Breast Cancer Research and Treatment

, Volume 140, Issue 2, pp 219–232

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

Authors

  • Zsuzsanna Mihály
    • 1st Department of PediatricsSemmelweis University
  • Máté Kormos
    • 1st Department of PediatricsSemmelweis University
  • András Lánczky
    • 1st Department of PediatricsSemmelweis University
  • Magdolna Dank
    • Department of Diagnostic Radiology and OncotherapySemmelweis University
  • Jan Budczies
    • Institut für PathologieCharité—Universitätsmedizin Berlin
  • Marcell A Szász
    • 2nd Department of PathologySemmelweis University
    • Research Laboratory for Pediatrics and Nephrology, 1st Department of PediatricsHungarian Academy of Sciences—Semmelweis University
Review

DOI: 10.1007/s10549-013-2622-y

Cite this article as:
Mihály, Z., Kormos, M., Lánczky, A. et al. Breast Cancer Res Treat (2013) 140: 219. doi:10.1007/s10549-013-2622-y

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.

Keywords

Breast cancerTamoxifenResistanceBiomarker

List of 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

Supplementary material

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Supplementary material 1 (R 16kb)
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Supplementary table 1 (TXT 196 kb)
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Supplementary table 2 (TXT 35 kb)
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Supplementary table 3 (TXT 59 kb)
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Supplementary table 6 (TXT 611 kb)

Copyright information

© Springer Science+Business Media New York 2013