Prognostic Impact of TP53 Mutations in Diffuse Large B-Cell Lymphoma

Prognostic Impact of TP53 Mutations in Diffuse Large B-Cell Lymphoma | 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 Diffuse Large B-Cell Lymphoma Jiayi Yu, Qiang He, Yuting Yan, Kunton Liu, Rong Xie, Ji Ma, Liang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8231260/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Feb, 2026 Read the published version in Annals of Hematology → Version 1 posted 9 You are reading this latest preprint version Abstract Objective: To evaluate the prognostic value of TP53 mutations in patients with diffuse large B-cell lymphoma (DLBCL). Methods: We retrospectively analyzed the clinical data and gene sequencing results of 253 newly diagnosed DLBCL. Survival and correlation analyses were performed. Results: We further revealed significant prognostic heterogeneity among different TP53 hotspot mutations, with mutations at codons G245, R175, R273, and R282 indicating a poorer prognosis. Within the DBD, mutations in exons 5, 7, and 8 were associated with poorer PFS, while mutations in exons 5, 6, and 8 were linked to poorer OS. Additionally, mutations in the Loop-L2, Loop-L3, and LSH motifs within the DBD were all significantly associated with unfavorable PFS and OS. Notably, in the cohort treated with R-CHOP plus novel agents (R-CHOP+X), there were no significant differences in response rates or survival between TP53-mut and TP53-wt patients, suggesting this combination may overcome the adverse prognosis associated with TP53 mutations. Conclusion: TP53 mutation is a crucial adverse prognostic factor in DLBCL. Given the significant prognostic heterogeneity among different TP53 hotspot mutations, a more refined risk stratification based on the TP53 mutational profile is warranted in clinical practice. For patients with high-risk mutations, combining R-CHOP with targeted therapies and exploring novel combination strategies targeting specific pathways are recommended. In contrast, standard R-CHOP may remain an appropriate option for patients with low-risk mutations. Future prospective trials are needed to validate the efficacy of R-CHOP combined with targeted agents in TP53-mutated DLBCL to optimize treatment strategies and improve patient outcomes. Diffuse large B-cell lymphoma TP53 mutation Prognosis Next-generation sequencing Precision medicine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Key Points TP53 mutations occur in approximately one-third of diffuse large B-cell lymphoma (DLBCL) patients Missense mutations in the DNA-binding domain are the most common type of TP53 alterations Specific hotspot mutations (R273, R248, R175) are associated with inferior prognosis TP53 mutation status provides independent prognostic information beyond the International Prognostic Index Combined analysis of TP53 mutation and protein expression improves risk stratification 1. INTRODUCTION Diffuse Large B-cell Lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL) in adults, accounting for 30–40% of all NHL cases[ 1 , 2 ]. It is an aggressive malignancy characterized by significant clinical and genetic heterogeneity[ 3 , 4 ]. Although the majority of DLBCL patients respond well to first-line immunochemotherapy with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) or similar regimens, approximately 30–40% of patients experience relapse or refractory disease[ 5 ]. For these relapsed/refractory (R/R) patients, achieving durable remission is challenging, even with second-line or subsequent therapies. Therefore, establishing more precise prognostic models to guide targeted therapy is crucial for improving clinical outcomes in DLBCL. In recent years, advances in genomic and transcriptomic analysis have led to significant progress in the molecular classification of DLBCL. This has evolved from the initial cell of origin (COO) classification [ 6 ] to the current identification of seven genetic subtypes [ 7 ], which exhibit distinct prognoses. Among these, the A53 subtype is characterized by key genetic alterations, including TP53 mutations. The TP53 gene is one of the most frequently mutated genes in human cancer, with sequencing data showing TP53 mutations in approximately half of all cancer cases, though its frequency and distribution vary significantly across tumor types [ 8 ]. The p53 protein, encoded by TP53 , is a critical tumor suppressor that plays a key role in cellular stress responses by regulating cell cycle arrest, DNA repair, apoptosis, senescence, and autophagy [ 9 , 10 ]. Previous studies have reported that TP53 mutations occur in about 20–30% of DLBCL patients [ 11 – 13 ] and are strongly associated with a poor prognosis [ 12 , 14 , 15 ]. However, research also indicates that not all TP53 mutations have the same biological effect; the functional consequences of a TP53 mutation may depend on the cellular and genomic context, leading to different clinical outcomes among patients with different mutations [ 16 – 18 ]. Consequently, simply using the TP53 gene status to determine prognosis may be insufficient. The impact of each specific mutation should be evaluated in detail based on its functional implications. This underscores the need for more precise risk stratification of TP53 -mutated DLBCL patients to explore effective anti-cancer drugs and treatment regimens aimed at improving their clinical outcomes. This study aims to systematically analyze the characteristics of TP53 mutations in newly diagnosed DLBCL patients using next-generation sequencing (NGS) of formalin-fixed paraffin-embedded (FFPE) samples and to correlate these findings with clinical outcomes. Our central hypothesis is that different TP53 mutation sites have heterogeneous prognostic impacts in DLBCL. Additionally, we will compare the prognostic differences of various treatment regimens in treatment-naive TP53-mutated DLBCL patients, to provide more instructive evidence for clinical practice. 2. METHODS Study Population This retrospective study included 253 patients newly diagnosed with DLBCL at Shandong Cancer Hospital and Shengli Oilfield Central Hospital between January 2018 and December 2023. Inclusion criteria were: (1) pathologically confirmed DLBCL; (2) newly diagnosed and previously untreated for DLBCL; and (3) complete next-generation sequencing data available. Exclusion criteria were: (1) primary central nervous system DLBCL; (2) primary mediastinal large B-cell lymphoma; (3) transformed large B-cell lymphoma; and (4) primary cutaneous DLBCL, leg type. Clinical Data Collection Detailed clinical characteristics were collected, including gender, age, p53 protein expression, ECOG performance status, Ann Arbor stage, serum lactate dehydrogenase (LDH) level, presence of bulky disease (greatest dimension ≥ 7.5 cm), B symptoms, International Prognostic Index (IPI) score, bone marrow infiltration, and number of extranodal sites. Pathological features (including genetic mutations), treatment regimens (R-CHOP, R-DAEPOCH, R-CHOP + X), and treatment responses were also recorded. Immunohistochemistry and FISH Immunohistochemistry (IHC) was performed on all tumor specimens for BCL6, MYC, and BCL2. Double-Expressor Lymphoma (DEL) was defined as C-MYC expression ≥ 40% and BCL2 expression ≥ 50% [ 19 ]. p53 IHC staining was performed on FFPE sections using the DO-7 monoclonal antibody (DAKO), with a positivity cutoff of 10% positive cells. Fluorescence in situ hybridization (FISH) was used to detect MYC , BCL2 , and BCL6 gene rearrangements. Double-Hit Lymphoma (DHL) was defined as rearrangement of MYC with either BCL2 or BCL6 [ 19 ]. Targeted DNA Sequencing DNA was extracted from FFPE tissue samples using the QIAamp DNA FFPE Tissue Kit (Qiagen, Hilden, Germany). A custom panel of 39 lymphoma-related genes, including TP53 , MYD88 , CD79B , MYC , and BCL2 , was used for detection. Amplicon-based sequencing was performed by Jinan King-Med Diagnostics using the Ion Torrent Proton platform (Thermo Fisher, USA) with a sequencing depth of ≥ 500×. Variants were filtered using the following criteria: single nucleotide variants (SNVs) and insertions/deletions (InDels) with a variant allele frequency (VAF) ≥ 2% were included, while polymorphic sites with a frequency > 5% in population databases (1000G, ExAC) were excluded. The biological significance of all detected mutations was evaluated using the IARC TP53 Database and the UMD TP53 Mutation Database [ 20 , 21 ] Follow-up and Response Assessment Follow-up began at the time of pathological diagnosis and ended in November 2024. Progression-Free Survival (PFS) was defined as the time from the start of treatment to disease progression, relapse, or death from any cause. Overall Survival (OS) was defined as the time from the start of treatment to death from any cause. Treatment response was assessed according to the 2014 Lugano Classification [ 22 ] as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD). The best response achieved was used for the final evaluation. Statistical Analysis All data were analyzed using SPSS version 26.0. Categorical data were presented as frequencies and percentages and compared using the Chi-square (χ²) test or Fisher's exact test. Survival curves were generated using the Kaplan-Meier method, and differences between groups were compared using the log-rank test. Univariate and multivariate analyses were performed using the Cox proportional hazards model; variables with P < 0.1 in the univariate analysis were included in the multivariate model. A P-value < 0.05 was considered statistically significant. 