Abstract A29: Mathematical models are not the be-all and end-all for breast cancer risk assessment
Authors: Schnabel Freya , Chun Jennifer , Schwartz Shira , Guth Amber , Axelrod Deborah , Shapiro Richard , Hiotis Karen , Smith Julia 
Published: May 2019

Purpose: Well-established risk factors for breast cancer include family history (FH), BRCA mutations and biopsies with atypical hyperplasia (AH) or lobular carcinoma in situ (LCIS). Several mathematical models, including the Gail and Tyrer-Cuzick models, have been developed to quantify a patient's risk for developing breast cancer. These models all differ in the list of variables and risk factors that are included in risk calculations. As a result, there is no single model that best estimates the risk for all high risk patients. The purpose of this study is to examine the application of the Gail and Tyrer-Cuzick models in a contemporary cohort of women who are enrolled in a comprehensive high-risk breast cancer database.

Methods: The institutional High Risk Breast Cancer Consortium (HRBCC) was established in January 2011. Patients who were at high risk for developing breast cancer based on family history (maternal and paternal), BRCA mutations, AH and LCIS were eligible to enroll in the database. The following variables were included in this analysis: age, family history, genetic testing results, reproductive history, AH, LCIS, Gail and Tyrer-Cuzick scores, risk reduction strategies, and outcomes. All clinical data are obtained from detailed questionnaires filled out by patients who consent to the database studies and from a review of electronic medical records. Descriptive statistics were performed.

Results: A total of 604 women were enrolled between 1/2011-2/2016. The median age was 51 years (range 20-87). The majority of women were Caucasian (83%). 52% had a strong FH, 13% were BRCA1 and 2 positive, 48% had AH, and 22% had LCIS. 47% of patients in our high risk program were not eligible for Gail model analysis (age &lt;35 years, BRCA mutation carriers, history of LCIS). Only one patient was not eligible for Tyrer-Cuzick model calculation based on age &gt;84 years. For patients who were eligible for Gail model analysis, 26 (8%) women did not meet criteria (5-year risk ≥1.7%) for being designated as high risk for breast cancer. 34 (6%) of our patients did not have Tyrer-Cuzick scores over 20% (criterion for high risk). Notably, majority of the patients (69%) who were not defined as high-risk based on Gail scores ≥1.7% or Tyrer-Cuzick scores ≥20%, had a strong family history of breast cancer. Only 14 (2%) patients developed breast cancer during our study period, and the majority (93%) of the cancers were early stage (stage 0,I).

Conclusions: Our institutional high-risk database includes women who are at high risk based on well-established risk factors for developing breast cancer (FH, BRCA mutations, AH, LCIS). Current mathematical models including the Gail and Tyrer-Cuzick models did not capture the increased risk of breast cancer in 8% of our population. While the models are helpful, in clinical practice they are not necessarily the be-all and end-all. Using heuristic risk factors is more time efficient and comprehensive risk assessment allows the clinicians and patients to better understand risk. Identifying patients as high risk and enrolling them in a high-risk database and program allow us to capture long term follow up, recommend surveillance for early detection, and better understand the effectiveness of different risk reduction and management strategies for this population.

Citation Format: Freya Schnabel, Jennifer Chun, Shira Schwartz, Amber Guth, Deborah Axelrod, Richard Shapiro, Karen Hiotis, Julia Smith. Mathematical models are not the be-all and end-all for breast cancer risk assessment. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A29.