Linear viral load increase of a single HPV-type in women with multiple HPV infections predicts progression to cervical cancer

Persistent high-risk human papillomavirus (HPV) infection is strongly associated with development of high-grade cervical intraepithelial neoplasia or cancer (CIN3+). In single type infections, serial type-specific viral-load measurements predict the natural history of the infection. In infections with multiple HPV-types, the individual type-specific viral-load profile could distinguish progressing HPV-infections from regressing infections. A case-cohort natural history study was established using samples from untreated women with multiple HPV-infections who developed CIN3+ (n=57) or cleared infections (n=88). Enriched cell pellet from liquid based cytology samples were subjected to a clinically validated real-time qPCR-assay (18 HPV-types). Using serial type-specific viral-load measurements (3) we calculated HPV-specific slopes and coefficient of determination (R-2) by linear regression. For each woman slopes and R-2 were used to calculate which HPV-induced processes were ongoing (progression, regression, serial transient, transient). In transient infections with multiple HPV-types, each single HPV-type generated similar increasing (0.27copies/cell/day) and decreasing (-0.27copies/cell/day) viral-load slopes. In CIN3+, at least one of the HPV-types had a clonal progressive course (R(2)0.85; 0.0025copies/cell/day). In selected CIN3+ cases (n=6), immunostaining detecting type-specific HPV 16, 31, 33, 58 and 67 RNA showed an even staining in clonal populations (CIN3+), whereas in transient virion-producing infections the RNA-staining was less in the basal layer compared to the upper layer where cells were ready to desquamate and release newly-formed virions. RNA-hybridization patterns matched the calculated ongoing processes measured by R-2 and slope in serial type-specific viral-load measurements preceding the biopsy. In women with multiple HPV-types, serial type-specific viral-load measurements predict the natural history of the different HPV-types and elucidates HPV-genotype attribution.