Affordable Access

Publisher Website

72-Gene Classifier for Predicting Prognosis of Estrogen Receptor–Positive and Node-Negative Breast Cancer Patients Using Formalin-Fixed, Paraffin-Embedded Tumor Tissues

Clinical Breast Cancer
DOI: 10.1016/j.clbc.2013.11.006
  • Dna Microarray
  • Ffpe
  • Gene Classifier
  • Gene Expression Analysis
  • Prognostic Prediction
  • Biology


Abstract 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.

There are no comments yet on this publication. Be the first to share your thoughts.