Approved Research

Overlap of high-risk individuals predicted by family history, genetic and non-genetic breast cancer risk prediction models

Lay summary

Currently, in many countries, population-based mammography screening is recommended based on age alone. However, not every woman is at the same level of risk of developing breast cancer. In this study, we calculate the breast cancer risk of individual women using different breast cancer risk prediction tools to assess the level of overlap in the high-risk individuals found. In particular, we investigate the performance of different tools in flagging high-risk individuals who are not yet at the conventional age to start breast cancer screening.

With the latest developments in genetic risk prediction, it is timely to consider whether every woman in the general population should be genetically profiled to inform screening programs. Our preliminary findings show that both genetic and conventional risk stratification tools have their own merits, and are able to identify unique individuals at risk. Each risk assessment tool is a partial predictor at best. The inclusion of multiple predictive tools can pick up additional high-risk individuals who are missed out from using any one tool alone. In our study, family history and genetic risk perform better for women below age 50, as compared to the Gail model. This is noteworthy as the entry age for subsidized breast screening in many countries is 50 years. Genetic risk profiles will help younger women in making informed decisions on whether they should start screening at an earlier age. High-risk individuals may benefit from specific recommendations or interventions based on their personal breast cancer risk profiles.

In view of public health policies, our findings may support the incorporation of genetic tools for breast cancer risk stratification. Given that screening uptake and adherence is alarmingly low even when cost is not a barrier to entry in some countries, the study team believes that increasing women's personal stakes in taking control of their own health may be the way forward. Individual risk profiles gathered from genetic information may help women make informed decisions and motivate them to attend screening.

In conclusion, family history, genetic and non-genetic risk stratification tools have the potential to complement one another to identify women at high risk in breast cancer screening programs. Our findings may add to the growing body of evidence to support a paradigm shift from an approach that is age-based to risk-based.