Revising ecological assumptions about Human papillomavirus interactions and type replacement

Changes in Genital Human Papillomavirus (HPV) Prevalence During 12 Years of Girls-Only Bivalent HPV Vaccination: Results From a Biennial Repeated Cross-sectional Study

BACKGROUND

Between 2009-2021, bivalent human papillomavirus (HPV) vaccination was offered to girls in the Netherlands. We studied the impact of girls-only HPV vaccination on genital HPV prevalence among young adults.

METHODS

PASSYON (2009-2021) is a study among sexual health clinic clients aged 16-24 years old. Questionnaires elicited data on demographics, sexual behavior, and HPV vaccination status. Genital samples were analyzed using a PCR-based assay (SPF10-LiPA25). Prevalence trends of 12 high-risk genotypes were assessed as adjusted average annual change (aAAC), estimated using Poisson generalized estimating equations models. The relation between aAAC and phylogenetic distance to HPV-16/18 was assessed by means of regression and rank correlation analysis. Data were collected from 8889 females and 3300 heterosexual males (HMs).

RESULTS

Among females (irrespective of vaccination status), prevalences of HPV-16/18/31/33/35/45 decreased significantly over time. Increasing trends were observed for HPV-39/52/56. Among both HMs and unvaccinated females (54.3%), HPV-16/18 significantly declined, as did HPV-31 among HMs. Contrastingly, HPV-52/58 increased significantly among HMs and unvaccinated females. The type-specific aAAC correlated well with the phylogenetic distance to HPV-16/18.

CONCLUSIONS

During 12 years of girls-only bivalent HPV vaccination in the Netherlands, decreasing trends of the vaccine types and cross-protected types were observed among females. Herd protection of vaccine types was observed for HMs and unvaccinated females, and 1 cross-protected type for HMs. Increasing prevalence trends of HPV types with large phylogenetic distance to the vaccine types might indicate type replacement.

A multi-strain model with asymptomatic transmission: Application to COVID-19 in the US

COVID-19, induced by the SARS-CoV-2 infection, has caused an unprecedented pandemic in the world. New variants of the virus have emerged and dominated the virus population. In this paper, we develop a multi-strain model with asymptomatic transmission to study how the asymptomatic or pre-symptomatic infection influences the transmission between different strains and control strategies that aim to mitigate the pandemic. Both analytical and numerical results reveal that the competitive exclusion principle still holds for the model with the asymptomatic transmission. By fitting the model to the COVID-19 case and viral variant data in the US, we show that the omicron variants are more transmissible but less fatal than the previously circulating variants. The basic reproduction number for the omicron variants is estimated to be 11.15, larger than that for the previous variants. Using mask mandate as an example of non-pharmaceutical interventions, we show that implementing it before the prevalence peak can significantly lower and postpone the peak. The time of lifting the mask mandate can affect the emergence and frequency of subsequent waves. Lifting before the peak will result in an earlier and much higher subsequent wave. Caution should also be taken to lift the restriction when a large portion of the population remains susceptible. The methods and results obtained her e may be applied to the study of the dynamics of other infectious diseases with asymptomatic transmission using other control measures.

Ability of epidemiological studies to monitor HPV post-vaccination dynamics: a simulation study

Genital human papillomavirus (HPV) infections are caused by a broad diversity of genotypes. As available vaccines target a subgroup of these genotypes, monitoring transmission dynamics of nonvaccine genotypes is essential. After reviewing the epidemiological literature on study designs aiming to monitor those dynamics, we evaluated their abilities to detect HPV-prevalence changes following vaccine introduction. We developed an agent-based model to simulate HPV transmission in a heterosexual population under various scenarios of vaccine coverage and genotypic interaction, and reproduced two study designs: post-vs.-prevaccine and vaccinated-vs.-unvaccinated comparisons. We calculated the total sample size required to detect statistically significant prevalence differences at the 5% significance level and 80% power. Although a decrease in vaccine-genotype prevalence was detectable as early as 1 year after vaccine introduction, simulations indicated that the indirect impact on nonvaccine-genotype prevalence (a decrease under synergistic interaction or an increase under competitive interaction) would only be measurable after >10 years whatever the vaccine coverage. Sample sizes required for nonvaccine genotypes were >5 times greater than for vaccine genotypes and tended to be smaller in the post-vs.-prevaccine than in the vaccinated-vs.-unvaccinated design. These results highlight that previously published epidemiological studies were not powerful enough to efficiently detect changes in nonvaccine-genotype prevalence.

