Cervical cancer etiology is better understood than the origins of most major malignancies. Within the Division of Cancer Epidemiology and Genetics, several groups continue to study the most interesting and important research topics. In the HPV Guanacaste Study, remaining topics in natural history of the causal virus (human papillomavirus, HPV) are the focus. The unusually high prevalence of HPV in sub Saharan Africa was another focus in the recently completed Nigeria Project Itoju. The now completed Portland study assessed the risk of cervical neoplasia in the 16 years following a single baseline HPV test and cytologic smear. Cervical Visualization Project: In this set of studies, HPV natural history is assessed visually (using a web-based open-source software system developed with the National Library of Medicine), microscopically (cytology and histology) and using a variety of molecular biomarkers. HPV Genome Project. Viral genomic studies are designed to determine why certain types of HPV, if persistent, are extremely powerful carcinogens (acquired genetic syndromes with high penetrance) while related HPV types are not. Viral methylation is also being considered. In the HPV Methylation Project, we are exploring the epigenetic changes in HPV and host genes in relationship to risk of HPV persistence, progression to CIN3, and invasion. The Persistence and Progression (PaP) cohort is a collaboration with Kaiser Permanente Northern California. More than 40,000 women with dual testing with positive HPV DNA assays and cytology are being followed for outcome. The New Mexico Pap Registry is a collaboration with Dr. Cosette Wheeler at U. New Mexico, consisting of a statewide surveillance of cytology and histology outcomes. In a subset, HPV testing is available. The goal eventually is to monitor the impact of HPV vaccination on cervical screening. HPV Cervical Cancer Risk Prediction. This study involves translation of what we have learned about HPV and cervical carcinogenesis into clnical guidelines, particularly via risk prediction models. This research project is human population based and etiologic.
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