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A meta-analysis of gene expression-based biomarkers predicting outcome after tamoxifen treatment in breast cancer

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Abstract

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 in breast cancer by evaluating these in a meta-analysis of available transcriptomic datasets with known treatment and follow-up. Biomarker candidates were identified in Pubmed and in the 2007–2012 ASCO and 2011–2012 SABCS abstracts. Breast cancer microarray datasets of endocrine therapy-treated patients were downloaded from GEO and EGA and RNAseq datasets from TCGA. Of the biomarker candidates, only those identified or already validated in a clinical cohort were included. Relapse-free survival (RFS) up to 5 years was used as endpoint in a ROC analysis in the GEO and RNAseq datasets. In the EGA dataset, Kaplan–Meier analysis was performed for overall survival. Statistical significance was set at p < 0.005. The transcriptomic datasets included 665 GEO-based and 1,208 EGA-based patient samples. All together 68 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), and SLC7A5 (AUC = 0.62, p = 9.2E−05). Further genes significantly correlated to RFS include FOS, TP53, BTG2, HOXB7, DRG1, CXCL10, and TPM4. In the RNAseq dataset, only ERBB2, EDF1, and MAPK1 reached statistical significance. We evaluated tamoxifen-resistance genes in three independent platforms and identified PGR, MAPT, and SLC7A5 as the most promising prognostic biomarkers in tamoxifen treated patients.

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Abbreviations

ASCO:

American Society of Clinical Oncology

EGA:

European genome–phenome archive

EGFR:

Epidermal growth factor receptor

ER:

Estrogen receptor

FFPE:

Formalin-fixed, paraffin-embedded

GEO:

Gene expression omnibus

NCCN:

National Comprehensive Cancer Network

NICE:

National Institute for Health and Clinical Excellence

PR:

Progesterone receptor

PRISMA:

Preferred reporting items for systematic reviews and meta-analyses

PROSPERO:

International prospective register of systematic reviews

RFS:

Relapse-free survival

ROC:

Receiver operating characteristic

SABCS:

San Antonio breast cancer symposium

TCGA:

The cancer genome atlas

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Acknowledgments

Our work was supported by the OTKA PD 83154 Grant, by the Predict Project (Grant No. 259303 of the EU Health.2010.2.4.1.-8 call), and by the KTIA EU_BONUS_12-1-2013-0003 Grant.

Ethical Standards

We declare that the experiments comply with the current laws of Hungary.

Conflict of interests

There are no disclaimers. The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. 1st Department of Pediatrics, Semmelweis University, Budapest, Hungary

    Zsuzsanna Mihály, Máté Kormos & András Lánczky

  2. Department of Diagnostic Radiology and Oncotherapy, Semmelweis University, Budapest, Hungary

    Magdolna Dank

  3. Institut für Pathologie, Charité—Universitätsmedizin Berlin, 10117, Berlin, Germany

    Jan Budczies

  4. 2nd Department of Pathology, Semmelweis University, Budapest, Hungary

    Marcell A Szász

  5. Research Laboratory for Pediatrics and Nephrology, 1st Department of Pediatrics, Hungarian Academy of Sciences—Semmelweis University, Bókay u. 53-54, Budapest, 1083, Hungary

    Balázs Győrffy

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  1. Zsuzsanna Mihály
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Correspondence to Balázs Győrffy.

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Mihály, Z., Kormos, M., Lánczky, A. et al. A meta-analysis of gene expression-based biomarkers predicting outcome after tamoxifen treatment in breast cancer. Breast Cancer Res Treat 140, 219–232 (2013). https://doi.org/10.1007/s10549-013-2622-y

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