Prognostic Impact of TP53 Mutations in Newly Diagnosed Pediatric B-Cell Acute Lymphoblastic Leukemia: A Single-Center Study from China

Prognostic Impact of TP53 Mutations in Newly Diagnosed Pediatric B-Cell Acute Lymphoblastic Leukemia: A Single-Center Study from China | 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 Prognostic Impact of TP53 Mutations in Newly Diagnosed Pediatric B-Cell Acute Lymphoblastic Leukemia: A Single-Center Study from China Qi Ji, Zhiqi Zhang, Senlin Zhang, Qingwei Wang, Li Gao, Yutan Chai, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7616674/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background: Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy, with cure rates exceeding 90% under modern treatment protocols. Nevertheless, 10–20% of patients relapse, and post-relapse survival remains poor. Although TP53 mutations are rare at initial diagnosis, they are enriched at relapse and have been implicated as adverse prognostic markers. However, their clinical significance in newly diagnosed pediatric B-ALL, particularly in Chinese cohorts, remains insufficiently defined. Methods: We retrospectively analyzed 460 children with newly diagnosed B-ALL enrolled in the CCCG-ALL2015 extended protocol at the Children’s Hospital of Soochow University (October 2020–July 2024). Baseline characteristics, cytogenetics, molecular genetics, and treatment outcomes were compared between TP53 wild-type (TP53wt, n = 443) and mutant (TP53mut, n = 17) patients. Next-generation sequencing was used to identify mutations. Minimal residual disease (MRD) was assessed by flow cytometry. Survival outcomes were evaluated using Kaplan–Meier and Cox regression analyses, and exploratory analyses examined the impact of variant allele frequency (VAF) on relapse and survival. Results: The frequency of TP53 mutations was 3.7%, predominantly clustering in the DNA-binding domain, with hotspot variants at G245, R248, R249, R273, and R282. Compared with TP53wt, patients with TP53mut were more frequently ≥ 10 years old and classified into intermediate-risk group. Kaplan–Meier analysis showed comparable overall survival (OS) between TP53mut and TP53wt patients (3-year OS: 92.3% vs. 98.2%; P = 0.14), but relapse-free survival (RFS) was significantly worse in the TP53mut group (3-year RFS: 49.5% vs. 89.6%; P < 0.001). Notably, all relapse events occurred in patients with pathogenic TP53 variants. Multivariate Cox analysis confirmed TP53 mutation as an independent adverse prognostic factor for RFS (HR 3.26, 95% CI 1.03–10.34, P = 0.045), alongside male sex, KMT2A rearrangements, and intermediate/high-risk classification. For OS, BCR::ABL1 was the only independent risk factor. Conclusions: Although rare at diagnosis, TP53 mutations in pediatric B-ALL are strongly associated with relapse and inferior RFS, underscoring their potential role as high-risk biomarkers. Incorporating TP53 status into risk stratification and exploring targeted or intensified therapeutic strategies may help improve outcomes in this high-risk subset. Figures Figure 1 Figure 2 Background Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy, accounting for approximately 25% of all cancers in children under 15 years of age, with nearly half of the cases occurring in children and adolescents 1 , 2 . Advances in risk stratification and precision medicine have markedly improved treatment outcomes, and the 5-year overall survival (OS) rate for pediatric ALL now exceeds 90% 3 . Nevertheless, 10–20% of patients experience relapse, which is associated with poor prognosis, and ALL continues to represent a leading cause of cancer-related mortality in children 3 , 4 . The tumor suppressor gene TP53 , located on chromosome 17p13, is essential for transcriptional regulation, cell cycle control, DNA repair, genomic stability, angiogenesis inhibition, and cellular integrity 5 . According to the International Agency for Research on Cancer (IARC), approximately 95% of TP53 mutations occur within the DNA-binding domain (p53-DBD) 6 , 7 . Mutations at several hotspots—most notably R175, G245, R248, R249, R273, and R282—disrupt p53 function and contribute to chemoresistance 8 . The R248Q variant has been reported to confer resistance to topoisomerase II inhibitors (daunorubicin, doxorubicin, idarubicin, and etoposide), the nucleoside analogue cytarabine, the antifolate methotrexate, and the microtubule inhibitor vincristine 9 . This mechanism constitutes one of the major causes of poor therapeutic response in TP53-mutated ALL. Although TP53 mutations are rare at initial ALL diagnosis (2–3%), they occur in more than 20% of relapsed cases and serve as an independent predictor of poor outcome 10 , 11 . However, due to their low prevalence and considerable heterogeneity in newly diagnosed pediatric ALL, the prognostic relevance of TP53 aberrations remains controversial. Inter-study variability further complicates interpretation, underscoring the need for cohort-specific studies to clarify the prognostic significance of TP53 alterations in childhood ALL. Based on this rationale, we conducted a single-center retrospective analysis of newly diagnosed pediatric B-ALL patients treated under the Chinese Children’s Cancer Group ALL-2015 (CCCG-ALL-2015) extended protocol, aiming to investigate the prevalence of TP53 mutations and their clinical correlations. Methods Patients and Study designs We retrospectively analyzed pediatric patients newly diagnosed with B-cell acute lymphoblastic leukemia (B-ALL) who were enrolled in the CCCG-ALL-2015 extended protocol at Children’s Hospital of Soochow University between October 2020 and July 2024. Diagnosis was established according to the 2016 World Health Organization (WHO) classification, based on morphology, immunophenotyping, cytogenetics, and molecular biology. Patients with incomplete clinical data or without available genetic testing were excluded. This study was approved by the institutional review board (2015004), and informed consent was obtained from patients’ guardians in accordance with the Declaration of Helsinki. Next-Generation Sequencing and Bioinformatics Total RNA was extracted from diagnostic bone marrow aspirates, depleted of ribosomal RNA (RiboZero), reverse-transcribed to cDNA, and sequenced on the Illumina NovaSeq 6000 platform, yielding an average of 16 Gb per sample. Reads were aligned to the UCSC hg19 reference genome using STAR and PCR duplicates were marked with Picard. Single-nucleotide variants (SNVs) and small indels were called with MuTect2 and Rnaindel, while gene fusions were detected using FusionCatcher. Internal tandem duplications (ITD) were analyzed using CICERO. Variants were annotated with VEP against ClinVar, COSMIC, dbSNP, 1000 Genomes, gnomAD, and ExAC. No pre-specified variant allele frequency (VAF) cutoff was applied at the variant calling stage to allow detection of low-frequency subclonal variants; all variants passing quality control (mapping quality, base quality, and ≥ 3 supporting mutant reads with depth ≥ 8) were retained, while those located in immunoglobulin or TCR regions and recurrent sequencing artifacts (internal blacklist) were excluded. We further restricted the analysis to coding and splice-related variants (missense, stop gained, frameshift, in-frame indels, splice donor/acceptor, stop lost, start lost, protein-altering, and splice region variants). Because matched normal samples were unavailable, germline variants could not be definitively excluded; common polymorphisms (allele frequency > 0.1% in gnomAD/ExAC/1000 Genomes) were filtered to minimize this risk. Final variant classification was performed according to ACMG guidelines. Cytogenetic and Molecular Evaluation Cytogenetic analysis was performed by conventional G-banding (≥ 20 metaphases). Recurrent molecular lesions including ETV6::RUNX1, BCR::ABL1, TCF3::PBX1, and KMT2A rearrangements were assessed by RT-qPCR and/or fluorescence in situ hybridization (FISH). Minimal Residual Disease (MRD) Assessment MRD was evaluated at day 19 and day 46 of induction therapy using multiparameter flow cytometry with a sensitivity of 0.01%. MRD positivity was defined as leukemic blasts ≥ 0.01% of nucleated cells. Endpoints and Follow-up OS was defined as the time from diagnosis to death from any