Dissecting diffuse large B-cell lymphoma heterogeneity: New insights from a state-specific molecular characterisation

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease.

In this study, we analysed a real-world cohort of 178 newly diagnosed DLBCL patients homogeneously treated with R-CHOP-like regimens, integrating RNA sequencing, targeted mutational profiling and digital deconvolution using the EcoTyper algorithm.

Five malignant B-cell states (S1-S5) were identified, each reflecting distinct differentiation and transcriptional programs with prognostic relevance, S1 and S5 representing the most favourable and adverse subtypes, respectively. EcoTyper-defined lymphoma ecotypes (LE1-LE9), capturing co-associations between malignant and microenvironmental cell states, further refined prognostic classification, with LE1/LE2 and LE9 associated with the worst and most favourable prognosis, respectively. Both B-cell states and LE retained independent prognostic value in multivariable analyses after adjusting for the cell of origin and the revised international prognostic index. Mutational profiling revealed state-specific patterns: S1 harboured mutations in genes involved in epigenetic regulation, cytoskeletal organisation and tumour microenvironment (TME) interactions (CREBBP, EZH2 and ACTB), whereas S5 was enriched for mutations promoting survival signalling, immune evasion and differentiation blockade (MYD88, CD79B and TP53). In vitro validation using human five DLBCL cell lines independently recapitulated the S1 and S5 transcriptomic states, supporting the existence of intrinsic, cell-autonomous biological programs. Pathway analysis indicated that S1 was linked to cell adhesion, extracellular matrix remodelling and inflammatory signalling, while S5 displayed enrichment of cell-cycle deregulation, metabolic reprogramming and immune evasion. Accordingly, S1 tumours appear reliant on TME interactions, whereas S5 tumours adopt a predominantly TME independent, intrinsically aggressive phenotype.

Collectively, these findings delineate a biologically coherent framework linking pathway deregulation with cell state and ecotype-specific mutational landscapes, refining DLBCL risk classification and adding an additional dimension to conventional prognostic models.