Abstract P2-11-21: Integration of an ancestrally unbiased polygenic risk score with the Tyrer-Cuzick breast cancer risk model
Authors: Elisha Hughes, Ryan Bernhisel, Holly Pederson, Braden Probst, Timothy Simmons, Susanne Wagner, Thaddeus Judkins, Eric Rosenthal, Benjamin Roa, Susan M. Domchek, Charis Eng, Judy Garber, Monique Gary, Ora K. Gordon, Jennifer Klemp, Semanti Mukherjee, Kenneth Offit, Funmi Olopade, Joseph Vijai, Jeffrey N. Weitzel, Pat Whitworth, Lamis Yehia, Allison Kurian, Mark Robson, Thomas P. Slavin, Alexander Gutin, Jerry S. Lanchbury
Journal: Cancer Research (2022)

Abstract Background: Polygenic risk scores (PRS) have been shown to provide genomically informed breast cancer risk assessment in both carriers and non-carriers of predisposing genetic mutations. Risk stratification is further improved by combining a PRS with risk models incorporating traditional risk factors to generate a Combined Risk Score (CRS). We recently developed and validated a breast cancer PRS for women of diverse ancestries using ancestry-informative genetic markers. Here, we combine the diverse ancestry PRS with a clinical and family history-based model to develop an integrated genomically-informed and ancestrally unbiased risk assessment tool. Methods: The study sample included women in the U.S. without a personal history of breast cancer, referred for clinical genetic testing between June 2020 and March 2021, and who tested negative for pathogenic or likely pathogenic variants in breast cancer susceptibility genes. A CRS, incorporating a validated PRS and the TC model, was generated using a previously described Fixed-Stratified method that accounts for association between PRS and clinical risk factors. Association between the PRS and each clinical risk factor included in the Tyrer-Cuzick (TC) breast cancer risk model (version 7) was tested using linear regression with PRS as the dependent variable and the TC factor as an independent variable with adjustment for age and ancestry. We examined the rate of reclassification resulting from incorporation of PRS into the CRS by classifying women as having high (&amp;gt;20%) remaining lifetime risk (RLR) versus low (≤20%) RLR according to both TC and CRS. Results: Among 68,803 women, 21,500 (31.2%) had one or more first degree relatives (FDR) with breast cancer. Approximately 10% of women reported only African ancestry and a similar percentage reported only Hispanic/Latina ethnicity (Table 1). Family history was significantly associated with PRS (p=1.0x10-76). After adjusting for multiple testing, no other factors showed significant association with PRS. Improved risk stratification of CRS over TC follows from two results: (1) We previously showed that PRS improved risk stratification above and beyond family history; (2) In the present study, PRS was not associated with any TC factor other than family history. Adding the PRS to the TC model significantly altered breast cancer risk estimates for women of all ancestries, with 17.3% of patients stratified differently by CRS versus TC alone. Differences in risk stratification (using the 20% threshold) for each self-reported ancestry are presented in Table 1. The CRS classified fewer patients (32.0%) as high RLR than the TC model alone (35.4%), with similar results for 5-year risk estimates. Conclusions: This is the first genomically-informed, integrated polygenic and traditional breast cancer risk model for US women referred for contemporary clinical genetic testing. This model advances the PRS component of a previously validated combined model. It effectively estimates 5-year and lifetime risk for breast cancer using a PRS with an objectively genetically determined ancestral composition, calibrated and validated for risk stratification in all ancestries. The model may reliably and responsibly inform risk reduction strategies such as enhanced surveillance and use of preventive medications. Table 1.Self-Reported Ancestry/EthnicityNumber (%) of patientsHigh TC High CRS High TC and Low CRSLow TC and High CRSAll68,803 (100%)24,332 (35.4%)22,041 (32.0%)7,080 . (10.3%)4,789 . (7.0%)Asian1,450. (2.1%)487 (33.6%)475 (32.8%)94 . (6.5%)82 . (5.7%)African7,909 (11.5%)2,540 (32.1%)2,473 (31.3%)435 . (5.5%)368 . (4.7%)Hispanic6,481 . (9.4%)1,614 (24.9%)1,345 (20.9%)606 . (9.4%)346 . (5.3%)Non-European*19,225 (27.9%)5,701 (29.7%)5,297 (27.6%)1,429 . (7.4%)1,025 . (5.3%)European**46,640 (67.8%)17,507 (37.5%)15,733 (33.7%)5,328 . (11.4%)3,554 . (7.6%)*Includes any combination of Black/African, Middle Eastern, Pacific Islander, Asian, Hispanic/Latino and/or Native American ancestry. **Includes White/Non-Hispanic and/or Ashkenazi Jewish. Citation Format: Elisha Hughes, Ryan Bernhisel, Holly Pederson, Braden Probst, Timothy Simmons, Susanne Wagner, Thaddeus Judkins, Eric Rosenthal, Benjamin Roa, Susan M. Domchek, Charis Eng, Judy Garber, Monique Gary, Ora K. Gordon, Jennifer Klemp, Semanti Mukherjee, Kenneth Offit, Funmi Olopade, Joseph Vijai, Jeffrey N. Weitzel, Pat Whitworth, Lamis Yehia, Allison Kurian, Mark Robson, Thomas P. Slavin, Alexander Gutin, Jerry S. Lanchbury. Integration of an ancestrally unbiased polygenic risk score with the Tyrer-Cuzick breast cancer risk model [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-11-21.