Assessment of attribution algorithms for resolving CIN3-related HPV genotype prevalence in mixed-genotype biopsy specimens using laser capture microdissection as the reference standard
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Alyssa Cornall, JULIA BROTHERTON, Emma T. Callegari, Fiona H. Tan, Marion Saville, Jan Pyman, Samuel Phillips, Michael J. Malloy, Sepehr Tabrizi, Suzanne M. Garland
Abstract
To make accurate determinations regarding potential and actual impact of HPV vaccine programs, precise estimates of genotype-specific contributions to disease are required for pre- and post-vaccine populations. Definitive determination of lesion-specific genotypes, particularly where multiple genotypes are detected in a sample, can be technically demanding and resource intensive; therefore, most prevalence studies use mathematical algorithms to adjust for multiple genotype detections. There are currently several algorithms, which can produce genotype estimates within a wide range of variability. The use of these for cervical cytology samples has recently been assessed for accuracy against a definitive reference standard, but none have yet been assessed for multiple-genotype-containing whole biopsy specimens. Using laser capture microdissection (LCM) on biopsy samples, lesion-specific genotype prevalence data were generated for a cohort of 516 young Australian women (aged 18-32 years) with cervical intraepithelial neoplasia grade 3 or adenocarcinoma in situ. Using whole tissue section genotype data from the same cohort, including 71 (13.7%) with multiple genotypes, lesion-associated genotype prevalence was estimated using four different attribution algorithms. The proportion of lesions attributable to HPV16 and HPV18 by LCM were 58.4% and 5%, respectively; hierarchical, proportional, single type/minimum and any type/maximum attribution estimates were comparable across genotypes. For analyses utilising whole tissue biopsy cervical specimens, attribution estimates are appropriate for estimating the proportional contribution of individual genotypes to lesions in a population.
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