Integrative Analysis of Diffuse Large B Cell Lymphoma Mutational Landscape: A Lysa Study

Introduction

Diffuse Large B Cell Lymphoma (DLBCL) is the most common lymphoid malignancy, accounting for 30-40% of all Non Hodgkin Lymphomas. Gene expression profiling (GEP) has identified three main subtypes of DLBCL: Germinal Center B-cell like (GCB), Activated B-Cell like (ABC) and Primary Mediastinal B-cell Lymphoma (PMBL). Recently, Next Generation Sequencing (NGS) has enabled a more detailed characterization of DLBCL mutational profiles. Conventional techniques such as immunohistochemistry (IHC) and FISH are also widely used to describe DLBCL. However, no study has yet performed an integrative analysis of the mutational, gene expression, IHC and FISH profiles of DLBCL, in order to provide a comprehensive view of this disease.

Methods

215 patients with de novo DLBCL in the prospective, multicenter and randomized LNH-03B clinical trials led by the LYmphoma Study Association (LYSA) were included in this study. Microarray-based GEP identified 81 ABC, 83 GCB, 18 PMBL and 33 other. Mutational profiles of patients’ tumor DNA were established using Lymphopanel NGS, designed to identify mutations in 34 genes important for lymphomagenesis. For each recurrently mutated gene, we applied ROMER (Ritchie, Nucleic Acids Res, 2015) to perform gene set enrichment analysis on differential expression profiles of mutant and wild-type patients, using a multifactorial model accounting for subtype. The gene sets were obtained from the MSIGDB Hallmarks (Subramanian A, PNAS, 2005) and Signaturedb (Schaffer, Immunol Rev, 2006) collections. When possible, IHC was performed for IgM (n=150), MYC (n=140), BCL2 (n=148), BCL6 (n=146), CD10 (n=152), FOXP1 (n=147) and MUM1 (n=152); FISH was performed for MYC (n=131), BCL2 (n=133) and BCL6 (n=131).

Results

As expected, EZH2 mutations were significantly associated with upregulation of GCB gene expression (p<10^-3), as well as downregulation of bivalent genes (p<10^-2), H3K27me3 targets (p<10^-2) and GSK343 upregulated genes (p=0.02) (Beguelin et al, Cancer Cell, 2013). IHC and FISH data further cemented EZH2 mutations’ link to GCB subtype, and particularly the t(14;18)-positive subset (CD10+: OR=3.9 and p=0.01, MUM1-: OR=0.12 and p<10^-3, BCL2+: OR=8.1 and p=0.04, BCL2 rearranged: OR=6.1 and p=0.04). BCL2 and CREBBP mutations were also linked to GCB subtype (CD10+ and MUM1-), and BCL2 mutations correlated with double-hit GCB DLBCL (Myc+: OR=6.6 and p<10^-2, MYC rearranged: OR=7.6 and p=0.03, BCL2 rearranged: OR=20 and p<10^-3).

An association between BCL6 translocations and ABC subtype was confirmed, via a correlation with ABC-enriched CD79B mutations (p=0.02), although interestingly not with MYD88 mutations. MYD88 mutations were correlated with an upregulation of genes involved in proliferation or repressed by PRDM1 (FDR=0.04 each), as well as with an upregulation of genes involved in checkpoint controls, such as E2F targets and genes involved in DNA repair (FDR=0.03 each). All MYD88 mutants expressed FOXP1 in IHC (p<10^-3) and MYD88 mutations were also correlated with IgM IHC positivity (OR=3.3 and p<10^-2). TNFAIP3 mutations, also involved in constitutive NFkB activation, were associated with an upregulation of genes regulated by NFkB in response to TNF (FDR=0.02), as well as with an upregulation of KRAS-activated genes (FDR<10^-2).

PMBL-enriched mutations in our cohort were frequently associated with IgM and FoxP1 negative IHC, as expected (WHO 2008 and Roschewski, Nat Rev Clin Onc). XPO1 and ITPKB mutations were correlated with JAK-STAT pathway activation in the total cohort, including upregulation of interferon-inducible genes for both gene mutations (FDR=0.02 and FDR=0.08 respectively) and upregulation of BCL6-repressed genes for XPO1 mutations only (FDR=0.02). Interestingly, CD58 mutations were significantly correlated with upregulation of Nfkb pathway target genes (FDR=0.06), perhaps due to their negative impact on CD2 activation and ROS production inhibition.

Conclusion

The results herein provide steps toward a comprehensive, multi-level overview of DLBCL. We highlight differential gene set expression linked to gene mutation status, as well as driver translocation-associated mutational profiles. By using an integrative analysis approach, this study broadens our understanding of DLBCL subtypes’ diverse genetic backgrounds.