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

doi:10.21203/rs.3.rs-8231260/v1

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

https://doi.org/10.21203/rs.3.rs-8231260/v1

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Objective: To evaluate the prognostic value of TP53 mutations in patients with diffuse large B-cell lymphoma (DLBCL).

Methods: We retrospectively analyzed the clinical data and gene sequencing results of 253 newly diagnosed DLBCL. Survival and correlation analyses were performed.

Results: We further revealed significant prognostic heterogeneity among different TP53 hotspot mutations, with mutations at codons G245, R175, R273, and R282 indicating a poorer prognosis. Within the DBD, mutations in exons 5, 7, and 8 were associated with poorer PFS, while mutations in exons 5, 6, and 8 were linked to poorer OS. Additionally, mutations in the Loop-L2, Loop-L3, and LSH motifs within the DBD were all significantly associated with unfavorable PFS and OS. Notably, in the cohort treated with R-CHOP plus novel agents (R-CHOP+X), there were no significant differences in response rates or survival between TP53-mut and TP53-wt patients, suggesting this combination may overcome the adverse prognosis associated with TP53 mutations.

Conclusion: TP53 mutation is a crucial adverse prognostic factor in DLBCL. Given the significant prognostic heterogeneity among different TP53 hotspot mutations, a more refined risk stratification based on the TP53 mutational profile is warranted in clinical practice. For patients with high-risk mutations, combining R-CHOP with targeted therapies and exploring novel combination strategies targeting specific pathways are recommended. In contrast, standard R-CHOP may remain an appropriate option for patients with low-risk mutations. Future prospective trials are needed to validate the efficacy of R-CHOP combined with targeted agents in TP53-mutated DLBCL to optimize treatment strategies and improve patient outcomes.

Diffuse large B-cell lymphoma

TP53 mutation

Prognosis

Next-generation sequencing

Precision medicine

TP53 mutations occur in approximately one-third of diffuse large B-cell lymphoma (DLBCL) patients
Missense mutations in the DNA-binding domain are the most common type of TP53 alterations
Specific hotspot mutations (R273, R248, R175) are associated with inferior prognosis
TP53 mutation status provides independent prognostic information beyond the International Prognostic Index
Combined analysis of TP53 mutation and protein expression improves risk stratification

Diffuse Large B-cell Lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL) in adults, accounting for 30–40% of all NHL cases[1, 2]. It is an aggressive malignancy characterized by significant clinical and genetic heterogeneity[3, 4]. Although the majority of DLBCL patients respond well to first-line immunochemotherapy with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) or similar regimens, approximately 30–40% of patients experience relapse or refractory disease[5]. For these relapsed/refractory (R/R) patients, achieving durable remission is challenging, even with second-line or subsequent therapies. Therefore, establishing more precise prognostic models to guide targeted therapy is crucial for improving clinical outcomes in DLBCL.

In recent years, advances in genomic and transcriptomic analysis have led to significant progress in the molecular classification of DLBCL. This has evolved from the initial cell of origin (COO) classification [6] to the current identification of seven genetic subtypes [7], which exhibit distinct prognoses. Among these, the A53 subtype is characterized by key genetic alterations, including TP53 mutations.

The TP53 gene is one of the most frequently mutated genes in human cancer, with sequencing data showing TP53 mutations in approximately half of all cancer cases, though its frequency and distribution vary significantly across tumor types [8]. The p53 protein, encoded by TP53, is a critical tumor suppressor that plays a key role in cellular stress responses by regulating cell cycle arrest, DNA repair, apoptosis, senescence, and autophagy [9, 10]. Previous studies have reported that TP53 mutations occur in about 20–30% of DLBCL patients [11–13] and are strongly associated with a poor prognosis [12, 14, 15]. However, research also indicates that not all TP53 mutations have the same biological effect; the functional consequences of a TP53 mutation may depend on the cellular and genomic context, leading to different clinical outcomes among patients with different mutations [16–18]. Consequently, simply using the TP53 gene status to determine prognosis may be insufficient. The impact of each specific mutation should be evaluated in detail based on its functional implications. This underscores the need for more precise risk stratification of TP53-mutated DLBCL patients to explore effective anti-cancer drugs and treatment regimens aimed at improving their clinical outcomes.

