Skip to main navigation Skip to search Skip to main content

Threshold-Based Overlap of Breast Cancer High-Risk Classification Using Family History, Polygenic Risk Scores, and Traditional Risk Models in 180,398 Women

  • on behalf of the ABCTB Investigators
  • , kConFab Investigators
  • , MyBrCa Investigators
  • , SGBCC Investigators
  • University of Sydney
  • Research Department
  • Peter Maccallum Cancer Centre
  • University of Melbourne
  • School of Mathematical Sciences
  • University of Nottingham Malaysia
  • Cancer Research Malaysia
  • Breast Cancer Research Unit
  • University of Malaya
  • Genome Institute of Singapore
  • National University of Singapore
  • Department of Surgery
  • MOH Holdings Pte Ltd.
  • Department of Surgery
  • Cancer Genetics Service
  • National Cancer Centre
  • Breast Department
  • KK Women's and Children's Hospital
  • Singapore Health Services
  • Department of General Surgery
  • Tan Tock Seng Hospital
  • Division of Surgery and Surgical Oncology
  • Singapore General Hospital
  • Division of Breast Surgery
  • Changi General Hospital
  • Division of Radiation Oncology
  • Division of Medical Oncology
  • National Cancer Institute (NCI)
  • University of Toronto
  • The N.N. Alexandrov Research Institute of Oncology and Medical Radiology
  • Queen's University
  • Lund University
  • German Cancer Research Center
  • American Cancer Society
  • Hannover Medical School
  • University of Cambridge
  • Harvard T.H. Chan School of Public Health
  • Dana-Farber Cancer Institute
  • University of Manchester
  • University of Utah School of Medicine
  • Complejo Hospitalario Universitario de Santiago
  • Intermountain Healthcare
  • University of Hamburg
  • QIMR Berghofer Medical Research Institute
  • Seoul National University
  • Seoul National University Cancer Research Institute
  • Karolinska Institutet
  • Fox Chase Cancer Center
  • KU Leuven
  • Brigham and Women’s Hospital
  • Leipzig University
  • Manchester University NHS Foundation Trust
  • Institute of Cancer Research
  • Curtin University
  • Columbia University
  • Erasmus University Rotterdam
  • Stockholm County Council
  • Hong Kong Sanatorium & Hospital
  • Erasmus MC Cancer Institute
  • Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology
  • University of Tübingen
  • Aichi Cancer Center Hospital and Research Institute
  • Nagoya University
  • National Cancer Center Japan
  • Pomeranian Medical University in Szczecin
  • Stanford University School of Medicine
  • Stanford University
  • Daerim Saint Mary’s Hospital
  • Evangelical Clinics of Bonn
  • Hong Kong Hereditary Breast Cancer Family Registry
  • The University of Hong Kong
  • Flanders Institute for Biotechnology
  • Uppsala University
  • Centre for Epidemiology and Biostatistics
  • Monash University
  • Maria Sklodowska-Curie Institute of Oncology
  • Cancer Council Victoria
  • University of Eastern Finland
  • IRCCS Fondazione Istituto Nazionale per lo studio e la cura dei tumori - Milano
  • Cyprus Institute of Neurology and Genetics
  • University of British Columbia
  • Provincial Health Services Authority
  • National Institute of Environmental Health Sciences (NIEHS)
  • The University of Chicago
  • Mississippi State University
  • European Institute of Oncology
  • Macedonian Academy of Sciences and Arts
  • University of Oulu
  • Northern Finland Laboratory Centre Oulu
  • Shaukat Khanum Memorial Cancer Hospital and Research Centre
  • Technion-Israel Institute of Technology
  • University of Thessaly
  • King's College London
  • Mayo Clinic Rochester, MN
  • Vanderbilt University
  • Clalit Regional Oncology Unit
  • Université Laval Research Center
  • University of Western Australia
  • University Hospitals Leuven
  • Sime Darby Berhad

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (<50 years) women (OR = 2.51, AUC = 0.622) than in women ≥ 50 years (OR = 2.06, AUC = 0.653) of European ancestry. PRS performance in Asians was lower (OR range = 1.62–1.64, AUC = 0.551–0.600). Gail performance was modest across groups and poor in younger Asian women (OR = 0.94–0.99, AUC = 0.523–0.533). Age interactions were observed for both PRS (p < 0.001) and Gail (p < 0.001) in Europeans, whereas in Asians, age interaction was observed only for Gail (invasive: p < 0.001; DCIS: p = 0.002). PRS identified more high-risk individuals than Gail in Asian populations, especially ≥50 years, while Gail identified more in Europeans. Overlap between PRS, Gail, and family history was limited at higher thresholds. Calibration analysis, comparing empirical and model-based ROC curves, showed divergence for both PRS and Gail (p < 0.001), which indicates miscalibration. In Europeans, family history and prior biopsies drove Gail discrimination. In younger Asians, age at first live birth was influential. Conclusions: PRS adds value to risk stratification beyond traditional tools, especially in younger women and Asian ancestry populations.

Original languageEnglish
Article number3561
JournalCancers
Volume17
Issue number21
DOIs
StatePublished - 03 Nov 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • BRCA1
  • BRCA2
  • Gail model
  • breast cancer
  • ductal carcinoma in situ (DCIS)
  • polygenic risk score (PRS)
  • risk stratification
  • risk-based screening

Fingerprint

Dive into the research topics of 'Threshold-Based Overlap of Breast Cancer High-Risk Classification Using Family History, Polygenic Risk Scores, and Traditional Risk Models in 180,398 Women'. Together they form a unique fingerprint.

Cite this