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Updated: May 3, 2026
Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
Published on: August 1, 2018
72-gene classifier for predicting prognosis of estrogen receptor-positive and node-negative breast cancer patients
Minako Nishio1, Yasuto Naoi1, Ryo Tsunashima1
1Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Osaka, Japan.
Background:
The 95-gene classifier (95-GC) can classify patients with estrogen receptor (ER)-positive and node-negative breast cancer into those with low and high risk of relapse with an accuracy similar to that of 21-GC (Oncotype DX). Because 95-GC uses RNA from fresh-frozen (FF) tumor tissues, we herein attempted to develop a gene classifier that is applicable to RNA from formalin-fixed paraffin-embedded (FFPE) tumor tissues.
Patients And Methods:
25 paired FF and FFPE tumor tissues were subjected to DNA microarray for gene-expression analysis. Of the 95 probes included in the 95-GC, 72 were selected for construction of the gene classifier for FFPE tumor tissues, because the gene expression detected by these 72 probes was well preserved in the FFPE tumor tissues.
Results:
The 72-GC was constructed with these 72 probes for the training set comprising 549 FF tumor tissues and validated with 434 FF tumor tissues (relapse-free survival at 10 years was 91% for the low-risk and 74% for the high-risk group (P = 3.74 × 10(-7)). The predictive capability of 72-GC for prognosis was found to be comparable to that of 95-GC. The 25 paired FF and FFPE tumor tissues from each of 25 patients were classified into the same risk group by 72-GC for 23 patients (92% concordance). 72-GC using the FFPE tumor tissues showed that the prognosis for the low-risk group was significantly (P = .007) better than for the high-risk group.
Conclusion:
72-GC is comparable to 95-GC in terms of accuracy of prognosis prediction, and may be effective for FFPE tumor tissues.
