Prognostic Landscape of TP53 Mutations in Hematologic Malignancies

Seo Yoon Jang; Joowon Jang; Jee-Soo Lee; Moon-Woo Seong; Songyi Park; Ja Min Byun; Youngil Koh; Junshik Hong; Inho Kim; Sung-Soo Yoon; Dong-Yeop Shin

doi:10.4143/crt.2025.297

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Original Article Hematologic malignancy Prognostic Landscape of TP53 Mutations in Hematologic Malignancies: Seo Yoon Jang¹, Joowon Jang², Jee-Soo Lee², Moon-Woo Seong^2,3, Songyi Park⁴, Ja Min Byun^1,3, Youngil Koh^1,3,5, Junshik Hong^1,3,5, Inho Kim^1,3, Sung-Soo Yoon^1,3,5, Dong-Yeop Shin^1,3,5; Cancer Research and Treatment : Official Journal of Korean Cancer Association 2026;58(2):656-663.
DOI: https://doi.org/10.4143/crt.2025.297
Published online: May 27, 2025

¹Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea

²Department of Laboratory Medicine, Seoul National University Hospital, Seoul, Korea

³Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea

⁴Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Korea

⁵Center for Medical Innovation, Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea

Correspondence: Dong-Yeop Shin, Department of Internal Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea
Tel: 82-2-2072-7209 E-mail: shindongyeop@snu.ac.kr

• Received: March 15, 2025 • Accepted: May 21, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

Purpose
While tumor protein p53 (TP53) mutations are well known to be associated with adverse prognosis in hematological diseases, their functional impact remains incompletely understood. This study examines the spectrum of TP53 mutations across various hematologic malignancies and evaluates their functional impact.
Materials and Methods
Using targeted sequencing panels, we analyzed TP53 mutations in the bone marrow aspiration samples of a retrospective cohort of 856 patients diagnosed with hematologic malignancies. To assess the impact of TP53 mutations, we applied the evolutionary action (EAp53) score and the relative fitness score (RFS), previously proposed functional scoring methods. The effects of variant allele frequency (VAF), disruptive mutations, EAp53 score, and RFS on overall survival (OS) were evaluated.
Results
TP53 mutations were associated with inferior OS compared with wildtype TP53 (median OS, 10.0 months vs. not estimable; hazard ratio [HR], 4.6; p < 0.001). In the acute myeloid leukemia, multiple myeloma, and myelodysplastic syndrome subgroups, TP53 mutations had a significant adverse impact on OS (HRs, 3.8, 4.2, and 6.0, respectively; p < 0.001, p=0.005, and p < 0.001, respectively). Patients with VAF > 50% had significantly poorer OS compared to those with VAF ≤ 50% (median OS, 7.5 months vs. 22.8 months; HR, 2.2, p=0.016). Moreover, patients in the high-risk RFS group (RFS > 0.22) had significantly worse OS compared to those in the low-risk RFS group (RFS ≤ 0.22) (median OS, 5.6 months vs. 16.3 months; HR, 2.2; p=0.041). However, no significant survival difference was observed between the EAp53 high-risk (> 75) and low-risk (≤ 75) groups, or between patients with disruptive and non-disruptive mutations.
Conclusion
Our findings highlight VAF and RFS as valuable tools for stratifying TP53-mutant patients into high-risk and low-risk groups.
Key words: Hematologic neoplasms, Genes, p53, Sequence analysis, DNA

Introduction

The tumor protein p53 (TP53) gene, which encodes the p53 transcription factor, is the most commonly mutated gene in human cancers [1]. First described in 1979, TP53 was the first tumor suppressor gene to be identified. Initially, p53 was considered to function as an oncogene, but a decade after its discovery, genetic studies revealed it to be a tumor suppressor gene.

Under normal physiological conditions, p53 expression is kept low primarily through degradation by E3 ubiquitin ligases, MDM2, Pirh2, and COP1 [2]. When cells are exposed to stress such as hypoxia and DNA damage, p53 is post-translationally modified through phosphorylation and acetylation and stabilized. As a result, the p53 protein accumulates in the nucleus triggering activation of downstream pathways of tumor suppressive mechanisms, including cell cycle arrest, DNA repair, senescence, and apoptosis [3]. Loss of the p53 function through mutations or deletions impairs the cell’s ability to regulate growth and apoptosis, contributing to tumorigenesis.

