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Translating a gene expression signature for multiple myeloma prognosis into a robust high-throughput assay for clinical use
- Source :
- BMC Medical Genomics
- Publisher :
- Springer Nature
-
Abstract
- Background Widespread adoption of genomic technologies in the management of heterogeneous indications, including Multiple Myeloma, has been hindered by concern over variation between published gene expression signatures, difficulty in physician interpretation and the challenge of obtaining sufficient genetic material from limited patient specimens. Methods Since 2006, the 70-gene prognostic signature, developed by the University of Arkansas for Medical Sciences (UAMS) has been applied to over 4,700 patients in studies performed in 4 countries and described in 17 peer-reviewed publications. Analysis of control sample and quality control data compiled over a 12-month period was performed. Results Over a 12 month period, the 70-gene prognosis score (range 0–100) of our multiple myeloma cell-line control sample had a standard deviation of 2.72 and a coefficient of variance of 0.03. The whole-genome microarray profile used to calculate a patient’s GEP70 score can be generated with as little as 15 ng of total RNA; approximately 30,000 CD-138+ plasma cells. Results from each GEP70 analysis are presented as either low (70-gene score
- Subjects :
- Microarray
Coefficient of variation
Plasma Cells
Biology
Bioinformatics
Biopsy
medicine
Genetics
Humans
Genetics(clinical)
Genetics (clinical)
Multiple myeloma
Oligonucleotide Array Sequence Analysis
Framingham Risk Score
medicine.diagnostic_test
Genome, Human
Gene Expression Profiling
Biopsy, Needle
Reproducibility of Results
medicine.disease
Prognosis
Human genetics
High-Throughput Screening Assays
Gene expression profiling
Gene Expression Regulation, Neoplastic
Treatment Outcome
Technical Advance
RNA
Syndecan-1
DNA microarray
Multiple Myeloma
Subjects
Details
- Language :
- English
- ISSN :
- 17558794
- Volume :
- 7
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- BMC Medical Genomics
- Accession number :
- edsair.doi.dedup.....e6bc89fe97322afcce91ace8a551047a
- Full Text :
- https://doi.org/10.1186/1755-8794-7-25