National screening for breast cancer using mammographic imaging, while shown to decrease breast cancer mortality, is associated with over-diagnosis and over-treatment. NICE has produced guidelines on the management of patients at increased risk of breast cancer due to a strong family history and/or mutations in rare, high-penetrance breast cancer genes such as BRCA1 and BRCA2. In recent years, GWASs have identified a number of more common low-penetrance susceptibility loci for breast cancer. Although each individual locus confers a relatively lower increase in risk, it is has been shown that when combined under a log-additive model they provide a modest level of risk discrimination in European populations. Genotyping for 18 of these loci was carried out in 2,301 Scottish women (870 women with breast cancer, 385 women with a strong family history and 1046 population controls), using a single iPLEX™ Assay as part of the MassARRAY® System by Sequenom®. Polygenic risk across 18 loci was found to follow a log-normal distribution with a mean close to zero in the Scottish population, with higher means for those with a family history and for those with breast cancer. The discriminatory accuracy of the polygenic risk profile was shown by an AUROC = 0.602, which is consistent with other risk estimation models. Polygenic risk was not found to correlate with other established risk factors such as breast tissue density or family history risk as determined by the BOADICEA risk estimation tool. However, there were stronger associations of polygenic risk in both ER-positive disease and for those diagnosed at a younger age. Further research involving a larger polygenic risk profile may yet show stronger discriminatory accuracy, especially if used in conjunction with breast tissue density and other established risk estimation tools. In conclusion, this research has provided further evidence to support the use of genotype data in breast cancer risk discrimination at a population level.
|Date of Award||2013|
|Supervisor||Jonathan Berg (Supervisor)|