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Phenotypic and functional characterization of neutrophils and monocytes from patients with myelodysplastic syndrome by flow cytometry
Cellular Immunology xxx (2016) xxx–xxx
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Research paper
Phenotypic and functional characterization of neutrophils and monocytes from patients with myelodysplastic syndrome by flow cytometry Cornelia S. Schmidt a, Pamela Aranda Lopez a, Jörn F. Dopheide b, Frank Schmidt b, Matthias Theobald a, Hansjörg Schild c, Evi Lauinger-Lörsch d, Florian Nolte d, Markus P. Radsak a,⇑ a
IIIrd Department of Medicine, Johannes-Gutenberg University Medical Center, Mainz, Germany IInd Department of Medicine, Johannes-Gutenberg University Medical Center, Mainz, Germany c Institute of Immunology, Johannes-Gutenberg University Medical Center, Mainz, Germany d Department of Hematology and Oncology, University Medical Center Mannheim, Mannheim, Germany b
a r t i c l e
i n f o
Article history: Received 3 March 2016 Revised 13 June 2016 Accepted 6 July 2016 Available online xxxx Keywords: Myelodysplastic syndrome Neutrophils Monocytes Phenotype Effector functions
a b s t r a c t Myelodysplastic syndrome (MDS) is a clonal stem cell disorder frequently associated with inefficient granulopoiesis showing dysplastic polymorphonuclear neutrophils (PMNs). To assess PMN functionality in MDS in a clinical routine setting, 30 MDS patients and ten healthy volunteers were analyzed for PMN and monocyte phenotype and function (degranulation, CD62L shedding, oxidative burst and phagocytosis) upon stimulation with lipopolysaccharide by multi-color flow cytometry (MCFC). Our data show a heterogeneous pattern for CD66, CD16 and CD64 expression on PMNs of MDS patients. CD62L shedding rate and CD66 degranulation were reduced. Interestingly, we detected correlations between the WHO adapted prognostic scoring system (WPSS) and CD16 expression on PMNs as well as the international prognostic scoring system (IPSS) and CD11b degranulation by MCFC, suggesting clinical relevance of MCFC based function testing. In conclusion, MCFC of myelodysplastic immunophenotypes and PMN functionality are applicable in clinical settings, but further prospective studies are needed to assess the practical clinical value of such analyses. Ó 2016 Elsevier Inc. All rights reserved.
1. Introduction Myelodysplastic syndrome (MDS) is a clonal hematopoeietic stem cell disorder characterized by a maturation defect in all hematopoietic cells in the myeloid lineage, most frequently affecting erythroid cell maturation resulting in anemia. MDS is a common myeloid neoplasm of the bone marrow with an increasing prevalence in the aging population, which is also believed to further increase over the next decades [1]. In addition, morphological as well as functional abnormalities of in polymorphonuclear neutrophils (PMNs) and monocytes are frequently found in MDS patients [2] and are believed to be associated with an increased susceptibility to bacterial infections which are a major cause of death in MDS patients [3]. One of the leading causes for death is neutropenia (refractory cytopenia, but also caused by hypomethy-
⇑ Corresponding author at: Department of Internal Medicine III, Hematology, Oncology and Pneumology, University Medical Center of the Johannes GutenbergUniversity, Langenbeckstr. 1, D-55131 Mainz, Germany. E-mail address: [email protected] (M.P. Radsak).
lating agents, Lenalidomid or chemotherapy). Beyond this, iron overload, stem cell transplantations, individual comorbidities, high age as well as immunosuppressive conditions such as B- or T-cell defects may contribute to the susceptibility to infections. Moreover, while multi-color flow cytometry (MCFC) analyses are used on a routine basis for the diagnosis and monitoring in many hematological malignancies, such MCFC assessments reveal a great variety of immunophenotypic aberrancies on the surface of myelodysplastic cells reflecting the heterogeneity of this disease. Anomalies include increased, decreased or cell-lineage unspecific (lineage infidelity) protein expression patterns on PMNs and monocytes [4]. Functional deficits of these cells have been described previously with heterogeneous results, i.e. affecting reactive oxygen species (ROS) production and phagocytic activity. Prodan and co-workers show that phagocytosis is the more conserved effector function for combating bacterial invasion in MDS since low risk MDS patients show no apparent functional defects [5]. Nevertheless, the oxidative burst activity is reduced in both low and high risk MDS patients. Interestingly, a correlation of cell counts and reduced functional activity has not been detected. Thus,
http://dx.doi.org/10.1016/j.cellimm.2016.07.005 0008-8749/Ó 2016 Elsevier Inc. All rights reserved.
Please cite this article in press as: C.S. Schmidt et al., Phenotypic and functional characterization of neutrophils and monocytes from patients with myelodysplastic syndrome by flow cytometry, Cell. Immunol. (2016), http://dx.doi.org/10.1016/j.cellimm.2016.07.005
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C.S. Schmidt et al. / Cellular Immunology xxx (2016) xxx–xxx
the scientists suggest an independent relation between cell count and leukocyte dysfunction which has to be further characterized. The introduction of MCFC as diagnostic co-criterion in MDS in 2007 underpins its accessory role in diagnosis and prognostication [4,6]. To refine and improve MDS classification and prognostic criteria, there have been many efforts towards the standardization of MCFC methods to achieve a reproducible clinical application [7– 13]. However, a full integration of MCFC analyses into the diagnostic strategies, particularly morphological and cytogenetic examination, has not yet been thoroughly accomplished. In our present study, we extend the current immunophenotypic characterization of PMNs and monocytes to also integrate parameters of PMN functionality of MDS patients in a clinical routine setting. Our results confirm an abnormal expression profile of myelodysplastic PMNs and monocytes. A heterogeneous pattern regarding the utilized surface markers allows the assumption of different phenotypic subgroups in MDS. Importantly, we demonstrate the feasibility of MCFC based PMN functional assays in a clinical routine setting. The correlations of our MCFC data with established clinical parameters for the risk stratification of MDS suggest that MCFC based phenotyping and function testing may be useful additional parameters to understand the clinical implications of possible functional defects in MDS. Hence, the present study opens the new opportunity to integrate functional qualities of leukocytes in MDS by MCFC for further risk stratification to potentially identify MDS patients at high risk for opportunistic infections or for disease progression. 2. Patients, materials and methods 2.1. Patients The study was carried out in accordance to the principles outlined in the Declaration of Helsinki and was approved by the local ethics committee (Landesärztekammer Rheinland-Pfalz, Mainz, Germany). Overall 30 MDS patients treated in the outpatients’ departments of the University Medical Centers Mainz and Mannheim were enrolled in this study. Clinical characteristics are displayed in Table 1. Ten healthy volunteers recruited form a cardiology aircraft-noise study of the IInd Department of Medicine of the University Medical Center in Mainz with no history of MDS or any hematological disease functioned as controls. After written informed consent a heparinized blood sample from a peripheral vein was drawn. Within eight hours of collection the blood specimen was prepared for flow cytometric analysis. 100 microliter (ll) whole blood was transferred into 5 milliliter (ml) FACS tubes (BD Falcon, San Diego, CA, USA). Subsequently, the staining procedures concerning the cells’ phenotype as well as functioning followed. 2.2. Reagents S. typhimurium lipopolysaccharide (LPS) [1 lg/ml] and dichlorfluorescein (DCF) were obtained from Sigma-Aldrich, Taufkirchen, Germany. PE-labeled polystyrene microspheres (56.9 106 particles/ml, diameter 1 lm, Fluoresbrite Plain Microspheres PCRed) were from Polysciences, Warrington, PA. MAbs used for flow cytometry are summarized in Table 2. 2.3. Flow cytometry 2.3.1. Surface markers For the purpose of identification of surface expression patterns, fluorochrome labeled monoclonal antibodies were added to the tubes and incubated for fifteen minutes at room temperature in
Table 1 Demographic data. MDS patients
Healthy controls
n Median age (range)
30 76.5 years (55–86)
Sex
56.7% male (n = 17) 43.3% female (n = 13)
10 64 years (47– 75) 90% male (n = 9) 10% female (n = 1)
MDS subtype (WHO 2008) RCUD (+/-RS), RA, RARS, Del-5q RCMD RAEB I RAEB II CMML/MDS-MPN/hypoplastic MDS
16 5 3 3 3
IPSS score Low Intermediate I Intermediate II High Unknown
12 10 4 1 3
IPSS-R score Very low Low Intermediate High Very high Unknown
1 14 7 4 1 3
WPSS score Very low Low Intermediate High Very high Unknown
8 10 2 2 2 6
Cell counts (median (range)) Hemoglobin g/dl WBC/nl ANC/nl Platelets/nl
9.2 (7.5–13.6) 4.4 (0.9–65.8) 2.5 (0.3–52) 212 (11–733)
15.0 (12.1–15.6) 6.72 (4.56–12.0) 4.19 (2.38–6.54) 241 (124–357)
n: number; RCUD: refractory cytopenia with unilineage dysplasia; RS: ringsideroblasts; RA: refractory anemia; RARS: refractory anemia with ring sideroblasts; Del5q: MDS with deletion 5q; RCMD: refractory cytopenia with multilineage dysplasia; CMML: chronic myelomonocytic leukemia; RAEB: refractory anemia with excess of blasts; MPN: myeloproliferative neoplasia: WBC: white blood cell count; ANC: absolute neutrophil count; IPSS: international prognostic scoring system; IPSS-R: revised – international prognostic scoring system; WPSS: WHO adapted prognostic scoring system; g: gram; dl: deciliter; nl: nanoliter.
the dark. After erythrocyte lysis with one milliliter ammonium chloride buffer, the cells were washed with phosphate buffered saline (PBS) buffer by centrifugation (1400 rotations per minute). Finally, the cells were resuspended in FACS buffer (1% bovine serum albumin, 0.02% natrium acetate and 1 mM ethylenediaminetetraacetic acid in PBS) and analyzed by flow cytometric examination.
