Identification of Factors Contributing to Variability in a Blood-Based Gene Expression Test

<div><h3>Background</h3><p>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.</p> <h3>Methods/Results</h3><p>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.</p> <h3>Conclusion</h3><p>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.</p> </div

Variability, as measured by SD, increases as gene expression levels decrease, reflecting the stochastic nature of PCR.

<p>Y axis depicts SD, in Cp units; X axis depicts gene expression, in Cp units. Higher Cp units equal lower gene expression. The dashed line represents a cubic regression model fitted to the data.</p

Components of Inter-Batch Variability Observed in the Study.

1<p>p values in bold are <0.05.</p

Measurements of Total, Intra-batch, and Clinical Variability.

1<p>SD given in Cp units and gene expression score points (GES).</p>2<p>Percent change in probability of subject having obstructive CAD.</p

Mean Deviation of GES from Target Score Across the Course of the Study.

<p>Solid black line  =  running mean deviation of GES across the 895 samples (x axis, chronological order samples were run; y axis, GES). Middle dashed line  =  target GES; upper and lower dashed lines  =  QC boundaries ±3 points target GES. 95% CI  =  grey area.</p

Gene Expression Score Component Variability.

1<p>Despite high having a high SD, TSPAN16 contributes little to variability in the GES due to its low weight in the control sample used; in clinical samples TSPAN16 is weighted according to sex <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040068#pone.0040068-Rosenberg1" target="_blank">[8]</a>.</p

Variability within Individual Laboratory Processes.

1<p>p values in bold are <0.05.</p

Depiction of sample flow and quality control points in the commercial laboratory.

<p>Process from whole blood sample to GES calculation consists of 4 laboratory steps and then quality control algorithm score calculation by a LIMS. Both sample controls (positive and negative) and process QC checks are used as indicated.</p