3. RESULTS 3.1 TP53 Gene Mutational Spectrum Among the 253 newly diagnosed DLBCL patients, 89 (35.2%) were found to have TP53 mutations. Two patients harbored two distinct mutations each (H179 and A159; G244 and N235). The majority of mutations were missense (n = 73, 82.0%), with the remainder being non-missense (n = 16, 18.0%), including 10 frameshift, 3 nonsense, 2 splice-site, and 1 in-frame mutation (Fig. 1 A). Most mutations (n = 81, 91.0%) were located in the DNA-binding domain (DBD), which encompasses exons 5 to 8. The distribution of mutations across exons 4–10 was as follows: exon 4 (6.7%), exon 5 (22.5%), exon 6 (4.5%), exon 7 (36.0%), exon 8 (28.1%), exon 9 (1.1%), and exon 10 (1.1%) (Fig. 1 B). Of the 89 mutations, 66 were located in codons involved in the central core domain's DNA-binding motifs: 26 in Loop-L3 (codons 237–250), which interacts with the minor groove of DNA; 14 in the LSH motif, which interacts with the major groove; and 26 in Loop-L2 (codons 119–135 and 272–287), which enhances DNA-binding affinity. The most common base substitution was G > A transition (n = 40, 44.94%) (Fig. 1 C). The highest VAF per sample was analyzed, with 61 patients having a VAF < 50% and 28 patients having a VAF ≥ 50% (Fig. 1 D). The most common hotspot codons were R273 (n = 14, 15.7%), R248 (n = 12, 13.5%), R175 (n = 7, 7.9%), R282 (n = 3, 3.4%), C242 (n = 3, 3.4%), G245 (n = 3, 3.4%), Y234 (n = 3, 3.4%), and Y236 (n = 3, 3.4%) (Fig. 1 E). R175, R248, R273, R282, and G245 are consistent with previously reported TP53 hotspots in DLBCL and solid tumors [ 23 – 25 ]. The relatively high frequency of C242, Y234, and Y236 mutations suggests they may be novel potential hotspots in DLBCL. In the 5 DHL patients, the most common hotspot was R248 (n = 2, 40%), while in the 38 DEL patients, it was R273 (n = 6, 15.8%) (Supplementary Table 1), suggesting distinct mutational profiles in different molecular subtypes of DLBCL. 3.2 Correlation between TP53 Mutation and Clinical Features Of the 253 patients, 164 were TP53 -wt and 89 were TP53 -mut. Significant differences were observed between the two groups regarding age ≥ 60 years (P = 0.023), MYD88 mutation (P = 0.002), CD79B mutation (P = 0.001), ECOG score ≥ 2 (P = 0.005), and bone marrow infiltration (P = 0.045) (Table 1 ). No significant associations were found between TP53 mutation characteristics (location, type, and VAF) and clinical features (all P > 0.05) (Supplementary Tables 2–4). Table 1 Clinical characteristics of DLBCL patients with and without TP53 mutation at diagnosis Characteristics TP53 wild type(n = 164) TP53 mutation(n = 89) p -value Age,median(range) 56 (10–81) 59 (17–90) 0.246 Age ≥ 60years 57/164 (34.8%) 44/89 (49.4%) 0.023 Gender: Male 80 /164 (48.8%) 51/89 (57.3%) 0.195 MYD88: Mutant 52 /161 (32.3%) 13 /89 (14.6%) 0.002 CD79B: Mutant 42 /161 (26.1%) 8/89 (9.0%) 0.001 Ann Arbor Stage: III-IV 103/163 (63.2%) 54 /87 (62.1%) 0.861 Cell of origin: non-GCB 104/156 (66.7%) 51 /88 (58.0%) 0.175 ECOG ≥ 2 47/150 (31.3%) 44 /89 (49.4%) 0.005 IPI score: 3–5 86/164 (52.4%) 45/86 (52.3%) 0.909 B symptoms 52/163 (31.9%) 26/87 (29.9%) 0.743 LDH>UNV 104/163 (63.8%) 53/89 (59.6%) 0.505 Extranodal involvement>1 119/163 (73.0%) 70 /89 (78.7%) 0.323 Bone marrow infiltration 16/148 (10.8%) 18/89 (20.2%) 0.045 Bulky disease 36/149 (24.2%) 19 /89 (21.4%) 0.618 Double-hit lymphoma 7/119 (5.9%) 5/63 (7.9%) 0.828 Double-expressor lymphoma 59/157 (37.6%) 38/82 (46.3%) 0.19 3.3 p53 Protein Expression Analysis p53 protein expression was assessed by IHC in 184 patients (62 TP53 -mut, 122 TP53 -wt). Most TP53 -mut cases showed high p53 expression, whereas TP53 -wt cases predominantly showed low or moderate expression, with a significant difference between the groups (P 75% and mutation location (P = 0.06), type (P = 0.093), or VAF (P = 0.254) (Supplementary Table 5). 3.4 Prognostic Impact of TP53 Mutation Sites In the entire cohort, median PFS was 9 months for TP53 -mut patients and 72 months for TP53 -wt patients. Median OS was 40 months for TP53 -mut patients and was not reached for TP53 -wt patients. Both PFS (P < 0.0001) and OS (P < 0.0001) were significantly worse for TP53 -mut patients (Supplementary Fig. 2). The 2-year PFS rates were 38.2% vs. 67.0%, and 2-year OS rates were 59.6% vs. 86.9% for TP53 -mut and TP53 -wt patients, respectively. The 5-year rates were 35.9% vs. 50.2% for PFS and 48.8% vs. 66.4% for OS. However, not all TP53 -mut patients had poor outcomes, necessitating a more refined risk stratification. When stratified by common hotspot mutations (C242, G245, R175, R248, R273, R282, Y234, and Y236), there was a significant difference in OS (P = 0.0393) but not PFS (Fig. 2 ), indicating prognostic heterogeneity. Compared to the TP53 -wt group, patients with G245, R175, R248, and R273 mutations had significantly shorter PFS (all P < 0.05), while those with G245, R175, and R282 mutations had significantly shorter OS (all P < 0.05) (Figs. 3 and 4 ). In contrast, patients with C242, Y234, and Y236 mutations showed no significant difference in OS or PFS compared to the TP53 -wt group, with survival curves approaching those of the wild-type group. Based on these outcomes, we classified hotspots into a high-risk group (G245, R175, R248, R273, R282) and a low-risk group (C242, Y234, Y236). Stratification by mutation characteristics showed no significant impact of mutation location, type, or VAF on PFS or OS (Supplementary Fig. 3). Analysis of individual exons in the DBD revealed that mutations in exons 5, 7, and 8 were associated with worse PFS (Supplementary Fig. 4A, C, E, G), while mutations in exons 5, 6, and 8 were associated with worse OS (Supplementary Fig. 4B, D, F, H). Mutations in the DBD motifs Loop-L2, Loop-L3, and LSH were all significantly associated with poor OS and PFS (Supplementary Fig. 5A-F). 3.5 Impact of TP53 Mutation on Prognosis in Different Treatment Contexts Among 220 patients (86.96%) who received at least two cycles of induction therapy and had a best response assessment, 130 (81 TP53 -wt, 49 TP53 -mut) received R-CHOP; 40 (28 TP53 -wt, 12 TP53 -mut) received R-DAEPOCH; and 50 (31 TP53 -wt, 19 TP53 -mut) received R-CHOP + X (including R2-CHOP, ZR-CHOP, and OR-CHOP). The overall CR rate for TP53 -mut patients was lower than for TP53 -wt patients (48.8% vs 68.6%, P = 0.004) (Table 2 ). In the R-CHOP group, the CR rate was significantly lower for TP53 -mut patients (38.8% vs 69.1%, P = 0.001). There was no significant difference in CR rates in the R-DAEPOCH (50.0% vs 67.8%, P = 0.476) or R-CHOP + X groups (73.7% vs 67.7%, P = 0.656). Notably, within the TP53 -mut group, the CR rate for R-CHOP + X was significantly better than for R-CHOP and R-DAEPOCH. Table 2 Response rates of DLBCL after 1st line treatment Best response TP53 mutation TP53 wild type p -value 0.004 CR 39/80 (48.8%) 96/140 (68.6%) No CR 41/80 (51.2%) 44/140 (31.4%) R-CHOP 0.001 CR 19/49 (38.8%) 56/81 (69.1%) No CR 30/49 (61.2%) 25/81( 30.9%) R-DAEPOCH 0.476 CR 6/12 (50.0%) 19/28 (67.8%) No CR 6/12 (50.0%) 9/28 (32.1%) R-CHOP + X 0.656 CR 14/19 (73.7%) 21/31 (67.7%) No CR 5/19 (26.3%) 10/31 (32.3%) In the R-CHOP group, TP53 -mut patients had significantly worse PFS (P < 0.0001) and OS (P < 0.0001) compared to TP53 -wt patients (Fig. 5 A, B). The 2-year PFS rates were 26.5% vs. 62.5%, and 2-year OS rates were 53.1% vs. 86.5% for TP53-mut vs. TP53-wt, respectively. In the R-DAEPOCH group, TP53 -mut patients had significantly worse PFS (P = 0.0323) but not OS (P = 0.1673) (Fig. 5 C, D). The 2-year PFS rates were 33.3% vs. 66.7%, and 2-year OS rates were 66.7% vs. 85.2% for TP53-mut vs. TP53-wt, respectively. In the R-CHOP + X group, there were no significant differences in PFS (P = 0.7812) or OS (P = 0.3245) between TP53 -mut and TP53 -wt patients (Fig. 5 E, F). The 2-year PFS rates were 73.7% vs. 82.1%, and 2-year OS rates were 78.9% vs. 96.4% for TP53-mut vs. TP53-wt, respectively. This pattern persisted in the ZR-CHOP subgroup (PFS P = 0.0832; OS P = 0.8903; Supplementary Fig. 6A, B) and when comparing TP53 high-risk hotspot mutations to TP53-wt (PFS P = 0.4593; OS P = 0.1958, Supplemental Fig. 7), suggesting targeted therapy may ameliorate the poor prognosis associated with high-risk mutations. Fourteen TP53 -wt and 5 TP53 -mut patients received autologous stem cell transplantation (ASCT). Eight TP53 -wt and 6 TP53 -mut patients received CAR-T cell therapy. Among patients receiving ASCT/CAR-T, TP53 -mut status was associated with significantly worse PFS (P = 0.0047) but not OS (P = 0.5418) (Supplementary Fig. 6C, D). 3.6 Impact of DLBCL Subtypes on Prognosis In the DEL subgroup, TP53 -mut patients had significantly worse PFS (P = 0.0001) and OS (P = 0.0084) than TP53 -wt patients (Supplementary Fig. 8A, B). No significant difference was observed in the DHL subgroup, likely due to small sample size (n = 12) (Supplementary Fig. 8C-D). Within the TP53 -mut cohort, there were no prognostic differences between DEL vs. non-DEL or DHL vs. non-DHL patients (Supplementary Fig. 9A-D). 3.7 Cox Regression Analysis Univariate analysis for PFS identified age ≥ 60 years, TP53 mutation, Ann-Arbor stage, ECOG score, IPI score, elevated LDH, and bone marrow involvement as adverse prognostic factors. Multivariate analysis confirmed that TP53 mutation, ECOG score, and IPI score were independent predictors of poor PFS (Supplementary Table 6). For OS, univariate analysis identified age ≥ 60 years, TP53 mutation, ECOG score, and DHL as adverse factors. Multivariate analysis confirmed TP53 mutation, ECOG score, and DHL as independent predictors of poor OS (Supplementary Table 7). 