Decoding the Heterogeneity of Diffuse Large B-Cell Lymphomas: A Comprehensive Genetic, Transcriptomic, and Phenotypic Profiling of B-Cell Lymphoma Cell Lines.

Diffuse large B-cell lymphoma (DLBCL) is the most prevalent form of non-Hodgkin lymphoma, exhibiting significant molecular and clinical heterogeneity. Advances in classification integrating phenotypic, genetic, and transcriptomic features have improved diagnosis and prognosis. However, a comprehensive and integrated molecular characterization of DLBCL cell lines is still lacking, which limits their optimal use as reliable experimental models. We employed fluorescence in situ hybridization, immunohistochemistry, and targeted DNA and RNA sequencing to identify genetic subtypes and determine the cell of origin, providing a comprehensive characterization of 29 DLBCL cell lines through the integration of phenotypic, genomic, and transcriptomic data. Principal component analysis, gene set enrichment analysis (GSEA), differential expression profiling, and regulon analysis enabled us to dissect molecular heterogeneity. We achieved high concordance in genetic subtype assignment using multiple classification algorithms (2-S, LymphGen, and DLBclass). The DHIT/DZ signature and transcriptional profiling further revealed additional molecular complexity. Some DLBCL-NOS cases exhibited high-grade features, suggesting that gene expression signatures may capture biological aggressiveness better than cytogenetic methods. GSEA confirmed the relevance of signaling pathways across DLBCL subtypes, and regulatory network analysis identified specific transcription-factor activities that support these pathways. MCD cell lines showed increased NF-κB and STAT signaling, while EZB/MYC+ cell lines demonstrated increased proliferation and cell cycle regulation, along with decreased NF-κB/STAT activity. Our study offers a detailed molecular overview of DLBCL cell lines, underscoring their relevance for mechanistic and therapeutic research. The data highlights how integrating genetic and transcriptomic analyses can refine disease classification and guide personalized therapy strategies.