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. 2013 Feb 5;108(2):370-9.
doi: 10.1038/bjc.2012.568. Epub 2013 Jan 3.

The epigenetic landscape of oral squamous cell carcinoma

P V Jithesh  1 , J M Risk, A G Schache, J Dhanda, B Lane, T Liloglou, R J Shaw
Affiliations

The epigenetic landscape of oral squamous cell carcinoma

P V Jithesh et al. Br J Cancer. .

Abstract

Background: There is relatively little methylation array data available specifically for oral squamous cell carcinoma (OSCC). This study aims to compare the DNA methylome across a large cohort of tumour/normal pairs.

Methods: DNA was extracted from 44 OSCCs and paired normal mucosa. DNA methylation analysis employed the Illumina GoldenGate high-throughput array comprising 1505 CpG loci selected from 807 epigenetically regulated genes. This data was correlated with extracapsular spread (ECS), human papilloma virus (HPV) status, recurrence and 5-year survival.

Results: Differential methylation levels of a number of genes distinguished the tumour tissue sample from the matched normal. Putative methylation signatures for ECS and recurrence were identified. The concept of concordant methylation or CpG island methylator phenotype (CIMP) in OSCC is supported by our data, with an association between 'CIMP-high' and worse prognosis. Epigenetic deregulation of NOTCH4 signalling in OSCC was also observed, as part of a possible methylation signature for recurrence, with parallels to recently discovered NOTCH mutations in HNSCC. Differences in methylation in HPV-driven cases were seen, but are less significant than that has been recently proposed in other series.

Conclusion: Although OSCC seems as much an 'epigenetic' as a genetic disease, the translational potential of cancer epigenetics has yet to be fully exploited. This data points to the application of epigenetic biomarkers and targets available to further the development of therapy in OSCC.

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Figures

Figure 1
Figure 1
Principal components analysis showing the distinction between tumour and normal samples based on methylation. Average β values from 43 tumour and matched normal samples were employed in the analysis. Separation between tumour (hexagon) and normal (sphere) samples can be visualised in the plot along the first principal component, with a few misplaced samples.
Figure 2
Figure 2
Principal components analysis showing HPV(+) (hexagon) and HPV(−) (sphere) tumour samples. Average β values from 43 tumour samples were employed in the analysis. One sample with unknown HPV status is also displayed.
Figure 3
Figure 3
Hierarchical clustering heatmap and dendrogram of tumour samples based on methylation markers differential between tumour and normal tissues. Average β values from the selected 48 probes were used for hierarchical agglomerative clustering using Euclidean distance and average linkage. CpG island methylator phenotype (CIMP) groups are shown by the two clusters on the dendrogram left of the heatmap, the bottom cluster designated as ‘CIMP-high' and the top as ‘CIMP-low'.
Figure 4
Figure 4
Kaplan–Meier plot of freedom from recurrence (FFR) showing a trend towards worse prognosis by patients in the CIMP-high group. FFR in CIMP-high and CIMP-low groups were not significantly different in log-rank test (P=0.06).
Figure 5
Figure 5
Hierarchical clustering heatmap and dendrogram of tumour samples based on methylation markers differential between patients with and without extracapsular spread. Average β values from the selected 15 probes were used for hierarchical agglomerative clustering using Euclidean distance and average linkage.
See this image and copyright information in PMC

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