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CD200 in acute myeloid leukemia: marked upregulation in CEBPA biallelic mutated cases

Diagnostic Pathology volume 20, Article number: 56 (2025) Cite this article

Abstract

CD200 is a glycoprotein that binds with its receptor CD200R, providing immunosuppressive signals to T and NK cells. CD200 is expressed by normal stem cells and progenitors committed to B-lymphopoiesis and myeloid development. CD200 biological relevance in acute leukemias is only partially understood.

The study included a consecutive series of four hundred thirty-one patients with acute myeloid leukemia (AML). Immunophenotype was established by multiparametric flow cytometry, and the genetic diagnosis was performed by PCR-based methods and a targeted resequencing method covering 42 genes.

66% of AML patients expressed CD200 being significantly associated with CD34 reactivity. The frequency of CD200 positivity was higher in cases with core-binding factor genetic lesions such as RUNX1-RUNX1T1 (81.3%) fusions and CBFB-MHY11 (63.2%) rearrangements and also with biallelic CEBPA mutations (100%). The molecular AML group with the lowest CD200 reactivity (19.1%) corresponded to AML with NPM1 mutations. RNA seq showed no uniform pattern of infiltrating cells in CEBPA mutated AML. Deconvolution analysis may be used to assess the immunoregulatory mechanisms of AML.

CD200 expression could help identify the more immature compartment and, combined with other markers, single out CEPA-mutated AML.

Introduction

Acute myeloid leukemia (AML) is a heterogeneous group of neoplastic disorders arising from malignant hematopoietic progenitors’ malignant transformation. It accounts for almost 10% of all cancers. AML diagnosis is based on morphology, flow cytometry, immunophenotyping, and genetic analysis. Those techniques are combined to set the proper diagnosis and treatment [1–2].

AML is sustained by a minor population of leukemia stem cells (LSCs) characterized by self-renewal capacity, immunologic privilege, and resistance to apoptosis. LSCs in AML patients may be genetically, immunophenotypically, and functionally heterogeneous [3]. Treatments should be addressed to eliminate the LSC population. Some immunophenotypic studies suggested that LSC may be found within the CD34 + and CD34- fractions [4]. Further phenotypic delineation of the LSC cells could be clinically helpful.

The discovery of immune checkpoints and their inhibition is a recently developed modality for AML treatment. T-cell immunoglobulin mucin-3 (TIM3), expressed on the surface of LSCs, is involved in AML progression. TIM3 binding by Galectin-9 inhibits the AML cell-killing activity by NK and activates different cellular survival pathways [5,6,7,8]. The CD200 antigen is gaining importance in the diagnosis and prognosis of AML because it has been suggested that it may behave like TIM3 [9]. CD200 is a type-I membrane glycoprotein that contains two extracellular domains: a transmembrane and a cytoplasmic domain [10]. This protein is expressed in immune cells, endothelial cells, neurons, and normal hematopoietic stem cells (HSCs). CD200 binds with its receptor CD200R on T- and B- and myeloid cells, providing immunosuppressive signals [11,12,13]. CD200 is overexpressed in various solid and hematologic neoplasms, as in the surface of LSCs in AML [14]. It has been suggested that CD200 could promote the growth of the more immature leukemic cell compartment [15,16,17,18]. CD200 is also expressed by chronic lymphocytic leukemia (CLL) cells, and its analysis is gaining acceptance as one key marker to differentiate CLL from mantle-cell lymphoma [19,20,21].

This study investigates the pattern of CD200 expression in patients with AML and establishes phenotypic and genetic correlations.

Patients and methods

Patients

This study included 431 patients diagnosed with AML based on standard WHO criteria 2017 [22], from 2017 to 2020, at the Hospital de la Santa Creu I Sant Pau in Barcelona. Patients referred to flow cytometry and molecular analysis were included in this series.

In an additional series, 60 adult AML diagnosed from the Hospital de la Santa Creu I Sant Pau were enrolled in the study to perform RNA analysis and gene expression by RT-PCR. Cases were categorized into different groups according to the molecular lesions as follows: Group 1 (t(8,21), n = 6); group 2 (inv(16), n = 6); group 3 (CEBPA-m, n = 8), group 4 (NPM1-m, n = 20); group 5 (Other AML, n = 20). Two normal bone marrow samples were used as calibrator samples.

An additional series of 13 AML cases with biallelic CEBPA were used for RNA seq (Suppl. Table 1).

