High-Resolution, Genome Wide Analysis of DNA Methylation Provides Insights Into the Epigenetic Architecture of t(4;14) Myeloma.

Abstract 2385

The t(4;14) group in multiple myeloma (MM) is associated with a significantly impaired prognosis based on the overexpression of MMSET, a histone methyltransferase and epigenetic modifier that is thought to play a major role in myeloma genesis. We have previously shown that t(4;14) myelomas are associated with a specific pattern of DNA hypermethylation using Illumina HumanMethylation27 BeadChip arrays. These results were interpreted as being consistent with that MMSET overexpression leads to specific changes in the epigenetic architecture of MM. However, the detailed mechanism underlying this remains unclear partly due to the low resolution of methylation array technology used. In order to address this we have performed high-resolution genome-wide analyses of DNA methylation for t(4;14) and t(11;14) samples from patients with myeloma, plasma cell leukemia (PCL) and myeloma cell lines (HMCL) using methyl binding domain next generation sequencing (MBD-seq) in order to define DNA methylation patterns specific for t(4;14) MM, as well as to analyse methylation changes accompanying the progression from MM to PCL at a high-resolution, genome-wide scale.

DNA from 6 myeloma patients at diagnosis [3 MM with t(4;14), 3 MM with t(11;14)], 6 patients with PCL [3 PCL with t(4;14), 3 PCL with t(11;14)] and 6 HMCL [3 with t(4;14), 3 non-t(4;14)] was fragmented. Methylated DNA fractions were captured using biotin-labelled methyl-binding domain 2 (MBD2) protein. Captured sequences bound to MBD were washed and eluted using elution buffers with increasing salt concentrations. Eluted fragments were purified and sequenced on an Illumina GAIIx, using 1.5 lanes per patient sample, generating 36 bp single-end reads. On average, 1.4 Gbases of reads were generated per sample. Sequences were aligned and de-duplicated using stampy and bwa algorithms. The reference genome (build hg19) was divided into overlapping bins of 200 bp (termed probes) and short read coverage per bin was normalised to per million reads aligned. Differentially methylated regions were defined by comparing normalised reads per probe between the t(4;14) and the t(11;14) groups for MM, PCL and HMCL groups.

We first compared probe values that were higher in all three t(4;14) MM samples compared to the three t(11;14) samples. About 16500 probe values were higher in t(4,14) cases compared the t(11;14) group, whereas only 470 probes values were higher in all t(11;14) cases compared to t(4;14) cases. This confirms our previous observation that t(4;14) MM cases are characterised by pronounced hypermethylation. Of the 16500 probes values higher in t(4;14), about 9500 probes mapped to gene bodies and 600 to gene promoters, affecting in total about 1600 genes, indicating that gene or gene regulatory sequence hypermethylation is a common feature in t(4;14). Gene set enrichment analyses of these genes demonstrated highly significant enrichment of KEGG pathways ‘pathways in cancer’, ‘cell adhesion molecules’, the GO term ‘cell development’, among others, and an overrepresentation of probes mapping to chromosomal regions on chromosome 1q. When comparing the progression from MM to PCL, about 2600 genomic probe values were higher in all 3 t(11;14) PCL vs all 3 t(11;14) MM and 1600 probes in all 3 t(4;14) PCL vs MM, indicating that hypermethylation from MM to PCL is more pronounced in t(11;14) than in t(4;14). Very few differences in probe values were present when comparing all 6 MM (both t(4;14) and t(11;14)) with the 6 PCL samples, indicating that the epigenetic mechanisms involved in progression from MM to PCL might be different between the cytogenetic subgroups. Enrichment of methylated sequences was strong for both translocation groups when comparing PCLs with HMCLs, demonstrating that the epigenetic architecture of HMCLs differs significantly even from late-stage patient tumour material.

This genome-wide methylation analysis provides us with candidate genes that are likely to be directly or indirectly epigenetically modified by MMSET. We are integrating this methylation data with gene expression data to identify expression-methylation correlations. Furthermore, additional experiments using MMSET knockout models will be used to further filter MMSET-specific effects on genome wide methylation. Finally, we go on to define epigenetic markers that could serve as biomarkers for future epigenetic therapies targeting epigenetic modifiers in t(4;14) myeloma.