Abstract
BackgroundWe 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.ResultsThe 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.ConclusionsCell-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.
Highlights
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
Each laboratory performed three separate RNA isolations per cell line, split their samples and shared them with the other site. Each laboratory used their own reverse transcription PCR (RT-PCR) methods to measure the miRNA profiles of both sets of samples
This cross-over approach was used to determine if robust differences in miRNA abundance were consistently observed among the four possible measurement processes resulting from all combinations of isolation laboratory and PCR laboratory
Summary
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. 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. Within the framework of the Early Detection Research Network (EDRN) of the National Cancer Institute [4], we are developing a measurement assurance paradigm for the first phase of this process, preclinical exploratory studies. Quality measurements during this phase are essential for the success of all subsequent phases of biomarker development
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