Identification of factors contributing to variability in a blood-based gene expression test

• Published in: PloS one 2012-07, Vol.7 (7), p.e40068-e40068
• Main Authors: Elashoff, Michael R, Nuttall, Rachel, Beineke, Philip, Doctolero, Michael H, Dickson, Mark, Johnson, Andrea M, Daniels, Susan E, Rosenberg, Steven, Wingrove, James A
• Format: Article
• Language: English
• Subjects: Analysis; Biology; Biomedical laboratories; Blood; Breast cancer; Cardiovascular disease; Clinical trials; Control algorithms; Coronary Artery Disease - diagnosis; Coronary Artery Disease - genetics; Coronary heart disease; Coronary vessels; Diabetes; Diagnostic tests; Disease control; DNA, Complementary - biosynthesis; Gene expression; Gene Expression Profiling - methods; Gene Expression Profiling - standards; Genes; Genomics; Heart diseases; Humans; Laboratories; Laboratory Personnel; Medicine; Patients; Quality control; Reagent Kits, Diagnostic - standards; Reagents; Real-Time Polymerase Chain Reaction - standards; Regression models; Reproducibility of Results; Ribonucleic acid; RNA; RNA - isolation & purification; Synthesis; Transcription; Trends; Variability; Variance; Variance analysis
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0040068

Corus CAD is a clinically validated test based on age, sex, and expression levels of 23 genes in whole blood that provides a score (1-40 points) proportional to the likelihood of obstructive coronary disease. Clinical laboratory process variability was examined using whole blood controls across a 24 month period: Intra-batch variability was assessed using sample replicates; inter-batch variability examined as a function of laboratory personnel, equipment, and reagent lots. To assess intra-batch variability, five batches of 132 whole blood controls were processed; inter-batch variability was estimated using 895 whole blood control samples. ANOVA was used to examine inter-batch variability at 4 process steps: RNA extraction, cDNA synthesis, cDNA addition to assay plates, and qRT-PCR. Operator, machine, and reagent lots were assessed as variables for all stages if possible, for a total of 11 variables. Intra- and inter-batch variations were estimated to be 0.092 and 0.059 Cp units respectively (SD); total laboratory variation was estimated to be 0.11 Cp units (SD). In a regression model including all 11 laboratory variables, assay plate lot and cDNA kit lot contributed the most to variability (p = 0.045; 0.009 respectively). Overall, reagent lots for RNA extraction, cDNA synthesis, and qRT-PCR contributed the most to inter-batch variance (52.3%), followed by operators and machines (18.9% and 9.2% respectively), leaving 19.6% of the variance unexplained. Intra-batch variability inherent to the PCR process contributed the most to the overall variability in the study while reagent lot showed the largest contribution to inter-batch variability.