Vitamin D receptor activation reduces VCaP xenograft tumor growth and counteracts ERG activity despite induction of TMPRSS2:ERG.

Whether vitamin D is chemopreventive and/or has potential therapeutically in prostate cancer is unresolved. One confounding factor is that many prostate cancers express a TMPRSS2:ERG fusion gene whose expression is increased both by androgens and by vitamin D receptor (VDR) activation. Two challenges that limit VDR agonist use clinically are hypercalcemia and the cooperation of VDR with ERG to hyper-induce the 1α,25-dihydroxyvitamin D3 metabolizing enzyme, CYP24A1, thus reducing VDR activity. Using the VCaP TMPRSS2:ERG positive cell line as a model, we found that a nonsecosteroidal CYP24A1 resistant VDR agonist, VDRM2, substantially reduces growth of xenograft tumors without inducing hypercalcemia. Utilizing next generation RNA sequencing, we found a very high overlap of 1,25D(OH)2D3 and VDRM2 regulated genes and by drawing upon previously published datasets to create an ERG signature, we found activation of VDR does not induce ERG activity above the already high basal levels present in VCaP cells. Moreover, we found VDR activation opposes 8 of the 10 most significant ERG regulated Hallmark gene set collection pathways from Gene Set Enrichment Analysis (GSEA). Thus, a CYP24A1 resistant VDR agonist may be beneficial for treatment of TMPRSS2:ERG positive prostate cancer; one negative consequence of TMPRSS2:ERG expression is inactivation of VDR signaling.

Integrative Pathway Analysis of Metabolic Signature in Bladder Cancer: A Linkage to The Cancer Genome Atlas Project and Prediction of Survival.

PURPOSE: We used targeted mass spectrometry to study the metabolic fingerprint of urothelial cancer and determine whether the biochemical pathway analysis gene signature would have a predictive value in independent cohorts of patients with bladder cancer. MATERIALS AND METHODS: Pathologically evaluated, bladder derived tissues, including benign adjacent tissue from 14 patients and bladder cancer from 46, were analyzed by liquid chromatography based targeted mass spectrometry. Differential metabolites associated with tumor samples in comparison to benign tissue were identified by adjusting the p values for multiple testing at a false discovery rate threshold of 15%. Enrichment of pathways and processes associated with the metabolic signature were determined using the GO (Gene Ontology) Database and MSigDB (Molecular Signature Database). Integration of metabolite alterations with transcriptome data from TCGA (The Cancer Genome Atlas) was done to identify the molecular signature of 30 metabolic genes. Available outcome data from TCGA portal were used to determine the association with survival. RESULTS: We identified 145 metabolites, of which analysis revealed 31 differential metabolites when comparing benign and tumor tissue samples. Using the KEGG (Kyoto Encyclopedia of Genes and Genomes) Database we identified a total of 174 genes that correlated with the altered metabolic pathways involved. By integrating these genes with the transcriptomic data from the corresponding TCGA data set we identified a metabolic signature consisting of 30 genes. The signature was significant in its prediction of survival in 95 patients with a low signature score vs 282 with a high signature score (p = 0.0458). CONCLUSIONS: Targeted mass spectrometry of bladder cancer is highly sensitive for detecting metabolic alterations. Applying transcriptome data allows for integration into larger data sets and identification of relevant metabolic pathways in bladder cancer progression.