Journal article
Performance of human papillomavirus (HPV) attribution algorithms to predict causative genotypes in anal high-grade lesions
The Journal of Infectious Diseases, Vol.227(12), pp.1407-1416
2023
PMID: 36591643
Abstract
Background: Gay and bisexual men (GBM) are at increased risk of human papillomavirus (HPV) associated anal high grade squamous intraepithelial lesions (HSILs). Understanding the fractions of HSILs attributable to HPV genotypes is important to inform potential impacts of screening and vaccination strategies. However, multiple infections are common, making attribution of causative types difficult. Algorithms developed for predicting HSIL causative genotype fractions have never been compared with a reference standard in GBM. Method: Samples were from the Study of the Prevention of Anal Cancer. Baseline HPV genotypes detected in anal swab samples (160 participants) were compared with HPV genotypes in anal HSILs (222 lesions) determined by laser capture microdissection (LCM). Five algorithms were compared: proportional, hierarchical, maximum, minimum, and maximum likelihood estimation. Results: All algorithms predicted HPV 16 as the most common HSIL causative genotype, and proportions differed from LCM detection (37.8%) by algorithm (with differences of −6.1%, +20.9%, −20.4%, +2.9%, and +2.2% respectively). Fractions predicted using the proportional method showed a strong positive correlation with LCM, overall (R = 0.73 and P = .002), and by human immunodeficiency virus (HIV) status (HIV positive, R = 0.74 and P = .001; HIV-negative, R = 0.68 and P = .005). Conclusions: Algorithms produced a range of inaccurate estimates of HSIL attribution, with the proportional algorithm performing best. The high occurrence of multiple HPV infections means that these algorithms may be of limited use in GBM.
Details
- Title
- Performance of human papillomavirus (HPV) attribution algorithms to predict causative genotypes in anal high-grade lesions
- Authors
- Samuel Phillips (Corresponding Author) - University of the Sunshine Coast, Queensland, Centre for BioinnovationAlyssa M Cornall (Author) - The University of MelbourneMonica Molano (Author) - Royal Women's HospitalFengyi Jin (Author) - UNSW SydneyJennifer M Roberts (Author) - Douglass Hanly Moir Pathology, Macquarie Park 2113, New South Wales, AustraliaAnnabelle Farnsworth (Author) - Douglass Hanly Moir PathologyRichard J Hillman (Author) - The University of SydneyDavid J Templeton (Author) - Sydney Local Health DistrictI Mary Poynten (Author) - UNSW SydneySuzanne M Garland (Author) - Department of Obstetrics and Gynecology, University of Melbourne, Parkville 3052, Victoria, AustraliaChristopher K Fairley (Author) - Monash UniversityGerald L Murray (Author) - The University of MelbourneSepehr N Tabrizi (Author) - The University of MelbourneAndrew E Grulich (Author) - UNSW SydneyDorothy A Machalek (Author) - UNSW Sydney
- Publication details
- The Journal of Infectious Diseases, Vol.227(12), pp.1407-1416
- Publisher
- Oxford University Press
- Date published
- 2023
- DOI
- 10.1093/infdis/jiac503
- ISSN
- 1537-6613; 0022-1899
- PMID
- 36591643
- Grants
- Grant note
- 13-11/ Cancer Council New South Wales Strategic Research Partnership Program ; 1130507/ Cancer Council Victoria
- Organisation Unit
- Centre for Bioinnovation
- Language
- English
- Record Identifier
- 99710098902621
- Output Type
- Journal article
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- Domestic collaboration
- Web Of Science research areas
- Immunology
- Infectious Diseases
- Microbiology
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