Flow cytometry analysis

Sample preparation

Immunophenotyping studies were performed upon diagnosis of erythrocyte-lysed bone marrow samples upon staining with monoclonal antibodies (MoAbs) directly conjugated with fluorochromes. Antigenic expression of leukemic cells was analyzed by four-color multiparametric flow cytometry; fluorescein isothiocyanate (FITC), phycoerythrin (PE), peridinin-chlorophyll protein (PerCP) or peridinin chlorophyll protein-Cyanine5.5 (PerCP-Cy5.5) and allophycocyanin (APC), in combination with MoAbs as follows: CD15/CD34/CD45/HLA-DR, CD10/CD20/CD34/ CD19, CD2/CD33/CD45/CD34, CD7/CD117/CD45/CD34, CD66/CD13/CD64/CD45, CD36/CD56/CD45/HLA-DR, CD14/CD123/CD45/CD34, CD36/Glycophorin A/CD45/HLA-DR, CD71/CD200/CD45/CD34, myeloperoxidase (MPO)/ CD79a/CD3/CD34, TdT/MPO/CD45/CD34 and lysozyme/lactoferrin/CD45/CD34, CD2/CD4/CD8/CD3, CD34/CD117/CD45/HLA-DR and CD38/CD33/CD45/CD34.

The MoAbs used in the study were (antibody clone, conjugated fluorochrome): TdT (HT-6 FITC), CD117 (10402 PE), MPO (MPO-7 FITC and PE), CD45 (H130 PerCP and PerCP-Cy5.5), CD71 (Be-Tq FITC), CD20 (B-Ly1 PE), CD79a (HM57 PE) from DAKO, Glostrup, Denmark; CD66 (GI55-228 FITC), CD64 (MOPC-21 PerCP-Cy5.5), CD19 (TB28.2 PerCP-Cy5.5), CD34 (8G12 FITC, PE and Per-CP-Cy5.5), HLA-DR (L243 PerCP-Cy5.5), CD10 (W8E7 FITC), CD2 (S5.2 FITC), CD33 (P67.6 PE), CD7 (4H9 FITC), CD13 (L138 PE), CD14 (M0P9 FITC), CD3 (SK7 PerCP and PerCP-Cy5.5), CD4 (SK4 FITC), CD56 (MY31 PE), CD15 (MMA FITC), CD123 (MOPC-21 PE), CD8 (SK1 PerCP), GA (GAR-1 PE), CD200 (MRC OX-104 PE), Lysozyme (EC 3.2.1 FITC), Lactoferrin (4C5 PE), CD38 (HB7 FITC) and CD36 (CB38 FITC) purchased from Becton Dickinson, San José, CA, USA (BDIS).

Data acquisition and analysis

Leukemic cells were acquired and analyzed on a FACSCalibur flow cytometer (Becton Dickinson, San Jose, CA, USA). We measured at least 10.000 events/tube. We used Infinicyt 2.0 software (Cytognos SL, Salamanca, Spain). We removed nonviable cells, doublets, and debris. Blasts were then identified based on CD45 + dim/low SSC properties. We determined the percentage of positive cells and the mean fluorescence intensity (MFI) values for CD200 and the rest of the antigens within the blast cell gate for each case. The positivity threshold was set at 20% except for CD117, MPO, TdT, and CD79a, for which a 10% value was used [19, 23]. We differentiated the threshold positivity of CD200 into 3 groups dependent on the percentage of CD200 expression: Pattern 0 or negative (0–20%), Pattern 1 or partially positive (20–50%), and Pattern 2 or fully positive (> 50%) corresponded to a homogeneous CD200 + cell population. P1 and P2 being positive cases with a progressive increase in the MFI and a simultaneous loss of negative cells (Fig. 1).

Fig. 1
figure 1

CD200 positivity patterns. Arbitrary positivity patterns were established based on the presence of negative cells and MFI. Pattern 0 or negative (P0) 0–20% expression of CD200 in blast CD45 + gate; Pattern 1 or partially positive (P1) 20–50% expression of CD200; Pattern 2 or fully positive (P2) > 50% expression of CD200

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Gene sequencing using NGS

Next-generation sequencing of 42 genes was performed with a customized panel using HaloPlexHS (Agilent Technologies®) and MiSeq platform (Illumina®). Library preparation and sequencing were performed according to the manufacturer’s instructions. The median reading depth was around 1000x, and the medium variant allele frequency (VAF) for variants was 5%. Only variants with a read depth > 100x and a minimum of 25 reads were analyzed. Pathogenic variants were classified using Varsome, COSMIC, ClinVar, PolyPhen2, and SIFT.

