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

Artikel i vetenskaplig tidskrift
Författare Jana Biermann
Szilard Nemes
Toshima Z Parris
Hanna Engqvist
Elisabeth Werner Rönnerman
Eva Forssell-Aronsson
Gunnar Steineck
Per Karlsson
Khalil Helou
Publicerad i Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
ISSN 1538-7755
Publiceringsår 2017
Publicerad vid Sahlgrenska Cancer Center
Institutionen för biomedicin, avdelningen för patologi
Institutionen för kliniska vetenskaper, sektionen för onkologi, radiofysik, radiologi och urologi, Avdelningen för onkologi
Institutionen för fysik (GU)
Språk en
Länkar dx.doi.org/10.1158/1055-9965.EPI-17...
www.ncbi.nlm.nih.gov/entrez/query.f...
Ämnesord breast cancer signature, prognostic biomarkers, outcome prediction, model validation
Ämneskategorier Biostatistik, Cell- och molekylärbiologi, Cancer och onkologi, Genetik, Bioinformatik och systembiologi

Sammanfattning

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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