2.3.2. Effector functions For the assessment of PMN effector functions (degranulation, CD62L shedding, oxidative burst and phagocytosis) four tubes containing 100 ll whole blood were incubated for 30 min at 37 °C in the presence or absence of S. typhimurium LPS after the addition of dichlorfluorescein (for the detection of ROS) and PE-labeled polystyrene microspheres (for the detection of phagocytic activity). Then, fluorochrome labeled antibodies (anti-CD11b, anti-CD62L, anti-CD66abce) were added and incubated for fifteen minutes on crushed ice. After two periods of erythrocyte lysis, the cells were washed twice with FACS-buffer, and subsequently analyzed by MCFC.
Please cite this article in press as: C.S. Schmidt et al., Phenotypic and functional characterization of neutrophils and monocytes from patients with myelodysplastic syndrome by flow cytometry, Cell. Immunol. (2016), http://dx.doi.org/10.1016/j.cellimm.2016.07.005
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C.S. Schmidt et al. / Cellular Immunology xxx (2016) xxx–xxx Table 2 Antibodies used for surface staining and analysis of effector function.
Table 3 Patient care – therapeutic methods.
Application
Antigen
Clone
Fluorochrome
Origin
Therapeutic strategy
Absolute (relative) patient count
Flow cytometry – surface stainings
TREM
6B1.1G12
FITC
CD13
Immu103.44
ECD
CD33
D3HL60.251
PE
CD10
ALB1
PE
‘‘Watch and wait” Transfusions Erythropoesis stimulating agents Hydroxyurea Lenalidomid Azacitidin Panobinostat Immunoglobulins
5 8 8 1 4 2 1 1
CD14
RMO52
ECD
HLA-DR
Immu357
PE-Cy7
CD16
3G8
APC
CD117
104D2D1
APC
CD64
10.1
APC-A700
CD34
581
APC-A700
CD11b CD45
ICRF44 J.33
Pacific Blue Krome Orange
Own production Beckman coulter Beckman coulter Beckman coulter Beckman coulter Beckman coulter Beckman coulter Beckman coulter BD biosciences Beckman coulter BioLegend Beckman coulter
CD11b CD62L CD66abce
ICRF44 DREG-56 Tet2
Pacific Blue APC-Cy7 APC
Flow cytometry – effector function
BioLegend BioLegend Miltenyi biotec
CD: cluster of differentiation.
2.3.3. Flow cytometric analysis All measurements were performed on a Navios flow cytometer (Beckman-Coulter, Krefeld, Germany) and analyzed by FlowJo software Version 9.6.4. (Copyright: Trustees of Leland Stanford Jr. University, 1996–97: Tree Star, Inc. 1997–2009). For analysis, PMN or monocytes were identified by gating on the respective cell populations on CD45/side scatter (SSC) plot. Debris and residual erythrocytes were excluded. In addition, leukocytes were separated as CD66abce positive PMNs and CD14 positive monocytes. Concerning all surface markers, mean fluorescence intensities (MFI) were applied for evaluation of the patients’ data. For degranulation, CD62L shedding, oxidative burst and phagocytosis an activation index was calculated by dividing the MFI of the indicated marker of the LPS stimulated sample by the respective unstimulated control. 2.4. Statistical analysis Regarding data management and statistical analysis, Prism V5.0a statistical software package (Graphpad, San Diego, CA,
(16.7%) (26.7%) (26.7%) (3.3%) (13.3%) (6.7%) (3.3%) (3.3%)
USA) was utilized. Individual patient measurements are presented as mean ± standard error of mean. For all analyses, a value of p < 0.05 was considered to be significant. Two group comparisons were performed by Mann-Whitney test. For correlations between flow cytometric parameters or with established clinical parameters of MDS patients and healthy volunteers, data sets were analyzed by Spearman’s correlation test. 3. Results 3.1. Demographic data For the analysis, the present study included only patients with the diagnosis of MDS. The main patients’ characteristics are reported in Table 1. Since we wanted to observe steady state conditions and exclude reactive changes in PMN or monocyte phenotypes or functions, patients who had to be hospitalized due to (infectious) complications of any cause were not included. Table 3 depicts the various treatments applied to the selected group of MDS patients at the time of analysis. 3.2. Heterogeneous expression of surface antigens on PMNs and monocytes in MDS Since we were interested to study not only phenotypic changes in surface marker expression, but also functional alterations in PMNs of MDS patients, we analyzed peripheral blood cells and not bone marrow samples, as this might more appropriately reflect functionally relevant changes that may have an impact on innate inflammatory responses. In this context, we observed a statistical significant increase of the CD66abce, CD16 and CD64 on PMNs (Fig. 1), compatible with an activated phenotype. In contrast other markers CD11b, CD62L, and TREM-1 displayed a more heterogenous expression pattern compared the healthy volunteer controls, but were overall not significantly different. Also in monocytes of MDS patients, we observed this heterogeneous distribution of all analyzed markers (Fig. 2), again not statistically different from the control group.
Fig. 1. Surface markers of myelodysplastic PMNs. MCFC results of antigen profiles on PMNs in MDS patients compared to a healthy reference group are represented. Among the patient collective heterogeneous expression levels of CD66abce, CD11b, CD62L, CD16, CD64 and TREM-1 become evident. Mean fluorescence intensities (MFIs) of the indicated markers from individual patient/volunteer samples are depicted. These data are presented as mean with standard error of the mean (SEM). ⁄indicates a significant difference (p < 0.05) by Mann-Whitney test.
Please cite this article in press as: C.S. Schmidt et al., Phenotypic and functional characterization of neutrophils and monocytes from patients with myelodysplastic syndrome by flow cytometry, Cell. Immunol. (2016), http://dx.doi.org/10.1016/j.cellimm.2016.07.005
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C.S. Schmidt et al. / Cellular Immunology xxx (2016) xxx–xxx
Fig. 2. Surface markers of myelodysplastic monocytes. MCFC results of protein patterns on monocytes in MDS patients in contrast to a healthy control collective are depicted. Within the patient group heterogeneous expression profiles of HLA-DR, CD11b and TREM-1 were detected. Mean fluorescence intensities (MFIs) of the indicated markers from individual patient/volunteer samples are shown. These data are presented as mean with standard error of the mean (SEM).
Taken together, compared to the healthy individuals, who appear to be a more homogenous group, the large variability of the measured parameters in MDS patients indicate relevant alterations in the phenotype of PMNs and monocytes in MDS. But this makes it also difficult to identify a clear distribution of surface marker patterns, given our small patient collective. 3.3. Heterogeneously altered PMN effector functions in MDS Beyond the phenotypic changes in PMNs and monocytes from MDS patients, we were also interested to document functional changes in the activation of PMN effector mechanisms. Therefore,
we used the TLR4 ligand LPS as a prototypic TLR agonist, important in the innate immune response to gram negative bacteria. We assessed the shedding rate of L-selectin (CD62L), degranulation of CD66abce and CD11b (Mac-1) as important adhesion molecules as well as the oxidative burst and phagocytic activity upon stimulation. As depicted in Fig. 3, we detected a more heterogeneous activation pattern in PMNs from MDS patients compared to the healthy controls. Interestingly, the CD62L shedding rate was somewhat reduced, although this was not statistically significant (Fig. 3A). For degranulation of CD11b and CD66abce (Fig. 3B and C, respectively), as well as the oxidative burst and phagocytosis (Fig. 3D and E, respectively) the activation indices
Fig. 3. PMN effector functions of MDS patients. MCFC reveals a reduced CD62L shedding rate of the patients’ PMNs (A). Degranulation processes of CD11b (B) and CD66abce (C) are furthermore heterogeneously impaired, as is the oxidative burst of the patients’ cells (D). Due to outliers the mean of the MDS phagocytic activation index appears to be increased respectively to the homogeneous reference group, however predominantly this function is defective in patients, too (E). Concerning data presentation, the displayed indices were calculated by dividing the MFI of the indicated marker of the LPS stimulated sample by the respective unstimulated control.
