Qi, Xinyue

AbstractMeta‐analysis allows researchers to combine evidence from multiple studies, making it a powerful tool for synthesizing information on the safety profiles of new medical interventions. There is a critical need to identify subgroups at high risk of experiencing treatment‐related toxicities. However, this remains quite challenging from a statistical perspective as there are a variety of clinical risk factors that may be relevant for different types of adverse events, and adverse events of interest may be rare or incompletely reported. We frame this challenge as a variable selection problem and propose a Bayesian hierarchical model which incorporates a horseshoe prior on the interaction terms to identify high‐risk groups. Our proposed model is motivated by a meta‐analysis of adverse events in cancer immunotherapy, and our results uncover key factors driving the risk of specific types of treatment‐related adverse events.

Abstract Mantle cell lymphoma (MCL) is an incurable rare subtype of non-Hodgkin lymphoma and is subject to relapse and therapeutic resistance. Molecular aberrations in MCL affect pathogenesis, prognosis, and therapeutic response. In this systematic review, we searched 3 databases and selected 32 articles that described mutations in MCL patients. We then conducted a meta-analysis using a Bayesian multiregression model to analyze patient-level data in 2127 MCL patients, including prevalence of mutations. In tumor or bone marrow samples taken at diagnosis or baseline, ATM was the most frequently mutated gene (43.5%) followed by TP53 (26.8%), CDKN2A (23.9%), and CCND1 (20.2%). Aberrations were also detected in IGH (38.4%) and MYC (20.8%), primarily through cytogenetic methods. Other common baseline mutations were NSD2 (15.0%), KMT2A (8.9%), S1PR1 (8.6%), and CARD11 (8.5%). Our data also show a change in mutational status from baseline samples to samples at disease progression and present mutations of interest in MCL that should be considered for future analysis. The genes with the highest mutational frequency difference (>5%) are TP53, ATM, KMT2A, MAP3K14, BTK, TRAF2, CHD2, TLR2, ARID2, RIMS2, NOTCH2, TET2, SPEN, NSD2, CARD11, CCND1, SP140, CDKN2A, and S1PR1. These findings provide a summary of the mutational landscape of MCL. The genes with the highest change in mutation frequency should be included in targeted next-generation sequencing panels for future studies. These findings also highlight the need for analysis of serial samples in MCL. Patient-level data of prevalent mutations in MCL provide additional evidence emphasizing molecular variability in advancing precision medicine initiatives in MCL.