TP53 mutations predict poor prognosis in diffuse large B-cell lymphoma: A single-center study

TP53 mutations predict poor prognosis in diffuse large B-cell lymphoma: A single-center study | 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 TP53 mutations predict poor prognosis in diffuse large B-cell lymphoma: A single-center study Haiyan Zhang, Xiang Zhang, Jinghan Wang, Xuewu Zhang, Yunfei Lv, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7797367/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jan, 2026 Read the published version in Annals of Hematology → Version 1 posted 9 You are reading this latest preprint version Abstract Evidence linking TP53 mutations to survival outcome and treatment efficacy in diffuse large B-cell lymphoma (DLBCL) remains limited. In this retrospective cohort of 436 newly diagnosed DLBCL patients, TP53 mutations were independent prognostic factors for poor overall survival (OS) and progression-free survival (PFS) (both P < 0.001). Compared with TP53 wild-type patients, those with TP53 mutations had poorer response to R-CHOP and higher rates of relapsed/refractory disease ( P = 0.001). R-CHOP combined with azacitidine significantly improved efficacy, showing higher complete remission (CR) (91.67% vs R-CHOP, P = 0.023) and overall response (OR) rates (100% vs R-CHOP, P = 0.029). In subgroup analysis, TP53 mutations were associated with unfavorable survival in GCB subtypes, but not in non-GCB subtypes, and it also indicates adverse prognosis in double-expressor lymphomas(DEL). Survival outcomes were worse in patients with Variant Allele Frequency (VAF) ≥ 60% and DNA-binding domains (DBD) mutations (particularly exons 5 and 8). No survival difference were observed between single-site and multiple-site mutations, nor gain-of-function (GOF) and loss-of-function (LOF) mutations. In addition, we adapted a p53 immunohistochemical classification from ovarian/endometrial cancer to enhance TP53 mutations prediction: overexpression (> 80%) or complete absence (< 1%) as high-risk, and heterogeneity (1%-80%) as low-risk. This approach achieved 70.83% sensitivity and 97.35% specificity, with specificity surpassed conventional ≥ 50% cutoff ( P < 0.001), indicating enhanced precision in predicting TP53 mutation risk. TP53 mutations p53 diffuse large B-cell lymphoma prognosis R-CHOP combined with azacitidine Figures Figure 1 Figure 2 Introduction As one of the most important and common tumor suppressor genes, TP53 was frequently mutated across all cancer types, including lymphoma, and closely related to tumorigenesis and cancer progression [ 1 – 6 ] . Recent studies have confirmed that TP53 mutations correlate with reduced immunochemotherapy efficacy and serve as an independent poor prognostic factor in indolent lymphomas [ 7 – 10 ] . However, few studies have explored the relationship between TP53 mutations and the survival outcomes or therapeutic efficacy in DLBCL. We retrospectively analyzed 436 newly diagnosed DLBCL patients to assess TP53 mutations significance and differential treatment responses. Detection of TP53 mutations through next-generation sequencing (NGS) is the most accurate method at present, but it remains costly, time-consuming, and requires specialized expertise for data analysis. Researchers have attempted to explore the expression of p53 (cut-off ≥ 50%) by immunohistochemistry (IHC) as an alternative to NGS, but the sensitivity and specificity were not particularly satisfactory [ 11 , 12 ] . Others proposed a 3-tier scoring system to describe p53 staining in ovarian carcinoma: overexpression (OE) (> 80%), complete absence (CA) (< 1%), cytoplasmic (CY) or wild-type (WT) (1%-80%), and suggested that both OE and CA would predict pathogenic TP53 mutations [ 13 – 15 ] . OE is most commonly associated with nonsynonymous mutations, which interfere with MDM2‐induced ubiquitination and degradation of p53, resulting in excessive p53 protein accumulation in the nucleus [ 14 , 16 ] . CA is associated with nonsense mutations, which introduce a premature stop codon that triggers nonsense‐mediated RNA decay, or indel and splice acceptor mutations that interfere with correct protein translation by introducing frame shifts or aberrant splicing [ 14 ] . WT expression is characterized by a variable staining intensity in a variable number of tumour cell nuclei. 14 The overall accuracy of optimized p53 IHC in predicting TP53 mutation was 0.97 (sensitivity 0.96, specificity 1.00) in ovarian carcinoma and 0.945 (sensitivity 0.95, specificity 0.941) in endometrial cancer [ 14 , 15 ] . However, it has not yet been determined whether this criterion is valid in DLBCL. Therefore, we analyzed 199 DLBCL cases with available NGS and p53 IHC results to evaluated the sensitivity and specificity of IHC in predicting TP53 mutations. Materials and Methods 1.Patients This study enrolled 436 DLBCL patients newly diagnosed and treated in our hospital between January 2018 to August 2023. The cohort consisted of 227 males and 209 females, with a median age of 60 years (IQR, 50–69). With a median follow-up of 22.75 months (IQR 17.06–32.54) for survivors, 90 deaths (20.6%) and 5 (1.1%) losses to follow-up occurred by July 8, 2024. All cases were diagnosed in accordance with 2016 WHO classification of lymphoid neoplasms [ 17 ] and underwent NGS, excluding those with other malignancies, HIV infection, pregnancy, primary central nervous system DLBCL, and transformed DLBCL. Response assessment standard by 2014 Lugano criteria for evaluation in Lymphoma [ 18 ] . OS was defined as the time from diagnosis to death from any cause or the last follow-up. PFS was calculated from the initiation of treatment to disease progression, death, or termination of follow-up. The study was approved by the ethics committee of our hospital. 2. mutations detection method and categorization TP53 mutations were detected by NGS in genomic DNA extracted from tumor-containing formalin-fixed paraffin-embedded (FFPE) tissue sections or bone marrow fluid. A 196-gene panel covering lymphoma diagnosis, prognosis, and targeted therapy was analyzed. The types of genetic variants screened included single nucleotide variants (SNVs), small insertions/deletions, copy number variations and BCl2/BCL6/MYC gene rearrangements. All detected genomic mutations are listed in Supplemental Table S1 . TP53 GOF mutations were defined as any nonsynonymous variants [ 14 , 19 ] TP53 LOF class mutations were defined as any stopgain, frameshift and splicing mutations [ 14 , 20 ] . 3. Immunohistochemistry and p53 expression classifications FFPE tumor tissues were sectioned at 4 µm for IHC analysis using an automated immunostainer (Benchmark XT system; Ventana Medical Systems, Tucson, AZ) with streptavidin-biotin peroxidase detection. The threshold definitions for high-level expression of MYC, BCL2, and Ki67 were ≥ 40%, > 50%, and ≥ 80%, respectively [ 17 , 21 ] . COO classification was determined by Hans algorithm [ 22 ] . DEL was defined as when both MYC and BCL2 were highly expressed. Two p53 IHC classifications were evaluated. Classification 1: High-expression (≥ 50%) and the low-expression (< 50%) group; Classification 2: CA ( 80%) and WT (1%-80%). CA and OE were classified as the high-risk group of TP53 mutations and WT being classified as the low-risk group. 4. Statistical Analysis Categorical variables are expressed as frequencies (percentages), compared by chi-squared or Fisher's exact tests. Survival rates were assessed by Kaplan–Meier analyses and survival curves were compared by the log-rank test. Statistically significant variables ( P < 0.1) with survival in univariate analysis continued to be analyzed by multivariate Cox regression to explore the independent risk factors. The optimal cut-off for VAF was obtained by X-tile software v3.6.1 (Yale University School of Medicine, New Haven, CT, USA) and The optimal cut-off values were determined using a minimal P value approach [ 23 ] . A two-sided P -values < 0.05 and was considered a statistically significant difference. All statistical analyses were performed with the Statistical Package for the Social Sciences, v. 27.0 (IBM SPSS, Chicago, IL, USA). Results 1. Characteristics of TP53 mutations in DLBCL patients TP53 mutations were identified in 106 patients (24.31%), comprising 131 mutations. Among these, 20 patients harbored multiple-site mutations. There were 113 (92.62%) mutations in the DBD, and the median level of VAF was 30.47% (IQR, 16.18%-55.90%). Missense mutations were the main mutation types (80.92%; Fig. 1 A). The mutation sites were concentrated in exons 4–8 (93.02%; Fig. 1 B). The most common mutations of the p53 protein were located in the 248, 175, 273, 234, 179, 245, and 282 (34.43%; Fig. 1 C), all within the DBD and previously reported as cancer hotspot mutations. Detailed mutation information is illustrated in Supplemental Table S2 . 2. The clinical and pathological features of patients with TP53 mutations. We compared the clinical and pathological features between patients with TP53 mutations (mut) and wild-type (wt). (for details, see Supplemental Table S3 and Table 1 ). No significant differences were observed between the two groups in terms of gender, ECOG performance status, Ann Arbor stage, extranodal involvement, IPI scores, B symptom, bulky disease (≥ 7.5cm), bone marrow infiltration, BCL-2 high expression, Hans classification (GCB vs non-GCB), or Ki67 high-expression. The TP53 mut group exhibited higher proportions of older age (> 60 years, P = 0.039) at diagnosis, elevated LDH ( P = 0.014), MYC high-expression ( P = 0.004), relapsed/refractory disease ( P < 0.001), and DEL ( P = 0.032) compared to the TP53 wt group. 