scholarly journals Prospective Evaluation of a Breast Cancer Risk Model Integrating Classical Risk Factors and Polygenic Risk in 15 Cohorts from Six Countries

2019 ◽  
Author(s):  
Amber N Wilcox ◽  
Parichoy Pal Choudhury ◽  
Chi Gao ◽  
Anika Hüsing ◽  
Mikael Eriksson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Cancer Prevention ◽  
Risk Model ◽  
European Ancestry ◽  
Polygenic Risk ◽  
The Us ◽  
Prospective Cohorts

ABSTRACTPURPOSERisk-stratified breast cancer prevention requires accurate identification of women at sufficiently different levels of risk. We conducted a comprehensive evaluation of a model integrating classical risk factors and a recently developed 313-variant polygenic risk score (PRS) to predict breast cancer risk.METHODSFifteen prospective cohorts from six countries with 237,632 women (7,529 incident breast cancer patients) of European ancestry aged 19-75 years at baseline were included. Calibration of five-year risk was assessed by comparing predicted and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50-70 years in those countries was evaluated by the proportion of women and future breast cancer cases crossing clinically-relevant risk thresholds.RESULTSThe model integrating classical risk factors and PRS accurately predicted five-year risk. For women younger than 50 years, median (range) expected-to-observed ratio across the cohorts was 0.94 (0.72 to 1.01) overall and 0.9 (0.7 to 1.4) at the highest risk decile. For women 50 years or older, these ratios were 1.04 (0.73 to 1.31) and 1.2 (0.7 to 1.6), respectively. The proportion of women in the general population identified above the 3% five-year risk threshold (used for recommending risk-reducing medications in the US) ranged from 7.0% in Germany (∼841,000 of 12 million) to 17.7% in the US (∼5.3 of 30 million). At this threshold, 14.7% of US women were re-classified by the addition of PRS to classical risk factors, identifying 12.2% additional future breast cancer cases.CONCLUSIONEvaluation across multiple prospective cohorts demonstrates that integrating a 313-SNP PRS into a risk model substantially improves its ability to stratify women of European ancestry for applying current breast cancer prevention guidelines.

scholarly journals Comparative validation of the BOADICEA and Tyrer-Cuzick breast cancer risk models incorporating classical risk factors and polygenic risk in a population-based prospective cohort

2020 ◽  
Author(s):  
Parichoy Pal Choudhury ◽  
Mark N. Brook ◽  
Amber N. Wilcox ◽  
Andrew Lee ◽  
Charlotta Mulder ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Older Women ◽  
Population Based ◽  
European Ancestry ◽  
Risk Models ◽  
Polygenic Risk ◽  
Comparative Validation

AbstractPurposeThe Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and the Tyrer-Cuzick breast cancer models have recently been extended to include polygenic risk scores (PRS). In addition, BOADICEA has also been extended to include reproductive and lifestyle factors, which were already part of Tyrer-Cuzick model. We conducted a comparative validation of the extended models including a recently developed 313-variant PRS in a population-based prospective cohort.MethodsWe used data from a nested case-control sample of 1,337 women of European ancestry (619 incident breast cancer cases) aged 23-75 years from the Generations Study. Models were evaluated for calibration of five-year absolute risk and risk discrimination.ResultsThe extended BOADICEA model with risk factors and PRS was well calibrated across risk deciles: expected-to-observed ratio (E/O) at the highest risk decile = 0.97 (95% Cl = 0.51 to 1.86) for women younger than 50 years and 1.09 (0.66 to 1.80) for women 50 years or older. Adding risk factors and PRS to the BOADICEA model improved discrimination modestly in younger women (Area Under the Curve (AUC): 69.7% vs. 69.1%) and substantially in older women (AUC: 64.6% vs. 56.8%). The Tyrer-Cuzick model with PRS had similar discrimination as the extended BOADICEA model for both age groups; but showed evidence of overestimation at the highest risk decile: E/O=1.54 (0.81 to 2.92) for younger and 1.73 (1.03 to 2.90) for older women.ConclusionThe extended BOADICEA model identified women in a European ancestry population at elevated breast cancer risk more accurately than the Tyrer-Cuzick model with PRS. These analyses can inform choice of risk models for risk stratified breast cancer prevention for women of European ancestry.

scholarly journals Integrating Clinical and Polygenic Factors to Predict Breast Cancer Risk in Women Undergoing Genetic Testing

2021 ◽  
pp. 307-316
Author(s):  
Elisha Hughes ◽  
Placede Tshiaba ◽  
Susanne Wagner ◽  
Thaddeus Judkins ◽  
Eric Rosenthal ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Risk Score ◽  
Risk Model ◽  
European Ancestry ◽  
Polygenic Risk ◽  
Screening And Prevention