Real-time PCR

Total RNA was extracted from bone marrow or peripheral blood samples. One µg of RNA was retrotranscribed in a total reaction volume of 20 µl. Samples were incubated for 2 min at 37ºC, 10 min at 25ºC, 50 min at 37ºC, and 15 min at 70ºC.

Meis1, HoxA9, and CD200 gene expression were monitored by quantitative real-time RT-PCR using the Assays on Demands on a QuantStudio 5 (Applied Biosystems, Foster City, CA, USA) and calculated using the DDCT method. PCR reactions were set up in MicroAmp optical 96-well reaction plates. After 2 min at 50ºC and 10 min at 95ºC, the amplification was carried out by 40 cycles at 95ºC for 15 s and 60ºC for 60 s. Each sample was analyzed in duplicate and normalized to the ABL levels, and a mix of two normal bone marrow samples was used as a calibrator.

RNA-Seq

cDNA was sequenced using the Illumina platform, obtaining ~ 34 to 46 million 75 bp paired-end reads per sample. Adapter sequences were trimmed with Trim Galore v.0.4.4. Raw sequencing reads in the fastq files were mapped with STAR (v.2.7.8) [24]. Gencode release 41 based on the GRCh38.p13 reference genome and the corresponding GTF file. The table of counts was obtained with feature Counts function in the package subread (v.2.0.3) [25]. Genes having more than 10 counts in 11 or more samples were kept to filter out lowly-expressed genes considering all samples (CEBPA and CEBPA_control).

Immune cell deconvolution was performed over the 13 AML cases with biallelic CEBPA with the CIBERSORT tool using as a reference the LM22 signature. CIBERSORTx was run with setting the permutations to 100. Expression data was imputed using TPMs considering all genes. Deconvolution was also performed over The Cancer Genome Atlas (TCGA) and BEAT2.0 cohorts. TCGA data was downloaded from cBioPortal [26,27,28], specifically, mRNA RSEM expression data from Acute Myeloid Leukemia (TCGA, PanCancer Atlas) was retrieved (n = 173). CEBPA mutation status was also retrieved com cBioportal. Any CEBPA status other than “No alterations” was considered as CEBPA-mutated. RSEM and mutation data merged led to a total of n = 166 samples. BEAT2.0 data was downloaded from Beat AML 2.0 project [29]. Specifically, normalized expression and clinical data were used. Only patients with expression data were used for the analysis (n = 671). CEBPA Biallelic variable (status “bi”) was used to classify 19 patients as CEBPA mutated. CD200 expression was compared between CEBPA-mutated and CEBPA WT patients (Wilcoxon test). To study differences of cell type abundances between CEBPA-mutated and CEBPA WT a meta-analysis of the two cohorts was performed with the meta package in R (v6.2.1) using a random effects model using the mean difference as summary measure. P-values of the overall random effects models are reported for each cell type.

Weighted correlation network analysis (WGCNA) package (v.1.72-5) [30] was employed to identify clusters (modules) of highly correlated genes using the log2(CPM) for the CEBPA mutated samples. These modules were subsequently correlated with CD200 expression and CEBPA status (either mutated or control) respectively. The top 5000 most variable genes were used for the analysis. CD200 was added to the 5000 most variable genes as it was a gene of interest.

Gene network plots were constructed using the igraph package (v.1.6.0). Spearman’s correlation was utilized to generate correlation matrices from the log2(CPM) expression values obtained from CEBPA patients (n = 13) and CEBPA_control patients (n = 11) independently. The analysis focused on genes within modules identified through WGCNA analysis that exhibited statistically significant correlations with CD200 expression (n = 903) solely in CEBPA patients. Additionally, CD200, CD200R1, and the downstream genes DOK2 and RASA1 from the CD200-CD200R pathway were included in the correlation analysis, despite not being part of the identified modules [18], resulting in a total of 907 genes. A correlation threshold of 0.75 was defined encompassing all genes. Only vertices (genes) connected to CD200 and/or CD200R1 were retained for visualization. Analyses were performed under R version 4.2.1.

