Speaker
Description
Human papillomavirus (HPV) infection is the causal agent of cervical cancer. While most HPV infections are transient and cleared by immune responses, a small fraction establish persistent infection, enabling cancer development through a multistage process from precancerous lesions to invasive disease. A distinctive feature of cervical cancer is the high rate of spontaneous remission at multiple stages.
We develop a stochastic mathematical model to analyze cervical cancer incidence and its age distribution, incorporating infection dynamics, immune control, progression, spontaneous remission, and age-dependent exposure risk. Model parameters are estimated by fitting age-specific incidence data using a Markov Chain Monte Carlo approach.
Our results show that remission rates substantially exceed progression rates, explaining why most persistent infections and precancerous lesions do not progress to malignancy. The characteristic age distribution of cervical cancer, peaking in the late 30s to early 40s, is primarily driven by age-dependent HPV persistence. Small changes in persistence probability can therefore produce large differences in long-term cancer risk, highlighting the importance of targeting persistent infection in prevention strategies.
[Reference]
Hayashi R, Hara A, Iwasa Y (2026) Human papillomavirus driving cervical cancer: a mathematical model with persistent infection, cancer progression, and spontaneous remission. J Theor Biol 617:112289.
