Estrogen receptor testing and 10-year mortality from breast cancer: A model for determining testing strategy

BACKGROUND: The use of adjuvant tamoxifen therapy in the treatment of estrogen receptor (ER) expressing breast carcinomas represents a major advance in personalized cancer treatment. Because there is no benefit (and indeed there is increased morbidity and mortality) associated with the use of tamoxifen therapy in ER-negative breast cancer, its use is restricted to women with ER expressing cancers. However, correctly classifying cancers as ER positive or negative has been challenging given the high reported false negative test rates for ER expression in surgical specimens. In this paper I model practice recommendations using published information from clinical trials to address the question of whether there is a false negative test rate above which it is more efficacious to forgo ER testing and instead treat all patients with tamoxifen regardless of ER test results. METHODS: I used data from randomized clinical trials to model two different hypothetical treatment strategies: (1) the current strategy of treating only ER positive women with tamoxifen and (2) an alternative strategy where all women are treated with tamoxifen regardless of ER test results. The variables used in the model are literature-derived survival rates of the different combinations of ER positivity and treatment with tamoxifen, varying true ER positivity rates and varying false negative ER testing rates. The outcome variable was hypothetical 10-year survival. RESULTS: The model predicted that there will be a range of true ER rates and false negative test rates above which it would be more efficacious to treat all women with breast cancer with tamoxifen and forgo ER testing. This situation occurred with high true positive ER rates and false negative ER test rates in the range of 20-30%. CONCLUSIONS: It is hoped that this model will provide an example of the potential importance of diagnostic error on clinical outcomes and furthermore will give an example of how the effect of that error could be modeled using real-world data from clinical trials.