Molecular diagnosis and outcome prediction in lymphoma

Division Of Basic Sciences - Nci

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Abstract

Previously, we profiled 574 diffuse large B-cell lymphoma (DLBCL) biopsies using whole exome sequencing, transcriptome sequencing, targeted amplicon sequencing and DNA copy number analysis to explore the genetic substructure of this heterogeneous cancer. We defined 6 genetic subtypes of DLBCL (MCD, BN2, A53, N1, GCB, ST2) by the pattern of co-occurring genetic aberrations, and showed that they differ profoundly in their oncogenic mechanisms and response to chemotherapy and targeted therapies. Recently we have extended this analysis by profiling DLBCL tumors using paired single cell RNA (scRNA) and ATAC (scATAC) sequencing, resulting in transcriptome and chromatin profiles of over 500,000 cells from 103 DLBCL biopsies and 3 human tonsil specimens. In parallel, we sequenced the exomes of these tumors, allowing us to assign them to genetic subtypes using the LymphGen algorithm that we developed. One important observation was that the malignant cells from each of the LymphGen subtypes could be distinguished from the other genetic subtypes by characteristic gene expression signatures, which we could validate using transcriptome data from 2 other large DLBCL cohorts. The gene expression signatures illuminated regulatory pathways and B cell differentiation differences between the genetic subtypes, demonstrating phenotypic differences that help to explain their divergent responses to therapy. In addition, we conducted 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 responses therapy. We computationally linked TF activators and repressors to expression of their direct genomic target genes, allowing us to define gene regulatory networks (GRNs) composed of enhancer-driven Regulons (eRegulons). Among these eRegulons, we identified major axes of variation that discriminated both DLBCL subtypes and normal B cell populations. The EZB and ST2 subtypes were significantly associated 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. The MCD, A53 and BN2 subtypes were enriched for the IRF4 eRegulon while MCD was additionally associated with BATF, SPIB, XBP1 and PRDM1 eRegulons. A TBL1XR1 eRegulon was significantly associated with the N1 subtype, 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. The key TFs that dictate the phenotypes of the genetic subtypes are rational therapeutic targets. Indeed, we showed that the drug lenalidomide is effect in MCD DLBCL models because it downmodulates expression of IRF4, which controls a prominent eRegulon in this genetic subtype.