|
| Status |
Public on Jul 30, 2015 |
| Title |
tumour tissue_robotic radical prostatetctomy_STKHLM4925 |
| Sample type |
RNA |
| |
|
| Source name |
primary tumour tissue from robotic radical prostatectomy (RRP) surgery
|
| Organism |
Homo sapiens |
| Characteristics |
tumour gleason: 9=4+5
tumour %: 70%
extra capsular extension (ece): Y
positive surgical margins (psm): Y
biochemical relapse (bcr): Y
time to bcr (months): 1.80821917808219
derived data (iclusterplus group): clust5
psa at diag: 15.4
clinical stage: T3a
pathology stage: T3aMxNx
total follow up (months): 85.7424657534246
|
| Treatment protocol |
Matched tumour and benign tissues were identified and prepared from RP surgical tissue as described in Warren et al. (2013). The Prostate. 73: 194-202. Prepared H&E slides of the tumour regions were macrodissected to yield samples of at least 70% tumour content.
|
| Growth protocol |
N/A - surgical tissue samples
|
| Extracted molecule |
total RNA |
| Extraction protocol |
Qiagen AllPrep, according to manufacturer's instructions.
|
| Label |
biotin
|
| Label protocol |
cRNA was generated and biotin-labelled using the Illumina TotalPrep RNA Amplification Kit
|
| |
|
| Hybridization protocol |
Standard Illumina hybridization protocol
|
| Scan protocol |
Standard Illumina scan protocol
|
| Data processing |
Bead level data were pre-processed to remove spatial artifacts, log2 transformed and quantile normalized using the beadarray package in Bioconductor prior to analysis (Dunning et al, 2007). The ComBAT method (Johnson et al., 2007), as implemented in the sva Bioconductor package, was used address batch effects in the expression data. Downstream analyses were restricted to 'perfect' probes only (Barbosa-Morais et al., 2010), and whenever a gene-centric analysis was required we chose the probe with the highest Inter-quartile range (IQR) to represent each gene. Probes (genes) were ranked by IQR values, and the 100 most variable probes across expression data were selected for clustering, based on k-means method (see Chalise et al. (2014) for a review on clustering methods), where each observation belongs to the cluster with the nearest mean that best describes that cluster. A linear modelling approach was used to estimate the expression of each probe in the five subtypes, and the set of matched benign samples. Differential expression statistics for the comparison of each subtype to benign were then generated following Bayes' shrinkage of variance (Smyth 2004).
|
| |
|
| Submission date |
Jul 10, 2015 |
| Last update date |
Dec 01, 2015 |
| Contact name |
Chandra Chilamakuri |
| E-mail(s) |
[email protected]
|
| Organization name |
Cancer Research UK Cambridge Institute
|
| Street address |
Robinson Way
|
| City |
Cambridge |
| ZIP/Postal code |
CB2 0RE |
| Country |
United Kingdom |
| |
|
| Platform ID |
GPL10558 |
| Series (2) |
| GSE70769 |
Prostate cancer stratification using molecular profiles [Stockholm ExpressionArray] |
| GSE70770 |
Prostate cancer stratification using molecular profiles |
|
