u003eKIT\u003c/em\u003e mutation is an independent adverse prognostic factor for resistance to VA; and that exon 17 mutations define a subgroup with particularly poor outcomes. Furthermore, we identify allo-HSCT as an effective strategy to overcome the poor prognosis associated with \u003cem\u003eKIT\u003c/em\u003e mutations in VA-treated patients. These findings position \u003cem\u003eKIT\u003c/em\u003e mutation status as a valuable biomarker for guiding initial therapy selection in AML.\u003c/p\u003e\u003cp\u003eAlthough \u003cem\u003eKIT\u003c/em\u003e mutations are well-established as a high-risk factor in CBF-AML [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], their prognostic significance in non-CBF-AML\u0026mdash;particularly in the context of the VA regimen\u0026mdash;has remained unclear. This study now establishes \u003cem\u003eKIT\u003c/em\u003e mutation as an important biomarker of primary resistance to first-line VA therapy. Our multivariate analysis confirmed it as an independent risk factor for both EFS and OS, thereby extending its adverse prognostic impact to the broader population of AML patients receiving low-intensity chemotherapy. Although survival differences were not statistically significant after PSM, likely due to limited sample size, the robust results from the multivariate analysis, which fully adjusted for confounding variables, strongly support the independent predictive value of \u003cem\u003eKIT\u003c/em\u003e mutations.\u003c/p\u003e\u003cp\u003eThe inferior efficacy of the VA regimen in \u003cem\u003eKIT\u003c/em\u003e-mutated AML is likely mediated by the constitutive activation of the \u003cem\u003eKIT\u003c/em\u003e signaling pathway, which enhances cell proliferation and survival [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Specifically, \u003cem\u003eKIT\u003c/em\u003e receptor mutations (notably D816V) cause ligand-independent activation of its tyrosine kinase domain, leading to persistent signaling through downstream pathways such as STAT5, PI3K/AKT/mTOR, and RAS/MAPK [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These pathways are key upstream regulators of MCL-1 expression [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Given that MCL-1 overexpression is a well-established core mechanism of resistance to venetoclax [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], its upregulation becomes critical: MCL-1 functionally complements BCL-2, so that even when venetoclax inhibits BCL-2, high levels of MCL-1 can bind and sequester pro-apoptotic proteins like BIM. This prevents the activation of BAX/BAK, maintains mitochondrial membrane integrity, and ultimately inhibits apoptosis [\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, \u003cem\u003eKIT\u003c/em\u003e mutation likely confers resistance to the VA regimen by driving MCL-1 expression [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This mechanism also explains the superior efficacy of IC observed in our study, as IC can rapidly clear leukemic clones independent of this specific anti-apoptotic pathway. These findings have immediate clinical implications, suggesting that for medically fit patients with \u003cem\u003eKIT\u003c/em\u003e-mutated AML, traditional intensive chemotherapy, which enables profound cytoreduction, should be prioritized as first-line treatment.\u003c/p\u003e\u003cp\u003eWithin \u003cem\u003eKIT\u003c/em\u003e-mutated AML, molecular heterogeneity was linked to distinct clinical outcomes. Patients with exon 17 mutations exhibited inferior EFS. Structurally, exon 17 encodes the activation loop, and mutations in this region (e.g., D816V) induce conformational changes that lead to potent constitutive \u003cem\u003eKIT\u003c/em\u003e activation [\u003cspan additionalcitationids=\"CR27 CR28 CR29\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], providing a molecular basis for the enhanced survival advantage of the leukemic cells and the consequent aggressive clinical behavior of this subtype. \u003cem\u003eKIT\u003c/em\u003e clearance kinetics further revealed unique behavior: although the 12-month clearance rate was similar to that of the non-exon 17 group, over half (51.9%) of all patients achieving molecular remission experienced \u003cem\u003eKIT\u003c/em\u003e mutation reemergence, which was predominantly driven by exon 17-mutated clones. This suggests that exon 17 mutations not only contribute to initial treatment resistance but may also enhance clonal regenerative capacity and molecular relapse risk. Thus, dynamic molecular monitoring and effective maintenance strategies are particularly important for these patients.\u003c/p\u003e\u003cp\u003eCo-mutation analysis identified that concurrent \u003cem\u003eNRAS\u003c/em\u003e mutations were associated with improved survival in \u003cem\u003eKIT\u003c/em\u003e-mutated patients\u0026mdash;a finding that contrasts with the traditional adverse prognostic role of \u003cem\u003eNRAS\u003c/em\u003e in AML. This may be explained by the genetic context of our cohort, which included a substantial proportion of CBF-AML, a subtype where \u003cem\u003eNRAS\u003c/em\u003e mutations have been previously reported to lack adverse or even exhibit neutral prognostic effects[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In contrast, \u003cem\u003eFLT3-ITD/TKD\u003c/em\u003e co-mutations showed a trend toward poorer outcomes. This is consistent with the known biology of \u003cem\u003eFLT3\u003c/em\u003e mutations, which drive leukemogenesis through the constitutive activation of multiple downstream signaling pathways, including STAT5, PI3K/AKT, and RAS/MAPK, leading to synergistic effects on cell proliferation, survival, and inhibition of apoptosis [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven the poor response to VA and high risk of molecular relapse observed in \u003cem\u003eKIT\u003c/em\u003e-mutated patients, the finding that allo-HSCT emerged as an independent protective factor in this context strongly supports its consideration as a key consolidation strategy for this high-risk subgroup.