cause or last follow-up. Relapse-free survival (RFS) was defined as the time from complete remission to hematologic relapse, death, or last follow-up. Patients without events were censored at the last follow-up date. The follow-up was censored on August 1, 2024. Statistical analysis Statistical analyses were performed using SPSS version 23.0. Continuous variables were expressed as median (range) and compared by the Mann–Whitney U test; categorical variables were expressed as counts (%) and compared by the chi-square test or Fisher’s exact test. OS and RFS were estimated using the Kaplan–Meier method and compared with the log-rank test. Prognostic factors were evaluated by Cox proportional hazards models; variables with P < 0.10 in univariate analysis were entered into multivariable models. Exploratory analyses were performed to assess the impact of TP53 variant allele frequency (VAF), using the cohort median as the cutoff. Two-sided P < 0.05 was considered statistically significant. Results Patient Clinical Characteristics A total of 460 patients diagnosed with B-ALL were enrolled, including 443 with TP53 wild-type (TP53 wt ) and 17 with TP53 mutations (TP53 mut ). All baseline characteristics are presented in Table 1 . Patients with TP53 mut were more frequently ≥ 10 years old at diagnosis compared with TP53 wt patients (41.2% vs. 17.2%, P = 0.027). No significant differences were observed between the two groups regarding gender distribution, white blood cell count, hemoglobin, platelet levels, bone marrow blast percentage, or fusion genes at diagnosis. Karyotypic abnormalities were detected in 52.8% of TP53 wt and 40% of TP53 mut cases, without statistical significance. Regarding risk stratification, patients with TP53 mut were more frequently classified into intermediate-risk groups both initially (70.6% vs. 30%, P = 0.002) and in the final risk assignment (75% vs. 37.8%, P = 0.011). No significant differences were found in CNS involvement or MRD positivity at day 19 or day 46 between the two groups. Table 1 Patient clinical characteristics Variable TP53 wt (n = 443) TP53 mut (n = 17) P value Age, years (%) 0.027 1 ~ 9 367 (82.8%) 10 (58.8%) ≥ 10 76 (17.2%) 7 (41.2%) Gender (%) 0.302 Female 186 (42%) 5 (29.4%) Male 257 (58%) 12 (70.6%) WBC, ×10 9 /L, median (IQR) 11.45 (5.54, 33.705) 32.57 (7.24, 57.02) 0.117 Hemoglobin, g/l, median (IQR) 80 (66, 96) 82 (69, 100) 0.650 Platelets, ×10 9 /L, median (IQR) 66 (31, 132.5) 54 (31, 145) 0.925 Bone marrow blasts, %, median (IQR) 90 (84.75, 94) 90 (77, 93) 0.500 Karyotype (%) 0.331 Normal 187 (47.2%) 9 (60%) Abnormal 209 (52.8%) 6 (40%) Genetic fusion subtype (%) 0.098 ETV6::RUNX1 94 (21.2%) 1 (5.9%) KMT2A rearrangements 10 (2.3%) 1 (5.9%) E2A::PBX1 23 (5.2%) 3 (17.6%) BCR::ABL1 24 (5.4%) 0 (0%) others 164 (37%) 5 (29.4%) Not found 128 (28.9%) 7 (41.2%) Initial stratification (%) 0.002 Low risk 309 (69.8%) 5 (29.4%) Intermediate risk 133 (30%) 12 (70.6%) High risk 1 (0.2%) 0 (0%) Final Risk stratification (%) 0.011 Low risk 257 (61.5%) 4 (25%) Intermediate risk 158 (37.8%) 12 (75%) High risk 3 (0.7%) 0 (0%) CNS involvement (%) 1.000 No 433 (97.7%) 17 (100%) Yes 10 (2.3%) 0 (0%) D19 MRD positive (%) 0.792 No 231 (53.3%) 8 (50%) Yes 202 (46.7%) 8 (50%) D46 MRD positive (%) 0.569 No 395 (93.2%) 16 (100%) Yes 29 (6.8%) 0 (0%) Distribution of TP53 Mutations As shown in Fig. 1 , a total of 17 TP53 mutations were identified in the cohort, predominantly clustered within the DNA-binding domain (DBD) of the p53 protein. Several hotspot mutations were observed, including R248, R249, R273, and R282. The majority of mutations were classified as pathogenic or likely pathogenic, while only one variant was located in the transactivation domain and was categorized as a variant of uncertain significance (VUS). Prognostic Impact of TP53 Mutations As shown in Fig. 2 , Kaplan–Meier analysis showed comparable OS between TP53-mutated and wild-type patients (3-year OS: 92.3%±7.4% vs. 98.2%±0.7%; P = 0.14) (Fig. 2A) . In contrast, patients harboring TP53 mutations exhibited significantly inferior RFS compared with those with TP53wt (3-year RFS: 49.5%±15.4% vs. 89.6%±2.1%; P < 0.001) (Fig. 2B) . Notably, all relapse cases occurred in patients carrying pathogenic TP53 mutations (Fig. 2C) . We also analyzed the impact of TP53 variant allele frequency (VAF) on relapse and survival. Patients with VAF above the cohort median had a higher relapse rate than those with VAF at or below the median (50% vs. 22%; P = 0.335), although the difference was not statistically significant. Only one death occurred in the subgroup with VAF above the cohort median, whereas no deaths were observed in patients with VAF at or below the median. (Fig. 2D-E) . In univariate Cox regression analysis, TP53 mutation was strongly associated with poor RFS (HR 6.59, 95% CI 2.73–15.88, P 50 × 10^9/L), abnormal karyotype, KMT2A rearrangement, and intermediate/high-risk classification. Conversely, the presence of ETV6::RUNX1 fusion was associated with improved RFS. Variables with P < 0.1 in univariate analysis were included in the multivariate model, which revealed that TP53 mutation remained an independent adverse prognostic factor (HR 3.26, 95% CI 1.03–10.34, P = 0.045). In addition, male sex (HR 3.45, 95% CI 1.20–9.89, P = 0.021), KMT2A rearrangement (HR 5.49, 95% CI 1.56–19.35, P = 0.008), and final intermediate/high-risk classification (HR 4.40, P = 0.039) were also identified as independent predictors of poor RFS ( Table 2 ) . Table 2 Univariate and multivariate analysis of RFS Characteristics Univariate analysis Multivariate analysis HR (95% CI) P -value HR (95% CI) P- value Male 2.06 (0.97 ~ 4.40) 0.062 3.45 (1.20 ~ 9.89) 0.021 Age (≥ 10 years) 2.04 (0.95 ~ 4.35) 0.066 1.02 (0.31 ~ 3.35) 0.97 WBC > 50 × 10 9 /L 4.65 (2.39 ~ 9.07) 80g/l 1.36 (0.70 ~ 2.66) 0.363 Platelets > 65.5×10 9 /L 0.57 (0.28 ~ 1.12) 0.103 Bone marrow blasts > 90% 1.11 (0.57 ~ 2.16) 0.757 CNS involvement 2.32 (0.56 ~ 9.68) 0.248 Abnormal karyotype 2.52 (1.10 ~ 5.76) 0.029 1.65 (0.66 ~ 4.10) 0.281 TP53mut 6.59 (2.73 ~ 15.88) < .001 3.26 (1.03 ~ 10.34) 0.045 ETV6::RUNX1 0.19 (0.05 ~ 0.81) 0.025 0.44 (0.05 ~ 3.75) 0.455 KMT2A rearrangements 9.20 (3.55 ~ 23.85) < .001 5.49 (1.56 ~ 19.35) 0.008 E2A::PBX1 1.01 (0.24 ~ 4.23) 0.987 BCR::ABL1 2.35 (0.72 ~ 7.70) 0.158 D19 MRD positive 1.75 (0.87 ~ 3.53) 0.116 D46 MRD positive 0.88 (0.21 ~ 3.70) 0.865 Initial stratification (IR\HR) 7.04 (3.38 ~ 14.68) < .001 1.08 (0.25 ~ 4.69) 0.918 Final Risk stratification (IR\HR) 7.22 (2.95 ~ 17.68) < .001 4.4 (1.07 ~ 18.02) 0.039 Similarly, univariate and multivariate analyses for OS demonstrated that BCR::ABL1 was the only independent adverse prognostic factor, whereas no other clinical or genetic variables showed a significant association with OS ( Table 3 ) . Table 3 Univariate and multivariate analysis of OS Characteristics Univariate analysis Multivariate analysis HR (95% CI) P -value HR (95% CI) P- value Male 0.92 (0.21 ~ 4.13) 0.917 Age (≥ 10 years) 2.09 (0.41 ~ 10.81) 0.378 WBC > 50 × 10 9 /L 3.50 (0.78 ~ 15.69) 0.101 Hemoglobin > 80g/l 0.76 (0.17 ~ 3.41) 0.725 Platelets > 65.5×10 9 /L 0.16 (0.02 ~ 1.35) 0.092 0.18 (0.02 ~ 1.54) 0.118 Bone marrow blasts > 90% 1.41 (0.32 ~ 6.31) 0.652 CNS involvement 0.00 (0.00 ~ Inf) 0.998 Normal karyotype 1.48 (0.25 ~ 8.89) 0.666 TP53mut 4.36 (0.53 ~ 36.27) 0.173 ETV6::RUNX1 0.00 (0.00 ~ Inf) 0.998 KMT2A rearrangements 0.00 (0.00 ~ Inf) 0.998 E2A::PBX1 2.88 (0.35 ~ 23.95) 0.327 BCR::ABL1 17.12 (3.81 ~ 76.99) < .001 5.74 (1.16 ~ 28.53) 0.033 D19 MRD positive 1.94 (0.32 ~ 11.59) 0.470 D46 MRD positive 3.26 (0.36 ~ 29.22) 0.291 Initial stratification (IR\HR) 14.45 (1.74 ~ 120.09) 0.013 8.03 (0.83 ~ 77.29) 0.071 Final Risk stratification (IR\HR) 120.69 (0.02 ~ 609016.10) 0.271 Discussion TP53 mutations are rare in newly diagnosed pediatric acute lymphoblastic leukemia (ALL), and data regarding their clinical characteristics and prognostic significance remain limited. In this single-center retrospective analysis of a large cohort, we found that TP53 mutation represents an independent adverse prognostic factor in children with B-ALL. Moreover, pathogenic TP53 mutations were more frequently associated with disease relapse. These findings suggest that patients harboring pathogenic TP53 mutations may benefit