Statistical methods

The Student’s t-test compared quantitative variables, and categorical variables were compared using Chi-square (X2) or Fisher’s exact test. Spearman coefficient correlation was performed to analyze markers related to CD200 with Bonferroni correction for multiple comparisons. Analysis was carried out using the statistical package (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp.). The One-way ANOVA followed by the Tukey HSP post-hoc test was employed to compare the gene expression levels.

Results

Immunophenotypic findings

66% of patients with AML expressed CD200. The immunophenotype analysis of the 431 cases showed that CD200 antigen was significantly associated whit CD34, CD117, HLA-DR, CD33, CD123, CD15, CD7, CD71, CD38, CD13 with a p-value < 0.001, and with CD36 and CD64 with a p-value < 0.05 (Table 1).

Table 1 Markers associated with CD200 expression in AML
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Next, we tried to establish correlations between CD200 positivity and genetic lesions in the AML group. (Table 2 and Suppl. Figure 1.1–1.5).

Table 2 CD200 expression is strongly associated with core-binding factor AML, CEBPA and RUNX1 mutations
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The strongest correlation corresponded to chimeric fusions at RUNX1/RUNX1T1 and CBFB-MYH11 and biallelic CEBPA mutations. Other mutations commonly associated with these categories, such as genetic lesions in RUNX1, GATA2, PHF6, ETV6, and ZBTB7A, were also associated with CD200 expression with p-values of < 0.05.

Thirteen out of 16 patients with the RUNX1/RUNX1T1 rearrangement were CD200+, with a predominant pattern 1 of CD200 expression. Moreover, 56% were CD34+, 87% were HLA-DR+, 75% CD33+, 93% were CD117+, 62% were CD15, four cases were CD56 + and only 1 case was CD7 + and CD36+ (Fig. 2).

Fig. 2
figure 2

CD200 expression in AML with CBF genetic lesions. Immunophenotype from one AML patient with a RUNX1/RUNX1T1 rearrangement (UPN 213) which is associated with P1 (partially positive), expressed 44.18% CD200-positive cells. Red: Leukemic cells expressing CD200

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Most CBFB-MYH11 cases (12/19) were also CD200+, most having a pattern 1. 63% of the cases with CBFB-MYH11 were HLA-DR + and CD33+, 73% were CD117+, 52% were CD15+, seven cases were CD34+, four cases were CD36+, and two cases were CD7+. No single case was CD56+ (Fig. 3).

Fig. 3
figure 3

CD200 expression in AML with CBF genetic lesions. Immunophenotype obtained from one AML patient with a CBFB-MYH11 rearrangement (UPN: 347) which is associated with P1 (partially positive), expressed 28.53% CD200-positive cells. Red: Leukemic cells expressing CD200

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All the AML cases with biallelic CEBPA mutations showed positivity of CD200+, four with a pattern 1, and the remaining 4 cases with a pattern 2. All eight cases also highly expressed HLA-DR, CD33, and CD117. CD7 was expressed in seven of the eight samples. CD15 and CD34 were expressed in six of the eight samples. Two samples expressed CD36, and only one expressed CD15. The presence of CD200 was correlated with CD117, CD33, CD15, HLA-DR (p < 0.001) and CD7 (p < 0.05) (Fig. 4 and Suppl. Figure 2.1–2.7).

Fig. 4
figure 4

CD200 expression in AML with biallelic CEBPA mutation. Immunophenotype from one AML patient with biallelic CEBPA mutation (UPN: 102; c.68delC // P23fsX137) which is associated with P2 (fully positive), expressed 80.08% CD200-positive cells. Red: Leukemic cells expressing CD200

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The lowest CD200 reactivity was found in the NPM1 group, with only 19.1% showing CD200 positivity. Most NPM1 mutated cases were also CD34- but it has been suggested that those cases with CD34 + positivity may represent a subgroup with a larger LSC compartment given that CD34 + NPM1 + cells can repopulate immunodeficient mice [31]. NPM1 + AML cases with high FLT3-ITD allelic ratios expressed more commonly CD34 and CD200 than the remaining NPM1 AML (Suppl. Table 2).

We assessed the lymphoid marrow populations using CD2, CD3, CD4, and CD8. If the CD4/CD8 ratio was > 1, we assumed a helper predominance, whereas cases with less than < 1 were considered cytotoxic dominant. The difference between CD2 and CD3 estimated the presence of Natural Killer (NK) cells. Most patients had more than 10% of CD2 + CD3-lymphocytes in the bone marrow. Leukemic CD200 expression was correlated with a lower percentage of T-Helper and NK lymphoid cells (Table 3), as it could be the case of an active immunosuppression mediated by the CD200-CD220R loop.