4. DISCUSSION This study systematically analyzed the TP53 mutation status in 253 DLBCL patients, exploring its mutational characteristics and prognostic value. While TP53 mutations are generally associated with poor treatment response and adverse prognosis in most cancers, there is significant heterogeneity in clinical outcomes among different TP53 mutants [ 17 ]. Functional assessment of TP53 genetic alterations is crucial for optimizing risk stratification, identifying prognostically distinct subgroups, and guiding individualized therapy for DLBCL patients. Our study revealed significant site-specific prognostic heterogeneity among TP53 hotspot mutations. The high-risk group (G245, R175, R248, R273, R282) was associated with significantly shorter PFS and OS, whereas the low-risk group (C242, Y234, Y236) showed outcomes statistically similar to TP53 -wt patients. This finding aligns with the concept of functional heterogeneity among TP53 mutants. For instance, Blandino et al. showed that specific mutants (e.g., His175, His179) can confer a selective survival advantage during chemotherapy[ 26 ]. Young et al. reported that in DLBCL, hotspot codons 158, 175, 245, 248, 273, 280, and 282 were associated with the highest mortality rates [ 23 ]. This site-specific prognostic difference may stem from distinct downstream oncogenic pathways activated by different mutants. For example, the R175H mutant has been shown to specifically bind the BACH1/LSD2 complex, promoting tumor metastasis by suppressing ferroptosis via histone demethylation [ 27 ]. G245S and R175H can cooperatively lift the transcriptional repression of IL-34, fostering an immunosuppressive microenvironment via a CSC–IL-34–macrophage axis [ 28 ]. The R273H mutant can reshape tumor cell glycosylation and metabolism by relieving negative regulation of MGAT4A [ 29 ]. These findings suggest that precision targeting of mutant-specific pathways is a promising future direction for treating TP53 -mutated DLBCL. Our findings support a refined risk stratification based on the TP53 mutational profile. Patients in the high-risk group who received R-CHOP plus targeted therapy had survival outcomes comparable to TP53 -wt patients, suggesting this combination may mitigate the adverse prognosis. We propose that patients with high-risk mutations should be considered for R-CHOP-based combination therapies and enrollment in trials exploring novel agents targeting pathways like BACH1/LSD2, IL-34, or MGAT4A. In contrast, standard R-CHOP may remain a viable option for patients with low-risk mutations. Consistent with previous research [ 30 ], we found that mutations in key DBD motifs (Loop-L2, Loop-L3, and LSH) were associated with poor prognosis. However, unlike some studies [ 24 ], we did not find that mutation type (missense vs. non-missense) or VAF (using a 50% cutoff) could stratify prognosis. This discrepancy with a study by Zhang et al., which found a VAF > 34% to be a poor prognostic marker [ 31 ], suggests that the prognostic value of VAF may depend on an optimized cutoff, which requires further investigation in larger cohorts. The treatment of TP53 -mutated DLBCL remains a major clinical challenge. Our results show that neither standard R-CHOP nor intensified R-DAEPOCH regimens significantly improved outcomes for these patients, consistent with other studies [ 15 , 32 ]. Importantly, in the R-CHOP + X cohort, the survival outcomes of TP53 -mut patients were not significantly different from TP53 -wt patients, reinforcing the potential of combination strategies. Bruton's Tyrosine Kinase (BTK) inhibitors like zanubrutinib, which block the essential B-cell receptor (BCR) signaling pathway, are a rational choice, as about 45% of A53-subtype DLBCLs have alterations in the BCR-dependent NF-κB pathway [ 7 , 33 ]. Our finding of no prognostic difference between TP53 -mut and TP53 -wt patients in the ZR-CHOP group supports this approach. This aligns with findings in other hematologic malignancies like CLL, where targeted therapies are prioritized for patients with TP53 alterations [ 34 ] While CAR-T cell therapy and ASCT are important salvage options for R/R DLBCL [ 35 , 36 ], our study suggests that TP53 mutations may limit their efficacy, as evidenced by the significantly lower 2-year PFS rate in TP53 -mut patients. This highlights the need for novel strategies, such as CAR-T followed by consolidative ASCT, which has shown promise in improving outcomes for this high-risk population. In the context of molecular subtypes, our study confirmed that TP53 mutations confer a poor prognosis in DEL, consistent with Meriranta et al[ 37 ]. The lack of a significant finding in DHL was likely due to the small sample size. Since there was no prognostic difference between DEL/DHL and non-DEL/non-DHL status within the TP53 -mut cohort, TP53 mutation may act as an independent, high-risk prognostic factor. At the protein expression level, p53 immunohistochemistry (IHC) serves as a surrogate marker for inferring TP53 mutation status, and previous studies have explored various positivity thresholds. Some studies have proposed that p53 expression > 50% or a pattern of either ≥ 65% (overexpression) or < 1% (complete absence) are effective cutoffs [ 30 , 38 ]. Our study found that in diffuse large B-cell lymphoma (DLBCL), the majority of cases with *TP53* mutations exhibited high levels of p53 protein expression (> 75%). This suggests that the optimal threshold may vary by tumor type and assay conditions, highlighting the need for future standardization in large, multicenter cohorts. In summary, TP53 mutation is a key adverse prognostic factor in DLBCL. Given the marked prognostic heterogeneity associated with different TP53 hotspot mutations, clinical practice should incorporate refined risk stratification based on the TP53 mutational profile. For patients harboring high-risk mutations (e.g., G245, R175, R248, R273, and R282), the exploration of novel combination therapies targeting relevant pathways is recommended. Conversely, for those with low-risk mutations (e.g., C242, Y234, and Y236), the standard R-CHOP regimen remains a viable treatment option. In the future, more prospective clinical trials are needed to validate the efficacy of R-CHOP combined with targeted agents in TP53 -mutated DLBCL. Such studies will be crucial for optimizing current therapeutic strategies to improve patient outcomes and quality of life. Declarations Acknowledgments We thank the staff of the Department of Hematology at Shandong Cancer Hospital and Shengli Oilfield Central Hospital for their valuable contributions to patient care and data collection. Funding This study was supported by grants from the Zhongguancun Precision Medicine Foundation's "New Life" Charity Project (Grant No. [GXZDH48]), the CSCO-Hengrui Oncology Research Fund (Grant No. [Y-HR2020QN-0175]), and the China Health & Medical Development Foundation (Grant No. [chmdf2025-xrky04-09]). Author information Contributions Conception and design: Ji Ma and Liang Wang Acquisition of data: Jiayi Yu, Kuntong Liu and Rong Xie Analysis and interpretation of data: Jiayi Yu and Qiang He Drafting of the manuscript: Jiayi Yu Critical revision of the manuscript for important intellectual content: Yuting Yan Statistical analysis: Jiayi Yu Obtained funding: Ji Ma Administrative, technical, or material support: Liang Wang Supervision: Liang Wang Corresponding author Correspondence to Ji Ma and Liang Wang Ethics declarations Ethics approval This study was approved by the Ethics Committee of Cancer Hospital of Shandong First Medical University (Approval No. SDTHEC2024006130) and Shengli Oilfield Central Hospital (Approval No. YXLL202504501). Given the retrospective design of the study and the use of archived specimens, the ethics committee waived the requirement for written informed consent. Competing interests The authors declare no competing interests. References Fisher SG, Fisher RI. 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Front Oncol. 2025;15:1550207. doi:10.3389/fonc.2025.1550207 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryTable2.docx SupplementaryTable3.docx SupplementaryTable4.docx SupplementaryTable5.docx SupplementaryTable6.docx SupplementaryTable7.docx SupplementaryFigure1.pdf SupplementaryFigure2.pdf SupplementaryFigure3.pdf SupplementaryFigure4.pdf SupplementaryFigure5.pdf SupplementaryFigure6.pdf SupplementaryFigure7.pdf SupplementaryFigure8.pdf SupplementaryFigure9.pdf SupplementaryFigureCaption.docx Cite Share Download PDF Status: Published Journal Publication published 10 Feb, 2026 Read the published version in Annals of Hematology → Version 1 posted Editorial decision: Revision requested 23 Dec, 2025 Reviews received at journal 20 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviews received at journal 09 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers invited by journal 08 Dec, 2025 Editor assigned by journal 02 Dec, 2025 Submission checks completed at journal 02 Dec, 2025 First submitted to journal 28 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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15:41:00","extension":"html","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":124095,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8231260/v1/f4ae7b72c7fe7a0c49df5181.html"},{"id":97899014,"identity":"3535d3bd-d15a-4285-9874-eeb7a2f9bb27","added_by":"auto","created_at":"2025-12-10 15:40:42","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130885,"visible":true,"origin":"","legend":"\u003cp\u003ePatterns of TP53 mutations in 89 DLBCL patients. (A) Donut graph demonstrating the proportion of TP53 mutation according to different exons. (B) Donut graph demonstrating the proportion of TP53 mutation according to different mutation types. (C) Donut graph demonstrating the proportion of TP53 mutation according to different base substitutions. (D) Donut graph demonstrating the proportion of TP53 mutation according to VAF. (E) Codon lollipop chart of TP53 mutations, marking the site with each mutation frequency.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8231260/v1/f1a546a4c46f7436b91449a4.jpeg"},{"id":97832913,"identity":"04397844-b1f0-4372-86f0-c2f0bb9a8253","added_by":"auto","created_at":"2025-12-10 00:39:48","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72831,"visible":true,"origin":"","legend":"\u003cp\u003e(A) PFS analysis based on different hotspot mutations in TP53-mut DLBCL patients. (B) OS analysis based on different hotspot mutations in TP53-mut DLBCL patients.