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Affymetrix data for training of Endopredict algorithm

ABSTRACT: These data, combined with other cohorts (GSE6532, GSE12093, and qRT-PCR based cohorts), was used to construct the EP algorithm, which predicts the likelihood of developing of a distant recurrence of early stage breast cancer under endocrine treatment. In addition, EPclin, a combination of the EP score, the nodal status and the tumor size, was constructed. All samples used for the development of the EP algorithm were taken from patients who received adjuvant tamoxifen only. Fresh-frozen tumours obtained from three centers were profiled on HG-U133A arrays. In brief, starting from 5 µg total RNA, labeled cRNA was prepared using the Roche Microarray cDNA Synthesis, Microarray RNA Target Synthesis (T7) and Microarray Target Purification Kit (Roche Applied Science, Mannheim, Germany), according to the manufacturer’s instructions. Raw .cel file data were processed by MAS 5.0 software (AFFYMETRIX, Santa Clara, CA). In the analysis settings, the global scaling procedure was chosen which multiplied the output signal intensities of each array to a mean target intensity of 500. We selected those samples from the GSE6532 and GSE12093 cohorts that we considered appropriate for our analyses (n=130 and n=130) and calculated MAS5 expression values from the corresponding .CEL-Files. One of the selection criteria was the GPL96 platform we also used for our measurements. GSE26971_GSE12093_dataset.txt.gz and GSE26971_GSE6532_dataset.txt.gz files contain data for both cohorts including the GSM numbers in row A and MAS5 expression data in the remaining rows. Processing of .CEL-files of these samples was exactly the same as the processing of the .CEL-files of our own samples: GPL96, MAS5, TGT 500.

ORGANISM(S): Homo sapiens

SUBMITTER: Filipits M

PROVIDER: S-ECPF-GEOD-26971 | biostudies-other | 2011 Sep

REPOSITORIES: biostudies-other

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Project description:BackgroundClinical Bioinformatics is currently growing and is based on the integration of clinical and omics data aiming at the development of personalized medicine. Thus the introduction of novel technologies able to investigate the relationship among clinical states and biological machineries may help the development of this field. For instance the Affymetrix DMET platform (drug metabolism enzymes and transporters) is able to study the relationship among the variation of the genome of patients and drug metabolism, detecting SNPs (Single Nucleotide Polymorphism) on genes related to drug metabolism. This may allow for instance to find genetic variants in patients which present different drug responses, in pharmacogenomics and clinical studies. Despite this, there is currently a lack in the development of open-source algorithms and tools for the analysis of DMET data. Existing software tools for DMET data generally allow only the preprocessing of binary data (e.g. the DMET-Console provided by Affymetrix) and simple data analysis operations, but do not allow to test the association of the presence of SNPs with the response to drugs.ResultsWe developed DMET-Analyzer a tool for the automatic association analysis among the variation of the patient genomes and the clinical conditions of patients, i.e. the different response to drugs. The proposed system allows: (i) to automatize the workflow of analysis of DMET-SNP data avoiding the use of multiple tools; (ii) the automatic annotation of DMET-SNP data and the search in existing databases of SNPs (e.g. dbSNP), (iii) the association of SNP with pathway through the search in PharmaGKB, a major knowledge base for pharmacogenomic studies. DMET-Analyzer has a simple graphical user interface that allows users (doctors/biologists) to upload and analyse DMET files produced by Affymetrix DMET-Console in an interactive way. The effectiveness and easy use of DMET Analyzer is demonstrated through different case studies regarding the analysis of clinical datasets produced in the University Hospital of Catanzaro, Italy.ConclusionDMET Analyzer is a novel tool able to automatically analyse data produced by the DMET-platform in case-control association studies. Using such tool user may avoid wasting time in the manual execution of multiple statistical tests avoiding possible errors and reducing the amount of time needed for a whole experiment. Moreover annotations and the direct link to external databases may increase the biological knowledge extracted. The system is freely available for academic purposes at: https://sourceforge.net/projects/dmetanalyzer/files/

Project description:Many of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously analyze data across a set of predetermined arrays to improve precision of the final measures of expression. One problem associated with these algorithms is that expression measurements for a particular sample are highly dependent on the set of samples used for normalization and results obtained by normalization with a different set may not be comparable. A related problem is that an organization producing and/or storing large amounts of data in a sequential fashion will need to either re-run the pre-processing algorithm every time an array is added or store them in batches that are pre-processed together. Furthermore, pre-processing of large numbers of arrays requires loading all the feature-level data into memory which is a difficult task even with modern computers. We utilize a scheme that produces all the information necessary for pre-processing using a very large training set that can be used for summarization of samples outside of the training set. All subsequent pre-processing tasks can be done on an individual array basis. We demonstrate the utility of this approach by defining a new version of the Robust Multi-chip Averaging (RMA) algorithm which we refer to as refRMA.We assess performance based on multiple sets of samples processed over HG U133A Affymetrix GeneChip arrays. We show that the refRMA workflow, when used in conjunction with a large, biologically diverse training set, results in the same general characteristics as that of RMA in its classic form when comparing overall data structure, sample-to-sample correlation, and variation. Further, we demonstrate that the refRMA workflow and reference set can be robustly applied to naïve organ types and to benchmark data where its performance indicates respectable results.Our results indicate that a biologically diverse reference database can be used to train a model for estimating probe set intensities of exclusive test sets, while retaining the overall characteristics of the base algorithm. Although the results we present are specific for RMA, similar versions of other multi-array normalization and summarization schemes can be developed.

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Affymetrix data for training of Endopredict algorithm

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