CpG Methylation Profiling in Multiple Myeloma.
Authors: Salhia Bodour , Baker Angela , Keats Jonathan J , Ahmann Gregory , Auclair Daniel , Mfuko Bunmi , Perkins Louise M. , Levy Joan , Bergsagel Leif , Fonseca Rafael , Carpten John 
Published: October 2019

Abstract
Abstract 604
We have conducted a pilot study to interrogate both CpG hyper- and hypomethylation in Multiple Myeloma (MM) using the Illumina GoldenGate (GG) BeadArrayTM high-throughput assay. Specifically, we utilized the Illumina Methylation Cancer Panel I, which spans 1,505 CpG loci selected from 807 genes including known tumor suppressor genes and oncogenes. To date we have profiled 50 CD138+ MM specimens, 4 MM cell lines and 6 normal CD138+ plasma cell samples (each representing a pool of 3-5 individuals) obtained from the bone marrow of patients who have undergone hip or knee replacement surgeries. We have obtained highly reproducible and reliable DNA methylation profiles with an average r2 of greater than 0.98 when the β values were compared between technical replicates. Further, we generated an internal control panel for the array by performing methylation specific PCR (MSP) to determine the methylation status of several genes found on the array (CDKN2A, E-Cadherin, MGMT, hMLH1) in 4 MM cell lines. A differential methylation analysis was conducted between normal plasma cells and MM samples using the BeadStudio Methylation Module v3.2. Differential scores for each CpG locus on the array were generated that correspond to significant differences between groups. Scores greater than (hypermethylation) or less than (hypomethylation) 13 represent ‘Diff scores' that are statistically significant (p values < 0.05) and this cut-off threshold is used to determine differentially methylated loci.
Globally, when comparing MM to normal plasma cells, hypomethylation occurred at 42% of probes on the array, and was more common than hypermethylation, which occurred at only 21% of probes. Interestingly, 77% of probes situated in non-CpG islands were hypomethylated compared to 26% or probes in CpG islands. On the contrary hypermethylation occurred mainly in CpG islands (26% of probes) whereas only 7.4% of probes in non-CpG islands were considered hypermethylated. A total of 946 CpG loci were differentially methylated between normal plasma cells and MM. Among the most significantly differentially methylated CpG loci between normal plasma cells and MM were FZD9, ALOX12, MMP-3, MMP-8, and p16.
Array comparative genomic hybridization (aCGH) data availabel for this same sample set was used to generate a hyperdiploid index for each sample by identifying those samples with whole chromosome gains at minimally three odd numbered chromosomes. There were 751 CpG loci that were differentially methylated between hypderdiploid and non-hyperdiploid tumors with WEE1, FRZB, ELK1, CLDN4, and SHH being among the most significant. We also applied the pathway enrichment analysis tool GeneGo v5 (Agilent) to these data, which identified cell adhesion/ECM remodeling, developmental pathways and several immune response pathways as the most significantly enriched pathways between normal and plasma cells with ephrin signaling pathways in the context of cell adhesion as the most significantly enriched pathway between hyperdiploid and non-hyperdiploid tumors. We hope to integrate methylation data with orthogonal datasets including gene expression, aCGH, and DNA sequence data being developed as part of the MMRF Genomics Initiative. Further validation of hits will also be followed up by functional studies to assess the true biological relevance of differentially methylated genes.
This study represents one of the first methylome interrogation studies in MM and points towards global hypomethylation as possibly an important mechanism driving myelomagenesis. Our data also suggest that differential methylation of genes may be a significant determinant of hyperdiploid biology. Determining the set of critical genes and pathways based on the myeloma methylome is likely to lead to an improved understanding of biological mechanisms involved in myelomagenesis, ultimately leading to new and improved methods of diagnosing and treating this disease.

Disclosures:
No relevant conflicts of interest to declare.