Unsupervised immunophenotypic profiling of chronic lymphocytic leukemia

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. q

Flow cytometric characterization of cerebrospinal fluid cells

“…However, visualizing these data becomes more and more complex and requires multiple sequential analyses to provide information about each cell subset. Therefore, automated classification systems are being developed (109)(110)(111)(112). Additionally, optimization of storage conditions to preserve CSF cells should result in higher cell yields and thereby increase the detection rate of flow cytometry in CSF samples with low cellularity.…”

The Diagnostic Value of Flow Cytometry Imunophenotyping in an Albanian Patient Population with a Preliminary Clinical Diagnosis of Chronic Lymphocytic Leukemia

“…In addition to the classical cytology, several diagnostic tools such as FCI, immunohistochemistry or cytogenetic and molecular biology methods are needed in order to establish a precise CLPD diagnosis [10,11]. However, in health care settings with limited financial and technological resources, it is often difficult to implement all these methods concomitantly, due to their high cost and also the need for well trained and skilled personnel Taking into account the fact that multiparametric FCI has been shown as a practical, rapid and powerful tool for the diagnosis of malignant hemopathies, we studied the impact of the implementation of this methodology into the more precise diagnosis of 84 consecutive patients sent at our laboratory with a preliminary CLL diagnosis established on the basis of their clinical and cytological data.…”

Analysis of clinical flow cytometric immunophenotyping data by clustering on statistical manifolds: Treating flow cytometry data as high‐dimensional objects

“…Indeed, flow cytometric immunophenotyping is at its essence a proteomic method, albeit on a relatively small scale (19)(20)(21). Our approach allows a proteomic-style analysis of the 5 TREATING FLOW DATA AS HIGH-DIMENSIONAL OBJECTS entire distribution formed by multicolor flow cytometric analysis of cell suspensions.…”