Global hypomethylation identifies Loci targeted for hypermethylation in head and neck cancer

Abstract

Purpose: The human epigenome is profoundly altered in cancers, with a characteristic loss of methylation in repetitive regions and concomitant accumulation of gene promoter methylation. The degree to which these processes are coordinated is unclear so we investigated both in head and neck squamous cell carcinomas.

Experimental design: Global methylation was measured using the luminometric methylation assay (LUMA) and pyrosequencing of LINE-1Hs and AluYb8 repetitive elements in a series of 138 tumors. We also measured methylation of more than 27,000 CpG loci with the Illumina HumanMethylation27 Microarray (n = 91).

Results: LINE-1 methylation was significantly associated with LUMA and Infinium loci methylation (Spearman's ρ = 0.52/ρ = 0.56, both P < 0.001) but not that of AluYb8. Methylation of LINE-1, AluYb8, and Infinium loci differed by tumor site (each Kruskal-Wallis, P < 0.05). Also, LINE-1 and LUMA methylation were associated with HPV16 E6 serology (each Mann-Whitney, P < 0.05). Comparing LINE-1 methylation to gene-associated methylation, we identified a distinct subset of CpG loci with significant hypermethylation associated with LINE-1 hypomethylation. An investigation of sequence features for these CpG loci revealed that they were significantly less likely to reside in repetitive elements (Gene Set Enrichment Analysis, P < 0.02), enriched in CpG islands (P < 0.001) and were proximal to transcription factor-binding sites (P < 0.05). We validated the top CpG loci that had significant hypermethylation associated with LINE-1 hypomethylation (at EVI2A, IFRD1, KLHL6, and PTPRCAP) by pyrosequencing independent tumors.

Conclusions: These data indicate that global hypomethylation and gene-specific methylation processes are associated in a sequence-dependent manner, and that clinical characteristics and exposures leading to HNSCC may be influencing these processes.

Figure 1

Summary HNSCCs. Red data points indicate tumors common across measurement methods. Black circles represent samples that were not used for all methods, however all tumors were subsets of those measured in the assay were n=138 (levels.

Figure 2

Tumors of different anatomical origin have distinct global methylation profiles. P-values in bold are considered significant by Kruskal-Wallis test (p<0.05). Plots show the effect of tumor site-stratification on percent methylation of (A) LINE-1, (B) AluYb8, (C) LUMA, and (D) averaged Infinium array loci.

Figure 3

Classes of array CpG loci are associated with global methylation levels in tumors but not in normal tissues. RPMM is used to cluster loci with similar methylation β values into sixteen methylation classes. Spearman’s correlation coefficients are calculated by comparing the mean methylation of class member loci for each sample to LINE-1 methylation level and plotted by class. Correlation points are colored according to their mean class methylation (as indicated in the color sidebars). Classes were ordered according to the percentage of their member loci mapping to CpG island regions. A separate RPMM model was applied to the data for each tissue type: (A) HNSCCs, (B) non-diseased head and neck tissues, and (C) normal peripheral blood. Blue shaded regions represent the 95% confidence limits of the observed maximums correlation from 10,000 random permutations (representing the null distribution). Therefore, correlations lying outside of these regions are considered statistically significant. Permutation test omnibus p-values are displayed at the bottom of each panel. Dotted blue lines indicate the zero correlation axis.

Figure 4

Methylation varies with the presence of local structural elements. LINE-1-discordantly methylated loci are enriched for (A) proximity to transcription factor binding sites and (B) CpG islands. Tumor data (for 61 oral/pharyngeal and 15 laryngeal tumors) are stratified by site and the distributions of loci, based on the Spearman correlations between CpG methylation and LINE-1 methylation, are plotted according to their respective kernel density estimations. GSEA permutation test p-values indicated in bold represent significant relationships (p<0.05). Dotted lines indicate the distribution of loci located within the sequence element, while solid lines indicate the distribution of loci located outside of the sequence element.