Please cite this article in press as: C.S. Schmidt et al., Phenotypic and functional characterization of neutrophils and monocytes from patients with myelodysplastic syndrome by flow cytometry, Cell. Immunol. (2016), http://dx.doi.org/10.1016/j.cellimm.2016.07.005
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C.S. Schmidt et al. / Cellular Immunology xxx (2016) xxx–xxx
were more heterogeneous in PMN from the MDS cohort compared to the healthy controls. However, significant differences concerning the effector functions between the MDS and control collective were not detected. Remarkably, there were patients with strongly deviant measurements compared to the majority of the group. In particular, CD11b degranulation, oxidative burst and phagocytic activity were affected (Fig. 3B,D and E). Due to those ‘‘outliers” the mean within the MDS patient collective was even increased compared to the reference group. This indicates that in addition to a reduced capability for the activation of PMN effector functions, there is also a subset of MDS patients where PMNs are hyper-reactive in response to LPS. This suggests that abnormal PMN functionality in MDS patients includes hypo- as well as hyper-responsiveness.
3.4. Correlation analysis of phenotype, function and clinical parameters in MDS and healthy donors To assess whether there are any relations of our MCFC based phenotypic and functional results with the clinical outcome of MDS, we analyzed the correlations of individual cell surface markers on PMN and monocytes as well as the functional tests with established clinical parameters for the risk stratification of MDS as detailed in Table 4. Importantly, we detected significantly
altered expression patterns of CD66, CD64, CD11b, CD16 and TREM-1 only in the cohort of MDS patients, but not in healthy donors (Table 4, printed in bold). As shown in Fig. 4, there was a strong correlation between CD16 and CD64 expression on PMNs of MDS patients, while this was not the case for CD66abce. Moreover, we found not only significant correlations between the individual surface markers on PMN and monocytes (e.g. CD11b and TREM-1 expression on PMN and monocytes), but we also detected significant correlations among functional parameters as well as functional and phenotypic markers in our patient cohort, but not in the healthy donor cohort, indicating on the one hand the consistency of our MCFC analyses and on the other hand a MDS specific relationship between surface expression of CD66 and CD11b with degranulation and CD62L shedding. Even more interestingly, we also detected correlations of our MCFC based assays with established clinical parameters that are relevant with respect to the risk stratification of MDS. Notably, PMN CD16 expression correlates with the WPSS and the degranulation of CD11b with the IPSS. Moreover, we found correlations between cell counts (for example the number of white blood cells or the quantity of PMN granulocytes) and various surface antigens on PMNs, but also degranulation and oxidative burst, further underlining the consistency of established parameters of MDS (blood cell counts) and MCFC derived markers. Finally, an interrelation between therapeutic strategy (symptomatic approach
Table 4 Correlations of flow cytometry based surface and functional markers. MDS patients
Healthy donors
r (CI;95%)
p
r (CI;95%)
p
Correlations of functional markers CD11b degran. – CD66 degran. CD66 degran. – CD62L shedding CD11b degran. – CD62L shedding
0.659 (0.366 to 0.821) 0.57 ( 0.779 to 0.260) 0.457 ( 0.708 to 0.107)
<0.001 0.001 0.010
0.417 ( 0.078 to 0.746) 0.533 ( 0.806 to 0.0735) 0.317 ( 0.691 to 0.191)
n.s. 0.023 n.s.
Correlations of surface markers CD16 expr. (PMN) – CD64 expr. (PMN) CD16 expr. (PMN) – CD66 expr. (PMN) CD16 expr. (PMN) – CD11b expr. (PMN) CD64 expr. (PMN) – CD66 expr. (PMN) CD64 expr. (PMN) – CD11b expr. (PMN) CD66 expr. (PMN) – CD11b expr. (PMN) TREM-1 expr. (PMN) – TREM-1 expr. (Mono) TREM-1 expr. (PMN) – CD11b expr. (Mono) CD11b expr. (PMN) – CD11b expr. (Mono) CD11b expr. (PMN) – TREM-1 expr. (Mono) CD16 expr. (PMN) – TREM-1 expr. (Mono)
0.993 (0.984 to 0.997) 0.362 ( 0.009 to 0.646) 0.539 (0.212 to 0.758) 0.367 ( 0.004 to 0.649) 0.564 (0.245 to 0.773) 0.686 (0.424 to 0.842) 0.524 (0.117 to 0.761) 0.464 ( 0.741 to 0.050) 0.546 (0.170 to 0.783) 0.402 ( 0.682 to 0.048) 0.413 ( 0.681 to 0.049)
<0.001 0.040 0.001 0.038 0.001 <0.001 0.007 0.026 0.006 0.046 0.040
0.976 0.267 0.430 0.417 0.479 0.100 0.079 0.176 0.745 0.139 0.006
<0.001 n.s. n.s. n.s. 0.032 n.s. n.s. n.s. 0.013 n.s. n.s.
Correlations of functional and surface markers CD66 expr. (PMN) – CD66 degran. CD66 expr. (PMN) – CD62L shedding CD11b expr. (PMN) – CD66 degran.
0.601 ( 0.796 to 0.301) 0.452 (0.0905 to 0.700) 0.444 ( 0.723 to 0.136)
<0.001 0.011 0.012
0.450 ( 0.764 to 0.0364) 0.133 ( 0.369 to 0.575) 0.133 ( 0.369 to 0.575)
n.s. n.s. n.s.
Correlations with clinical parameters WPSS – CD16 expr. (PMN) WPSS – PLTs IPSS – CD11b degran. Therapy – CD62L shedding WBC – CD11b expr. (PMN) WBC – oxidative burst WBC – CD66 degran. WBC – CD11b degran. ANC – CD16 expr. (PMN) ANC – CD64 expr. (PMN) ANC – CD11b expr. (PMN) ANC – CD11b degran.
0.421 ( 0.711 to 0.009) 0.422 ( 0.711 to 0.009) 0.478 (0.108 to 0.732) 0.496 (0.155 to 0.732) 0.396 (0.0229 to 0.672) 0.433 (0.0676 to 0.696) 0.463 ( 0.715 to 0.104) 0.492 ( 0.733 to 0.142) 0.390 (0.0230 to 0.664) 0.395 (0.0295 to 0.668) 0.472 (0.124 to 0.717) 0.385 ( 0.661 to 0.017)
0.040 0.04 0.012 0.005 0.034 0.019 0.012 0.007 0.033 0.031 0.008 0.036
– – – –
– – – – n.s. n.s. n.s. n.s. 0.025 0.014 n.s. n.s.
(0.936 0.991) ( 0.243 to 0.661) ( 0.029 to 0.740) ( 0.077 to 0.746) (0.0312 to 0.766) ( 0.398 to 0.552) ( 0.389 to 0.5142) ( 0.302 to 0.583) (0.441 to 0.896) ( 0.335 to 0.558) ( 0.449 to 0.459)
0.233 ( 0.640 to 0.276) 0.267 ( 0.661 to 0.243) 0.143 ( 0.394 to 0.607) 0.300 ( 0.209 to 0.681) 0.667 (0.2762 to 0.8682) 0.567 (0.121 to 0.822) 0.167 ( 0.339 to 0.598) 0.033 ( 0.453 to 0.504)
r: correlation coefficient (Spearman’s test); CI: 95 % confidence interval; CD: cluster of differentiation; PMN: polymorphnuclear granulocyte; Mono: monocyte; PLTs: platelets; WBC: white blood cell count; ANC: absolute neutrophil count; WPSS: WHO adapted prognostic scoring system; n.s.: non-significant; degran.: degranulation; CD66: CD66abce. Correlation analyses among the entire set of surface markers, functional markers, IPSS, IPSS-R, WPSS, diagnostic subgroups, other clinical parameters (WBC, ANC, Hb and PLTs) and therapy revealed varied relevant correspondences between the contemplated variables. Emphasized (in bold print) are the antigen expressions that correlate significantly only within the MDS collective compared to the healthy donor group.
