72 Why TP53 expression varies in lymphoma: Insights from a Bayesian meta-analysis

Objectives/Goals: To quantify between-study heterogeneity in TP53 expression across DLBCL studies using frequentist and Bayesian meta-analytic frameworks; estimate the association between TP53 status and cell-of-origin (GCB vs. non-GCB); and identify methodological and biological sources of variation, using study-level data. Methods/Study Population: Four retrospective study-level datasets (n=457) from France, Canada, Brazil, and the USA were included. TP53 status was primarily assessed by immunohistochemistry (IHC) with study-specific thresholds (20–50% positive cells). Cell-of-origin classification followed the Hans or Choi algorithms as reported by each study. We fit frequentist random effects models with REML (R metafor) and Bayesian hierarchical models (R rstan) Results/Anticipated Results: Frequentist random effects meta-analysis indicated moderate-to-high heterogeneity (I² = 66.7%, p = 0.038) and a pooled odds ratio for GCB among TP53-positive vs. TP53-negative cases of 0.953 (95% CI: 0.682–1.330). The Bayesian hierarchical model estimated P(GCB | TP53+) with a posterior mean of 0.71 (95% CrI: 0.38–0.93); study-level posterior means ranged from 0.37 to 0.53. Both approaches confirm substantial between-study variation attributable to methodological factors (IHC thresholds, COO algorithms) and biological differences Discussion/Significance of Impact: This analysis clarifies the heterogeneity of TP53 expression in lymphoma and shows how methodological and biological variability influence results. Bayesian modeling enhances reliability in small, heterogeneous biomarker syntheses, supporting standardization of TP53 assessment and improving reproducibility in DLBCL.