A breast cancer prediction model incorporating familial and personal risk factors

TLDR

Breast cancer risk is influenced by genetic and personal factors, yet prior studies have not combined detailed genetic analysis with personal history to fully explain familial aggregation. The study aims to integrate genetic and personal risk factors to improve overall breast cancer risk prediction. The model applies Bayesian inference to family history to estimate gene carrier probabilities, then refines risk using personal history. The resulting model, implemented in a computer program, provides personalized breast cancer risk estimates by combining BRCA, low‑penetrance genes, and personal factors.

Abstract

Many factors determine a woman's risk of breast cancer. Some of them are genetic and relate to family history, others are based on personal factors such as reproductive history and medical history. While many papers have concentrated on subsets of these risk factors, no papers have incorporated personal risk factors with a detailed genetic analysis. There is a need to combine these factors to provide a better overall determinant of risk. The discovery of the BRCA1 and BRCA2 genes has explained some of the genetic determinants of breast cancer risk, but these genes alone do not explain all of the familial aggregation of breast cancer. We have developed a model incorporating the BRCA genes, a low penetrance gene and personal risk factors. For an individual woman her family history is used in conjuction with Bayes theorem to iteratively produce the likelihood of her carrying any genes predisposing to breast cancer, which in turn affects her likelihood of developing breast cancer. This risk was further refined based on the woman's personal history. The model has been incorporated into a computer program that gives a personalised risk estimate.