Peripheral blood DNA methylation profiles are indicative of head and neck squamous cell carcinoma: an epigenome-wide association study

Abstract

Head and neck cancer accounts for an estimated 47,560 new cases and 11,480 deaths annually in the United States, the majority of which are squamous cell carcinomas (HNSCC). The overall 5 year survival is approximately 60% and declines with increasing stage at diagnosis, indicating a need for non-invasive tests that facilitate the detection of early disease. DNA methylation is a stable epigenetic modification that is amenable to measurement and readily available in peripheral blood. We used a semi-supervised recursively partitioned mixture model (SS-RPMM) approach to identify novel blood DNA methylation markers of HNSCC using genome-wide methylation array data for peripheral blood samples from 92 HNSCC cases and 92 cancer-free control subjects. To assess the performance of the resultant markers, we constructed receiver operating characteristic (ROC) curves and calculated the corresponding area under the curve (AUC). Cases and controls were best differentiated by a methylation profile of six CpG loci (associated with FGD4, SERPINF1, WDR39, IL27, HYAL2 and PLEKHA6), with an AUC of 0.73 (95% CI: 0.62-0.82). After adjustment for subject age, gender, smoking, alcohol consumption and HPV16 serostatus, the AUC increased to 0.85 (95% CI: 0.76-0.92). We have identified a novel blood-based methylation profile that is indicative of HNSCC with a high degree of accuracy. This profile demonstrates the potential of DNA methylation measured in blood for development of non-invasive applications for detection of head and neck cancer.

Figure 1

Schematic of the semi-supervised RPMM approach used for identification of novel DNA methylation profiles in peripheral blood associated with HNSCC.

Figure 2

DNA methylation profiles of the 6 CpG loci identified as indicative of HNSCC. (A) The recursively partitioned mixture model (RPMM) classification of 92 subjects in the testing set (rows) by the 6 CpG loci used for discriminating HNSCC case-control status (columns), and class-specific mean methylation (right side). (B) The proportion of cases (n = 46) and controls (n = 46) from the testing set following application of the predicted classes.

Figure 3

Classification performance of methylation profiles for discrimination of HNSCC. (A) Receiver operating characteristic (RJC) curve for methylation class only for prediction of HNSCC, with corresponding area-under the curve (AUC). (B) RJC curve for methylation class prediction of HNSCC, adjusted for age, gender, smoking, alcohol consumption and HPV16 serostatus.

Figure 4

Gene-set enrichment analysis (GSEA) for DNA methylation data. The lower half of the figure displays KEGG pathways that are significantly overrepresented in genes associated with differentially methylated CpG loci, while the upper half depicts transcription factor binding sites (TFBS) located within 1 kb of differentially methylated CpG loci. Pathways or TFBS associated with HPV16 serostatus are shown on the left and those associated with HNSCC are shown on the right, with the sole overlapping TFBS (POU2F1) displayed in the center.