This study aims to systematically analyze the characteristics of TP53 mutations in newly diagnosed DLBCL patients using next-generation sequencing (NGS) of formalin-fixed paraffin-embedded (FFPE) samples and to correlate these findings with clinical outcomes. Our central hypothesis is that different TP53 mutation sites have heterogeneous prognostic impacts in DLBCL. Additionally, we will compare the prognostic differences of various treatment regimens in treatment-naive TP53-mutated DLBCL patients, to provide more instructive evidence for clinical practice.

Study Population

This retrospective study included 253 patients newly diagnosed with DLBCL at Shandong Cancer Hospital and Shengli Oilfield Central Hospital between January 2018 and December 2023. Inclusion criteria were: (1) pathologically confirmed DLBCL; (2) newly diagnosed and previously untreated for DLBCL; and (3) complete next-generation sequencing data available. Exclusion criteria were: (1) primary central nervous system DLBCL; (2) primary mediastinal large B-cell lymphoma; (3) transformed large B-cell lymphoma; and (4) primary cutaneous DLBCL, leg type.

Clinical Data Collection

Detailed clinical characteristics were collected, including gender, age, p53 protein expression, ECOG performance status, Ann Arbor stage, serum lactate dehydrogenase (LDH) level, presence of bulky disease (greatest dimension ≥ 7.5 cm), B symptoms, International Prognostic Index (IPI) score, bone marrow infiltration, and number of extranodal sites. Pathological features (including genetic mutations), treatment regimens (R-CHOP, R-DAEPOCH, R-CHOP + X), and treatment responses were also recorded.

Immunohistochemistry and FISH

Immunohistochemistry (IHC) was performed on all tumor specimens for BCL6, MYC, and BCL2. Double-Expressor Lymphoma (DEL) was defined as C-MYC expression ≥ 40% and BCL2 expression ≥ 50% [19]. p53 IHC staining was performed on FFPE sections using the DO-7 monoclonal antibody (DAKO), with a positivity cutoff of 10% positive cells. Fluorescence in situ hybridization (FISH) was used to detect MYC, BCL2, and BCL6 gene rearrangements. Double-Hit Lymphoma (DHL) was defined as rearrangement of MYC with either BCL2 or BCL6 [19].

Targeted DNA Sequencing

DNA was extracted from FFPE tissue samples using the QIAamp DNA FFPE Tissue Kit (Qiagen, Hilden, Germany). A custom panel of 39 lymphoma-related genes, including TP53, MYD88, CD79B, MYC, and BCL2, was used for detection. Amplicon-based sequencing was performed by Jinan King-Med Diagnostics using the Ion Torrent Proton platform (Thermo Fisher, USA) with a sequencing depth of ≥ 500×. Variants were filtered using the following criteria: single nucleotide variants (SNVs) and insertions/deletions (InDels) with a variant allele frequency (VAF) ≥ 2% were included, while polymorphic sites with a frequency > 5% in population databases (1000G, ExAC) were excluded. The biological significance of all detected mutations was evaluated using the IARC TP53 Database and the UMD TP53 Mutation Database [20, 21]

Follow-up and Response Assessment

Follow-up began at the time of pathological diagnosis and ended in November 2024. Progression-Free Survival (PFS) was defined as the time from the start of treatment to disease progression, relapse, or death from any cause. Overall Survival (OS) was defined as the time from the start of treatment to death from any cause. Treatment response was assessed according to the 2014 Lugano Classification [22] as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD). The best response achieved was used for the final evaluation.

