Abstract
BackgroundGene expression profiling is an important approach for detecting diagnostic and prognostic biomarkers, and predicting drug safety. The development of a wide range of technologies and platforms for measuring mRNA expression makes the evaluation and standardization of transcriptomic data problematic due to differences in protocols, data processing and analysis methods. Thus, universal RNA standards, such as those developed by the External RNA Controls Consortium (ERCC), are proposed to aid validation of research findings from diverse platforms such as microarrays and RT-qPCR, and play a role in quality control (QC) processes as transcriptomic profiling becomes more commonplace in the clinical setting.ResultsPanels of ERCC RNA standards were constructed in order to test the utility of these reference materials (RMs) for performance characterization of two selected gene expression platforms, and for discrimination of biomarker profiles between groups. The linear range, limits of detection and reproducibility of microarray and RT-qPCR measurements were evaluated using panels of RNA standards. Transcripts of low abundance (≤ 10 copies/ng total RNA) showed more than double the technical variability compared to higher copy number transcripts on both platforms. Microarray profiling of two simulated 'normal' and 'disease' panels, each consisting of eight different RNA standards, yielded robust discrimination between the panels and between standards with varying fold change ratios, showing no systematic effects due to different labelling and hybridization runs. Also, comparison of microarray and RT-qPCR data for fold changes showed agreement for the two platforms.ConclusionsERCC RNA standards provide a generic means of evaluating different aspects of platform performance, and can provide information on the technical variation associated with quantification of biomarkers expressed at different levels of physiological abundance. Distinct panels of standards serve as an ideal quality control tool kit for determining the accuracy of fold change cut-off threshold and the impact of experimentally-derived noise on the discrimination of normal and disease profiles.
Highlights
Gene expression profiling is an important approach for detecting diagnostic and prognostic biomarkers, and predicting drug safety
RNA standards to characterize different methods for gene expression quantification and provide technical information which can be applied to mRNA biomarkers of differing levels of abundance
The results show that pair-wise correlation of replicate arrays of the same sample exhibited a high degree of correlation for the platform (R2 > 0.985) (Additional File 3)
Summary
Gene expression profiling is an important approach for detecting diagnostic and prognostic biomarkers, and predicting drug safety. Compared to traditional clinical outcome measurements where a single biochemical measurement or histopathological score is interpreted, gene expression signatures resulting from microarray experiments generate a LGC Limited, Queens Road, Teddington, Middlesex, TW11 0LY, UK molecular fingerprint consisting of multiple biomarkers which cannot otherwise be interpreted in isolation This approach has been applied successfully in the area of breast cancer prognosis, where the first in vitro diagnostic multi-variate index assay (IVDMIA) using gene expression measurements, MammaPrint (a microarraybased expression profile of 70 genes [5]), was approved for use by the FDA in 2007 [4], while OncotypeDx, a reverse-transcription quantitative PCR (RT-qPCR) -based assay profiles 21 genes in proliferation and estrogen receptor-related pathways [6]. Upon approval of multigene biomarker tests for clinical applications, reference materials would play an integral part in ongoing quality control (QC)
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