from intensified or alternative therapeutic strategies. The clinical relevance of TP53 aberrations in childhood ALL has remained controversial due to their low prevalence and inter-study heterogeneity. In our cohort, the incidence of TP53 mutations was 3.7% , consistent with previous reports 12 – 14 . Several studies have demonstrated their prognostic impact. Zhang 12 et al. reported significantly lower relapse-free survival and a higher risk of secondary malignancies in TP53 -mutated ALL, establishing TP53 mutation as an independent predictor of poor outcome. Similarly, Firtina 15 et al. confirmed its association with unfavorable prognosis in pediatric ALL, while Qian et al., analyzing 3,801 children from COG trials, found germline TP53 pathogenic variants to be strongly associated with inferior event-free and OS 13 . Recent study also demonstrated that TP53 deletions are associated with inferior survival, with significantly reduced 5-year OS and DFS, particularly in MRD-positive patients after induction, indicating an MRD-dependent prognostic effect 16 . Our data likewise showed that TP53 mutations significantly affected RFS, consistent with the literature, whereas no difference in OS was observed. This discrepancy may be attributable to the limited sample size in our single-center cohort, as well as the relatively short follow-up period. Chemotherapy remains a cornerstone of cancer treatment; however, the emergence of resistance poses a major barrier to long-term efficacy. Substantial evidence indicates that mutant p53 expression is positively correlated with chemoresistance across multiple tumor types, particularly involving hotspot residues such as R175, G245, R248, R249, R273, and R282 17 . Our findings support this concept, as pathogenic variants within the DNA-binding domain—particularly at hotspot residues R248, R273, and R282—were predominantly associated with relapse. Mechanistically, Wild-type p53 promotes apoptosis through mitochondrial and Fas-mediated pathways by inducing Bax/Bak oligomerization and regulating VDAC, thereby enhancing chemosensitivity. In contrast, hotspot mutants (e.g., R175H, L194F, R249S, R280K) disrupt mitochondrial apoptotic signaling 18 – 20 . In osteosarcoma, the p53 R273H mutant downregulates procaspase-3, thereby impairing apoptosis and conferring resistance to agents such as methotrexate and doxorubicin 21 . In ALL, Li et al. 9 demonstrated that substitution of endogenous wild-type TP53 with the R248 variant conferred resistance to idarubicin and vincristine, while abolishing p53-mediated apoptosis and cell-cycle arrest. Similarly, Yang et al. 22 reported that R248Q mutations were relapse-specific, often induced by thiopurine exposure and mismatch repair deficiency, and conferred resistance to multiple drug classes, including topoisomerase II inhibitors (daunorubicin, doxorubicin, idarubicin, etoposide), cytarabine, methotrexate, and vincristine. These mechanisms may partially explain why TP53 mutations are strongly associated with poor therapeutic response and adverse prognosis in ALL and other cancers. In newly diagnosed ALL, the prevalence of TP53 mutations is very low; thus, they have not yet been incorporated into current risk stratification systems, and large-scale randomized controlled trials are lacking. Since its discovery more than four decades ago, TP 53 has been extensively studied in tumor biology 23 . However, the development of TP 53-targeted therapies remains challenging, as agents must selectively target TP 53mut in cancer cells without affecting TP 53 wt in normal tissues 24 . Therapeutic strategies can generally be classified into two categories: restoring TP 53 wt function or eliminating TP 53 mut. Reports of TP53-directed therapy in pediatric ALL are limited. Demir et al. 25 showed that APR-246 restores wild-type p53 conformation, reactivates downstream signaling, and resensitizes cells to genotoxic agents, reducing leukemic burden and synergizing with doxorubicin in preclinical TP53-mutated ALL models. Similarly, Richmond et al. 26 demonstrated that the p53–MDM2 inhibitor RG7112 induced regression, delayed progression, and enhanced the efficacy of induction-type regimens in infant KMT2A -rearranged ALL xenografts. However, these findings are confined to preclinical models, and no pediatric clinical trials have yet been conducted. Beyond targeted therapy, immunotherapeutic approaches such as blinatumomab and CAR-T cells represent promising strategies for relapsed/refractory ALL. However, emerging evidence suggests that TP53 mutations may be associated with CD19-negative relapse and poor outcomes following blinatumomab or CAR-T therapy in adult ALL 27 – 29 . Similar mechanisms of antigen loss have also been implicated in pediatric settings, underscoring the need to elucidate TP53 -related immune escape pathways to optimize the use of immunotherapy in this high-risk subset. This study has several limitations. First, the prevalence of TP53 mutations at initial ALL diagnosis is very low, resulting in a relatively small sample size that may introduce bias which might affect its significant impact on OS. Future investigations should incorporate larger, multi-center cohorts to validate these findings. Second, mechanistic insights into how TP53 mutations influence prognosis in pediatric ALL remain largely unexplored, and further experimental studies are warranted. In summary, we report one of the first prospective single-center studies from China characterizing the clinical features and prognostic significance of TP53 mutations in newly diagnosed pediatric B-ALL. Our results demonstrate that TP53 mutations, although rare at diagnosis, are associated with adverse outcomes, underscoring their potential role as a high-risk biomarker. These findings highlight the need for larger-scale studies and mechanistic research to refine risk stratification and guide therapeutic strategies in this subset of patients. Declarations Data availability statement The datasets generated and/or analyzed during the current study are available from the corresponding author at [email protected] upon reasonable request. All relevant data are presented in the manuscript, tables, and figures. The data was available from the corresponding author on reasonable request. Ethics statement The study was approved by the Children’s Hospital of Soochow University Institutional Review Board in accordance with the Declaration of Helsinki. Informed consents were obtained from patients and/or their legal guardians. Funding This work was supported by following grants: National Key R&D Program of China (2022YFC2502700), the National Natural Science Foundation of China (No. 82470221, 82170218, 82100229, 82200177, 82300244, 82470127, 82300182, 82470160, 82400264), Jiangsu Natural Science Foundation (No. BK20240373), Gusu Innovation and Entrepreneurship Leading Talent Program (ZXL2024387), Suzhou project (No. DZXYJ202305, SZS201615, SKY2022012, SZS2023014 and SYW2025121), Soochow Medical School project(ML13101223) and Children’s Hospital of Soochow University grant(2023SYLCYJ01). Author Contribution QJ, ZZ, SZ, QW, and LG contributed equally to this work. QJ, ZZ, SZ, QW, LG, YC, YH, YZ, BL, PC, HW, SL, HL, XB, JG, JJZ, and YL collected clinical samples and patient data. QJ, ZZ, SZ, QW, and LG performed data analysis and interpreted the results. YL, PX, and SH supervised the study. QJ, ZZ, SZ, QW, and LG drafted the manuscript. YL, PX, and SH critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript. Acknowledgments We also thank all the patients who participated in this study. References Kantarjian H, Pui C-H, Jabbour E. Acute lymphocytic leukaemia. The Lancet . doi: 10.1016/S0140-6736(25)00864-5 Ekpa QL, Akahara PC, Anderson AM, et al. A Review of Acute Lymphocytic Leukemia (ALL) in the Pediatric Population: Evaluating Current Trends and Changes in Guidelines in the Past Decade. Cureus . Dec 2023;15(12):e49930. doi: 10.7759/cureus.49930 Inaba H, Mullighan CG. Pediatric acute lymphoblastic leukemia. Haematologica . 