Flow cytometry “Ogata score” for the diagnosis of myelodysplastic syndromes in a real‐life setting. A Latin American experience

Comparison of five diagnostic flow cytometry scores in patients with myelodysplastic syndromes: Diagnostic power and prognostic impact

BACKGROUND

Flow cytometry (FCM) is a co-criterion in myelodysplastic syndromes (MDS) diagnostics according to the WHO classification. The presented study compared diagnostic power and prognostic impact of different FCM-based scores.

METHODS

A total of 807 bone marrow (BM) samples of patients with cytopenia (543 MDS, 153 non-clonal cytopenias, 111 non-MDS myeloid malignancies) and 78 healthy controls have been investigated using a standardized 8-color-FCM procedure. FCSS, Ogata-score, iFS, RED-score, and ELN-NEC were analyzed for sensitivity and specificity in comparison to standard diagnostic tools. Median follow up for patients was 26 month (range: 0.2-89).

RESULTS

The iFS showed the highest accuracy (80%) with the best balance between sensitivity (79%) and specificity (86%). This was also valid in MDS with very low IPSS-R and even in MDS without ring sideroblasts, with normal blast count and karyotype, where iFS could confirm diagnosis in 62% and 65% of patients. Besides the high diagnostic power, the established iFS category "consistent with MDS" was associated with inferior overall survival (OS) independent from WHO classification (median: 51 month vs. not reached, p < 0.0001). Remarkably, this iFS category redefined a subgroup of patients with worse OS within IPSS-R low-risk category (73 month vs. not reached, p = 0.0433). Finally, multivariable analysis showed that iFS added independent prognostic information regarding OS besides IPSS-R.

CONCLUSIONS

The iFS separates non-clonal cytopenias and MDS with the highest accuracy, provided information in addition to standard diagnostic procedures, and refined established prognostic tools for outcome prediction.

Multiparameter flow cytometry in the evaluation of myelodysplasia: Analytical issues: Recommendations from the European LeukemiaNet/International Myelodysplastic Syndrome Flow Cytometry Working Group

Multiparameter flow cytometry (MFC) is one of the essential ancillary methods in bone marrow (BM) investigation of patients with cytopenia and suspected myelodysplastic syndrome (MDS). MFC can also be applied in the follow-up of MDS patients undergoing treatment. This document summarizes recommendations from the International/European Leukemia Net Working Group for Flow Cytometry in Myelodysplastic Syndromes (ELN iMDS Flow) on the analytical issues in MFC for the diagnostic work-up of MDS. Recommendations for the analysis of several BM cell subsets such as myeloid precursors, maturing granulocytic and monocytic components and erythropoiesis are given. A core set of 17 markers identified as independently related to a cytomorphologic diagnosis of myelodysplasia is suggested as mandatory for MFC evaluation of BM in a patient with cytopenia. A myeloid precursor cell (CD34⁺ CD19^- ) count >3% should be considered immunophenotypically indicative of myelodysplasia. However, MFC results should always be evaluated as part of an integrated hematopathology work-up. Looking forward, several machine-learning-based analytical tools of interest should be applied in parallel to conventional analytical methods to investigate their usefulness in integrated diagnostics, risk stratification, and potentially even in the evaluation of response to therapy, based on MFC data. In addition, compiling large uniform datasets is desirable, as most of the machine-learning-based methods tend to perform better with larger numbers of investigated samples, especially in such a heterogeneous disease as MDS.

A simple score derived from bone marrow immunophenotyping is important for prognostic evaluation in myelodysplastic syndromes

Immunophenotyping of bone marrow (BM) precursors has been used as an ancillary diagnostic tool in myelodysplastic syndromes (MDS), but there is no general agreement about which variables are the most relevant for prognosis. We developed a parsimonious prognostic model based on BM cell populations well-defined by phenotype. We analyzed 95 consecutive patients with primary MDS diagnosed at our Institution between 2005 and 2012 where BM immunophenotyping had been performed at diagnosis. Median follow-up: 42 months (4-199). Median age: 67 years (33-79). According to IPSS-R, 71 cases were low or intermediate risk. Flow variables significant in the univariate Cox analysis: "%monocytes/TNCs", "% CD16⁺ monocytes/TNCs", "total alterations in monocytes", "% myeloid CD34⁺ cells", "number of abnormal expressions in myeloblasts" and "% of B-cell progenitors". In the multivariate model remained independent: "% myeloid CD34⁺ cells", B-cell progenitors" and "% CD16⁺ monocytes/TNCs". These variables were categorized by the extreme quartile risk ratio strategy in order to build the score: % myeloid CD34⁺ cells" (≥ 2.0% = 1 point), B-cell progenitors" (< 0.05% 1 point) and "CD16⁺ monocytes/TNCs" (≥ 1.0% 1 point). This score could separate patients with a different survival. There was a weak correlation between the score and IPSS-R. Both had independent prognostic values and so, the flow score adds value for the prognostic evaluation in MDS.