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8231260/v1/10ad19385a58075fa69a8d9f.jpeg"},{"id":97898459,"identity":"8f66f71c-0360-4a9b-8f27-f152c1a3bedf","added_by":"auto","created_at":"2025-12-10 15:39:11","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109606,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve of PFS comparing TP53 hotspot mutations with TP53-wt. (A)C242. (B) G245. (C) R175. (D) R248. (E) R273. (F) Y234. (G) Y234. (H) Y236.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8231260/v1/e6c3ba9ab0792e69203fff2f.jpeg"},{"id":97898082,"identity":"c4b6ef06-2db4-4a20-b235-51e4fd9a2943","added_by":"auto","created_at":"2025-12-10 15:38:40","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":109967,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve of OS comparing TP53 hotspot mutations with TP53-wt. (A)C242. (B) G245. (C) R175. (D) R248. (E) R273. (F) Y234. (H) Y236.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8231260/v1/b5dbbc5d8552348eb6d8c7a2.jpeg"},{"id":97897963,"identity":"92e263f7-7f68-417c-8896-559e7c7912fb","added_by":"auto","created_at":"2025-12-10 15:38:30","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":134305,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves of treatment for DLBCL patients. (A) R-CHOP regimen, PFS. (B) R-CHOP regimen, OS. (C) R-DAEPOCH regimen, PFS. (D) R-DAEPOCH regimen, OS. (E) R-CHOP+X regimen, PFS. (E) R-CHOP+X regimen, OS.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8231260/v1/fd1ad8de0ee8fddbf373804d.jpeg"},{"id":102785493,"identity":"da86a23e-d02e-4f7a-87ba-24593773364a","added_by":"auto","created_at":"2026-02-16 16:07:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1377027,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8231260/v1/83a4db18-6f7c-4a9b-a319-a8be3267fefa.pdf"},{"id":97832917,"identity":"59a3dabb-7180-4d12-88e0-3464cd015d57","added_by":"auto","created_at":"2025-12-10 00:39:48","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19326,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8231260/v1/2d0d7a569b3c8a55c1ea4d19.docx"},{"id":97898055,"identity":"49fe25c4-80e9-4de0-842e-23234cd3bfc3","added_by":"auto","created_at":"2025-12-10 15:38:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16912,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8231260/v1/536d41501653b5b6549a0aa9.docx"},{"id":97899360,"identity":"9837dd2b-38d1-4eb3-ad50-71a25ab919fc","added_by":"auto","created_at":"2025-12-10 15:43:18","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17035,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8231260/v1/8ffc7f7c10a79e128147d0a3.docx"},{"id":97832925,"identity":"58a3db4a-54b1-4ac7-90bf-b6220cf347d0","added_by":"auto","created_at":"2025-12-10 00:39:48","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17270,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8231260/v1/8daa23033fc74d0e3c06f50e.docx"},{"id":97897777,"identity":"d6881183-e8d1-4ec1-98de-323441d5986c","added_by":"auto","created_at":"2025-12-10 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00:39:48","extension":"pdf","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":8672,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8231260/v1/ff464346f8357eaef845cc6e.pdf"},{"id":97897947,"identity":"2259fa66-4d6c-44ec-9b63-ad566156d790","added_by":"auto","created_at":"2025-12-10 15:38:30","extension":"pdf","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":9461,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure9.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8231260/v1/355ef3e0a58cd75b0ad285f0.pdf"},{"id":97832945,"identity":"25a07d67-6e52-457e-afc6-91f0f30a79af","added_by":"auto","created_at":"2025-12-10 00:39:48","extension":"docx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":16348,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureCaption.docx","url":"https://assets-eu.researchsquare.com/files/rs-8231260/v1/c42473a5803d90a56a623b4f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Impact of TP53 Mutations in Diffuse Large B-Cell Lymphoma","fulltext":[{"header":"Key Points","content":"\u003cul\u003e\n \u003cli\u003eTP53 mutations occur in approximately one-third of diffuse large B-cell lymphoma (DLBCL) patients\u003c/li\u003e\n \u003cli\u003eMissense mutations in the DNA-binding domain are the most common type of TP53 alterations\u003c/li\u003e\n \u003cli\u003eSpecific hotspot mutations (R273, R248, R175) are associated with inferior prognosis\u003c/li\u003e\n \u003cli\u003eTP53 mutation status provides independent prognostic information beyond the International Prognostic Index\u003c/li\u003e\n \u003cli\u003eCombined analysis of TP53 mutation and protein expression improves risk stratification\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. INTRODUCTION","content":"\u003cp\u003eDiffuse Large B-cell Lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL) in adults, accounting for 30\u0026ndash;40% of all NHL cases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is an aggressive malignancy characterized by significant clinical and genetic heterogeneity[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although the majority of DLBCL patients respond well to first-line immunochemotherapy with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) or similar regimens, approximately 30\u0026ndash;40% of patients experience relapse or refractory disease[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For these relapsed/refractory (R/R) patients, achieving durable remission is challenging, even with second-line or subsequent therapies. Therefore, establishing more precise prognostic models to guide targeted therapy is crucial for improving clinical outcomes in DLBCL.\u003c/p\u003e\u003cp\u003eIn recent years, advances in genomic and transcriptomic analysis have led to significant progress in the molecular classification of DLBCL. This has evolved from the initial cell of origin (COO) classification [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] to the current identification of seven genetic subtypes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], which exhibit distinct prognoses. Among these, the A53 subtype is characterized by key genetic alterations, including TP53 mutations.\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eTP53\u003c/em\u003e gene is one of the most frequently mutated genes in human cancer, with sequencing data showing \u003cem\u003eTP53\u003c/em\u003e mutations in approximately half of all cancer cases, though its frequency and distribution vary significantly across tumor types [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The p53 protein, encoded by \u003cem\u003eTP53\u003c/em\u003e, is a critical tumor suppressor that plays a key role in cellular stress responses by regulating cell cycle arrest, DNA repair, apoptosis, senescence, and autophagy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Previous studies have reported that \u003cem\u003eTP53\u003c/em\u003e mutations occur in about 20\u0026ndash;30% of DLBCL patients [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and are strongly associated with a poor prognosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, research also indicates that not all \u003cem\u003eTP53\u003c/em\u003e mutations have the same biological effect; the functional consequences of a \u003cem\u003eTP53\u003c/em\u003e mutation may depend on the cellular and genomic context, leading to different clinical outcomes among patients with different mutations [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Consequently, simply using the \u003cem\u003eTP53\u003c/em\u003e gene status to determine prognosis may be insufficient. The impact of each specific mutation should be evaluated in detail based on its functional implications. This underscores the need for more precise risk stratification of \u003cem\u003eTP53\u003c/em\u003e-mutated DLBCL patients to explore effective anti-cancer drugs and treatment regimens aimed at improving their clinical outcomes.\u003c/p\u003e\u003cp\u003eThis study aims to systematically analyze the characteristics of TP53 mutations in newly diagnosed DLBCL patients using next-generation sequencing (NGS) of formalin-fixed paraffin-embedded (FFPE) samples and to correlate these findings with clinical outcomes. Our central hypothesis is that different TP53 mutation sites have heterogeneous prognostic impacts in DLBCL. Additionally, we will compare the prognostic differences of various treatment regimens in treatment-naive TP53-mutated DLBCL patients, to provide more instructive evidence for clinical practice.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cp\u003e\u003cb\u003eStudy Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis retrospective study included 253 patients newly diagnosed with DLBCL at Shandong Cancer Hospital and Shengli Oilfield Central Hospital between January 2018 and December 2023. Inclusion criteria were: (1) pathologically confirmed DLBCL; (2) newly diagnosed and previously untreated for DLBCL; and (3) complete next-generation sequencing data available. Exclusion criteria were: (1) primary central nervous system DLBCL; (2) primary mediastinal large B-cell lymphoma; (3) transformed large B-cell lymphoma; and (4) primary cutaneous DLBCL, leg type.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical Data Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDetailed clinical characteristics were collected, including gender, age, p53 protein expression, ECOG performance status, Ann Arbor stage, serum lactate dehydrogenase (LDH) level, presence of bulky disease (greatest dimension\u0026thinsp;\u0026ge;\u0026thinsp;7.5 cm), B symptoms, International Prognostic Index (IPI) score, bone marrow infiltration, and number of extranodal sites. Pathological features (including genetic mutations), treatment regimens (R-CHOP, R-DAEPOCH, R-CHOP\u0026thinsp;+\u0026thinsp;X), and treatment responses were also recorded.