Transcriptomic profiling of Prostate Cancer
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA956563 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The transcriptomic heterogeneity of the prostate cancer was tested by profiling histologically distinct but equally graded (Gleason score 4+5=9/10) cancer nodules from a surgically removed prostate cancer. We found that not only that the genes were differently regulated in the two nodules but also that expression fluctuations were differently controlled and the gene networks differently remodeled.
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Project description:The integration of diverse ‘omic’ datasets will increase our understanding of the key signaling pathways that drive disease. Here, we used clinical tissue cohorts corresponding to lethal metastatic castration resistant prostate cancer (CRPC) obtained at rapid autopsy to integrate mutational, transcriptomic, and phosphoproteomic datasets for pathway analysis. Using Tied Diffusion through Interacting Events (TieDIE), we integrated differentially expressed transcriptional master regulators, functionally mutated genes, and differentially ‘activated’ kinases in CRPC tissues to synthesize a robust signaling network consisting of pathways with known and novel gene interactions. For 6 individual CRPC patients for which we had transcriptomic and phosphoproteomic data we observed distinct pathway activation states for each patient profile. In one patient, the activated pathways were strikingly similar to a prostate cancer cell line, 22Rv1, providing us with a good pre-clinical model to test targeted, combination therapies. In all, synthesis of multiple ‘omic’ datasets revealed a plethora of pathway information suitable for targeted therapies in lethal prostate cancer.
2016-08-19 | PXD002286 | Pride
Project description:The integration of diverse ‘omic’ datasets will increase our understanding of the key signaling pathways that drive disease. Here, we used clinical tissue cohorts corresponding to lethal metastatic castration resistant prostate cancer (CRPC) obtained at rapid autopsy to integrate mutational, transcriptomic, and phosphoproteomic datasets for pathway analysis. Using Tied Diffusion through Interacting Events (TieDIE), we integrated differentially expressed transcriptional master regulators, functionally mutated genes, and differentially ‘activated’ kinases in CRPC tissues to synthesize a robust signaling network consisting of pathways with known and novel gene interactions. For 6 individual CRPC patients for which we had transcriptomic and phosphoproteomic data we observed distinct pathway activation states for each patient profile. In one patient, the activated pathways were strikingly similar to a prostate cancer cell line, 22Rv1, providing us with a good pre-clinical model to test targeted, combination therapies. In all, synthesis of multiple ‘omic’ datasets revealed a plethora of pathway information suitable for targeted therapies in lethal prostate cancer.
2017-03-29 | MSV000080776 | MassIVE
Project description:Transcriptomic profiling of metastasis tissue samples obtained from prostate cancer patients was performed using Clariom D human arrays.
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Project description:Androgen receptor (AR) signaling is a major driver and therapy target in prostate cancer. Several inhibitors of AR function are approved for different stages of the disease and their impact on downstream gene transcription has been described. However, the ensuing effects of androgen and anti-androgens at the protein level are less well understood. Here, we focused on the AR inhibitor darolutamide which has recently been approved for non-metastatic castration-resistant prostate cancer. Here we determined the impact of darolutamide, a recently approved AR antagonist which significantly extends progression-free and overall survival in non-metastatic CRPC (31, 32), on the prostate cancer proteome. We first determined the direct binding between darolutamide and the AR in living prostate cancer cells in a label-free context using the cellular high throughput thermal shift assay (CETSA HT). We then generated comprehensive proteomic profiles of prostate cancer cells treated with androgen and darolutamide, and compared them with transcriptomic profiles. We found a generally high concordance between proteomic and transcriptomic data, both on the level of detected expressed genes and their protein products, as well as in terms of the corresponding biological programs. However there were cases where protein and gene expression levels were not regulated in parallel, suggesting an additional post-transcriptional regulation step controlling protein abundance to occur in several instances.
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