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A novel 18-marker panel predicting clinical outcome in breast cancer

Journal article
Authors Jana Biermann
Szilard Nemes
Toshima Z Parris
Hanna Engqvist
Elisabeth Werner Rönnerman
Eva Forssell-Aronsson
Gunnar Steineck
Per Karlsson
Khalil Helou
Published in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
Volume 26
Issue 11
ISSN 1538-7755
Publication year 2017
Published at Sahlgrenska Cancer Center
Institute of Biomedicine, Department of Pathology
Institute of Clinical Sciences, Section for Oncology, Radiation Physics, Radiology and Urology, Department of Oncology
Department of Physics (GU)
Language en
Links dx.doi.org/10.1158/1055-9965.EPI-17...
www.ncbi.nlm.nih.gov/entrez/query.f...
Keywords breast cancer signature, prognostic biomarkers, outcome prediction, model validation
Subject categories Bioinformatics and Systems Biology, Genetics, Cancer and Oncology, Cell and molecular biology, Biostatistics

Abstract

Gene expression profiling has made considerable contributions to our understanding of cancer biology and clinical care. This study describes a novel gene expression signature for breast cancer-specific survival that was validated using external datasets. Gene expression signatures for invasive breast carcinomas (mainly Luminal B subtype) corresponding to 136 patients were analysed using Cox regression and the effect of each gene on disease-specific survival (DSS) was estimated. Iterative Bayesian Model Averaging was applied on multivariable Cox regression models resulting in an 18-marker panel, which was validated using three external validation datasets. The 18 genes were analysed for common pathways and functions using the Ingenuity Pathway Analysis software. This study complied with the REMARK criteria. The 18-gene multivariable model showed a high predictive power for DSS in the training and validation cohort and a clear stratification between high- and low-risk patients. The differentially expressed genes were predominantly involved in biological processes such as cell cycle, DNA replication, recombination, and repair. Furthermore, the majority of the 18 genes were found to play a pivotal role in cancer. Our findings demonstrated that the 18 molecular markers were strong predictors of breast cancer-specific mortality. The stable time-dependent area under the ROC curve function (AUC(t)) and high C-indices in the training and validation cohorts were further improved by fitting a combined model consisting of the 18-marker panel and established clinical markers. Our work supports the applicability of this 18-marker panel to improve clinical outcome prediction for breast cancer patients.

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