Identification of a methylation panel as an alternative triage to detect CIN3+ in hrHPV-positive self-samples from the population-based cervical cancer screening programme

Author:

de Waard J.,Bhattacharya A.,de Boer M. T.,van Hemel B. M.,Esajas M. D.,Vermeulen K. M.,de Bock G. H.,Schuuring E.,Wisman G. B. A.

Abstract

Abstract Background The Dutch population-based cervical cancer screening programme (PBS) consists of primary high-risk human papilloma virus (hrHPV) testing with cytology as triage test. In addition to cervical scraping by a general practitioner (GP), women are offered self-sampling to increase participation. Because cytological examination on self-sampled material is not feasible, collection of cervical samples from hrHPV-positive women by a GP is required. This study aims to design a methylation marker panel to detect CIN3 or worse (CIN3+) in hrHPV-positive self-samples from the Dutch PBS as an alternative triage test for cytology. Methods Fifteen individual host DNA methylation markers with high sensitivity and specificity for CIN3+ were selected from literature and analysed using quantitative methylation-specific PCR (QMSP) on DNA from hrHPV-positive self-samples from 208 women with CIN2 or less (< CIN2) and 96 women with CIN3+. Diagnostic performance was determined by area under the curve (AUC) of receiver operating characteristic (ROC) analysis. Self-samples were divided into a train and test set. Hierarchical clustering analysis to identify input methylation markers, followed by model-based recursive partitioning and robustness analysis to construct a predictive model, was applied to design the best marker panel. Results QMSP analysis of the 15 individual methylation markers showed discriminative DNA methylation levels between < CIN2 and CIN3+ for all markers (p < 0.05). The diagnostic performance analysis for CIN3+ showed an AUC of ≥ 0.7 (p < 0.001) for nine markers. Hierarchical clustering analysis resulted in seven clusters with methylation markers with similar methylation patterns (Spearman correlation> 0.5). Decision tree modeling revealed the best and most robust panel to contain ANKRD18CP, LHX8 and EPB41L3 with an AUC of 0.83 in the training set and 0.84 in the test set. Sensitivity to detect CIN3+ was 82% in the training set and 84% in the test set, with a specificity of 74% and 71%, respectively. Furthermore, all cancer cases (n = 5) were identified. Conclusion The combination of ANKRD18CP, LHX8 and EPB41L3 revealed good diagnostic performance in real-life self-sampled material. This panel shows clinical applicability to replace cytology in women using self-sampling in the Dutch PBS programme and avoids the extra GP visit after a hrHPV-positive self-sampling test.

Funder

ZonMw

Publisher

Springer Science and Business Media LLC

Subject

Genetics (clinical),Developmental Biology,Genetics,Molecular Biology

Reference49 articles.

1. Arbyn M, Raifu AO, Weiderpass E, Bray F, Anttila A. Trends of cervical cancer mortality in the member states of the European Union. Eur J Cancer. 2009;45:2640–8.

2. Peto PJ, Gilham PC, Fletcher O, Matthews FE. The cervical cancer epidemic that screening has prevented in the UK. Lancet. 2004;364:249–56.

3. Jansen EEL, Zielonke N, Gini A, Anttila A, Segnan N, Vokó Z, et al. Effect of organised cervical cancer screening on cervical cancer mortality in Europe: a systematic review. Eur J Cancer. 2020;127:207–23.

4. The National Institute for Public Health and the Environment (RIVM). Framework for the execution of the Dutch cervical cancer screening programme. 2021. Available from: https://www.rivm.nl/documenten/framework-for-execution-of-cervical-cancer-population-screening

5. The National Institute for Public Health and the Environment (RIVM). Bevolkingsonderzoek baarmoederhalskanker. 2022. Available from: https://www.rivm.nl/bevolkingsonderzoek-baarmoederhalskanker

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3