Myelodysplastic Syndromes

When the diagnosis of acute leukaemia or LPDs was excluded, the flow-cytometry allows to evaluate the presence of immunophenotypic lineage- associated dysplasia. From the working conference of LeukemiaNet on the flow Cytometry of Myelodisplastic Syndromes (van de Loosdrecht et al, 2009) have been identified the following features of progenitor cells as the most relevant: increased percentage of myeloblasts, abnormal intensity of lineage-specific markers and the expression of lineage- infidelity markers . In particular, blasts are considered as pathological when the percentage values are greater or equal to 3 % of the global cellularity and the expression of the lineage markers (CD45, CD34, CD117, CD13, CD33, HLADR) is increased, reduced or lacking compared to normal myeloblasts (Fig.1).The presence of myeloid markers such as CD11b antigen or one of the lymphoid markers (CD7, CD56, CD19, TdT) would describe the pathological characteristics of myeloblasts. Hypogranularity of granulocytes is a well-known phenomenon in MDS. Hence an abnormal SSC is amongst the most frequently observed FCM aberrancies within this compartement. Abnormal granulocytes can also be recognized by aberrant expression of antigens, including the increased or decreased expression of CD13, CD16, CD11b (Fig. 2). The lack of CD10 antigen, normally expressed in the neutrophils mature, as well as the expression of lymphoid makers (CD2, CD7, CD56) represent further dysplastic features. Within the monocytes, the altered ratio (increased or reduced) compared to the lymphocytes and the expression of lymphoid markers (CD2, CD56, CD7, CD19) identify the dysplastic phenotype. Markers as CD36, CD64 or CD33 with CD14 are useful to distinguish monocytes from dysplastic granulocytes.Although, only limited numbers of antibodies are available to study erythroid dysplasia, the abnormal expression profile of glycoforine A (CD235a) and CD71 as well as the abnormal pattern CD71/CD235a are parameters scored as aberrant. The parameters described are scored to identify different prognostic groups (Wells DA et al, 2003; van de Loosdrecht et al, 2008). The flow-score correlates significantly with WHO subgroups, transfusion-dependency and clinical outcome.

 

References Mateo G, Montalban A, Vidriales MB et al. Prognostic Value of Immunophenotyping in Multiple Myeloma: A Study by the PETHEMA/GEM Cooperative Study Groups on Patients Uniformly Treated With High- Dose Therapy. J Clin Oncol 2011; 26:2737-2744 Paiva B, Martinez-Lopez J, Vidriales MB et al. Comparison of Immunofixation, Serum Free Light Chain, and Immunophenotyping for Response Evaluation and Prognostication in Multiple Myeloma. J Clin Oncol 2011;29:1627-1633. Rawstron AC, Orfao A, Beksac M et al Report of the European Myeloma Network on multiparametric flow cytometry in multiple myeloma and related disorders. Haematologica 2008;93 (3) 431-438 van de Loosdrecht AA, Alhan C, Bene MC et al. Standardization of flow cytometry in myelodysplastic syndromes: report from the first European LeukemiaNet working conference on flow cytometry in myelodysplastic syndromes. Haematologica 2009;94:1124–34. van de Loosdrecht AA, Westers TM, Westra AH et al. Identification of distinct prognostic subgroups in lowand intermediate-1-risk myelodysplastic syndromes by flow cytometry. Blood 2008;111:1067–77. Wells DA, Benesch M, Loken MR et al. Myeloid and monocytic dyspoiesis as determined by flow cytometric scoring in myelodysplastic syndrome correlates with the IPSS and with outcome after hematopoietic stem cell transplantation. Blood 2003; 102: 394–403

Figura 2. Dysplastic pattern of CD13/CD16: comparison between normal (a) and dysplastic patterns (b-c). Wells DA et al, 2003

Figura 2. Dysplastic pattern of CD13/CD16:  comparison between normal (a) and dysplastic patterns  (b-c). Wells DA et al, 2003

Figura 2. Dysplastic pattern of CD13/CD16: comparison between normal (a) and dysplastic patterns (b-c). Wells DA et al, 2003

 

 

