Identify truly high-risk TP53-mutated diffuse large B cell lymphoma patients and explore the underlying biological mechanisms

Kai-Xin Du ^1,2,3, Yi-Fan Wu ^1,2,3, Wei Hua ^1,2,3

¹Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China.
²Key Laboratory of Hematology, Nanjing Medical University, Nanjing, 210029, China.
³Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China.
⁴Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China.
⁵Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, OX3 7LE, UK.
⁶Nanjing Geneseeq Technology Inc, Nanjing, Jiangsu, China.
⁷Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China. liangjinhua1990@126.com.
⁸Key Laboratory of Hematology, Nanjing Medical University, Nanjing, 210029, China. liangjinhua1990@126.com.
⁹Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China. liangjinhua1990@126.com.
¹⁰Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China. xuwei10000@hotmail.com.
¹¹Key Laboratory of Hematology, Nanjing Medical University, Nanjing, 210029, China. xuwei10000@hotmail.com.
¹²Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China. xuwei10000@hotmail.com.

⁰Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China.

Cell Communication and Signaling : Ccs +

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August 15, 2024

View abstract on PubMed

Summary

TP53 mutations impact diffuse large B-cell lymphoma (DLBCL) survival. A new TP53 Prognostic Index (TP53PI) model accurately identifies high-risk TP53-mutated DLBCL patients for better stratification.

Area Of Science

Oncology
Genetics
Immunology

Background

TP53 mutations (TP53-mut) are linked to poor outcomes in many cancers, but their prognostic value in diffuse large B-cell lymphoma (DLBCL) remains debated.
Precise risk stratification is crucial for TP53-mut DLBCL patients to guide treatment decisions.

Purpose Of The Study

To develop a robust risk stratification model for TP53-mut DLBCL patients.
To identify specific TP53 mutation subtypes and their impact on prognosis and biological pathways.

Main Methods

Analysis of 2637 DLBCL cases from multiple cohorts.
Construction of a TP53 missense mutation risk model using a machine learning framework.
Development of the TP53 Prognostic Index (TP53PI) model combining clinical data and mutation status.

Main Results

14.0% of DLBCL patients harbored TP53 mutations, with missense mutations being most common.
The machine learning model demonstrated high accuracy in predicting prognosis for TP53-mut DLBCL.
High-risk missense mutations were associated with early progression, immune and metabolic pathway dysregulation, and a suppressed immune microenvironment.

Conclusions

The study successfully identified high-risk TP53-mut DLBCL patients.
The TP53PI model offers improved risk stratification for TP53-mut DLBCL.
Understanding mutation and transcriptional alterations provides insights into disease progression and immune evasion.

Keywords:

TP53 Biological mechanism Diffuse large B cell lymphoma Missense mutation Prognostic index