3. Exploration of treatment for patients with TP53 mutations in DLBCL. A total of 282 patients treated with R-CHOP or R-CHOP–like regimen as frontline treatment and underwent efficacy evaluation, including 61 patients in TP53 mut group and 221 in TP53 wt group. The TP53 mut group showed significantly lower CR rates (55.74% vs. 76.47%; P = 0.001) and OR rates (68.85% vs. 89.59%; P < 0.001) compared to the TP53 wt group (Table 2 ). Table 1 Clinical and pathological features of DLBCL patients TP53 wt, TP53 mut and overall Characteristics TP53 wt (n = 330) TP53 mut (n = 106) P Overall (n = 397) gender 0.530 Male, n (%) 169 (51.2) 58 (54.7) 227 (52.1) Female, n (%) 161 (48.8) 48 (45.3) 209 (47.9) Age, Median (IQR) 0.039 60 (50–69) ≤ 60yrs, n (%) 175 (53.0) 44 (41.5) 219 (50.2) > 60yrs, n (%) 155 (47.0) 62 (58.5) 217 (49.8) ECOG* 0.199 <2, n (%) 251 (76.1) 74 (69.8) 325 (74.5) ≥ 2, n (%) 79 (23.9) 32 (30.2) 111 (25.5) LDH*, Median (IQR) 0.014 254 (182-424.5) Elevated, n (%) 157 (47.6) 65 (61.3) 222 (50.9) normal, n (%) 173 (52.4) 41 (38.7) 214 (49.1) Ann Arbor stage 0.058 I-II, n (%) 101 (30.6) 43 (40.6) 144 (33.0) III-IV, n (%) 229 (69.4) 63 (59.4) 292 (67.0) Extranodal involvement 0.326 ≤ 1, n (%) 210 (63.6) 73 (68.9) 283 (64.9) >1, n (%) 120 (36.4) 33 (31.1) 153 (35.1) IPI* 0.468 0–1, n (%) 101 (30.6) 35 (33.0) 136 (31.2) 2, n (%) 71 (21.5) 19 (17.9) 90 (20.6) 3, n (%) 84 (25.5) 22 (20.8) 106 (24.3) 4–5, n (%) 74 (22.4) 30 (28.3) 104 (23.9) B symptom 0.769 Yes, n (%) 92 (27.9) 28 (26.4) 120 (27.5) No, n (%) 238 (72.1) 78 (73.6) 316 (72.5) bulky disease (≥ 7.5cm) 0.168 Yes, n (%) 21 (6.4) 11 (10.4) 32 (7.3) No, n (%) 309 (93.6) 95 (89.6) 404 (92.7) Bone marrow infiltration 0.813 Yes, n (%) 53 (16.1) 16 (5.1) 69 (15.8) No, n (%) 277 (83.9) 90 (84.9) 367 (84.2) MYC high-expression 0.004 Yes, n (%) 157 (51.8) 70 (68.0) 227 (55.9) No, n (%) 146 (48.2) 33 (32.0) 179 (44.1) Unknown 27 3 30 BCL-2 high-expression Yes, n (%) 252 (79.2) 76 (72.4) 0.144 328 (77.5) No, n (%) 66 (20.8) 29 (27.6) 95 (22.5) Unknown 12 1 13 DEL* 0.032 Yes, n (%) 122 (39.9) 54 (51.9) 176 (42.9) No, n (%) 184 (60.1) 50 (48.1) 234 (57.1) Unknown 24 2 26 Hans classification 0.277 GCB, n (%) 105 (2.0) 40 (37.7) 145 (33.4) non-GCB, n (%) 223 (68.0) 66 (62.3) 289 (66.6) Unknown 2 0 2 Ki67 0.218 highly expressed, n (%) 199 (61.2) 70 (68.0) 269 (62.9) non-highly expressed, n (%) 126 (38.8) 33 (32.0) 159 (37.1) Unknown, n (%) 5 3 8 Relapsed/ Refractory disease <0.001 Yes, n (%) 82 (24.9) 44 (41.9) 126 (29.0) No, n (%) 247 (75.1) 61 (58.1) 308 (71.0) Unknown 1 1 2 ECOG, Eastern Cooperative Oncology Group performance status; LDH, lactate dehydrogenase; IPI, International Prognostic Index. DEL, double-expressor lymphomas; Data are expressed as n(%) unless otherwise specified. Table 2 Response rates to first-line R-CHOP in TP53 mut vs. TP53 wt patients TP53 mut TP53 wt CR 34(55.74%) 169(76.47%) P = 0.001 PR 8(13.11%) 29(13.12%) OR 42(68.85%) 198(89.59%) P <0.001 SD 2(3.28%) 2(0.90%) PD 17(27.87%) 21(9.50%) total 61 221 CR, complete remission; PR, partial response; OR, overall response; SD, stable disease; PD, progressive disease We further analyzed the treatment responses in other patients with TP53 mutations who did not receive R-CHOP or R-CHOP-like regimens. 12 patients received R-CHOP combined with azacitidine (AZA 100mg, d1-5, Q21d) (R-CHOP + AZA). 11 patients (91.67%) achieved CR and 1 (8.33%) had PR, yielding a significantly higher CR rate ( P = 0.023) and OR rate ( P = 0.029) compared to the R-CHOP group (Table 3 ). The PR patient converted to CR after AZA maintenance therapy (100 mg/day, days 1–5, Q28D). With a median follow-up of 13.07 months (IQR 10.23–19.77), no progression occurred. Among 11 patients treated with R-CHOP combined with Bruton tyrosine kinase inhibitors (BTKis) (R-CHOP + BTKis), there were 5 CR (45.5%), 4 PR (36.4%), and 2 cases of disease progression. Although the OR rate of 81.82% was higher than that of the R-CHOP group, but the difference did not reach statistically significant ( P = 0.491, Table 3 ). The clinical and pathological features of TP53 mut patients treated with R-CHOP, R-CHOP + AZA, and R-CHOP + BTKi were compared and presented in Supplementary Table S4. Table 3 Response rates in TP53 mut DLBCL: R-CHOP vs R-CHOP + AZA and R-CHOP vs R-CHOP + BTKis. R-CHOP R-CHOP + AZA P (R-CHOP vs. R-CHOP + AZA) R-CHOP + BTKis P (R-CHOP vs. R-CHOP + BTKi) CR 34(55.74%) 11(91.67%) 0.023 5(45.45%) 0.529 PR 8(13.11%) 1(8.33%) 4(36.36%) OR 42(68.85%) 12(100%) 0.029 9(81.82%) 0.491 SD 2(3.28%) 0 0 PD 17(27.87%) 0 2(18.18%) total 61 12 11 R-CHOP + AZA, R-CHOP combined with azacitidine; R-CHOP + BTKis, R-CHOP combined with Bruton tyrosine kinase inhibitors; CR, complete remission; PR, partial response; OR, overall response; SD, stable disease; PD, progressive disease 4. Prognostic significance of mutations in DLBCL patients 1)Univariate and multivariate analysis Kaplan-Meier analysis with log-rank test revealed that DLBCL patients with TP53 mut had inferior OS and PFS compared to TP53 wt (both P < 0.001; Fig. 2 A and 2 B). TP53 mut patients had lower 3-year OS (66.0% vs 84.8%) and PFS (58.5% vs 75.8%) than TP53 wt. After adjusting for clinical and pathological features, multivariate Cox regression confirmed TP53 mutations as an independent risk factor for OS and PFS (both P < 0.001). Patients with TP53 mutations had a significantly higher risk of death (HR = 2.772, 95% CI: 1.745–4.404; P < 0.001) and disease progression (HR = 2.285, 95% CI: 1.533–3.407; P < 0.001) compared to TP53 wt (Table 4 ). Table 4 Univariate and multivariate analysis of OS and PFS in patients with DLBCL. univariate analysis multivariate analysis P P HR 95.0% CI PFS TP53 mutations <0.001 60yrs <0.001 0.01 1.692 1.134–2.526 ECOG performance status <0.001 0.013 1.628 1.108–2.392 elevated LDH <0.001 0.001 2.160 1.348–3.463 Ann Arbor III-IV 1) <0.001 0.058 1.463 0.987–2.169 B symptom <0.001 <0.001 1.931 1.324–2.816 bulky disease(≥ 7.5cm) 0.078 0.498 1.242 0.672–2.295 bone marrow infiltration <0.001 0.002 2.021 1.306–3.128 MYC high-expression 0.035 0.855 1.037 0.706–1.522 BCL-2 high-expression 0.065 0.195 1.389 0.845–2.282 Hans classification 0.741 Ki67 high-expression 0.431 OS TP53 mutations <0.001 <0.001 2.772 1.745–4.404 Age <0.001 0.006 1.957 1.209–3.168 ECOG score <0.001 0.046 1.582 1.008–2.482 elevated LDH <0.001 0.018 2.017 1.130–3.598 Ann Arbor III-IV 1) <0.001 0.033 1.674 1.042–2.690 B symptom <0.001 0.002 2.006 1.290–3.120 bone marrow infiltration <0.001 0.016 1.906 1.126–3.229 MYC high-expression <0.001 0.100 1.517 0.923–2.494 BCL-2 high-expression 0.337 bulky disease(≥ 7.5cm) 0.428 Hans classification 0.238 Ki67 high-expression 0.684 ECOG, Eastern Cooperative Oncology Group performance status; LDH, lactate dehydrogenase; IPI, International Prognostic Index. DEL, double-expressor lymphomas 2) Prognostic effect of mutations on subgroup of DLBCL To determine whether TP53 mutations have a poor prognostic effect in all DLBCL patients, we performed subgroup analyses (Fig. 2 C-J). Our results show that TP53 mut were significantly associated with worse OS and PFS (both P < 0.001; Fig. 2 C and 2 D) in the GCB subtype, but not in non-GCB patients (OS: P = 0.258; PFS: P = 0.279; Fig. 2 E and 2 F). Additionally, in DEL, TP53 mut correlated with inferior survival compared to TP53 wt (OS: P = 0.003; PFS: P < 0.001; Fig. 2 G and 2 H). Based on TP53 mutations status and DEL classification, patients were categorized into four groups: (1) TP53 wt/non-DEL, (2) TP53 wt/DEL+, (3) TP53 mut/non-DEL, and (4) TP53 mut/DEL+. TP53 wt/non-DEL patients had the best prognosis, whereas TP53 mut/DEL + patients exhibited the worst survival (Fig. 2 I and 2 J). 3)Prognostic effect of mutations subsets We further performed subgroup analyses of the mutation types and the sites (Fig. 2 K-V and Supplemental Fig. S1 ). The result demonstrated that both single-site and multiple-site mutations, nor GOF and LOF mutations had worse OS and PFS compared to TP53 wt (all P < 0.001; Supplemental Fig. S1 A-H). However, no significant survival differences were observed between single-site vs. multiple-site mutations (OS: P = 0.602; PFS: P = 0.835; Fig. 2 K and 2 L, or between GOF vs. LOF mutations (OS: P = 0.247; PFS: P = 0.237; Fig. 2 M and 2 N). The optimal VAF cut-off determined by X-tile software was 60%. Both patients with VAF < 60% (OS: P = 0.002; PFS: P = 0.004) and VAF ≥ 60% (both P < 0.001) had worse survival than TP53 wt (Supplemental Fig. S1 J–N). Notably, the VAF ≥ 60% subgroup had significantly poorer outcomes than the VAF < 60% subgroup (both P < 0.001; Fig. 2 O and 2 P). TP53 mutations located in the DBD were associated with inferior survival (both P < 0.001; Fig. 2 Q and 2 R), whereas non-DBD mutations did not differ from the TP53 wt (OS: P = 0.790; PFS: P = 0.461; Fig. 2 S and 2 T). Mutations in exon 5 and 8 had the poorest survival (both P < 0.001; Fig. 2 U and 2 V). 5. p53 IHC predicts the risk of TP53 mutations A total of 199 patients underwent NGS and p53 IHC at the same time, NGS confirmed 151 as TP53 wt and 48 as TP 53 mut. Using NGS-detected TP53 mutations as the gold standard, we compared the sensitivity and specificity of the two classifications in predicting TP53 mutations (Table 5 and Table 6 ). Classification 2 predicted 38 patients as high-risk for TP53 mutations. Among these, 34 patients were TP53 mutations confirmed by NGS. The sensitivity of Classification 2 was 70.83%, which was higher than that of classifications 1 (66.67%), but the difference was not statistically significant ( P = 0.660). Classification 2 predicted 161 patients as low-risk for TP53 mutations. Among these, 147 patients were confirmed as TP53 wt by NGS. The specificity of Classification 2 was 97.35%, significantly higher than that of classifications 1 (70.83%; P < 0.001). Table 5 The results of two classifications of p53 IHC in predicting TP53 mutations NGS Classifications 1 Classifications 2 total p53 high expression p53 low expression High risk of TP53 mutations Low risk of TP53 mutations TP53 mutations 32 (66.67%) 16 (33.33%) 34 (70.83%) 14 (29.17%) 48 TP53 wild-type 28 (18.54%) 123 (81.46%) 4 (2.65%) 147 (97.35%) 151 total 60 139 38 161 191 Table 6 The sensitivity and specificity of the two classifications in predicting TP53 mutations. Accuracy Classifications 1 Classifications 2 P Sensitivity 66.67% 70.83% 0.660 Specificity 81.46% 97.35% <0.001 Discussion Tumors with TP53 mutations often progress more quickly, have a poor response to treatment, and have a poor prognosis. In our study, the mutation patterns of TP53 in DLBCL were similar to those in solid tumors. Our cohort exhibited a slightly higher rate (24.3%) compared to previous reports [ 11 , 24 – 27 ] . On the one hand, it may be related to race and geographical environment. on the other hand, it may be related to the detection method. NGS can detect mutations with lower VAF more sensitively and accurately [ 28 ] . In our study, TP53 mutations were independent prognostic factors for poor OS and PFS. Compared with TP53 wild-type patients, those with TP53 mutations had poorer response to R-CHOP and