Investigation of recurrence prediction ability of EndoPredict using microarray data from fresh frozen tissues in ER-positive, HER2-negative breast cancer and indication expansion of EndoPredict from microarray data from fresh-frozen to FFPE tissues.

Background: EndoPredict (EP) is a multigene assay to predict distant recurrence risk in luminal breast cancer. EP measures the expression of 12 genes in primary tumor by qRT-PCR from formalin-fixed paraffin-embedded (FFPE) tissues and calculates EP risk score that indicates the risk of distant recurrence. We evaluated the performance of EP in predicting distant recurrence risk using microarray data from fresh frozen (FF) tissues. We also examined the applicability of EP to microarray data from FFPE tissues.

Methods: We analyzed the publicly available data of 431 node-negative and 270 node-positive patients with luminal breast cancer who received endocrine therapy alone. We evaluated the prognostic value of EP using microarray data from FF tissues. Next, we created an algorithm to calculate EP risk score using microarray data from FFPE tissues. We examined the correlation coefficient of EP risk score and concordance rate of EP risk high/low using microarray data from FFPE/FF tissue pairs in a validation set of 39 patients.

Results: In 431 node-negative patients, the distant recurrence-free survival (DRFS) rate was significantly worse in those with high EP risk scores (P = 3.68 × 10, log-rank). The 5-year DRFS was 95.2% in those with low EP risk score. In the validation set, the correlation coefficient of EP risk score was 0.93 and the concordance rate of EP risk high/low was 91.7%.

Conclusions: EP using microarray data from FF tissues was useful in predicting distant recurrence risk in luminal breast cancer, and EP might be utilized in microarray data from FFPE tissues.