A Model for Individualized Risk Prediction of Contralateral Breast Cancer

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Project Summary Patients diagnosed with invasive breast cancer or ductal carcinoma in situ are increasingly choosing to undergo contralateral prophylactic mastectomy (CPM) to reduce their risk of contralateral breast cancer (CBC). This is a particularly disturbing trend as a large number of these CPMs are believed to be medically unnecessary. This is because the risk of CBC has dropped markedly for most patients in the last two decades due to availability of effective adjuvant therapies. Despite this fact, patients diagnosed with first primary breast cancer tend to substantially overestimate their CBC risk. At the same time, they underestimate the complications, risks, and negative effects associated with CPM. These incorrect perceptions partly explain the rising CPM rates in the U.S. Morever, there is little evidence that CPM helps in prolonging survival. Thus, the benefits of CPM need to be weighed properly with its drawbacks. Given the invasive and irreversible nature of CPM, it behooves us to provide sound and effective education to breast cancer patients, who are going through an emotionally challenging period. Physicians do try to educate their patients; however, they lack tools that can help them in this endeavor. In particular, they need a CBC risk prediction model that can provide individualized risk estimates for sporadic (non-genetic) breast cancer patients. This project aims to fill this need by developing such a model, validating it, and implementing it in a freely available software package for immediate clinical use. To build the model, we will use data from Surveillance, Epidemiology, and End Results (SEER) Program and meta-analysis of risk estimates from literature. The proposed model will be in the style of the Gail model - a popular tool for counseling women on the risk of developing breast cancer - but one that is exclusively designed for counseling women with unilateral breast cancer on the risk of developing CBC. After building the model, we will validate it on prospectively collected data on breast cancer patients from four institutions - University of Texas at Southwestern Medical Center, Parkland Memorial Hospital in Dallas, M D Anderson Cancer Center, and Dartmouth Medical School. Once the model is validated, we will create a user- friendly package in statistical software R for implementing the model. Then we will integrate the package into CancerGene, a widely used and freely available clinical software for counseling patients on the risk of breast cancer. CancerGene is licensed to more than 4000 sites worldwide and therefore will be a perfect gateway to make the new model available to a large number of practitioners. We believe our proposed model will greatly facilitate patients' education and will help stem the increasing trend of medically unnecessary CPMs.