The Utility of the Oncotype DX Test for Breast Cancer Patients in an Australian Multidisciplinary Setting

. The Oncotype DX test is a genomic assay that generates a Recurrence Score (RS) predicting the 10-year risk of recurrence and response to adjuvant chemotherapy in ER+/HER2− breast cancer patients. The aims were to determine breast cancer distant recurrence and correlate with adjuvant chemoendocrine prescribing patterns based on the Oncotype DX recurrence score. Methods. We conducted a retrospective single-institution case series of 71 patients who had Oncotype DX assay testing after definitive surgery between 2012 and 2016. Both node-positive and node-negative patients were included. Patients were divided into Oncotype DX low risk (RS   25) (n = 16, 23%). Median follow-up was 6.1 years (range 4–8.9 years). Adjuvant treatment regimens and oncological outcomes were determined. Results. Mean age at diagnosis was 56 years (range, 33–77). Invasive ductal carcinoma (IDC) accounted for the majority (87%), with most tumors measuring between 10–20 mm (52%). 48% of the cohort were node positive. 15 of 16 high-risk patients (94%) received chemotherapy. 96% of intermediate-risk patients received endocrine therapy alone, one patient received chemoendocrine therapy (2%), and one declined systemic therapy (2%). In the low-risk group, 100% received endocrine therapy only. The high-risk group had the lowest mean ER% ( P < 0.05 ), greatest mean mitotic rate ( P < 0.05 ), and greatest proportion of Ki67% > 14. Five patients developed distant recurrence (7%): three from the intermediate-risk group (7%), one from the low-risk group (10%), and one from the high-risk group (6%). Conclusion. This is the first Australian study reporting the experience with medium-term recurrence outcomes of using the Oncotype DX assay in breast cancer. Chemotherapy was rarely given for patients with low-to-intermediate RS and always offered in high RS. This pattern of prescribing was associated with low rates of distant recurrence. National funding models should be considered.