Molecular subclusters of follicular lymphoma: a report from the United Kingdom’s Haematological Malignancy Research Network

•Subtypes of follicular lymphoma are identified by model-based clustering of genetic mutations in a large (n = 548) population-based cohort.•These subtypes imply distinct mechanistic subsets of follicular lymphoma with implications for patient risk and treatment opportunities. [Display omitted] Follicular lymphoma (FL) is morphologically and clinically diverse, with mutations in epigenetic regulators alongside t(14;18) identified as disease-initiating events. Identification of additional mutational entities confirms this cancer’s heterogeneity, but whether mutational data can be resolved into mechanistically distinct subsets remains an open question. Targeted sequencing was applied to an unselected population-based FL cohort (n = 548) with full clinical follow-up (n = 538), which included 96 diffuse large B-cell lymphoma (DLBCL) transformations. We investigated whether molecular subclusters of FL can be identified and whether mutational data provide predictive information relating to transformation. DNA extracted from FL samples was sequenced with a 293-gene panel representing genes frequently mutated in DLBCL and FL. Three clusters were resolved using mutational data alone, independent of translocation status: FL_aSHM, with high burden of aberrant somatic hypermutation (aSHM) targets; FL_STAT6, with high STAT6 & CREBBP mutation and low aSHM; and FL_Com, with the absence of features of other subtypes and enriched KMT2D mutation. Analysis of mutation signatures demonstrated differential enrichment of predicted mutation signatures between subgroups and a dominant preference in the FL_aSHM subgroup for G(C>T)T and G(C>T)C transitions consistent with previously defined aSHM-like patterns. Of transformed cases with paired samples, 17 of 26 had evidence of branching evolution. Poorer overall survival (OS) in the aSHM group (P = .04) was associated with older age; however, overall tumor genetics provided limited information to predict individual patient risk. Our approach identifies 3 molecular subclusters of FL linked to differences in underlying mechanistic pathways. These clusters, which may be further resolved by the inclusion of translocation status and wider mutation profiles, have implications for understanding pathogenesis as well as improving treatment strategies in the future.