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J Clin Microbiol. 2012 May;50(5):1564-70. doi: 10.1128/JCM.06240-11. Epub 2012 Feb 15.

Human papillomavirus load measured by Linear Array correlates with quantitative PCR in cervical cytology specimens.

Author information

1
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA. wentzenn@mail.nih.gov

Abstract

Carcinogenic human papillomavirus (HPV) infections are necessary causes of most anogenital cancers. Viral load has been proposed as a marker for progression to cancer precursors but has been confirmed only for HPV16. Challenges in studying viral load are related to the lack of validated assays for a large number of genotypes. We compared viral load measured by Linear Array (LA) HPV genotyping with the gold standard, quantitative PCR (Q-PCR). LA genotyping and Q-PCR were performed in 143 cytology specimens from women referred to colposcopy. LA signal strength was measured by densitometry. Correlation coefficients and receiver operating characteristic (ROC) analyses were used to evaluate analytical and clinical performance. We observed a moderate to strong correlation between the two quantitative viral load measurements, ranging from an R value of 0.61 for HPV31 to an R value of 0.86 for HPV52. We also observed agreement between visual LA signal strength evaluation and Q-PCR. Both quantifications agreed on the disease stages with highest viral load, which varied by type (cervical intraepithelial neoplasia grade 2 [CIN2] for HPV52, CIN3 for HPV16 and HPV33, and cancer for HPV18 and HPV31). The area under the curve (AUC) for HPV16 Q-PCR at the CIN3 cutoff was 0.72 (P = 0.004), and the AUC for HPV18 LA at the CIN2 cutoff was 0.78 (P = 0.04). Quantification of LA signals correlates with the current gold standard for viral load, Q-PCR. Analyses of viral load need to address multiple infections and type attribution to evaluate whether viral load has clinical value beyond the established HPV16 finding. Our findings support conducting comprehensive studies of viral load and cervical cancer precursors using quantitative LA genotyping data.

PMID:
22337992
PMCID:
PMC3347116
DOI:
10.1128/JCM.06240-11
[Indexed for MEDLINE]
Free PMC Article

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