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Gene expression profiling assays are frequently used to guide adjuvant chemotherapy decisions in hormone receptor-positive, lymph node-negative breast cancer. We hypothesized that the clinical value of these new tools would be more fully realized when appropriately integrated with high-quality clinicopathologic data. Hence, we developed a model that uses routine pathologic parameters to estimate Oncotype DX recurrence score (ODX RS) and independently tested its ability to predict ODX RS in clinical samples. Patients and MethodsWe retrospectively reviewed ordered ODX RS and pathology reports from five institutions (n = 1,113) between 2006 and 2013. We used locally performed histopathologic markers (estrogen receptor, progesterone receptor, Ki-67, human epidermal growth factor receptor 2, and Elston grade) to develop models that predict RS-based risk categories. Ordering patterns at one site were evaluated under an integrated decision-making model incorporating clinical treatment guidelines, immunohistochemistry markers, and ODX. Final locked models were independently tested (n = 472). Results Distribution of RS ConclusionThe proposed model accurately predicts high-and low-risk RS categories (. 25 or # 25) in a majority of cases. Integrating histopathologic and molecular information into the decision-making process allows refocusing the use of new molecular tools to cases with uncertain risk.

Predicting recurrence risk and chemotherapy benefit in early-stage breast cancer is challenging. The Oncotype DX gene assay is often used. Using a database of 221 patients a simple 2-rule model was developed and validated on an independent group of 319 patients. The model categorizes patients unlikely to benefit from the test thus achieving significant avoidance of cost. Background Predicting recurrence risk and chemotherapy benefit in early-stage breast cancer can be challenging, and Oncotype DX (ODX) is often used to gain insight. However, it is still unclear whether ODX can benefit in all cases. To clarify ODX’s usefulness we sought to develop a model using readily available pathologic markers to help clinicians make that determination. Patients and Methods Clinical pathologic data from 221 hormone receptor-positive, HER2-negative invasive breast cancer patients was used to create a model. The model was then validated on a second institution’s set of 319 patients. Results The model has 2 simple rules: low grade and positive progesterone receptor tumors (LG+PR) are low risk, and high grade or low estrogen receptor (ER) (ER < 20%) tumors (HG/LER) are high risk. The TAILORx (Trial Assigning Individualized Options for Treatment (Rx)) trial thresholds of Recurrence Score (RS) ≤ 10, when chemotherapy is of little benefit, and RS ≥ 26 when chemotherapy might be beneficial were used to judge model performance. Impressively, the misclassifications of an HG/LER patient who has an RS ≤ 10 were 0% and 2%, and for LG+PR patients who had an RS ≥ 26 were 0% and 2.6%. In the validation set, 28% (66 of 232) of the indeterminate group (neither in the HG/LER nor the LG + PR groups) had an RS ≤ 10 or an RS ≥ 26; this group might clinically benefit from ODX. Conclusion A simple 2-rule model based on readily available pathologic data was developed and validated, which categorized patients into high and low risk for recurrence. Identification of patients who are unlikely to benefit from ODX testing could result in significant cost avoidance.