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

Abstract

Background: 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.

Methods/results: 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.

Conclusion: 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.

Figure 1. Depiction of sample flow and quality control points in the commercial laboratory.

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.

Figure 2. Percentages of Overall and Laboratory Variability by Component.

(A) Overall variability in the laboratory process; PCR process  = 70% (speckled white); reagent lots, operators, or machines  = 24% total (grey); unaccounted = 6%. (B) Contributions of laboratory processes to non-PCR associated variability; RNA extraction (white), cDNA (light grey), qRT-PCR (mid grey), sample addition (dark grey), unexplained (black lines).

Figure 3. Variability, as measured by SD, increases as gene expression levels decrease, reflecting the stochastic nature of PCR.

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.

Figure 4. Mean Deviation of GES from Target Score Across the Course of the Study.

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.