DNA Methylation Epitypes of Burkitt Lymphoma with Distinct Molecular and Clinical Features

Abstract

The genetic subtypes of Burkitt lymphoma have been defined, but the role of epigenetics remains to be comprehensively characterized. We searched genomic DNA from 218 patients across four continents for recurrent DNA methylation patterns and their associations with clinical and molecular features. We identified DNA methylation patterns that were not fully explained by the Epstein-Barr virus status or mutation status, leading to two epitypes described here as HypoBL and HyperBL. Each is characterized by distinct genomic and clinical features including global methylation, mutation burden, aberrant somatic hypermutation, and survival outcomes. Methylation, gene expression, and mutational differences between the epitypes support a model in which each arises from a distinct cell of origin. These results, pending validation in external cohorts, point to a refined risk assessment for patients with Burkitt lymphoma who may experience inferior outcomes.

Significance: Burkitt lymphoma can be divided into two epigenetic subtypes (epitypes), each carrying distinct biological, transcriptomic, genomic, and clinical features. Epitype is more strongly associated with clinical and mutational features than the Epstein-Barr virus status or genetic subtype, highlighting an important additional layer of Burkitt lymphoma pathogenesis.

Figure 1.

Burkitt lymphoma (BL) genomes contain global demethylation and localized hypermethylation. A, Global averaged methylation of normal centroblast (n = 6) and EBV-positive BL (n = 130) samples. Methylation values from individual EPIC array probes were binned into 1-Mbp windows, and the average of each bin is shown on a heat scale. Data are arranged in concentric rings starting from the outermost ring: normal centroblasts, the difference in methylation levels between EBV-positive BL and centroblasts, and EBV-positive BL. B, Global averaged methylation of normal centroblast (n = 6) and EBV-negative BL (n = 88) samples. Methylation values from individual EPIC array probes were binned into 1-Mbp windows, and the average of each bin is shown on a heat scale. Data are arranged in concentric rings starting from the outermost ring: normal centroblasts, the difference between EBV-negative BL and normal centroblasts, and EBV-negative BL. C, A comparison of the number of significant DMPs (|abs(logFC)|> 0.25 and Q < 0.05) from Illumina EPIC array that were hypermethylated and hypomethylated among each EBV-positive BL (n = 130) and EBV-negative BL (n = 88) samples when compared with normal centroblast (n = 6) samples. D, The chromatin state associated with the DMPs in (C) are shown as a total number of probes with DMP in BL vs. normal centroblasts. E, The averaged methylation of samples across DMRs shared between EBV-positive (n = 130) and EBV-negative (n = 88) samples when compared with normal centroblast (n = 6) samples. CNV, copy-number variation; Heterochrom/lo, heterochromatin/low signal; Txn, transcriptional.

Figure 2.

Methylation patterns in Burkitt lymphoma (BL) are associated with EBV status. A, The chromatin state associated with significant DMPs from the EPIC array that were hypermethylated and hypomethylated from comparing EBV-positive (n = 130) against EBV-negative (n = 88) BLs shown as a number of all significant DMPs. The number of EPIC array probes contained within each chromatin region that was differentially methylated between BL and centroblasts (CB) (from Fig. 1C) is displayed as the background reference. B, The averaged methylation of samples across DMRs identified from the EPIC array comparing EBV-positive (n = 130) and EBV-negative (n = 88) BLs. Normal centroblast samples (n = 6) are shown as the reference. C, The heatmap on the left represents averaged methylation of samples across DMRs identified from the EPIC array comparing EBV-positive (n = 130) and EBV-negative (n = 88) BLs that were significantly (R² > 0.4 and Q < 0.01) correlated with gene expression as quantified by RNA-seq analysis. The heatmap on the right indicates the expression of genes associated with each DMR. Rows in both heatmaps are in the same order with each row depicting a DMR and the expression of its associated gene. CNV, copy-number variation; Heterochrom/lo, heterochromatin/low signal; neg, negative; pos, positive; Txn, transcriptional.

Figure 3.

Identification of distinct epitypes in Burkitt lymphoma. A, Heatmap depicting the optimal 60 probes from the EPIC array which achieved the greatest accuracy for classifying samples into one of the two epitypes (LPS class). Samples are ordered on the basis of the probability score associated with belonging to the HyperBL epitype. Alluvial plots showing the distribution of epitype membership by EBV status (B), age group (C), and genetic subgroup (D). All 218 samples with the available EPIC array profiling are included in the analysis.

Figure 4.

