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Ta, Casey N

0000-0002-4679-805X

Mayo Clinic

Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States.

Citations 1

Implementing integrated genomic risk assessments for breast cancer: lessons learned from the Electronic Medical Records and Genomics study
Liu, Cong , Morse, Jennifer, Linder, Jodell E. , Antoniou, Antonis C. , Carver, Tim , Cortopassi, Josh , Peterson, Josh F. , Ta, Casey , Hoell, Christin , Prows, Cynthia A. , Miller, Emily , Perez, Emma , Jarvik, Gail P, Odgis, Jacqueline A. , Bonini, Katherine E. , McGuffin, Kyle, Maradik, Mary, Limdi, Nita, Marathe, Priya , Suckiel, Sabrina A. , Aguilar, Sienna , Lewis, Toni J., Wei, Wei‐Qi, Freimuth, Robert R, Håkonarson, Håkon , Wiesner, Georgia L. , Jarvik, Gail P. , Carver, Tim , Ta, Casey N , Hoell, Christin , Bland, Sarah T , Kottyan, Leah C , Luo, Yuan , Weng, Chunhua , Hakonarson, Hakon , Jarvik, Gail P , Liu, Cong , Peterson, Josh F , Crew, Katherine D. , Morse, Jennifer, Kenny, Eimear E , Bland, Harris T. , Mittendorf, Kathleen F. , McGuffin, Kyle, Kottyan, Leah C. , Abul‐Husn, Noura S. , Luo, Yuan , Freimuth, Robert R. , Weng, Chunhua , Chung, Wendy K.

JAMIA Open | October 4, 2025 | Volume: 8 | Issue: 5 | Pages: No page available

10.1093/jamiaopen/ooaf113 Get It

Abstract Objectives To implementation an automated multi-institutional pipeline that delivers breast-cancer risk integrated with polygenic risk scores, monogenic variants, family history, and clinical factors, emphasizing operational challenges and their solutions. Materials and Methods A five-stage process was executed at ten sites. Data streams from REDCap surveys, PRS and monogenic reports, and MeTree pedigrees were normalized and forwarded through a REDCap plug-in to the CanRisk API. Results Integrated risk was returned to >10 000 women; 3.6% were ≥25 % lifetime risk and 0.9% carried pathogenic variants. Pipeline generated score aligns well with manual generated ones. Major barriers such as heterogeneous pedigree formats, missing data, edge-case handling, and evolving model versions were identified and resolved through mapping rules, imputations, and iterative testing. Discussion Cross-platform data harmonization and stakeholder alignment were decisive for success. Borderline-risk communication and model-version drift remain open issues. Conclusion Large-scale PRS-integrated breast-cancer risk reporting is feasible but requires robust interoperability standards and iterative governance.