exhibited a higher proportion of relapsed/refractory disease. Although most studies confirm that TP53 mutations have very poor prognostic significance in DLBCL, the prognostic value of TP53 mutations in different subtypes remains controversial. Xu-Monette et al. found that TP53 mutations predict poor survival in both the GCB and ABC-DLBCL subtypes [ 11 ] . In contrast, studies by Zainuddin et al. suggests that TP53 mutations were associated with poor prognosis in GCB-DLBCL patients, but not in non-GCB-DLBCL patients [ 29 ] . Our findings are consistent with the latter observations. mutant p53 can increase NF-κB activity [ 30 ] . The unfavorable prognosis of TP53 mutations in non-GCB-DLBCL may be limited, as the NF-κB signaling pathway is activated in this subtype. The TP53 mut group exhibit a higher proportion of DEL than the TP53 wt group. Notably, even among patients with DEL, TP53 mut group have worse OS and PFS than TP53 wt group. This result suggests that the adverse prognostic impact of TP53 mutations is not solely attributable to the associated higher frequency of DEL. TP53 mutations have obvious heterogeneity, and different mutation types and locations have different clinical outcomes. DBD is the critical domain for p53 to exert its function. TP53 mutations in the DBD were the strongest predictors of poor OS in DLBCL [ 11 ] .However, no difference in PFS was observed between DBD and non-DBD TP53 mutations in the Ruijin cohort [ 31 ] . We found that DBD mutations correlated with inferior survival versus wild-type, whereas non-DBD mutations showed no significant difference. LOF mutations have weaker tumourigenic effects than GOF mutations in genetically engineered mouse models [ 21 ] . In our study, no survival difference were observed between single-site and multiple-site mutations, nor gain-of-function and loss-of-function mutations, while VAF ≥ 60% had worse survival than VAF < 60%. There is no unified standard treatment regimen for DLBCL with TP53 mutations. Previous studies confirmed that R-CHOP regimen does not benefit patients with TP53 mutations [ 11 ] . BTKis have been preferred as the treatment approach for CLL/SLL patients with TP53 mutations [ 32 ] . R-CHOP + BTKis failed to show significant efficacy benefits in our DLBCL cohort. DLBCL cells show aberrant DNA methylation promoting progression, treatment resistance, and immune evasion [ 33 ] . Studies have shown that decitabine + R-CHOP may improve the prognosis of DLBCL patients with TP53 mutations [ 26 ] . In our study, R-CHOP + AZA showed significantly higher CRR and ORR than R-CHOP. Maybe, R-CHOP + AZA can overcome the negative prognostic value of TP53 mutations, but its efficacy still needs to be examined by further expanding the number of cases and extending the follow-up time. In our study, we found that both TP53 GOF and LOF mutations had a worse prognosis. This suggests that it is necessary and meaningful to classify complete absence of p53 as a distinct high-risk group of TP53 mutations. We adapted p53 IHC classification method from ovarian and endometrial cancer to enhance TP53 mutations prediction. This classification achieved 70.83% sensitivity and 97.35% specificity. Such high specificity is very significant, that is, it has a very good ability to correctly identify TP53 wt patients and a very low risk of misjudging TP53 mut patients, which ensures the accuracy of diagnosis. p53 IHC is widely available, can be interpreted well by pathologists, and is faster and inexpensive compared to NGS. This enables timely intervention for high-risk TP53 mut DLBCL patients prior to NGS confirmation, potentially improving clinical outcomes. The sensitivity of this classification in DLBCL does not appear to be as high as that in ovarian carcinoma and endometrial cancer. It may be affected by factors such as the sensitivity of the test method and the post-transcriptional regulation [ 16 ] . In conclusion, this study identified TP53 mutations as an independent unfavourable prognostic factor in DLBCL. DLBCL patients with TP53 mutations showed poorer responses to R-CHOP and a higher incidence of relapse or refractory disease. while R-CHOP + A-AZA may potentially adverse outcomes in DLBCL patients with TP53 mutations. p53 IHC with either CA or OE had higher specificity than the high expression, which could more accurately predict the risk of TP53 mutations. Declarations Acknowledgements: This study was conducted during my research visit at hematological department of the First Affiliated Hospital of Zhejiang University. Thank you Professor Yu Wenjuan for your technical guidance. Author Contribution(s): H.Z. participated in the design of the study, carried out data collection, analysis and interpretation, and drafted and revised the manuscript. X.Z. contributed to the study conception, design, and supervision, and reviewed and revised manuscript drafts. W.Yu. was responsible for conceptualization, funding acquisition, project administration, and manuscript review and editing. H.Tong. contributed to supervision and project administration. J. Wang and J.J. were responsible for data acquisition. X. Zhang. and Y.Lv. performed the statistical analysis. Y.Zhu., X.Ye., J.Wei., M.Y., G.Xu., C.Yang., H.Meng. and W.Xie. participated in data collection. All authors reviewed the manuscript. Funding Information: Funding was provided by National Natural Science Foundation of China [Grant Numbers 82370151] Data availability: Data available in article supplementary material. Ethics approval and consent to participate: This study was approved by the Ethics Committee of the First Affiliated Hospital of Zhejiang University School of Medicin. All research was performed in accordance with the Declaration of Helsinki. 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Supplementary Files SupplementalTable.pdf SupplementalTableS2.ThecharacteristicsoftheTP53mutations.xlsx SupplementalTableS3.Clinicalandpathologicalfeaturesofthe436DLBCLpatients.xls Cite Share Download PDF Status: Published Journal Publication published 29 Jan, 2026 Read the published version in Annals of Hematology → Version 1 posted Editorial decision: Revision requested 23 Oct, 2025 Reviews received at journal 21 Oct, 2025 Reviewers agreed at journal 18 Oct, 2025 Reviews received at journal 14 Oct, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers invited by journal 13 Oct, 2025 Editor assigned by journal 10 Oct, 2025 Submission checks completed at journal 10 Oct, 2025 First submitted to journal 07 Oct, 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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17:41:17","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152890,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7797367/v1/7ff7e91d3e7737faa50c9f39.html"},{"id":94546978,"identity":"3378d3f7-54f3-47e2-9c30-91dc601314e7","added_by":"auto","created_at":"2025-10-28 17:41:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":553073,"visible":true,"origin":"","legend":"\u003cp\u003eThe features of TP53 mutation (A) Type of TP53 mutations and Composition ratio. (B) Exon and Inclusion frequency distribution. (C) Frequency and position of TP53 mutations.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7797367/v1/1d5874a0ce5d8d405207f9c9.png"},{"id":94547044,"identity":"dcfae466-a335-4f7a-b35c-58b532936b7a","added_by":"auto","created_at":"2025-10-28 17:41:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1234290,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis of \u003cem\u003eTP53\u003c/em\u003e mutations in patients with DLBCL. (A) Overall survival analysis of \u003cem\u003eTP53\u003c/em\u003e mutations in patients with DLBCL. (B) Progression-free survival analysis of \u003cem\u003eTP53\u003c/em\u003e mutations in patients with DLBCL. (C) Overall survival analysis of \u003cem\u003eTP53\u003c/em\u003e mutations in patients with GCB-DLBCL. (D) Progression-free survival analysis of \u003cem\u003eTP53\u003c/em\u003e mutations in patients with GCB-DLBCL. (E) Overall survival analysis of \u003cem\u003eTP53\u003c/em\u003e mutations in patients with non–GCB-DLBCL. (F) Progression-free survival analysis of \u003cem\u003eTP53\u003c/em\u003emutations in patients with non–GCB-DLBCL. (G) Overall survival analysis of \u003cem\u003eTP53\u003c/em\u003emutations in patients with DEL. (H) Progression-free survival analysis of \u003cem\u003eTP53\u003c/em\u003emutations in patients with DEL. (I) Overall survival analysis of patients with different \u003cem\u003eTP53 \u003c/em\u003emutations status and DEL classification. (J) Progression-free survival analysis of patients with different \u003cem\u003eTP53 \u003c/em\u003emutations status and DEL classification. (K) Overall survival analysis of \u003cem\u003eTP53\u003c/em\u003esingle and multiple site mutations in DLBCL. (L) Progression-free survival analysis of \u003cem\u003eTP53\u003c/em\u003e single and multiple site mutations in DLBCL. (M) Overall survival analysis of \u003cem\u003eTP53\u003c/em\u003e GOF and LOF class mutations in DLBCL. (N) Progression-free survival analysis of \u003cem\u003eTP53\u003c/em\u003e GOF and LOF class mutations in DLBCL.(O) Overall survival analysis of \u003cem\u003eTP53\u003c/em\u003e mutations VAF\u0026lt;60% and VAF≥60%. (P) Progression-free survival analysis of \u003cem\u003eTP53\u003c/em\u003e mutations VAF\u0026lt;60% and VAF≥60%. (Q) Overall survival analysis of \u003cem\u003eTP53\u003c/em\u003e mutations occurred in the DBD. (R) Progression-free survival analysis of \u003cem\u003eTP53\u003c/em\u003emutations occurred in the DBD. (S) Overall survival analysis of \u003cem\u003eTP53\u003c/em\u003emutations occurred in the non-DBD. (T) Progression-free survival analysis of \u003cem\u003eTP53\u003c/em\u003emutations occurred in the non-DBD. (U) Overall survival analysis of \u003cem\u003eTP53\u003c/em\u003emutations on EXON 5-8, the p-value was for each exon compared to the \u003cem\u003eTP53\u003c/em\u003ewt group. (V) Progression-free survival analysis of \u003cem\u003eTP53\u003c/em\u003e mutations on EXON 5-8, the p-value was for each exon compared to the \u003cem\u003eTP53\u003c/em\u003e wt group.