Gene-regulatory axes of diffuse large B-cell lymphoma subtypes

Abstract Introduction: Diffuse large B-cell lymphoma (DLBCL) is stratified into genetic subtypes (MCD, BN2, A53, N1, EZB, ST2) that differ in their gene expression profiles, oncogenic mechanisms, and response to therapy. Using paired single cell RNA (scRNA) and ATAC (scATAC) sequencing in DLBCL tumors, we previously identified gene expression themes reflecting B cell differentiation, cell growth, and cell cycle that distinguished intratumoral genetic subclones (Wang B, ASH, 2024). Here, we present a global analysis of transcription factor (TF) binding and activity in normal B cells and DLBCL tumors that revealed epigenetic heterogeneity among the DLBCL genetic subtypes, which underpins their divergent therapeutic responses. Methods: Paired scRNA and scATAC sequencing was performed on 102 DLBCL cases (504,444 cells) and 3 tonsils (12,227 cells). Gene expression and genetic subtypes were determined from matched bulk samples by RNA and whole exome sequencing. Computational analysis was performed using R/python and custom bioinformatic pipelines. Results: By linking TF activators (+/+) and repressors (-/+) to target gene expression using SCENIC+ (Bravo Gonzalez-Blas C, Nat Methods, 2023), we identified gene regulatory networks (GRNs) composed of enhancer-driven Regulons (eRegulons). In tonsillar B cell subpopulations, we identified 173 eRegulons that linked TF binding to 9,456 genomic regions and 3,535 target genes. A subset of these eRegulons were differentially active (p<0.05) in germinal center (GC) B cells (FOXO1, MEF2B, EBF1, PAX5, TCF3), plasma cells (PC; IRF4, XBP1, PRDM1) and memory B cells (KLF2, STAT1, ETV6). Importantly, cell lineage analysis traced the activity of these eRegulons along 3 differentiation trajectories stemming from naïve B cells towards either GC dark zone, PC, or memory B cells. Next, we used TF binding to define the epigenetic landscape of the DLBCL genetic subtypes, which could be distinguished from each other using subtype-specific gene expression signatures. Chromatin binding by TFs that regulate PC differentiation (IRF4, POU2F2, TCF4) correlated with the MCD, BN2 and A53 gene expression signatures as well as with gene expression themes reflecting PC differentiation, cell cycle, and cell growth. Binding by another group of TFs (FOXO1, MYBL1, STAT6, PAX5) was associated with the EZB signature and the GC B cell gene expression theme. Binding by BCL6 was anticorrelated with signatures of the N1 subtype and memory differentiation, suggesting that BCL6 antagonizes memory B cell differentiation and the generation of N1 DLBCL. To define GRNs in DLBCL genetic subtypes and genetic subclones, we used SCENIC+ to infer 289 eRegulons, comprised of 12,016 TF binding regions and 4,723 target genes. By integrating the eRegulon RNA and ATAC scores using multiomics factor analysis (Argelaguet R, Genome Biol, 2020), we identified major axes of variation that discriminated both DLBCL subtypes and normal B cell populations. The EZB and ST2 subtypes were significantly associated (p<0.05) with eRegulons that typify normal GC B cells (MEF2B, MEF2C, IRF8, FOXO1). Within these subtypes, subclones with REL amplification had significantly greater activity of a REL +/+ eRegulon than those with wild type REL (p<0.001). The MCD, A53 and BN2 subtypes were enriched (p<0.05) for the IRF4 +/+ eRegulon while MCD was additionally associated (p<0.05) with BATF, SPIB, XBP1 and PRDM1 eRegulons. A TBL1XR1 –/+ eRegulon was significantly associated with the N1 subtype (p<0.001), which is notable given that TBL1XR1 is a tumor suppressor that is frequently inactivated in N1. The subtype-associated eRegulons were also differentially active in normal B cell populations, with several MCD eRegulons active in PCs, N1 eRegulons active in memory B cells, and EZB eRegulons active in GC B cells. Accordingly, eRegulon scores correlated with the B cell differentiation themes across DLBCL subclones. Conclusions: By paired scRNA and scATAC sequencing, we identified GRNs present in normal and malignant B cells that highlight transcriptional states of DLBCL genetic subtypes which vary along three principal differentiation axes – GC B cell, memory B cell and PC. Our analysis illuminates the biological heterogeneity of DLBCL molecular subtypes and offers rationale targets for future therapeutic development.