Quantitative measurement of Human Papillomavirus type 16 L1/L2 DNA methylation correlates with cervical disease grade

Bryant, D., Tristram, Amanda, Liloglou, T., Hibbitts, S., Fiander, Alison and Powell, Ned (2013) Quantitative measurement of Human Papillomavirus type 16 L1/L2 DNA methylation correlates with cervical disease grade. Journal of Clinical Virology, 59 (1), 24 - 29. (doi:10.1016/j.jcv.2013.10.029).

Abstract

Background
Persistent infection with Human Papillomavirus (HPV) type 16 causes the majority of cervical cancers. Genital HPV infection is very common, but neoplastic progression is uncommon. There is an urgent need to identify biomarkers associated with cervical neoplasia that can be used to triage women who test positive for HPV.
Objectives
To assess the ability of quantitative measurement of HPV16 DNA methylation to separate samples of different cytology grades and cervical cancers, and determine which of the assessed regions of the HPV genome and individual CpGs are most informative.
Study design
DNA methylation was quantified by pyrosequencing of bisulphite converted DNA from liquid based cytology samples from 17 women with normal cytology and 20 women with severe dyskaryosis, and from fixed tissue from 24 women with cervical cancer. Methylation was assessed in the HPV Long Control Region (LCR), E2 and L1/L2 regions.
Results
In cervical cancers, increased HPV DNA methylation was present in all regions. Increased methylation was also observed in severely dyskaryotic relative to normal samples, but only in the E2 and L1/L2 regions. The ability of methylation based classifiers to separate the three classes of material was assessed by ROC curve analyses. The best separation between normal and dyskaryotic samples was achieved by assessment of the L1/L2 CpGs at nucleotide positions 5600 and 5609 (AUC = 0.900, 95% CI: 0.793–1).
Conclusions
This study demonstrates the potential of quantification of HPV DNA methylation as a biomarker of cervical neoplasia. An algorithm considering methylation at specific L1/L2 CpGs appeared the most promising model.

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Contributors

Author: D. Bryant

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