Transcriptional profiling of primary prostate tumor in metastatic hormonesensitive prostate cancer and association with clinical outcomes: correlative analysis of the E3805 CHAARTED study
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PROVIDER: PRJNA833082 | ENA |
REPOSITORIES: ENA
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Project description:Accurate prediction of prostate cancer clinical courses remains elusive. In this study, we performed whole genome copy number analysis on leukocytes of 273 prostate cancer patients using Affymetrix SNP6.0 chip. Copy number variations (CNV) were found across all chromosomes of the human genome. An average of 152 CNV fragments per genome was identified in the leukocytes from prostate cancer patients. The size distributions of CNV in the genome of leukocytes were highly correlative with prostate cancer aggressiveness. A prostate cancer outcome prediction model was developed based on large size ratio of CNV from the leukocyte genomes. This prediction model generated an average prediction rate of 75.2%, with sensitivity of 77.3% and specificity of 69.0% for prostate cancer recurrence. When combined with Nomogram and the status of fusion transcripts, the average prediction rate was improved to 82.5% with sensitivity of 84.8% and specificity of 78.2%. In addition, the leukocyte prediction model was 62.6% accurate in predicting short prostate specific antigen doubling time. When combined with Gleason’s grade, Nomogram and the status of fusion transcripts, the prediction model generated a correct prediction rate of 77.5% with 73.7% sensitivity and 80.1% specificity. To our knowledge, this is the first study showing that CNVs in leukocyte genomes are predictive of clinical outcomes of a human malignancy.
2017-02-09 | GSE70650 | GEO
Project description:Gene expression study of the ECOG 3805 randomized controlled trial
2022-05-01 | GSE201805 | GEO
Project description:To identify genes with the potential target of differentially expressed miRNA in metastatic prostate cancer, we have employed whole genome microarray expression profiling. Transplantable metastatic versus a non-metastatic prostate cancer xenograft line, both derived from one patient’s primary cancer, were developed via sub-renal capsule grafting of cancer tissue into NOD/SCID mice. The same RNA samples from both lines were also used for miRNA sequencing. Overlapped genes of predicted targets of differentially expressed miRNAs and differentially expressed in microarray platform showed potential markers of combination of miRNA and gene in metastatic prostate cancer. Gene expression in metastatic/non-metastatic prostate cancer was measured. Same total RNA samples were used for small RNA library construction for miRNA sequencing. This submission represents the gene expression component of the study.
2012-03-11 | E-GEOD-28029 | biostudies-arrayexpress
Project description:An integrative analysis of this compendium of proteomic alterations and transcriptomic data was performed revealing only 48-64% concordance between protein and transcript levels. Importantly, differential proteomic alterations between metastatic and clinically localized prostate cancer that mapped concordantly to gene transcripts served as predictors of clinical outcome in prostate cancer as well as other solid tumors. Keywords: prostate cancer progression 13 individual benign prostate, primary and metastatic prostate cancer samples and 6 pooled samples from benign,primary or metastatic prostate cancer tissues.
2005-09-20 | E-GEOD-3325 | biostudies-arrayexpress
Project description:To identify genes with the potential target of differentially expressed miRNA in metastatic prostate cancer, we have employed whole genome microarray expression profiling. Transplantable metastatic versus a non-metastatic prostate cancer xenograft line, both derived from one patient’s primary cancer, were developed via sub-renal capsule grafting of cancer tissue into NOD/SCID mice. The same RNA samples from both lines were also used for miRNA sequencing. Overlapped genes of predicted targets of differentially expressed miRNAs and differentially expressed in microarray platform showed potential markers of combination of miRNA and gene in metastatic prostate cancer.