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Mar 15 2015;21(6):1395–405. doi: 10.1158/1078-0432.Ccr-14-2300 Aldoss I, Li S, Zhang J, et al. TP53 mutations are associated with CD19- relapse and inferior outcomes after blinatumomab in adults with ALL. Blood Adv . May 13 2025;9(9):2159–2172. doi: 10.1182/bloodadvances.2024014986 Locatelli F, Shah B, Thomas T, et al. Incidence of CD19-negative relapse after CD19-targeted immunotherapy in R/R BCP acute lymphoblastic leukemia: a review. Leuk Lymphoma . Oct 2023;64(10):1615–1633. doi: 10.1080/10428194.2023.2232496 Pan J, Tan Y, Deng B, et al. Frequent occurrence of CD19-negative relapse after CD19 CAR T and consolidation therapy in 14 TP53-mutated r/r B-ALL children. Leukemia . Dec 2020;34(12):3382–3387. doi: 10.1038/s41375-020-0831-z Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Jan, 2026 Reviews received at journal 28 Nov, 2025 Reviews received at journal 23 Nov, 2025 Reviews received at journal 23 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers invited by journal 28 Oct, 2025 Editor assigned by journal 19 Sep, 2025 Submission checks completed at journal 19 Sep, 2025 First submitted to journal 15 Sep, 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. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7616674","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540700475,"identity":"06e5171f-fa5e-4502-94dc-ea4caa2b142a","order_by":0,"name":"Qi Ji","email":"","orcid":"","institution":"Children’s Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Ji","suffix":""},{"id":540700476,"identity":"eb3d92a6-fa6c-451c-9f0d-ef6e6cc82c22","order_by":1,"name":"Zhiqi Zhang","email":"","orcid":"","institution":"Children’s Hospital of Soochow 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1","display":"","copyAsset":false,"role":"figure","size":119150,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7616674/v1/59ffefb5997cf68379dbcc26.png"},{"id":95503510,"identity":"7edde593-801d-483b-a6ef-ef6e9e7b5de6","added_by":"auto","created_at":"2025-11-10 05:40:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":362780,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7616674/v1/4b1d3f1e8192c4195e4af59c.png"},{"id":95531688,"identity":"7e609e43-625d-4b90-99f0-b6a60a440bb3","added_by":"auto","created_at":"2025-11-10 10:23:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1421345,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7616674/v1/a74c097e-cc5d-4fac-b80b-ad96ca41f644.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Impact of TP53 Mutations in Newly Diagnosed Pediatric B-Cell Acute Lymphoblastic Leukemia: A Single-Center Study from China","fulltext":[{"header":"Background","content":"\u003cp\u003eAcute lymphoblastic leukemia (ALL) is the most common pediatric malignancy, accounting for approximately 25% of all cancers in children under 15 years of age, with nearly half of the cases occurring in children and adolescents\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Advances in risk stratification and precision medicine have markedly improved treatment outcomes, and the 5-year overall survival (OS) rate for pediatric ALL now exceeds 90%\u003csup\u003e3\u003c/sup\u003e. Nevertheless, 10\u0026ndash;20% of patients experience relapse, which is associated with poor prognosis, and ALL continues to represent a leading cause of cancer-related mortality in children\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe tumor suppressor gene \u003cem\u003eTP53\u003c/em\u003e, located on chromosome 17p13, is essential for transcriptional regulation, cell cycle control, DNA repair, genomic stability, angiogenesis inhibition, and cellular integrity\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. According to the International Agency for Research on Cancer (IARC), approximately 95% of TP53 mutations occur within the DNA-binding domain (p53-DBD)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Mutations at several hotspots\u0026mdash;most notably R175, G245, R248, R249, R273, and R282\u0026mdash;disrupt p53 function and contribute to chemoresistance\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The R248Q variant has been reported to confer resistance to topoisomerase II inhibitors (daunorubicin, doxorubicin, idarubicin, and etoposide), the nucleoside analogue cytarabine, the antifolate methotrexate, and the microtubule inhibitor vincristine\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This mechanism constitutes one of the major causes of poor therapeutic response in TP53-mutated ALL.\u003c/p\u003e\u003cp\u003eAlthough TP53 mutations are rare at initial ALL diagnosis (2\u0026ndash;3%), they occur in more than 20% of relapsed cases and serve as an independent predictor of poor outcome\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. However, due to their low prevalence and considerable heterogeneity in newly diagnosed pediatric ALL, the prognostic relevance of \u003cem\u003eTP53\u003c/em\u003e aberrations remains controversial. Inter-study variability further complicates interpretation, underscoring the need for cohort-specific studies to clarify the prognostic significance of \u003cem\u003eTP53\u003c/em\u003e alterations in childhood ALL.\u003c/p\u003e\u003cp\u003eBased on this rationale, we conducted a single-center retrospective analysis of newly diagnosed pediatric B-ALL patients treated under the Chinese Children\u0026rsquo;s Cancer Group ALL-2015 (CCCG-ALL-2015) extended protocol, aiming to investigate the prevalence of \u003cem\u003eTP53\u003c/em\u003e mutations and their clinical correlations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatients and Study designs\u003c/h2\u003e\u003cp\u003eWe retrospectively analyzed pediatric patients newly diagnosed with B-cell acute lymphoblastic leukemia (B-ALL) who were enrolled in the CCCG-ALL-2015 extended protocol at Children\u0026rsquo;s Hospital of Soochow University between October 2020 and July 2024. Diagnosis was established according to the 2016 World Health Organization (WHO) classification, based on morphology, immunophenotyping, cytogenetics, and molecular biology. Patients with incomplete clinical data or without available genetic testing were excluded. This study was approved by the institutional review board (2015004), and informed consent was obtained from patients\u0026rsquo; guardians in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eNext-Generation Sequencing and Bioinformatics\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from diagnostic bone marrow aspirates, depleted of ribosomal RNA (RiboZero), reverse-transcribed to cDNA, and sequenced on the Illumina NovaSeq 6000 platform, yielding an average of 16 Gb per sample. Reads were aligned to the UCSC hg19 reference genome using STAR and PCR duplicates were marked with Picard. Single-nucleotide variants (SNVs) and small indels were called with MuTect2 and Rnaindel, while gene fusions were detected using FusionCatcher. Internal tandem duplications (ITD) were analyzed using CICERO. Variants were annotated with VEP against ClinVar, COSMIC, dbSNP, 1000 Genomes, gnomAD, and ExAC.\u003c/p\u003e\u003cp\u003eNo pre-specified variant allele frequency (VAF) cutoff was applied at the variant calling stage to allow detection of low-frequency subclonal variants; all variants passing quality control (mapping quality, base quality, and \u0026ge;\u0026thinsp;3 supporting mutant reads with depth\u0026thinsp;\u0026ge;\u0026thinsp;8) were retained, while those located in immunoglobulin or TCR regions and recurrent sequencing artifacts (internal blacklist) were excluded. We further restricted the analysis to coding and splice-related variants (missense, stop gained, frameshift, in-frame indels, splice donor/acceptor, stop lost, start lost, protein-altering, and splice region variants). Because matched normal samples were unavailable, germline variants could not be definitively excluded; common polymorphisms (allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.1% in gnomAD/ExAC/1000 Genomes) were filtered to minimize this risk. Final variant classification was performed according to ACMG guidelines.\u003c/p\u003e\n\u003ch3\u003eCytogenetic and Molecular Evaluation\u003c/h3\u003e\n\u003cp\u003eCytogenetic analysis was performed by conventional G-banding (\u0026ge;\u0026thinsp;20 metaphases). Recurrent molecular lesions including ETV6::RUNX1, BCR::ABL1, TCF3::PBX1, and KMT2A rearrangements were assessed by RT-qPCR and/or fluorescence in situ hybridization (FISH).