Molecular classification and identification of an aggressive signature in low-grade B-cell lymphomas

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.

Automated summary

Non-follicular low-grade B-cell lymphomas (LGBCL) are biologically diverse entities that are difficult to categorize pathologically. By using RNA sequencing and machine learning approaches, this study identified five clusters of patients that were independent of subtype. One cluster had inferior outcome, upregulated cell cycle genes, and increased tumor immune cell content.