2023 ACMG Annual Clinical Genetics Meeting Digital Edition: Poster Gallery-Prospective Longitudinal Validation of a Breast Cancer Risk Prediction Model in a Cohort of 130,058 Individuals

Prospective Longitudinal Validation of a Breast Cancer Risk Prediction Model in a Cohort of 130,058 Individuals

Pdf Summary

This study aimed to validate a breast cancer risk prediction model called the Combined Risk Score (CRS) by conducting a prospective longitudinal study. The cohort consisted of 130,058 individuals with a median follow-up of 12.1 months, during which 340 incident breast cancer events were observed. The CRS was found to be well-calibrated in the overall cohort, with an observed-to-expected ratio of 1.11. In the highest risk decile, the CRS outperformed the traditional risk factor model, with an observed-to-expected ratio of 0.91 compared to 0.67 for the traditional model alone. The CRS significantly improved upon the traditional risk factor model and may lead to improved breast cancer prevention and screening strategies.<br /><br />The study also evaluated the discriminative accuracy of the CRS, Tyrer-Cuzick model (TC), and polygenic risk score (PRS). The CRS was found to be more informative than both the TC and PRS in predicting breast cancer risk. Among individuals classified as high-risk by the CRS, the incidence of breast cancer was nearly four times higher than those classified as low-risk. There was also a discordance in classification between the CRS and TC models, with 32.6% of individuals classified as high-risk by the TC being reclassified as low-risk by the CRS.<br /><br />In conclusion, the study validated the CRS as a predictor of breast cancer risk and demonstrated its superiority over traditional risk factor models. The CRS showed improved calibration and provided more informative risk predictions compared to the TC and PRS. The clinical use of the CRS may improve breast cancer prevention and screening strategies.

Asset Subtitle

Presenting Author - Brent Mabey, MSc; Co-Author - Elisha Hughes, PhD; Co-Author - Braden Probst, MStat; Co-Author - Holly Pederson, MD; Co-Author - Timothy Simmons, MStat; Co-Author - Brian Morris, BS; Co-Author - Brooke Hullinger, JD; Co-Author - Susan Domchek, MD; Co-Author - Charis Eng, MD; Co-Author - Monique Gary, MD; Co-Author - Jennifer Klemp, PhD, MPH; Co-Author - Semanti Mukherjee, PhD; Co-Author - Joseph Vijai, PhD; Co-Author - Kenneth Offit, MD; Co-Author - Olufunmilayo Olopade, MD; Co-Author - Sandhya Pruthi, MD; Co-Author - Allison W. Kurian, MD, MSc; Co-Author - Mark Robson, MD; Co-Author - Pat Whitworth, MD; Co-Author - Susanne Wagner, PhD; Co-Author - Jerry Lanchbury, PhD; Co-Author - Thomas Slavin; Co-Author - Alexander Gutin, PhD;

Meta Tag

Bioinformatics

Clinical History

Genetic Diversity

Genetic Testing

Polygenic risk scores

Risk Assessment

Co-Author Elisha Hughes, PhD

Co-Author Braden Probst, MStat

Co-Author Holly Pederson, MD

Co-Author Timothy Simmons, MStat

Co-Author Brian Morris, BS

Co-Author Brooke Hullinger, JD

Co-Author Susan Domchek, MD

Co-Author Charis Eng, MD

Co-Author Monique Gary, MD

Co-Author Jennifer Klemp, PhD, MPH

Co-Author Semanti Mukherjee, PhD

Co-Author Joseph Vijai, PhD

Co-Author Kenneth Offit, MD

Co-Author Olufunmilayo Olopade, MD

Co-Author Sandhya Pruthi, MD

Co-Author Allison W. Kurian, MD, MSc

Co-Author Mark Robson, MD

Co-Author Pat Whitworth, MD

Co-Author Susanne Wagner, PhD

Co-Author Jerry Lanchbury, PhD

Co-Author Thomas Slavin

Co-Author Alexander Gutin, PhD

Presenting Author Brent Mabey, MSc

Keywords

breast cancer risk prediction model

Combined Risk Score

prospective longitudinal study

cohort

traditional risk factor model

breast cancer prevention

screening strategies

Tyrer-Cuzick model

polygenic risk score

informative risk predictions