\u003c/p\u003e\u003cp\u003eAlthough our exploratory analysis of nine patients receiving the \u003cem\u003eKIT\u003c/em\u003e inhibitor avapritinib [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] did not demonstrate a significant survival benefit, it enabled rapid treatment responses and successfully bridged refractory patients to transplant. These clinical observations support its further evaluation in larger cohorts.\u003c/p\u003e\u003cp\u003eThis study has several limitations. Firstly, its retrospective, single-center design may introduce selection bias. Secondly, the relatively limited sample size in some analytical groups may have reduced statistical power. Additionally, although treatment strategies followed clinical standards, variations in specific drug dosing and treatment cycles existed; such real-world heterogeneity might introduce unmeasured confounding factors. Future multicenter, prospective studies are essential to validate these findings, explore targeted and combination therapies for \u003cem\u003eKIT\u003c/em\u003e-mutated AML, and further elucidate the molecular mechanisms of treatment resistance in this patient subset.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study elucidates the dual guiding value of \u003cem\u003eKIT\u003c/em\u003e mutation status in first-line treatment for newly diagnosed AML. Our findings confirm that \u003cem\u003eKIT\u003c/em\u003e mutations, particularly the exon 17 subtype, are associated with poor response to the VA regimen and a high risk of molecular relapse, thereby supporting the prioritization of IC as the preferred treatment option. Collectively, \u003cem\u003eKIT\u003c/em\u003e mutation status provides a crucial basis for individualized treatment decision-making in AML.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThis study was approved by the Ethics Committee of Qilu Hospital of Shandong University (KYLL-202502-054), with a waiver for informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eData are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e Qingli Ji: Designed the study, collected and analyzed data, and wrote the manuscript. Xinwen Jiang, Xiaoqing Li, Chen Cao, Xinrui Zhang: Collected data and performed patient follow-up. Minran Zhou and Sai Ma: Reviewed and edited the manuscript. Chunyan Chen: Supervised the research and critically reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eD\u0026ouml;hner H, Wei AH, Appelbaum FR et al (2022) Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. 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Pediatr Blood Cancer 71:e30898. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/pbc.30898\u003c/span\u003e\u003cspan address=\"10.1002/pbc.30898\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Acute Myeloid Leukemia, KIT, Intensive Chemotherapy, Venetoclax","lastPublishedDoi":"10.21203/rs.3.rs-8030948/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8030948/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough KIT mutations hold significant prognostic value in acute myeloid leukemia (AML), their impact on selecting first-line treatment remains unclear. This retrospective study of 222 newly diagnosed AML patients therefore compared the efficacy of venetoclax plus azacitidine (VA) versus intensive chemotherapy (IC) in KIT-mutated AML, while also exploring the prognostic implications of KIT mutation subtypes and their role in predicting VA response. Among patients with KIT mutations, IC was superior to VA, yielding significantly longer median event-free survival (EFS) (14.5 vs. 2.4 months, p\u0026thinsp;=\u0026thinsp;0.011) and overall survival (OS) (not reached vs. 9.8 months, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and a higher complete remission (CR) rate (80.0% vs. 17.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Exon 17 mutations were associated with significantly shorter EFS relative to other KIT mutations (7.3 vs. 18.8 months; p\u0026thinsp;=\u0026thinsp;0.046). Moreover, among all VA-treated patients, KIT mutation was an independent adverse prognostic factor for both EFS (HR\u0026thinsp;=\u0026thinsp;3.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and OS (HR\u0026thinsp;=\u0026thinsp;3.31, p\u0026thinsp;=\u0026thinsp;0.001). This study establishes the superiority of IC over VA in KIT-mutated AML and identifies KIT mutation as an important biomarker of resistance to venetoclax-based therapy, providing valuable guidance for first-line treatment decisions.\u003c/p\u003e","manuscriptTitle":"Superior Response and Survival of Intensive Chemotherapy Over Venetoclax Plus Azacitidine in Newly Diagnosed KIT-Mutated Acute Myeloid Leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 08:25:12","doi":"10.21203/rs.3.rs-8030948/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-03T21:16:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-02T14:08:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-29T20:11:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263421884208304650891672758938744771408","date":"2025-11-13T04:07:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49985148624374333017260555684437410531","date":"2025-11-11T17:24:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-10T21:27:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-10T09:00:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-10T08:57:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Hematology","date":"2025-11-04T16:20:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bfdbdd92-c835-43cf-87ef-0e247b16ae59","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:07:26+00:00","versionOfRecord":{"articleIdentity":"rs-8030948","link":"https://doi.org/10.1007/s00277-026-06841-4","journal":{"identity":"annals-of-hematology","isVorOnly":false,"title":"Annals of Hematology"},"publishedOn":"2026-02-16 15:59:41","publishedOnDateReadable":"February 16th, 2026"},"versionCreatedAt":"2025-11-20 08:25:12","video":"","vorDoi":"10.1007/s00277-026-06841-4","vorDoiUrl":"https://doi.org/10.1007/s00277-026-06841-4","workflowStages":[]},"version":"v1","identity":"rs-8030948","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8030948","identity":"rs-8030948","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}