Abstract

6583 Background: Characterizing estrogen receptor (ER) and progesterone receptor (PR) status has long been standard practice with newly diagnosed breast cancers. ER and PR expression suggests a more favorable prognosis and predicts tumor responsiveness to hormonal therapy. However, this testing comes with considerable cost. At our institution, the Medicare reimbursement rate for a single ER/PR assay is $310. With over 300,000 newly diagnosed breast cancers in the US each year, the total cost of ER/PR testing is close to 100 million dollars. Furthermore, the 2010 American Society of Clinical Oncology (ASCO) and the College of American Pathologists (CAP) guidelines suggest that up to 20% of ER/PR testing worldwide may be inaccurate with potential for false negative results. A falsely negative result could lead clinicians to not offer treatment with hormonal therapies. The purpose of this study was to investigate whether nuclear grade could be used to predict ER status, thus saving cost and reducing the potential for false negative test results and the inappropriate withholding of beneficial treatment. Methods: A retrospective analysis of prospectively collected data from the University of Vermont Breast Cancer Database identified all newly diagnosed breast cancers of any histologic type with available data on nuclear grade and ER status (2/2008-10/2016). The relationship between nuclear grade and ER status was analyzed (Table). Results: Nuclear grades 1 and 2 were associated with a significantly higher proportion of ER positive results than high nuclear grades (chi-square test p <.001). Conclusions: Nuclear grade 1 and 2 breast cancers are nearly universally ER positive (99% and 97%, respectively). Thus, ER testing may be redundant in these tumors. Elimination of ER testing in nuclear grade 1 and 2 tumors could save tens of millions of dollars each year in the US alone. It could also reduce the potential for false negative test results and the inappropriate withholding of beneficial treatment. [Table: see text]

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