Abstract A28: Risk model for clinical management of HPV-infected women
Authors: Demarco Maria , Hyun Noorie , Katki Hormuzd , Befano Brian , Cheung Li , Raine-Bennett Tina R. , Fetterman Barbara , Lorey Thomas , Poitras Nancy , Gage Julia C. , Castle Phillip E. , Wentzensen Nicolas , Schiffman Mark 
Published: May 2019

Background: The natural history of human papillomavirus (HPV) and the steps leading to cervical cancer are well-known; the steps include infection with one of the 13 carcinogenic HPV genotypes, viral persistence, progression to precancer, and invasion. Cervical screening programs target treatable cervical precancer to prevent cancer mortality and morbidity. HPV infections are very common and only those causing precancer pose a risk of cancer. In addition to HPV genotype, multiple established co-factors can be combined to predict with unparalleled accuracy and precision the broad range of risks for the critical transition from common HPV infection to uncommon cervical precancer. Thus, there are three types of factors predicting risk of precancer: viral (e.g., HPV genotype and viral load), host (e.g., age, race/ethnicity) and behavioral (e.g., oral contraceptive use, smoking, BMI, co-infection with other sexually transmitted agents). We are building a risk prediction model for clinical use that reflects the determinants of HPV natural history. The absolute-risk based model will consider the three possible HPV outcomes: HPV progression, else HPV “clearance” (immune suppression) signifying low risk of subsequent precancer from that infection, else persistence of HPV infection without either progression or clearance (i.e., still unresolved outcome). To estimate these competing risks for all the factors, cofactors and their combinations requires very large cohorts of HPV-infected women.

Methods: Our analysis makes use of data from a uniquely large cohort study of HPV-infected women, specifically, the 35,000 HPV-positive women, 30 years or older, from the NCI-Kaiser Permanente Northern California Persistence and Progression cohort study. The median time of follow-up is 3 years (maximum &gt;7 years). Risk predictors already recorded include: woman's age, HPV infection status, HPV genotype, viral load, concurrent cervical cytology result, and the range of behavioral cofactors. We will present at the meeting the steps leading to the final model: 1) univariate, then multivariate, absolute risks of progression, clearance, or persistence for each HPV genotype; 2) the same risks accounting for time to event and loss-to-followup; and 3) the novel statistic mean risk stratification (MRS), which measures how well the model predicts the crucial dichotomous outcome (progression vs. not). MRS identifies which combination of variables, by virtue of frequency of positive results and strength of risk stratification, is most promising in deciding risk-based clinical management (i.e., who needs colposcopic biopsy due to high risk of precancer). We present the univariate absolute risks for HPV genotypes here, but will show the full multivariate proportional hazards and MRS analyses at the conference.

Results: Risk of progression (29.4% for HPV16 to 7.2% for HPV68) varied inversely with risk of clearance (60.1% for HPV16 to 81.6% for HPV68), by HPV type. Relatively few (~10%) of infections of any carcinogenic type persisted without progression. The most important univariate cofactors in preliminary analyses are viral load (for HPV16 mainly), woman's age, and concurrent cytology. No behavioral risk factors are especially important. Time to clearance and time to progression did not vary by HPV type, with median time to events of 1.5-2 years.

Conclusions: Based on our preliminary results, the fate of most HPV infections is determined within a few years of first detection, based mainly on characteristics of the virus. MRS summarizes the average risk discrimination of the prediction model compared to pre-test probability, permitting estimation of its expected benefit. We hypothesize and will test whether multivariate calculations of absolute risks and the use of mean risk stratification can lead to improved risk-based clinical management of HPV-infected women.

Citation Format: Maria Demarco, Noorie Hyun, Hormuzd Katki, Brian Befano, Li Cheung, Tina R. Raine-Bennett, Barbara Fetterman, Thomas Lorey, Nancy Poitras, Julia C. Gage, Phillip E. Castle, Nicolas Wentzensen, Mark Schiffman. Risk model for clinical management of HPV-infected women. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A28.