Abstract
BackgroundMolecular biomarkers that are based on mRNA transcripts are being developed for the diagnosis and treatment of a number of diseases. DNA microarrays are one of the primary technologies being used to develop classifiers from gene expression data for clinically relevant outcomes. Microarray assays are highly multiplexed measures of comparative gene expression but have a limited dynamic range of measurement and show compression in fold change detection. To increase the clinical utility of microarrays, assay controls are needed that benchmark performance using metrics that are relevant to the analysis of genomic data generated with biological samples.ResultsRatiometric controls were prepared from commercial sources of high quality RNA from human tissues with distinctly different expression profiles and mixed in defined ratios. The samples were processed using six different target labeling protocols and replicate datasets were generated on high density gene expression microarrays. The area under the curve from receiver operating characteristic plots was calculated to measure diagnostic performance. The reliable region of the dynamic range was derived from log2 ratio deviation plots made for each dataset. Small but statistically significant differences in diagnostic performance were observed between standardized assays available from the array manufacturer and alternative methods for target generation. Assay performance using the reliable range of comparative measurement as a metric was improved by adjusting sample hybridization conditions for one commercial kit.ConclusionsProcess improvement in microarray assay performance was demonstrated using samples prepared from commercially available materials and two metrics - diagnostic performance and the reliable range of measurement. These methods have advantages over approaches that use a limited set of external controls or correlations to reference sets, because they provide benchmark values that can be used by clinical laboratories to help optimize protocol conditions and laboratory proficiency with microarray assays.
Highlights
Molecular biomarkers that are based on mRNA transcripts are being developed for the diagnosis and treatment of a number of diseases
We developed a system for assessing technical performance with microarray assays that involves two biologically complex samples (mixed tissue ratiometric controls (MTRC)) that are representative of experimental samples [14]
The four components of the human MTRC-4 used as the example in this study are universal human reference RNA (UHRR), human brain reference RNA (HBRR), liver RNA, and skeletal muscle RNA
Summary
Molecular biomarkers that are based on mRNA transcripts are being developed for the diagnosis and treatment of a number of diseases. Microarray assays are highly multiplexed measures of comparative gene expression but have a limited dynamic range of measurement and show compression in fold change detection. To increase the clinical utility of microarrays, assay controls are needed that benchmark performance using metrics that are relevant to the analysis of genomic data generated with biological samples. Microarrays are designed for high throughput assessment of relative mRNA levels, but comparative expression measurements are constrained by limits in the dynamic range of detection [6]. The MAQC samples have been used for performance comparisons by correlation of results to the reference microarray datasets or to the qRT-PCR data generated for a subset of analytes in the MAQC samples on the TaqMan platform (for examples, see [12,13]). Correlations to historical reference sets measure the degree of similarity to benchmark data but not necessarily an improvement in performance over reference set levels
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