SMZL comprises four distinct genetically defined molecular clusters, and two distinct phenotypically defined immune-microenvironment classes
Molecular based nosology of SMZL can improve disease classification and the discovery of novel biomarkers and therapeutic vulnerabilities
Splenic marginal zone B-cell lymphoma (SMZL) is a heterogeneous clinico-biological entity. The clinical course is variable, multiple genes are mutated with no unifying mechanism, essential regulatory pathways and surrounding microenvironments are diverse. We sought to clarify the heterogeneity of SMZL by resolving different subgroups and their underlying genomic abnormalities, pathway signatures and microenvironment compositions to uncover biomarkers and therapeutic vulnerabilities. We studied 303 SMZL spleen samples collected through the IELSG46 multicenter, international study (NCT02945319) by using a multiplatform approach. We carried out genetic and phenotypic analyses, defined self-organized signatures, validated the findings in independent primary tumor meta-data and in genetically modified mouse models, and determined correlations with outcome data. We identified two prominent genetic clusters in SMZL, termed NNK (58% of cases, harboring NF-κB, NOTCH and KLF2 modules) and DMT (32% of cases, with DNA-damage response, MAPK and TLR modules). Genetic aberrations in multiple genes as well as cytogenetic and immunogenetic features distinguished NNK- from DMT-SMZLs. These genetic clusters not only have distinct underpinning biology, as judged by differences in gene-expression signatures, but also different outcome, with inferior survival in NNK-SMZLs. Digital cytometry and in situ profiling segregated two basic types of SMZL immune microenvironments termed immune-suppressive SMZL (50% of cases, associated with inflammatory cells and immune checkpoint activation) and immune-silent SMZL (50% of cases, associated with an immune-excluded phenotype) with distinct mutational and clinical connotations. In summary, we propose a nosology of SMZL that can implement its classification and also aid in the development of rationally targeted treatments.