The p53 protein consisted of five main domains: (1) N-terminal domain, (2) central DNA-binding domain (DBD), (3) oligomerization domain, (4) proline-rich domain, and the (5) C-terminal domain [4]. While tumor suppressor genes are often inactivated through frameshift or deletions, p53 is most commonly inactivated by missense mutations in the coding region.

TP53 mutations have been observed in more than 50% of all human cancers, primarily in solid cancers. In hematologic malignancies, TP53 mutations are less prevalent [5,6]. In multiple myeloma, the frequency of TP53 mutations is reported to be 5%-8% [7,8], and in the later stages of the disease, it increases to 25% in plasma cell leukemia [9]. The prevalence in acute myeloid leukemia (AML) is around 8%, as well [10].

Several studies have reported a relationship between TP53 aberrations and aggressive disease, chemotherapy resistance, and adverse prognosis in hematologic diseases [11,12]. However, due to the low prevalence of TP53 mutations in these malignancies compared to solid tumors, research on their prevalence and impact is limited.

In this study, we aimed to investigate the spectrum of TP53 mutation across various hematologic malignancies and assess the prognostic significance of different types of TP53 mutations.

Materials and Methods

1. Study design and patients

This was a retrospective study including patients diagnosed with hematologic malignancies between January 2018 and September 2022 at Seoul National University Hospital in the Republic of Korea. Eligible patients were aged 18 years or older and were diagnosed with a hematological malignancy including the following: AML, chronic myeloid leukemia, acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia, multiple myeloma, amyloidosis, myelodysplastic syndrome (MDS), myeloproliferative neoplasm (including essential thrombocytopenia, polycythemia vera, myelofibrosis), chronic myelomonocytic leukemia, lymphoma, or other leukemia.

2. Genetic testing of TP53

Bone marrow aspiration samples were stored at room temperature and processed for DNA extraction on the day of collection or the following day. Genomic DNA was then extracted using the QIAamp DNA Blood Mini Kit (Qiagen) according to the manufacturer’s instructions, and tested for TP53 mutations using two types of targeted sequencing panels. Samples from 2018 to 2020 were evaluated with an in-house panel covering 76 genes, while samples from 2021 to 2022 were analyzed with a custom ArcherDX VariantPlex panel (ArcherDX Inc.) covering 104 genes. Germline TP53 testing was not performed.

3. Disruptive and non-disruptive mutations

We analyzed the location of TP53 mutations and the amino acid alteration in our cohort, classifying mutations as either “disruptive” or “non-disruptive”. The prognostic value of disruptive mutations has been demonstrated in head and neck cancers [13]. Mutations classified as “disruptive” included (1) non-conservative mutations that replace an amino acid with one from a different polarity or charge category, particularly within the DBD (L2-L3 region, codons 163-195 or 236-251) and (2) stop codons in any region of the gene, resulting in premature protein truncation. Non-disruptive mutations were defined as conservative mutations or non-conservative mutations occurring outside the DBD, excluding stop codons.

4. Evolutionary action score and relative fitness score

The evolutionary action score (EAp53) and relative fitness score (RFS) were investigated in our cohort. EAp53 is a computational tool that accounts for evolutionary sensitivity and is designed to quantify the deleterious impact of different missense TP53 mutations [14]. Initially validated in head and neck cancer, EAp53 ranges from 0 to 100, with higher scores indicating greater adverse impact and 0 representing wildtype TP53. RFS was developed to measure the functional impact of TP53 mutations located in the DBD in human cells in vitro and in vivo [15]. A high RFS indicates a growth advantage in culture and higher fitness, while a low RFS indicates preferential depletion. We obtained the RFS for DBD TP53 mutations in the AMLSG cohort from the online data resource (GSE115072). We conducted a time-dependent receiver operating characteristic (ROC) analysis to assess RFS in predicting 2-year mortality. Using Youden’s index [16], we determined the optimal threshold and classified patients as high- or low-risk.

5. Statistical analysis

The impact of variant allele frequency (VAF), disruptive/non-disruptive, EAp53, and RFS on overall survival (OS) was assessed. Cox regression analyses were performed to determine hazard ratios (HRs) with 95% confidence intervals (CIs) for TP53 wildtype versus mutant in the overall population and within each disease subgroup with at least 10 mutant patients. OS was compared among different TP53 functional groups using the Kaplan-Meier method and the log-rank test. Comparisons of continuous variables were performed using the Wilcoxon rank-sum test. Two-sided p-values ≤ 0.05 were considered statistically significant. All statistics were performed using R version 4.4.1 (R Foundation for Statistical Computing).

