IDENTIFY TRULY HIGH‐RISK TP53‐MUTATED DLBCL AND EXPLORE THE UNDERLYING BIOLOGICAL MECHANISMS

Abstract

Introduction: TP53 mutation (TP53-mut) correlates with inferior survivals in many cancers, whereas its prognostic role in diffuse large B-cell lymphoma (DLBCL) is still in controversy. TP53-mut is frequently enriched in A53 subtype and also can be found in other gene subtypes. However, A53 subtype which was associated with fatal prognosis cannot be identified using the next-generation sequencing (NGS) which used mostly in clinical practice. Therefore, more precise risk stratification is need to be further explored for TP53-mut DLBCL patients. Methods: The available clinical information and corresponding mutation data of DLBCL were retrieved and obtained from published articles. Ultimately, 2637 DLBCL patients in six cohorts were enrolled in the final analysis. Among the 109 DLBCL patients in the Jiangsu Province Hospital (JSPH) study cohort, all tumor tissue samples were collected to perform NGS while 104 samples were analyzed the gene expression levels using RNA-seq. Results: Among the 2637 DLBCL patients from the integrated cohort, 14.0% patients (370/2637) had TP53-mut. The distribution of mutation events was mainly located in the DNA binding domain (N = 333, 83.3%), containing 34 at Arg248, corresponding to the TP53 hotspots in non-Hodgkin lymphoma described in previous studies (Figure 1A). Compared with TP53 wild type (TP53-wt) patients, significant p value was generated by Breslow test (p = 0.0014), whereas Log Rank test uncovered border-line p value for overall survival (OS) (p = 0.0840). Such a result indicated that adverse survival of TP53-mut patients just occurred during early survival while the 10-year OS was even slightly better than the TP53-wrt patients (Figure 1B). Accordingly, we sought to construct a model to identify the truly high-risk patients. As shown in Figure 1C, three variables (age, international prognostic index score and MCD subtype) retained independent prognostic factors for progression free survival (PFS) and OS in TP53-mut patients. The TP53 prognostic index (TP53-PI) model could significantly distinguish the prognosis of TP53-mut DLBCL patients (p < 0.0001, Figure 1D). To calculate the weight of each selected factor, a nomogram was generated, which was used to predict the survival rates. Differential expression analysis by RNA-seq analysis in JSPH cohort offered insights into the underlying biological mechanisms of poor survivals for high TP53-PI risk DLBCL patients. The immune-associated biological processes occupied a large proportion in the result of Gene Ontology functional enrichment analysis between low and high TP53-PI risk DLBCL patients (Figure 1E). Of note, the TP53-mut group had a unique immune microenvironment. Keywords: aggressive B-cell non-Hodgkin lymphoma, diagnostic and prognostic biomarkers, microenvironment