Greater obesity among breast cancer cases compared to controls from the Breast Cancer Risk Model in Pacific Islander Study in Guam and Saipan (628.8)

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.

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Clinical validity assessment of a breast cancer risk model combining genetic and clinical information

Nature Precedings ◽

10.1038/npre.2010.4295.1 ◽

2010 ◽

Author(s):

Matthew Mealiffe ◽

Renee Stokowski ◽

Brian Rhees ◽

Ross Prentice ◽

Mary Pettinger ◽

...

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Risk Model ◽

Clinical Information ◽

Clinical Validity ◽

Validity Assessment ◽

Model Combining

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

Journal of Breast Imaging ◽

10.1093/jbi/wbz006 ◽

2019 ◽

Vol 1 (2) ◽

pp. 99-106 ◽

Cited By ~ 8

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.

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