1. Evaluation of 95-Gene Classifier of Formalin-fixed Paraffin-embedded Tissues in ER-positive, HER2-negative, and Node-negative Breast Cancer
- Author
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Yuria Saito, Kanako C. Hatanaka, Ayae Nange, Mitsuru Taniguchi, Keisuke Yamagishi, Yoshihiro Matsuno, Asami Okumura, Kengo Hamasaki, Yutaka Hatanaka, and Hiroko Yamashita
- Subjects
Cancer Research ,genetic structures ,Formalin fixed paraffin embedded ,business.industry ,HER2 negative ,General Medicine ,Biology ,medicine.disease ,Node negative ,Text mining ,Breast cancer ,Oncology ,Cancer research ,medicine ,business ,Classifier (UML) ,Gene - Abstract
Background 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. 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.Conclusions Our study shows that 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.
- Published
- 2023