The Validation Set for the initial development of the 95GC in 2011.

Our aim to develop the 95GC (gene classifier) was to make an accurate diagnostic system using gene expression analysis by means of DNA microarray for prognosis of node-negative and estrogen receptor (ER)-positive breast cancer patients in order to identify a subset of patients who can be safely spared adjuvant chemotherapy. A diagnostic system comprising a 95-gene classifier was developed for predicting the prognosis of node-negative and ER-positive breast cancer patients by using DNA microarray (gene expression) data (n = 549) as the training set and the DNA microarray data (n = 105) as the validation set (= this data set). With the 95-gene classifier we could classify the 105 patients　receiving only endocrine therapy without chemotherapy in the validation set into a high-risk (n = 44) and a low-risk (n = 61) group with 10-year recurrence-free survival rates of 93 and 53%, respectively (P = 8.6e-7). Multivariate analysis demonstrated that the 95-gene classifier was the most important and significant predictor of recurrence (P = 9.6e-4) independently of tumor size, histological grade, progesterone receptor, HER2, Ki67, or GGI. The 95-gene classifier developed by us can predict the prognosis of node-negative and ER-positive breast cancer patients with high accuracy. *Note: This old data has been updated multiple times by others. Then, there are some differences from the original 2011 paper and unclear points still remain. Therefore, do not use it for formal analysis aimed at public insurance coverage etc. This is for research purposes only. Please cite this paper when writing a new paper. DOI: 10.1007/s10549-010-1145-z RNA was extracted from 105 fresh frozen tumor samples obtained from radical breast cancer surgery and hybridized on Affymetrix microarrays.