The potential use of expression profiling: implications for predicting treatment response in rheumatoid arthritis
Identifieur interne : 002838 ( Main/Exploration ); précédent : 002837; suivant : 002839The potential use of expression profiling: implications for predicting treatment response in rheumatoid arthritis
Auteurs : Samantha Louise Smith [Royaume-Uni] ; Darren Plant [Royaume-Uni] ; Stephen Eyre [Royaume-Uni] ; Anne Barton [Royaume-Uni]Source :
- Annals of the Rheumatic Diseases [ 0003-4967 ] ; 2013-07.
English descriptors
- Teeft :
- Abundance genes, American society, Arthritis, Arthritis research, Baseline, Batch effects, Beadchip microarray, Biological therapies, Biological therapy, Biomarkers, Breast cancer, Clin, Clin pharmacol, Clinical oncology, Clinical outcome, Clinical practice, Clinical response, Coeliac disease, Different platforms, Drug labelling, Endocrine therapy, Epidemiology unit, Expression levels, Future studies, Gene, Gene expression, Gene expression measurements, Genetic, Genetic predictors, Genetic variants, Genome, Guideline, Health sciences centre, High levels, Human epidermal growth factor receptor, Human genome, Interferon type, Locus, Meanage, Microarray, Microarray data, Microarrays, Oestrogen receptor, Oncology, Peripheral blood, Peripheral blood rituximab, Personalised medicine, Predictor, Receptor, Responder, Response rates, Rheum, Rheumatoid, Rheumatoid arthritis, Rheumatoid arthritis patients, Rituximab, Tamoxifen, Thiopurine methyltransferase, Tpmt, Translational medicine, Treatment response, Whole blood.
Abstract
Whole genome expression profiling, or transcriptomics, is a high throughput technology with the potential for major impacts in both clinical settings and drug discovery and diagnostics. In particular, there is much interest in this technique as a mechanism for predicting treatment response. Gene expression profiling entails the quantitative measurement of messenger RNA levels for thousands of genes simultaneously with the inherent possibility of identifying biomarkers of response to a particular therapy or by singling out those at risk of serious adverse events. This technology should contribute to the era of stratified medicine, in which patient specific populations are matched to potentially beneficial drugs via clinical tests. Indeed, in the oncology field, gene expression testing is already recommended to allow rational use of therapies to treat breast cancer. However, there are still many issues surrounding the use of the various testing platforms available and the statistical analysis associated with the interpretation of results generated. This review will discuss the implications this promising technology has in predicting treatment response and outline the various advantages and pitfalls associated with its use.
Url:
DOI: 10.1136/annrheumdis-2012-202743
Affiliations:
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