Abstract
There are two main questions when assessing a woman for interventions to reduce her risks of developing or dying from breast cancer, the answers of which will determine her access: What are her chances of carrying a mutation in a high-risk gene such as BRCA1 or BRCA2? What are her risks of developing breast cancer with or without such a mutation? These risks taken together with the risks and benefits of the intervention will then determine whether an intervention is appropriate. A number of models have been developed for assessing these risks with varying degrees of validation. With further improvements in our knowledge of how to integrate risk factors and to eventually integrate further genetic variants into these models, we are confident we will be able to discriminate with far greater accuracy which women are most likely to develop breast cancer.
Highlights
Breast cancer is the most common form of cancer affecting women
We have recently shown that at least 20% of breast cancer patients aged 30 years and younger are due to mutations in the known high-risk genes BRCA1, BRCA2 and TP53 [10]
The major limitation of the Gail model is the inclusion of only first-degree relatives, which results in underestimating risk in the 50% of families with cancer in the paternal lineage and takes no account of the age of onset of breast cancer. As such it performed less well in our own validation set from a family history clinic (Table 1), substantially underestimating risk overall and in most subgroups assessed [27]
Summary
Breast cancer is the most common form of cancer affecting women. One in eight to one in 12 women will develop the disease in their lifetime in the developed world. The major limitation of the Gail model is the inclusion of only first-degree relatives, which results in underestimating risk in the 50% of families with cancer in the paternal lineage and takes no account of the age of onset of breast cancer As such it performed less well in our own validation set from a family history clinic (Table 1), substantially underestimating risk overall and in most subgroups assessed [27]. As these were the only models to take account of ovarian cancer in their risk-assessment algorithm, this confirmed that ovarian cancer has a significant effect on breast cancer risk. With improvements in the computer models such as the BOADICEA model, we will hopefully achieve a more accurate and discriminatory cutoff point
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