The critical clinical question in prostate cancer research is to develop means of distinguishing aggressive from indolent disease. Using a combination of proteomic and expression array data, we identified a set of 40 genes with concordant dysregulation of protein products that could be evaluated in situ by quantitative immunohistochemistry. Using linear discriminant analysis, we determined that the optimal model to predict prostate cancer progression consisted of 12 proteins. Using a separate patient population, the transcriptional levels of the 12 genes encoding for these proteins predicted PSAfailure in 79 men following surgery for clinically localized prostate cancer (p=0.0015). This study demonstrates that cross platform models can lead to predictive models with the possible advantage of being more robust through this selection process
Defining Aggressive Prostate Cancer Using a 12 Gene Model
Demichelis, Francesca;
2005-01-01
Abstract
The critical clinical question in prostate cancer research is to develop means of distinguishing aggressive from indolent disease. Using a combination of proteomic and expression array data, we identified a set of 40 genes with concordant dysregulation of protein products that could be evaluated in situ by quantitative immunohistochemistry. Using linear discriminant analysis, we determined that the optimal model to predict prostate cancer progression consisted of 12 proteins. Using a separate patient population, the transcriptional levels of the 12 genes encoding for these proteins predicted PSAfailure in 79 men following surgery for clinically localized prostate cancer (p=0.0015). This study demonstrates that cross platform models can lead to predictive models with the possible advantage of being more robust through this selection processI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
