Gene Panel Model Predictive of Outcome in Men at High-Risk of Systemic Progression and Death From Prostate Cancer After Radical Retropubic Prostatectomy

Abstract

A B S T R A C T PurposeIn men who are at high-risk of prostate cancer, progression and death from cancer after radical retropubic prostatectomy (RRP), limited prognostic information is provided by established prognostic features. The objective of this study was to develop a model predictive of outcome in this group of patients. MethodsCandidate genes were identified from microarray expression data from 102 laser capture microdissected prostate tissue samples. Candidates were overexpressed in tumor compared with normal prostate and more frequently in Gleason patterns 4 and 5 than in 3. A case control study of 157 high-risk patients, matched on Gleason score and stage with systemic progression or death of prostate cancer as the end point, was used to evaluate the expression of candidate genes and build a multivariate model. Tumor was collected from the highest Gleason score in paraffinembedded blocks and the gene expression was quantified by real-time reverse transcription polymerase chain reaction. Validation of the final model was performed on a separate case-control study of 57 high-risk patients who underwent RRP. ResultsA model incorporating gene expression of topoisomerase-2a, cadherin-10, the fusion status based on ERG, ETV1, and ETV4 expression, and the aneuploidy status resulted in a 0.81 area under the curve (AUC) in receiver operating characteristic statistical analysis for the identification of men with systemic progression and death from high grade prostate cancer. The AUC was 0.79 in the independent validation study. ConclusionThe model can identify men with high-risk prostate cancer who may benefit from more intensive postoperative follow-up and adjuvant therapies.

A 17-gene Assay to Predict Prostate Cancer Aggressiveness in the Context of Gleason Grade Heterogeneity, Tumor Multifocality, and Biopsy Undersampling

“…Although multiple prior studies have related genomic markers to PCa clinical outcomes [17][18][19][20]51], the development of GPS specifically addresses the impact of tumor sampling in predicting aggressive PCa and included central pathology review and a large number of clinical recurrence events providing robust statistical power. By adding independent molecular information to established risk parameters, the GPS improves risk stratification at time of diagnosis and may favorably shift the balance of risks and benefits for men who are candidates for AS and facing challenging decisions regarding optimal management of localized PCa.…”

“…Although many groups have demonstrated the potential of gene expression analysis to predict outcome in localized PCa [17][18][19][20], frequent genetic differences between regions of individual tumors and limited tumor sampling by needle biopsy pose challenges to molecular-based assays in PCa [21,22]. With these challenges in mind, we conducted two studies to identify genes for which expression in both prostatectomy and biopsy tissues consistently correlates with tumor aggressiveness regardless of multifocality, heterogeneity, or technical challenges associated with limited tumor obtained through biopsy.…”

Genomic Predictors of Outcome in Prostate Cancer

“…The design of discovery studies included several approaches: single-and multicenter studies and correlation of gene panel data to outcomes of the full cohort or selected subgroups [42,51,52,57,58,62] or a case-control population selected on a particular outcome [53,59,60,63]. All except one study reported that the applied expression panel offered significant prognostic information in the particular study cohort.…”

“…The biomarkers to be included in clinical decision making have to provide additional independent prognostic information or additive value together with established clinical and pathologic variables in a multivariate setting like the Memorial Sloan Kettering Cancer Center or Cleveland Clinic nomograms or Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) risk stratification for PCa. [54,60,61]. A few studies investigated TURP tissue to predict the outcome of men undergoing conservative treatment [51,62,63].…”

“…Studies have utilized different methodological approaches to assess the value of genomic tests. These approaches included traditional statistical methods (survival analyses, multivariable models with other clinicopathologic variables, and receiver operating characteristic analysis) [42,[51][52][53]59,60,[62][63][64]. In some studies, results from expression panels were combined with other variables or a nomogram to determine if genomic data added prognostic information above the baseline models [54,57,58,61,65].…”