DOI: 10.6084/m9.figshare.2069706.v7
title: Biomarker Benchmark - GSE37199

description: <b><br>[NOTICE: This data set has been deprecated. Please see our new version of the data (and additional data sets) here: https://osf.io/mhk93 ]</b><br>"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.<br>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).<br>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%).<br>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"<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37199<br><br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).<br><br><br>