Automated peak detection and cell cycle analysis of flow cytometric DNA histograms.

Abstract

We describe an algorithm for fully automated flow cytometric DNA histogram classification and analysis that provides rapid, reproducible determination of DNA index and S-phase fraction (SPF). Automated classification agreed with subjective assessment of DNA ploidy in 96-98% of DNA histograms. Automated and conventional analyses of DNA index (r = 0.95) and SPF (r = 0.89) were also highly correlated with one another. In a series of 86 node-negative breast carcinomas, SPF calculated with the fully automated method was a significant predictor of 10 year survival (p = 0.009). Automation greatly increased the speed of DNA histogram analysis, allowing evaluation of the same set of histograms with different methods. In a preliminary study exploring the optimization of DNA histogram analysis, the best association between SPF and prognosis of breast cancer patients was achieved using sliced nuclei debris modeling, reporting only the aneuploid SPF (in aneuploid histograms), while excluding small aneuploid clones (< 15% of total cell count) from evaluation. In conclusion, automated DNA histogram analysis does not replace the need for close human supervision but provides a useful guideline for less experienced users, facilitates interlaboratory comparisons, and makes possible extensive reanalyses of large data sets.