\u003c/p\u003e\n\u003ch3\u003eMinimal Residual Disease (MRD) Assessment\u003c/h3\u003e\n\u003cp\u003eMRD was evaluated at day 19 and day 46 of induction therapy using multiparameter flow cytometry with a sensitivity of 0.01%. MRD positivity was defined as leukemic blasts\u0026thinsp;\u0026ge;\u0026thinsp;0.01% of nucleated cells.\u003c/p\u003e\n\u003ch3\u003eEndpoints and Follow-up\u003c/h3\u003e\n\u003cp\u003eOS was defined as the time from diagnosis to death from any cause or last follow-up. Relapse-free survival (RFS) was defined as the time from complete remission to hematologic relapse, death, or last follow-up. Patients without events were censored at the last follow-up date. The follow-up was censored on August 1, 2024.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using SPSS version 23.0. Continuous variables were expressed as median (range) and compared by the Mann\u0026ndash;Whitney U test; categorical variables were expressed as counts (%) and compared by the chi-square test or Fisher\u0026rsquo;s exact test. OS and RFS were estimated using the Kaplan\u0026ndash;Meier method and compared with the log-rank test. Prognostic factors were evaluated by Cox proportional hazards models; variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in univariate analysis were entered into multivariable models. Exploratory analyses were performed to assess the impact of TP53 variant allele frequency (VAF), using the cohort median as the cutoff. Two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003ePatient Clinical Characteristics\u003c/h2\u003e\u003cp\u003eA total of 460 patients diagnosed with B-ALL were enrolled, including 443 with TP53 wild-type (TP53\u003csup\u003ewt\u003c/sup\u003e) and 17 with TP53 mutations (TP53\u003csup\u003emut\u003c/sup\u003e). All baseline characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients with TP53\u003csup\u003emut\u003c/sup\u003e were more frequently\u0026thinsp;\u0026ge;\u0026thinsp;10 years old at diagnosis compared with TP53\u003csup\u003ewt\u003c/sup\u003e patients (41.2% vs. 17.2%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027). No significant differences were observed between the two groups regarding gender distribution, white blood cell count, hemoglobin, platelet levels, bone marrow blast percentage, or fusion genes at diagnosis. Karyotypic abnormalities were detected in 52.8% of TP53\u003csup\u003ewt\u003c/sup\u003e and 40% of TP53\u003csup\u003emut\u003c/sup\u003e cases, without statistical significance. Regarding risk stratification, patients with TP53\u003csup\u003emut\u003c/sup\u003e were more frequently classified into intermediate-risk groups both initially (70.6% vs. 30%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and in the final risk assignment (75% vs. 37.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011). No significant differences were found in CNS involvement or MRD positivity at day 19 or day 46 between the two groups.\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\u003ePatient clinical characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTP53\u003csup\u003ewt\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;443)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTP53\u003csup\u003emut\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026thinsp;~\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e367 (82.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (58.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (17.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (41.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.302\u003c/p\u003e\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\u003e186 (42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (29.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e257 (58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (70.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.45 (5.54, 33.705)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.57 (7.24, 57.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin, g/l, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80 (66, 96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (69, 100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelets, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (31, 132.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (31, 145)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBone marrow blasts, %, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90 (84.75, 94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90 (77, 93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKaryotype (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e187 (47.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e209 (52.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenetic fusion subtype (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETV6::RUNX1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94 (21.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eKMT2A\u003c/em\u003e rearrangements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE2A::PBX1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (5.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (17.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCR::ABL1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (5.4%)\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eothers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164 (37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (29.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot found\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e128 (28.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (41.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInitial stratification (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e309 (69.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (29.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (70.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.2%)\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinal Risk stratification (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e257 (61.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e158 (37.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (0.7%)\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCNS involvement (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e433 (97.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (2.3%)\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD19 MRD positive (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.792\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e231 (53.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e202 (46.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD46 MRD positive (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.569\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e395 (93.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (6.8%)\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDistribution of TP53 Mutations\u003c/h2\u003e\u003cp\u003eAs shown in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e, a total of 17 \u003cem\u003eTP53\u003c/em\u003e mutations were identified in the cohort, predominantly clustered within the DNA-binding domain (DBD) of the p53 protein. Several hotspot mutations were observed, including R248, R249, R273, and R282. The majority of mutations were classified as pathogenic or likely pathogenic, while only one variant was located in the transactivation domain and was categorized as a variant of uncertain significance (VUS).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePrognostic Impact of TP53 Mutations\u003c/h2\u003e\u003cp\u003eAs shown in \u003cb\u003eFig.\u0026nbsp;2\u003c/b\u003e, Kaplan\u0026ndash;Meier analysis showed comparable OS between TP53-mutated and wild-type patients (3-year OS: 92.3%\u0026plusmn;7.4% vs. 98.2%\u0026plusmn;0.7%; P\u0026thinsp;=\u0026thinsp;0.14)\u003cb\u003e(Fig.\u0026nbsp;2A)\u003c/b\u003e. In contrast, patients harboring TP53 mutations exhibited significantly inferior RFS compared with those with TP53wt (3-year RFS: 49.5%\u0026plusmn;15.4% vs. 89.6%\u0026plusmn;2.1%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(Fig.\u0026nbsp;2B)\u003c/b\u003e. Notably, all relapse cases occurred in patients carrying pathogenic TP53 mutations \u003cb\u003e(Fig.