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7797367/v1/d96f500cb65191fbed70fb22.png"},{"id":101691062,"identity":"f8822512-43fa-4519-91d6-317d613bcbb7","added_by":"auto","created_at":"2026-02-02 16:11:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3067181,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7797367/v1/191c2481-bc36-425e-b7b6-a1035d029a77.pdf"},{"id":94547160,"identity":"c0a7941d-f2ac-4dc7-97ac-4b9b2e9ba1d0","added_by":"auto","created_at":"2025-10-28 17:42:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":551433,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7797367/v1/a0e82d9bf324afb201549892.pdf"},{"id":94546684,"identity":"28050ff7-3851-475f-a53d-74d0d2963ee0","added_by":"auto","created_at":"2025-10-28 17:40:18","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31248,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS2.ThecharacteristicsoftheTP53mutations.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7797367/v1/8e68fa287170f6eef6cb4cc1.xlsx"},{"id":94546984,"identity":"ab6576b4-37d7-4039-873b-66839f20b255","added_by":"auto","created_at":"2025-10-28 17:41:40","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":186368,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS3.Clinicalandpathologicalfeaturesofthe436DLBCLpatients.xls","url":"https://assets-eu.researchsquare.com/files/rs-7797367/v1/1183785da0656dc4685dcf95.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"TP53 mutations predict poor prognosis in diffuse large B-cell lymphoma: A single-center study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs one of the most important and common tumor suppressor genes, \u003cem\u003eTP53\u003c/em\u003e was frequently mutated across all cancer types, including lymphoma, and closely related to tumorigenesis and cancer progression\u003csup\u003e[\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Recent studies have confirmed that \u003cem\u003eTP53\u003c/em\u003e mutations correlate with reduced immunochemotherapy efficacy and serve as an independent poor prognostic factor in indolent lymphomas\u003csup\u003e[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, few studies have explored the relationship between \u003cem\u003eTP53\u003c/em\u003e mutations and the survival outcomes or therapeutic efficacy in DLBCL. We retrospectively analyzed 436 newly diagnosed DLBCL patients to assess \u003cem\u003eTP53\u003c/em\u003e mutations significance and differential treatment responses.\u003c/p\u003e\u003cp\u003eDetection of \u003cem\u003eTP53\u003c/em\u003e mutations through next-generation sequencing (NGS) is the most accurate method at present, but it remains costly, time-consuming, and requires specialized expertise for data analysis. Researchers have attempted to explore the expression of p53 (cut-off \u0026ge;\u0026thinsp;50%) by immunohistochemistry (IHC) as an alternative to NGS, but the sensitivity and specificity were not particularly satisfactory\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Others proposed a 3-tier scoring system to describe p53 staining in ovarian carcinoma: overexpression (OE) (\u0026gt;\u0026thinsp;80%), complete absence (CA) (\u0026lt;\u0026thinsp;1%), cytoplasmic (CY) or wild-type (WT) (1%-80%), and suggested that both OE and CA would predict pathogenic \u003cem\u003eTP53\u003c/em\u003e mutations\u003csup\u003e[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. OE is most commonly associated with nonsynonymous mutations, which interfere with MDM2‐induced ubiquitination and degradation of p53, resulting in excessive p53 protein accumulation in the nucleus\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. CA is associated with nonsense mutations, which introduce a premature stop codon that triggers nonsense‐mediated RNA decay, or indel and splice acceptor mutations that interfere with correct protein translation by introducing frame shifts or aberrant splicing\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. WT expression is characterized by a variable staining intensity in a variable number of tumour cell nuclei.\u003csup\u003e14\u003c/sup\u003e The overall accuracy of optimized p53 IHC in predicting \u003cem\u003eTP53\u003c/em\u003e mutation was 0.97 (sensitivity 0.96, specificity 1.00) in ovarian carcinoma and 0.945 (sensitivity 0.95, specificity 0.941) in endometrial cancer\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. However, it has not yet been determined whether this criterion is valid in DLBCL. Therefore, we analyzed 199 DLBCL cases with available NGS and p53 IHC results to evaluated the sensitivity and specificity of IHC in predicting \u003cem\u003eTP53\u003c/em\u003e mutations.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\n\u003ch3\u003e1.Patients\u003c/h3\u003e\n\u003cp\u003eThis study enrolled 436 DLBCL patients newly diagnosed and treated in our hospital between January 2018 to August 2023. The cohort consisted of 227 males and 209 females, with a median age of 60 years (IQR, 50\u0026ndash;69). With a median follow-up of 22.75 months (IQR 17.06\u0026ndash;32.54) for survivors, 90 deaths (20.6%) and 5 (1.1%) losses to follow-up occurred by July 8, 2024. All cases were diagnosed in accordance with 2016 WHO classification of lymphoid neoplasms\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e and underwent NGS, excluding those with other malignancies, HIV infection, pregnancy, primary central nervous system DLBCL, and transformed DLBCL. Response assessment standard by 2014 Lugano criteria for evaluation in Lymphoma\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. OS was defined as the time from diagnosis to death from any cause or the last follow-up. PFS was calculated from the initiation of treatment to disease progression, death, or termination of follow-up. The study was approved by the ethics committee of our hospital.\u003c/p\u003e\n\u003ch3\u003e2. mutations detection method and categorization\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e mutations were detected by NGS in genomic DNA extracted from tumor-containing formalin-fixed paraffin-embedded (FFPE) tissue sections or bone marrow fluid. A 196-gene panel covering lymphoma diagnosis, prognosis, and targeted therapy was analyzed. The types of genetic variants screened included single nucleotide variants (SNVs), small insertions/deletions, copy number variations and \u003cem\u003eBCl2/BCL6/MYC\u003c/em\u003e gene rearrangements. All detected genomic mutations are listed in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/span\u003e. \u003cem\u003eTP53\u003c/em\u003e GOF mutations were defined as any nonsynonymous variants\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e \u003cem\u003eTP53\u003c/em\u003e LOF class mutations were defined as any stopgain, frameshift and splicing mutations\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e3. Immunohistochemistry and p53 expression classifications\u003c/h3\u003e\n\u003cp\u003eFFPE tumor tissues were sectioned at 4 \u0026micro;m for IHC analysis using an automated immunostainer (Benchmark XT system; Ventana Medical Systems, Tucson, AZ) with streptavidin-biotin peroxidase detection. The threshold definitions for high-level expression of MYC, BCL2, and Ki67 were \u0026ge;\u0026thinsp;40%, \u0026gt;\u0026thinsp;50%, and \u0026ge;\u0026thinsp;80%, respectively\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. COO classification was determined by Hans algorithm\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. DEL was defined as when both MYC and BCL2 were highly expressed. Two p53 IHC classifications were evaluated. Classification 1: High-expression (\u0026ge;\u0026thinsp;50%) and the low-expression (\u0026lt;\u0026thinsp;50%) group; Classification 2: CA (\u0026lt;\u0026thinsp;1%), OE (\u0026gt;\u0026thinsp;80%) and WT (1%-80%). CA and OE were classified as the high-risk group of \u003cem\u003eTP53\u003c/em\u003e mutations and WT being classified as the low-risk group.\u003c/p\u003e\n\u003ch3\u003e4. Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eCategorical variables are expressed as frequencies (percentages), compared by chi-squared or Fisher's exact tests. Survival rates were assessed by Kaplan\u0026ndash;Meier analyses and survival curves were compared by the log-rank test. Statistically significant variables (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) with survival in univariate analysis continued to be analyzed by multivariate Cox regression to explore the independent risk factors. The optimal cut-off for VAF was obtained by X-tile software v3.6.1 (Yale University School of Medicine, New Haven, CT, USA) and The optimal cut-off values were determined using a minimal \u003cem\u003eP\u003c/em\u003e value approach\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. A two-sided \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and was considered a statistically significant difference. All statistical analyses were performed with the Statistical Package for the Social Sciences, v. 27.0 (IBM SPSS, Chicago, IL, USA).\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003e1. Characteristics of TP53 mutations in DLBCL patients\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e mutations were identified in 106 patients (24.31%), comprising 131 mutations. Among these, 20 patients harbored multiple-site mutations. There were 113 (92.62%) mutations in the DBD, and the median level of VAF was 30.47% (IQR, 16.18%-55.90%). Missense mutations were the main mutation types (80.92%; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The mutation sites were concentrated in exons 4\u0026ndash;8 (93.02%; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The most common mutations of the p53 protein were located in the 248, 175, 273, 234, 179, 245, and 282 (34.43%; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), all within the DBD and previously reported as cancer hotspot mutations. Detailed mutation information is illustrated in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplemental Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e2. The clinical and pathological features of patients with TP53 mutations.