Identification and functional validation of HPV-mediated hypermethylation in head and neck squamous cell carcinoma - PubMed (original) (raw)
doi: 10.1186/gm419. eCollection 2013.
Tim Fenton 2, James West 2, Gareth Wilson 2, Andrew Feber 2, Stephen Henderson 2, Christina Thirlwell 2, Harpreet K Dibra 2, Amrita Jay 3, Lee Butcher 2, Ankur R Chakravarthy 2, Fiona Gratrix 2, Nirali Patel 2, Francis Vaz 4, Paul O'Flynn 4, Nicholas Kalavrezos 4, Andrew E Teschendorff 2, Chris Boshoff 2, Stephan Beck 2
Affiliations
- PMID: 23419152
- PMCID: PMC3706778
- DOI: 10.1186/gm419
Identification and functional validation of HPV-mediated hypermethylation in head and neck squamous cell carcinoma
Matthias Lechner et al. Genome Med. 2013.
Abstract
Background: Human papillomavirus-positive (HPV+) head and neck squamous cell carcinoma (HNSCC) represents a distinct clinical and epidemiological condition compared with HPV-negative (HPV-) HNSCC. To test the possible involvement of epigenetic modulation by HPV in HNSCC, we conducted a genome-wide DNA-methylation analysis.
Methods: Using laser-capture microdissection of 42 formalin-fixed paraffin wax-embedded (FFPE) HNSCCs, we generated DNA-methylation profiles of 18 HPV+ and 14 HPV- samples, using Infinium 450 k BeadArray technology. Methylation data were validated in two sets of independent HPV+/HPV- HNSCC samples (fresh-frozen samples and cell lines) using two independent methods (Infinium 450 k and whole-genome methylated DNA immunoprecipitation sequencing (MeDIP-seq)). For the functional analysis, an HPV- HNSCC cell line was transduced with lentiviral constructs containing the two HPV oncogenes (E6 and E7), and effects on methylation were assayed using the Infinium 450 k technology.
Results and discussion: Unsupervised clustering over the methylation variable positions (MVPs) with greatest variation showed that samples segregated in accordance with HPV status, but also that HPV+ tumors are heterogeneous. MVPs were significantly enriched at transcriptional start sites, leading to the identification of a candidate CpG island methylator phenotype in a sub-group of the HPV+ tumors. Supervised analysis identified a strong preponderance (87%) of MVPs towards hypermethylation in HPV+ HNSCC. Meta-analysis of our HNSCC and publicly available methylation data in cervical and lung cancers confirmed the observed DNA-methylation signature to be HPV-specific and tissue-independent. Grouping of MVPs into functionally more significant differentially methylated regions identified 43 hypermethylated promoter DMRs, including for three cadherins of the Polycomb group target genes. Integration with independent expression data showed strong negative correlation, especially for the cadherin gene-family members. Combinatorial ectopic expression of the two HPV oncogenes (E6 and E7) in an HPV- HNSCC cell line partially phenocopied the hypermethylation signature seen in HPV+ HNSCC tumors, and established E6 as the main viral effector gene.
Conclusions: Our data establish that archival FFPE tissue is very suitable for this type of methylome analysis, and suggest that HPV modulates the HNSCC epigenome through hypermethylation of Polycomb repressive complex 2 target genes such as cadherins, which are implicated in tumor progression and metastasis.
Figures
Figure 1
Unsupervised analysis of the top 250 methylation variable positions (MVPs) in formalin-fixed, paraffin wax-embedded (FFPE) human papillomavirus-positive (HPV+) and HPV-negative (HPV-) tumor samples. (A) Singular value decomposition: PC-k denotes the kth principal component, DA denotes survival at censoring date. The first two principal components PC-1 and PC-2 most strongly correlated with HPV status, whereas the remaining significant components associated with clinical parameters, including alcohol consumption, smoking, age, sex, tumor stage, and grade. No association was found with technical factors (such as Sentrix position and Sentrix ID) on the array. (B) The first two principal components clearly distinguish HPV status. HPV+ samples are plotted in black, HPV- samples are in red, and m/f indicates male/female. (C) Clusters inferred by the unsupervised consensus-clustering algorithm for the top 250 MVPs as found using the MAD estimator.
Figure 2
Supervised differential methylation analysis reveals human papillomavirus-positive (HPV+) signature, showing a skew towards hypermethylation. (A) Histogram of P-values from the supervised analysis a clear trend towards small significant P values. (B) Independence of HPV status and sex: scatter plot of t-statistics of individual CpGs reflecting HPV status (positive t-statistics indicate hypermethylation in HPV-infected samples). wiht P value computed using Wilcoxon rank sum test. (C) Methlyation status according to gene-feature annotation, showing a clear trend towards hypermethylation (P = 0.017). Gene features: TSS1500, TSS200, 5' untranslated region (UTR), first exon, gene body, 3' UTR.