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImmunohistochemistry and FISH\u003c/b\u003e\u003c/p\u003e\u003cp\u003eImmunohistochemistry (IHC) was performed on all tumor specimens for BCL6, MYC, and BCL2. Double-Expressor Lymphoma (DEL) was defined as C-MYC expression\u0026thinsp;\u0026ge;\u0026thinsp;40% and BCL2 expression\u0026thinsp;\u0026ge;\u0026thinsp;50% [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. p53 IHC staining was performed on FFPE sections using the DO-7 monoclonal antibody (DAKO), with a positivity cutoff of 10% positive cells. Fluorescence in situ hybridization (FISH) was used to detect \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eBCL2\u003c/em\u003e, and \u003cem\u003eBCL6\u003c/em\u003e gene rearrangements. Double-Hit Lymphoma (DHL) was defined as rearrangement of \u003cem\u003eMYC\u003c/em\u003e with either \u003cem\u003eBCL2\u003c/em\u003e or \u003cem\u003eBCL6\u003c/em\u003e [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eTargeted DNA Sequencing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDNA was extracted from FFPE tissue samples using the QIAamp DNA FFPE Tissue Kit (Qiagen, Hilden, Germany). A custom panel of 39 lymphoma-related genes, including \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eMYD88\u003c/em\u003e, \u003cem\u003eCD79B\u003c/em\u003e, \u003cem\u003eMYC\u003c/em\u003e, and \u003cem\u003eBCL2\u003c/em\u003e, was used for detection. Amplicon-based sequencing was performed by Jinan King-Med Diagnostics using the Ion Torrent Proton platform (Thermo Fisher, USA) with a sequencing depth of \u0026ge;\u0026thinsp;500\u0026times;. Variants were filtered using the following criteria: single nucleotide variants (SNVs) and insertions/deletions (InDels) with a variant allele frequency (VAF)\u0026thinsp;\u0026ge;\u0026thinsp;2% were included, while polymorphic sites with a frequency\u0026thinsp;\u0026gt;\u0026thinsp;5% in population databases (1000G, ExAC) were excluded. The biological significance of all detected mutations was evaluated using the IARC TP53 Database and the UMD TP53 Mutation Database [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\u003cp\u003e\u003cb\u003eFollow-up and Response Assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollow-up began at the time of pathological diagnosis and ended in November 2024. Progression-Free Survival (PFS) was defined as the time from the start of treatment to disease progression, relapse, or death from any cause. Overall Survival (OS) was defined as the time from the start of treatment to death from any cause. Treatment response was assessed according to the 2014 Lugano Classification [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD). The best response achieved was used for the final evaluation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll data were analyzed using SPSS version 26.0. Categorical data were presented as frequencies and percentages and compared using the Chi-square (χ\u0026sup2;) test or Fisher's exact test. Survival curves were generated using the Kaplan-Meier method, and differences between groups were compared using the log-rank test. Univariate and multivariate analyses were performed using the Cox proportional hazards model; variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in the univariate analysis were included in the multivariate model. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 \u003cem\u003eTP53\u003c/em\u003e Gene Mutational Spectrum\u003c/h2\u003e\u003cp\u003eAmong the 253 newly diagnosed DLBCL patients, 89 (35.2%) were found to have \u003cem\u003eTP53\u003c/em\u003e mutations. Two patients harbored two distinct mutations each (H179 and A159; G244 and N235). The majority of mutations were missense (n\u0026thinsp;=\u0026thinsp;73, 82.0%), with the remainder being non-missense (n\u0026thinsp;=\u0026thinsp;16, 18.0%), including 10 frameshift, 3 nonsense, 2 splice-site, and 1 in-frame mutation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Most mutations (n\u0026thinsp;=\u0026thinsp;81, 91.0%) were located in the DNA-binding domain (DBD), which encompasses exons 5 to 8. The distribution of mutations across exons 4\u0026ndash;10 was as follows: exon 4 (6.7%), exon 5 (22.5%), exon 6 (4.5%), exon 7 (36.0%), exon 8 (28.1%), exon 9 (1.1%), and exon 10 (1.1%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Of the 89 mutations, 66 were located in codons involved in the central core domain's DNA-binding motifs: 26 in Loop-L3 (codons 237\u0026ndash;250), which interacts with the minor groove of DNA; 14 in the LSH motif, which interacts with the major groove; and 26 in Loop-L2 (codons 119\u0026ndash;135 and 272\u0026ndash;287), which enhances DNA-binding affinity. The most common base substitution was G\u0026thinsp;\u0026gt;\u0026thinsp;A transition (n\u0026thinsp;=\u0026thinsp;40, 44.94%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The highest VAF per sample was analyzed, with 61 patients having a VAF\u0026thinsp;\u0026lt;\u0026thinsp;50% and 28 patients having a VAF\u0026thinsp;\u0026ge;\u0026thinsp;50% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The most common hotspot codons were R273 (n\u0026thinsp;=\u0026thinsp;14, 15.7%), R248 (n\u0026thinsp;=\u0026thinsp;12, 13.5%), R175 (n\u0026thinsp;=\u0026thinsp;7, 7.9%), R282 (n\u0026thinsp;=\u0026thinsp;3, 3.4%), C242 (n\u0026thinsp;=\u0026thinsp;3, 3.4%), G245 (n\u0026thinsp;=\u0026thinsp;3, 3.4%), Y234 (n\u0026thinsp;=\u0026thinsp;3, 3.4%), and Y236 (n\u0026thinsp;=\u0026thinsp;3, 3.4%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). R175, R248, R273, R282, and G245 are consistent with previously reported \u003cem\u003eTP53\u003c/em\u003e hotspots in DLBCL and solid tumors [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The relatively high frequency of C242, Y234, and Y236 mutations suggests they may be novel potential hotspots in DLBCL. In the 5 DHL patients, the most common hotspot was R248 (n\u0026thinsp;=\u0026thinsp;2, 40%), while in the 38 DEL patients, it was R273 (n\u0026thinsp;=\u0026thinsp;6, 15.8%) (Supplementary Table\u0026nbsp;1), suggesting distinct mutational profiles in different molecular subtypes of DLBCL.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Correlation between \u003cem\u003eTP53\u003c/em\u003e Mutation and Clinical Features\u003c/h2\u003e\u003cp\u003eOf the 253 patients, 164 were \u003cem\u003eTP53\u003c/em\u003e-wt and 89 were \u003cem\u003eTP53\u003c/em\u003e-mut. Significant differences were observed between the two groups regarding age\u0026thinsp;\u0026ge;\u0026thinsp;60 years (P\u0026thinsp;=\u0026thinsp;0.023), \u003cem\u003eMYD88\u003c/em\u003e mutation (P\u0026thinsp;=\u0026thinsp;0.002), \u003cem\u003eCD79B\u003c/em\u003e mutation (P\u0026thinsp;=\u0026thinsp;0.001), ECOG score\u0026thinsp;\u0026ge;\u0026thinsp;2 (P\u0026thinsp;=\u0026thinsp;0.005), and bone marrow infiltration (P\u0026thinsp;=\u0026thinsp;0.045) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No significant associations were found between \u003cem\u003eTP53\u003c/em\u003e mutation characteristics (location, type, and VAF) and clinical features (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Supplementary Tables\u0026nbsp;2\u0026ndash;4).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical characteristics of DLBCL patients with and without TP53 mutation at diagnosis\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\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTP53 wild type(n\u0026thinsp;=\u0026thinsp;164)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTP53 mutation(n\u0026thinsp;=\u0026thinsp;89)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-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,median(range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (10\u0026ndash;81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (17\u0026ndash;90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;60years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57/164 (34.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44/89 (49.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender: Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80 /164 (48.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51/89 (57.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMYD88: Mutant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 /161 (32.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 /89 (14.6%)\u003c/p\u003e\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\u003eCD79B: Mutant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 /161 (26.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8/89 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnn Arbor Stage: III-IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103/163 (63.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 /87 (62.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCell of origin: non-GCB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104/156 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 /88 (58.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECOG\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47/150 (31.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 /89 (49.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPI score: 3\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86/164 (52.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45/86 (52.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52/163 (31.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26/87 (29.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDH\u0026gt;UNV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104/163 (63.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53/89 (59.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtranodal involvement\u0026gt;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119/163 (73.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70 /89 (78.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBone marrow infiltration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16/148 (10.