Please cite this article in press as: C.S. Schmidt et al., Phenotypic and functional characterization of neutrophils and monocytes from patients with myelodysplastic syndrome by flow cytometry, Cell. Immunol. (2016), http://dx.doi.org/10.1016/j.cellimm.2016.07.005
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C.S. Schmidt et al. / Cellular Immunology xxx (2016) xxx–xxx
Fig. 4. Coexpression of CD16, CD64 and CD66abce on PMNs of MDS patients. The correlation (A) of surface expression of CD16 (x axis) and CD64/CD66abce (y axis) of MDS patients and (B) the correlation of surface expression of CD64 (x axis) and CD16/CD66abce (y axis) shows that CD16 and CD64 expression are strongly associated in contrast to CD66abce.
against Lenalidomid/Azacitidin/Panobinostat) and CD62L shedding was identified. No correlations of the MCFC data were detected with respects to diagnostic subgroups, IPSS-R and hemoglobin, most likely due to our limited data set. 4. Discussion Many other groups have analyzed bone marrow samples to characterize immunophenotypes in MDS [4]. In contrast, we have focused our attention on analyses from the peripheral blood, since we were not only interested in marker profiles, but also functional changes. While altered phenotypes may be more accurately described by the examination of bone marrow samples, the direct analyses of peripheral blood has the advantage that samples can be processed in a more standardized fashion and without having to consider artifacts due to contamination with peripheral blood. In addition, comparisons with healthy control samples can easily be made, which is of special importance with respect to the functional assays. Moreover, it is rational to assume that the analysis of cells in the periphery might more accurately reflect relevant defects in PMN function (over bone marrow). Concerning surface markers in MDS, increased or reduced expression patterns as well as aberrant antigen expression patterns have been described (lineage infidelity) [4,14]. In line with others, we detected in our cohort of 30 MDS patients a more heterogeneous expression pattern of all markers analyzed compared to the control group reflecting the heterogeneity of the disease. Hence the existence of distinct phenotypic subgroups of PMNs and monocytes in MDS can be assumed. As other investigators before [14–16], we are unable to attribute clear marker patterns to specific MDS subgroups. This is mostly due to the small sample size of the MDS cohort (n = 30). For CD66abce, CD16 and CD64 on PMNs, we showed a significantly increased expression compared to the healthy individuals (Fig. 1). Interestingly, the surface coexpression of CD16 and CD64 on PMNs of MDS patients are strongly correlated suggesting similar mechanisms of (possibly transcriptional) regulation as both a Fc-c receptors. In contrast, this is not the case for CD66abce expression (Fig. 4), suggesting a distinct mechanism for the regulation of CD66 surface expression. An altered CD66 expression on PMNs of MDS patients has already been found previously by Cherian et al. and has been integrated into a peripheral blood MDS score [17]. Together, this marker combination is compatible with an activated PMN phenotype that is MDS specific (at least compared to our healthy donor cohort), since the majority of correlations of altered marker expression and functional tests are only detected in the MDS cohort, but not the healthy individuals (Table 4). This allows the assumption that PMNs in MDS may be continuously activated in vivo, as previously proposed by Ohsaka et al. concerning the CD62L shedding rate [15]. This is further
corroborated by the fact that CD66abce, CD11b and CD16 are not only constitutively present in the outer plasma membrane, but also in storage compartments where they are ready to be recruited to the surface upon activation [14]. While others have demonstrated aberrancies of the oxidative burst, degranulation, CD62L shedding and phagocytic activity in PMNs from patients with MDS [2,18–24], our present work shows a strong heterogeneity in the activation of these effector functions in MDS patients compared to healthy volunteers Fig. 3), but apparently no significant differences. Interestingly, Prodan et al. demonstrate a less preserved oxidative burst that reacts more sensitive to pathologic stimuli and is already reduced in patients with low risk MDS (in contrast to the phagocytic activity). Thus, they conclude impairment of PMN functions in MDS occurs autonomously [5]. This is apparently different in our cohort. Firstly and in agreement with others [17] we detected significant positive correlations for expression of CD66abce and CD11b as surface and activation markers (Table 4) suggesting that there is indeed a connection between steady state expression and PMN functions, at least in terms of degranulation of CD66abce and CD11b as well as shedding of CD62L. Consistent with Prodan et al. we detected no correlations with the oxidative burst or phagocytosis. Deregulation of innate immune and inflammatory signals in MDS may contribute to the observed MDS phenotypes. In this context, Peng et al. describe an abnormal overexpression of the p38 mitogen-activated protein kinase (MAPK) signaling pathway [25]. Triggered by the extrinsic microenvironment including high concentrations of cytokines, p38MAPK overactivation in MDS marrow leads to increased apoptosis of normal progenitors and inhibition of p38MAPK results in a reduction of TNF-a and IL-1b generation [26]. Moreover, Gañán-Gómez et al. report on the overexpression and constitutive activation of Fas, TNF-R1/2, TLRs, type II IFN-R, and their associated signal transducers [27]. Along these lines, a recent report also describes increased expression levels of the TLR adapter protein MyD88 in CD34+ cells of MDS patients [28]. Furthermore, Varney and coworkers demonstrate a direct mechanism and relevance of MDS mutations for hyperactived TLR signaling in MDS [29]. They show that the loss of the Tifab gene in MDS with Del(5q) results in a derepression of NFjB since a complex of TIFAB and TRAF6 is necessary to inhibit NFjB activation. Although they did not examine PMN functionalities, this all may contribute to the heterogeneity of altered PMN responsiveness to LPS in MDS patients. Towards the clinical significance of our findings, we found correlations of phenotypic and functional features of PMNs and monocytes with clinical parameters in MDS patients. Though expression profiles of the studied antigens are extremely complex and diverse, there are markers on both cell types (i.e. TREM-1 and CD11b) that correlate with each other, suggesting a similar regulation of their expression in PMNs and monocytes. In agreement with established flow cytometry scores [30–32], we detected a negative correlation of CD16 expression on PMN with the WPSS, and CD11b degranulation with the IPSS, but not the IPSS-R. Presumably, the reason for this is related to our small data set. Finally of note, we detected a correlation of treatment with CD62L expression on PMN. However, concerning the clinical meaning of this observation we are currently unable to draw conclusions due to the design of our study. Further prospective studies are needed to address the question whether CD62L might be a suitable marker to monitor MDS treatments beyond supportive care. Concerning correlations of MDS related mutations with phenotypic and functional alterations in PMNs of MDS patients, we had only information of standard cytogenetic alterations available integrated into the MDS risk scores. This is certainly a limitation of our study. The evolving more sophisticated molecular diagnostics of MDS covering the most common mutations SF3B1,
Please cite this article in press as: C.S. Schmidt et al., Phenotypic and functional characterization of neutrophils and monocytes from patients with myelodysplastic syndrome by flow cytometry, Cell. Immunol. (2016), http://dx.doi.org/10.1016/j.cellimm.2016.07.005
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TET2, SRSF2, ASXL1, DNMT3A, RUNX1, U2AF1, TP53, and EZH2 as proposed by the WHO 2016 classification [33] might be useful to establish a better correlation of phenotypic and functional aberrancies in MDS.
[6]
5. Conclusions
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Our results confirm the heterogeneity of aberrant marker profiles in PMN and monocytes of MDS patients and demonstrate the feasibility of PMN function testing in a clinical routine setting. In addition, we propose that CD16 and CD11b expression levels may be useful surrogate markers for MDS risk stratification. Other markers such as CD66abce or CD64 need further evaluation to determine whether they have additional value for MDS risk stratification by flow cytometry. Based on our data, further prospective studies are still required to validate these parameters and eventually distinguish subgroups of patients with greater risk of infections and MDS progression. Additional information The authors declare that there are no completing financial interests concerning this manuscript.
[8]
[9]
[10]
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Author contributions C. Schmidt performed the MCFC stainings and analysis as well as the analyses of the clinical patient data. P. Stein designed MCFC analyses for PMN phenotype and functions. J. Dopheide contributed the monocyte panel for MCFC. F. Schmidt contributed the healthy volunteer donors. M. Theobald and H. Schild contributed to the design of the research and writing of the manuscript. E. Lauinger-Lörsch and F. Nolte contributed some of the MDS patient blood samples and patient data. M. Radsak designed research and wrote the manuscript. Acknowledgments The authors would like to thank Annekatrin Klaric, Andrea Drescher and Ursel Petrat for excellent technical assistance. This work was supported by grants from the CTH/BMBF (BMBF01EO1003; to M.P.R.), ‘‘Naturwissenschaftlich-Medizinis ches Forschungszentrum (NMFZ), ‘‘Forschungszentrum Immunologie (FZI)” of the University Medical Center Mainz (to M.P.R.). This work includes results of Cornelia Schmidt’s medical doctoral thesis ‘‘Phänotypische und funktionelle Charakterisierung von neutrophilen Granulozyten und Monozyten bei Myelodysplastischem Syndrom mittels Durchflusszytometrie”.
