ABSTRACT
BACKGROUND: Proteomics and functional genomics have revolutionized approaches to disease classification. Like proteomics, flow cytometry (FCM) assesses concurrent expression of many proteins, with the advantage of using intact cells that may be differentially selected during analysis. However, FCM has generally been used for incremental marker validation or construction of predictive models based on known patterns, rather than as a tool for unsupervised class discovery. We undertook a retrospective analysis of clinical FCM data to assess the feasibility of a cell-based proteomic approach to FCM by unsupervised cluster analysis. METHODS: Multicolor FCM data on peripheral blood (PB) and bone marrow (BM) lymphocytes from 140 consecutive patients with B-cell chronic lymphoproliferative disorders (LPDs), including 81 chronic lymphocytic leukemia (CLLs), were studied. Expression was normalized for CD19 totals, and recorded for 10 additional B-cell markers. Data were subjected to hierarchical cluster analysis using complete linkage by Pearson's correlation. Analysis of CLL in PB samples (n = 63) discovered three major clusters. One cluster (14 patients) was skewed toward "atypical" CLL and was characterized by high CD20, CD22, FMC7, and light chain, and low CD23. The remaining two clusters consisted almost entirely (48/49) of cases recorded as typical BCLL. The smaller "typical" BCLL cluster differed from the larger cluster by high CD38 (P = 0.001), low CD20 (P = 0.001), and low CD23 (P = 0.016). These two typical BCLL clusters showed a trend toward a difference in survival (P = 0.1090). Statistically significant cluster stability was demonstrated by expanding the dataset to include BM samples, and by using a method of random sampling with replacement. CONCLUSIONS: This study supports the concept that unsupervised immunophenotypic profiling of FCM data can yield reproducible subtypes of lymphoma/chronic leukemia. Expanded studies are warranted in the use of FCM as an unsupervised class discovery tool, akin to other proteomic methods, rather than as a validation tool.
