Pathobiologic Stratification of Oncotype DX Recurrence Scores and Comparative Validation of 3 Surrogate Models

Context.— The Oncotype DX Recurrence Score (RS) predicts recurrence and chemotherapy benefit in early-stage estrogen receptor positive breast cancer patients. Cost and unavailability are 2 major disadvantages of the assay. Multiple models have been developed to predict the RS. Objective.— To predict RS based on histopathologic and biomarker features, and to measure concordance and correlation with RS of the following 3 algorithms: breast cancer prognostic score, Magee0, and Magee2. Design.— Breast cancer cases with available RS were reviewed (n = 442). RS categories were stratified by pathologic and biomarker variables. Histopathologic and biomarker data were abstracted from pathology reports, and RS was calculated by each model. Correlation and concordance between models and RS were calculated. Results.— Less than 5% of breast cancers with lobular features, low-grade tumors, carcinomas with high progesterone receptor content, or luminal A tumors had an RS greater than 25. Breast cancer prognostic score, Magee0, and Magee2 demonstrated correlation coefficients with RS of 0.63, 0.61, and 0.62, respectively. Two-step discordances were uncommon. When an RS of 25 was used to separate high-risk from non–high-risk cases, concordance rates of 86% to 88% were achieved. Conclusions.— High RS was observed only in a small percentage of pure or mixed lobular carcinomas, low-grade or luminal A tumors, and tumors with high progesterone receptor expression, suggesting that these cancers may not require Oncotype testing. All 3 surrogate models demonstrated comparable correlation and high concordance with the RS when a cutoff of 25 was used, suggesting their utility in cases where the actual RS is unavailable.