Table 3 Bone marrow lymphoid ratios and leukemic CD200 positivity
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RNA analysis

CD200 expression was significantly higher in the t(8,21), inv(16) and CEBPA-m group than in NPM1-m and Other AML (p < 0.001 and p < 0.05 respectively, (Fig. 5). Downregulation of Meis1 and HoxA9 expression in t(8,21), inv(16) and CEBPA-m group compared with NPM1-m and Other AML groups supports the observed findings on gene expression analysis (Suppl. Figure 3.1–3.2).

Fig. 5
figure 5

CD200 expression by RT-PCR. CD200 expression comparing 3 groups: t(8,21), inv(16) and CEBPA-m vs. NPM1-m vs. Other AML. p < 0.001 (***); p < 0.05 (*)

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We were interested in the lymphoid populations observed in AML cases with CEBPA mutations, given that this category typically overexpresses CD200. Indeed, CD200 expression was higher in CEBPA-mutated patients than WT in external cohorts (Fig. 6A). We applied deconvolution protocols to investigate the immune cells present in CEBPA-mutated AML cases and in TCGA-LAML and BEAT data (Fig. 6B). Deconvolution showed similar overall cell type composition between cohorts. Cell abundances were compared between CEBPA-mutated and CEBPA WT patients performing a meta-analysis of both cohorts. Our CEBPA-mutated AML cases were not included in this comparison as no WT group was available. Random effect models showed statistical differences between CEBPA-mutated and WT patients of B cells memory (p = 3.53e-5) and dendritic cells activated (p = 0.0117) proportions. B cells naive (p = 0.0867) and monocytes (p = 0.074) showed a trend for significance (Fig. 6C and Suppl. Figure 4.1). Interestingly, CD200 expression correlated with B cells naïve abundance only in WT patients (TCGA R = 0.3, p = 0.00018, BEAT R = 0.26, p = 9.1e − 12) whereas it correlated with B cells memory only in CEBPA mutated patients (TCGA R = 0.57, p = 0.067; R = 0.38, p = 0.11) (Fig. 6D). CD200 expression correlation with dendritic cells was weak (TCGA R = 0.2, p = 0.013, BEAT R = − 0.062, p = 0.12) and monocytes showed a negative correlation in WT patients (Suppl. Figure 4.2). Despite not establishing a clear predominant cell population, uneven distribution of lymphoid cells may be used to analyze leukemic-immune interactions. We also checked the CD200 regulatory pathways in these samples. We found that the inhibitory effects of CD200 on lymphoid cells could be mediated through CD200R1 and RASA1. This protein behaves as a RAS inhibitor and allows control of cellular proliferation and differentiation. At the same time, CD200 overexpression in CEBPA leukemic cells was inversely correlated with PNMA3, this protein shares homology with retroviral Gag proteins, MMP19, a protein that plays a major role in the breakdown of extracellular matrix, and ARHGAP22, an insulin-dependent protein which regulates cell motility (Fig. 7).

Fig. 6
figure 6

Immune cell deconvolution of AML biallelic CEBPA patients, TCGA-AML and BEAT patients. (A) CD200 expression (log2 normalized) in CEBPA mutated patients and WT patients in BEAT and TCGA cohorts. P-values from Wilcoxon rank sum test. (B) Deconvolution results of the RNA-seq data from 13 AML cases with biallelic CEBPA (top left panel), TCGA-AML CEBPA-mutated patients (top center panel) and TCGA-AML CEBPA WT (top right panel), BEAT CEBPA-mutated patients (bottom left panel) and BEAT CEPBA-WT (bottom right panel). Cell type proportions are shown in the y axis. Samples were clustered with euclidean distance and complete method. (C) Cell types with significantly different proportions between CEBPA-mutated and CEBPA-WT in the meta-analysis of the TCGA and BEAT cohorts. Random effects models p-value: B cells naive (p-value = 0.0867), B cells memory (p-value = 3.53E-5), dendritic cells activated (p = 0.0117) and Monocytes (p-value = 0.074). Meta-analysis plots can be found in Suppl. Figure 4.1. (D) Correlation of CD200 expression (log2 normalized) with B cells naïve and memory. Pearson correlation coefficient (R) and p-values are shown separately for CEBPA mutated and WT patients

