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Open Access Research article

The Gene expression Grade Index: a potential predictor of relapse for endocrine-treated breast cancer patients in the BIG 1–98 trial

Christine Desmedt1, Anita Giobbie-Hurder2, Patrick Neven3, Robert Paridaens3, Marie-Rose Christiaens3, Ann Smeets3, Françoise Lallemand1, Benjamin Haibe-Kains1, Giuseppe Viale4, Richard D Gelber5, Martine Piccart1 and Christos Sotiriou1*

Author Affiliations

1 Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium

2 IBCSG Statistical Center, Dana-Farber Cancer Institute, Boston, MA, USA

3 U.Z. Gasthuisberg, Katholieke Universtiteit Leuven, Leuven, Belgium

4 European Institute of Oncology, Milan, Italy

5 IBCSG Statistical Center, Dana-Farber Cancer Institute, Frontier Science and Technology Research Foundation, Harvard School of Public Health, Boston, MA, USA

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BMC Medical Genomics 2009, 2:40  doi:10.1186/1755-8794-2-40

Published: 2 July 2009

Abstract

Background

We have previously shown that the Gene expression Grade Index (GGI) was able to identify two subtypes of estrogen receptor (ER)-positive tumors that were associated with statistically distinct clinical outcomes in both untreated and tamoxifen-treated patients. Here, we aim to investigate the ability of the GGI to predict relapses in postmenopausal women who were treated with tamoxifen (T) or letrozole (L) within the BIG 1–98 trial.

Methods

We generated gene expression profiles (Affymetrix) and computed the GGI for a matched, case-control sample of patients enrolled in the BIG 1–98 trial from the two hospitals where frozen samples were available. All relapses (cases) were identified from patients randomized to receive monotherapy or from the switching treatment arms for whom relapse occurred before the switch. Each case was randomly matched with four controls based upon nodal status and treatment (T or L). The prognostic value of GGI was assessed as a continuous predictor and divided at the median. Predictive accuracy of GGI was estimated using time-dependent area under the curve (AUC) of the ROC curves.

Results

Frozen samples were analyzable for 48 patients (10 cases and 38 controls). Seven of the 10 cases had been assigned to receive L. Cases and controls were comparable with respect to menopausal and nodal status, local and chemotherapy, and HER2 positivity. Cases were slightly older than controls and had a larger proportion of large, poorly differentiated ER+/PgR- tumors. The GGI was significantly and linearly related to risk of relapse: each 10-unit increase in GGI resulted in an increase of approximately 11% in the hazard rate (p = 0.02). Within the subgroups of patients with node-positive disease or who were treated with L, the hazard of relapse was significantly greater for patients with GGI at or above the median. AUC reached a maximum of 78% at 27 months.

Conclusion

This analysis supports the GGI as a good predictor of relapse for ER-positive patients, even among patients who receive L. Validation of these results, in a larger series from BIG 1–98, is planned using the simplified GGI represented by a smaller set of genes and tested by qRT-PCR on paraffin-embedded tissues.