Clinical impact of a novel model predictive of oncotype DX recurrence score in breast cancer

Abstract

Background/Aim: Oncotype DX recurrence score (RS) for breast cancer is a useful tool for determining chemotherapy indication but it is expensive and time-consuming. We determined whether four immuno-histochemical markers, namely human epidermal growth factor 2 (HER2), estrogen receptor (ER), progesterone receptor (PgR), and Ki-67, are predictive of an RS ≥26 in Japanese patients. Patients and Methods: The study included 95 Japanese patients evaluated for RS. A predictive model was created using logistic regression analysis. Results: The discriminant function was calculated as follows: p=1/{1+exp [−(4.611+1.2342×HER2−0.0813×ER− 0.0489 ×PgR+0.0857×Ki67)]}. Using a probability of 0.5 as the cutoff, the accuracy, sensitivity, specificity, positive predictive and negative predictive values were 90.5%, 72.2%, 94.8%, 76.4% and 93.5%, respectively. Conclusion: The model had a high negative predictive value in predicting RS ≥26 in Japanese patients, indicating that Oncotype DX testing may be omitted in patients with a negative result according to the predictive model.