Search

Abstract Background The 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. Results Three 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. Conclusions The 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.

Abstract Background We demonstrate the feasibility of creating a pair of reference samples to be used as surrogates for clinical samples measured in either a research or clinical laboratory setting. The reference sample paradigm presented and evaluated here is designed to assess the capability of a measurement process to detect true differences between two biological samples. Cell-based reference samples can be created with a biomarker signature pattern designed in silico. Clinical laboratories working in regulated applications are required to participate in proficiency testing programs; research laboratories doing discovery typically do not. These reference samples can be used in proficiency tests or as process controls that allow a laboratory to evaluate and optimize its measurement systems, monitor performance over time (process drift), assess changes in protocols, reagents, and/or personnel, maintain standard operating procedures, and most importantly, provide evidence for quality results. Results The biomarkers of interest in this study are microRNAs (miRNAs), small non-coding RNAs involved in the regulation of gene expression. Multiple lung cancer associated cell lines were determined by reverse transcription (RT)-PCR to have sufficiently different miRNA profiles to serve as components in mixture designs as reference samples. In silico models based on the component profiles were used to predict miRNA abundance ratios between two different cell line mixtures, providing target values for profiles obtained from in vitro mixtures. Two reference sample types were tested: total RNA mixed after extraction from cell lines, and intact cells mixed prior to RNA extraction. MicroRNA profiling of a pair of samples composed of extracted RNA derived from these cell lines successfully replicated the target values. Mixtures of intact cells from these lines also approximated the target values, demonstrating potential utility as mimics for clinical specimens. Both designs demonstrated their utility as reference samples for inter- or intra-laboratory testing. Conclusions Cell-based reference samples can be created for performance assessment of a measurement process from biomolecule extraction through quantitation. Although this study focused on miRNA profiling with RT-PCR using cell lines associated with lung cancer, the paradigm demonstrated here should be extendable to genome-scale platforms and other biomolecular endpoints.

Abstract Background Circulating microRNAs are undergoing exploratory use as safety biomarkers in drug development. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is one common approach used to quantitate levels of microRNAs in samples that includes the use of a standard curve of calibrators fit to a regression model. Guidelines are needed for setting assay quantitation thresholds that are appropriate for this method and to biomarker pre-validation. Results In this report, we develop two workflows for determining a lower limit of quantitation (LLOQ) for RT-qPCR assays of microRNAs in exploratory studies. One workflow is based on an error threshold calculated by a logistic model of the calibration curve data. The second workflow is based on a threshold set by the sample blank, which is the no template control for RT-qPCR. The two workflows are used to set lower thresholds of reportable microRNA levels for an example dataset in which miR-208a levels in biofluids are quantitated in a cardiac injury model. LLOQ thresholds set by either workflow are effective in filtering out microRNA values with large uncertainty estimates. Conclusions Two workflows for LLOQ determinations are presented in this report that provide methods that are easy to implement in investigational studies of microRNA safety biomarkers and offer choices in levels of conservatism in setting lower limits of acceptable values that facilitate interpretation of results.

Genome-scale gene expression technologies are increasingly being applied for biological research as a whole and toxicological screening in particular. In order to monitor data quality and process drift, we adopted the use of two rat-tissue mixtures (brain, liver, kidney, and testis) previously introduced as RNA reference samples. These samples were processed every time a microarray experiment was hybridized, thereby verifying the comparability of the resulting expression data for cross-study comparison. This study presents the analysis of 21 technical replicates of these two mixed-tissue samples using Affymetrix RAE230_2 GeneChip over a period of 12 months. The results show that detection sensitivity, measured by the number of present and absent sequences, is robust, and data correlation, indicated by scatter plots, varies little over time. Receiver operating characteristic (ROC) curves show the sensitivity and specificity of the current measurements are consistent with arrays previously classified as well performing. Overall, this paper shows that the inclusion of standard samples during microarray labeling and hybridization experiments is useful to benchmark the performance of microarray experiments over time and allows discovery of any process drift that, if it occurs, may confound the comparison of these datasets.