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Fig. 7
figure 7

Pathway analysis on bulk RNAseq experiments from CEBPA mutated AML. Gene network plot of CD200-CD200R1 related genes. Genes analyzed were previously identified as WGCNA modules that significantly correlated with CD200 expression in CEBPA mutated patients. CD200, CD200R1, DOK2 and RASA1 from the CD200-CD200R pathway were included in the correlation analysis. Only genes connected to CD200 and/or CD200R1 were retained for visualization. A correlation threshold of 0.75 was set for the network

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Discussion

In this work, we consecutively analyzed the expression of CD200 in a series of 431 patients with AML. We showed that 66% of AML patients unequivocally expressed CD200, and its expression was significantly associated with simultaneous CD34 reactivity. We also identified the molecular subgroups with the highest probability of CD200 positivity: cases with RUNX1/RUNX1T1, CBFB-MHY11 rearrangements, and AML with biallelic CEBPA mutations. Conversely, we found the lowest CD200 in AML with NPM1 mutations. However, in this AML group, CD200 + was related to NPM1+/FLT3-ITDhigh ratios, suggesting that this marker could indicate an enlarged leukemic stem cell compartment and be a surrogate marker of a bad outcome.

Regarding CD200 in AML, we found that two-thirds of cases were positive. Damiani D. et al. [16] analyzed a cohort of 244 patients, finding that CD200 was expressed in 136 out of 244 (56%). Tribelli M. et al. [32] CD200 was expressed in 67/139 patients (48%). In both studies and in line with our findings, CD200 was most frequently expressed in CD34-positive blast cells.

We established correlations between CD200 expression and molecular findings. The most remarkable association was with core-binding factor molecular alterations. The study of Tonks A. et al. [33], using mainly transcriptomic data, reported that, in AML, there was a correlation between CD200 expression and the presence of core-binding factor-associated abnormalities such as t(8;21) and inv(16) (p = 0.0001). Coustan-Smith E. et al. [34] analyzed 370 bone marrow samples from patients with de novo or secondary AML and found that CD200 may be helpful to MRD studies. CD200 was significantly overexpressed in patients with RUNX1/RUNX1T1 alterations in this mainly pediatric cohort. The survey of Ho JM. et al. [14], with a series of 65 AML patients, also showed that 5/65 patients presented RUNX1 mutation, and four out of the five had high levels of CD200 expression (more than 86% of the total of myeloblasts). Herein, we add biallelic CEBPA mutation as typically CD200 overexpressing categories [35]. Our findings suggest that the immunophenotypic pattern at diagnosis (CD7, CD34, CD117, CD33, CD123), including CD200 (bright positivity), may be a reliable way to identify AML with biallelic CEBPA mutation. The study of Dentesano G. et al. [36] reported a relationship between the CD200 and CEBPB in microglial cells and suggested that CEBPB could regulate the expression of CD200; it remains to be tested if CEBPA is also involved in regulating CD200 in hematopoietic cells.

We have seen that AML patients with NPM1 mutation have lower CD200 expression levels than other genetic alterations [37–38]. It is known that most NPM1 mutated AML are CD34 negative [39–40]; the association between CD200 and CD34 could explain why both antigens are low in NPM1 mutated AML. FLT3-ITD mutations are subclonal events that provide an adverse prognosis, especially in NPM1 + patients with high FLT3-ITD allelic ratios (> 0.5). These patients have been included in the ELN high-risk group [41–42]. CD200 differentiated a subgroup of 27 AML patients with NPM1+/FLT3-ITDhigh ratios in our study. Tribelli M. et al. [32] showed that CD200 expression identified a group NPM1+/FLT3-ITD- (n = 37) characterized by poor prognosis. These observations suggest that CD200 reactivity needs to be tested in NPM1 mutated AML cases.

Several studies have described the immunosuppressive effects through the CD200/CD200R signal pathway in solid cancers and hematological malignancies [43–44]. In our research, we suggest the role of CD200 by inhibiting NK cell activation and cytotoxic T-cell functions in AML patients. We detected a potential inhibitory loop on lymphoid populations mediated by RASA1, but these findings need to be confirmed by additional experiments.

Furthermore, we validate our cytometry results by analyzing 60 more AML patients with RT-PCR, and our results confirm the relation between CD200 expression and core-binding factors and the inverse correlation between CD200 expression and NPM1 mutation [45].