Results

1. Patient cohort and baseline characteristics

A total of 856 patients were included in the study and the baseline characteristics are presented in Table 1. The median age at diagnosis was 62.4 years (range, 18.8 to 97.0 years) and 475 patients (55.5%) were male. Among the 856 patients, 243 (28.4%) were diagnosed with AML, and 158 (18.5%) with multiple myeloma. TP53 mutations were detected in 70 patients (8.2%). Of the 48 patients diagnosed with lymphoma, 42 (87.5%) had bone marrow involvement.

2. TP53 mutation frequency and type

The frequency of TP53 mutation in each hematologic disease is shown in Fig. 1A. The highest frequency was observed in MDS (14.3%), followed by AML (12.8%) and lymphoma (10.4%). No TP53 mutations were identified in chronic lymphocytic leukemia and amyloidosis. The median TP53 mutation VAF was 48.9 (range, 2.4 to 93.5). The distribution of TP53 VAF across each hematologic disease is shown in Fig. 1B. Excluding chronic myeloid leukemia, which had only one TP53 mutant case, MDS and AML had the highest median VAFs (MDS, 58.2; range, 24.0 to 91.4; AML, 52.1; range, 2.7 to 93.5). Among lymphoid malignancies, the median VAFs of ALL (median VAF, 45.8; range, 32.5 to 68.9) and lymphoma (median VAF, 44.1; range, 33.5 to 67.7) approached 50%, while that of multiple myeloma was less than 20% (median VAF, 15.7; range, 2.4 to 53.0).

The most common TP53 mutations were missense mutations inside the DBD, accounting for 81.3% of all mutations. Additionally, 8.0% were missense mutations outside the DBD, 6.7% were frameshift or indels, 2.7% were nonsense mutations, and 1.3% were copy number variations (Fig. 2).

3. TP53 wildtype versus mutant

Patients with TP53 mutations had significantly poorer OS compared to those with wildtype TP53 (median OS, 10.0 months vs. not estimable; HR, 4.6; p < 0.001) (Fig. 3). Within the AML, multiple myeloma, and MDS subgroups, the deleterious effect of TP53 mutation on survival was evident, with the MDS subgroup showing the highest HR (HR, 6.0; 95% CI, 2.7 to 13.2) (Fig. 4).

The Revised International Prognostic Scoring System (IPSS-R) risk categories were identified in the MDS subgroup, and dichotomized into higher-risk (high, very high) and lower-risk (very low, low, intermediate) groups. Notably, no TP53 mutant case was observed in the lower-risk group. To better assess the prognostic impact of TP53 mutations in MDS, patients were stratified by IPSS-R risk category and TP53 mutational status into three groups: higher-risk MDS, TP53 mutant (n=16), higher-risk MDS, TP53 wildtype (n=29), and lower-risk MDS, TP53 wildtype (n=67). Among these, the higher-risk MDS, TP53 mutant group had the shortest median OS of 8.3 months and a significantly increased risk of death, with an HR of 11.6 compared to the lower-risk, wildtype group (Fig. 5).

4. TP53 mutation VAF

Patients were dichotomized into two groups based on a TP53 mutation VAF threshold of 50%, with 32 patients in the VAF > 50% group and 37 patients in the VAF ≤ 50% group. The VAF > 50% group had a significantly poorer OS compared to the VAF ≤ 50% group (median OS, 7.5 months vs. 22.8 months; HR, 2.2; p=0.016) (Fig. 6A).

5. RFS high-risk versus low-risk

The RFS was identified for 59 patients with TP53 mutations inside the DBD. ROC analysis revealed an area under curve of 0.74 and identified an optimal RFS threshold of 0.22 for predicting 2-year mortality (S1 Fig.). Based on this threshold, we classified the patients into RFS high-risk (RFS > 0.22) and low-risk (RFS ≤ 0.22) groups, with 15 patients in the high-risk group and 44 in the low-risk group. Patients in the high-risk RFS group had significantly poorer OS compared to those in the low-risk RFS group (median OS, 5.6 months vs. 16.3 months; HR, 2.2; p=0.041) (Fig. 6B).

6. EAp53 and disruptive mutations

The EAp53 were available for 64 patients, with 36 classified into the high-risk (EAp53 > 75) group and 28 into the low-risk (EAp53 ≤ 75) group. No significant difference in OS was observed between the EAp53 high-risk and low-risk groups (Fig. 6C).

