Abstract
e11564 Background: To date, three molecular markers (ER, PR and CYP2D6) have been used in clinical setting to predict the benefit of the anti-estrogen tamoxifen therapy. Our aim was to validate new biomarker candidates predicting response to tamoxifen treatment by evaluating these in a meta-analysis of available microarray datasets with known treatment and follow-up. Methods: Biomarker candidates were identified in Pubmed 2007-2012 and in the 2010-2012 ASCO and SABCS abstracts. Breast cancer microarray datasets were downloaded from GEO and EGA. Of the biomarker candidates, only those identified or already validated in a clinical cohort were included. In the transcriptomic datasets, only patients with tamoxifen treatment for relapse-free survival and endocrine treatment for overall survival were eligible. The raw microarray data was re-processed and integrated into two databases. Relapse free survival (RFS) up to 5 years was used as endpoint in a ROC analysis in the GEO datasets. In the EGA dataset, Kaplan-Meier analysis was performed for overall survival (OS). Statistical significance was set at p<0.01. Results: The transcriptomic datasets included 667 GEO-based and 1208 EGA-based patient samples. All together 59 biomarker candidates were identified. Of these, the best performing genes were PGR (AUC=0.64, p=2.3E-07), MAPT (AUC=0.62, p=7.8E-05), SLC7A5 (AUC=0.62, p=9.2E-05) and TP53 (AUC=0.60, p=1.2E-03). Further genes significantly correlated to relapse-free survival include BTG2, HOXB7, DRG1, CXCL10, BCL2, TPM4, IGF1R and SMC3. Correlation to overall survival was significant for PGR (HR=0.67, p=1.7E-04), MAPT (HR=0.7, p=7.2E-04) and SLC7A5 (HR=1.6, p=1.6E-05). None of the remaining genes including ESR1 reached statistical significance for relapse-free survival. Conclusions: We validated two genes (MAPT and SLC7A5) as being capable to select those patients most likely benefit from tamoxifen treatment.
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