Can measurements of potential doubling time (Tpot) be compared between laboratories? A quality control study

The purpose of this study was to investigate the reproducibility of potential doubling time measurements of human tumors in different laboratories and to distinguish which steps in the measurement procedure were subject to the greatest variation. This was achieved by comparing measurements on the same source material in two different laboratories in which three aspects of the technique were separately studied, namely, preparation and staining of the nuclear suspensions, running the samples on the flow cytometer (FCM), and analyzing the two-parameter FCM data. This involved exchange between the two centers of fixed tumor material, stained nuclear suspensions, and FCM data on floppy disks. The analysis step was found to be the least variable step. For DNA synthesis time, Ts, and the labeling index, LI, the coefficients of determination (R2) ranged from 92% to 95.4%. A small systematic bias was observed, with one center measuring approximately 15% higher values for both LI and Ts. Different criteria for window placements were found to be a contributing factor. Variations in the FCM step were approximately equal to those for analysis, with no systematic deviations. Variations for the preparation and staining step were the largest (R2 = 60.5% and 38.1% for LI and Ts, respectively). However, this step was the only one subject to intratumoral variability, which was the largest contributing factor to the variations observed. In addition, however, LI was on average 41% higher in one center compared to the other, resulting in a systematic bias. Differences in the level of green fluorescence of the labeled cells implicated antibody differences as a possible cause. The variations found here for the three procedural aspects were significantly smaller than variations observed between tumors, a requirement for a predictive assay. Suggestions for implementation of quality control procedures include objective (computer-assisted) data analysis on two-parameter histograms and optimization of antibody combinations.