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[14]
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[16]
[17]
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Research paper
Phenotypic and functional characterization of neutrophils and monocytes from patients with myelodysplastic syndrome by flow cytometry Cornelia S. Schmidt a, Pamela Aranda Lopez a, Jörn F. Dopheide b, Frank Schmidt b, Matthias Theobald a, Hansjörg Schild c, Evi Lauinger-Lörsch d, Florian Nolte d, Markus P. Radsak a,⇑ a
IIIrd Department of Medicine, Johannes-Gutenberg University Medical Center, Mainz, Germany IInd Department of Medicine, Johannes-Gutenberg University Medical Center, Mainz, Germany c Institute of Immunology, Johannes-Gutenberg University Medical Center, Mainz, Germany d Department of Hematology and Oncology, University Medical Center Mannheim, Mannheim, Germany b
a r t i c l e
i n f o
Article history: Received 3 March 2016 Revised 13 June 2016 Accepted 6 July 2016 Available online xxxx Keywords: Myelodysplastic syndrome Neutrophils Monocytes Phenotype Effector functions
a b s t r a c t Myelodysplastic syndrome (MDS) is a clonal stem cell disorder frequently associated with inefficient granulopoiesis showing dysplastic polymorphonuclear neutrophils (PMNs). To assess PMN functionality in MDS in a clinical routine setting, 30 MDS patients and ten healthy volunteers were analyzed for PMN and monocyte phenotype and function (degranulation, CD62L shedding, oxidative burst and phagocytosis) upon stimulation with lipopolysaccharide by multi-color flow cytometry (MCFC). Our data show a heterogeneous pattern for CD66, CD16 and CD64 expression on PMNs of MDS patients. CD62L shedding rate and CD66 degranulation were reduced. Interestingly, we detected correlations between the WHO adapted prognostic scoring system (WPSS) and CD16 expression on PMNs as well as the international prognostic scoring system (IPSS) and CD11b degranulation by MCFC, suggesting clinical relevance of MCFC based function testing. In conclusion, MCFC of myelodysplastic immunophenotypes and PMN functionality are applicable in clinical settings, but further prospective studies are needed to assess the practical clinical value of such analyses. Ó 2016 Elsevier Inc. All rights reserved.
1. Introduction Myelodysplastic syndrome (MDS) is a clonal hematopoeietic stem cell disorder characterized by a maturation defect in all hematopoietic cells in the myeloid lineage, most frequently affecting erythroid cell maturation resulting in anemia. MDS is a common myeloid neoplasm of the bone marrow with an increasing prevalence in the aging population, which is also believed to further increase over the next decades [1]. In addition, morphological as well as functional abnormalities of in polymorphonuclear neutrophils (PMNs) and monocytes are frequently found in MDS patients [2] and are believed to be associated with an increased susceptibility to bacterial infections which are a major cause of death in MDS patients [3]. One of the leading causes for death is neutropenia (refractory cytopenia, but also caused by hypomethy-
⇑ Corresponding author at: Department of Internal Medicine III, Hematology, Oncology and Pneumology, University Medical Center of the Johannes GutenbergUniversity, Langenbeckstr. 1, D-55131 Mainz, Germany. E-mail address: [email protected] (M.P. Radsak).
lating agents, Lenalidomid or chemotherapy). Beyond this, iron overload, stem cell transplantations, individual comorbidities, high age as well as immunosuppressive conditions such as B- or T-cell defects may contribute to the susceptibility to infections. Moreover, while multi-color flow cytometry (MCFC) analyses are used on a routine basis for the diagnosis and monitoring in many hematological malignancies, such MCFC assessments reveal a great variety of immunophenotypic aberrancies on the surface of myelodysplastic cells reflecting the heterogeneity of this disease. Anomalies include increased, decreased or cell-lineage unspecific (lineage infidelity) protein expression patterns on PMNs and monocytes [4]. Functional deficits of these cells have been described previously with heterogeneous results, i.e. affecting reactive oxygen species (ROS) production and phagocytic activity. Prodan and co-workers show that phagocytosis is the more conserved effector function for combating bacterial invasion in MDS since low risk MDS patients show no apparent functional defects [5]. Nevertheless, the oxidative burst activity is reduced in both low and high risk MDS patients. Interestingly, a correlation of cell counts and reduced functional activity has not been detected. Thus,
http://dx.doi.org/10.1016/j.cellimm.2016.07.005 0008-8749/Ó 2016 Elsevier Inc. All rights reserved.
Please cite this article in press as: C.S. Schmidt et al., Phenotypic and functional characterization of neutrophils and monocytes from patients with myelodysplastic syndrome by flow cytometry, Cell. Immunol. (2016), http://dx.doi.org/10.1016/j.cellimm.2016.07.005
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the scientists suggest an independent relation between cell count and leukocyte dysfunction which has to be further characterized. The introduction of MCFC as diagnostic co-criterion in MDS in 2007 underpins its accessory role in diagnosis and prognostication [4,6]. To refine and improve MDS classification and prognostic criteria, there have been many efforts towards the standardization of MCFC methods to achieve a reproducible clinical application [7– 13]. However, a full integration of MCFC analyses into the diagnostic strategies, particularly morphological and cytogenetic examination, has not yet been thoroughly accomplished. In our present study, we extend the current immunophenotypic characterization of PMNs and monocytes to also integrate parameters of PMN functionality of MDS patients in a clinical routine setting. Our results confirm an abnormal expression profile of myelodysplastic PMNs and monocytes. A heterogeneous pattern regarding the utilized surface markers allows the assumption of different phenotypic subgroups in MDS. Importantly, we demonstrate the feasibility of MCFC based PMN functional assays in a clinical routine setting. The correlations of our MCFC data with established clinical parameters for the risk stratification of MDS suggest that MCFC based phenotyping and function testing may be useful additional parameters to understand the clinical implications of possible functional defects in MDS. Hence, the present study opens the new opportunity to integrate functional qualities of leukocytes in MDS by MCFC for further risk stratification to potentially identify MDS patients at high risk for opportunistic infections or for disease progression. 2. Patients, materials and methods 2.1. Patients The study was carried out in accordance to the principles outlined in the Declaration of Helsinki and was approved by the local ethics committee (Landesärztekammer Rheinland-Pfalz, Mainz, Germany). Overall 30 MDS patients treated in the outpatients’ departments of the University Medical Centers Mainz and Mannheim were enrolled in this study. Clinical characteristics are displayed in Table 1. Ten healthy volunteers recruited form a cardiology aircraft-noise study of the IInd Department of Medicine of the University Medical Center in Mainz with no history of MDS or any hematological disease functioned as controls. After written informed consent a heparinized blood sample from a peripheral vein was drawn. Within eight hours of collection the blood specimen was prepared for flow cytometric analysis. 100 microliter (ll) whole blood was transferred into 5 milliliter (ml) FACS tubes (BD Falcon, San Diego, CA, USA). Subsequently, the staining procedures concerning the cells’ phenotype as well as functioning followed. 2.2. Reagents S. typhimurium lipopolysaccharide (LPS) [1 lg/ml] and dichlorfluorescein (DCF) were obtained from Sigma-Aldrich, Taufkirchen, Germany. PE-labeled polystyrene microspheres (56.9 106 particles/ml, diameter 1 lm, Fluoresbrite Plain Microspheres PCRed) were from Polysciences, Warrington, PA. MAbs used for flow cytometry are summarized in Table 2. 2.3. Flow cytometry 2.3.1. Surface markers For the purpose of identification of surface expression patterns, fluorochrome labeled monoclonal antibodies were added to the tubes and incubated for fifteen minutes at room temperature in
Table 1 Demographic data. MDS patients
Healthy controls
n Median age (range)
30 76.5 years (55–86)
Sex
56.7% male (n = 17) 43.3% female (n = 13)
10 64 years (47– 75) 90% male (n = 9) 10% female (n = 1)
MDS subtype (WHO 2008) RCUD (+/-RS), RA, RARS, Del-5q RCMD RAEB I RAEB II CMML/MDS-MPN/hypoplastic MDS
16 5 3 3 3
IPSS score Low Intermediate I Intermediate II High Unknown
12 10 4 1 3
IPSS-R score Very low Low Intermediate High Very high Unknown
1 14 7 4 1 3
WPSS score Very low Low Intermediate High Very high Unknown
8 10 2 2 2 6
Cell counts (median (range)) Hemoglobin g/dl WBC/nl ANC/nl Platelets/nl
9.2 (7.5–13.6) 4.4 (0.9–65.8) 2.5 (0.3–52) 212 (11–733)
15.0 (12.1–15.6) 6.72 (4.56–12.0) 4.19 (2.38–6.54) 241 (124–357)
n: number; RCUD: refractory cytopenia with unilineage dysplasia; RS: ringsideroblasts; RA: refractory anemia; RARS: refractory anemia with ring sideroblasts; Del5q: MDS with deletion 5q; RCMD: refractory cytopenia with multilineage dysplasia; CMML: chronic myelomonocytic leukemia; RAEB: refractory anemia with excess of blasts; MPN: myeloproliferative neoplasia: WBC: white blood cell count; ANC: absolute neutrophil count; IPSS: international prognostic scoring system; IPSS-R: revised – international prognostic scoring system; WPSS: WHO adapted prognostic scoring system; g: gram; dl: deciliter; nl: nanoliter.
the dark. After erythrocyte lysis with one milliliter ammonium chloride buffer, the cells were washed with phosphate buffered saline (PBS) buffer by centrifugation (1400 rotations per minute). Finally, the cells were resuspended in FACS buffer (1% bovine serum albumin, 0.02% natrium acetate and 1 mM ethylenediaminetetraacetic acid in PBS) and analyzed by flow cytometric examination.