Statistical Analysis

All data were analyzed using SPSS version 26.0. Categorical data were presented as frequencies and percentages and compared using the Chi-square (χ²) test or Fisher's exact test. Survival curves were generated using the Kaplan-Meier method, and differences between groups were compared using the log-rank test. Univariate and multivariate analyses were performed using the Cox proportional hazards model; variables with P < 0.1 in the univariate analysis were included in the multivariate model. A P-value < 0.05 was considered statistically significant.

3.1 TP53 Gene Mutational Spectrum

Among the 253 newly diagnosed DLBCL patients, 89 (35.2%) were found to have TP53 mutations. Two patients harbored two distinct mutations each (H179 and A159; G244 and N235). The majority of mutations were missense (n = 73, 82.0%), with the remainder being non-missense (n = 16, 18.0%), including 10 frameshift, 3 nonsense, 2 splice-site, and 1 in-frame mutation (Fig. 1A). Most mutations (n = 81, 91.0%) were located in the DNA-binding domain (DBD), which encompasses exons 5 to 8. The distribution of mutations across exons 4–10 was as follows: exon 4 (6.7%), exon 5 (22.5%), exon 6 (4.5%), exon 7 (36.0%), exon 8 (28.1%), exon 9 (1.1%), and exon 10 (1.1%) (Fig. 1B). Of the 89 mutations, 66 were located in codons involved in the central core domain's DNA-binding motifs: 26 in Loop-L3 (codons 237–250), which interacts with the minor groove of DNA; 14 in the LSH motif, which interacts with the major groove; and 26 in Loop-L2 (codons 119–135 and 272–287), which enhances DNA-binding affinity. The most common base substitution was G > A transition (n = 40, 44.94%) (Fig. 1C). The highest VAF per sample was analyzed, with 61 patients having a VAF < 50% and 28 patients having a VAF ≥ 50% (Fig. 1D). The most common hotspot codons were R273 (n = 14, 15.7%), R248 (n = 12, 13.5%), R175 (n = 7, 7.9%), R282 (n = 3, 3.4%), C242 (n = 3, 3.4%), G245 (n = 3, 3.4%), Y234 (n = 3, 3.4%), and Y236 (n = 3, 3.4%) (Fig. 1E). R175, R248, R273, R282, and G245 are consistent with previously reported TP53 hotspots in DLBCL and solid tumors [23–25]. The relatively high frequency of C242, Y234, and Y236 mutations suggests they may be novel potential hotspots in DLBCL. In the 5 DHL patients, the most common hotspot was R248 (n = 2, 40%), while in the 38 DEL patients, it was R273 (n = 6, 15.8%) (Supplementary Table 1), suggesting distinct mutational profiles in different molecular subtypes of DLBCL.

3.2 Correlation between TP53 Mutation and Clinical Features

Of the 253 patients, 164 were TP53-wt and 89 were TP53-mut. Significant differences were observed between the two groups regarding age ≥ 60 years (P = 0.023), MYD88 mutation (P = 0.002), CD79B mutation (P = 0.001), ECOG score ≥ 2 (P = 0.005), and bone marrow infiltration (P = 0.045) (Table 1). No significant associations were found between TP53 mutation characteristics (location, type, and VAF) and clinical features (all P > 0.05) (Supplementary Tables 2–4).

Table 1
Clinical characteristics of DLBCL patients with and without TP53 mutation at diagnosis
Characteristics	TP53 wild type(n = 164)	TP53 mutation(n = 89)	p-value
Age,median(range)	56 (10–81)	59 (17–90)	0.246
Age ≥ 60years	57/164 (34.8%)	44/89 (49.4%)	0.023
Gender: Male	80 /164 (48.8%)	51/89 (57.3%)	0.195
MYD88: Mutant	52 /161 (32.3%)	13 /89 (14.6%)	0.002
CD79B: Mutant	42 /161 (26.1%)	8/89 (9.0%)	0.001
Ann Arbor Stage: III-IV	103/163 (63.2%)	54 /87 (62.1%)	0.861
Cell of origin: non-GCB	104/156 (66.7%)	51 /88 (58.0%)	0.175
ECOG ≥ 2	47/150 (31.3%)	44 /89 (49.4%)	0.005
IPI score: 3–5	86/164 (52.4%)	45/86 (52.3%)	0.909
B symptoms	52/163 (31.9%)	26/87 (29.9%)	0.743
LDH>UNV	104/163 (63.8%)	53/89 (59.6%)	0.505
Extranodal involvement>1	119/163 (73.0%)	70 /89 (78.7%)	0.323
Bone marrow infiltration	16/148 (10.8%)	18/89 (20.2%)	0.045
Bulky disease	36/149 (24.2%)	19 /89 (21.4%)	0.618
Double-hit lymphoma	7/119 (5.9%)	5/63 (7.9%)	0.828
Double-expressor lymphoma	59/157 (37.6%)	38/82 (46.3%)	0.19

