Developing a reliable and quantitative assessment of the potential virulence of a malignancy has been a long-standing goal in clinical cytometry. DNA histogram analysis provides valuable information on the cycling activity of a tumor population through S-phase estimates; it also identifies nondiploid populations, a possible indicator of genetic instability and subsequent predisposition to metastasis. Because of conflicting studies in the literature, the clinical relevance of both of these potential prognostic markers has been questioned for the management of breast cancer patients. The purposes of this study are to present a set of 10 adjustments derived from a single large study that optimizes the prognostic strength of both DNA ploidy and S-phase and to test the validity of this approach on two other large multicenter studies. Ten adjustments to both DNA ploidy and S-phase were developed from a single node-negative breast cancer database from Baylor College (n ‫؍‬ 961 cases). Seven of the adjustments were used to reclassify histograms into low-risk and high-risk ploidy patterns based on aneuploid fraction and DNA index optimum thresholds resulting in prognostic P values changing from little (P < 0.02) or no significance to P < 0.000005. Other databases from Sweden (n ‫؍‬ 210 cases) and France (n ‫؍‬ 220 cases) demonstrated similar improvement of DNA ploidy prognostic significance, P < 0.02 to P < 0.0009 and P < 0.12 to P < 0.002, respectively. Three other adjustments were applied to diploid and aneuploid S-phases. These adjustments eliminated a spurious correlation between DNA ploidy and S-phase and enabled them to combine independently into a powerful prognostic model capable of stratifying patients into low, intermediate, and high-risk groups (P < 0.000005). When the Baylor prognostic model was applied to the Sweden and French databases, similar significant patient stratifications were observed (P < 0.0003 and P < 0.00001, respectively). The successful transference of the Baylor prognostic model to other studies suggests that the proposed adjustments may play an important role in standardizing this test and provide valuable prognostic information to those involved in the management of breast cancer patients. Cytometry (Comm. Clin. Cytometry) 46: 121-135, 2001.

Mass cytometry is an emerging technology capable of 40 or more correlated measurements on a single cell. The complexity and volume of data generated by this platform have accelerated the creation of novel methods for high-dimensional data analysis and visualization. A key step in any high-level data analysis is the removal of unwanted events, a process often referred to as data cleanup. Data cleanup as applied to mass cytometry typically focuses on elimination of dead cells, debris, normalization beads, true aggregates, and coincident ion clouds from raw data. We describe a probability state modeling (PSM) method that automatically identifies and removes these elements, resulting in FCS files that contain mostly live and intact events. This approach not only leverages QC measurements such as DNA, live/dead, and event length but also four additional pulse-processing parameters that are available on Fluidigm Helios™ and CyTOF ® (Fluidigm, Markham, Canada) 2 instruments with software versions of 6.3 or higher. These extra Gaussian-derived parameters are valuable for detecting well-formed pulses and eliminating coincident positive ion clouds. The automated nature of this new routine avoids the subjectivity of other gating methods and results in unbiased elimination of unwanted events. CYTOF instruments were invented as an alternative technology to flow cytometry (1,2). Instead of fluorescent molecules, heavy metals are conjugated to antibodies, which bind to specific epitopes on cells (3). Metal-labeled cells are detected and quantified by inductively coupled plasma mass spectrometry (ICP-MS) with time-of-flight detection (4). Because this technology avoids spectral overlap of fluorescent dyes and isotopic metal contamination is well less than 5%, the number of correlated measurements is mainly limited by the number of stable isotopes of rare earth metals, which realistically could soon reach 100 or more (5).Whether a cytometer is fluorescence-based or metal-based, there are always undesired events that need to be eliminated prior to analysis. Typical flow cytometers have internal circuitry or logic that ignores signal-derived pulses that are partially formed or abnormally long. Flow cytometry often employs a forward by 90 light-scatter gate to eliminate debris and aggregates. Also, pulse processing features such as peak height, width, and area can be leveraged to reduce the number of aggregates.Since mass cytometry atomizes particles into clouds of positively charged ions, its pulse processing capabilities are mainly targeted at detecting and eliminating coincident ion clouds or poorly formed pulses. Mass cytometry also has DNA intercalators (1) that can eliminate debris and some true aggregates. Both technologies 1 Verity Software House, Topsham, Maine

High‐dimensional mass cytometry data potentially enable a comprehensive characterization of immune cells. In order to positively affect clinical trials and translational clinical research, this advanced technology needs to demonstrate a high reproducibility of results across multiple sites for both peripheral blood mononuclear cells (PBMC) and whole blood preparations. A dry 30‐marker broad immunophenotyping panel and customized automated analysis software were recently engineered and are commercially available as the Fluidigm® Maxpar® Direct™ Immune Profiling Assay™. In this study, seven sites received whole blood and six sites received PBMC samples from single donors over a 2‐week interval. Each site labeled replicate samples and acquired data on Helios™ instruments using an assay‐specific acquisition template. All acquired sample files were then automatically analyzed by Maxpar Pathsetter™ software. A cleanup step eliminated debris, dead cells, aggregates, and normalization beads. The second step automatically enumerated 37 immune cell populations and performed label intensity assessments on all 30 markers. The inter‐site reproducibility of the 37 quantified cell populations had consistent population frequencies, with an average %CV of 14.4% for whole blood and 17.7% for PBMC. The dry reagent coupled with automated data analysis is not only convenient but also provides a high degree of reproducibility within and among multiple test sites resulting in a comprehensive yet practical solution for deep immune phenotyping.

As the technology of cytometry matures, there is mounting pressure to address two major issues with data analyses. The first issue is to develop new analysis methods for high-dimensional data that can directly reveal and quantify important characteristics associated with complex cellular biology. The other issue is to replace subjective and inaccurate gating with automated methods that objectively define subpopulations and account for population overlap due to measurement uncertainty. Probability state modeling (PSM) is a technique that addresses both of these issues. The theory and important algorithms associated with PSM are presented along with simple examples and general strategies for autonomous analyses. PSM is leveraged to better understand B-cell ontogeny in bone marrow in a companion Cytometry Part B manuscript. Three short relevant videos are available in the online supporting information for both of these papers. PSM avoids the dimensionality barrier normally associated with high-dimensionality modeling by using broadened quantile functions instead of frequency functions to represent the modulation of cellular epitopes as cells differentiate. Since modeling programs ultimately minimize or maximize one or more objective functions, they are particularly amenable to automation and, therefore, represent a viable alternative to subjective and inaccurate gating approaches. V C 2015 International Society for Advancement of Cytometry

Automated PSM analysis of fetal RBCs strongly correlates with expert traditional manual analysis. PSM enumerates fetal RBCs accurately with significantly greater objectivity and lower imprecision than the traditional manual gating method. Thus, PSM provides a means to markedly improve interlaboratory variance with FMH assays based upon subjective gating strategies.