作者: Wei, Wei-Qi ; Suckiel, Sabrina A ; Marathe, Priya N ; Limdi, Nita ; Perez, Emma ; Hakonarson, Hakon ; Ta, Casey N ; Prows, Cynthia ; Crew, Katherine D ; Odgis, Jacqueline A ; Cortopassi, Josh ; Morse, Jennifer ; Jarvik, Gail P ; Chung, Wendy K ; Abul-Husn, Noura S ; Linder, Jodell E ; Miller, Emily ; Freimuth, Robert R ; Maradik, Mary ; Carver, Tim ; Kottyan, Leah C ; Bonini, Katherine E ; Kenny, Eimear E ; Hoell, Christin ; Weng, Chunhua ; Peterson, Josh F ; Mittendorf, Kathleen F ; Wiesner, Georgia L ; Lewis, Toni J ; Bland, Harris T ; Aguilar, Sienna ; Luo, Yuan ; Liu, Cong ; McGuffin, Kyle ; Antoniou, Antonis C
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.