Comprehensive Driver Mutation Landscape of DLBCL Informed by Integrated Tissue and Circulating DNA Analyses

Diffuse large B-cell lymphoma (DLBCL) is genetically heterogeneous. We aimed to define a compartment-aware driver mutation landscape of DLBCL by integrating tumor tissue and circulating cell-free DNA (cfDNA) sequencing, and to assess its biological and prognostic relevance.

Somatic mutations were analyzed from targeted sequencing of tumor tissue or bone marrow and cfDNA from peripheral blood or cerebrospinal fluid, including paired tissue-liquid samples, together with whole-genome sequencing data from TCGA. Driver genes were identified using four complementary algorithms. Functional enrichment and protein-protein interaction analyses were performed. Prognostic relevance was evaluated using multigene expression-based modeling with Cox and LASSO regression in independent cohorts.

Recurrent driver alterations converged on core pathogenic pathways, including B-cell receptor signaling, NF-κB activation, epigenetic regulation, and immune escape. Twenty-two high-confidence driver genes, including PIM1, KMT2D, CD79B, B2M, TP53, MYC, and EZH2, were consistently identified across clinical cohorts and supported by TCGA data. cfDNA profiling showed substantial concordance with tissue-derived drivers while revealing gene-specific differences in mutation burden and co-mutation patterns, indicating complementary capture of clinically relevant heterogeneity. Network analysis highlighted TP53, MYC, EP300, and CREBBP as central hubs. A four-gene expression-based model (MYC, PLCL1, IRF8, LNPEP) stratified patients by overall survival and modestly improved prognostic discrimination beyond the International Prognostic Index.

Integrated analysis of tumor tissue and cfDNA sequencing defines a clinically relevant driver framework for DLBCL. cfDNA preserves core oncogenic signals while complementing tissue profiling, supporting refined genomic risk stratification.