Practical data handling pipeline improves performance of qPCR-based circulating miRNA measurements

Since numerous miRNAs have been shown to be present in circulation, these so-called circulating miRNAs have emerged as potential biomarkers for disease. However, results of qPCR studies on circulating miRNA biomarkers vary greatly and many experiments cannot be reproduced. Missing data in qPCR experiments often occur due to off-target amplification, nonanalyzable qPCR curves and discordance between replicates. The low concentration of most miRNAs leads to most, but not all missing data. Therefore, failure to distinguish between missing data due to a low concentration and missing data due to randomly occurring technical errors partly explains the variation within and between otherwise similar studies. Based on qPCR kinetics, an analysis pipeline was developed to distinguish missing data due to technical errors from missing data due to a low concentration of the miRNA-equivalent cDNA in the PCR reaction. Furthermore, this pipeline incorporates a method to statistically decide whether concentrations from replicates are sufficiently concordant, which improves stability of results and avoids unnecessary data loss. By going through the pipeline's steps, the result of each measurement is categorized as "valid, invalid, or undetectable." Together with a set of imputation rules, the pipeline leads to more robust and reproducible data as was confirmed experimentally. Using two validation approaches, in two cohorts totaling 2214 heart failure patients, we showed that this pipeline increases both the accuracy and precision of qPCR measurements. In conclusion, this statistical data handling pipeline improves the performance of qPCR studies on low-expressed targets such as circulating miRNAs.