Figure 3
Unsupervised analysis of the top 1,000 MVPs in formalin-fixed, paraffin wax-embedded (FFPE) human papillomavirus-positive (HPV+) and HPV-negative (HPV-) tumor samples. (A) Consensus clustering identified four sub-groups in HPV+ and HPV- groups, revealing sub-group 1a as candidate CIMP within the HPV+ group. (B) Clusters inferred by the unsupervised consensus-clustering algorithm for the top 1,000 MVPs as found using the MAD estimator.
Figure 4
Validation of human papillomavirus-positive (HPV+) and HPV-negative (HPV-) methylation signature. (A-C) Validation of consistency of _t_-statistics between formalin-fixed, paraffin wax-embedded (FFPE) and (A) fresh-frozen (FF) samples, (B) FF control probes and (C) HPV+ against HPV- cell lines. (D) Heatmap representation of signature of consistent hypermethylated methylation variable positions (hyper-MVPs; top) and hypomethylated MVPs (hypo-MVPs; bottom) in the Infinium DNA-methylation data. The DNA-methylation (β) values are represented using a color scale from yellow (low DNA methylation) to blue (high DNA methylation) normalized across each MVP. The HPV+ head and neck squamous cell cancer (HNSCC) methylation signature contains 2,194 consistent hyper-MVPs and 74 consistent hypo-MVPs across all three datasets. The six HNSCC cell-line samples were run in duplicate.
Figure 5
Validation of consistent hypermethylated methylation variable positions (hyper-MVPs) in E6 and E6&E7 infected cell-line clones. (A) The formalin-fixed, paraffin wax-embedded (FFPE) hyper-MVP signature consistent with E6 (infected with E6 or E6&E7) versus empty vector controls (Monte Carlo _P_= 0.007). Volcano plot shows t-statistics of E6 versus empty clones plotted against log10 FFPE P values. (B) Heat-map representation of consistent hyper-MVPs in clones infected with E6, E6&E7, or E7 and empty vector controls. Yellow indicates relative hypomethylation in HPV+ samples and blue indicates hypermethylation (MVPs normalized across samples).
Figure 6
Multidimensional scaling using the four datasets. These datasets were comprised of 48 cervical-cancer samples (CERV; pink), 59 lung-cancer samples (LUNG; purple), 18 human papillomavirus-positive (HPV+) head and neck squamous cell cancer (HNSCC) samples (HPV1; light-blue) and 14 HPV-negative (HPV-) HNSCC samples (HPV0; green), using a selection of HPV-associated versus smoking-associated features identified by comparing HPV+ versus HPV- HNSCC.
Figure 7
Exemplar profiles of a hypermethylated differentially methylated region (hyper-DMR) for CDH8 and a hypomethylated DMR (hypo-DMR) for MEI1. Comparison of DMR profiles obtained from formalin-fixed, paraffin wax-embedded (FFPE) head and neck squamous cell cancers (HNSCCs), fresh-frozen (FF) HNSCCs, and HNSCC cell lines. The profiles clearly show the increasing power to detect methylation variable positions and differentially methylated regions (DMRs) is dependent on cell-type purity (cell line > laser-capture microdissected FFPE > FF). Feature annotation is as provided by BeadChip, and methylation values are color-coded accordingly: TSS1500, orange (1500 bp to 200 bp upstream of the transcription start site (TSS)); TSS200, red (200 bp upstream of the TSS); 5' untranslated region (UTR), yellow; gene body, blue; CpG islands, black; CpG shores, grey; and CpG shelves, light grey.
Figure 8
Integration of DNA-methylation data with public gene-expression data. (A) DNA methylation correlates with decreased gene expression: scatter plot of t-statistics between human papillomavirus-positive (HPV+) and HPV-negative (HPV-) formalin-fixed, paraffin wax-embedded (FFPE) cancer samples (top 500 differentially methylated MVPs restricted to CpG islands) shows significant anti-correlation between DNA methylation and gene expression. Gene expression-data were taken from Pyeon et al., [14]. (B) List of top 10 anti-correlated targets: Differentially methylated genes in promoter region (TSS200) which also exhibit differential gene expression in the independent Pyeon gene-expression dataset. 3 Cadherin genes were found among the top 10 hits (bold).
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