Abstract C150: Evaluating breast cancer risk by race and ethnicity using the Tyrer Cuzick and Gail models in the Komen Tissue Bank cohort

Abstract Introduction: Breast cancer is the second leading cause of cancer death in women after lung cancer in the United States. While early detection is critical to reducing morbidity and mortality, predication of individualized risk is important in prevention and could also reduce unnecessary costs related to screening. The Susan G. Komen Tissue Bank (KTB) at Indiana University is the only tissue bank in the world that provides breast tissue specimens donated by healthy individuals to further to breast cancer research; however, as donors are volunteers, it is not clear how representative they are with respect to breast cancer risk compared to the general population. Methods: In this study, we calculated Breast Cancer Risk Assessment scores using Tyrer Cuzick (TCZ) and the Breast Cancer Risk Assessment Tool (The Gail Model, GM) in the KTB cohort by race and ethnicity. Recruited between 2007 and 2023, the participants completed a questionnaire encompassing their demographics, personal health and reproductive history, family history, and modifiable risk factors like smoking and alcohol use. In both models, participants with a lifetime risk of breast cancer of more than 15% were classified as having higher than average risk, while those with a risk of less than 15% were classified as having an average risk. Data analysis was performed using SAS v.9.4 software. Statistical significance was determined by p-values <0.05. Results: Out of 5118 participants, 206 with prior breast cancer and 37 male donors were excluded, resulting in a final sample of 4875 participants (aged 18–90) for analysis. The cohort was predominantly non-Hispanic white (NHW, 69.73%), followed by non-Hispanic black (NHB, 16.7%), Hispanic (8. 6%), and Asian individuals (3.3%), with 1.8% reporting multiracial or other race. The GM consistently categorized fewer women as having higher than average risk compared to the TZM. Across both TCZ and GM, the percentages of individuals classified as higher than average risk was greatest for NHW women, with 29.2% being categorized as higher than average risk in the TCZ, compared to 21.0% in the GM. The greatest discordance between the models was seen in NHB women, with the TCZ estimating 25.2% were at greater than average risk, and the GM only reporting 3.7% to be at greater than average risk. Overall, risk assessment scores varied across racial and ethnic groups (p- value<0.001). Factors driving these differences will be presented in additional analyses. Conclusion: There are known limitations in breast cancer risk assessment models by race and ethnicity that were confirmed in the women who comprise the KTB cohort. Donors to the KTB are generally representative of the US population with respect to breast cancer risk. As the KTB cohort continues to grow and mature, it can be utilized to assess breast cancer risk models for various racial and ethnic groups to assist in the identification of high-risk individuals that may benefit from prevention strategies and targeted screening. Citation Format: Vidya Patil, Michele L. Cote, Jenny Cui. Evaluating breast cancer risk by race and ethnicity using the Tyrer Cuzick and Gail models in the Komen Tissue Bank cohort [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C150.

Authors

Institutions

Publication Details

Journal
Cancer Epidemiology Biomarkers & Prevention
Published
2024-09-21
DOI
https://doi.org/10.1158/1538-7755.disp24-c150
Primary Topic
Cancer Risks and Factors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract C150: Evaluating breast cancer risk by race and ethnicity using the Tyrer Cuzick and Gail models in the Komen Tissue Bank cohort

Michele L. Coté, Vidya S. Patil, Jenny Cui
Cancer Epidemiology Biomarkers & Prevention
Cancer Risks and Factors
article

Abstract C150: Evaluating breast cancer risk by race and ethnicity using the Tyrer Cuzick and Gail models in the Komen Tissue Bank cohort

Michele L. Coté, Vidya S. Patil, Jenny Cui
article en

Abstract

Abstract Introduction: Breast cancer is the second leading cause of cancer death in women after lung cancer in the United States. While early detection is critical to reducing morbidity and mortality, predication of individualized risk is important in prevention and could also reduce unnecessary costs related to screening. The Susan G. Komen Tissue Bank (KTB) at Indiana University is the only tissue bank in the world that provides breast tissue specimens donated by healthy individuals to further to breast cancer research; however, as donors are volunteers, it is not clear how representative they are with respect to breast cancer risk compared to the general population. Methods: In this study, we calculated Breast Cancer Risk Assessment scores using Tyrer Cuzick (TCZ) and the Breast Cancer Risk Assessment Tool (The Gail Model, GM) in the KTB cohort by race and ethnicity. Recruited between 2007 and 2023, the participants completed a questionnaire encompassing their demographics, personal health and reproductive history, family history, and modifiable risk factors like smoking and alcohol use. In both models, participants with a lifetime risk of breast cancer of more than 15% were classified as having higher than average risk, while those with a risk of less than 15% were classified as having an average risk. Data analysis was performed using SAS v.9.4 software. Statistical significance was determined by p-values <0.05. Results: Out of 5118 participants, 206 with prior breast cancer and 37 male donors were excluded, resulting in a final sample of 4875 participants (aged 18–90) for analysis. The cohort was predominantly non-Hispanic white (NHW, 69.73%), followed by non-Hispanic black (NHB, 16.7%), Hispanic (8. 6%), and Asian individuals (3.3%), with 1.8% reporting multiracial or other race. The GM consistently categorized fewer women as having higher than average risk compared to the TZM. Across both TCZ and GM, the percentages of individuals classified as higher than average risk was greatest for NHW women, with 29.2% being categorized as higher than average risk in the TCZ, compared to 21.0% in the GM. The greatest discordance between the models was seen in NHB women, with the TCZ estimating 25.2% were at greater than average risk, and the GM only reporting 3.7% to be at greater than average risk. Overall, risk assessment scores varied across racial and ethnic groups (p- value<0.001). Factors driving these differences will be presented in additional analyses. Conclusion: There are known limitations in breast cancer risk assessment models by race and ethnicity that were confirmed in the women who comprise the KTB cohort. Donors to the KTB are generally representative of the US population with respect to breast cancer risk. As the KTB cohort continues to grow and mature, it can be utilized to assess breast cancer risk models for various racial and ethnic groups to assist in the identification of high-risk individuals that may benefit from prevention strategies and targeted screening. Citation Format: Vidya Patil, Michele L. Cote, Jenny Cui. Evaluating breast cancer risk by race and ethnicity using the Tyrer Cuzick and Gail models in the Komen Tissue Bank cohort [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C150.

Cancer Epidemiology Biomarkers & PreventionVol. 33(9_Supplement)
Indiana University Health (US), Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indiana University – Purdue University Indianapolis (US)
Good health and well-being
Openalex Percentile: Top 73%
Cancer Risks and Factors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.