Evaluation of 95-Gene Classifier of Formalin-fixed Paraffin-embedded Tissues in ER-positive, HER2-negative, and Node-negative Breast Cancer

Abstract

Background/aim: A subset of patients with estrogen receptor (ER)-positive, HER2-negative, and node-negative breast cancer experience recurrences. Predicting patients who will have recurrences within 5 years of surgery is essential so that patients can be selected to receive adjuvant chemotherapy. The 95-gene classifier (95-GC) has been validated as a method to differentiate patients into high and low-risk groups for early recurrence.

Patients and methods: In this study, we performed 95-GC analysis on 56 formalin-fixed paraffin-embedded (FFPE) tissue samples from patients who underwent surgery for ER-positive, HER2-negative, and node-negative breast cancer and did not receive adjuvant chemotherapy. We associated the obtained high- and low-risk groups with clinicopathological characteristics and recurrence-free survival (RFS).

Results: We classified 12 out of 56 patients into the high-risk recurrence group. We found significantly higher KI67 scores in patients in the high-risk group. Other clinicopathological characteristics were not associated with the 95-GC risk groups. Patients in the 95-GC low-risk group had a significantly better prognosis than those in the high-risk group (p=0.0387). The 5-year RFS rate was 97.6% in the low-risk group and 74.1% in the high-risk group, while the 10-year RFS rates were 90.1% and 74.1%, respectively.

Conclusion: The 95-GC score can accurately predict RFS within 5 years of surgery for ER-positive, HER2-negative, and node-negative breast cancer using FFPE tissue samples. These prediction models could help assign patients to the most effective treatment regimen.

Keywords: 95-Gene classifier; ER-positive node-negative breast cancer; prediction model; recurrence.