Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Blood mRNA expression signatures derived from unsupervised analyses identify prostate cancers with poor outcome

ABSTRACT: Background: Inter-patient prostate cancer (PrCa) heterogeneity results in highly variable patient outcomes. Multi-purpose biomarkers to dissect this heterogeneity are urgently required to improve treatment and accelerate drug development in PrCa. Circulating biomarkers are most practical for evaluating this disease. We pursued the analytical validation and clinical qualification of blood mRNA expression arrays. Methods: Whole blood samples were collected into PaxGeneTM tubes from PrCa patients: 31 good prognosis patients selected for active surveillance (AS) and 63 advanced castration resistant PrCa (CRPC) patients. RNA was extracted, amplified, biotinylated, and hybridised to Affymetrix U133plus2 microarrays and analysis of genome-wide expression profiles were analysed using Bayesian Latent Process Decomposition (LPD). Findings: LPD analysis of the mRNA expression data divided the evaluable patients (n=94) into 4 separate groups. LPD1 and LPD2 consisted almost entirely of CRPC patients (14/14; 17/18); LPD3 (15/31) and LDP4 (12/21) comprised both AS and CRPC. Ten patients were unclassifiable by LPD analysis. LPD1 CRPC patients had significantly poorer overall survival (median 10.7 months, CI-95% 4.2-17.2) than CRPC patients in LPD2 to 4 (median 26.5 months, CI-95% 18.1-34.9, p=0.00007). LPD 1 membership remained the strongest prognostic factor in a multivariate analysis (HR 5.0, CI-95% 2.1-11.9, p=0.0002). Gene signatures in the poor prognosis LPD group 1 were associated with increased CD71+ early erythroid cells and decreased B-cell and T-cell immune response. A 9-gene signature, that was validated by RT-PCR studies, classified patients as group LPD 1 with a very low misclassification rate (1.2%). Interpretation: Poor PrCa outcome can be predicted using gene expression signatures from whole blood and merits further evaluation in predictive and pharmacodynamic biomarker studies for novel anticancer drugs including immune therapies. 107 blood samples were collected in paxgene tubes from 94 prostate cancer patients (31 good prognosis & 63 advanced castration resistant PrCa). Replicates include 9 biological replicates and four technical replicates

ORGANISM(S): Homo sapiens

SUBMITTER: Ian Giddings

PROVIDER: E-GEOD-37199 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Prognostic value of blood mRNA expression signatures in castration-resistant prostate cancer: a prospective, two-stage study.

Olmos David D Brewer Daniel D Clark Jeremy J Danila Daniel C DC Parker Chris C Attard Gerhardt G Fleisher Martin M Reid Alison Hm AH Castro Elena E Sandhu Shahneen K SK Barwell Lorraine L Oommen Nikhil Babu NB Carreira Suzanne S Drake Charles G CG Jones Robert R Cooper Colin S CS Scher Howard I HI de Bono Johann S JS

The Lancet. Oncology 20121009 11

<h4>Background</h4>Biomarkers are urgently needed to dissect the heterogeneity of prostate cancer between patients to improve treatment and accelerate drug development. We analysed blood mRNA expression arrays to identify patients with metastatic castration-resistant prostate cancer with poorer outcome.<h4>Methods</h4>Whole blood was collected into PAXgene tubes from patients with castration-resistant prostate cancer and patients with prostate cancer selected for active surveillance. In stage I ...[more]

PMID: 23059046

Similar Datasets

Project description:This study aimed to identify the genetic signatures associated with disease prognosis in bladder cancer. We used 165 primary bladder cancer samples, 23 recurrent non-muscle invasive tumor tissues, 58 normal looking bladder mucosae surrounding cancer and 10 normal bladder mucosae for microarray analysis. Hierarchical clustering was used to stratify the prognosis-related gene classifiers. For validation, real-time reverse-transcriptase polymerase chain reaction (RT-PCR) of top-ranked 14 genes was performed. On unsupervised hierarchical clustering using prognosis related gene-classifier, tumors were divided into 2 groups. The high risk gene signatures had significantly poor prognosis compared to low risk gene signatures (P<0.001 by the log-rank test, respectively). The prognosis-related gene classifiers correlated significantly with recurrence of non-muscle invasive bladder cancer (hazard ratio, 4.09; 95% confidence interval [CI], 1.94 to 8.64; P<0.001), and progression (hazard ratio, 23.68; 95% confidence interval [CI], 4.91 to 114.30; P<0.001), cancer-specific survival (hazard ratio, 29.25; 95% confidence interval [CI], 3.47 to 246.98; P=0.002) and overall survival (hazard ratio, 23.33; 95% confidence interval [CI], 4.97 to 109.50; P<0.001) of muscle invasive bladder cancer (p < 0.001, respectively). No patient with non-muscle invasive bladder cancer experienced cancer progression in low risk gene signature group. Prognosis-related gene classifiers validated by RT- PCR showed identical results. Prognosis related gene-classifiers provided strong predictive value for disease outcome. These gene classifiers could assist in selecting patients who might benefit from more aggressive therapeutic intervention or surveillance. Keywords: Gene expression, Bladder cancer, Prognosis 165 primary bladder cancer samples and 23 recurrent non-muscle invasive tumor tissues from 14 patients were taken in the Chungbuk National University Hospital. Only histologically verified transitional cell carcinoma samples were selected. Simultaneously 58 normal looking bladder mucosae surrounding cancer were obtained during the operation, which were histologically confirmed normal. Also, 10 normal bladder mucosae were obtained from patients with benign disease. The normal controls were determined to be free of cancer after revealing no malignant cells on urine cytology and no observable bladder cancer on cystoscopic examination during operation for their diseases, and were histologically reconfirmed normal.

Dataset Information

Blood mRNA expression signatures derived from unsupervised analyses identify prostate cancers with poor outcome

Publications

Prognostic value of blood mRNA expression signatures in castration-resistant prostate cancer: a prospective, two-stage study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure