Christine Kottaridi1, Maria Kyrgiou2,3, Abraham Pouliakis1, Maria Magkana1, Evangelia Aga1, Aris Spathis1, Anita Mitra2,3, George Makris4, Charalampos Chrelias4, Vassiliki Mpakou5, Evangelos Paraskevaidis6, John G Panayiotides7, Petros Karakitsos1. 1. Department of Cytopathology, National and Kapodistrian University of Athens Medical School, "ATTIKON" University Hospital, Athens, Greece. 2. Department of Surgery and Cancer, Institute of Reproductive and Developmental Biology, Faculty of Medicine, Imperial College London, UK. 3. West London Gynaecological Cancer Center, Queen Charlotte's and Chelsea, Hammersmith Hospital, Imperial Healthcare NHS Trust, London, UK. 4. Third Department of Obstetrics and Gynecology, University of Athens Medical School, "ATTIKON" University Hospital, Athens. 5. Second Department of Internal Medicine and Research Institute, University of Athens Medical School, "ATTIKON" University Hospital, Athens. 6. Department of Obstetrics and Gynecology, University Hospital of Ioannina, Ioannina, Greece. 7. Second Department of Pathology, University of Athens Medical School, "ATTIKON" University Hospital, Athens, Greece.
Abstract
Background: Methylation of the human papillomavirus (HPV) DNA has been proposed as a novel biomarker. Here, we correlated the mean methylation level of 12 CpG sites within the L1 gene, to the histological grade of cervical precancer and cancer. We assessed whether HPV L1 gene methylation can predict the presence of high-grade disease at histology in women testing positive for HPV16 genotype. Methods: Pyrosequencing was used for DNA methylation quantification and 145 women were recruited. Results: We found that the L1 HPV16 mean methylation (±SD) significantly increased with disease severity (cervical intraepithelial neoplasia [CIN] 3, 17.9% [±7.2] vs CIN2, 11.6% [±6.5], P < .001 or vs CIN1, 9.0% [±3.5], P < .001). Mean methylation was a good predictor of CIN3+ cases; the area under the curve was higher for sites 5611 in the prediction of CIN2+ and higher for position 7145 for CIN3+. The evaluation of different methylation thresholds for the prediction of CIN3+ showed that the optimal balance of sensitivity and specificity (75.7% and 77.5%, respectively) and positive and negative predictive values (74.7% and 78.5%, respectively) was achieved for a methylation of 14.0% with overall accuracy of 76.7%. Conclusions: Elevated methylation level is associated with increased disease severity and has good ability to discriminate HPV16-positive women that have high-grade disease or worse.
Background: Methylation of the human papillomavirus (HPV) DNA has been proposed as a novel biomarker. Here, we correlated the mean methylation level of 12 CpG sites within the L1 gene, to the histological grade of cervical precancer and cancer. We assessed whether HPV L1 gene methylation can predict the presence of high-grade disease at histology in women testing positive for HPV16 genotype. Methods: Pyrosequencing was used for DNA methylation quantification and 145 women were recruited. Results: We found that the L1 HPV16 mean methylation (±SD) significantly increased with disease severity (cervical intraepithelial neoplasia [CIN] 3, 17.9% [±7.2] vs CIN2, 11.6% [±6.5], P < .001 or vs CIN1, 9.0% [±3.5], P < .001). Mean methylation was a good predictor of CIN3+ cases; the area under the curve was higher for sites 5611 in the prediction of CIN2+ and higher for position 7145 for CIN3+. The evaluation of different methylation thresholds for the prediction of CIN3+ showed that the optimal balance of sensitivity and specificity (75.7% and 77.5%, respectively) and positive and negative predictive values (74.7% and 78.5%, respectively) was achieved for a methylation of 14.0% with overall accuracy of 76.7%. Conclusions: Elevated methylation level is associated with increased disease severity and has good ability to discriminate HPV16-positive women that have high-grade disease or worse.
Authors: Helen Kelly; Yolanda Benavente; Miquel Angel Pavon; Silvia De Sanjose; Philippe Mayaud; Attila Tibor Lorincz Journal: Br J Cancer Date: 2019-10-16 Impact factor: 7.640
Authors: Jasmin Fertey; Jörg Hagmann; Hans-Joachim Ruscheweyh; Christian Munk; Susanne Kjaer; Daniel Huson; Juliane Haedicke-Jarboui; Frank Stubenrauch; Thomas Iftner Journal: Cancer Med Date: 2019-12-19 Impact factor: 4.452
Authors: Sarah J Bowden; Ilkka Kalliala; Areti A Veroniki; Marc Arbyn; Anita Mitra; Kostas Lathouras; Lisa Mirabello; Marc Chadeau-Hyam; Evangelos Paraskevaidis; James M Flanagan; Maria Kyrgiou Journal: EBioMedicine Date: 2019-11-12 Impact factor: 8.143
Authors: Karoliina Tainio; Antonios Athanasiou; Kari A O Tikkinen; Riikka Aaltonen; Jovita Cárdenas; Sivan Glazer-Livson; Maija Jakobsson; Kirsi Joronen; Mari Kiviharju; Karolina Louvanto; Sanna Oksjoki; Riikka Tähtinen; Seppo Virtanen; Pekka Nieminen; Maria Kyrgiou; Ilkka Kalliala Journal: BMJ Date: 2018-02-27