2012-03-11 | GSE28029 | GEO
Project description:BackgroundThe phase III CHAARTED trial established upfront androgen-deprivation therapy (ADT) plus docetaxel (D) as a standard for metastatic hormone-sensitive prostate cancer (mHSPC) based on meaningful improvement in overall survival (OS). Biological prognostic markers of outcomes and predictors of chemotherapy benefit are undefined.Patients and methodsWhole transcriptomic profiling was performed on primary PC tissue obtained from patients enrolled in CHAARTED prior to systemic therapy. We adopted an a priori analytical plan to test defined RNA signatures and their associations with HSPC clinical phenotypes and outcomes. Multivariable analyses (MVAs) were adjusted for age, Eastern Cooperative Oncology Group status, de novo metastasis presentation, volume of disease, and treatment arm. The primary endpoint was OS; the secondary endpoint was time to castration-resistant PC.ResultsThe analytic cohort of 160 patients demonstrated marked differences in transcriptional profile compared with localized PC, with a predominance of luminal B (50%) and basal (48%) subtypes, lower androgen receptor activity (AR-A), and high Decipher risk disease. Luminal B subtype was associated with poorer prognosis on ADT alone but benefited significantly from ADT + D [OS: hazard ratio (HR) 0.45; P = 0.007], in contrast to basal subtype which showed no OS benefit (HR 0.85; P = 0.58), even in those with high-volume disease. Higher Decipher risk and lower AR-A were significantly associated with poorer OS in MVA. In addition, higher Decipher risk showed greater improvements in OS with ADT + D (HR 0.41; P = 0.015).ConclusionThis study demonstrates the utility of transcriptomic subtyping to guide prognostication in mHSPC and potential selection of patients for chemohormonal therapy, and provides proof of concept for the possibility of biomarker-guided selection of established combination therapies in mHSPC.
| S-EPMC8463957 | biostudies-literature
Project description:Genomic sequencing of many thousands of tumors has revealed many genes associated with specific types of cancer. Similarly, large scale CRISPR functional genomics efforts have mapped genes required for proliferation or survival in hundreds of cancer cell lines. Despite this, for specific disease subtypes, such as metastatic prostate cancer, it is likely that there exist many undiscovered tumor specific genetic dependencies, such as prostate cancer specific drivers, that represent drug targets. To identify such genetic dependencies, we performed genome-scale CRISPRi screens in metastatic prostate cancer models. We then created a pipeline in which we integrated publicly available pan-cancer functional genomics data with our metastatic prostate cancer functional and clinical genomics data to identify genes that can drive aggressive prostate cancer phenotypes. Our integrative analysis of these data revealed two known prostate cancer specific driver genes, AR and HOXB13, as the top two hits and also nominated a number of unexpected genes. In this study we highlight the strength of an integrated clinical and functional genomics pipeline and focus on two hit genes, KIF4A and WDR62. We demonstrate that both KIF4A and WDR62 drive aggressive prostate cancer phenotypes in vitro and in vivo in multiple models, irrespective of AR-status, and are also associated with poor patient outcome.
2021-06-17 | GSE178329 | GEO
Project description:Genomic sequencing of many thousands of tumors has revealed many genes associated with specific types of cancer. Similarly, large scale CRISPR functional genomics efforts have mapped genes required for proliferation or survival in hundreds of cancer cell lines. Despite this, for specific disease subtypes, such as metastatic prostate cancer, it is likely that there exist many undiscovered tumor specific genetic dependencies, such as prostate cancer specific drivers, that represent drug targets. To identify such genetic dependencies, we performed genome-scale CRISPRi screens in metastatic prostate cancer models. We then created a pipeline in which we integrated publicly available pan-cancer functional genomics data with our metastatic prostate cancer functional and clinical genomics data to identify genes that can drive aggressive prostate cancer phenotypes. Our integrative analysis of these data revealed two known prostate cancer specific driver genes, AR and HOXB13, as the top two hits and also nominated a number of unexpected genes. In this study we highlight the strength of an integrated clinical and functional genomics pipeline and focus on two hit genes, KIF4A and WDR62. We demonstrate that both KIF4A and WDR62 drive aggressive prostate cancer phenotypes in vitro and in vivo in multiple models, irrespective of AR-status, and are also associated with poor patient outcome.
2021-06-17 | GSE178300 | GEO