\u003c/h3\u003e\n\u003cp\u003eWe compared the clinical and pathological features between patients with \u003cem\u003eTP53\u003c/em\u003e mutations (mut) and wild-type (wt). (for details, see \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplemental Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No significant differences were observed between the two groups in terms of gender, ECOG performance status, Ann Arbor stage, extranodal involvement, IPI scores, B symptom, bulky disease (\u0026ge;\u0026thinsp;7.5cm), bone marrow infiltration, BCL-2 high expression, Hans classification (GCB vs non-GCB), or Ki67 high-expression. The \u003cem\u003eTP53\u003c/em\u003e mut group exhibited higher proportions of older age (\u0026gt;\u0026thinsp;60 years, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.039) at diagnosis, elevated LDH (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.014), MYC high-expression (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.004), relapsed/refractory disease (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and DEL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) compared to the TP53 wt group.\u003c/p\u003e\n\u003ch3\u003e3. Exploration of treatment for patients with TP53 mutations in DLBCL.\u003c/h3\u003e\n\u003cp\u003eA total of 282 patients treated with R-CHOP or R-CHOP\u0026ndash;like regimen as frontline treatment and underwent efficacy evaluation, including 61 patients in \u003cem\u003eTP53\u003c/em\u003e mut group and 221 in \u003cem\u003eTP53\u003c/em\u003e wt group. The \u003cem\u003eTP53\u003c/em\u003e mut group showed significantly lower CR rates (55.74% vs. 76.47%; \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.001) and OR rates (68.85% vs. 89.59%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to the \u003cem\u003eTP53\u003c/em\u003e wt group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical and pathological features of DLBCL patients \u003cem\u003eTP53\u003c/em\u003e wt, \u003cem\u003eTP53\u003c/em\u003e mut and overall\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003e\u003cem\u003eTP53\u003c/em\u003e wt\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;330)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e mut\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;397)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e169 (51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (54.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e227 (52.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e161 (48.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (45.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e209 (47.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, Median (IQR)\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\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60 (50\u0026ndash;69)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;60yrs, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e175 (53.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (41.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e219 (50.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60yrs, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e155 (47.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (58.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e217 (49.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECOG*\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\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;2, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e251 (76.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74 (69.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e325 (74.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;2, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (30.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111 (25.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDH*, Median (IQR)\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\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e254 (182-424.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevated, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157 (47.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (61.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e222 (50.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enormal, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e173 (52.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (38.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e214 (49.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnn Arbor stage\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\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI-II, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101 (30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (40.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e144 (33.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII-IV, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e229 (69.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (59.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e292 (67.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtranodal involvement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e210 (63.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73 (68.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e283 (64.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;1, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120 (36.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (31.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e153 (35.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPI*\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\u003e0.468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;1, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101 (30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (33.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e136 (31.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (21.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e90 (20.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84 (25.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e106 (24.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026ndash;5, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (22.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (28.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e104 (23.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB symptom\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\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (27.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e120 (27.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e238 (72.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78 (73.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e316 (72.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ebulky disease (\u0026ge;\u0026thinsp;7.5cm)\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\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 (7.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e309 (93.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95 (89.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e404 (92.7)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69 (15.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e277 (83.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90 (84.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e367 (84.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMYC high-expression\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\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157 (51.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70 (68.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e227 (55.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e146 (48.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e179 (44.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCL-2 high-expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e252 (79.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76 (72.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e328 (77.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (27.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95 (22.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDEL*\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\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122 (39.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (51.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e176 (42.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e184 (60.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (48.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e234 (57.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHans classification\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\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCB, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (37.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e145 (33.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enon-GCB, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e223 (68.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 (62.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e289 (66.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKi67\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\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehighly expressed, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e199 (61.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70 (68.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e269 (62.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enon-highly expressed, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e126 (38.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e159 (37.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelapsed/ Refractory disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82 (24.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (41.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e126 (29.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e247 (75.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (58.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e308 (71.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eECOG, Eastern Cooperative Oncology Group performance status; LDH, lactate dehydrogenase; IPI, International Prognostic Index. DEL, double-expressor lymphomas; Data are expressed as n(%) unless otherwise specified.\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 to first-line R-CHOP in \u003cem\u003eTP53\u003c/em\u003e mut vs. \u003cem\u003eTP53\u003c/em\u003e wt patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e mut\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e wt\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34(55.