Jessica Minnier1,2,3, Nallakkandi Rajeevan4,5, Lina Gao1,2,3, Byung Park1,2,3, Saiju Pyarajan6,7, Paul Spellman2,3, Sally G Haskell8,9, Cynthia A Brandt5,8, Shiuh-Wen Luoh2,3. 1. OHSU-PSU School of Public Health, Oregon Health & Science University, Portland, OR. 2. Knight Cancer Institute, Oregon Health & Science University, Portland, OR. 3. VA Portland Health Care System, Portland, OR. 4. Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT. 5. Yale Center for Medical Informatics (YCMI), Yale School of Medicine, New Haven, CT. 6. VA Boston Healthcare System, Boston, MA. 7. Department of Medicine, Harvard Medical School, Boston, MA. 8. VA Connecticut Healthcare System, West Haven, CT. 9. Department of Internal Medicine, Yale School of Medicine, New Haven, CT.
Abstract
Accurate breast cancer (BC) risk assessment allows personalized screening and prevention. Prospective validation of prediction models is required before clinical application. Here, we evaluate clinical- and genetic-based BC prediction models in a prospective cohort of women from the Million Veteran Program. MATERIALS AND METHODS: Clinical BC risk prediction models were validated in combination with a genetic polygenic risk score of 313 (PRS313) single-nucleotide polymorphisms in genetic females without prior BC diagnosis (n = 35,130, mean age 49 years) with 30% non-Hispanic African ancestry (AA). Clinical risk models tested were Breast and Prostate Cancer Cohort Consortium, literature review, and Breast Cancer Risk Assessment Tool, and implemented with or without PRS313. Prediction accuracy and association with incident breast cancer was evaluated with area under the receiver operating characteristic curve (AUC), hazard ratios, and proportion with high absolute lifetime risk. RESULTS: Three hundred thirty-eight participants developed incident breast cancers with a median follow-up of 3.9 years (2.5 cases/1,000 person-years), with 196 incident cases in women of European ancestry and 112 incident cases in AA women. Individualized Coherent Absolute Risk Estimator-literature review in combination with PRS313 had an AUC of 0.708 (95% CI, 0.659 to 0.758) in women with European or non-African ancestries and 0.625 (0.539 to 0.711) in AA women. Breast Cancer Risk Assessment Tool with PRS313 had an AUC of 0.695 (0.62 to 0.729) in European or non-AA and 0.675 (0.626 to 0.723) in AA women. Incorporation of PRS313 with clinical models improved prediction in European but not in AA women. Models estimated up to 9% of European and 18% of AA women with absolute lifetime risk > 20%. CONCLUSION: Clinical and genetic BC risk models predict incident BC in a large prospective multiracial cohort; however, more work is needed to improve genetic risk estimation in AA women.
Accurate breast cancer (BC) risk assessment allows personalized screening and prevention. Prospective validation of prediction models is required before clinical application. Here, we evaluate clinical- and genetic-based BC prediction models in a prospective cohort of women from the Million Veteran Program. MATERIALS AND METHODS: Clinical BC risk prediction models were validated in combination with a genetic polygenic risk score of 313 (PRS313) single-nucleotide polymorphisms in genetic females without prior BC diagnosis (n = 35,130, mean age 49 years) with 30% non-Hispanic African ancestry (AA). Clinical risk models tested were Breast and Prostate Cancer Cohort Consortium, literature review, and Breast Cancer Risk Assessment Tool, and implemented with or without PRS313. Prediction accuracy and association with incident breast cancer was evaluated with area under the receiver operating characteristic curve (AUC), hazard ratios, and proportion with high absolute lifetime risk. RESULTS: Three hundred thirty-eight participants developed incident breast cancers with a median follow-up of 3.9 years (2.5 cases/1,000 person-years), with 196 incident cases in women of European ancestry and 112 incident cases in AA women. Individualized Coherent Absolute Risk Estimator-literature review in combination with PRS313 had an AUC of 0.708 (95% CI, 0.659 to 0.758) in women with European or non-African ancestries and 0.625 (0.539 to 0.711) in AA women. Breast Cancer Risk Assessment Tool with PRS313 had an AUC of 0.695 (0.62 to 0.729) in European or non-AA and 0.675 (0.626 to 0.723) in AA women. Incorporation of PRS313 with clinical models improved prediction in European but not in AA women. Models estimated up to 9% of European and 18% of AA women with absolute lifetime risk > 20%. CONCLUSION: Clinical and genetic BC risk models predict incident BC in a large prospective multiracial cohort; however, more work is needed to improve genetic risk estimation in AA women.
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