PURPOSE: Primary systemic therapy (PST) with gemcitabine (G), epirubicin (E), and docetaxel (Doc) has resulted in a pathologic complete response (pCR) in 26% of primary breast cancer patients. This study was aimed at the identification of a gene expression signature in diagnostic core biopsy tissue samples that predicts pCR. PATIENTS AND METHODS: Core biopsy samples from patients with operable primary breast cancer, T2-4N0-2M0, enrolled onto two phase I and II trials evaluating GEDoc (n = 48) and GE sequentially followed by Doc (GEsDoc; n = 52) as PST were snap frozen and subjected to RNA expression profiling. A signature predicting pCR was discovered in the training set (GEsDoc) applying a support vector machine algorithm, and performance of this classifier was validated on the independent test set (GEDoc) by receiver operator characteristics analysis. RESULTS: We identified a signature consisting of 512 genes, which was enriched in genes involved in transforming growth factor beta and RAS-mediated signaling pathways, that predicts pCR with a sensitivity of 78%, a specificity of 90%, and an overall accuracy of 88% (95% CI, 75% to 95%). Apart from our signature, only HER2 overexpression was an independent predictor of pCR in multivariate analysis. CONCLUSION: In conclusion, our gene expression signature allows prediction of pCR to PST containing G, E, and Doc with unprecedented high overall accuracy and robustness.
PURPOSE: Primary systemic therapy (PST) with gemcitabine (G), epirubicin (E), and docetaxel (Doc) has resulted in a pathologic complete response (pCR) in 26% of primary breast cancerpatients. This study was aimed at the identification of a gene expression signature in diagnostic core biopsy tissue samples that predicts pCR. PATIENTS AND METHODS: Core biopsy samples from patients with operable primary breast cancer, T2-4N0-2M0, enrolled onto two phase I and II trials evaluating GEDoc (n = 48) and GE sequentially followed by Doc (GEsDoc; n = 52) as PST were snap frozen and subjected to RNA expression profiling. A signature predicting pCR was discovered in the training set (GEsDoc) applying a support vector machine algorithm, and performance of this classifier was validated on the independent test set (GEDoc) by receiver operator characteristics analysis. RESULTS: We identified a signature consisting of 512 genes, which was enriched in genes involved in transforming growth factor beta and RAS-mediated signaling pathways, that predicts pCR with a sensitivity of 78%, a specificity of 90%, and an overall accuracy of 88% (95% CI, 75% to 95%). Apart from our signature, only HER2 overexpression was an independent predictor of pCR in multivariate analysis. CONCLUSION: In conclusion, our gene expression signature allows prediction of pCR to PST containing G, E, and Doc with unprecedented high overall accuracy and robustness.
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