3.3 p53 Protein Expression Analysis

p53 protein expression was assessed by IHC in 184 patients (62 TP53-mut, 122 TP53-wt). Most TP53-mut cases showed high p53 expression, whereas TP53-wt cases predominantly showed low or moderate expression, with a significant difference between the groups (P < 0.001, Supplementary Fig. 1). However, there was no significant association between p53 expression > 75% and mutation location (P = 0.06), type (P = 0.093), or VAF (P = 0.254) (Supplementary Table 5).

3.4 Prognostic Impact of TP53 Mutation Sites

In the entire cohort, median PFS was 9 months for TP53-mut patients and 72 months for TP53-wt patients. Median OS was 40 months for TP53-mut patients and was not reached for TP53-wt patients. Both PFS (P < 0.0001) and OS (P < 0.0001) were significantly worse for TP53-mut patients (Supplementary Fig. 2). The 2-year PFS rates were 38.2% vs. 67.0%, and 2-year OS rates were 59.6% vs. 86.9% for TP53-mut and TP53-wt patients, respectively. The 5-year rates were 35.9% vs. 50.2% for PFS and 48.8% vs. 66.4% for OS.

However, not all TP53-mut patients had poor outcomes, necessitating a more refined risk stratification. When stratified by common hotspot mutations (C242, G245, R175, R248, R273, R282, Y234, and Y236), there was a significant difference in OS (P = 0.0393) but not PFS (Fig. 2), indicating prognostic heterogeneity. Compared to the TP53-wt group, patients with G245, R175, R248, and R273 mutations had significantly shorter PFS (all P < 0.05), while those with G245, R175, and R282 mutations had significantly shorter OS (all P < 0.05) (Figs. 3 and 4). In contrast, patients with C242, Y234, and Y236 mutations showed no significant difference in OS or PFS compared to the TP53-wt group, with survival curves approaching those of the wild-type group. Based on these outcomes, we classified hotspots into a high-risk group (G245, R175, R248, R273, R282) and a low-risk group (C242, Y234, Y236).

Stratification by mutation characteristics showed no significant impact of mutation location, type, or VAF on PFS or OS (Supplementary Fig. 3). Analysis of individual exons in the DBD revealed that mutations in exons 5, 7, and 8 were associated with worse PFS (Supplementary Fig. 4A, C, E, G), while mutations in exons 5, 6, and 8 were associated with worse OS (Supplementary Fig. 4B, D, F, H). Mutations in the DBD motifs Loop-L2, Loop-L3, and LSH were all significantly associated with poor OS and PFS (Supplementary Fig. 5A-F).

3.5 Impact of TP53 Mutation on Prognosis in Different Treatment Contexts

Among 220 patients (86.96%) who received at least two cycles of induction therapy and had a best response assessment, 130 (81 TP53-wt, 49 TP53-mut) received R-CHOP; 40 (28 TP53-wt, 12 TP53-mut) received R-DAEPOCH; and 50 (31 TP53-wt, 19 TP53-mut) received R-CHOP + X (including R2-CHOP, ZR-CHOP, and OR-CHOP). The overall CR rate for TP53-mut patients was lower than for TP53-wt patients (48.8% vs 68.6%, P = 0.004) (Table 2). In the R-CHOP group, the CR rate was significantly lower for TP53-mut patients (38.8% vs 69.1%, P = 0.001). There was no significant difference in CR rates in the R-DAEPOCH (50.0% vs 67.8%, P = 0.476) or R-CHOP + X groups (73.7% vs 67.7%, P = 0.656). Notably, within the TP53-mut group, the CR rate for R-CHOP + X was significantly better than for R-CHOP and R-DAEPOCH.