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18/89 (20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBulky disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36/149 (24.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 /89 (21.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDouble-hit lymphoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7/119 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5/63 (7.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDouble-expressor lymphoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59/157 (37.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38/82 (46.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 p53 Protein Expression Analysis\u003c/h2\u003e\u003cp\u003ep53 protein expression was assessed by IHC in 184 patients (62 \u003cem\u003eTP53\u003c/em\u003e-mut, 122 \u003cem\u003eTP53\u003c/em\u003e-wt). Most \u003cem\u003eTP53\u003c/em\u003e-mut cases showed high p53 expression, whereas \u003cem\u003eTP53\u003c/em\u003e-wt cases predominantly showed low or moderate expression, with a significant difference between the groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Supplementary Fig.\u0026nbsp;1). However, there was no significant association between p53 expression\u0026thinsp;\u0026gt;\u0026thinsp;75% and mutation location (P\u0026thinsp;=\u0026thinsp;0.06), type (P\u0026thinsp;=\u0026thinsp;0.093), or VAF (P\u0026thinsp;=\u0026thinsp;0.254) (Supplementary Table\u0026nbsp;5).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Prognostic Impact of \u003cem\u003eTP53\u003c/em\u003e Mutation Sites\u003c/h2\u003e\u003cp\u003eIn the entire cohort, median PFS was 9 months for \u003cem\u003eTP53\u003c/em\u003e-mut patients and 72 months for \u003cem\u003eTP53\u003c/em\u003e-wt patients. Median OS was 40 months for \u003cem\u003eTP53\u003c/em\u003e-mut patients and was not reached for \u003cem\u003eTP53\u003c/em\u003e-wt patients. Both PFS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and OS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) were significantly worse for \u003cem\u003eTP53\u003c/em\u003e-mut patients (Supplementary Fig.\u0026nbsp;2). The 2-year PFS rates were 38.2% vs. 67.0%, and 2-year OS rates were 59.6% vs. 86.9% for \u003cem\u003eTP53\u003c/em\u003e-mut and \u003cem\u003eTP53\u003c/em\u003e-wt patients, respectively. The 5-year rates were 35.9% vs. 50.2% for PFS and 48.8% vs. 66.4% for OS.\u003c/p\u003e\u003cp\u003eHowever, not all \u003cem\u003eTP53\u003c/em\u003e-mut patients had poor outcomes, necessitating a more refined risk stratification. When stratified by common hotspot mutations (C242, G245, R175, R248, R273, R282, Y234, and Y236), there was a significant difference in OS (P\u0026thinsp;=\u0026thinsp;0.0393) but not PFS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating prognostic heterogeneity. Compared to the \u003cem\u003eTP53\u003c/em\u003e-wt group, patients with G245, R175, R248, and R273 mutations had significantly shorter PFS (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while those with G245, R175, and R282 mutations had significantly shorter OS (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, patients with C242, Y234, and Y236 mutations showed no significant difference in OS or PFS compared to the \u003cem\u003eTP53\u003c/em\u003e-wt group, with survival curves approaching those of the wild-type group. Based on these outcomes, we classified hotspots into a high-risk group (G245, R175, R248, R273, R282) and a low-risk group (C242, Y234, Y236).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eStratification by mutation characteristics showed no significant impact of mutation location, type, or VAF on PFS or OS (Supplementary Fig.\u0026nbsp;3). Analysis of individual exons in the DBD revealed that mutations in exons 5, 7, and 8 were associated with worse PFS (Supplementary Fig.\u0026nbsp;4A, C, E, G), while mutations in exons 5, 6, and 8 were associated with worse OS (Supplementary Fig.\u0026nbsp;4B, D, F, H). Mutations in the DBD motifs Loop-L2, Loop-L3, and LSH were all significantly associated with poor OS and PFS (Supplementary Fig.\u0026nbsp;5A-F).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Impact of \u003cem\u003eTP53\u003c/em\u003e Mutation on Prognosis in Different Treatment Contexts\u003c/h2\u003e\u003cp\u003eAmong 220 patients (86.96%) who received at least two cycles of induction therapy and had a best response assessment, 130 (81 \u003cem\u003eTP53\u003c/em\u003e-wt, 49 \u003cem\u003eTP53\u003c/em\u003e-mut) received R-CHOP; 40 (28 \u003cem\u003eTP53\u003c/em\u003e-wt, 12 \u003cem\u003eTP53\u003c/em\u003e-mut) received R-DAEPOCH; and 50 (31 \u003cem\u003eTP53\u003c/em\u003e-wt, 19 \u003cem\u003eTP53\u003c/em\u003e-mut) received R-CHOP\u0026thinsp;+\u0026thinsp;X (including R2-CHOP, ZR-CHOP, and OR-CHOP). The overall CR rate for \u003cem\u003eTP53\u003c/em\u003e-mut patients was lower than for \u003cem\u003eTP53\u003c/em\u003e-wt patients (48.8% vs 68.6%, P\u0026thinsp;=\u0026thinsp;0.004) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the R-CHOP group, the CR rate was significantly lower for \u003cem\u003eTP53\u003c/em\u003e-mut patients (38.8% vs 69.1%, P\u0026thinsp;=\u0026thinsp;0.001). There was no significant difference in CR rates in the R-DAEPOCH (50.0% vs 67.8%, P\u0026thinsp;=\u0026thinsp;0.476) or R-CHOP\u0026thinsp;+\u0026thinsp;X groups (73.7% vs 67.7%, P\u0026thinsp;=\u0026thinsp;0.656). Notably, within the \u003cem\u003eTP53\u003c/em\u003e-mut group, the CR rate for R-CHOP\u0026thinsp;+\u0026thinsp;X was significantly better than for R-CHOP and R-DAEPOCH.\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\u003eResponse rates of DLBCL after 1st line treatment\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBest response\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTP53 mutation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTP53 wild type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39/80 (48.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96/140 (68.6%)\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\u003eNo CR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41/80 (51.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44/140 (31.4%)\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\u003eR-CHOP\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\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19/49 (38.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56/81 (69.1%)\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\u003eNo CR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30/49 (61.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25/81( 30.9%)\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\u003eR-DAEPOCH\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\u003e0.476\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6/12 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19/28 (67.8%)\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\u003eNo CR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6/12 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9/28 (32.1%)\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\u003eR-CHOP\u0026thinsp;+\u0026thinsp;X\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\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14/19 (73.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21/31 (67.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo CR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5/19 (26.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10/31 (32.3%)\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\u003cp\u003eIn the R-CHOP group, \u003cem\u003eTP53\u003c/em\u003e-mut patients had significantly worse PFS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and OS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) compared to \u003cem\u003eTP53\u003c/em\u003e-wt patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). The 2-year PFS rates were 26.5% vs. 62.5%, and 2-year OS rates were 53.1% vs. 86.5% for TP53-mut vs. TP53-wt, respectively. In the R-DAEPOCH group, \u003cem\u003eTP53\u003c/em\u003e-mut patients had significantly worse PFS (P\u0026thinsp;=\u0026thinsp;0.0323) but not OS (P\u0026thinsp;=\u0026thinsp;0.1673) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D). The 2-year PFS rates were 33.3% vs. 66.7%, and 2-year OS rates were 66.7% vs. 85.2% for TP53-mut vs. TP53-wt, respectively. In the R-CHOP\u0026thinsp;+\u0026thinsp;X group, there were no significant differences in PFS (P\u0026thinsp;=\u0026thinsp;0.7812) or OS (P\u0026thinsp;=\u0026thinsp;0.3245) between \u003cem\u003eTP53\u003c/em\u003e-mut and \u003cem\u003eTP53\u003c/em\u003e-wt patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, F). The 2-year PFS rates were 73.7% vs. 82.1%, and 2-year OS rates were 78.9% vs. 96.4% for TP53-mut vs. TP53-wt, respectively. This pattern persisted in the ZR-CHOP subgroup (PFS P\u0026thinsp;=\u0026thinsp;0.0832; OS P\u0026thinsp;=\u0026thinsp;0.8903; Supplementary Fig.\u0026nbsp;6A, B) and when comparing TP53 high-risk hotspot mutations to TP53-wt (PFS P\u0026thinsp;=\u0026thinsp;0.4593; OS P\u0026thinsp;=\u0026thinsp;0.1958, Supplemental Fig.\u0026nbsp;7), suggesting targeted therapy may ameliorate the poor prognosis associated with high-risk mutations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFourteen \u003cem\u003eTP53\u003c/em\u003e-wt and 5 \u003cem\u003eTP53\u003c/em\u003e-mut patients received autologous stem cell transplantation (ASCT). Eight \u003cem\u003eTP53\u003c/em\u003e-wt and 6 \u003cem\u003eTP53\u003c/em\u003e-mut patients received CAR-T cell therapy. Among patients receiving ASCT/CAR-T, \u003cem\u003eTP53\u003c/em\u003e-mut status was associated with significantly worse PFS (P\u0026thinsp;=\u0026thinsp;0.0047) but not OS (P\u0026thinsp;=\u0026thinsp;0.5418) (Supplementary Fig.\u0026nbsp;6C, D).