\u0026nbsp;2C)\u003c/b\u003e. We also analyzed the impact of TP53 variant allele frequency (VAF) on relapse and survival. Patients with VAF above the cohort median had a higher relapse rate than those with VAF at or below the median (50% vs. 22%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.335), although the difference was not statistically significant. Only one death occurred in the subgroup with VAF above the cohort median, whereas no deaths were observed in patients with VAF at or below the median. \u003cb\u003e(Fig.\u0026nbsp;2D-E)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eIn univariate Cox regression analysis, TP53 mutation was strongly associated with poor RFS (HR 6.59, 95% CI 2.73\u0026ndash;15.88, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), along with elevated white blood cell (WBC) count (\u0026gt;\u0026thinsp;50 \u0026times; 10^9/L), abnormal karyotype, \u003cem\u003eKMT2A\u003c/em\u003e rearrangement, and intermediate/high-risk classification. Conversely, the presence of \u003cem\u003eETV6::RUNX1\u003c/em\u003efusion was associated with improved RFS. Variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in univariate analysis were included in the multivariate model, which revealed that TP53 mutation remained an independent adverse prognostic factor (HR 3.26, 95% CI 1.03\u0026ndash;10.34, P\u0026thinsp;=\u0026thinsp;0.045). In addition, male sex (HR 3.45, 95% CI 1.20\u0026ndash;9.89, P\u0026thinsp;=\u0026thinsp;0.021), \u003cem\u003eKMT2A\u003c/em\u003e rearrangement (HR 5.49, 95% CI 1.56\u0026ndash;19.35, P\u0026thinsp;=\u0026thinsp;0.008), and final intermediate/high-risk classification (HR 4.40, P\u0026thinsp;=\u0026thinsp;0.039) were also identified as independent predictors of poor RFS \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate analysis of RFS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.06 (0.97\u0026thinsp;~\u0026thinsp;4.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.45 (1.20\u0026thinsp;~\u0026thinsp;9.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (\u0026ge;\u0026thinsp;10 years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.04 (0.95\u0026thinsp;~\u0026thinsp;4.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.02 (0.31\u0026thinsp;~\u0026thinsp;3.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u0026thinsp;\u0026gt;\u0026thinsp;50\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.65 (2.39\u0026thinsp;~\u0026thinsp;9.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.21 (0.39\u0026thinsp;~\u0026thinsp;3.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.738\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin\u0026thinsp;\u0026gt;\u0026thinsp;80g/l\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.36 (0.70\u0026thinsp;~\u0026thinsp;2.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelets\u0026thinsp;\u0026gt;\u0026thinsp;65.5\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.57 (0.28\u0026thinsp;~\u0026thinsp;1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBone marrow blasts\u0026thinsp;\u0026gt;\u0026thinsp;90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.11 (0.57\u0026thinsp;~\u0026thinsp;2.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCNS involvement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.32 (0.56\u0026thinsp;~\u0026thinsp;9.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal karyotype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.52 (1.10\u0026thinsp;~\u0026thinsp;5.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.65 (0.66\u0026thinsp;~\u0026thinsp;4.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTP53mut\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.59 (2.73\u0026thinsp;~\u0026thinsp;15.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.26 (1.03\u0026thinsp;~\u0026thinsp;10.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eETV6::RUNX1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.19 (0.05\u0026thinsp;~\u0026thinsp;0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.44 (0.05\u0026thinsp;~\u0026thinsp;3.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eKMT2A\u003c/em\u003e rearrangements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.20 (3.55\u0026thinsp;~\u0026thinsp;23.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e5.49 (1.56\u0026thinsp;~\u0026thinsp;19.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eE2A::PBX1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01 (0.24\u0026thinsp;~\u0026thinsp;4.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBCR::ABL1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.35 (0.72\u0026thinsp;~\u0026thinsp;7.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD19 MRD positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.75 (0.87\u0026thinsp;~\u0026thinsp;3.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD46 MRD positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.88 (0.21\u0026thinsp;~\u0026thinsp;3.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInitial stratification (IR\\HR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.04 (3.38\u0026thinsp;~\u0026thinsp;14.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.08 (0.25\u0026thinsp;~\u0026thinsp;4.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.918\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinal Risk stratification (IR\\HR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.22 (2.95\u0026thinsp;~\u0026thinsp;17.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.4 (1.07\u0026thinsp;~\u0026thinsp;18.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSimilarly, univariate and multivariate analyses for OS demonstrated that \u003cem\u003eBCR::ABL1\u003c/em\u003e was the only independent adverse prognostic factor, whereas no other clinical or genetic variables showed a significant association with OS \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate analysis of OS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.92 (0.21\u0026thinsp;~\u0026thinsp;4.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (\u0026ge;\u0026thinsp;10 years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.09 (0.41\u0026thinsp;~\u0026thinsp;10.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u0026thinsp;\u0026gt;\u0026thinsp;50\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.50 (0.78\u0026thinsp;~\u0026thinsp;15.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin\u0026thinsp;\u0026gt;\u0026thinsp;80g/l\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.76 (0.17\u0026thinsp;~\u0026thinsp;3.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelets\u0026thinsp;\u0026gt;\u0026thinsp;65.5\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16 (0.02\u0026thinsp;~\u0026thinsp;1.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.18 (0.02\u0026thinsp;~\u0026thinsp;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBone marrow blasts\u0026thinsp;\u0026gt;\u0026thinsp;90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.41 (0.32\u0026thinsp;~\u0026thinsp;6.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCNS involvement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00 (0.00\u0026thinsp;~\u0026thinsp;Inf)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal karyotype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.48 (0.25\u0026thinsp;~\u0026thinsp;8.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTP53mut\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.36 (0.53\u0026thinsp;~\u0026thinsp;36.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eETV6::RUNX1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00 (0.00\u0026thinsp;~\u0026thinsp;Inf)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eKMT2A\u003c/em\u003e rearrangements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00 (0.00\u0026thinsp;~\u0026thinsp;Inf)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eE2A::PBX1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.88 (0.35\u0026thinsp;~\u0026thinsp;23.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBCR::ABL1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.12 (3.81\u0026thinsp;~\u0026thinsp;76.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e5.74 (1.16\u0026thinsp;~\u0026thinsp;28.