Burkitt lymphoma (BL) epitypes have distinct methylation patterns. A, Global averaged methylation of normal centroblast (n = 6) and HyperBL (n = 102) samples. Methylation values from individual EPIC array probes were binned into 1-Mbp windows and the average of each bin is shown on a heat scale. Data are arranged in concentric rings starting from the outermost ring: normal centroblast, the difference in methylation levels between HyperBL and centroblast, and HyperBL samples. B, Global averaged methylation of normal centroblast (n = 6) and HypoBL (n = 88) samples. Methylation values from individual EPIC array probes are binned into 1-Mbp windows and the average of each bin is shown on a heat scale. Data are arranged in concentric rings starting from the outermost ring: normal centroblast, the difference in methylation levels between HypoBL and centroblast, and HypoBL samples. C, The chromatin state associated with significant DMPs from the EPIC array that were hypermethylated and hypomethylated from comparing HyperBL (n = 102) against HypoBL (n = 88) samples is shown as a number of all significant DMPs (|abs(logFC)|> 0.25 and Q < 0.05). The number of EPIC array probes contained within each chromatin region that were differentially methylated between BL and centroblasts (CB) (from Fig. 1C) is displayed as the background reference. D, The averaged methylation of samples across DMRs identified from the EPIC array comparing HyperBL (n = 102) and HypoBL (n = 88). Normal centroblast samples (n = 6) are shown as the reference, and unclassified samples (n = 28) are shown for comparison. E, The heatmap on the left represents averaged methylation of samples across DMRs identified from the EPIC array comparing HyperBL (n = 102) and HypoBL (n = 88) that were significantly correlated with gene expression. The heatmap on the right indicates the expression of the genes associated with each DMR. Rows in both heatmaps are in the same order with each row depicting a DMR and the expression of its associated gene. Unclassified samples (n = 28) are included for comparison. F, Dot plot depicting the top 15 enriched families of TFs with binding sites identified within the DMRs based on epitype. The x-axis depicts the different families of TFs, and the y-axis depicts the −log₁₀(P value) of each TF. CNV, copy-number variation; Heterochrom/lo, heterochromatin/low signal; Txn, transcriptional.

Figure 5.

Mutational profiles associated with the Burkitt lymphoma (BL) epitypes. A, Oncoplot of coding mutations identified by WGS in the genes determined to be associated with BL (29) and their occurrence across epitypes. The percentages on the left indicate the frequency of coding mutations of each specific gene across all samples. Each column of the oncoplot represents an individual sample. The mutations are colored according to their type. Gray tiles on the oncoplot represent the absence of mutations of the specific gene. Samples (n = 190) within each epitype are ordered on the basis of EBV status to highlight key differences. B, Box and whisker plots showing the mutation burden across HyperBL (n = 102), HypoBL (n = 88), and unclassified (n = 28) samples. In order from left to right the plots show the total number of coding mutations, driver mutations, and genome-wide mutation load as identified from the WGS. Samples were subjected to the Wilcoxon rank-sum test. C, Estimated number of mutations per COSMIC signatures SBS1, SBS5, and SBS9 in BL tumors (n = 218) stratified by epitype. Each point represents individual sample, and the y-axis shows the total number of mutations associated with that signature. Samples were subjected to the Wilcoxon rank-sum test. *, P < 0.05; **, P < 0.01; ***, P < 0.001 (P values at or above 0.05 are not shown).

Figure 6.

Patterns of gene expression associated with epitype membership. A, The heatmap displays the 213 differentially expressed genes between epitypes, with rows representing differentially expressed genes and columns representing samples. Rows and columns are clustered using Manhattan distances. The epitype membership and EBV status of each sample is shown at the top. Samples are split on the basis of epitype with HyperBL (n = 102) cases on the left, HypoBL (n = 88) cases in the middle, and unclassified (n = 28) cases on the right. B, Heatmap representing the expression of 32 gene sets from SignatureDB with significant (Q ≤ 0.0025) differences between epitypes. Samples from 218 patients were clustered and ordered on their expression of genes within each gene set. Rows represent the gene sets and columns represent samples. Row and columns were clustered using Manhattan distances.

Figure 7.

Survival outcomes of adult patients with Burkitt lymphoma (BL) stratified by epitype. Kaplan–Meier survival analyses were conducted within the adult cases for whom the follow-up data were most complete. Patients with HyperBL were compared with those with HypoBL to analyze progression-free survival (A) and overall survival (B), including the unclassified cases. All times are shown in years. A log-rank P value is shown, and pairwise comparisons were conducted separately across the indicated groups. The risk tables show the number of patients at the specified timepoint.