Factors associated with human papillomavirus (HPV) virus load variations in genital infections in young women

Abstract

AbstractHuman papillomaviruses (HPVs) are the most oncogenic viruses known to humans, with 12 high-risk (HR) genotypes causing nearly all cervical cancers. Cytology is commonly used to screen for cervical lesions but is currently being replaced by testing for high-risk HPV (HR HPV). Although HR HPV screening has a higher sensitivity, its specificity is limited, and it is currently advised to repeat the first screening 4 to 6 months later. To increase the sensitivity of the screening triage, other biomarkers have been suggested, including HPV viral load. Indeed, since 1999, several independent studies have found an association between HR HPV viral load in cervical samples and the severity of cervical disease. Here, we further explore the determinants of variations in HPV viral load in genital infections in young adult women.We analysed samples collected in the PAPCLEAR clinical cohort for participants who were infected by HPV genotypes for which we quantified virus load using qPCR targeting 13 genotypes. We developed a Bayesian statistical model estimating the effect of covariates of interest on the HPV viral load. To analyse precisely the viral load difference between HPV genotypes, phylogenetic distances between HPVs were also integrated in the Bayesian model.Our results fail to identify an effect of anti-HPV vaccination, co-infections by multiple HPVs or tobacco smoking on the detected viral load. On the opposite, swabs contained significantly more viral copies than cervical smears. Our results also highlight that most of the viral load variance could be explained at the genotype level (80%) rather than at the individual level (20%). Our model reveals important differences in viral load detected between the different genotypes tested, with HPV16 being the highest and HPV18 the lowest. The impact of phylogenetic signal on viral load was also estimated to be low, except for a cluster comprised of HPV53, HPV66 and HPV56. These results contribute to identifying the main drivers of HPV viral load detected and could help design needed future screening policies.