Abstract
The highly heterogeneous clinical course of human prostate cancer has prompted the development of multiple RNA biomarkers and diagnostic tools to predict outcome for individual patients. Biomarker discovery is often unstable with, for example, small changes in discovery dataset configuration resulting in large alterations in biomarker composition. Our hypothesis, which forms the basis of this current study, is that highly significant overlaps occurring between gene signatures obtained using entirely different approaches indicate genes fundamental for controlling cancer progression. For prostate cancer, we found two sets of signatures that had significant overlaps suggesting important genes (p < 10−34 for paired overlaps, hypergeometrical test). These overlapping signatures defined a core set of genes linking hormone signalling (HES6-AR), cell cycle progression (Prolaris) and a molecular subgroup of patients (PCS1) derived by Non Negative Matrix Factorization (NNMF) of control pathways, together designated as SIG-HES6. The second set (designated SIG-DESNT) consisted of the DESNT diagnostic signature and a second NNMF signature PCS3. Stratifications using SIG-HES6 (HES6, PCS1, Prolaris) and SIG-DESNT (DESNT) classifiers frequently detected the same individual high-risk cancers, indicating that the underlying mechanisms associated with SIG-HES6 and SIG-DESNT may act together to promote aggressive cancer development. We show that the use of combinations of a SIG-HES6 signature together with DESNT substantially increases the ability to predict poor outcome, and we propose a model for prostate cancer development involving co-operation between the SIG-HES6 and SIG-DESNT pathways that has implication for therapeutic design.
Highlights
A major problem in management of human prostate cancer is the high variability in its clinical course making prediction of outcome at the time of diagnosis or following radical therapy extremely difficult [1,2]
Upon investigation of whether poor outcome was determined by Gleason Score, we found that the number of signatures that indicated that a patient was at high risk was an independent prognostic indicator when Gleason was included as a covariate
Our results have an implication for the use of biomarkers in general since the use of DESNT together with a SIG-HES6 biomarker may represent a much more effective method for detecting patients with aggressive disease. To our knowledge this is the first publication to systematically analyze the relationships between multiple distinct prognostic signatures for prostate cancer
Summary
A major problem in management of human prostate cancer is the high variability in its clinical course making prediction of outcome at the time of diagnosis or following radical therapy extremely difficult [1,2]. A critical challenge is to improve prediction of outcome beyond the use of standard clinical predictors including D’Amico stratification and CAPRA score [3]. The development of expression-based prognostic biomarkers has proven very fruitful with over 20 predictive signatures and classifications reported. Many signatures were derived using supervised approaches involving. The Prolaris biomarker [19] contains genes known to be involved in controlling transition through the cell cycle
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.
