Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification

Abstract

Background External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification. Methods Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35–74 years. Risk projections in a target population of US white non-Hispanic women age 50–70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS). Results The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years. Conclusions iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.

Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions

“…First, UK Biobank participants tend to be healthier than the general population, and Color Genomics participants are likely to be enriched for genetic disease compared to the general population 35 . Within any target population, a comparison of predicted and observed disease rates can allow for recalibration of risk estimates, as recently performed for a breast cancer prediction tool 36 . Second, our analysis focused on the role of monogenic variants in the nine genes associated with the three CDC tier 1 genomic conditions; additional efforts are needed to include variants in other known or newly discovered genes in the future.…”

Using Whole Breast Ultrasound Tomography to Improve Breast Cancer Risk Assessment: A Novel Risk Factor Based on the Quantitative Tissue Property of Sound Speed

“…Given that breast cancer risk prediction models under-perform with regard to estimating individual risk, researchers have attempted to incorporate MPD into such models to improve their performance (as recently reviewed in Louro et al [2]). Multiple studies [2] have found that adding MPD to risk models improves breast cancer risk prediction, and efforts to incorporate MPD in newer risk models are ongoing [3,4]; however, to date, improvements in discriminatory accuracy have been modest.…”

Distinct temporal trends in breast cancer incidence from 1997 to 2016 by molecular subtypes: a population-based study of Scottish cancer registry data

“…Furthermore, specific molecular subtypes, such as ER− breast cancers and the subset of ER− basal-like tumours, were more likely to be interval cancers. Predictive modelling of breast cancer has been proposed as a potential tool for personalised medicine and risk-stratified screening, [45][46][47] and future efforts might be used within screening programmes to improve the detection of more aggressive ER− breast cancers, particularly amongst those at higher risk of developing such cancers. With increased technologic advances in imaging modalities, it will be important to assess how these impact screen-detected tumours, and whether they can also improve detection for more aggressive ER-negative tumours that are more likely to be diagnosed outside of most screening programmes' age ranges.…”