2.3.2. Effector functions For the assessment of PMN effector functions (degranulation, CD62L shedding, oxidative burst and phagocytosis) four tubes containing 100 ll whole blood were incubated for 30 min at 37 °C in the presence or absence of S. typhimurium LPS after the addition of dichlorfluorescein (for the detection of ROS) and PE-labeled polystyrene microspheres (for the detection of phagocytic activity). Then, fluorochrome labeled antibodies (anti-CD11b, anti-CD62L, anti-CD66abce) were added and incubated for fifteen minutes on crushed ice. After two periods of erythrocyte lysis, the cells were washed twice with FACS-buffer, and subsequently analyzed by MCFC.
Please cite this article in press as: C.S. Schmidt et al., Phenotypic and functional characterization of neutrophils and monocytes from patients with myelodysplastic syndrome by flow cytometry, Cell. Immunol. (2016), http://dx.doi.org/10.1016/j.cellimm.2016.07.005
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C.S. Schmidt et al. / Cellular Immunology xxx (2016) xxx–xxx Table 2 Antibodies used for surface staining and analysis of effector function.
Table 3 Patient care – therapeutic methods.
Application
Antigen
Clone
Fluorochrome
Origin
Therapeutic strategy
Absolute (relative) patient count
Flow cytometry – surface stainings
TREM
6B1.1G12
FITC
CD13
Immu103.44
ECD
CD33
D3HL60.251
PE
CD10
ALB1
PE
‘‘Watch and wait” Transfusions Erythropoesis stimulating agents Hydroxyurea Lenalidomid Azacitidin Panobinostat Immunoglobulins
5 8 8 1 4 2 1 1
CD14
RMO52
ECD
HLA-DR
Immu357
PE-Cy7
CD16
3G8
APC
CD117
104D2D1
APC
CD64
10.1
APC-A700
CD34
581
APC-A700
CD11b CD45
ICRF44 J.33
Pacific Blue Krome Orange
Own production Beckman coulter Beckman coulter Beckman coulter Beckman coulter Beckman coulter Beckman coulter Beckman coulter BD biosciences Beckman coulter BioLegend Beckman coulter
CD11b CD62L CD66abce
ICRF44 DREG-56 Tet2
Pacific Blue APC-Cy7 APC
Flow cytometry – effector function
BioLegend BioLegend Miltenyi biotec
CD: cluster of differentiation.
2.3.3. Flow cytometric analysis All measurements were performed on a Navios flow cytometer (Beckman-Coulter, Krefeld, Germany) and analyzed by FlowJo software Version 9.6.4. (Copyright: Trustees of Leland Stanford Jr. University, 1996–97: Tree Star, Inc. 1997–2009). For analysis, PMN or monocytes were identified by gating on the respective cell populations on CD45/side scatter (SSC) plot. Debris and residual erythrocytes were excluded. In addition, leukocytes were separated as CD66abce positive PMNs and CD14 positive monocytes. Concerning all surface markers, mean fluorescence intensities (MFI) were applied for evaluation of the patients’ data. For degranulation, CD62L shedding, oxidative burst and phagocytosis an activation index was calculated by dividing the MFI of the indicated marker of the LPS stimulated sample by the respective unstimulated control. 2.4. Statistical analysis Regarding data management and statistical analysis, Prism V5.0a statistical software package (Graphpad, San Diego, CA,
(16.7%) (26.7%) (26.7%) (3.3%) (13.3%) (6.7%) (3.3%) (3.3%)
USA) was utilized. Individual patient measurements are presented as mean ± standard error of mean. For all analyses, a value of p < 0.05 was considered to be significant. Two group comparisons were performed by Mann-Whitney test. For correlations between flow cytometric parameters or with established clinical parameters of MDS patients and healthy volunteers, data sets were analyzed by Spearman’s correlation test. 3. Results 3.1. Demographic data For the analysis, the present study included only patients with the diagnosis of MDS. The main patients’ characteristics are reported in Table 1. Since we wanted to observe steady state conditions and exclude reactive changes in PMN or monocyte phenotypes or functions, patients who had to be hospitalized due to (infectious) complications of any cause were not included. Table 3 depicts the various treatments applied to the selected group of MDS patients at the time of analysis. 3.2. Heterogeneous expression of surface antigens on PMNs and monocytes in MDS Since we were interested to study not only phenotypic changes in surface marker expression, but also functional alterations in PMNs of MDS patients, we analyzed peripheral blood cells and not bone marrow samples, as this might more appropriately reflect functionally relevant changes that may have an impact on innate inflammatory responses. In this context, we observed a statistical significant increase of the CD66abce, CD16 and CD64 on PMNs (Fig. 1), compatible with an activated phenotype. In contrast other markers CD11b, CD62L, and TREM-1 displayed a more heterogenous expression pattern compared the healthy volunteer controls, but were overall not significantly different. Also in monocytes of MDS patients, we observed this heterogeneous distribution of all analyzed markers (Fig. 2), again not statistically different from the control group.
Fig. 1. Surface markers of myelodysplastic PMNs. MCFC results of antigen profiles on PMNs in MDS patients compared to a healthy reference group are represented. Among the patient collective heterogeneous expression levels of CD66abce, CD11b, CD62L, CD16, CD64 and TREM-1 become evident. Mean fluorescence intensities (MFIs) of the indicated markers from individual patient/volunteer samples are depicted. These data are presented as mean with standard error of the mean (SEM). ⁄indicates a significant difference (p < 0.05) by Mann-Whitney test.
Please cite this article in press as: C.S. Schmidt et al., Phenotypic and functional characterization of neutrophils and monocytes from patients with myelodysplastic syndrome by flow cytometry, Cell. Immunol. (2016), http://dx.doi.org/10.1016/j.cellimm.2016.07.005
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C.S. Schmidt et al. / Cellular Immunology xxx (2016) xxx–xxx
Fig. 2. Surface markers of myelodysplastic monocytes. MCFC results of protein patterns on monocytes in MDS patients in contrast to a healthy control collective are depicted. Within the patient group heterogeneous expression profiles of HLA-DR, CD11b and TREM-1 were detected. Mean fluorescence intensities (MFIs) of the indicated markers from individual patient/volunteer samples are shown. These data are presented as mean with standard error of the mean (SEM).
Taken together, compared to the healthy individuals, who appear to be a more homogenous group, the large variability of the measured parameters in MDS patients indicate relevant alterations in the phenotype of PMNs and monocytes in MDS. But this makes it also difficult to identify a clear distribution of surface marker patterns, given our small patient collective. 3.3. Heterogeneously altered PMN effector functions in MDS Beyond the phenotypic changes in PMNs and monocytes from MDS patients, we were also interested to document functional changes in the activation of PMN effector mechanisms. Therefore,
we used the TLR4 ligand LPS as a prototypic TLR agonist, important in the innate immune response to gram negative bacteria. We assessed the shedding rate of L-selectin (CD62L), degranulation of CD66abce and CD11b (Mac-1) as important adhesion molecules as well as the oxidative burst and phagocytic activity upon stimulation. As depicted in Fig. 3, we detected a more heterogeneous activation pattern in PMNs from MDS patients compared to the healthy controls. Interestingly, the CD62L shedding rate was somewhat reduced, although this was not statistically significant (Fig. 3A). For degranulation of CD11b and CD66abce (Fig. 3B and C, respectively), as well as the oxidative burst and phagocytosis (Fig. 3D and E, respectively) the activation indices
Fig. 3. PMN effector functions of MDS patients. MCFC reveals a reduced CD62L shedding rate of the patients’ PMNs (A). Degranulation processes of CD11b (B) and CD66abce (C) are furthermore heterogeneously impaired, as is the oxidative burst of the patients’ cells (D). Due to outliers the mean of the MDS phagocytic activation index appears to be increased respectively to the homogeneous reference group, however predominantly this function is defective in patients, too (E). Concerning data presentation, the displayed indices were calculated by dividing the MFI of the indicated marker of the LPS stimulated sample by the respective unstimulated control.
