Abstract
AbstractAbstract 1285 Introduction:Recent studies have now demonstrated that both genic and global hypomethylation characterizes the multiple myeloma (MM) epigenome. We previously conducted a study to measure changes in DNA methylation at approximately 1500 CpG (GoldenGate Methylation BeadArray, Illumina) loci in 193 MM samples used in the Multiple Myeloma Research Consortium Genomics Initiative. The purpose of this study was to determine the extent to which DNA methylation changes in MM correlate to changes at the gene expression level. Methods:To evaluate this correlation we performed three independent analyses. In the first approach we analyzed matching gene expression and DNA methylation data using a Pearson correlation to assess the linear relationship between the datasets. In the second approach methylation and gene expression data from 193 matching samples were quantile-normalized and standardized across genes and samples. Methylation data was then discretized into three groups (methylated, unchanged, unmethylated) according to the mean and standard deviation of the values for each probe on the array. We then computed a t-test to compare the gene expression differences for each gene represented on the methylation array between samples in the methylated and unmethylated groups. We also performed a class enrichment analysis to assess DNA methylation/gene expression correlations that might be associated with hyperdiploidy or molecular subtype. In the third approach we performed RT-PCR and methylation pyrosequencing to validate results in a subset of samples used for the analysis and in an independent cohort comprised of 50 MM samples. Results:By Pearson correlation, only 31 (2.1%) CpG loci (corresponding to 24 unique genes) had a gene expression probe with a statistically significant correlation. Using the discretization approach, we identified 382 loci (25.3%, 309 unique genes) with methylation-expression correlations. Although we identified more correlations by discretizing the data, methylated and unmethylated samples were represented by an average of only 30 and 27 samples per group. We identified correlations where the directionality of methylation and expression values were either in the opposite (negative) or same direction (positive). Among the genes identified, the tumor suppressor genes CDKN2A and DLC1 interestingly demonstrated a positive correlation (methylated and expressed) and the IGF1R and IL17RB genes were negatively correlated (unmethylated and expressed). A class enrichment analysis revealed that samples with DLC1, CDKN2A, IGF1R or IL17RB methylation were associated with hyperdiploid MM. Conversely, samples that were unmethylation for these genes were generally non-hyperdiploid. These findings were validated by RT-PCR and methylation pyrosequencing. Conclusion:Overall, our findings demonstrated, albeit in a limited number of genes, that DNA methylation changes are weakly associated to gene expression. These data suggest that CpG methylation may have other functional consequences such as predisposing the genome to global gene transcriptional changes or chromosomal instability. While future studies are needed to determine the exact role of DNA methylation, we identified a number of genes regulated by an epigenetic mechanism with important clinical and biological implications to MM and warrant further study. Disclosures:No relevant conflicts of interest to declare.
Highlights
Multiple myeloma (MM) is an incurable late-stage plasma cell malignancy which accounts for about 10% of all hematological cancers [1]
The gene expression dataset was downloaded from the Multiple Myeloma Genomics Portal (MMGP; http:// www.broadinstitute.org/mmgp) which was generated as part of the Multiple Myeloma Research Consortium (MMRC) Genomics Initiative
To identify DNA methylation events that correlate to gene expression levels we first computed a Pearson correlation coefficient in a cohort size consisting of 193 MM samples
Summary
Multiple myeloma (MM) is an incurable late-stage plasma cell malignancy which accounts for about 10% of all hematological cancers [1]. Extensive analyses of gene expression profiles, genomic copy number and whole genomic sequencing have provided valuable insights into the molecular basis of MM [1,2,3]. These studies have led to the identification of several genetic and molecular subtypes that are associated with unique clinical and prognostic features. Epigenetic modifications constitute a number of complex and interdependent mechanisms that have become recognized as critical facets of cancer development and progression [5,6]. CpG dinucleotides are largely concentrated in small regions termed ‘‘CpG islands’’, which are found in about
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