Episome partitioning and symmetric cell divisions: Quantifying the role of random events in the persistence of HPV infections

Human Papillomaviruses (HPV) are one of the most prevalent sexually transmitted infections (STI) and the most oncogenic viruses known to humans. The vast majority of HPV infections clear in less than 3 years, but the underlying mechanisms, especially the involvement of the immune response, are still poorly known. Building on earlier work stressing the importance of randomness in the type of cell divisions in the clearance of HPV infection, we develop a stochastic mathematical model of HPV dynamics that combines the previous aspect with an explicit description of the intracellular level. We show that the random partitioning of virus episomes upon stem cell division and the occurrence of symmetric divisions dramatically affect viral persistence. These results call for more detailed within-host studies to better understand the relative importance of stochasticity and immunity in HPV infection clearance.

Every year, infections by Human Papillomaviruses (HPV) are responsible for a large share of infectious cancers. The prevalence of HPVs is very high, which makes it a major public health issue. Fortunately, most HPV infections (80 to 90%) are cleared naturally within three years. Among the few that persist into chronic infections, the majority also naturally regress. Hence for a given HPV infection, the risk of progression towards cancerous status is low. The immune response is often invoked to explain HPV clearance in non-persisting infections, but many uncertainties remain. Besides immunity, randomness was also suggested to play an important role. Here, we examine how random events occurring during the life cycle of the virus could alter the persistence of the virus inside the host. We develop a mechanistic model that explicitly follows the dynamic of viral copies inside host cells, as well as the dynamics of the epithelium. In our model, infection extinction occurs when all viral copies end up in differentiated cells and migrate towards the surface. This can happen upon cell division during the random allocation of the episomes (i.e. independent circular DNA copies of the viral genome) or when a stem cell divides symmetrically to generate two differentiated cells. We find that the combination of these random events drastically affects infection persistence. More generally, the importance of random fluctuations could match that of immunity and calls for further studies at the within-host and the epidemiological level.

The ratio of single to co-colonization is key to complexity in interacting systems with multiple strains

The high number and diversity of microbial strains circulating in host populations have motivated extensive research on the mechanisms that maintain biodiversity. However, much of this work focuses on strain-specific and cross-immunity interactions. Another less explored mode of pairwise interaction is via altered susceptibilities to co-colonization in hosts already colonized by one strain. Diversity in such interaction coefficients enables strains to create dynamically their niches for growth and persistence, and "engineer" their common environment. How such a network of interactions with others mediates collective coexistence remains puzzling analytically and computationally difficult to simulate. Furthermore, the gradients modulating stability-complexity regimes in such multi-player endemic systems remain poorly understood. In a recent study (Madec & Gjini, Bulletin of Mathematical Biology, 82), we obtained an analytic representation for N-type coexistence in an SIS epidemiological model with co-colonization. We mapped multi-strain dynamics to a replicator equation using timescale separation. Here, we examine what drives coexistence regimes in such co-colonization system. We find the ratio of single to co-colonization, µ, critically determines the type of equilibrium and number of coexisting strains, and encodes a trade-off between overall transmission intensity R 0 and mean interaction coefficient in strain space, k. Preserving a given coexistence regime, under fixed trait variation, requires balancing between higher mean competition in favorable environments, and higher cooperation in harsher environments, and is consistent with the stress gradient hypothesis. Multi-strain coexistence tends to steady-state attractors for small µ, whereas as µ increases, dynamics tend to more complex attractors. Following strain frequencies, evolutionary dynamics in the system also display contrasting patterns with µ, interpolating between multi-stable and fluctuating selection for cooperation and mean invasion fitness, in the two extremes. This co-colonization framework could be applied more generally, to study invariant principles in collective coexistence, and to quantify how critical shifts in community dynamics get potentiated by mean-field and environmental gradients.

