Hopper, Melissa A

ABSTRACTDiffuse large B‐cell lymphoma (DLBCL) patients that fail to achieve a complete metabolic response with frontline immunochemotherapy have a poor prognosis. Genomic profiling has led to a broader understanding of the molecular drivers in DLBCL, but it is unknown how well current classifiers identify patients that will experience primary treatment resistance (PTR). Using whole exome and RNA sequencing data from newly diagnosed DLBCL patients, we evaluated the genomic landscape of PTR and compared it to that of non‐PTR DLBCL. We found a significant increase in the frequency of TP53 (34% vs. 15%, p = 0.005) and ARID1A mutations (21% vs. 7%, p = 0.007) in PTR cases, with pathway analysis further demonstrating a downregulation of TP53 and an increase in chromatin modifying pathways. These results suggest that TP53 and ARID1A may be key mediators of PTR and important pathways contributing to the poor outcomes. We found that the current molecular classifiers were unable to identify PTR cases at diagnosis. However, our newly identified high‐risk signature identified 46% of PTR cases at diagnosis. Overall, these results contribute to our understanding of the genomic landscape of patients with primary treatment resistance.

Abstract Non‐follicular low‐grade B‐cell lymphomas (LGBCL) are biologically diverse entities that share clinical and histologic features that make definitive pathologic categorization challenging. While most patients with LGBCL have an indolent course, some experience aggressive disease, highlighting additional heterogeneity across these subtypes. To investigate the potential for shared biology across subtypes, we performed RNA sequencing and applied machine learning approaches that identified five clusters of patients that grouped independently of subtype. One cluster was characterized by inferior outcome, upregulation of cell cycle genes, and increased tumor immune cell content. Integration of whole exome sequencing identified novel LGBCL mutations and enrichment of TNFAIP3 and BCL2 alterations in the poor survival cluster. Building on this, we further refined a transcriptomic signature associated with early clinical failure in two independent cohorts. Taken together, this study identifies unique clusters of LGBCL defined by novel gene expression signatures and immune profiles associated with outcome across diagnostic subtypes.