TP53 突变 (TP53-mut) 与许多癌症的较差生存率相关，而其在弥漫性大 B 细胞淋巴瘤 (DLBCL) 中的预后作用仍存在争议。因此，对于TP53突变的DLBCL患者，需要进一步探索更精确的风险分层。我们的分析纳入了来自多个队列的 2637 例 DLBCL 病例。在2637名DLBCL患者中，14.0%的患者（370/2637）患有TP53突变。由于错义突变占TP53-mut DLBCL患者的绝大多数，并且大多数非错义突变影响P53蛋白的功能，导致生存率较差，因此我们区分了错义突变的患者。基于150个组合的机器学习计算框架构建了TP53错义突变风险模型，在预测预后方面表现出优异的性能。进一步分析显示，具有高风险错义突变的患者与早期进展显着相关，并在转录水平上表现出多种免疫和代谢途径的失调。此外，高危人群表现出绝对抑制的免疫微环境。为了对整个 TP53 突变 DLBCL 队列进行分层，我们结合临床特征，最终构建了 TP53 预后指数 (TP53PI) 模型。总之，我们确定了真正高风险的 TP53 突变 DLBCL 患者，并在突变和转录水平上解释了这种差异。© 2024。作者。

TP53 mutation (TP53-mut) correlates with inferior survival in many cancers, whereas its prognostic role in diffuse large B-cell lymphoma (DLBCL) is still in controversy. Therefore, more precise risk stratification needs to be further explored for TP53-mut DLBCL patients. A set of 2637 DLBCL cases from multiple cohorts, was enrolled in our analysis. Among the 2637 DLBCL patients, 14.0% patients (370/2637) had TP53-mut. Since missense mutations account for the vast majority of TP53-mut DLBCL patients, and most non-missense mutations affect the function of the P53 protein, leading to worse survival rates, we distinguished patients with missense mutations. A TP53 missense mutation risk model was constructed based on a 150-combination machine learning computational framework, demonstrating excellent performance in predicting prognosis. Further analysis revealed that patients with high-risk missense mutations are significantly associated with early progression and exhibit dysregulation of multiple immune and metabolic pathways at the transcriptional level. Additionally, the high-risk group showed an absolutely suppressed immune microenvironment. To stratify the entire cohort of TP53-mut DLBCL, we combined clinical characteristics and ultimately constructed the TP53 Prognostic Index (TP53PI) model. In summary, we identified the truly high-risk TP53-mut DLBCL patients and explained this difference at the mutation and transcriptional levels.© 2024. The Author(s).