HPV cervical infections and serological status in vaccinated and unvaccinated women

Understanding genital infections by Human papillomaviruses (HPVs) remains a major public health issue, especially in countries where vaccine uptake is low. We investigate HPV prevalence and antibody status in 150 women (ages 18 to 25) in Montpellier, France. At inclusion and one month later, cervical swabs, blood samples and questionnaires (for demographics and behavioural variables) were collected. Oncogenic, non-vaccine genotypes HPV51, HPV66, HPV53, and HPV52 were the most frequently detected viral genotypes overall. Vaccination status, which was well-balanced in the cohort, showed the strongest (protective) effect against HPV infections, with an associated odds ratio for alphapapillomavirus detection of 0.45 (95% confidence interval: [0.22;0.58]). We also identified significant effects of age, number of partners, body mass index, and contraception status on HPV detection and on coinfections. Type-specific IgG serological status was also largely explained by the vaccination status. IgM seropositivity was best explained by HPV detection at inclusion only. Finally, we identify a strong significant effect of vaccination on genotype prevalence, with a striking under-representation of HPV51 in vaccinated women. Variations in HPV prevalence correlate with key demographic and behavioural variables. The cross-protective effect of the vaccine against HPV51 merits further investigation.

Interplay between competitive and cooperative interactions in a three-player pathogen system

In ecological systems, heterogeneous interactions between pathogens take place simultaneously. This occurs, for instance, when two pathogens cooperate, while at the same time, multiple strains of these pathogens co-circulate and compete. Notable examples include the cooperation of human immunodeficiency virus with antibiotic-resistant and susceptible strains of tuberculosis or some respiratory infections with Streptococcus pneumoniae strains. Models focusing on competition or cooperation separately fail to describe how these concurrent interactions shape the epidemiology of such diseases. We studied this problem considering two cooperating pathogens, where one pathogen is further structured in two strains. The spreading follows a susceptible-infected-susceptible process and the strains differ in transmissibility and extent of cooperation with the other pathogen. We combined a mean-field stability analysis with stochastic simulations on networks considering both well-mixed and structured populations. We observed the emergence of a complex phase diagram, where the conditions for the less transmissible, but more cooperative strain to dominate are non-trivial, e.g. non-monotonic boundaries and bistability. Coupled with community structure, the presence of the cooperative pathogen enables the coexistence between strains by breaking the spatial symmetry and dynamically creating different ecological niches. These results shed light on ecological mechanisms that may impact the epidemiology of diseases of public health concern.

Untangling the dynamics of persistence and colonization in microbial communities

A central goal of community ecology is to infer biotic interactions from observed distributions of co-occurring species. Evidence for biotic interactions, however, can be obscured by shared environmental requirements, posing a challenge for statistical inference. Here, we introduce a dynamic statistical model, based on probit regression, that quantifies the effects of spatial and temporal covariance in longitudinal co-occurrence data. We separate the fixed pairwise effects of species occurrences on persistence and colonization rates, a potential signal of direct interactions, from latent pairwise correlations in occurrence, a potential signal of shared environmental responses. We first validate our modeling framework with several simulation studies. Then, we apply the approach to a pressing epidemiological question by examining how human papillomavirus (HPV) types coexist. Our results suggest that while HPV types respond similarly to common host traits, direct interactions are sparse and weak, so that HPV type diversity depends largely on shared environmental drivers. Our modeling approach is widely applicable to microbial communities and provides valuable insights that should lead to more directed hypothesis testing and mechanistic modeling.

Epithelial stratification shapes infection dynamics

Infections of stratified epithelia contribute to a large group of common diseases, such as dermatological conditions and sexually transmitted diseases. To investigate how epithelial structure affects infection dynamics, we develop a general ecology-inspired model for stratified epithelia. Our model allows us to simulate infections, explore new hypotheses and estimate parameters that are difficult to measure with tissue cell cultures. We focus on two contrasting pathogens: Chlamydia trachomatis and Human papillomaviruses (HPV). Using cervicovaginal parameter estimates, we find that key infection symptoms can be explained by differential interactions with the layers, while clearance and pathogen burden appear to be bottom-up processes. Cell protective responses to infections (e.g. mucus trapping) generally lowered pathogen load but there were specific effects based on infection strategies. Our modeling approach opens new perspectives for 3D tissue culture experimental systems of infections and, more generally, for developing and testing hypotheses related to infections of stratified epithelia.

