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BackgroundThe potential utility of microRNA as biomarkers for early detection of cancer and other diseases is being investigated with genome-scale profiling of differentially expressed microRNA. Processes for measurement assurance are critical components of genome-scale measurements. Here, we evaluated the utility of a set of total RNA samples, designed with between-sample differences in the relative abundance of miRNAs, as process controls.ResultsThree pure total human RNA samples (brain, liver, and placenta) and two different mixtures of these components were evaluated as measurement assurance control samples on multiple measurement systems at multiple sites and over multiple rounds. In silico modeling of mixtures provided benchmark values for comparison with physical mixtures. Biomarker development laboratories using next-generation sequencing (NGS) or genome-scale hybridization assays participated in the study and returned data from the samples using their routine workflows. Multiplexed and single assay reverse-transcription PCR (RT-PCR) was used to confirm in silico predicted sample differences. Data visualizations and summary metrics for genome-scale miRNA profiling assessment were developed using this dataset, and a range of performance was observed. These metrics have been incorporated into an online data analysis pipeline and provide a convenient dashboard view of results from experiments following the described design. The website also serves as a repository for the accumulation of performance values providing new participants in the project an opportunity to learn what may be achievable with similar measurement processes.ConclusionsThe set of reference samples used in this study provides benchmark values suitable for assessing genome-scale miRNA profiling processes. Incorporation of these metrics into an online resource allows laboratories to periodically evaluate their performance and assess any changes introduced into their measurement process.

Within the International Life Sciences Institute Committee on Genomics, a working group was formed to focus on the application of microarray technology to preclinical assessments of drug-induced nephrotoxicity. As part of this effort, Sprague-Dawley rats were treated with the nephrotoxicant cisplatin at doses of 0.3-5 mg/kg over a 4- to 144-hr time course. RNA prepared from these animals was run on a variety of microarray formats at multiple sites. A set of 93 differentially expressed genes associated with cisplatin-induced renal injury was identified on the National Institute of Environmental Health Sciences (NIEHS) custom cDNA microarray platform using quadruplicate measurements of pooled animal RNA. The reproducibility of this profile of statistically significant gene changes on other platforms, in pooled and individual animal replicate samples, and in an independent study was investigated. A good correlation in response between platforms was found among the 48 genes in the NIEHS data set that could be matched to probes on the Affymetrix RGU34A array by UniGene identifier or sequence alignment. Similar results were obtained with genes that could be linked between the NIEHS and Incyte or PHASE-1 arrays. The degree of renal damage induced by cisplatin in individual animals was commensurate with the number of differentially expressed genes in this data set. These results suggest that gene profiles linked to specific types of tissue injury or mechanisms of toxicity and identified in well-performed replicated microarray experiments may be extrapolatable across platform technologies, laboratories, and in-life studies.