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Implementing Integrated Genomic Risk Assessments for Breast Cancer: Lessons Learned from the eMERGE Study.

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Summary

This study developed a pipeline for integrated breast cancer risk assessment combining polygenic risk scores (PRS), genetic variants, and family history. The system successfully assessed risk in over 10,000 women, identifying high-risk individuals and actionable genetic findings.

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Area of Science:

  • Genetics and Genomics
  • Clinical Oncology
  • Bioinformatics

Background:

  • Accurate breast cancer risk assessment is crucial for personalized prevention and screening strategies.
  • Integrating polygenic risk scores (PRS) with clinical and genetic data offers a more comprehensive risk evaluation.
  • The eMERGE network provides a valuable infrastructure for implementing and evaluating such integrated risk models.

Purpose of the Study:

  • To develop and implement a computational pipeline for integrated breast cancer risk assessment using the BOADICEA model.
  • To incorporate polygenic risk scores (PRS), monogenic variants, family history, and clinical factors into a unified risk prediction framework.
  • To deploy and evaluate this pipeline within the multi-site eMERGE study.

Main Methods:

  • A pipeline was designed, customized, and deployed across ten eMERGE clinical sites.
  • Data integration involved REDCap surveys, PRS reports, monogenic variant reports, and pedigree data.
  • The CanRisk Application Programming Interface (API) was utilized for data processing and risk score generation.

Main Results:

  • The pipeline successfully generated integrated breast cancer risk scores for over 10,000 females.
  • 3.6% of participants were classified as high-risk (≥25% lifetime risk), and 0.9% carried pathogenic variants in key breast cancer genes (BRCA1, BRCA2, PALB2, PTEN).
  • High PRS alone was found in 5.6% of participants, with 34% of these also having high integrated risk scores; API and User Interface (UI) results showed high concordance (0.13% average difference).

Conclusions:

  • The study demonstrates the feasibility of integrating PRS into clinical breast cancer risk assessment.
  • Challenges in data integration and risk communication were identified, highlighting areas for improvement.
  • Future work should focus on recalibrating models for diverse populations and streamlining risk interpretation workflows.