Discovery and Validation of a Prostate Cancer Genomic Classifier that Predicts Early Metastasis Following Radical Prostatectomy

Abstract

PurposeClinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis.MethodsA nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 who experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set.ResultsExpression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67–0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression.ConclusionA genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.

Altered interactions between unicellular and multicellular genes drive hallmarks of transformation in a diverse range of solid tumors

“…The TAI showed a strong decreasing trend with Gleason score, in both the TCGA (Fig. 1E) and an independent dataset (18), and similar results were obtained using the grade of LIHC and STAD tumors (SI Appendix, Figs. S10 and S11).…”

High-Throughput Transcriptomic Analysis Nominates Proteasomal Genes As Age Specific Biomarkers and Therapeutic Targets in Prostate Cancer

“…The normalization and summarization of the microarray samples were done with the Single Channel Array Normalization algorithm with quality control performed as described previously. [16][17][18][19] Gene expression for each gene was calculated using the Affymetrix Core level summaries for annotated genes. Microarray data are available on the NCBI Gene Expression Omnibus as accession numbers GSE46691 (MCI), GSE62116 (MCII) and GSE62667 (CC).…”

Construction of a set of novel and robust gene expression signatures predicting prostate cancer recurrence

“…Both the 17‐gene Oncotype DX and the 31‐gene Prolaris improve risk stratification of patients with high risk of PC recurrence at time of diagnosis (Albala et al ., 2016; Cuzick et al ., 2011; Klein et al ., 2014; Knezevic et al ., 2013; Oderda et al ., 2017) and after radical prostatectomy (RP) (Cooperberg et al ., 2013; Cullen et al ., 2015). The 22‐gene Decipher predicts metastasis following RP (Erho et al ., 2013; Karnes et al ., 2013; Klein et al ., 2016). While these and other biomarkers assist decision making and thus improve patient management, their clinical application requires further validation (Lamy et al ., 2017; Martin, 2016; McGrath et al ., 2016; Patel and Gnanapragasam, 2016; Ross et al ., 2016; Zhuang and Johnson, 2016).…”