Distinct geographic clustering of oncogenic human papillomaviruses multiple infections in cervical cancers: Results from a worldwide cross-sectional study

Coinfections by multiple Human Papillomaviruses (HPVs) are observed in approximately 6-8% of invasive cervical cancer (ICC) cases worldwide. But neither the presence of persistent HPVs coinfections nor their etiological role in the development of ICC is well understood. Cervical HPVs coinfections have been observed randomly, mostly in women with preneoplastic lesions, and only few studies have globally analyzed ICC cases. Here we explored the HPVs multiple infection patterns in a large worldwide sample of cross-sectional ICC cases. Paraffin-embedded ICC biopsy samples were tested using stringent HPV genotyping. Logistic regression models were used to identify the most likely pairwise HPV types in multiple infections. Multivariate analysis was applied to detect significant HPV coinfection patterns beyond pairwise HPVs comparison. Among 8780 HPV DNA-positive ICC cases worldwide, 6.7% (N = 587) contained multiple HPVs. Pairwise analysis revealed that HPV16|74, HPV31|33, HPV31|44, HPV33|44 and HPV45|70 pairs were significantly more frequently found together in multiple infections compared to any other HPV type combination, which supports the occasional role of Alpha-10 LR-HPVs in cervical cancers. In contrast, HPV16|31, HPV16|45, HPV16|51 and HPV18|HPV45 pairs were significantly less frequently found together than with any other HPV pair combination. Multivariate analysis sustained the results and revealed for the first time a distinct coinfection pattern in African ICCs stemming from the clustering of oncogenic HPV51/35/18/52 coinfections in African women. We suggest that the differential geographic HPVs coinfections clustering observed might be compatible with a specific modulation of the natural history/oncogenic potential of particular HPVs multiple infections and warrant monitoring for post-vaccinated.

Mathematical analysis of a two-sex Human Papillomavirus (HPV) model

A two-sex deterministic model for Human Papillomavirus (HPV) that assesses the impact of treatment and vaccination on its transmission dynamics is designed and rigorously analyzed. The model is shown to exhibit the phenomenon of backward bifurcation, caused by the imperfect vaccine as well as the re-infection of individuals who recover from a previous infection, when the associated reproduction number is less than unity. Analysis of the reproduction number reveals that the impact of treatment on effective control of the disease is conditional, and depends on the sign of a certain threshold unlike when preventive measures are implemented (i.e. condom use and vaccination of both males and females). Numerical simulations of the model showed that, based on the parameter values used therein, a vaccine (with 75% efficacy) for male population with about 40% condom compliance by females will result in a significant reduction in the disease burden in the population. Also, the numerical simulations of the model reveal that with 70% condom compliance by the male population, administering female vaccine (with 45% efficacy) is sufficient for effective control of the disease.

Recurring infection with ecologically distinct HPV types can explain high prevalence and diversity

The high prevalence of human papillomavirus (HPV), the most common sexually transmitted infection, arises from the coexistence of over 200 genetically distinct types. Accurately predicting the impact of vaccines that target multiple types requires understanding the factors that determine HPV diversity. The diversity of many pathogens is driven by type-specific or "homologous" immunity, which promotes the spread of variants to which hosts have little immunity. To test for homologous immunity and to identify mechanisms determining HPV transmission, we fitted nonlinear mechanistic models to longitudinal data on genital infections in unvaccinated men. Our results provide no evidence for homologous immunity, instead showing that infection with one HPV type strongly increases the risk of infection with that type for years afterward. For HPV16, the type responsible for most HPV-related cancers, an initial infection increases the 1-year probability of reinfection by 20-fold, and the probability of reinfection remains 14-fold higher 2 years later. This increased risk occurs in both sexually active and celibate men, suggesting that it arises from autoinoculation, episodic reactivation of latent virus, or both. Overall, our results suggest that high HPV prevalence and diversity can be explained by a combination of a lack of homologous immunity, frequent reinfections, weak competition between types, and variation in type fitness between host subpopulations. Because of the high risk of reinfection, vaccinating boys who have not yet been exposed may be crucial to reduce prevalence, but our results suggest that there may also be large benefits to vaccinating previously infected individuals.