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e169(76.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8(13.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29(13.12%)\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\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42(68.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e198(89.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2(3.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(0.90%)\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\u003ePD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17(27.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21(9.50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e221\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\u003eCR, complete remission; PR, partial response; OR, overall response; SD, stable disease; PD, progressive disease\u003c/p\u003e\u003cp\u003eWe further analyzed the treatment responses in other patients with TP53 mutations who did not receive R-CHOP or R-CHOP-like regimens. 12 patients received R-CHOP combined with azacitidine (AZA 100mg, d1-5, Q21d) (R-CHOP\u0026thinsp;+\u0026thinsp;AZA). 11 patients (91.67%) achieved CR and 1 (8.33%) had PR, yielding a significantly higher CR rate (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) and OR rate (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.029) compared to the R-CHOP group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The PR patient converted to CR after AZA maintenance therapy (100 mg/day, days 1\u0026ndash;5, Q28D). With a median follow-up of 13.07 months (IQR 10.23\u0026ndash;19.77), no progression occurred. Among 11 patients treated with R-CHOP combined with Bruton tyrosine kinase inhibitors (BTKis) (R-CHOP\u0026thinsp;+\u0026thinsp;BTKis), there were 5 CR (45.5%), 4 PR (36.4%), and 2 cases of disease progression. Although the OR rate of 81.82% was higher than that of the R-CHOP group, but the difference did not reach statistically significant (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.491, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The clinical and pathological features of \u003cem\u003eTP53\u003c/em\u003e mut patients treated with R-CHOP, R-CHOP\u0026thinsp;+\u0026thinsp;AZA, and R-CHOP\u0026thinsp;+\u0026thinsp;BTKi were compared and presented in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table S4.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResponse rates in TP53 mut DLBCL: R-CHOP vs R-CHOP\u0026thinsp;+\u0026thinsp;AZA and R-CHOP vs R-CHOP\u0026thinsp;+\u0026thinsp;BTKis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR-CHOP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR-CHOP\u0026thinsp;+\u0026thinsp;AZA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(R-CHOP vs. R-CHOP\u0026thinsp;+\u0026thinsp;AZA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eR-CHOP\u0026thinsp;+\u0026thinsp;BTKis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(R-CHOP vs. R-CHOP\u0026thinsp;+\u0026thinsp;BTKi)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34(55.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11(91.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5(45.45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8(13.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(8.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4(36.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42(68.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9(81.82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2(3.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17(27.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2(18.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eR-CHOP\u0026thinsp;+\u0026thinsp;AZA, R-CHOP combined with azacitidine; R-CHOP\u0026thinsp;+\u0026thinsp;BTKis, R-CHOP combined with Bruton tyrosine kinase inhibitors; CR, complete remission; PR, partial response; OR, overall response; SD, stable disease; PD, progressive disease\u003c/p\u003e\n\u003ch3\u003e4. Prognostic significance of mutations in DLBCL patients\u003c/h3\u003e\n\u003cp\u003e1)Univariate and multivariate analysis\u003c/p\u003e\u003cp\u003eKaplan-Meier analysis with log-rank test revealed that DLBCL patients with \u003cem\u003eTP53\u003c/em\u003e mut had inferior OS and PFS compared to \u003cem\u003eTP53\u003c/em\u003e wt (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). \u003cem\u003eTP53\u003c/em\u003e mut patients had lower 3-year OS (66.0% vs 84.8%) and PFS (58.5% vs 75.8%) than \u003cem\u003eTP53\u003c/em\u003e wt. After adjusting for clinical and pathological features, multivariate Cox regression confirmed \u003cem\u003eTP53\u003c/em\u003e mutations as an independent risk factor for OS and PFS (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with \u003cem\u003eTP53\u003c/em\u003e mutations had a significantly higher risk of death (HR\u0026thinsp;=\u0026thinsp;2.772, 95% CI: 1.745\u0026ndash;4.404; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and disease progression (HR\u0026thinsp;=\u0026thinsp;2.285, 95% CI: 1.533\u0026ndash;3.407; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to \u003cem\u003eTP53\u003c/em\u003e wt (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate analysis of OS and PFS in patients with DLBCL.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eunivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003emultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95.0% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e mutations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.533\u0026ndash;3.407\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;60yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.134\u0026ndash;2.526\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eECOG performance status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.108\u0026ndash;2.392\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eelevated LDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.348\u0026ndash;3.463\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnn Arbor III-IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.301\u0026ndash;5.160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enumber of extranodal sites(\u0026gt;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.987\u0026ndash;2.169\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB symptom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.324\u0026ndash;2.816\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebulky disease(\u0026ge;\u0026thinsp;7.5cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.672\u0026ndash;2.295\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebone marrow infiltration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.306\u0026ndash;3.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMYC high-expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.706\u0026ndash;1.522\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBCL-2 high-expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.845\u0026ndash;2.282\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHans classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKi67 high-expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e mutations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.745\u0026ndash;4.404\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.209\u0026ndash;3.168\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eECOG score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.008\u0026ndash;2.482\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eelevated LDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.130\u0026ndash;3.598\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnn Arbor III-IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.913\u0026ndash;4.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enumber of extranodal sites(\u0026gt;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.042\u0026ndash;2.690\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB symptom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.290\u0026ndash;3.120\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebone marrow infiltration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.126\u0026ndash;3.229\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMYC high-expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.923\u0026ndash;2.494\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBCL-2 high-expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebulky disease(\u0026ge;\u0026thinsp;7.5cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHans classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKi67 high-expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eECOG, Eastern Cooperative Oncology Group performance status; LDH, lactate dehydrogenase; IPI, International Prognostic Index. DEL, double-expressor lymphomas\u003c/p\u003e\n\u003ch3\u003e2) Prognostic effect of mutations on subgroup of DLBCL\u003c/h3\u003e\n\u003cp\u003eTo determine whether \u003cem\u003eTP53\u003c/em\u003e mutations have a poor prognostic effect in all DLBCL patients, we performed subgroup analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-J). Our results show that \u003cem\u003eTP53\u003c/em\u003e mut were significantly associated with worse OS and PFS (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) in the GCB subtype, but not in non-GCB patients (OS: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.258; PFS: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.279; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Additionally, in DEL, \u003cem\u003eTP53\u003c/em\u003e mut correlated with inferior survival compared to \u003cem\u003eTP53\u003c/em\u003e wt (OS: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; PFS: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). Based on \u003cem\u003eTP53\u003c/em\u003e mutations status and DEL classification, patients were categorized into four groups: (1) \u003cem\u003eTP53\u003c/em\u003e wt/non-DEL, (2) \u003cem\u003eTP53\u003c/em\u003e wt/DEL+, (3) \u003cem\u003eTP53\u003c/em\u003e mut/non-DEL, and (4) \u003cem\u003eTP53\u003c/em\u003e mut/DEL+. \u003cem\u003eTP53\u003c/em\u003e wt/non-DEL patients had the best prognosis, whereas \u003cem\u003eTP53\u003c/em\u003e mut/DEL\u0026thinsp;+\u0026thinsp;patients exhibited the worst survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ).