Performance of human papillomavirus (HPV) attribution algorithms to predict causative genotypes in anal high-grade lesions

Samuel Phillips; Alyssa M Cornall; Monica Molano; Fengyi Jin; Jennifer M Roberts; Annabelle Farnsworth; Richard J Hillman; David J Templeton; I Mary Poynten; Suzanne M Garland; Christopher K Fairley; Gerald L Murray; Sepehr N Tabrizi; Andrew E Grulich; Dorothy A Machalek

doi:10.1093/infdis/jiac503

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Performance of human papillomavirus (HPV) attribution algorithms to predict causative genotypes in anal high-grade lesions

Journal article

Peer reviewed

Performance of human papillomavirus (HPV) attribution algorithms to predict causative genotypes in anal high-grade lesions

Samuel Phillips, Alyssa M Cornall, Monica Molano, Fengyi Jin, Jennifer M Roberts, Annabelle Farnsworth, Richard J Hillman, David J Templeton, I Mary Poynten, Suzanne M Garland, …

The Journal of Infectious Diseases, Vol.227(12), pp.1407-1416

2023

DOI: https://doi.org/10.1093/infdis/jiac503

PMID: 36591643

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Abstract

Hierarchical

Maximum Likelihood Estimation

Any type/Maximum

Laser Capture Microdissection

Anal high-grade squamous intraepithelial lesion

Single type/Minimum

HPV

Proportional

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 Bioinnovation
Alyssa M Cornall (Author) - The University of Melbourne
Monica Molano (Author) - Royal Women's Hospital
Fengyi Jin (Author) - UNSW Sydney
Jennifer M Roberts (Author) - Douglass Hanly Moir Pathology, Macquarie Park 2113, New South Wales, Australia
Annabelle Farnsworth (Author) - Douglass Hanly Moir Pathology
Richard J Hillman (Author) - The University of Sydney
David J Templeton (Author) - Sydney Local Health District
I Mary Poynten (Author) - UNSW Sydney
Suzanne M Garland (Author) - Department of Obstetrics and Gynecology, University of Melbourne, Parkville 3052, Victoria, Australia
Christopher K Fairley (Author) - Monash University
Gerald L Murray (Author) - The University of Melbourne
Sepehr N Tabrizi (Author) - The University of Melbourne
Andrew E Grulich (Author) - UNSW Sydney
Dorothy 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
PMID: 36591643
Grants: Sexually transmitted infections: Causes, consequences and interventions, 568971, National Health and Medical Research Council (Australia, Canberra) - NHMRC
New interventions to control sexually transmitted infections and their consequences, 1071269, National Health and Medical Research Council (Australia, Canberra) - NHMRC
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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Web Of Science research areas: Immunology; Infectious Diseases; Microbiology

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