Molecular Features of Diffuse Large B-Cell Lymphoma Associated With Primary Treatment Resistance

Abstract

Diffuse large B-cell lymphoma (DLBCL) patients that fail to achieve a complete metabolic response with frontline immunochemotherapy have a poor prognosis. Genomic profiling has led to a broader understanding of the molecular drivers in DLBCL, but it is unknown how well current classifiers identify patients that will experience primary treatment resistance (PTR). Using whole exome and RNA sequencing data from newly diagnosed DLBCL patients, we evaluated the genomic landscape of PTR and compared it to that of non-PTR DLBCL. We found a significant increase in the frequency of TP53 (34% vs. 15%, p = 0.005) and ARID1A mutations (21% vs. 7%, p = 0.007) in PTR cases, with pathway analysis further demonstrating a downregulation of TP53 and an increase in chromatin modifying pathways. These results suggest that TP53 and ARID1A may be key mediators of PTR and important pathways contributing to the poor outcomes. We found that the current molecular classifiers were unable to identify PTR cases at diagnosis. However, our newly identified high-risk signature identified 46% of PTR cases at diagnosis. Overall, these results contribute to our understanding of the genomic landscape of patients with primary treatment resistance.

Keywords: genomics; large B‐cell lymphoma; primary refractory DLBCL; primary treatment resistance.

FIGURE 1

Study design. Consort diagram showing flowchart of patients included for analysis. CNA, copy number analysis; CR, complete response; DH, MYC rearrangement with BCL2 or BCL6, “double hit”; DLBCL, diffuse large B‐cell lymphoma; EFS24, event free survival status at 24 months; WES, whole exome sequencing. ¹ Primary refractory defined as SD or PD as best response to frontline therapy.

FIGURE 2

Genetic features of PTR DLBCL. (A) Oncoprint of lymphoma driver genes in PTR, (B) Oncoprint of copy number events in PTR, (C) Forest plot showing enrichment of mutations and copy number events between PTR and non‐PTR cases, and (D) Dot plot showing the percentage of samples which have mutations in the represented pathways. Red dots represent the percentage of samples in PTR while blue dots represent the percentage of cases in non‐PTR. Shown pathways have at least a 1.3 fold increase or decrease between groups. ABC, activated B‐cell subtype; CI, 95% confidence interval; COO, cell of origin; DHL/THL, double hit lymphoma/triple hit lymphoma; DHSig, double hit signature; freq, frequency; GCB, germinal center B‐cell subtype; non‐PTR, non primary treatment resistance cases; OR, odds ratio; PTR, primary treatment resistance.

FIGURE 3

Differential RNA expression in PTR cases. (A) Volcano plot showing differentially expressed genes (FDR < 0.15) between PTR versus non‐PTR cases. Downstream analysis of differentially expressed genes was limited to protein coding genes only. Red dots represent upregulated genes and blue dots represent downregulated genes. Representative examples in known lymphoma driver genes are labeled in gray. (B) GSEA analysis of stromal‐1‐dlbcl survival predictor for PTR versus non‐PTR, and (C) Bar plot showing classification of PTR cases using integrated high‐risk signature. DLBCL, diffuse large B‐cell lymphoma; FDR, false discovery rate; GSEA, gene set enrichment analysis; non‐PTR, non primary treatment resistance cases; PTR, primary treatment resistance; RNAsig, RNA risk signature.

FIGURE 4

DLBCL classification of PTR and non‐PTR. Bar plots showing DLBCL classification using LymphGen, HMRN, Lymphoma microenvironment (LME), Lymphoma Ecotype and B‐cell state classification. not‐PTR, non primary treatment resistance cases; PTR, primary treatment resistance.