Table 1 Breast cancer gene expression profiling datasets analyzed in this study.
Reference | Study summary | Sample Size | Microarray platforms | Data download | How dataset was used in this study |
|---|---|---|---|---|---|
Van de Vijver et al. [3] | Demonstrated that a 70-gene expression signature is a more powerful predictor for outcome than standard clinical and histological criteria in 295 primary breast cancer patients | 295 | Inkjet Oligo | Initial unsupervised analysis to identify outcome associated pathways. | |
Wang et al. [8] | Developed a 76-gene signature to predict distant metastasis using gene expression profiling data in 286 node negative primary breast cancer tumors | 286 | U133A | Initial unsupervised analysis to identify outcome associated pathways; Training dataset to build prognostic gene signature models. | |
Miller et al. [22] | Identified a 32-gene signature from 251 primary breast cancers to distinguish p53-mutant and wild-type tumors and to predict prognosis. | 251 | U133A | Initial unsupervised analysis to identify outcome associated pathways; Independent dataset for validating the prognostic gene signature models. | |
Pawitan et al. [7] | Identified a subset of 64 genes from gene expression profiles in 159 primary breast cancers that give an optimal separation of good and poor outcomes. | 159 | U133A | Initial unsupervised analysis to identify outcome associated pathways; Independent dataset for validating the prognostic gene signature models. | |
Bild et al. [21] | Developed gene expression signatures for oncogenic pathways and demonstrated these signatures are predictive of clinical outcomes in lung, breast and ovarian cancers. | 171 | U95Av2 | Initial unsupervised analysis to identify outcome associated pathways. |