\u003c/p\u003e\n\u003ch3\u003e3)Prognostic effect of mutations subsets\u003c/h3\u003e\n\u003cp\u003eWe further performed subgroup analyses of the mutation types and the sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK-V and Supplemental Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The result demonstrated that both single-site and multiple-site mutations, nor GOF and LOF mutations had worse OS and PFS compared to \u003cem\u003eTP53\u003c/em\u003e wt (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Supplemental Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e A-H). However, no significant survival differences were observed between single-site vs. multiple-site mutations (OS: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.602; PFS: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.835; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eL, or between GOF vs. LOF mutations (OS: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.247; PFS: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.237; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eM and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eN). The optimal VAF cut-off determined by X-tile software was 60%. Both patients with VAF\u0026thinsp;\u0026lt;\u0026thinsp;60% (OS: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; PFS: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and VAF\u0026thinsp;\u0026ge;\u0026thinsp;60% (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had worse survival than \u003cem\u003eTP53\u003c/em\u003e wt (Supplemental Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eJ\u0026ndash;N). Notably, the VAF\u0026thinsp;\u0026ge;\u0026thinsp;60% subgroup had significantly poorer outcomes than the VAF\u0026thinsp;\u0026lt;\u0026thinsp;60% subgroup (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eO and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eP). \u003cem\u003eTP53\u003c/em\u003e mutations located in the DBD were associated with inferior survival (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eQ and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eR), whereas non-DBD mutations did not differ from the \u003cem\u003eTP53\u003c/em\u003e wt (OS: \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.790; PFS: \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.461; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eS and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eT). Mutations in exon 5 and 8 had the poorest survival (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eU and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eV).\u003c/p\u003e\n\u003ch3\u003e5. p53 IHC predicts the risk of TP53 mutations\u003c/h3\u003e\n\u003cp\u003eA total of 199 patients underwent NGS and p53 IHC at the same time, NGS confirmed 151 as TP53 wt and 48 as TP\u003cem\u003e53\u003c/em\u003e mut. Using NGS-detected \u003cem\u003eTP53\u003c/em\u003e mutations as the gold standard, we compared the sensitivity and specificity of the two classifications in predicting \u003cem\u003eTP53\u003c/em\u003e mutations (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eand\u003c/span\u003e Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Classification 2 predicted 38 patients as high-risk for \u003cem\u003eTP53\u003c/em\u003e mutations. Among these, 34 patients were \u003cem\u003eTP53\u003c/em\u003e mutations confirmed by NGS. The sensitivity of Classification 2 was 70.83%, which was higher than that of classifications 1 (66.67%), but the difference was not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.660). Classification 2 predicted 161 patients as low-risk for \u003cem\u003eTP53\u003c/em\u003e mutations. Among these, 147 patients were confirmed as \u003cem\u003eTP53\u003c/em\u003e wt by NGS. The specificity of Classification 2 was 97.35%, significantly higher than that of classifications 1 (70.83%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe results of two classifications of p53 IHC in predicting TP53 mutations\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNGS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eClassifications 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eClassifications 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003etotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep53 high expression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep53 low expression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh risk of TP53 mutations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow risk of TP53 mutations\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTP53 mutations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (66.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (33.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (70.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (29.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTP53 wild-type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (18.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123 (81.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (2.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e147 (97.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e191\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe sensitivity and specificity of the two classifications in predicting TP53 mutations.\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\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClassifications 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClassifications 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66.67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70.83%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.660\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e81.46%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97.35%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTumors with \u003cem\u003eTP53\u003c/em\u003e mutations often progress more quickly, have a poor response to treatment, and have a poor prognosis. In our study, the mutation patterns of \u003cem\u003eTP53\u003c/em\u003e in DLBCL were similar to those in solid tumors. Our cohort exhibited a slightly higher rate (24.3%) compared to previous reports\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. On the one hand, it may be related to race and geographical environment. on the other hand, it may be related to the detection method. NGS can detect mutations with lower VAF more sensitively and accurately\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In our study, \u003cem\u003eTP53\u003c/em\u003e mutations were independent prognostic factors for poor OS and PFS. Compared with \u003cem\u003eTP53\u003c/em\u003e wild-type patients, those with \u003cem\u003eTP53\u003c/em\u003e mutations had poorer response to R-CHOP and exhibited a higher proportion of relapsed/refractory disease.\u003c/p\u003e\u003cp\u003eAlthough most studies confirm that \u003cem\u003eTP53\u003c/em\u003e mutations have very poor prognostic significance in DLBCL, the prognostic value of \u003cem\u003eTP53\u003c/em\u003e mutations in different subtypes remains controversial. Xu-Monette et al. found that \u003cem\u003eTP53\u003c/em\u003e mutations predict poor survival in both the GCB and ABC-DLBCL subtypes\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In contrast, studies by Zainuddin et al. suggests that \u003cem\u003eTP53\u003c/em\u003e mutations were associated with poor prognosis in GCB-DLBCL patients, but not in non-GCB-DLBCL patients\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Our findings are consistent with the latter observations. mutant p53 can increase NF-κB activity\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. The unfavorable prognosis of \u003cem\u003eTP53\u003c/em\u003e mutations in non-GCB-DLBCL may be limited, as the NF-κB signaling pathway is activated \u003cem\u003ein this subtype.\u003c/em\u003e The \u003cem\u003eTP53\u003c/em\u003e mut group exhibit a higher proportion of DEL than the \u003cem\u003eTP53\u003c/em\u003e wt group. Notably, even among patients with DEL, \u003cem\u003eTP53\u003c/em\u003e mut group have worse OS and PFS than \u003cem\u003eTP53\u003c/em\u003e wt group. This result suggests that the adverse prognostic impact of \u003cem\u003eTP53\u003c/em\u003e mutations is not solely attributable to the associated higher frequency of DEL.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e mutations have obvious heterogeneity, and different mutation types and locations have different clinical outcomes. DBD is the critical domain for p53 to exert its function. \u003cem\u003eTP53\u003c/em\u003e mutations in the DBD were the strongest predictors of poor OS in DLBCL\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.However, no difference in PFS was observed between DBD and non-DBD \u003cem\u003eTP53\u003c/em\u003e mutations in the Ruijin cohort\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. We found that DBD mutations correlated with inferior survival versus wild-type, whereas non-DBD mutations showed no significant difference. LOF mutations have weaker tumourigenic effects than GOF mutations in genetically engineered mouse models\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In our study, no survival difference were observed between single-site and multiple-site mutations, nor gain-of-function and loss-of-function mutations, while VAF\u0026thinsp;\u0026ge;\u0026thinsp;60% had worse survival than VAF\u0026thinsp;\u0026lt;\u0026thinsp;60%.\u003c/p\u003e\u003cp\u003eThere is no unified standard treatment regimen for DLBCL with TP53 mutations. Previous studies confirmed that R-CHOP regimen does not benefit patients with \u003cem\u003eTP53\u003c/em\u003e mutations\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. BTKis have been preferred as the treatment approach for CLL/SLL patients with \u003cem\u003eTP53\u003c/em\u003e mutations\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. R-CHOP\u0026thinsp;+\u0026thinsp;BTKis failed to show significant efficacy benefits in our DLBCL cohort. DLBCL cells show aberrant DNA methylation promoting progression, treatment resistance, and immune evasion\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that decitabine\u0026thinsp;+\u0026thinsp;R-CHOP may improve the prognosis of DLBCL patients with \u003cem\u003eTP53\u003c/em\u003e mutations\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. In our study, R-CHOP\u0026thinsp;+\u0026thinsp;AZA showed significantly higher CRR and ORR than R-CHOP. Maybe, R-CHOP\u0026thinsp;+\u0026thinsp;AZA can overcome the negative prognostic value of \u003cem\u003eTP53\u003c/em\u003e mutations, but its efficacy still needs to be examined by further expanding the number of cases and extending the follow-up time.