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD19 MRD positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.94 (0.32\u0026thinsp;~\u0026thinsp;11.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD46 MRD positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.26 (0.36\u0026thinsp;~\u0026thinsp;29.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInitial stratification (IR\\HR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.45 (1.74\u0026thinsp;~\u0026thinsp;120.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e8.03 (0.83\u0026thinsp;~\u0026thinsp;77.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinal Risk stratification (IR\\HR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120.69 (0.02\u0026thinsp;~\u0026thinsp;609016.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e mutations are rare in newly diagnosed pediatric acute lymphoblastic leukemia (ALL), and data regarding their clinical characteristics and prognostic significance remain limited. In this single-center retrospective analysis of a large cohort, we found that \u003cem\u003eTP53\u003c/em\u003e mutation represents an independent adverse prognostic factor in children with B-ALL. Moreover, pathogenic \u003cem\u003eTP53\u003c/em\u003e mutations were more frequently associated with disease relapse. These findings suggest that patients harboring pathogenic \u003cem\u003eTP53\u003c/em\u003e mutations may benefit from intensified or alternative therapeutic strategies.\u003c/p\u003e\u003cp\u003eThe clinical relevance of \u003cem\u003eTP53\u003c/em\u003e aberrations in childhood ALL has remained controversial due to their low prevalence and inter-study heterogeneity. In our cohort, the incidence of \u003cem\u003eTP53\u003c/em\u003e mutations was \u003cb\u003e3.7%\u003c/b\u003e, consistent with previous reports\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Several studies have demonstrated their prognostic impact. Zhang\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e et al. reported significantly lower relapse-free survival and a higher risk of secondary malignancies in \u003cem\u003eTP53\u003c/em\u003e-mutated ALL, establishing \u003cem\u003eTP53\u003c/em\u003e mutation as an independent predictor of poor outcome. Similarly, Firtina\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e et al. confirmed its association with unfavorable prognosis in pediatric ALL, while Qian et al., analyzing 3,801 children from COG trials, found germline \u003cem\u003eTP53\u003c/em\u003e pathogenic variants to be strongly associated with inferior event-free and OS\u003csup\u003e13\u003c/sup\u003e. Recent study also demonstrated that \u003cem\u003eTP53\u003c/em\u003e deletions are associated with inferior survival, with significantly reduced 5-year OS and DFS, particularly in MRD-positive patients after induction, indicating an MRD-dependent prognostic effect\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Our data likewise showed that \u003cem\u003eTP53\u003c/em\u003e mutations significantly affected RFS, consistent with the literature, whereas no difference in OS was observed. This discrepancy may be attributable to the limited sample size in our single-center cohort, as well as the relatively short follow-up period.\u003c/p\u003e\u003cp\u003eChemotherapy remains a cornerstone of cancer treatment; however, the emergence of resistance poses a major barrier to long-term efficacy. Substantial evidence indicates that mutant p53 expression is positively correlated with chemoresistance across multiple tumor types, particularly involving hotspot residues such as R175, G245, R248, R249, R273, and R282\u003csup\u003e17\u003c/sup\u003e. Our findings support this concept, as pathogenic variants within the DNA-binding domain\u0026mdash;particularly at hotspot residues R248, R273, and R282\u0026mdash;were predominantly associated with relapse. Mechanistically, Wild-type p53 promotes apoptosis through mitochondrial and Fas-mediated pathways by inducing Bax/Bak oligomerization and regulating VDAC, thereby enhancing chemosensitivity. In contrast, hotspot mutants (e.g., R175H, L194F, R249S, R280K) disrupt mitochondrial apoptotic signaling\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In osteosarcoma, the p53 R273H mutant downregulates procaspase-3, thereby impairing apoptosis and conferring resistance to agents such as methotrexate and doxorubicin\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In ALL, Li et al.\u003csup\u003e9\u003c/sup\u003e demonstrated that substitution of endogenous wild-type \u003cem\u003eTP53\u003c/em\u003e with the R248 variant conferred resistance to idarubicin and vincristine, while abolishing p53-mediated apoptosis and cell-cycle arrest. Similarly, Yang et al.\u003csup\u003e22\u003c/sup\u003e reported that R248Q mutations were relapse-specific, often induced by thiopurine exposure and mismatch repair deficiency, and conferred resistance to multiple drug classes, including topoisomerase II inhibitors (daunorubicin, doxorubicin, idarubicin, etoposide), cytarabine, methotrexate, and vincristine. These mechanisms may partially explain why \u003cem\u003eTP53\u003c/em\u003e mutations are strongly associated with poor therapeutic response and adverse prognosis in ALL and other cancers.\u003c/p\u003e\u003cp\u003eIn newly diagnosed ALL, the prevalence of \u003cem\u003eTP53\u003c/em\u003e mutations is very low; thus, they have not yet been incorporated into current risk stratification systems, and large-scale randomized controlled trials are lacking. Since its discovery more than four decades ago, \u003cem\u003eTP\u003c/em\u003e 53 has been extensively studied in tumor biology\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. However, the development of \u003cem\u003eTP\u003c/em\u003e 53-targeted therapies remains challenging, as agents must selectively target \u003cem\u003eTP\u003c/em\u003e 53mut in cancer cells without affecting \u003cem\u003eTP\u003c/em\u003e 53 wt in normal tissues\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Therapeutic strategies can generally be classified into two categories: restoring \u003cem\u003eTP\u003c/em\u003e 53 wt function or eliminating \u003cem\u003eTP\u003c/em\u003e 53 mut.\u003c/p\u003e\u003cp\u003eReports of TP53-directed therapy in pediatric ALL are limited. Demir et al.\u003csup\u003e25\u003c/sup\u003e showed that APR-246 restores wild-type p53 conformation, reactivates downstream signaling, and resensitizes cells to genotoxic agents, reducing leukemic burden and synergizing with doxorubicin in preclinical TP53-mutated ALL models. Similarly, Richmond et al.\u003csup\u003e26\u003c/sup\u003e demonstrated that the p53\u0026ndash;MDM2 inhibitor RG7112 induced regression, delayed progression, and enhanced the efficacy of induction-type regimens in infant \u003cem\u003eKMT2A\u003c/em\u003e -rearranged ALL xenografts. However, these findings are confined to preclinical models, and no pediatric clinical trials have yet been conducted. Beyond targeted therapy, immunotherapeutic approaches such as blinatumomab and CAR-T cells represent promising strategies for relapsed/refractory ALL. However, emerging evidence suggests that \u003cem\u003eTP53\u003c/em\u003e mutations may be associated with CD19-negative relapse and poor outcomes following blinatumomab or CAR-T therapy in adult ALL\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Similar mechanisms of antigen loss have also been implicated in pediatric settings, underscoring the need to elucidate \u003cem\u003eTP53\u003c/em\u003e-related immune escape pathways to optimize the use of immunotherapy in this high-risk subset.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the prevalence of \u003cem\u003eTP53\u003c/em\u003e mutations at initial ALL diagnosis is very low, resulting in a relatively small sample size that may introduce bias which might affect its significant impact on OS. Future investigations should incorporate larger, multi-center cohorts to validate these findings. Second, mechanistic insights into how \u003cem\u003eTP53\u003c/em\u003e mutations influence prognosis in pediatric ALL remain largely unexplored, and further experimental studies are warranted.