Multiple Myeloma

Despite to difficulties for the quantification of plasmacells, the immunophenotypic study has shown one uncontested value in the diagnostic and prognostic evaluation of patients with multiple mieloma. From a experts consensus (Rawstron AC et al, 2008) have been published immunophenotypic features to identify pathological plasmacells. Markers selected to distinguish normal from pathological plasmacells are the lack of the CD19 antigen and the strong intensity of the CD56. These markers assume the greatest discriminating capability while others resulted recommended ( CD117, CD28, CD27 and CD20) (Fig.3). The diagnosis of multiple myeloma may come from the frequency of pathological plasmacells compared to normal ones: pathological plasmacells > 97% of the total ones are commonly observed in the multiple myeloma while her normal plasmacells > 3% are observed in the MGUSs. On the prognostic value of immunophenotype, Mateo et al showed poor prognosis in patients with the expression of CD19, CD28 and lack of the CD117 antigen (Mateo G et al, 2009).

 

Figura 3. Multiple myeloma: two distinct pathological phenotypes (CD138+,CD56+,CD81+dim,CD19- (red) vs CD138+,CD81+dim,CD56-,CD19- (green)) with a population phenotipically  normal (CD138+,CD81+,CD19+,CD56-)(violet)

Figura 3. Multiple myeloma: two distinct pathological phenotypes (CD138+, CD56+, CD81+dim, CD19- (red) vs CD138+, CD81+dim, CD56-, CD19- (green)) with a population phenotipically normal (CD138+, CD81+, CD19+, CD56-) (violet)

 

Minimal residual disease in multiple myeloma

In multiple myeloma flow cytometry can be easily used for the study of MRD. From a comparison with other techniques as free- light chains quantification, the immunophenotypic analysis has demonstrated a greater sensitiveness (Paiva B et al, 2011) and a. At least 1000000 of events should ne acquired to have a sensitivity of 0.01 % (Rawstron AC et al, 2008). The most frequently aberrant phenotypes observed in multiple myeloma are the combination CD19-/CD56 + (60 % of cases) and CD19-/CD56 – (30 % of cases). Anyway, these phenotypes represent the respective percentage of 10 % and 20 % of total plasmacells in the normal bone marrow (Peceliunas V et al, 2011). Hence, the identification of further markers and the study of fluorescence intensity should constitute a valid help to discriminate pathological from normal plasmacells in recovery BM (Fig 4).

 

Figura 4. MRD in multiple myeloma.  Positive population CD138+, CD56+ (a)  after induction (b) but absent after autologous transplant (normal plasmacells. CD138+, CD19+, CD56-) (c,d)

Figura 4. MRD in multiple myeloma. Positive population CD138+, CD56+ (a) after induction (b) but absent after autologous transplant (normal plasmacells. CD138+, CD19+, CD56-) (c,d)

 

 

References

  • Mateo G, Montalban A, Vidriales MB et al. Prognostic Value of Immunophenotyping in Multiple Myeloma: A Study by the PETHEMA/GEM Cooperative Study Groups on Patients Uniformly Treated With High- Dose Therapy. J Clin Oncol 2011; 26:2737-2744
  • Paiva B, Martinez-Lopez J, Vidriales MB et al. Comparison of Immunofixation, Serum Free Light Chain, and Immunophenotyping for Response Evaluation and Prognostication in Multiple Myeloma. J Clin Oncol 2011;29:1627-1633.
  • Rawstron AC, Orfao A, Beksac M et al Report of the European Myeloma Network on multiparametric flow cytometry in multiple myeloma and related disorders. Haematologica 2008;93 (3) 431-438
  • van de Loosdrecht AA, Alhan C, Bene MC et al. Standardization of flow cytometry in myelodysplastic syndromes: report from the first European LeukemiaNet working conference on flow cytometry in myelodysplastic syndromes. Haematologica 2009;94:1124–34.
  • van de Loosdrecht AA, Westers TM, Westra AH et al. Identification of distinct prognostic subgroups in lowand intermediate-1-risk myelodysplastic syndromes by flow cytometry. Blood 2008;111:1067–77.
  • Wells DA, Benesch M, Loken MR et al. Myeloid and monocytic dyspoiesis as determined by flow cytometric scoring in myelodysplastic syndrome correlates with the IPSS and with outcome after hematopoietic stem cell transplantation. Blood 2003;102:394–403