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	http://www.rii.com/publications/2002/nejm.html	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	http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2034	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	http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3494	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	http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1456	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	http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3143	Initial unsupervised analysis to identify outcome associated pathways.