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

Author:

Hughes Elisha1,Tshiaba Placede1,Wagner Susanne1,Judkins Thaddeus1,Rosenthal Eric1,Roa Benjamin1,Gallagher Shannon1,Meek Stephanie1,Dalton Kathryn2,Hedegard Wade3,Adami Carol A.4,Grear Danna F.5,Domchek Susan M.6,Garber Judy7,Lancaster Johnathan M.1,Weitzel Jeffrey N.8,Kurian Allison W.9,Lanchbury Jerry S.1,Gutin Alexander1,Robson Mark E.10

Affiliation:

1. Myriad Genetics, Inc., Salt Lake City, UT

2. Cape Cod Healthcare, Mashpee, MA

3. Elizabeth Wende Breast Care, Rochester, NY

4. Bethesda Health, Boynton Beach, FL

5. The Breast Center of NWA-Medical Associates of Northwest Arkansas, Fayetteville, AR

6. Basser Center for BRCA, University of Pennsylvania, Philadelphia, PA

7. Dana-Farber Cancer Institute, Boston, MA

8. City of Hope, Duarte, CA

9. Stanford University School of Medicine, Stanford, CA

10. Memorial Sloan Kettering Cancer Center, New York, NY

Abstract

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.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

Cancer Research,Oncology

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