A total of 20 patients were classified into the disruptive mutation group, and 52 into the non-disruptive mutation group. Similarly, there was no significant difference in OS between the disruptive and non-disruptive groups (Fig. 6D).

Discussion

Our study demonstrated that TP53 mutation VAF and RFS are strong predictors of survival outcomes in patients with hematologic malignancies. Patients with TP53 VAF ≤ 50% and RFS < 0.22 had median OS values of 22.8 months and 16.3 months, respectively, both of which were superior to the median OS of the overall TP53 mutant cohort (10.0 months). While TP53-mutated hematologic diseases are generally associated with poor prognosis, we identified a subset of patients with relatively favorable outcomes. VAF and RFS may serve as a valuable tool for identifying patients with TP53 mutations who might still benefit from intensive treatment approaches.

Our results align with previously published data, which indicate that RFS is a valuable tool for distinguishing between low-risk and high-risk AML, although the AML-specific threshold used in that study was –0.135 [17]. Additionally, the impact of TP53 mutation VAF on clinical outcomes was evaluated in a prior study of AML patients, which reported that TP53 VAF > 40% was independently associated with significantly higher relapse rates, worse relapse-free survival, and poorer OS [18].

In our study, VAF > 50% effectively stratified patients according to survival outcomes. A VAF > 50% suggests a possible loss of heterozygosity (LOH) in TP53. LOH refers to the loss of one allele of the gene, either through deletion or uniparental disomy, in a cancer cell that was originally heterozygous, carrying both a wildtype and a mutant allele. As a result of LOH, TP53 gene inactivation occurs through a two-hit mechanism. A high VAF may also reflect a germline TP53 mutation, but in the absence of germline testing in our study, we were unable to differentiate between somatic and germline origins in these cases. Studies on the prognostic impact of biallelic TP53 alterations have shown conflicting results. Bernard et al. [19] reported that multiple hits of TP53, caused by either LOH or multiple mutations, were associated with adverse clinical outcomes in a cohort of MDS patients. However, in patients with AML or high-risk MDS with excess blasts, TP53 allelic status was found to be less predictive of clinical outcomes [20].

The International Consensus Classification, revised in 2022, introduced a minimum TP53 VAF cutoff of 10% for the diagnosis of AML, highlighting the influence of TP53 VAF on the current diagnosis of AML/MDS [21]. The influence of TP53 VAF on the prognosis of MDS patients has been explored in previous studies. Sallman et al. [22] identified TP53 VAF as a critical determinant of prognosis in MDS patients, proposing a VAF threshold of 20% as the optimal cutoff for stratifying survival. Pellagatii et al. [23] demonstrated that TP53 VAF showed its largest increase at the time of progression to AML or higher-risk disease. Our results show that TP53 VAF values vary among different hematologic malignancies. Unlike in myeloid malignancies, where VAF values tend to be higher, patients with multiple myeloma have lower VAF values. A low VAF may indicate a TP53 minor clone or TP53-mutated hematopoietic stem cells, but the inability to distinguish between the two is a limitation of our study [24]. Another limitation is the retrospective design and the lack of consideration for the differing prognoses among various hematologic diseases. However, the strength of our study is that we assessed the functional impact of TP53 in a large cohort of hematologic patients.

Discrimination of TP53 mutations into disruptive and non-disruptive did not show a statistically significant impact on survival in our patients, despite its demonstrated prognostic relevance in head and neck cancer patients [13]. Similarly, while the EAp53 score was a successful tool for predicting clinical prognosis of head and neck cancer patients harboring TP53 mutations [14], it did not reveal a survival difference among hematologic patients in our cohort. These findings, which contradict the well-documented prognostic relevance in patients with head and neck cancers warrant further investigation into their disease-specific implications of TP53 mutations.

The frequency of TP53 mutation was higher in MDS (14.3%) than in AML (12.8%), despite AML generally being associated with worse outcomes. This difference may be attributable, at least in part, to age, as patients with MDS in our cohort were significantly older than those with AML (median age 64.7 years versus 61.6 years; p=0.016).

The association between TP53 mutations and resistance to conventional cytotoxic chemotherapy is well established in AML and other malignancies [25,26]. Even when responses are observed, they have shown limited correlation with improved OS. In the VIALE trial, which compared the combination of azacitidine and venetoclax with the single-agent azacitidine, response rates were similar between TP53 mutant and wildtype patients. However, survival was markedly poor in the TP53 mutant group [27].