Please cite this article in press as: C.S. Schmidt et al., Phenotypic and functional characterization of neutrophils and monocytes from patients with myelodysplastic syndrome by flow cytometry, Cell. Immunol. (2016), http://dx.doi.org/10.1016/j.cellimm.2016.07.005
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C.S. Schmidt et al. / Cellular Immunology xxx (2016) xxx–xxx
were more heterogeneous in PMN from the MDS cohort compared to the healthy controls. However, significant differences concerning the effector functions between the MDS and control collective were not detected. Remarkably, there were patients with strongly deviant measurements compared to the majority of the group. In particular, CD11b degranulation, oxidative burst and phagocytic activity were affected (Fig. 3B,D and E). Due to those ‘‘outliers” the mean within the MDS patient collective was even increased compared to the reference group. This indicates that in addition to a reduced capability for the activation of PMN effector functions, there is also a subset of MDS patients where PMNs are hyper-reactive in response to LPS. This suggests that abnormal PMN functionality in MDS patients includes hypo- as well as hyper-responsiveness.
3.4. Correlation analysis of phenotype, function and clinical parameters in MDS and healthy donors To assess whether there are any relations of our MCFC based phenotypic and functional results with the clinical outcome of MDS, we analyzed the correlations of individual cell surface markers on PMN and monocytes as well as the functional tests with established clinical parameters for the risk stratification of MDS as detailed in Table 4. Importantly, we detected significantly
altered expression patterns of CD66, CD64, CD11b, CD16 and TREM-1 only in the cohort of MDS patients, but not in healthy donors (Table 4, printed in bold). As shown in Fig. 4, there was a strong correlation between CD16 and CD64 expression on PMNs of MDS patients, while this was not the case for CD66abce. Moreover, we found not only significant correlations between the individual surface markers on PMN and monocytes (e.g. CD11b and TREM-1 expression on PMN and monocytes), but we also detected significant correlations among functional parameters as well as functional and phenotypic markers in our patient cohort, but not in the healthy donor cohort, indicating on the one hand the consistency of our MCFC analyses and on the other hand a MDS specific relationship between surface expression of CD66 and CD11b with degranulation and CD62L shedding. Even more interestingly, we also detected correlations of our MCFC based assays with established clinical parameters that are relevant with respect to the risk stratification of MDS. Notably, PMN CD16 expression correlates with the WPSS and the degranulation of CD11b with the IPSS. Moreover, we found correlations between cell counts (for example the number of white blood cells or the quantity of PMN granulocytes) and various surface antigens on PMNs, but also degranulation and oxidative burst, further underlining the consistency of established parameters of MDS (blood cell counts) and MCFC derived markers. Finally, an interrelation between therapeutic strategy (symptomatic approach
Table 4 Correlations of flow cytometry based surface and functional markers. MDS patients
Healthy donors
r (CI;95%)
p
r (CI;95%)
p
Correlations of functional markers CD11b degran. – CD66 degran. CD66 degran. – CD62L shedding CD11b degran. – CD62L shedding
0.659 (0.366 to 0.821) 0.57 ( 0.779 to 0.260) 0.457 ( 0.708 to 0.107)
<0.001 0.001 0.010
0.417 ( 0.078 to 0.746) 0.533 ( 0.806 to 0.0735) 0.317 ( 0.691 to 0.191)
n.s. 0.023 n.s.
Correlations of surface markers CD16 expr. (PMN) – CD64 expr. (PMN) CD16 expr. (PMN) – CD66 expr. (PMN) CD16 expr. (PMN) – CD11b expr. (PMN) CD64 expr. (PMN) – CD66 expr. (PMN) CD64 expr. (PMN) – CD11b expr. (PMN) CD66 expr. (PMN) – CD11b expr. (PMN) TREM-1 expr. (PMN) – TREM-1 expr. (Mono) TREM-1 expr. (PMN) – CD11b expr. (Mono) CD11b expr. (PMN) – CD11b expr. (Mono) CD11b expr. (PMN) – TREM-1 expr. (Mono) CD16 expr. (PMN) – TREM-1 expr. (Mono)
0.993 (0.984 to 0.997) 0.362 ( 0.009 to 0.646) 0.539 (0.212 to 0.758) 0.367 ( 0.004 to 0.649) 0.564 (0.245 to 0.773) 0.686 (0.424 to 0.842) 0.524 (0.117 to 0.761) 0.464 ( 0.741 to 0.050) 0.546 (0.170 to 0.783) 0.402 ( 0.682 to 0.048) 0.413 ( 0.681 to 0.049)
<0.001 0.040 0.001 0.038 0.001 <0.001 0.007 0.026 0.006 0.046 0.040
0.976 0.267 0.430 0.417 0.479 0.100 0.079 0.176 0.745 0.139 0.006
<0.001 n.s. n.s. n.s. 0.032 n.s. n.s. n.s. 0.013 n.s. n.s.
Correlations of functional and surface markers CD66 expr. (PMN) – CD66 degran. CD66 expr. (PMN) – CD62L shedding CD11b expr. (PMN) – CD66 degran.
0.601 ( 0.796 to 0.301) 0.452 (0.0905 to 0.700) 0.444 ( 0.723 to 0.136)
<0.001 0.011 0.012
0.450 ( 0.764 to 0.0364) 0.133 ( 0.369 to 0.575) 0.133 ( 0.369 to 0.575)
n.s. n.s. n.s.
Correlations with clinical parameters WPSS – CD16 expr. (PMN) WPSS – PLTs IPSS – CD11b degran. Therapy – CD62L shedding WBC – CD11b expr. (PMN) WBC – oxidative burst WBC – CD66 degran. WBC – CD11b degran. ANC – CD16 expr. (PMN) ANC – CD64 expr. (PMN) ANC – CD11b expr. (PMN) ANC – CD11b degran.
0.421 ( 0.711 to 0.009) 0.422 ( 0.711 to 0.009) 0.478 (0.108 to 0.732) 0.496 (0.155 to 0.732) 0.396 (0.0229 to 0.672) 0.433 (0.0676 to 0.696) 0.463 ( 0.715 to 0.104) 0.492 ( 0.733 to 0.142) 0.390 (0.0230 to 0.664) 0.395 (0.0295 to 0.668) 0.472 (0.124 to 0.717) 0.385 ( 0.661 to 0.017)
0.040 0.04 0.012 0.005 0.034 0.019 0.012 0.007 0.033 0.031 0.008 0.036
– – – –
– – – – n.s. n.s. n.s. n.s. 0.025 0.014 n.s. n.s.
(0.936 0.991) ( 0.243 to 0.661) ( 0.029 to 0.740) ( 0.077 to 0.746) (0.0312 to 0.766) ( 0.398 to 0.552) ( 0.389 to 0.5142) ( 0.302 to 0.583) (0.441 to 0.896) ( 0.335 to 0.558) ( 0.449 to 0.459)
0.233 ( 0.640 to 0.276) 0.267 ( 0.661 to 0.243) 0.143 ( 0.394 to 0.607) 0.300 ( 0.209 to 0.681) 0.667 (0.2762 to 0.8682) 0.567 (0.121 to 0.822) 0.167 ( 0.339 to 0.598) 0.033 ( 0.453 to 0.504)
r: correlation coefficient (Spearman’s test); CI: 95 % confidence interval; CD: cluster of differentiation; PMN: polymorphnuclear granulocyte; Mono: monocyte; PLTs: platelets; WBC: white blood cell count; ANC: absolute neutrophil count; WPSS: WHO adapted prognostic scoring system; n.s.: non-significant; degran.: degranulation; CD66: CD66abce. Correlation analyses among the entire set of surface markers, functional markers, IPSS, IPSS-R, WPSS, diagnostic subgroups, other clinical parameters (WBC, ANC, Hb and PLTs) and therapy revealed varied relevant correspondences between the contemplated variables. Emphasized (in bold print) are the antigen expressions that correlate significantly only within the MDS collective compared to the healthy donor group.
