Validating risk prediction models for common hormonal cancers
University of Cambridge
Professor Douglas Easton
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Genetic and other risk-factor data can potentially be used to predict a person?s risk of developing a particular type of cancer. These individual risks could be used to focus screening programs or risk-lowering interventions towards those at highest risk. We are developing risk prediction models for breast, ovarian, endometrial and prostate cancer which include information about common genetic variants and other risk factors. We would like to use Biobank data to evaluate the performance of our models.
Separately, we would like to estimate the effects of hormone levels and genetic variants on cancer risk and survival. This research is both health-related and in the public interest. The very large, prospective UK Biobank cohort is an ideal resource for validating our cancer risk prediction models ? it is essential that these models are thoroughly evaluated before they can be considered for clinical use. We will use our risk-prediction models to predict each participant?s risk of developing breast, ovarian, endometrial or prostate cancer within five or ten years of enrolment (excluding those with any prior cancer diagnosis), based on their genotypes, family history of cancer and other variables. We will divide participants into groups depending on their risk level, then compare the predicted numbers of cancers in each group with the number observed in that time period.
Separately, we will compare circulating hormone levels at enrolment between those who did or did not later develop cancer and perform GWASs using baseline cases. We are requesting access to data for the whole UKBB cohort.