Currently, no targeted therapy has been identified as effective against TP53 mutant clones. One of the most extensively studied agents for TP53 mutant MDS and AML is APR-246 (eprenetapopt), a p53 reactivator. While phase II trials combining APR-246 with azacitidine demonstrated high complete response rates of 40%-50% and a median OS of 11.8 months [28], the combination failed to meet its primary endpoint in the phase III trial. Another targeted agent investigated for TP53-mutated MDS/AML patients is magrolimab, an anti-CD47 antibody. However, studies of magrolimab failed to show improved activity in TP53-mutated MDS and AML, and the agent was placed under a full clinical hold due to increased risk of death. Emerging therapies, such as the bispecific CD123×CD3 agent (MGD024) [29] and the CD123-targeting antibody-drug conjugate (pivekimab sunirine) [30], have shown efficacy in pre-clinical or early-phase clinical trials.

The treatment of TP53-mutated hematologic malignancies remains a significant clinical challenge, with current therapeutic options yielding suboptimal outcomes. Our findings underscore the potential for TP53 VAF and RFS as pan-hematologic prognostic biomarkers. As not all TP53 mutations confer uniformly poor outcomes, functional characterization of TP53 alterations may refine risk stratification and identify prognostically distinct subgroups within this high-risk population, ultimately guiding tailored treatment strategies.

Electronic Supplementary Material

Supplementary materials are available at Cancer Research and Treatment website (https://www.e-crt.org).

crt-2025-297_S1_Fig.pdf

NOTES

Ethical Statement

This retrospective study was approved by the institutional review board of Seoul National University Hospital (IRB number H-2312-052-1491). All procedures in this study were in accordance with the Declaration of Helsinki and ethical standards of the Institutional Review Board (IRB). The requirement for informed consent was waived by the IRB because this was a retrospective study with minimal risk.

Author Contributions

Conceived and designed the analysis: Jang SY, Shin DY.

Collected the data: Jang SY, Shin DY.

Contributed data or analysis tools: Jang SY, Jang J, Lee JS, Seong MW, Park S, Byun JM, Koh Y, Hong J, Kim I, Yoon SS.

Performed the analysis: Jang SY.

Wrote the paper: Jang SY, Shin DY.

Conflicts of Interest

Conflict of interest relevant to this article was not reported.

Fig. 1.

TP53 frequency and the distribution of TP53 variant allele frequency (VAF). (A) TP53 mutation frequency. (B) Distribution of TP53 VAF of each hematologic disease. ALL, acute lymphocytic leukemia; AML, acute myeloid leukemia; CML, chronic myeloid leukemia; CMML, chronic myelomonocytic leukemia; MDS, myelodysplastic syndrome; MM, multiple myeloma; MPN, myeloproliferative neoplasm.

Fig. 2.

Proportion of TP53 mutation types. CNV, copy number variation; DBD, DNA-binding domain.

Fig. 3.

Kaplan-Meier analysis of overall survival (OS) grouped by TP53 mutational status. CI, confidence interval; NE, not estimable; OS, overall survival.

Fig. 4.

Forest plot of hazard ratio (HR; dot) and 95% confidence interval (CI; line) for overall survival according to each hematologic disease. AML, acute myeloid leukemia; MDS, myelodysplastic syndrome.

Fig. 5.

Overall survival (OS) of myelodysplastic syndrome (MDS) patients grouped by revised International Prognostic Scoring System (IPSS-R) higher/lower-risk and TP53 mutational status. CI, confidence interval; NE, not estimable. The IPSS-R risk categories were dichotomized into higher-risk (high, very high) and lower-risk (very low, low, intermediate) groups.

Fig. 6.

Comparison of overall survival (OS) between functional groups: (A) OS in variant allele frequency (VAF) > 50% and VAF ≤ 50% groups; (B) OS in relative fitness score (RFS) high-risk and low-risk groups; (C) OS in EAp53 high-risk and low-risk groups; (D) OS in disruptive and non-disruptive groups. CI, confidence interval; EAp53, evolutionary action score; NE, not estimable.

Table 1.

Baseline characteristics

	Total (n=856)
Age at diagnosis (yr)	62.4 (18.8-97.0)
Age group (yr)
18-39	96 (11.2)
40-59	264 (30.8)
60-79	436 (50.9)
≥ 80	60 (7.0)
Sex
Male	475 (55.5)
Female	381 (44.5)
Diagnosis
Acute myeloid leukemia	243 (28.4)
Chronic myeloid leukemia	36 (4.2)
Acute lymphocytic leukemia	56 (6.5)
Chronic lymphocytic leukemia	26 (3.0)
Multiple myeloma	158 (18.5)
Amyloidosis	8 (0.9)
Myelodysplastic syndrome	112 (13.1)
Myeloproliferative neoplasm^a), chronic myelomonocytic leukemia	143 (16.7)
Lymphoma	48 (5.6)
Other leukemia^b)	26 (3.0)
TP53 mutational status
Wildtype	786 (91.8)
Mutant	70 (8.2)

Values are presented as median (range) or number (%).

