Methods Microarray and clinical data The microarray data used for our analyses was obtained from the Stanford microarray repository (downloaded
from http://microarray-pubs.stanford.edu/wound_NKI/explore.html, Selleckchem NCT-501 henceforth called NKI dataset). A matrix containing clinical data for the patients that provided samples for the microarray profiles used in the present study was AR-13324 research buy downloaded from the same location. This data consists of the gene expression profiles of primary breast tumors biopsied from 295 human breast cancer patients. All patients had either stage I or stage II breast cancer, and were younger than 53 years old. The prevalence of lymph-node positive and lymph-node negative disease was 49% and 51%, respectively. Selleckchem CBL0137 We combined these data into one matrix containing indices for survival, metastasis,
and the gene expression profiles for each patient. We used 12 year overall survival as the clinical endpoint for this study. Organization of data We blindly divided the patients into two groups consisting of similar numbers of patients, one for algorithm training (144 patients) and the other for algorithm validation (151 patients). Defining levels of gene expression In order to rank the predictive ability of a gene, we first needed to assess its expression in each given patient tumor relative to its expression in the tumors of all patients. To this end we first calculated the 95% confidence interval for expression of each gene. The level of expression for each gene was then defined as the following: i) If the expression of a gene in a given patient’s tumor was greater than the upper limit of the 95% confidence interval for the expression of the same gene across all patient tumors, then the Florfenicol gene’s expression was scored high for that patient’s tumor. ii) If the expression of a gene in a given patient’s tumor was less than the lower limit of the 95% confidence interval
for the expression of the same gene across all patient tumors, then the gene’s expression was scored low for that patient’s tumor. iii) If the expression of a gene in a given patient’s tumor was within the 95% confidence interval for the expression of the gene across all patient tumors, then the gene’s expression was scored average for that patient’s tumor. These steps were completed for every gene across every patient tumor. This new matrix consisting of clinical patient data, as well as the gene expression score for each gene, represented by either high, average or low, was then used to rank the genes based on their predictive capacity. Ranking the predictive capacity of each gene We ranked each gene in the training set according to its expression in the tumor of patients who either survived or died from breast cancer.