Performance of Human Papillomavirus Attribution Algorithms to Predict Causative Genotypes in Anal High-Grade Lesions

S Phillips; AM Cornall; M Molano; F Jin; JM Roberts; A Farnsworth; RJ Hillman; DJ Templeton; IM Poynten; SM Garland; CK Fairley; GL Murray; SN Tabrizi; AE Grulich; DA Machalek

Journal article

Performance of Human Papillomavirus Attribution Algorithms to Predict Causative Genotypes in Anal High-Grade Lesions

S Phillips, AM Cornall, M Molano, F Jin, JM Roberts, A Farnsworth, RJ Hillman, DJ Templeton, IM Poynten, SM Garland, CK Fairley, GL Murray, SN Tabrizi, AE Grulich, DA Machalek

Journal of Infectious Diseases | OXFORD UNIV PRESS INC | Published : 2023

DOI: 10.1093/infdis/jiac503

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 genotyp..

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University of Melbourne Researchers

Suzanne Wark Author

Gerald Murray Author

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Awarded by Merck

Funding Acknowledgements

This work was supported by the National Health and Medical Research Council (program grants 568971 and 1071269), the Cancer Council New South Wales Strategic Research Partnership Program (grant 13-11), and Cancer Council Victoria (grant 1130507 for funding of laser capture microdissection genotyping)