A Mathematical Model of Cell Cycle Dysregulation Due to Human Papillomavirus Infection

Human papillomaviruses (HPVs) that infect mucosal epithelium can be classified as high risk or low risk based on their propensity to cause lesions that can undergo malignant progression. HPVs produce the E7 protein that binds to cell cycle regulatory proteins including the retinoblastoma tumor suppressor protein (RB) to modulate cell cycle control. Generally, high-risk HPV E7 proteins bind to RB with a higher affinity than low-risk HPV E7s, but both are able to deactivate RB and trigger S phase progression. In uninfected cells, RB inactivation is a tightly controlled process that must coincide with growth factor stimulation to commit cells to division. High-risk HPV E7 proteins short-circuit this control by decreasing growth factor requirement for cell division. We develop a mathematical model to examine the role that RB binding affinity, growth factor concentration, and E7 concentration have on cell cycle progression. Our model predicts that high RB binding affinity and E7 concentration accelerate the [Formula: see text] to S phase transition and weaken the dependence on growth factor. This model thus captures a key step in high-risk HPV oncogenesis.

Multiple high-risk HPV genotypes are grouped by type and are associated with viral load and risk factors

Investigating whether high-risk human papillomavirus (HR-HPV) types tend to become grouped in a particular way and whether factors are associated with such grouping is important for measuring the real impact of vaccination. In total, 219 women proving positive for HPV as detected by real-time PCR were included in the study. Each sample was analysed for detecting and quantifying six viral types and the hydroxymethylbilane synthase gene. Multiple correspondence analysis led to determining grouping patterns for six HR-HPV types and simultaneous association with multiple variables and whether viral load was related to the coexistence of other viral types. Two grouping profiles were identified: the first included HPV-16 and HPV-45 and the second profile was represented by HPV-31, HPV-33 and HPV-58. Variables such as origin, contraceptive method, births and pregnancies, educational level, healthcare affiliation regime, atypical squamous cells of undetermined significance and viral load were associated with these grouping profiles. Different socio-demographic characteristics were found when coinfection occurred by phylogenetically related HPV types and when coinfection was due to non-related types. Biological characteristics, the number of viral copies, temporality regarding acquiring infection and competition between viral types could influence the configuration of grouping patterns. Characteristics related to women and HPV, influence such interactions between coexisting HPV types reflecting the importance of their evaluation.

Mechanistic mathematical models: An underused platform for HPV research

Health economic modeling has become an invaluable methodology for the design and evaluation of clinical and public health interventions against the human papillomavirus (HPV) and associated diseases. At the same time, relatively little attention has been paid to a different yet complementary class of models, namely that of mechanistic mathematical models. The primary focus of mechanistic mathematical models is to better understand the intricate biologic mechanisms and dynamics of disease. Inspired by a long and successful history of mechanistic modeling in other biomedical fields, we highlight several areas of HPV research where mechanistic models have the potential to advance the field. We argue that by building quantitative bridges between biologic mechanism and population level data, mechanistic mathematical models provide a unique platform to enable collaborations between experimentalists who collect data at different physical scales of the HPV infection process. Through such collaborations, mechanistic mathematical models can accelerate and enhance the investigation of HPV and related diseases.

Modeling the Mechanisms by Which HIV-Associated Immunosuppression Influences HPV Persistence at the Oral Mucosa

Human immunodeficiency virus (HIV)-infected patients are at an increased risk of co-infection with human papilloma virus (HPV), and subsequent malignancies such as oral cancer. To determine the role of HIV-associated immune suppression on HPV persistence and pathogenesis, and to investigate the mechanisms underlying the modulation of HPV infection and oral cancer by HIV, we developed a mathematical model of HIV/HPV co-infection. Our model captures known immunological and molecular features such as impaired HPV-specific effector T helper 1 (Th1) cell responses, and enhanced HPV infection due to HIV. We used the model to determine HPV prognosis in the presence of HIV infection, and identified conditions under which HIV infection alters HPV persistence in the oral mucosa system. The model predicts that conditions leading to HPV persistence during HIV/HPV co-infection are the permissive immune environment created by HIV and molecular interactions between the two viruses. The model also determines when HPV infection continues to persist in the short run in a co-infected patient undergoing antiretroviral therapy. Lastly, the model predicts that, under efficacious antiretroviral treatment, HPV infections will decrease in the long run due to the restoration of CD4+ T cell numbers and protective immune responses.