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Impact of DLBCL Subtypes on Prognosis\u003c/h2\u003e\u003cp\u003eIn the DEL subgroup, \u003cem\u003eTP53\u003c/em\u003e-mut patients had significantly worse PFS (P\u0026thinsp;=\u0026thinsp;0.0001) and OS (P\u0026thinsp;=\u0026thinsp;0.0084) than \u003cem\u003eTP53\u003c/em\u003e-wt patients (Supplementary Fig.\u0026nbsp;8A, B). No significant difference was observed in the DHL subgroup, likely due to small sample size (n\u0026thinsp;=\u0026thinsp;12) (Supplementary Fig.\u0026nbsp;8C-D). Within the \u003cem\u003eTP53\u003c/em\u003e-mut cohort, there were no prognostic differences between DEL vs. non-DEL or DHL vs. non-DHL patients (Supplementary Fig.\u0026nbsp;9A-D).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Cox Regression Analysis\u003c/h2\u003e\u003cp\u003eUnivariate analysis for PFS identified age\u0026thinsp;\u0026ge;\u0026thinsp;60 years, \u003cem\u003eTP53\u003c/em\u003e mutation, Ann-Arbor stage, ECOG score, IPI score, elevated LDH, and bone marrow involvement as adverse prognostic factors. Multivariate analysis confirmed that \u003cem\u003eTP53\u003c/em\u003e mutation, ECOG score, and IPI score were independent predictors of poor PFS (Supplementary Table\u0026nbsp;6). For OS, univariate analysis identified age\u0026thinsp;\u0026ge;\u0026thinsp;60 years, \u003cem\u003eTP53\u003c/em\u003e mutation, ECOG score, and DHL as adverse factors. Multivariate analysis confirmed \u003cem\u003eTP53\u003c/em\u003e mutation, ECOG score, and DHL as independent predictors of poor OS (Supplementary Table\u0026nbsp;7).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThis study systematically analyzed the \u003cem\u003eTP53\u003c/em\u003e mutation status in 253 DLBCL patients, exploring its mutational characteristics and prognostic value. While \u003cem\u003eTP53\u003c/em\u003e mutations are generally associated with poor treatment response and adverse prognosis in most cancers, there is significant heterogeneity in clinical outcomes among different \u003cem\u003eTP53\u003c/em\u003e mutants [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Functional assessment of \u003cem\u003eTP53\u003c/em\u003e genetic alterations is crucial for optimizing risk stratification, identifying prognostically distinct subgroups, and guiding individualized therapy for DLBCL patients.\u003c/p\u003e\u003cp\u003eOur study revealed significant site-specific prognostic heterogeneity among \u003cem\u003eTP53\u003c/em\u003e hotspot mutations. The high-risk group (G245, R175, R248, R273, R282) was associated with significantly shorter PFS and OS, whereas the low-risk group (C242, Y234, Y236) showed outcomes statistically similar to \u003cem\u003eTP53\u003c/em\u003e-wt patients. This finding aligns with the concept of functional heterogeneity among \u003cem\u003eTP53\u003c/em\u003e mutants. For instance, Blandino et al. showed that specific mutants (e.g., His175, His179) can confer a selective survival advantage during chemotherapy[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Young et al. reported that in DLBCL, hotspot codons 158, 175, 245, 248, 273, 280, and 282 were associated with the highest mortality rates [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis site-specific prognostic difference may stem from distinct downstream oncogenic pathways activated by different mutants. For example, the R175H mutant has been shown to specifically bind the BACH1/LSD2 complex, promoting tumor metastasis by suppressing ferroptosis via histone demethylation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. G245S and R175H can cooperatively lift the transcriptional repression of IL-34, fostering an immunosuppressive microenvironment via a CSC\u0026ndash;IL-34\u0026ndash;macrophage axis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The R273H mutant can reshape tumor cell glycosylation and metabolism by relieving negative regulation of MGAT4A [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These findings suggest that precision targeting of mutant-specific pathways is a promising future direction for treating \u003cem\u003eTP53\u003c/em\u003e-mutated DLBCL.\u003c/p\u003e\u003cp\u003eOur findings support a refined risk stratification based on the \u003cem\u003eTP53\u003c/em\u003e mutational profile. Patients in the high-risk group who received R-CHOP plus targeted therapy had survival outcomes comparable to \u003cem\u003eTP53\u003c/em\u003e-wt patients, suggesting this combination may mitigate the adverse prognosis. We propose that patients with high-risk mutations should be considered for R-CHOP-based combination therapies and enrollment in trials exploring novel agents targeting pathways like BACH1/LSD2, IL-34, or MGAT4A. In contrast, standard R-CHOP may remain a viable option for patients with low-risk mutations.\u003c/p\u003e\u003cp\u003eConsistent with previous research [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], we found that mutations in key DBD motifs (Loop-L2, Loop-L3, and LSH) were associated with poor prognosis. However, unlike some studies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], we did not find that mutation type (missense vs. non-missense) or VAF (using a 50% cutoff) could stratify prognosis. This discrepancy with a study by Zhang et al., which found a VAF\u0026thinsp;\u0026gt;\u0026thinsp;34% to be a poor prognostic marker [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], suggests that the prognostic value of VAF may depend on an optimized cutoff, which requires further investigation in larger cohorts.\u003c/p\u003e\u003cp\u003eThe treatment of \u003cem\u003eTP53\u003c/em\u003e-mutated DLBCL remains a major clinical challenge. Our results show that neither standard R-CHOP nor intensified R-DAEPOCH regimens significantly improved outcomes for these patients, consistent with other studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Importantly, in the R-CHOP\u0026thinsp;+\u0026thinsp;X cohort, the survival outcomes of \u003cem\u003eTP53\u003c/em\u003e-mut patients were not significantly different from \u003cem\u003eTP53\u003c/em\u003e-wt patients, reinforcing the potential of combination strategies.\u003c/p\u003e\u003cp\u003eBruton's Tyrosine Kinase (BTK) inhibitors like zanubrutinib, which block the essential B-cell receptor (BCR) signaling pathway, are a rational choice, as about 45% of A53-subtype DLBCLs have alterations in the BCR-dependent NF-κB pathway [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our finding of no prognostic difference between \u003cem\u003eTP53\u003c/em\u003e-mut and \u003cem\u003eTP53\u003c/em\u003e-wt patients in the ZR-CHOP group supports this approach. This aligns with findings in other hematologic malignancies like CLL, where targeted therapies are prioritized for patients with \u003cem\u003eTP53\u003c/em\u003e alterations [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eWhile CAR-T cell therapy and ASCT are important salvage options for R/R DLBCL [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], our study suggests that \u003cem\u003eTP53\u003c/em\u003e mutations may limit their efficacy, as evidenced by the significantly lower 2-year PFS rate in \u003cem\u003eTP53\u003c/em\u003e-mut patients. This highlights the need for novel strategies, such as CAR-T followed by consolidative ASCT, which has shown promise in improving outcomes for this high-risk population.\u003c/p\u003e\u003cp\u003eIn the context of molecular subtypes, our study confirmed that \u003cem\u003eTP53\u003c/em\u003e mutations confer a poor prognosis in DEL, consistent with Meriranta et al[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The lack of a significant finding in DHL was likely due to the small sample size. Since there was no prognostic difference between DEL/DHL and non-DEL/non-DHL status within the \u003cem\u003eTP53\u003c/em\u003e-mut cohort, \u003cem\u003eTP53\u003c/em\u003e mutation may act as an independent, high-risk prognostic factor.\u003c/p\u003e\u003cp\u003eAt the protein expression level, p53 immunohistochemistry (IHC) serves as a surrogate marker for inferring \u003cem\u003eTP53\u003c/em\u003e mutation status, and previous studies have explored various positivity thresholds. Some studies have proposed that p53 expression\u0026thinsp;\u0026gt;\u0026thinsp;50% or a pattern of either \u0026ge;\u0026thinsp;65% (overexpression) or \u0026lt;\u0026thinsp;1% (complete absence) are effective cutoffs [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Our study found that in diffuse large B-cell lymphoma (DLBCL), the majority of cases with *TP53* mutations exhibited high levels of p53 protein expression (\u0026gt;\u0026thinsp;75%). This suggests that the optimal threshold may vary by tumor type and assay conditions, highlighting the need for future standardization in large, multicenter cohorts.\u003c/p\u003e\u003cp\u003eIn summary, \u003cem\u003eTP53\u003c/em\u003e mutation is a key adverse prognostic factor in DLBCL. Given the marked prognostic heterogeneity associated with different \u003cem\u003eTP53\u003c/em\u003e hotspot mutations, clinical practice should incorporate refined risk stratification based on the \u003cem\u003eTP53\u003c/em\u003e mutational profile. For patients harboring high-risk mutations (e.g., G245, R175, R248, R273, and R282), the exploration of novel combination therapies targeting relevant pathways is recommended. Conversely, for those with low-risk mutations (e.g., C242, Y234, and Y236), the standard R-CHOP regimen remains a viable treatment option. In the future, more prospective clinical trials are needed to validate the efficacy of R-CHOP combined with targeted agents in \u003cem\u003eTP53\u003c/em\u003e-mutated DLBCL. Such studies will be crucial for optimizing current therapeutic strategies to improve patient outcomes and quality of life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the staff of the Department of Hematology at Shandong Cancer Hospital and Shengli Oilfield Central Hospital for their valuable contributions to patient care and data collection. