A novel 18-marker panel predicting clinical outcome in breast cancer
Menée à partir de l'analyse du profil génétique d'échantillons tumoraux prélevés sur 136 patientes atteintes d'un carcinome invasif du sein, puis validée à l'aide de trois séries de données portant respectivement sur 720, 237 et 128 patientes complémentaires, cette étude met en évidence l'intérêt d'une signature, basée sur l'expression de 18 gènes impliqués principalement dans divers processus (cycle cellulaire, réplication, recombinaison et réparation de l'ADN), pour prédire la survie spécifique
Background : 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.
Methods : 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.
Results : 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.
Conclusion : 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.
Impact : Our work supports the applicability of this 18-marker panel to improve clinical outcome prediction for breast cancer patients.
Cancer Epidemiology Biomarkers & Prevention , résumé, 2016