National- and state-level impact and cost-effectiveness of nonavalent HPV vaccination in the United States

Every year in the United States more than 12,000 women are diagnosed with cervical cancer, a disease principally caused by human papillomavirus (HPV). Bivalent and quadrivalent HPV vaccines protect against 66% of HPV-associated cervical cancers, and a new nonavalent vaccine protects against an additional 15% of cervical cancers. However, vaccination policy varies across states, and migration between states interdependently dilutes state-specific vaccination policies. To quantify the economic and epidemiological impacts of switching to the nonavalent vaccine both for individual states and for the nation as a whole, we developed a model of HPV transmission and cervical cancer incidence that incorporates state-specific demographic dynamics, sexual behavior, and migratory patterns. At the national level, the nonavalent vaccine was shown to be cost-effective compared with the bivalent and quadrivalent vaccines at any coverage despite the greater per-dose cost of the new vaccine. Furthermore, the nonavalent vaccine remains cost-effective with up to an additional 40% coverage of the adolescent population, representing 80% of girls and 62% of boys. We find that expansion of coverage would have the greatest health impact in states with the lowest coverage because of the decreasing marginal returns of herd immunity. Our results show that if policies promoting nonavalent vaccine implementation and expansion of coverage are coordinated across multiple states, all states benefit both in health and in economic terms.

Accuracy of EasyChip HPV blot genotyping assay to detect high-risk HPV genotypes in SurePath Papanicolaou specimens

INTRODUCTION

EasyChip HPV blot is a human papillomavirus (HPV) genotyping assay that can be potentially used for HPV assay validation or clinical HPV research. To evaluate its genotyping accuracy, we compared EasyChip HPV blot with quantitative real-time polymerase chain reaction (qRT-PCR)/type-specific PCR assays in the detection of 8 high-risk HPV genotypes.

MATERIALS AND METHODS

Archival SurePath Papanicolaou specimens with abnormal results and follow-up biopsy (n = 154) were selected retrospectively for HPV genotyping by EasyChip HPV blot. To determine the accuracy of the assay, qRT-PCR and type-specific PCR also were performed and results for 8 high-risk HPV genotypes were compared (HPV16, 18, 31, 33, 35, 45, 52, and 58).

RESULTS

A total of 95 Papanicolaou specimens were qualified for data analysis. Concordance between EasyChip HPV blot and qRT-PCR/type-specific PCR assays was high, with a very good agreement for the 8 high-risk HPV genotypes (95%; kappa value: 0.894, 95% CI: 0.805-0.984) and for HPV16 and HPV18 (96%; kappa value: 0.899, 95% CI: 0.802-0.996). HPV16 was the most frequent HPV genotype by EasyChip HPV blot. The odds ratio of HPV16/18 for high-grade cervical intraepithelial neoplasia was 11.25 (95% CI: 3.93-32.31).

CONCLUSIONS

EasyChip HPV blot is a reliable HPV genotyping assay that can be used for HPV assay validation or clinical HPV studies.

How direct competition shapes coexistence and vaccine effects in multi-strain pathogen systems

We describe an integrated modeling framework for understanding strain coexistence in polymorphic pathogen systems. Previous studies have debated the utility of neutral formulations and focused on cross-immunity between strains as a major stabilizing mechanism. Here we convey that direct competition for colonization mediates stable coexistence only when competitive abilities amongst pathogen clones satisfy certain pairwise asymmetries. We illustrate our ideas with nested SIS models of single and dual colonization, applied to polymorphic pneumococcal bacteria. By fitting the models to cross-sectional prevalence data from Portugal (before and after the introduction of a seven-valent pneumococcal conjugate vaccine), we are able to not only statistically compare neutral and non-neutral epidemiological formulations, but also estimate vaccine efficacy, transmission and competition parameters simultaneously. Our study highlights that the response of polymorphic pathogen populations to interventions holds crucial information about strain interactions, which can be extracted by suitable nested modeling.