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the Zhongguancun Precision Medicine Foundation\u0026apos;s \u0026quot;New Life\u0026quot; Charity Project (Grant No. [GXZDH48]), the CSCO-Hengrui Oncology Research Fund (Grant No. [Y-HR2020QN-0175]), and the China Health \u0026amp; Medical Development Foundation (Grant No. [chmdf2025-xrky04-09]).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContributions\u003c/p\u003e\n\u003cp\u003eConception and design: Ji Ma and Liang Wang\u003c/p\u003e\n\u003cp\u003eAcquisition of data: Jiayi Yu, Kuntong Liu and Rong Xie\u003c/p\u003e\n\u003cp\u003eAnalysis and interpretation of data: Jiayi Yu and Qiang He\u003c/p\u003e\n\u003cp\u003eDrafting of the manuscript: Jiayi Yu\u003c/p\u003e\n\u003cp\u003eCritical revision of the manuscript for important intellectual content: Yuting Yan\u003c/p\u003e\n\u003cp\u003eStatistical analysis: Jiayi Yu\u003c/p\u003e\n\u003cp\u003eObtained funding: Ji Ma\u003c/p\u003e\n\u003cp\u003eAdministrative, technical, or material support: Liang Wang\u003c/p\u003e\n\u003cp\u003eSupervision: Liang Wang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Ji Ma and Liang Wang\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Cancer Hospital of Shandong First Medical University (Approval No. SDTHEC2024006130) and Shengli Oilfield Central Hospital (Approval No. YXLL202504501). Given the retrospective design of the study and the use of archived specimens, the ethics committee waived the requirement for written informed consent. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFisher SG, Fisher RI. The epidemiology of non-Hodgkin\u0026apos;s lymphoma. Oncogene. 2004;23(38):6524-6534. doi:10.1038/sj.onc.1207843\u003c/li\u003e\n\u003cli\u003eLi S, Young KH, Medeiros LJ. Diffuse large B-cell lymphoma. Pathology. 2018;50(1):74-87. doi:10.1016/j.pathol.2017.09.006\u003c/li\u003e\n\u003cli\u003eKorkolopoulou P, Vassilakopoulos T, Milionis V, et al. Recent Advances in Aggressive Large B-cell Lymphomas: A Comprehensive Review. 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Haematologica. 2022;107(5):1153-1162. doi:10.3324/haematol.2021.278638\u003c/li\u003e\n\u003cli\u003eKastenhuber ER, Lowe SW. Putting p53 in Context. Cell. 2017;170(6):1062-1078. doi:10.1016/j.cell.2017.08.028\u003c/li\u003e\n\u003cli\u003eSabapathy K, Lane DP. Therapeutic targeting of p53: all mutants are equal, but some mutants are more equal than others. Nat Rev Clin Oncol. 2018;15(1):13-30. doi:10.1038/nrclinonc.2017.151\u003c/li\u003e\n\u003cli\u003eBieging KT, Mello SS, Attardi LD. Unravelling mechanisms of p53-mediated tumour suppression. Nat Rev Cancer. 2014;14(5):359-370. doi:10.1038/nrc3711\u003c/li\u003e\n\u003cli\u003eRiedell PA, Smith SM. Double hit and double expressors in lymphoma: Definition and treatment. Cancer. 2018;124(24):4622-4632. doi:10.1002/cncr.31646\u003c/li\u003e\n\u003cli\u003eEdlund K, Larsson O, Ameur A, et al. Data-driven unbiased curation of the TP53 tumor suppressor gene mutation database and validation by ultradeep sequencing of human tumors. 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Identify truly high-risk TP53-mutated diffuse large B cell lymphoma patients and explore the underlying biological mechanisms. Cell Commun Signal. 2024;22(1):401. doi: 10.1186/s12964-024-01765-w\u003c/li\u003e\n\u003cli\u003eHong Y, Ren T, Wang X, et al. APR-246 triggers ferritinophagy and ferroptosis of diffuse large B-cell lymphoma cells with distinct TP53 mutations. Leukemia. 2022;36(9):2269-2280. doi: 10.1038/s41375-022-01634-w\u003c/li\u003e\n\u003cli\u003eBlandino G, Levine AJ, Oren M. Mutant p53 gain of function: differential effects of different p53 mutants on resistance of cultured cells to chemotherapy. Oncogene. 1999;18(2):477-485. doi: 10.1038/sj.onc.1202314\u003c/li\u003e\n\u003cli\u003eSu Z, Kon N, Yi J, et al. Specific regulation of BACH1 by the hotspot mutant p53R175H reveals a distinct gain-of-function mechanism. Nat Cancer. 2023;4(4):564-581. doi: 10.1038/s43018-023-00532-z\u003c/li\u003e\n\u003cli\u003eNian Z, Dou Y, Shen Y, et al. 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The Prognostic Predictive Value of TP53 mutation Variant Allele Frequency in Diffuse Large B-Cell Lymphoma Zhongguo Shi Yan Xue Ye Xue Za Zhi. 2024;32(6):1719-1725. doi:10.19746/j.cnki.issn.1009-2137.2024.06.013\u003c/li\u003e\n\u003cli\u003eZayac AS, Landsburg DJ, Hughes ME, et al. High-grade B-cell lymphoma, not otherwise specified: a multi-institutional retrospective study. Blood Adv. 2023;7(21):6381-6394. doi:10.1182/bloodadvances.2023009731\u003c/li\u003e\n\u003cli\u003eZhang Q, Wen C, Zhao L, Wang Y. A Comprehensive Review of Small-Molecule Inhibitors Targeting Bruton Tyrosine Kinase: Synthetic Approaches and Clinical Applications. Molecules. 2023;28(24):8037. doi:10.3390/molecules28248037\u003c/li\u003e\n\u003cli\u003eHallek M, Al-Sawaf O. Chronic lymphocytic leukemia: 2022 update on diagnostic and therapeutic procedures. Am J Hematol. 2021;96(12):1679-1705. doi:10.1002/ajh.26367\u003c/li\u003e\n\u003cli\u003eTrabolsi A, Arumov A, Schatz JH. Bispecific antibodies and CAR-T cells: dueling immunotherapies for large B-cell lymphomas. Blood Cancer J. 2024;14(1):27. doi:10.1038/s41408-024-00997-w\u003c/li\u003e\n\u003cli\u003eZhao Y, Wang H, Jin S, et al. Prognostic analysis of DLBCL patients and the role of upfront ASCT in high-intermediate and high-risk patients. Oncotarget. 2017;8(42):73168-73176. Published 2017 Apr 21. doi:10.18632/oncotarget.17324\u003c/li\u003e\n\u003cli\u003eMeriranta L, Pasanen A, Alkodsi A, et al. Molecular background delineates outcome of double protein expressor diffuse large B-cell lymphoma. Blood Adv. 2020;4(15):3742-3753. doi:10.1182/bloodadvances.2020001727\u003c/li\u003e\n\u003cli\u003eJin Y, Wang Y, Wang L, et al. TP53 mutation and immunohistochemical p53 expression characteristics in diffuse large B-cell lymphoma. Front Oncol. 2025;15:1550207. doi:10.3389/fonc.2025.1550207\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Diffuse large B-cell lymphoma, TP53 mutation, Prognosis, Next-generation sequencing, Precision medicine","lastPublishedDoi":"10.21203/rs.3.rs-8231260/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8231260/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To evaluate the prognostic value of TP53 mutations in patients with diffuse large B-cell lymphoma (DLBCL).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We retrospectively analyzed the clinical data and gene sequencing results of 253 newly diagnosed DLBCL. Survival and correlation analyses were performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e We further revealed significant prognostic heterogeneity among different TP53 hotspot mutations, with mutations at codons G245, R175, R273, and R282 indicating a poorer prognosis. Within the DBD, mutations in exons 5, 7, and 8 were associated with poorer PFS, while mutations in exons 5, 6, and 8 were linked to poorer OS. Additionally, mutations in the Loop-L2, Loop-L3, and LSH motifs within the DBD were all significantly associated with unfavorable PFS and OS. Notably, in the cohort treated with R-CHOP plus novel agents (R-CHOP+X), there were no significant differences in response rates or survival between TP53-mut and TP53-wt patients, suggesting this combination may overcome the adverse prognosis associated with TP53 mutations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e TP53 mutation is a crucial adverse prognostic factor in DLBCL. Given the significant prognostic heterogeneity among different TP53 hotspot mutations, a more refined risk stratification based on the TP53 mutational profile is warranted in clinical practice. For patients with high-risk mutations, combining R-CHOP with targeted therapies and exploring novel combination strategies targeting specific pathways are recommended. In contrast, standard R-CHOP may remain an appropriate option for patients with low-risk mutations. Future prospective trials are needed to validate the efficacy of R-CHOP combined with targeted agents in TP53-mutated DLBCL to optimize treatment strategies and improve patient outcomes.\u003c/p\u003e","manuscriptTitle":"Prognostic Impact of TP53 Mutations in Diffuse Large B-Cell Lymphoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-10 00:39:43","doi":"10.21203/rs.3.rs-8231260/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-23T22:40:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-20T17:07:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196408764169301973161449745420089914512","date":"2025-12-10T17:03:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T08:50:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145770760658819754261054936229451277854","date":"2025-12-08T08:39:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-08T07:16:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-02T15:07:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-02T15:04:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Hematology","date":"2025-11-28T14:15:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3318dace-185d-4226-98c8-a4bab85edf16","owner":[],"postedDate":"December 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:05:09+00:00","versionOfRecord":{"articleIdentity":"rs-8231260","link":"https://doi.org/10.1007/s00277-026-06882-9","journal":{"identity":"annals-of-hematology","isVorOnly":false,"title":"Annals of Hematology"},"publishedOn":"2026-02-10 15:57:28","publishedOnDateReadable":"February 10th, 2026"},"versionCreatedAt":"2025-12-10 00:39:43","video":"","vorDoi":"10.1007/s00277-026-06882-9","vorDoiUrl":"https://doi.org/10.1007/s00277-026-06882-9","workflowStages":[]},"version":"v1","identity":"rs-8231260","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8231260","identity":"rs-8231260","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}