Abstract
One of the major challenges in the development of prostate cancer prognostic biomarkers is the cellular heterogeneity in tissue samples. We developed an objective Cluster-Correlation (CC) analysis to identify gene expression changes in various cell types that are associated with progression. In the Cluster step, samples were clustered (unsupervised) based on the expression values of each gene through a mixture model combined with a multiple linear regression model in which cell-type percent data were used for decomposition. In the Correlation step, a Chi-square test was used to select potential prognostic genes. With CC analysis, we identified 324 significantly expressed genes (68 tumor and 256 stroma cell expressed genes) which were strongly associated with the observed biochemical relapse status. Significance Analysis of Microarray (SAM) was then utilized to develop a seven-gene classifier. The Classifier has been validated using two independent Data Sets. The overall prediction accuracy and sensitivity is 71% and 76%, respectively. The inclusion of the Gleason sum to the seven-gene classifier raised the prediction accuracy and sensitivity to 83% and 76% respectively based on independent testing. These results indicated that our prognostic model that includes cell type adjustments and using Gleason score and the seven-gene signature has some utility for predicting outcomes for prostate cancer for individual patients at the time of prognosis. The strategy could have applications for improving marker performance in other cancers and other diseases.
Highlights
Prostate cancer is the most frequently diagnosed male cancer and the second leading cause of cancer death in men in the United States [1]
One reason accounting for such inconsistency across studies might be the heterogeneity in terms of cell composition, i.e., the tissue samples used for assays were usually mixture of various cell types with varying percentages [14,15,16] as well as genetic heterogeneity of the polyclonal and multifocal nature of prostate cancer
It would not be appropriate to perform differential expression analysis of the tumor component directly with all the 130 samples of Data Set 1 because the estimated tissue components showed a large variation of the cell type composition percentage among these samples, including samples with almost exclusively stroma
Summary
Prostate cancer is the most frequently diagnosed male cancer and the second leading cause of cancer death in men in the United States [1]. A major current issue in clinical management is determining reliable prognostic indicators that distinguish indolent cancer from those that will recur. Classification systems such as the Kattan nomograms [6], D’Amico classification [7], and CAPRA (Cancer of the Prostate Risk Assessment) score [8] that incorporate the measurement of several preoperative and postoperative clinical markers can be used to predict the probability of recurrence after radical prostatectomy. Extensive previous efforts have attempted to identify gene expression changes between aggressive cases and indolent cases [9,10,11] Standard analytical approaches, such as ttest, significance analysis of microarray (SAM) [12] and linear models for microarray data (LIMMA) [13], have been applied to these studies. Such composition heterogeneity is rarely taken into account in biomarker studies because there has been no straightforward way to deal with such variation through regular gene expression analyses
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
