Distinct transcriptional repertoire of the androgen receptor in ETS fusion-negative prostate cancer

Anders E Berglund, Robert J Rounbehler, Travis Gerke, Shivanshu Awasthi, Chia-Ho Cheng, Mandeep Takhar, Elai Davicioni, Mohammed Alshalalfa, Nicholas Erho, Eric A Klein, Stephen J Freedland, Ashley E Ross, Edward M Schaeffer, Bruce J Trock, Robert B Den, John L Cleveland, Jong Y Park, Jasreman Dhillon, Kosj Yamoah, Anders E Berglund, Robert J Rounbehler, Travis Gerke, Shivanshu Awasthi, Chia-Ho Cheng, Mandeep Takhar, Elai Davicioni, Mohammed Alshalalfa, Nicholas Erho, Eric A Klein, Stephen J Freedland, Ashley E Ross, Edward M Schaeffer, Bruce J Trock, Robert B Den, John L Cleveland, Jong Y Park, Jasreman Dhillon, Kosj Yamoah

Abstract

Background: Prostate cancer (PCa) tumors harboring translocations of ETS family genes with the androgen responsive TMPRSS2 gene (ETS+ tumors) provide a robust biomarker for detecting PCa in approximately 70% of patients. ETS+ PCa express high levels of the androgen receptor (AR), yet PCa tumors lacking ETS fusions (ETS-) also express AR and demonstrate androgen-regulated growth. In this study, we evaluate the differences in the AR-regulated transcriptomes between ETS+ and ETS- PCa tumors.

Methods: 10,608 patient tumors from three independent PCa datasets classified as ETS+ (samples overexpressing ERG or other ETS family members) or ETS- (all other PCa) were analyzed for differential gene expression using false-discovery-rate adjusted methods and gene-set enrichment analysis (GSEA).

Results: Based on the expression of AR-dependent genes and an unsupervised Principal Component Analysis (PCA) model, AR-regulated gene expression alone was able to separate PCa samples into groups based on ETS status in all PCa databases. ETS status distinguished several differentially expressed genes in both TCGA (6.9%) and GRID (6.6%) databases, with 413 genes overlapping in both databases. Importantly, GSEA showed enrichment of distinct androgen-responsive genes in both ETS- and ETS+ tumors, and AR ChIP-seq data identified 131 direct AR-target genes that are regulated in an ETS-specific fashion. Notably, dysregulation of ETS-dependent AR-target genes within the metabolic and non-canonical WNT pathways was associated with clinical outcomes.

Conclusions: ETS status influences the transcriptional repertoire of the AR, and ETS- PCa tumors appear to rely on distinctly different AR-dependent transcriptional programs to drive and sustain tumorigenesis.

Trial registration: ClinicalTrials.gov NCT02609269.

Conflict of interest statement

Anders E. Berglund. Patents, Royalties, Other Intellectual Property: H. Lee Moffitt Cancer Center & Research Institute. Mandeep Takhar Employment: GenomeDx Biosciences, Mohammed Alshalalfa Employment: GenomeDx Biosciences, Elai Davicioni Employment: GenomeDx Biosciences, Leadership: GenomeDx Biosciences, Stock or Other Ownership: GenomeDx Biosciences, Nicholas Erho, Employment: GenomeDx Biosciences, Ashley E. Ross Stock or Other Ownership: GenomeDx Biosciences, Consulting or Advisory Role: GenomeDx Biosciences, Research Funding: GenomeDx Biosciences, Edward M. Schaeffer Consulting or Advisory Role: GenomeDx Biosciences, Metama, Eric A. Klein Consulting or Advisory Role: GenomeDx Biosciences, Berg, Genomic Health, Speakers’ Bureau: Genomic Health, Robert B. Den Research Funding: GenomeDx Biosciences, Bruce J. Trock Consulting or Advisory Role: GenomeDx Biosciences, Research Funding: Myriad Genetics, John L. Cleveland: No relationship to disclose, Jong Y. Park: No relationship to disclose, Jasreman Dhillon: No relationship to disclose, Stephen J. Freedland: No relationship to disclose, Shivanshu Awasthi: No relationship to disclose, Chia-Ho Cheng: No relationship to disclose, Travis Gerke: No relationship to disclose, Robert J. Rounbehler: No relationship to disclose, Kosj Yamoah: No relationship to disclose.

Figures

Fig. 1
Fig. 1
AR-regulated genes discriminate tumors based on ETS status. An unsupervised PCA model of 101 AR-regulated genes affirms the distinct AR signatures of ETS+ and the ETS− samples in the first principal component for the TCGA RNA-seq (a) and in the second principal component for the GRID microarray profiling (b) PCa datasets. c and d show the relative contribution of the individual AR-regulated genes to the PCA models in a and b, respectively. The t-SNE model shows similar results to the PCA model for the TCGA (e) and the GRID (f) dataset, where there is a clear separation between the ETS− and ETS+ samples. The results also indicate that the molecular subtypes used for ETS+ (1-ERG, 2-ETV1, 3-ETV4, 4-FLI1) and ETS− (5-SPOP, 6-FOXA1, 7-IDH1, 8-other) fall into the correct ETS category
Fig. 2
Fig. 2
Genes differentially expressed in ETS+ versus ETS− PCa tumors. a The GRID dataset has 46050 genes with 3047 (6.6%) being differentially expressed when comparing ETS+ vs. ETS− PCa. The TCGA dataset has 20531 genes with 1423 (6.9%) being differentially expressed based on ETS status. Using a false-discovery-rate adjusted (q < 0.05) Mann–Whitney U test and a fold-change cut-off of 0.585 (TCGA) and 0.05 (GRID), 413 differentially expressed genes based on ETS status were defined in both PCa databases. b There is no significant difference in AR expression between ETS− and ETS+ tumors in either TCGA or GRID. The 413 significant differentially expressed genes (c) in the TCGA and GRID PCa based on ETS status were analyzed by GSEA and the HALLMARK gene sets [43]. Significant gene sets (d) overexpressed in ETS+ are shown at the top and those gene sets for ETS− shown below. Androgen response was the top-ranked gene set for both ETS+ and ETS− significantly overexpressed genes
Fig. 3
Fig. 3
Distinct direct AR target genes are regulated in ETS+ and ETS− PCa tumors. a Schematic of pipeline used to define 5 categories from the 131 direct AR transcription targets in ETS+ and ETS− PCa tumors. b Examples of genes whose expression is significantly different in adjacent normal tissue (N) in ETS+ PCa or ETS− PCa. c Heatmap of differentially expressed direct AR target genes in ETS- Up pink, ETS- Down gray, ETS+ Up blue, ETS+ Down green, and that are upregulated in both ETS+ and ETS− PCa (brown). Each row/gene is normalized to median expression in adjacent normal tissue. The 8 subtypes of ETS+ and ETS− PCa, as defined by their expression of ERG, ATV1, ETV4, FLI1, SPOP, FOXA1, IDH1 and “other” are shown beneath the heatmap
Fig. 4
Fig. 4
Specific AR target pathways show ETS status and BCR dependency. Gene ontology analysis and literature searches revealed that among ETS status-dependent AR target genes, metabolic pathway genes and non-canonical WNT pathway genes are up-regulated in ETS− tumors, whereas signaling and ion transport pathway genes are up-regulated in ETS+ tumors (a). Kaplan–Meier curves for metabolic (b) and non-canonical WNT (c) genes show significance to BCR in ETS+ tumors

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