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From a 2DE-Gel Spot to Protein Function: Lesson Learned From HS1 in Chronic Lymphocytic Leukemia
Published on: October 19, 2014
Unsupervised immunophenotypic profiling of chronic lymphocytic leukemia
Luzette K Habib1, William G Finn
1Department of Pathology, University of Michigan Medical School, Ann Arbor, 48109, USA.
Insights
Unsupervised analysis of flow cytometry data revealed distinct subtypes of B-cell chronic lymphoproliferative disorders. This approach offers a proteomic-like method for disease classification beyond traditional validation.
Area of Science:
- Immunology
- Computational Biology
- Oncology
Background:
- Proteomics and functional genomics advance disease classification.
- Flow cytometry (FCM) analyzes protein expression on intact cells.
- FCM traditionally validates markers or predicts outcomes, not for unsupervised discovery.
Purpose of the Study:
- To assess feasibility of unsupervised cluster analysis for FCM data.
- To explore FCM as a cell-based proteomic approach for disease classification.
Main Methods:
- Retrospective analysis of multicolor FCM data from 140 patients with B-cell chronic lymphoproliferative disorders.
- Hierarchical cluster analysis of peripheral blood and bone marrow lymphocyte data.
- Normalized expression of CD19 and 10 additional B-cell markers.
Main Results:
- Three major clusters identified in chronic lymphocytic leukemia (CLL) peripheral blood samples.
- One cluster showed "atypical" CLL markers (high CD20, CD22, FMC7, light chain; low CD23).
- Two clusters of "typical" BCLL distinguished by CD38, CD20, and CD23 expression, with a trend toward survival differences.
Conclusions:
- Unsupervised immunophenotypic profiling of FCM data can identify reproducible lymphoma/leukemia subtypes.
- FCM shows potential as an unsupervised class discovery tool, similar to proteomic methods.
- Further studies are warranted to establish FCM as a primary discovery tool.
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.
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