Conversely, we expected genes whose expression was associated with good prognosis to generally be highly expressed

in patients who survived and to be expressed at low levels in those patients who succumbed. Therefore, the ranking of the genes was performed as follows for genes predictive of poor or good prognosis. Genes predictive of poor prognosis i) A predictive score for each gene was computed for each gene across all patients, and was initially set at 0.   ii) 1. The score for each gene was increased by 1 when the patient had both high gene expression and died, or had both low gene expression and survived.   2. The score was decreased by 1 when the patient had both low CP673451 cell line gene expression and died, or had both high gene expression and survived.   3. Average gene expression levels did not lead to any changes in the predictive score.     Genes predictive of good prognosis i) A predictive score for each gene is computed for each gene across all patients, and was initially set at 0.   ii) 1. The score was increased by 1 when the patient had both high gene expression and survived, or had both low gene expression

and died.   2. The score is decreased by 1 when the patient had both low gene expression and survived, or had both high gene Captisol solubility dmso expression and died.   3. Average gene expression levels did not lead to any changes in the predictive score.     We then combined the top ranked genes from both the Amisulpride poor-prognosis and good-prognosis gene lists to generate a predictor gene signature. We completed all of the steps described above using Microsoft Excel™ 2007. Template file available upon request. Measuring the predictive ability of the gene signature In order to separate the training data set into good prognosis and poor prognosis groups we summed the expression of both poor-prognosis genes (poor-prognosis gene score) and good-prognosis genes (good-prognosis gene score) for all the patients in our training set. To give each patient a single overall-prognosis score we subtracted the good-prognosis gene score from the poor-prognosis

gene score, and ranked the patients according to this new total. This led patients with the highest and lowest expression of poor-prognosis and good-prognosis genes, respectively, to receive the highest scores, and patients with the lowest and highest expression of poor-prognosis and good-prognosis genes, respectively, to receive the lowest scores. In this fashion, high scores were indicative of poor prognosis and low scores were indicative of good prognosis. In order to determine a optimal cut-off score which would yield prognosis predictions with the highest possible specificity and sensitivity, we used receiver-operator characteristic curves (ROC) [6]. This generated a list of possible cut-off scores, as well as each score’s associated specificity and sensitivity.