In:
Cancer Epidemiology, Biomarkers & Prevention, American Association for Cancer Research (AACR), Vol. 28, No. 11 ( 2019-11-01), p. 1816-1824
Abstract:
Human papillomavirus (HPV) testing provides a much more sensitive method of detection for high-grade lesions than cytology, but specificity is low. Here, we explore the extent to which full HPV genotyping, viral load, and multiplicity of types can be used to improve specificity. Methods: A population-based sample of 47,120 women undergoing cervical screening was tested for 13 high-risk HPV genotypes. Positive predictive values (PPV) for cervical intraepithelial neoplasia (CIN) grade 2 or worse (CIN2+; N = 3,449) and CIN3 or worse (CIN3+; N = 1,475) over 3 years of follow-up were estimated for HPV genotype and viral load. Weighted multivariate logistic regression models were used to estimate the odds of CIN2+ or CIN3+ according to genotype, multiplicity of types, and viral load. Results: High-risk HPV was detected in 15.4% of women. A hierarchy of HPV genotypes based on sequentially maximizing PPVs for CIN3+ found HPV16 & gt;33 & gt;31 to be the most predictive, followed sequentially by HPV18 & gt;35 & gt;58 & gt;45 & gt;52 & gt;59 & gt;51 & gt;39 & gt;56 & gt;68. After adjusting for higher ranked genotypes, the inclusion of multiple HPV infections added little to risk prediction. High viral loads for HPV18, 35, 52, and 58 carried more risk than low viral loads for HPV16, 31, and 33. High viral load for HPV16 was significantly more associated with CIN3+ than low viral load. Conclusions: HPV genotype and viral load, but not multiplicity of HPV infections, are important predictors of CIN2+ and CIN3+. Impact: The ability to identify women at higher risk of CIN2+ and CIN3+ based on both HPV genotype and viral load could be important for individualizing triage plans, particularly as HPV becomes the primary screening test.
Type of Medium:
Online Resource
ISSN:
1055-9965
,
1538-7755
DOI:
10.1158/1055-9965.EPI-19-0239
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2019
detail.hit.zdb_id:
2036781-8
detail.hit.zdb_id:
1153420-5
