Systematic assessment of prognostic gene signatures for breast cancer shows distinct influence of time and ER status
1 Department of Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Montebello 0310 Oslo, Norway
2 The K.G. Jebsen Center for Breast Cancer Research, Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
3 Department of Radiology, Center for Cancer Systems Biology, School of Medicine, Stanford University, Stanford, USA
4 Department of Informatics, Biomedical Research Group, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
5 Center for Cancer Biomedicine, University of Oslo, Oslo, Norway
6 Department of Oncology, Division of Cancer Medicine, Surgery and Transplantation, Oslo University Hospital Radiumhospitalet, Montebello 0310 Oslo, Norway
7 Department of Pathology, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Montebello 0310 Oslo, Norway
BMC Cancer 2014, 14:211 doi:10.1186/1471-2407-14-211Published: 19 March 2014
The aim was to assess and compare prognostic power of nine breast cancer gene signatures (Intrinsic, PAM50, 70-gene, 76-gene, Genomic-Grade-Index, 21-gene-Recurrence-Score, EndoPredict, Wound-Response and Hypoxia) in relation to ER status and follow-up time.
A gene expression dataset from 947 breast tumors was used to evaluate the signatures for prediction of Distant Metastasis Free Survival (DMFS). A total of 912 patients had available DMFS status. The recently published METABRIC cohort was used as an additional validation set.
Survival predictions were fairly concordant across most signatures. Prognostic power declined with follow-up time. During the first 5 years of followup, all signatures except for Hypoxia were predictive for DMFS in ER-positive disease, and 76-gene, Hypoxia and Wound-Response were prognostic in ER-negative disease. After 5 years, the signatures had little prognostic power. Gene signatures provide significant prognostic information beyond tumor size, node status and histological grade.
Generally, these signatures performed better for ER-positive disease, indicating that risk within each ER stratum is driven by distinct underlying biology. Most of the signatures were strong risk predictors for DMFS during the first 5 years of follow-up. Combining gene signatures with histological grade or tumor size, could improve the prognostic power, perhaps also of long-term survival.