PURPOSE Screening and prevention decisions for women at increased risk of developing breast cancer depend on genetic and clinical factors to estimate risk and select appropriate interventions. Integration of polygenic risk into clinical breast cancer risk estimators can improve discrimination. However, correlated genetic effects must be incorporated carefully to avoid overestimation of risk. MATERIALS AND METHODS A novel Fixed-Stratified method was developed that accounts for confounding when adding a new factor to an established risk model. A combined risk score (CRS) of an 86–single-nucleotide polymorphism polygenic risk score and the Tyrer-Cuzick v7.02 clinical risk estimator was generated with attenuation for confounding by family history. Calibration and discriminatory accuracy of the CRS were evaluated in two independent validation cohorts of women of European ancestry (N = 1,615 and N = 518). Discrimination for remaining lifetime risk was examined by age-adjusted logistic regression. Risk stratification with a 20% risk threshold was compared between CRS and Tyrer-Cuzick in an independent clinical cohort (N = 32,576). RESULTS Simulation studies confirmed that the Fixed-Stratified method produced accurate risk estimation across patients with different family history. In both validation studies, CRS and Tyrer-Cuzick were significantly associated with breast cancer. In an analysis with both CRS and Tyrer-Cuzick as predictors of breast cancer, CRS added significant discrimination independent of that captured by Tyrer-Cuzick ( P < 10−11 in validation 1; P < 10−7 in validation 2). In an independent cohort, 18% of women shifted breast cancer risk categories from their Tyrer-Cuzick–based risk compared with risk estimates by CRS. CONCLUSION Integrating clinical and polygenic factors into a risk model offers more effective risk stratification and supports a personalized genomic approach to breast cancer screening and prevention.

scholarly journals Modifiable Lifestyle Factors: Opportunities for (Hereditary) Breast Cancer Prevention - a Narrative Review

Breast Care ◽  
2018 ◽  
Vol 13 (2) ◽  
pp. 108-113 ◽  
Author(s):  
Jacqueline Lammert ◽  
Sabine Grill ◽  
Marion Kiechle
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Cancer Prevention ◽  
Healthy Lifestyle ◽  
Absolute Risk ◽  
Lifestyle Factors ◽  
Breast Cancer Prevention ◽  
The Impact

Increasing rates of obesity, lack of physical activity, sedentary behavior, and frequent alcohol consumption are major lifestyle-related risk factors for breast cancer. In fact, it has been estimated that about one-third of breast cancer cases are attributable to factors women can change. Most research has focused on examining the impact of one single exposure on breast cancer risk while adjusting for other risk modifiers. Capitalizing on big data, major efforts have been made to evaluate the combined impact of well-established lifestyle factors on overall breast cancer risk. At the individual level, data indicate that even simple behavior modifications could have a considerable impact on breast cancer prevention. Moreover, there is emerging new evidence that adopting a healthy lifestyle may be particularly relevant for women with hereditary susceptibility to breast cancer. On the absolute risk scale, studies suggest that the presence of certain risk factors, such as excessive body weight, had a substantially higher impact on breast cancer risk if women had a hereditary predisposition to cancer. The existing body of knowledge gives the medical professionals guidance as to which factors to focus on when counseling patients. However, well-designed randomized controlled trials utilizing objective methods are crucial to providing concrete recommendations.

scholarly journals A Case-Control Study to Add Volumetric or Clinical Mammographic Density into the Tyrer-Cuzick Breast Cancer Risk Model

2019 ◽  
Vol 1 (2) ◽  
pp. 99-106 ◽  
Author(s):  
Adam R Brentnall ◽  
Wendy F Cohn ◽  
William A Knaus ◽  
Martin J Yaffe ◽  
Jack Cuzick ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Mammographic Density ◽  
Case Control Study ◽  
Risk Model ◽  
Case Control ◽  
Percent Density ◽  
Control Study

Abstract Background Accurate breast cancer risk assessment for women attending routine screening is needed to guide screening and preventive interventions. We evaluated the accuracy of risk predictions from both visual and volumetric mammographic density combined with the Tyrer-Cuzick breast cancer risk model. Methods A case-control study (474 patient participants and 2243 healthy control participants) of women aged 40–79 years was performed using self-reported classical risk factors. Breast density was measured by using automated volumetric software and Breast Imaging and Reporting Data System (BI-RADS) density categories. Odds ratios (95% CI) were estimated by using logistic regression, adjusted for age, demographic factors, and 10-year risk from the Tyrer-Cuzick model, for a change from the 25th to 75th percentile of the adjusted percent density distribution in control participants (IQ-OR). Results After adjustment for classical risk factors in the Tyrer-Cuzick model, age, and body mass index (BMI), BI-RADS density had an IQ-OR of 1.55 (95% CI = 1.33 to 1.80) compared with 1.40 (95% CI = 1.21 to 1.60) for volumetric percent density. Fibroglandular volume (IQ-OR = 1.28, 95% CI = 1.12 to 1.47) was a weaker predictor than was BI-RADS density (Pdiff = 0.014) or volumetric percent density (Pdiff = 0.065). In this setting, 4.8% of women were at high risk (8% + 10-year risk), using the Tyrer-Cuzick model without density, and 7.1% (BI-RADS) compared with 6.8% (volumetric) when combined with density. Conclusion The addition of volumetric and visual mammographic density measures to classical risk factors improves risk stratification. A combined risk could be used to guide precision medicine, through risk-adapted screening and prevention strategies.

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.

scholarly journals Risk factors for breast cancer in the breast cancer risk model study of Guam and Saipan

2017 ◽  
Vol 50 ◽  
pp. 221-233 ◽  
Author(s):  
Rachael T. Leon Guerrero ◽  
Rachel Novotny ◽  
Lynne R. Wilkens ◽  
Marie Chong ◽  
Kami K. White ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Model ◽  
Model Study

scholarly journals Breast cancer risk prediction using a clinical risk model and polygenic risk score

2016 ◽  
Vol 159 (3) ◽  
pp. 513-525 ◽  
Author(s):  
Yiwey Shieh ◽  
Donglei Hu ◽  
Lin Ma ◽  
Scott Huntsman ◽  
Charlotte C. Gard ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Risk Score ◽  
Risk Model ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Clinical Risk
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