\u003c/p\u003e\u003cp\u003eIn our study, we found that both \u003cem\u003eTP53\u003c/em\u003e GOF and LOF mutations had a worse prognosis. This suggests that it is necessary and meaningful to classify complete absence of p53 as a distinct high-risk group of \u003cem\u003eTP53\u003c/em\u003e mutations. We adapted p53 IHC classification method from ovarian and endometrial cancer to enhance \u003cem\u003eTP53\u003c/em\u003e mutations prediction. This classification achieved 70.83% sensitivity and 97.35% specificity. Such high specificity is very significant, that is, it has a very good ability to correctly identify \u003cem\u003eTP53\u003c/em\u003e wt patients and a very low risk of misjudging \u003cem\u003eTP53\u003c/em\u003e mut patients, which ensures the accuracy of diagnosis. p53 IHC is widely available, can be interpreted well by pathologists, and is faster and inexpensive compared to NGS. This enables timely intervention for high-risk \u003cem\u003eTP53\u003c/em\u003e mut DLBCL patients prior to NGS confirmation, potentially improving clinical outcomes. The sensitivity of this classification in DLBCL does not appear to be as high as that in ovarian carcinoma and endometrial cancer. It may be affected by factors such as the sensitivity of the test method and the post-transcriptional regulation\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn conclusion, this study identified \u003cem\u003eTP53\u003c/em\u003e mutations as an independent unfavourable prognostic factor in DLBCL. DLBCL patients with \u003cem\u003eTP53\u003c/em\u003e mutations showed poorer responses to R-CHOP and a higher incidence of relapse or refractory disease. while R-CHOP\u0026thinsp;+\u0026thinsp;A-AZA may potentially adverse outcomes in DLBCL patients with \u003cem\u003eTP53\u003c/em\u003e mutations. p53 IHC with either CA or OE had higher specificity than the high expression, which could more accurately predict the risk of \u003cem\u003eTP53\u003c/em\u003e mutations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThis study was conducted during my research visit at hematological department of the First Affiliated Hospital of Zhejiang University. Thank you Professor Yu Wenjuan for your technical guidance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution(s):\u0026nbsp;\u003c/strong\u003eH.Z. participated in the design of the study, carried out data collection, analysis and interpretation, \u0026nbsp;and drafted and revised the manuscript. X.Z. contributed to the study conception, design, and supervision, and reviewed and revised manuscript drafts. W.Yu. was responsible for conceptualization, funding acquisition, project administration, and manuscript review and editing. H.Tong. contributed to supervision and project administration. J. Wang and J.J. were responsible for data acquisition. X. Zhang. and Y.Lv. performed the statistical analysis. Y.Zhu., X.Ye., J.Wei., M.Y., G.Xu., C.Yang., H.Meng. and W.Xie. participated in data collection. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information:\u003c/strong\u003eFunding was provided by National Natural Science Foundation of China [Grant Numbers 82370151]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eData available in article supplementary material.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study was approved by the Ethics Committee of the First Affiliated Hospital of Zhejiang University School of Medicin. All research was performed in accordance with the Declaration of Helsinki. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThere are no conflicts of interest to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBykov VJN, Eriksson SE, Bianchi J et al (2018) Targeting mutant p53 for efficient cancer therapy. Nat Rev Cancer. 18(2): 89\u0026ndash;102. doi.org/10.1038/nrc.2017.109.\u003c/li\u003e\n\u003cli\u003eHassin O, Oren M (2023) Drugging p53 in cancer: one protein, many targets. Nat Rev Drug Discov. 22(2): 127\u0026ndash;44. doi.org/10.1038/s41573-022-00571-8.\u003c/li\u003e\n\u003cli\u003eWang Z, Strasser A, Kelly GL (2022) Should mutant \u003cem\u003eTP53\u003c/em\u003e be targeted for cancer therapy? 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Epub 2022 Jul 14. PMID: 35835991.\u003c/li\u003e\n\u003cli\u003eHu T, Chitnis N, Monos D etal (2021) Next-generation sequencing technologies: An overview. Hum Immunol. 82(11):801-811. doi: 10.1016/j.humimm.2021.02.012. Epub 2021 Mar 19. PMID: 33745759.\u003c/li\u003e\n\u003cli\u003eZainuddin N, Berglund M, Wanders A et al (2009) \u003cem\u003eTP53\u003c/em\u003e mutations predict for poor survival in de novo diffuse large B-cell lymphoma of germinal center subtype. Leuk Res. 33(1):60-6. doi: 10.1016/j.leukres.2008.06.022. Epub 2008 Aug 15. PMID: 18706692.\u003c/li\u003e\n\u003cli\u003eScian MJ, Stagliano KE, Anderson MA et al (2005) Tumor-derived p53 mutants induce NF-kappaB2 gene expression. Mol Cell Biol. 25(22):10097-110. doi: 10.1128/MCB.25.22.10097-10110.2005. PMID: 16260623; PMCID: PMC1280285.\u003c/li\u003e\n\u003cli\u003eShen R, Fu D, Dong L et al (2023) Simplified algorithm for genetic subtyping in diffuse large B-cell lymphoma. Signal Transduct Target Ther. 8(1):145. doi: 10.1038/s41392-023-01358-y. PMID: 37032379; PMCID: PMC10083170.\u003c/li\u003e\n\u003cli\u003eAhn IE, Farooqui MZH, Tian X et al (2018) Depth and durability of response to ibrutinib in CLL: 5-year follow-up of a phase 2 study. Blood. 131(21):2357-2366. doi: 10.1182/blood-2017-12-820910. Epub 2018 Feb 26. PMID: 29483101; PMCID: PMC5969380.\u003c/li\u003e\n\u003cli\u003eKotlov N, Bagaev A, Revuelta MV et al (2021) Clinical and Biological Subtypes of B-cell Lymphoma Revealed by Microenvironmental Signatures. . Cancer Discov. 11(6):1468-1489. doi: 10.1158/2159-8290.CD-20-0839. Epub 2021 Feb 4. PMID: 33541860; PMCID: PMC8178179.\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":"TP53 mutations, p53, diffuse large B-cell lymphoma, prognosis, R-CHOP combined with azacitidine","lastPublishedDoi":"10.21203/rs.3.rs-7797367/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7797367/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEvidence linking \u003cem\u003eTP53\u003c/em\u003e mutations to survival outcome and treatment efficacy in diffuse large B-cell lymphoma (DLBCL) remains limited. In this retrospective cohort of 436 newly diagnosed DLBCL patients, \u003cem\u003eTP53\u003c/em\u003e mutations were independent prognostic factors for poor overall survival (OS) and progression-free survival (PFS) (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared with \u003cem\u003eTP53\u003c/em\u003e wild-type patients, those with \u003cem\u003eTP53\u003c/em\u003e mutations had poorer response to R-CHOP and higher rates of relapsed/refractory disease (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.001). R-CHOP combined with azacitidine significantly improved efficacy, showing higher complete remission (CR) (91.67% vs R-CHOP, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.023) and overall response (OR) rates (100% vs R-CHOP, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.029). In subgroup analysis, \u003cem\u003eTP53\u003c/em\u003e mutations were associated with unfavorable survival in GCB subtypes, but not in non-GCB subtypes, and it also indicates adverse prognosis in double-expressor lymphomas(DEL). Survival outcomes were worse in patients with Variant Allele Frequency (VAF)\u0026thinsp;\u0026ge;\u0026thinsp;60% and DNA-binding domains (DBD) mutations (particularly exons 5 and 8). No survival difference were observed between single-site and multiple-site mutations, nor gain-of-function (GOF) and loss-of-function (LOF) mutations. In addition, we adapted a p53 immunohistochemical classification from ovarian/endometrial cancer to enhance \u003cem\u003eTP53\u003c/em\u003e mutations prediction: overexpression (\u0026gt;\u0026thinsp;80%) or complete absence (\u0026lt;\u0026thinsp;1%) as high-risk, and heterogeneity (1%-80%) as low-risk. This approach achieved 70.83% sensitivity and 97.35% specificity, with specificity surpassed conventional\u0026thinsp;\u0026ge;\u0026thinsp;50% cutoff (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating enhanced precision in predicting \u003cem\u003eTP53\u003c/em\u003e mutation risk.\u003c/p\u003e","manuscriptTitle":"TP53 mutations predict poor prognosis in diffuse large B-cell lymphoma: A single-center study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 02:12:58","doi":"10.21203/rs.3.rs-7797367/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-23T16:15:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T01:26:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307490782883191271120044503019058919868","date":"2025-10-18T20:19:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T04:19:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145770760658819754261054936229451277854","date":"2025-10-14T00:03:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-13T15:41:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-10T07:32:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-10T07:29:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Hematology","date":"2025-10-07T08:21:06+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":"4da7fbcc-c518-4d47-88f7-d6865acf5abb","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:08:40+00:00","versionOfRecord":{"articleIdentity":"rs-7797367","link":"https://doi.org/10.1007/s00277-026-06821-8","journal":{"identity":"annals-of-hematology","isVorOnly":false,"title":"Annals of Hematology"},"publishedOn":"2026-01-29 15:59:18","publishedOnDateReadable":"January 29th, 2026"},"versionCreatedAt":"2025-10-28 02:12:58","video":"","vorDoi":"10.1007/s00277-026-06821-8","vorDoiUrl":"https://doi.org/10.1007/s00277-026-06821-8","workflowStages":[]},"version":"v1","identity":"rs-7797367","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7797367","identity":"rs-7797367","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}