Non-invasive methylation test to detect cervical pre-cancer in self-collected vaginal and urine specimens
Session type: Poster / e-Poster / Silent Theatre session
1Queen Mary University of London, London, UK
The implementation of HPV testing as a primary screen will soon become the norm worldwide. Because HPV testing is a very sensitive method, but not specific enough, the choice of an appropriate triage strategy for hrHPV positive women will be one of the future key issues facing the cervical screening community. Clinician taken samples are the gold standard but self-sampling including urine may be a useful alternative. We have developed a triage classifier for the detection of CIN2+, based on DNA methylation of HPV16, HPV18, HPV31 and HPV33 and the human gene EPB41L3. We will test S5 classifier on two non-invasive specimens: a self-collected vaginal sample and urine. We aim to assess whether S5 can identify women who are CIN2+ using self-collected samples and comparing several collection devices.
MethodWomen attending the colposcopy clinic at The Royal London Hospital as a consequence of abnormal screening cytology and/or a positive HPV result were recruited as part of the ‘Self-sampling for vaginal HPV. 503 women provided a urine sample using the Colli-Pee™ device. In total 600 women provided self-collected vaginal samples using either Flocked swab and Diagene or HerSwab and Qvintip. DNA was extracted, Bisulfite converted, followed by pyrosequencing assays for the 6 S5 markers. Average methylation was calculated to generate the S5 score.
ResultsS5 showed a good and statistically significant separation between <CIN2 and CIN2+ samples for both urine and vagina self-samples (p=<0.0001). The area under the ROC curve was 0.7254 (p=<0.0001) for urine samples and 0.7388 (p=<0.0001) for vaginal self-samples. At the pre-defined cut-off of 0.8, the sensitivity for urine samples was 66% and specificity 72% and vaginal self-samples was 71% and specificity 68%.
We demonstrated that S5 can be successfully amplified in urine and vaginal self-collected samples and that the classifier is able to correctly identify CIN2+ women.