Pocket Oncology (Pocket Notebook Series), 1st Ed.


Chung-Han Lee

Two different fluorophores are used to label the cells


Y-Axis APC

96.4% of cells are high for FITC labeled marker

3.58% of cells are high for both APC & FITC labeled markers (Transfus Sci 1995;16:303)

Figure 5-3 Example of Flow Cytometry Data

Clinical Applications in Hematology/Oncology

• Phenotypic characterization of leukemia & lymphomas

• Measurement of DNA content & proliferation markers (Ki-67, proliferating cell nuclear antigen (PCNA))

• Histocompatibility cross-matching

• HLA-B27 detection

• Immunodeficiency studies (CD4, CD8)

• Isolating progenitor cells (CD34) (Clin Chem 2000;46:1221)

Sources of False Negatives with Flow Cytometry

• Sampling error

Rare in liquid samples, but possible w/tissue samples such as touch preps

• Cell loss during processing

Large cells (large lymphoid cells, plasma cells) more likely to be lost

Samples from smears/touch preps should be compared to cytospin

• Rare neoplastic cells

Marginal zone lymphoma—neoplasm hidden among reactive B cells

Min. residual disease detection requires screening of 500000–1000000 cells, typical clinical lab screens 30000–100000 cells

• Neoplastic cell difficult to identify

Abn B cells lacking CD20 overlooked after anti-CD20 Rx

B cells lacking surface Igs (Blood 2008;111:3941)

Immunophenotyping of NonHodgkin Lymphoma

• B vs. T cell neoplasm

Pan-B Ags (CD19, CD20, CD79a, PAX5)

Pan-T Ags (CD2, CD3, CD5, CD7, & negative for B-cell Ags)

• Morphologically/clinically stratify B & T cell neoplasms

B cell neoplasm: Small cells, medium cells, large cells, cutaneous

T cell neoplasm: Anaplastic, cutaneous, extranodal, nodal

• B cell neoplasms