Our study has some limitations. Most of our population corresponded to adults, so the meaning CD200 in pediatric patients needs to be clarified. Also, we have yet to study the clinical outcomes of AML patients.

The discovery that CD200 plays a vital role in human neoplasia prompted the use of therapies to block the CD200-CD200R binding and suppress the overexpression of this antigen in leukemia. The study of Rastogi N. et al. [46] proposed a fully human anti-CD200 antibody (TTI-CD200) that can block the interaction of CD200 with its receptor and restore AML immune responses in vitro and in vivo. Another study [47] suggested that a recombinant humanized monoclonal antibody called Samalizumab targeted CD200 and was associated with reduced tumor burden in advanced CLL. It remains to be tested if these therapeutic tools help target the AML molecular subgroups with high CD200 expression.

In conclusion, CD200 is a valuable addition to a flow cytometry marker panel and is commonly expressed in AML patients, especially those with core-binding factor alterations. Its upregulation in some categories may suggest that it may be an indirect measure of the leukemic stem cell size or the development of inhibitory immune mechanisms. Further studies are needed to fully understand the prognostic role of CD200 in AML, its association with clonal evolution, and its effects on immunoregulatory cells.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

Supported this work: PI16/094 and PI20/00867 from the Instituto de Salud Carlos III, Ministerio de Economía y Competitividad and Generalitat de Catalunya AGAUR 2014-SGR-383, 2017-SGR-999.

Funding

PI16/094 and PI20/00867 from the Instituto de Salud Carlos III, Ministerio de Economía y Competitividad and Generalitat de Catalunya AGAUR 2014-SGR-383, 2017-SGR-999.

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Authors and Affiliations

  1. Department of Hematology, Hospital de la Santa Creu i Sant Pau. Universitat Autònoma de Barcelona. IIB Sant Pau, Institut Josep Carreras, Sant Quintí, 89, Barcelona, 08041, Spain

    Laura González-Guerrero, Helena Castellet, Clara Martínez, Nuria González, Marta Pratcorona & Josep F. Nomdedéu

  2. Unitat d’Hematopatologia, Servei d’Anatomia Patológica, Hospital Clínic. IDIBAPS, Barcelona, Spain

    Francesca Guijarro & Neus Villamor

  3. Laboratori Hematologia. Hospital Dr. Josep Trueta. ICO Girona, Girona, Spain

    Natalia Lloveras

  4. Epidemiology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain

    Ignasi Gich

  5. Bioniformatics Unit (BU), MARData, Hospital del Mar Research Institute (HMRIB), Barcelona, Spain

    Pau Berenguer-Molins & Júlia Perera-Bel

  6. Hematology Department, Hospital Germans Trias i Pujol. ICO Badalona. Institut Josep Carreras, Badalona, Spain

    Lurdes Zamora & Susana Vives

  7. Hematology Department, Hospital de Son Llàtzer, Palma de Mallorca, Spain

    Martí Mascaró

  8. Hematology Department, Hospital de Son Espases, Palma de Mallorca, Spain

    Antonia Sampol

  9. Servei Hematologia, Hospital Universitari Arnau de Vilanova, Lleida, Spain

    Antoni Garcia-Guiñón

  10. Laboratorio de Hematología. Hospital Clínico de Valencia, Universidad de Valencia, Valencia, Spain

    Mar Tormo

  11. Clinical Hematology Department, ICO-Hospital Duran i Reynals. Institut d’Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain

    Montserrat Arnan

Authors
  1. Laura González-Guerrero
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  2. Helena Castellet
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  3. Clara Martínez
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  4. Nuria González
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  19. Josep F. Nomdedéu
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Contributions

LGG, HC, CM and JFN designed the study. LGG, HC, CM, NG, FG, NL, MP, PB, JP, LZ, MM, IG, AS, AGG, SV, MT, MA, NV and JFN obtained clinical and biological data. LGG, HC, NG and JFN wrote the manuscript with input from all authors. The study received approval from the CEIC (Ethical Committee number IIBSP-LEU-2021-120).

Corresponding author

Correspondence to Josep F. Nomdedéu.

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The study received approval from the CEIC (Ethical Committee number IIBSP-LEU-2021-120).

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González-Guerrero, L., Castellet, H., Martínez, C. et al. CD200 in acute myeloid leukemia: marked upregulation in CEBPA biallelic mutated cases. Diagn Pathol 20, 56 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13000-025-01655-w

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Keywords

Diagnostic Pathology

ISSN: 1746-1596

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