TY - JOUR
T1 - Simplified breast risk tool integrating questionnaire risk factors, mammographic density, and polygenic risk score
T2 - Development and validation
AU - Rosner, Bernard
AU - Tamimi, Rulla M.
AU - Kraft, Peter
AU - Gao, Chi
AU - Mu, Yi
AU - Scott, Christopher
AU - Winham, Stacey J.
AU - Vachon, Celine M.
AU - Colditz, Graham A.
N1 - Publisher Copyright:
© 2021 American Association for Cancer Research.
PY - 2021/4
Y1 - 2021/4
N2 - Background: Clinical use of breast cancer risk prediction requires simplified models. We evaluate a simplified version of the validated Rosner-Colditz model and add percent mammographic density (MD) and polygenic risk score (PRS), to assess performance from ages 45-74. We validate using the Mayo Mammography Health Study (MMHS). Methods: We derived the model in the Nurses' Health Study (NHS) based on: MD, 77 SNP PRS and a questionnaire score (QS; lifestyle and reproductive factors). A total of 2,799 invasive breast cancer cases were diagnosed from 1990-2000. MD (using Cumulus software) and PRS were assessed in a nested case-control study. We assess model performance using this case-control dataset and evaluate 10-year absolute breast cancer risk. The prospective MMHS validation dataset includes 21.8% of women age <50, and 434 incident cases identified over 10 years of follow-up. Results: In the NHS, MD has the highest odds ratio (OR) for 10-year risk prediction: ORper SD = 1.48 [95% confidence interval (CI): 1.31-1.68], followed by PRS, ORper SD = 1.37 (95% CI: 1.21-1.55) and QS, ORper SD = 1.25 (95% CI: 1.11-1.41). In MMHS, the AUC adjusted for age + MD + QS 0.650; for age + MD + QS + PRS 0.687, and the NRI was 6% in cases and 16% in controls. Conclusion: A simplified assessment of QS, MD, and PRS performs consistently to discriminate those at high 10-year breast cancer risk. Impact: This simplified model provides accurate estimation of 10-year risk of invasive breast cancer that can be used in a clinical setting to identify women who may benefit from chemopreventive intervention.
AB - Background: Clinical use of breast cancer risk prediction requires simplified models. We evaluate a simplified version of the validated Rosner-Colditz model and add percent mammographic density (MD) and polygenic risk score (PRS), to assess performance from ages 45-74. We validate using the Mayo Mammography Health Study (MMHS). Methods: We derived the model in the Nurses' Health Study (NHS) based on: MD, 77 SNP PRS and a questionnaire score (QS; lifestyle and reproductive factors). A total of 2,799 invasive breast cancer cases were diagnosed from 1990-2000. MD (using Cumulus software) and PRS were assessed in a nested case-control study. We assess model performance using this case-control dataset and evaluate 10-year absolute breast cancer risk. The prospective MMHS validation dataset includes 21.8% of women age <50, and 434 incident cases identified over 10 years of follow-up. Results: In the NHS, MD has the highest odds ratio (OR) for 10-year risk prediction: ORper SD = 1.48 [95% confidence interval (CI): 1.31-1.68], followed by PRS, ORper SD = 1.37 (95% CI: 1.21-1.55) and QS, ORper SD = 1.25 (95% CI: 1.11-1.41). In MMHS, the AUC adjusted for age + MD + QS 0.650; for age + MD + QS + PRS 0.687, and the NRI was 6% in cases and 16% in controls. Conclusion: A simplified assessment of QS, MD, and PRS performs consistently to discriminate those at high 10-year breast cancer risk. Impact: This simplified model provides accurate estimation of 10-year risk of invasive breast cancer that can be used in a clinical setting to identify women who may benefit from chemopreventive intervention.
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U2 - 10.1158/1055-9965.EPI-20-0900
DO - 10.1158/1055-9965.EPI-20-0900
M3 - Article
C2 - 33277321
AN - SCOPUS:85103863234
SN - 1055-9965
VL - 30
SP - 600
EP - 607
JO - Cancer Epidemiology Biomarkers and Prevention
JF - Cancer Epidemiology Biomarkers and Prevention
IS - 4
ER -