HPV clearance and the neglected role of stochasticity

Clearance of anogenital and oropharyngeal HPV infections is attributed primarily to a successful adaptive immune response. To date, little attention has been paid to the potential role of stochastic cell dynamics in the time it takes to clear an HPV infection. In this study, we combine mechanistic mathematical models at the cellular level with epidemiological data at the population level to disentangle the respective roles of immune capacity and cell dynamics in the clearing mechanism. Our results suggest that chance—in form of the stochastic dynamics of basal stem cells—plays a critical role in the elimination of HPV-infected cell clones. In particular, we find that in immunocompetent adolescents with cervical HPV infections, the immune response may contribute less than 20% to virus clearance—the rest is taken care of by the stochastic proliferation dynamics in the basal layer. In HIV-negative individuals, the contribution of the immune response may be negligible.

Worldwide, 5% of all cancers are associated with the sexually transmitted human papillomavirus (HPV). The most common cancer types attributed to HPV are cervical and anal cancers, but HPV-related head and neck cancers are on the rise, too. Even though the lifetime risk of infection with HPV is as high as 80%, most infections clear spontaneously within 1–2 years, and only a small fraction progress to cancer. In order to identify who is at risk for HPV-related cancer, a better understanding of the underlying biology is of great importance. While it is generally accepted that the immune system plays a key role in HPV clearance, we investigate here a mechanism which could be equally important: the stochastic division dynamics of stem cells in the infected tissues. Combining mechanistic mathematical models at the cell-level with population-level data, we disentangle the contributions from immune system and cellular dynamics in the clearance process. We find that cellular stochasticity may play an even more important role than the immune system. Our findings shed new light onto open questions in HPV immunobiology, and may influence the way we vaccinate and screen individuals at risk of HPV-related cancers.

Papillomaviruses: Viral evolution, cancer and evolutionary medicine

Papillomaviruses (PVs) are a numerous family of small dsDNA viruses infecting virtually all mammals. PVs cause infections without triggering a strong immune response, and natural infection provides only limited protection against reinfection. Most PVs are part and parcel of the skin microbiota. In some cases, infections by certain PVs take diverse clinical presentations from highly productive self-limited warts to invasive cancers. We propose PVs as an excellent model system to study the evolutionary interactions between the immune system and pathogens causing chronic infections: genotypically, PVs are very diverse, with hundreds of different genotypes infecting skin and mucosa; phenotypically, they display extremely broad gradients and trade-offs between key phenotypic traits, namely productivity, immunogenicity, prevalence, oncogenicity and clinical presentation. Public health interventions have been launched to decrease the burden of PV-associated cancers, including massive vaccination against the most oncogenic human PVs, as well as systematic screening for PV chronic anogenital infections. Anti-PVs vaccines elicit protection against infection, induce cross-protection against closely related viruses and result in herd immunity. However, our knowledge on the ecological and intrapatient dynamics of PV infections remains fragmentary. We still need to understand how the novel anthropogenic selection pressures posed by vaccination and screening will affect viral circulation and epidemiology. We present here an overview of PV evolution and the connection between PV genotypes and the phenotypic, clinical manifestations of the diseases they cause. This differential link between viral evolution and the gradient cancer-warts-asymptomatic infections makes PVs a privileged playground for evolutionary medicine research.

Dynamics of high-risk nonvaccine human papillomavirus types after actual vaccination scheme

Human papillomavirus (HPV) has been identified as the main etiological factor in the developing of cervical cancer (CC). This finding has propitiated the development of vaccines that help to prevent the HPVs 16 and 18 infection. Both genotypes are associated with 70% of CC worldwide. In the present study, we aimed to determine the emergence of high-risk nonvaccine HPV after actual vaccination scheme to estimate the impact of the current HPV vaccines. A SIR-type model was used to study the HPV dynamics after vaccination. According to the results, our model indicates that the application of the vaccine reduces infection by target or vaccine genotypes as expected. However, numerical simulations of the model suggest the presence of the phenomenon called vaccine-induced pathogen strain replacement. Here, we report the following replacement mechanism: if the effectiveness of cross-protective immunity is not larger than the effectiveness of the vaccine, then the high-risk nonvaccine genotypes emerge. In this scenario, further studies of infection dispersion by HPV are necessary to ascertain the real impact of the current vaccines, primarily because of the different high-risk HPV types that are found in CC.