^a) Myeloproliferative neoplasm includes polycythemia vera, essential thrombocythemia, and myelofibrosis,

^b) Other leukemia includes rare leukemias, such as natural killer cell leukemia, mixed phenotype acute leukemia, plasma cell leukemia, T-large granular lymphocytic leukemia, and hairy cell leukemia.

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Prognostic Landscape of TP53 Mutations in Hematologic Malignancies

Cancer Res Treat. 2026;58(2):656-663. Published online May 27, 2025

DOI: https://doi.org/10.4143/crt.2025.297

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Abstract
Introduction
Materials and Methods
Results
Discussion
Electronic Supplementary Material
NOTES
REFERENCES

Genomic Landscape of Pulmonary Sarcomatoid Carcinoma

Prognostic Landscape of TP53 Mutations in Hematologic Malignancies

Fig. 1. TP53 frequency and the distribution of TP53 variant allele frequency (VAF). (A) TP53 mutation frequency. (B) Distribution of TP53 VAF of each hematologic disease. ALL, acute lymphocytic leukemia; AML, acute myeloid leukemia; CML, chronic myeloid leukemia; CMML, chronic myelomonocytic leukemia; MDS, myelodysplastic syndrome; MM, multiple myeloma; MPN, myeloproliferative neoplasm.

Fig. 2. Proportion of TP53 mutation types. CNV, copy number variation; DBD, DNA-binding domain.

Fig. 3. Kaplan-Meier analysis of overall survival (OS) grouped by TP53 mutational status. CI, confidence interval; NE, not estimable; OS, overall survival.

Fig. 4. Forest plot of hazard ratio (HR; dot) and 95% confidence interval (CI; line) for overall survival according to each hematologic disease. AML, acute myeloid leukemia; MDS, myelodysplastic syndrome.

Fig. 5. Overall survival (OS) of myelodysplastic syndrome (MDS) patients grouped by revised International Prognostic Scoring System (IPSS-R) higher/lower-risk and TP53 mutational status. CI, confidence interval; NE, not estimable. The IPSS-R risk categories were dichotomized into higher-risk (high, very high) and lower-risk (very low, low, intermediate) groups.

Fig. 6. Comparison of overall survival (OS) between functional groups: (A) OS in variant allele frequency (VAF) > 50% and VAF ≤ 50% groups; (B) OS in relative fitness score (RFS) high-risk and low-risk groups; (C) OS in EAp53 high-risk and low-risk groups; (D) OS in disruptive and non-disruptive groups. CI, confidence interval; EAp53, evolutionary action score; NE, not estimable.

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

Fig. 6.

Prognostic Landscape of TP53 Mutations in Hematologic Malignancies

	Total (n=856)
Age at diagnosis (yr)	62.4 (18.8-97.0)
Age group (yr)
18-39	96 (11.2)
40-59	264 (30.8)
60-79	436 (50.9)
≥ 80	60 (7.0)
Sex
Male	475 (55.5)
Female	381 (44.5)
Diagnosis
Acute myeloid leukemia	243 (28.4)
Chronic myeloid leukemia	36 (4.2)
Acute lymphocytic leukemia	56 (6.5)
Chronic lymphocytic leukemia	26 (3.0)
Multiple myeloma	158 (18.5)
Amyloidosis	8 (0.9)
Myelodysplastic syndrome	112 (13.1)
Myeloproliferative neoplasm^a), chronic myelomonocytic leukemia	143 (16.7)
Lymphoma	48 (5.6)
Other leukemia^b)	26 (3.0)
TP53 mutational status
Wildtype	786 (91.8)
Mutant	70 (8.2)

Table 1. Baseline characteristics

Values are presented as median (range) or number (%).

Myeloproliferative neoplasm includes polycythemia vera, essential thrombocythemia, and myelofibrosis,

Other leukemia includes rare leukemias, such as natural killer cell leukemia, mixed phenotype acute leukemia, plasma cell leukemia, T-large granular lymphocytic leukemia, and hairy cell leukemia.