Abstract
Abstract One of the major problems in the development of prostate cancer prognostic biomarkers is the composition heterogeneity in tissue samples, which causes the inconsistency among different studies. To deal with this problem, we developed two-step strategy. In the first step, the proposed two-step cluster analysis model accounting for the cell-type composition percentage was used to identify 324 genes strongly associated with biochemical recurrence status and in the second step the 324 identified genes was further investigated by SAM (significance analysis of microarray) leading to a seven-gene classifier. The classifier was then validated on two independent data sets with overall accuracy and sensitivity of 71% and 76%, respectively. The seven-gene classifier stratified the test samples into two risk groups that are highly associated with biochemical relapse status (hazard ratio = 2.6 with p value = 0.03). The study also showed that the inclusion of the Gleason sum to the seven-gene classifier raised the sensitivity and accuracy to 76% and 83% respectively when tested on the independent data sets. These results indicated that our seven-gene classifier with the Gleason score can be used for predicting outcomes for prostate cancer patients. Citation Format: Xin Chen, Zhenyu Jia, Mercola Dan. An accurate seven-gene signature plus Gleason score for prostate cancer prognosis [abstract]. In: Proceedings of the AACR Special Conference on Advances in Prostate Cancer Research; 2012 Feb 6-9; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2012;72(4 Suppl):Abstract nr B43.
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