AUTOMATED PEAK DETECTION AND CELL-CYCLE ANALYSIS OF FLOW CYTOMETRIC DNA HISTOGRAMS

We describe an algorithm for fully automated flow cytometric DNA histo gram classification and analysis that provides rapid, reproducible det ermination of DNA index and S-phase fraction (SPF). Automated classifi cation agreed with subjective assessment of DNA ploidy in 96-98% of DN A histograms. Automated and conventional analyses of DNA index (r = 0. 95) and SPF (r = 0.89) were also highly correlated with one another. I n a series of 86 node-negative breast carcinomas, SPF calculated with the fully automated method was a significant predictor of 10 year surv ival (p = 0.009). Automation greatly increased the speed of DNA histog ram analysis, allowing evaluation of the same set of histograms with d ifferent methods. In a preliminary study exploring the optimization of DNA histogram analysis, the best association between SPF and prognosi s of breast cancer patients was achieved using sliced nuclei debris mo deling, reporting only the aneuploid SPF (in aneuploid histograms), wh ile excluding small aneuploid clones (<15% of total cell count) from e valuation. In conclusion, automated DNA histogram analysis does not re place the need for close human supervision but provides a useful guide line for less experienced users, facilitates interlaboratory compariso ns, and makes possible extensive reanalyses of large data sets. (C) 19 94 Wiley-Liss,Inc.