The surface molecule signature of primary human acute myeloid leukemia (AML) cells is highly associated with NPM1 mutation status

Even though human acute myeloid leukemia (AML) is characterized by an expansion of malignant myeloblasts, these immature leukemic cells can show signs of differentiation both by morphological examination and by flow-cytometric analysis of membrane molecules.1 Analysis of this molecular profile is mainly used as a diagnostic tool to detect panmyeloid or lineage-associated differentiation markers, and thereby distinguish between myeloblasts and lymphoid blasts.2 On the other hand, cytogenetic and molecular genetic analyses are mainly used for prognostic classification of patients. The most important mutations are then FLT3-ITD, which has an adverse prognostic impact, and NPM-1 mutations, which can be associated with a favorable prognosis.2 Certain cytogenetic and molecular genetic abnormalities are associated with signs of AML cell differentiation, including NPM-1 mutations, which are associated with monocytic morphology and low expression of the CD34 stem cell marker.3 These last observations suggest that the cell of origin of the NPM-1 mutation is a myeloid progenitor and not the most immature stem cells. In this context we have therefore investigated in more detail the associations between NPM-1 mutations and AML cell differentiation as defined by a limited number of well-characterized surface membrane molecules.

The membrane molecule profile was investigated by flow cytometry for 184 consecutive AML patients. All patients were examined with regard to the expression of CD11c (expressed on monocytes), CD13 (monocytes, neutrophils), CD14 (mainly expressed on monocytes, at lower levels on neutrophils), CD15 (mainly neutrophils, also monocytes), CD33 (appears on myelomonocytic precursors after CD34), CD34 (immature hematopoietic cells), CD45 (leukocytes) and HLA-DR.4 The French–American–British (FAB) classification was used for morphological evaluation of the differentiation status,5 and analysis for FLT3 and NPM-1 mutations was performed as previously described.6 Bioinformatical analyses were performed using the J-Express 2009 analysis suite (MolMine AS, Bergen, Norway)7 and hierarchical clustering was performed with Pearson's correlation as distance measure and complete weighted linkage.

Based on the surface expression level of this limited number of differentiation markers, the patients could be classified by hierarchical clustering into distinct subsets (Figure 1). The patients were classified into two major clusters, the most important difference between these two subsets being a low CD34 expression in Cluster I and a high expression in Cluster II. The heterogeneity within each of these two patient subsets was limited (Figure 2).

Figure 1

Unsupervised hierarchical cluster analyses compeered with clinical data for 184 consecutive acute myeloid leukemia (AML) patients. The expression of CD11c, CD13, CD14, CD15, CD33, CD34, CD45 and HLA-DR was examined by flow cytometry for primary human AML cells derived from 184 consecutive patients. The data are presented as percent positive cells, for example, 0–100%. The Pearson's correlation test with complete linkage was used to make a heat map with additional unsupervised hierarchical clustering analyses. The upper panel shows the expression profile, with low expression being marked with deep green color and high expression with deep red color. The patients could then be divided into two distinct clusters: I and II, each with two distinct subclusters: Ia/Ib and IIa/IIb. The lower panel compares the patient clusters with the established clinical parameters (from top to bottom) of FAB classification, FLT3 mutation status and NPM-1 mutation status.

Figure 2

The correlation visualization with distance matrix separates the CD34+ and CD34 populations. The figure displays the pairwise correlation between the 184 patient samples based on the AML cell expression of cell surface differentiation markers. Red and green colors indicate a high positive or negative correlation between samples, respectively. Based on this identification CD34 expression was identified as the main discriminative marker, and the patients could then be divided into CD34high and CD34low subsets. Each subset showed a limited heterogeneity when comparing patients within the same subset, indicated by red color in the upper left and lower right squares, whereas there was a considerable difference between the two subsets, as indicated by the green color in the lower right and upper left squares.

Cluster I patients could be further subdivided into two different subsets (Figure 1): Cluster Ia was characterized by the phenotype CD34lowHLA-DRlow, whereas Cluster Ib patients showed the phenotype CD34lowHLA-DRhigh and a high frequency of patients with morphological signs of monocytic differentiation (FAB AML-M4/M5). The lack of HLA-CR suggests a more mature phenotype of patients in this subset.4 Cluster II patients could also be divided into two subsets: patients in the IIa cluster showed the phenotype CD34highCD33high, whereas patients in Cluster IIb showed the more immature phenotype, CD34highCD33low. FLT3 mutations were observed at similar frequencies in all the four subsets. In contrast, NPM-1 mutations showed a significantly increased frequency in Cluster I compared with Cluster II (51.5% versus 4.8%, P<0.0001, χ2-test) and were not detected for any patient in the immature Cluster IIb. Finally, the frequencies of patients with both FLT3-ITD and NPM-1 mutations did not differ between the four subsets.

In our present study we included a large number of consecutive/unselected AML patients and the patient clusters were defined on the basis of membrane molecule analyses for all patients. A majority of these patients were investigated for FLT3 and NPM-1 mutations, and these analyzed patients also represent a consecutive or unselected subset of the whole group. Thus, even though mutational data were not available for all patients, the absence of patient selection allows a reliable analysis of mutational frequencies for the various patient subsets.

Previous studies have shown that NPM-1 mutations are associated with FAB-M4/M5 morphology and low or absent CD34 expression.3 Our present results show that NPM-1 mutations were seen especially in CD34-negative patients, and with no difference between CD34-negative patients with or without morphological signs of monocytic differentiation. Furthermore, a very striking observation was the absence of NPM-1 mutations among CD34+CD33 patients, the patient subset showing the most immature surface marker phenotype.

On the basis of our present study, we conclude that the most striking observation is not the previously described positive association between monocytic differentiation and NPM-1 mutations or the negative association between these mutations and CD34 expression,3, 8 but rather the absence of NPM-1 mutations among patients with the most immature CD33CD34+ AML cell phenotype. Thus, NPM-1 mutations are associated with myeloid differentiation, an observation consistent with the hypothesis that the cell of origin of the NPM-1 mutation is a common myeloid progenitor.8


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This study received financial support from the Norwegian Cancer Association.

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Correspondence to Ø Bruserud.

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Tsykunova, G., Reikvam, H., Hovland, R. et al. The surface molecule signature of primary human acute myeloid leukemia (AML) cells is highly associated with NPM1 mutation status. Leukemia 26, 557–559 (2012).

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