Table 2
Response rates of DLBCL after 1st line treatment
Best response	TP53 mutation	TP53 wild type	p-value
			0.004
CR	39/80 (48.8%)	96/140 (68.6%)
No CR	41/80 (51.2%)	44/140 (31.4%)
R-CHOP			0.001
CR	19/49 (38.8%)	56/81 (69.1%)
No CR	30/49 (61.2%)	25/81( 30.9%)
R-DAEPOCH			0.476
CR	6/12 (50.0%)	19/28 (67.8%)
No CR	6/12 (50.0%)	9/28 (32.1%)
R-CHOP + X			0.656
CR	14/19 (73.7%)	21/31 (67.7%)
No CR	5/19 (26.3%)	10/31 (32.3%)

In the R-CHOP group, TP53-mut patients had significantly worse PFS (P < 0.0001) and OS (P < 0.0001) compared to TP53-wt patients (Fig. 5A, B). The 2-year PFS rates were 26.5% vs. 62.5%, and 2-year OS rates were 53.1% vs. 86.5% for TP53-mut vs. TP53-wt, respectively. In the R-DAEPOCH group, TP53-mut patients had significantly worse PFS (P = 0.0323) but not OS (P = 0.1673) (Fig. 5C, D). The 2-year PFS rates were 33.3% vs. 66.7%, and 2-year OS rates were 66.7% vs. 85.2% for TP53-mut vs. TP53-wt, respectively. In the R-CHOP + X group, there were no significant differences in PFS (P = 0.7812) or OS (P = 0.3245) between TP53-mut and TP53-wt patients (Fig. 5E, F). The 2-year PFS rates were 73.7% vs. 82.1%, and 2-year OS rates were 78.9% vs. 96.4% for TP53-mut vs. TP53-wt, respectively. This pattern persisted in the ZR-CHOP subgroup (PFS P = 0.0832; OS P = 0.8903; Supplementary Fig. 6A, B) and when comparing TP53 high-risk hotspot mutations to TP53-wt (PFS P = 0.4593; OS P = 0.1958, Supplemental Fig. 7), suggesting targeted therapy may ameliorate the poor prognosis associated with high-risk mutations.

Fourteen TP53-wt and 5 TP53-mut patients received autologous stem cell transplantation (ASCT). Eight TP53-wt and 6 TP53-mut patients received CAR-T cell therapy. Among patients receiving ASCT/CAR-T, TP53-mut status was associated with significantly worse PFS (P = 0.0047) but not OS (P = 0.5418) (Supplementary Fig. 6C, D).

3.6 Impact of DLBCL Subtypes on Prognosis

In the DEL subgroup, TP53-mut patients had significantly worse PFS (P = 0.0001) and OS (P = 0.0084) than TP53-wt patients (Supplementary Fig. 8A, B). No significant difference was observed in the DHL subgroup, likely due to small sample size (n = 12) (Supplementary Fig. 8C-D). Within the TP53-mut cohort, there were no prognostic differences between DEL vs. non-DEL or DHL vs. non-DHL patients (Supplementary Fig. 9A-D).

3.7 Cox Regression Analysis

Univariate analysis for PFS identified age ≥ 60 years, TP53 mutation, Ann-Arbor stage, ECOG score, IPI score, elevated LDH, and bone marrow involvement as adverse prognostic factors. Multivariate analysis confirmed that TP53 mutation, ECOG score, and IPI score were independent predictors of poor PFS (Supplementary Table 6). For OS, univariate analysis identified age ≥ 60 years, TP53 mutation, ECOG score, and DHL as adverse factors. Multivariate analysis confirmed TP53 mutation, ECOG score, and DHL as independent predictors of poor OS (Supplementary Table 7).