\u003c/p\u003e\u003cp\u003eIn summary, we report one of the first prospective single-center studies from China characterizing the clinical features and prognostic significance of \u003cem\u003eTP53\u003c/em\u003e mutations in newly diagnosed pediatric B-ALL. Our results demonstrate that \u003cem\u003eTP53\u003c/em\u003e mutations, although rare at diagnosis, are associated with adverse outcomes, underscoring their potential role as a high-risk biomarker. These findings highlight the need for larger-scale studies and mechanistic research to refine risk stratification and guide therapeutic strategies in this subset of patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author at [email protected] upon reasonable request. All relevant data are presented in the manuscript, tables, and figures. The data was available from the corresponding author on reasonable request.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eEthics statement\u003c/h2\u003e\u003cp\u003e The study was approved by the Children\u0026rsquo;s Hospital of Soochow University Institutional Review Board in accordance with the Declaration of Helsinki. Informed consents were obtained from patients and/or their legal guardians.\u003c/p\u003e\u003c/div\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by following grants: National Key R\u0026amp;D Program of China (2022YFC2502700), the National Natural Science Foundation of China (No. 82470221, 82170218, 82100229, 82200177, 82300244, 82470127, 82300182, 82470160, 82400264), Jiangsu Natural Science Foundation (No. BK20240373), Gusu Innovation and Entrepreneurship Leading Talent Program (ZXL2024387), Suzhou project (No. DZXYJ202305, SZS201615, SKY2022012, SZS2023014 and SYW2025121), Soochow Medical School project(ML13101223) and Children\u0026rsquo;s Hospital of Soochow University grant(2023SYLCYJ01).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eQJ, ZZ, SZ, QW, and LG contributed equally to this work. QJ, ZZ, SZ, QW, LG, YC, YH, YZ, BL, PC, HW, SL, HL, XB, JG, JJZ, and YL collected clinical samples and patient data. QJ, ZZ, SZ, QW, and LG performed data analysis and interpreted the results. YL, PX, and SH supervised the study. QJ, ZZ, SZ, QW, and LG drafted the manuscript. YL, PX, and SH critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eWe also thank all the patients who participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKantarjian H, Pui C-H, Jabbour E. Acute lymphocytic leukaemia. \u003cem\u003eThe Lancet\u003c/em\u003e. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(25)00864-5\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(25)00864-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEkpa QL, Akahara PC, Anderson AM, et al. 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TP53 mutations are associated with CD19- relapse and inferior outcomes after blinatumomab in adults with ALL. \u003cem\u003eBlood Adv\u003c/em\u003e. May 13 2025;9(9):2159\u0026ndash;2172. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/bloodadvances.2024014986\u003c/span\u003e\u003cspan address=\"10.1182/bloodadvances.2024014986\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLocatelli F, Shah B, Thomas T, et al. Incidence of CD19-negative relapse after CD19-targeted immunotherapy in R/R BCP acute lymphoblastic leukemia: a review. \u003cem\u003eLeuk Lymphoma\u003c/em\u003e. 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Dec 2020;34(12):3382\u0026ndash;3387. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41375-020-0831-z\u003c/span\u003e\u003cspan address=\"10.1038/s41375-020-0831-z\" 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":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7616674/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7616674/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eAcute lymphoblastic leukemia (ALL) is the most common pediatric malignancy, with cure rates exceeding 90% under modern treatment protocols. Nevertheless, 10\u0026ndash;20% of patients relapse, and post-relapse survival remains poor. Although \u003cem\u003eTP53\u003c/em\u003e mutations are rare at initial diagnosis, they are enriched at relapse and have been implicated as adverse prognostic markers. However, their clinical significance in newly diagnosed pediatric B-ALL, particularly in Chinese cohorts, remains insufficiently defined.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eWe retrospectively analyzed 460 children with newly diagnosed B-ALL enrolled in the CCCG-ALL2015 extended protocol at the Children\u0026rsquo;s Hospital of Soochow University (October 2020\u0026ndash;July 2024). Baseline characteristics, cytogenetics, molecular genetics, and treatment outcomes were compared between \u003cem\u003eTP53\u003c/em\u003e wild-type (TP53wt, n\u0026thinsp;=\u0026thinsp;443) and mutant (TP53mut, n\u0026thinsp;=\u0026thinsp;17) patients. Next-generation sequencing was used to identify mutations. Minimal residual disease (MRD) was assessed by flow cytometry. Survival outcomes were evaluated using Kaplan\u0026ndash;Meier and Cox regression analyses, and exploratory analyses examined the impact of variant allele frequency (VAF) on relapse and survival.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eThe frequency of \u003cem\u003eTP53\u003c/em\u003e mutations was 3.7%, predominantly clustering in the DNA-binding domain, with hotspot variants at G245, R248, R249, R273, and R282. Compared with TP53wt, patients with TP53mut were more frequently\u0026thinsp;\u0026ge;\u0026thinsp;10 years old and classified into intermediate-risk group. Kaplan\u0026ndash;Meier analysis showed comparable overall survival (OS) between TP53mut and TP53wt patients (3-year OS: 92.3% vs. 98.2%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14), but relapse-free survival (RFS) was significantly worse in the TP53mut group (3-year RFS: 49.5% vs. 89.6%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, all relapse events occurred in patients with pathogenic \u003cem\u003eTP53\u003c/em\u003e variants. Multivariate Cox analysis confirmed \u003cem\u003eTP53\u003c/em\u003e mutation as an independent adverse prognostic factor for RFS (HR 3.26, 95% CI 1.03\u0026ndash;10.34, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045), alongside male sex, \u003cem\u003eKMT2A\u003c/em\u003e rearrangements, and intermediate/high-risk classification. For OS, \u003cem\u003eBCR::ABL1\u003c/em\u003e was the only independent risk factor.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eAlthough rare at diagnosis, \u003cem\u003eTP53\u003c/em\u003e mutations in pediatric B-ALL are strongly associated with relapse and inferior RFS, underscoring their potential role as high-risk biomarkers. Incorporating \u003cem\u003eTP53\u003c/em\u003e status into risk stratification and exploring targeted or intensified therapeutic strategies may help improve outcomes in this high-risk subset.\u003c/p\u003e","manuscriptTitle":"Prognostic Impact of TP53 Mutations in Newly Diagnosed Pediatric B-Cell Acute Lymphoblastic Leukemia: A Single-Center Study from China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 05:40:03","doi":"10.21203/rs.3.rs-7616674/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-21T01:43:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-28T21:10:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-23T15:58:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-23T07:17:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53593338306998920927105432317809119515","date":"2025-11-14T18:21:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21164671196845505510566678758645250506","date":"2025-11-13T20:36:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186404773856181219756719266862183734077","date":"2025-10-31T00:35:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-28T20:44:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-19T14:57:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-19T14:37:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-09-15T05:58:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"44204154-bcf5-4cc1-9aa8-0a5aa458a3fd","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-22T00:38:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 05:40:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7616674","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7616674","identity":"rs-7616674","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}