Please cite this article in press as: C.S. Schmidt et al., Phenotypic and functional characterization of neutrophils and monocytes from patients with myelodysplastic syndrome by flow cytometry, Cell. Immunol. (2016), http://dx.doi.org/10.1016/j.cellimm.2016.07.005
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C.S. Schmidt et al. / Cellular Immunology xxx (2016) xxx–xxx
Fig. 4. Coexpression of CD16, CD64 and CD66abce on PMNs of MDS patients. The correlation (A) of surface expression of CD16 (x axis) and CD64/CD66abce (y axis) of MDS patients and (B) the correlation of surface expression of CD64 (x axis) and CD16/CD66abce (y axis) shows that CD16 and CD64 expression are strongly associated in contrast to CD66abce.
against Lenalidomid/Azacitidin/Panobinostat) and CD62L shedding was identified. No correlations of the MCFC data were detected with respects to diagnostic subgroups, IPSS-R and hemoglobin, most likely due to our limited data set. 4. Discussion Many other groups have analyzed bone marrow samples to characterize immunophenotypes in MDS [4]. In contrast, we have focused our attention on analyses from the peripheral blood, since we were not only interested in marker profiles, but also functional changes. While altered phenotypes may be more accurately described by the examination of bone marrow samples, the direct analyses of peripheral blood has the advantage that samples can be processed in a more standardized fashion and without having to consider artifacts due to contamination with peripheral blood. In addition, comparisons with healthy control samples can easily be made, which is of special importance with respect to the functional assays. Moreover, it is rational to assume that the analysis of cells in the periphery might more accurately reflect relevant defects in PMN function (over bone marrow). Concerning surface markers in MDS, increased or reduced expression patterns as well as aberrant antigen expression patterns have been described (lineage infidelity) [4,14]. In line with others, we detected in our cohort of 30 MDS patients a more heterogeneous expression pattern of all markers analyzed compared to the control group reflecting the heterogeneity of the disease. Hence the existence of distinct phenotypic subgroups of PMNs and monocytes in MDS can be assumed. As other investigators before [14–16], we are unable to attribute clear marker patterns to specific MDS subgroups. This is mostly due to the small sample size of the MDS cohort (n = 30). For CD66abce, CD16 and CD64 on PMNs, we showed a significantly increased expression compared to the healthy individuals (Fig. 1). Interestingly, the surface coexpression of CD16 and CD64 on PMNs of MDS patients are strongly correlated suggesting similar mechanisms of (possibly transcriptional) regulation as both a Fc-c receptors. In contrast, this is not the case for CD66abce expression (Fig. 4), suggesting a distinct mechanism for the regulation of CD66 surface expression. An altered CD66 expression on PMNs of MDS patients has already been found previously by Cherian et al. and has been integrated into a peripheral blood MDS score [17]. Together, this marker combination is compatible with an activated PMN phenotype that is MDS specific (at least compared to our healthy donor cohort), since the majority of correlations of altered marker expression and functional tests are only detected in the MDS cohort, but not the healthy individuals (Table 4). This allows the assumption that PMNs in MDS may be continuously activated in vivo, as previously proposed by Ohsaka et al. concerning the CD62L shedding rate [15]. This is further
corroborated by the fact that CD66abce, CD11b and CD16 are not only constitutively present in the outer plasma membrane, but also in storage compartments where they are ready to be recruited to the surface upon activation [14]. While others have demonstrated aberrancies of the oxidative burst, degranulation, CD62L shedding and phagocytic activity in PMNs from patients with MDS [2,18–24], our present work shows a strong heterogeneity in the activation of these effector functions in MDS patients compared to healthy volunteers Fig. 3), but apparently no significant differences. Interestingly, Prodan et al. demonstrate a less preserved oxidative burst that reacts more sensitive to pathologic stimuli and is already reduced in patients with low risk MDS (in contrast to the phagocytic activity). Thus, they conclude impairment of PMN functions in MDS occurs autonomously [5]. This is apparently different in our cohort. Firstly and in agreement with others [17] we detected significant positive correlations for expression of CD66abce and CD11b as surface and activation markers (Table 4) suggesting that there is indeed a connection between steady state expression and PMN functions, at least in terms of degranulation of CD66abce and CD11b as well as shedding of CD62L. Consistent with Prodan et al. we detected no correlations with the oxidative burst or phagocytosis. Deregulation of innate immune and inflammatory signals in MDS may contribute to the observed MDS phenotypes. In this context, Peng et al. describe an abnormal overexpression of the p38 mitogen-activated protein kinase (MAPK) signaling pathway [25]. Triggered by the extrinsic microenvironment including high concentrations of cytokines, p38MAPK overactivation in MDS marrow leads to increased apoptosis of normal progenitors and inhibition of p38MAPK results in a reduction of TNF-a and IL-1b generation [26]. Moreover, Gañán-Gómez et al. report on the overexpression and constitutive activation of Fas, TNF-R1/2, TLRs, type II IFN-R, and their associated signal transducers [27]. Along these lines, a recent report also describes increased expression levels of the TLR adapter protein MyD88 in CD34+ cells of MDS patients [28]. Furthermore, Varney and coworkers demonstrate a direct mechanism and relevance of MDS mutations for hyperactived TLR signaling in MDS [29]. They show that the loss of the Tifab gene in MDS with Del(5q) results in a derepression of NFjB since a complex of TIFAB and TRAF6 is necessary to inhibit NFjB activation. Although they did not examine PMN functionalities, this all may contribute to the heterogeneity of altered PMN responsiveness to LPS in MDS patients. Towards the clinical significance of our findings, we found correlations of phenotypic and functional features of PMNs and monocytes with clinical parameters in MDS patients. Though expression profiles of the studied antigens are extremely complex and diverse, there are markers on both cell types (i.e. TREM-1 and CD11b) that correlate with each other, suggesting a similar regulation of their expression in PMNs and monocytes. In agreement with established flow cytometry scores [30–32], we detected a negative correlation of CD16 expression on PMN with the WPSS, and CD11b degranulation with the IPSS, but not the IPSS-R. Presumably, the reason for this is related to our small data set. Finally of note, we detected a correlation of treatment with CD62L expression on PMN. However, concerning the clinical meaning of this observation we are currently unable to draw conclusions due to the design of our study. Further prospective studies are needed to address the question whether CD62L might be a suitable marker to monitor MDS treatments beyond supportive care. Concerning correlations of MDS related mutations with phenotypic and functional alterations in PMNs of MDS patients, we had only information of standard cytogenetic alterations available integrated into the MDS risk scores. This is certainly a limitation of our study. The evolving more sophisticated molecular diagnostics of MDS covering the most common mutations SF3B1,
Please cite this article in press as: C.S. Schmidt et al., Phenotypic and functional characterization of neutrophils and monocytes from patients with myelodysplastic syndrome by flow cytometry, Cell. Immunol. (2016), http://dx.doi.org/10.1016/j.cellimm.2016.07.005
C.S. Schmidt et al. / Cellular Immunology xxx (2016) xxx–xxx
TET2, SRSF2, ASXL1, DNMT3A, RUNX1, U2AF1, TP53, and EZH2 as proposed by the WHO 2016 classification [33] might be useful to establish a better correlation of phenotypic and functional aberrancies in MDS.
[6]
5. Conclusions
[7]
Our results confirm the heterogeneity of aberrant marker profiles in PMN and monocytes of MDS patients and demonstrate the feasibility of PMN function testing in a clinical routine setting. In addition, we propose that CD16 and CD11b expression levels may be useful surrogate markers for MDS risk stratification. Other markers such as CD66abce or CD64 need further evaluation to determine whether they have additional value for MDS risk stratification by flow cytometry. Based on our data, further prospective studies are still required to validate these parameters and eventually distinguish subgroups of patients with greater risk of infections and MDS progression. Additional information The authors declare that there are no completing financial interests concerning this manuscript.
[8]
[9]
[10]
[11]
[12]
Author contributions C. Schmidt performed the MCFC stainings and analysis as well as the analyses of the clinical patient data. P. Stein designed MCFC analyses for PMN phenotype and functions. J. Dopheide contributed the monocyte panel for MCFC. F. Schmidt contributed the healthy volunteer donors. M. Theobald and H. Schild contributed to the design of the research and writing of the manuscript. E. Lauinger-Lörsch and F. Nolte contributed some of the MDS patient blood samples and patient data. M. Radsak designed research and wrote the manuscript. Acknowledgments The authors would like to thank Annekatrin Klaric, Andrea Drescher and Ursel Petrat for excellent technical assistance. This work was supported by grants from the CTH/BMBF (BMBF01EO1003; to M.P.R.), ‘‘Naturwissenschaftlich-Medizinis ches Forschungszentrum (NMFZ), ‘‘Forschungszentrum Immunologie (FZI)” of the University Medical Center Mainz (to M.P.R.). This work includes results of Cornelia Schmidt’s medical doctoral thesis ‘‘Phänotypische und funktionelle Charakterisierung von neutrophilen Granulozyten und Monozyten bei Myelodysplastischem Syndrom mittels Durchflusszytometrie”.
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
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