This study systematically analyzed the TP53 mutation status in 253 DLBCL patients, exploring its mutational characteristics and prognostic value. While TP53 mutations are generally associated with poor treatment response and adverse prognosis in most cancers, there is significant heterogeneity in clinical outcomes among different TP53 mutants [17]. Functional assessment of TP53 genetic alterations is crucial for optimizing risk stratification, identifying prognostically distinct subgroups, and guiding individualized therapy for DLBCL patients.

Our study revealed significant site-specific prognostic heterogeneity among TP53 hotspot mutations. The high-risk group (G245, R175, R248, R273, R282) was associated with significantly shorter PFS and OS, whereas the low-risk group (C242, Y234, Y236) showed outcomes statistically similar to TP53-wt patients. This finding aligns with the concept of functional heterogeneity among TP53 mutants. For instance, Blandino et al. showed that specific mutants (e.g., His175, His179) can confer a selective survival advantage during chemotherapy[26]. Young et al. reported that in DLBCL, hotspot codons 158, 175, 245, 248, 273, 280, and 282 were associated with the highest mortality rates [23].

This site-specific prognostic difference may stem from distinct downstream oncogenic pathways activated by different mutants. For example, the R175H mutant has been shown to specifically bind the BACH1/LSD2 complex, promoting tumor metastasis by suppressing ferroptosis via histone demethylation [27]. G245S and R175H can cooperatively lift the transcriptional repression of IL-34, fostering an immunosuppressive microenvironment via a CSC–IL-34–macrophage axis [28]. The R273H mutant can reshape tumor cell glycosylation and metabolism by relieving negative regulation of MGAT4A [29]. These findings suggest that precision targeting of mutant-specific pathways is a promising future direction for treating TP53-mutated DLBCL.

Our findings support a refined risk stratification based on the TP53 mutational profile. Patients in the high-risk group who received R-CHOP plus targeted therapy had survival outcomes comparable to TP53-wt patients, suggesting this combination may mitigate the adverse prognosis. We propose that patients with high-risk mutations should be considered for R-CHOP-based combination therapies and enrollment in trials exploring novel agents targeting pathways like BACH1/LSD2, IL-34, or MGAT4A. In contrast, standard R-CHOP may remain a viable option for patients with low-risk mutations.

Consistent with previous research [30], we found that mutations in key DBD motifs (Loop-L2, Loop-L3, and LSH) were associated with poor prognosis. However, unlike some studies [24], we did not find that mutation type (missense vs. non-missense) or VAF (using a 50% cutoff) could stratify prognosis. This discrepancy with a study by Zhang et al., which found a VAF > 34% to be a poor prognostic marker [31], suggests that the prognostic value of VAF may depend on an optimized cutoff, which requires further investigation in larger cohorts.

The treatment of TP53-mutated DLBCL remains a major clinical challenge. Our results show that neither standard R-CHOP nor intensified R-DAEPOCH regimens significantly improved outcomes for these patients, consistent with other studies [15, 32]. Importantly, in the R-CHOP + X cohort, the survival outcomes of TP53-mut patients were not significantly different from TP53-wt patients, reinforcing the potential of combination strategies.

Bruton's Tyrosine Kinase (BTK) inhibitors like zanubrutinib, which block the essential B-cell receptor (BCR) signaling pathway, are a rational choice, as about 45% of A53-subtype DLBCLs have alterations in the BCR-dependent NF-κB pathway [7, 33]. Our finding of no prognostic difference between TP53-mut and TP53-wt patients in the ZR-CHOP group supports this approach. This aligns with findings in other hematologic malignancies like CLL, where targeted therapies are prioritized for patients with TP53 alterations [34]

While CAR-T cell therapy and ASCT are important salvage options for R/R DLBCL [35, 36], our study suggests that TP53 mutations may limit their efficacy, as evidenced by the significantly lower 2-year PFS rate in TP53-mut patients. This highlights the need for novel strategies, such as CAR-T followed by consolidative ASCT, which has shown promise in improving outcomes for this high-risk population.

