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

Desmedt C, Giobbie-Hurder A, Neven P, Paridaens R, Christiaens MR, Smeets A, Lallemand F, Haibe-Kains B, Viale G, Gelber RD, Piccart M, Sotiriou C - BMC Med Genomics (2009)

Bottom Line: 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.AUC reached a maximum of 78% at 27 months.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium. christine.desmedt@bordet.be

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.

No MeSH data available.


Related in: MedlinePlus

The predictive accuracy of the GGI estimated using time-dependent area under the curve (AUC) of the ROC curves.
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Figure 1: The predictive accuracy of the GGI estimated using time-dependent area under the curve (AUC) of the ROC curves.

Mentions: The prognostic potential of the GGI was assessed using Cox proportional hazards regression with GGI as the single covariate to estimate time-specific ROC curves. ROC curves were constructed for times between 0 and 50 months and the areas under the ROC curves were then plotted to obtain the AUC(t) function (100% = perfect classification, 50% = no discrimination). Estimates of AUC(t) are shown in Figure 1. Over the first 24 months of follow-up, the AUC(t) ranged between 73% and 74%. This may be interpreted to say that for any time, t, between 0 and 24 months, the probability was at least 73% that a patient who relapsed at time t had a GGI score greater than a patient who had not relapsed at time t. AUC(t) reached a maximum value of 77.6% at 27 months, with maximal discrimination occurring at approximately the median follow-up time observed in the data.


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

Desmedt C, Giobbie-Hurder A, Neven P, Paridaens R, Christiaens MR, Smeets A, Lallemand F, Haibe-Kains B, Viale G, Gelber RD, Piccart M, Sotiriou C - BMC Med Genomics (2009)

The predictive accuracy of the GGI estimated using time-dependent area under the curve (AUC) of the ROC curves.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2713270&req=5

Figure 1: The predictive accuracy of the GGI estimated using time-dependent area under the curve (AUC) of the ROC curves.
Mentions: The prognostic potential of the GGI was assessed using Cox proportional hazards regression with GGI as the single covariate to estimate time-specific ROC curves. ROC curves were constructed for times between 0 and 50 months and the areas under the ROC curves were then plotted to obtain the AUC(t) function (100% = perfect classification, 50% = no discrimination). Estimates of AUC(t) are shown in Figure 1. Over the first 24 months of follow-up, the AUC(t) ranged between 73% and 74%. This may be interpreted to say that for any time, t, between 0 and 24 months, the probability was at least 73% that a patient who relapsed at time t had a GGI score greater than a patient who had not relapsed at time t. AUC(t) reached a maximum value of 77.6% at 27 months, with maximal discrimination occurring at approximately the median follow-up time observed in the data.

Bottom Line: 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.AUC reached a maximum of 78% at 27 months.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium. christine.desmedt@bordet.be

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.

No MeSH data available.


Related in: MedlinePlus