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

2019 ◽  
Vol 112 (3) ◽  
pp. 278-285 ◽  
Author(s):  
Parichoy Pal Choudhury ◽  
Amber N Wilcox ◽  
Mark N Brook ◽  
Yan Zhang ◽  
Thomas Ahearn ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Hispanic Women ◽  
Target Population ◽  
Mammographic Breast Density ◽  
Polygenic Risk Score ◽  
Breast Cancer Risk Assessment

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.

scholarly journals A streamlined model for use in clinical breast cancer risk assessment maintains predictive power and is further improved with inclusion of a polygenic risk score

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245375
Author(s):  
Richard Allman ◽  
Erika Spaeth ◽  
John Lai ◽  
Susan J. Gross ◽  
John L. Hopper
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
African American ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Score ◽  
Clinical Risk Factors ◽  
Polygenic Risk Score ◽  
Gail Model ◽  
Clinical Risk

Five-year absolute breast cancer risk prediction models are required to comply with national guidelines regarding risk reduction regimens. Models including the Gail model are under-utilized in the general population for various reasons, including difficulty in accurately completing some clinical fields. The purpose of this study was to determine if a streamlined risk model could be designed without substantial loss in performance. Only the clinical risk factors that were easily answered by women will be retained and combined with an objective validated polygenic risk score (PRS) to ultimately improve overall compliance with professional recommendations. We first undertook a review of a series of 2,339 Caucasian, African American and Hispanic women from the USA who underwent clinical testing. We first used deidentified test request forms to identify the clinical risk factors that were best answered by women in a clinical setting and then compared the 5-year risks for the full model and the streamlined model in this clinical series. We used OPERA analysis on previously published case-control data from 11,924 Gail model samples to determine clinical risk factors to include in a streamlined model: first degree family history and age that could then be combined with the PRS. Next, to ensure that the addition of PRS to the streamlined model was indeed beneficial, we compared risk stratification using the Streamlined model with and without PRS for the existing case-control datasets comprising 1,313 cases and 10,611 controls of African-American (n = 7421), Caucasian (n = 1155) and Hispanic (n = 3348) women, using the area under the curve to determine model performance. The improvement in risk discrimination from adding the PRS risk score to the Streamlined model was 52%, 46% and 62% for African-American, Caucasian and Hispanic women, respectively, based on changes in log OPERA. There was no statistically significant difference in mean risk scores between the Gail model plus risk PRS compared to the Streamlined model plus PRS. This study demonstrates that validated PRS can be used to streamline a clinical test for primary care practice without diminishing test performance. Importantly, by eliminating risk factors that women find hard to recall or that require obtaining medical records, this model may facilitate increased clinical adoption of 5-year risk breast cancer risk prediction test in keeping with national standards and guidelines for breast cancer risk reduction.

scholarly journals Review of: Are breast density and bone mineral density independent risk factors for breast cancer?

2005 ◽  
Vol 8 (11) ◽  
Author(s):  
J. L. Hopper
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Bone Mineral Density ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Correlation Coefficient ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Mineral Density ◽  
Increased Risk

Citation of original article:K. Kerlikowske, J. Shepherd, J. Creasman, J. A. Tice, E. Ziv, S. R. Cummings. Are breast density and bone mineral density independent risk factors for breast cancer. Journal of the National Cancer Institute 2005; 97(7): 368–74.Abstract of the original articleBackground: Mammographic breast density and bone mineral density (BMD) are markers of cumulative exposure to estrogen. Previous studies have suggested that women with high mammographic breast density or high BMD are at increased risk of breast cancer. We determined whether mammographic breast density and BMD of the hip and spine are correlated and independently associated with breast cancer risk. Methods: We conducted a cross-sectional study (N = 15 254) and a nested case-control study (of 208 women with breast cancer and 436 control subjects) among women aged 28 years or older who had a screening mammography examination and hip BMD measurement within 2 years. Breast density for 3105 of the women was classified using the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) categories, and percentage mammographic breast density among the case patients and control subjects was quantified with a computer-based threshold method. Spearman rank partial correlation coefficient and Pearson's correlation coefficient were used to examine correlations between BI-RADS breast density and BMD and between percentage mammographic breast density and BMD, respectively, in women without breast cancer. Logistic regression was used to examine the association of breast cancer with percentage mammographic breast density and BMD. All statistical tests were two-sided. Results: Neither BI-RADS breast density nor percentage breast density was correlated with hip or spine BMD (correlation coefficient = −.02 and −.01 for BI-RADS, respectively, and −2.06 and .01 for percentage breast density, respectively). Neither hip BMD nor spine BMD had a statistically significant relationship with breast cancer risk. Women with breast density in the highest sextile had an approximately threefold increased risk of breast cancer compared with women in the lowest sextile (odds ratio: 2.7; 95% confidence interval: 1.4–5.4); adjusting for hip or spine BMD did not change the association between breast density and breast cancer risk. Conclusion: Breast density is strongly associated with increased risk of breast cancer, even after taking into account reproductive and hormonal risk factors, whereas BMD, although a possible marker of lifetime exposure to estrogen, is not. Thus, a component of breast density that is independent of estrogen-mediated effects may contribute to breast cancer risk.

scholarly journals Combined Associations of a Polygenic Risk Score and Classical Risk Factors With Breast Cancer Risk

2020 ◽  
Author(s):  
Pooja Middha Kapoor ◽  
Nasim Mavaddat ◽  
Parichoy Pal Choudhury ◽  
Amber N Wilcox ◽  
Sara Lindström ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Genetic Risk ◽  
Goodness Of Fit ◽  
European Ancestry ◽  
Individual Risk ◽  
Polygenic Risk Score ◽  
Increased Risk

Abstract We evaluated the joint associations between a new 313-variant PRS (PRS313) and questionnaire-based breast cancer risk factors for women of European ancestry, using 72 284 cases and 80 354 controls from the Breast Cancer Association Consortium. Interactions were evaluated using standard logistic regression and a newly developed case-only method for breast cancer risk overall and by estrogen receptor status. After accounting for multiple testing, we did not find evidence that per-standard deviation PRS313 odds ratio differed across strata defined by individual risk factors. Goodness-of-fit tests did not reject the assumption of a multiplicative model between PRS313 and each risk factor. Variation in projected absolute lifetime risk of breast cancer associated with classical risk factors was greater for women with higher genetic risk (PRS313 and family history) and, on average, 17.5% higher in the highest vs lowest deciles of genetic risk. These findings have implications for risk prevention for women at increased risk of breast cancer.

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