In the context of molecular subtypes, our study confirmed that TP53 mutations confer a poor prognosis in DEL, consistent with Meriranta et al[37]. The lack of a significant finding in DHL was likely due to the small sample size. Since there was no prognostic difference between DEL/DHL and non-DEL/non-DHL status within the TP53-mut cohort, TP53 mutation may act as an independent, high-risk prognostic factor.

At the protein expression level, p53 immunohistochemistry (IHC) serves as a surrogate marker for inferring TP53 mutation status, and previous studies have explored various positivity thresholds. Some studies have proposed that p53 expression > 50% or a pattern of either ≥ 65% (overexpression) or < 1% (complete absence) are effective cutoffs [30, 38]. Our study found that in diffuse large B-cell lymphoma (DLBCL), the majority of cases with *TP53* mutations exhibited high levels of p53 protein expression (> 75%). This suggests that the optimal threshold may vary by tumor type and assay conditions, highlighting the need for future standardization in large, multicenter cohorts.

In summary, TP53 mutation is a key adverse prognostic factor in DLBCL. Given the marked prognostic heterogeneity associated with different TP53 hotspot mutations, clinical practice should incorporate refined risk stratification based on the TP53 mutational profile. For patients harboring high-risk mutations (e.g., G245, R175, R248, R273, and R282), the exploration of novel combination therapies targeting relevant pathways is recommended. Conversely, for those with low-risk mutations (e.g., C242, Y234, and Y236), the standard R-CHOP regimen remains a viable treatment option. In the future, more prospective clinical trials are needed to validate the efficacy of R-CHOP combined with targeted agents in TP53-mutated DLBCL. Such studies will be crucial for optimizing current therapeutic strategies to improve patient outcomes and quality of life.

Acknowledgments

We thank the staff of the Department of Hematology at Shandong Cancer Hospital and Shengli Oilfield Central Hospital for their valuable contributions to patient care and data collection.

Funding

This study was supported by grants from the Zhongguancun Precision Medicine Foundation's "New Life" Charity Project (Grant No. [GXZDH48]), the CSCO-Hengrui Oncology Research Fund (Grant No. [Y-HR2020QN-0175]), and the China Health & Medical Development Foundation (Grant No. [chmdf2025-xrky04-09]).

Author information

Contributions

Conception and design: Ji Ma and Liang Wang

Acquisition of data: Jiayi Yu, Kuntong Liu and Rong Xie

Analysis and interpretation of data: Jiayi Yu and Qiang He

Drafting of the manuscript: Jiayi Yu

Critical revision of the manuscript for important intellectual content: Yuting Yan

Statistical analysis: Jiayi Yu

Obtained funding: Ji Ma

Administrative, technical, or material support: Liang Wang

Supervision: Liang Wang

Corresponding author

Correspondence to Ji Ma and Liang Wang

Ethics declarations

Ethics approval

This study was approved by the Ethics Committee of Cancer Hospital of Shandong First Medical University (Approval No. SDTHEC2024006130) and Shengli Oilfield Central Hospital (Approval No. YXLL202504501). Given the retrospective design of the study and the use of archived specimens, the ethics committee waived the requirement for written informed consent.

Competing interests

The authors declare no competing interests.

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

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Abstract

Figures

Key Points

1. INTRODUCTION

2. METHODS

3. RESULTS

3.1 TP53 Gene Mutational Spectrum

3.2 Correlation between TP53 Mutation and Clinical Features

3.3 p53 Protein Expression Analysis

3.4 Prognostic Impact of TP53 Mutation Sites

3.5 Impact of TP53 Mutation on Prognosis in Different Treatment Contexts

3.6 Impact of DLBCL Subtypes on Prognosis

3.7 Cox Regression Analysis

4. DISCUSSION

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1