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Evaluating the role of stochasticity in the clearance of HPV infections in young adults.








Evaluating the role of stochasticity in the clearance of HPV infections in young adults.


Samuel ALIZON


MIVEGEC department, CNRS (Montpellier, France)






05 September 2022


Human Papillomaviruses (HPVs) are DNA-viruses that comprise more than 150 different types [Doorbar, 2012, Vaccine]. They represent one of the most prevalent sexually-transmitted infection (STIs) [Chesson, 2014, Sexually Transmitted Diseases] but, fortunately, most of them are non-persistent and clear (i.e.~absence of detectable viruses by PCR) spontaneously within the three first years [Schiffman, 2011, JNCI].The mechanisms underlying the clearance patterns are still poorly understood. Indeed, immune mechanisms are often invoked to explain virus clearance but HPVs are non-lytic viruses, i.e. virus copies follow the life of the cell without killing it, virions are released during cell shedding at the surface of the epithelium. Therefore, they cause limited inflammation, which likely indicates a limited involvement of innate immune responses. Besides, in natural infection, there is no viremia (i.e. presence of viruses in the blood) and the production of circulating antibodies against the virus is low [Stanley, 2008, Gynecol Oncol]. Recently, an alternative hypothesis was put forward to explain those patterns, which resides on chance itself (or `stochasticity'). As shown by Ryser et al [Ryser, 2015, PLoS Comput Biol] using a mathematical model, symmetric divisions of stem cells can lead to the clearance of HPV infections. We extended this framework by including additional random phenomena that could impact the life cycle of the virus, especially the episome partitioning upon cellular division, i.e. the random allocation of the viral copies in the two daughter cells. We find that this more realistic model further increases the potential importance of random processes in HPV infection clearance.To estimate the importance of stochasticity in explaining HPV infection clearance patterns, we need to confront simulations from our model with longitudinal follow-up data from clinical trials. In particular, control arms of HPV vaccine trials contain the information we need since they consist in longitudinal follow-ups of unvaccinated individuals exposed to HPV.Using statistical analyses, we propose to first describe the dynamics of natural HPV infections via classical survival analysis theory (Kaplan-Meier/Nelson-Allen estimates, Cox regression and log-rank test). Second, we will perform a more elaborate statistical analysis of the estimated cumulative probability of persistent detection of the first episode of HPV infection by fitting our simulations to these estimates. Analysing longitudinal follow-ups will shed new light on the natural history of HPV infections and on the potential importance of stochasticity in explaining clearance patterns.



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Statistical Analysis Plan