Systematically defining single gene determinants of response to neoadjuvant chemotherapy reveals specific biomarkers
A partir d'une analyse systématique de plusieurs bases de données, cette étude identifie neuf gènes dont l'expression est associée à la réponse à une chimiothérapie néoadjuvante chez des patientes atteintes d'un cancer du sein
Purpose: We sought to systematically define determinants of response to neoadjuvant chemotherapy to elucidate predictive biomarkers for breast cancer.
Experimental Design:An unbiased systematic analysis was performed in multiple independent datasets to define genes predictive of complete pathological response (pCR) following treatment with neoadjuvant chemotherapy. These genes were interrogated across ER-positive and ER-negative breast cancer and those in common across three different treatment regimens were analyzed for optimal predictive power. Subsequent validation was performed on independent cohorts by gene expression and immunohistochemical analyses.
Results:Genes that were highly associated with the response to neoadjuvant chemotherapy in breast cancer were defined using a computational method ranking individual genes by their respective receiver operating characteristics. Such predictive genes of response to taxane associated therapies were strongly enriched for cell cycle control processes in both ER-positive and ER-negative breast cancer and correlated with pCR. However, other genes that were specifically associated with residual disease were also identified under other treatment conditions. Using the intersection between treatment groups, nine genes were identified that harbored strong predictive power in multiple contexts and validation cohort. In particular the nuclear oncogene DEK was strongly associated with pCR, whereas the cell surface protein BCAM was strongly associated with residual disease. By immunohistochemical staining these markers exhibited potent predictive power that remained significant in multivariate analysis.
Conclusions:: Systematic computational approaches can define key genes that will be able to predict response to chemotherapy across multiple treatment modalities yielding a small collection of biomarkers that can be readily deployed by immunohistochemical analyses.
Clinical Cancer Research , résumé, 2014