Background
Leukemia is the most common malignancy and the second most frequent general cause of childhood death. It is classified as acute lymphoid of the B- or T-lineage being the most prevalent type in children, and as acute myeloid. Although tremendous improvements have been made in the treatment of leukemia in the past few years, there is still a large proportion of patients who do not benefit from the available therapy. The overall survival of pediatric acute lymhoblastic leukemia (ALL) and acute myeloid leukemia (AML) patients treated with the current chemotherapy regimens is above 80 and 70%, respectively [
1,
2].
The standardized treatment protocols consist of the same repertoire of cytostatic drugs which have been used for the past decades. They differ in the drug dosage, the time of administration and in the drug combinations. State-of-the-art genomic techniques have enabled the identification of new genetic alterations, which could be targeted by novel compounds; however, genetic-based approaches have not fully revealed the direct cause of inter-individual differences in the sensitivity to cytostatic drugs [
3‐
6]. Therefore, understanding the mechanism of action and being able to predict the impaired effect of commonly administered drugs is crucial for improving patient outcomes.
We turned our attention to cellular metabolism which represents a limiting process in cell proliferation and survival. Recent data have indicated that a number of cytostatic drugs trigger metabolic reprogramming, which impairs their effects and, in the long run, could cause resistance. Our group previously described that leukemia cells treated with L-asparaginase (ASNase) reprogrammed their metabolism and increased fatty acid oxidation (FAO) together with autophagy in order to compensate for asparagine and glutamine depletion [
7]. Pharmacological inhibition of FAO increased the sensitivity to ASNase in leukemia cells, which supported its pro-survival effect and its potential role in the mechanism of resistance. A similar phenomenon was described in chronic lymphoblastic leukemia, where the administration of dexamethasone led to the increase in FAO [
8]. Since treatment can dramatically influence the metabolic setup, we assume that reciprocally, the metabolic predisposition of cancer cells could also interfere with their response to treatment.
Our aim was to investigate how the basal metabolic profile of leukemic cells interferes with the effectiveness of the compounds currently used for treatment. We focused on cytostatic drugs used in the treatment of childhood leukemia with an emphasis on ASNase. ASNase represents a crucial drug used in the treatment of ALL; it is also incorporated in the front-line treatment of adult leukemia [
9,
10]. Unfortunately, the sensitivity to ASNase differs significantly among ALL patients as described by Ramarkers et al. [
11]. A specific phenotypic subgroup of ALL derived from a T lymphoid lineage (T-ALL) was also shown to be more resistant to ASNase treatment [
12,
13]. In the current study, we have determined the metabolic phenotype of different types of leukemia in order to better understand their differences, metabolic demands, and their connection with the selective sensitivity of ASNase.
Methods
Cell culture
Human B-cell precursor leukemia cell lines (BCP-ALL; TOM-1, HB11;19, RS4;11, UOC-B6, REH, SUP-B15, NALM6), T-cell leukemia cell lines (HPB-ALL, CCRF-CEM, JURKAT, MOLT-4), AML cell lines (MV4;11, KASUMI-1, NB-4, THP-1, MOLM-13) and cell lines derived from the blast crisis of chronic myeloid leukemia (CML), which manifest as AML (K-562, LAMA-84) and as ALL (BV-173), were used (Table S
1). The HB11;19 cell line was kindly provided by Dr. Anthony Ford from the Institute of Cancer Research (London, UK), and the UOC-B6 cell line was provided by Dr. Ondrej Krejci (Massachusetts General Hospital, Boston) [
14]. The rest of the cell lines were purchased from the German Collection of Microorganisms and Cell Cultures (DSMZ, Braunschweig, Germany). The cell lines were negative for mycoplasma contamination and cultivated in RPMI-1640 medium with GlutaMAX™ supplemented with 10% fetal calf serum, penicillin (100 U/mL) and streptomycin (100 μg/mL) under controlled conditions (37 °C, 5% CO
2). The cultured cells were split every 2 to 3 days and maintained in exponential growth phase.
Patient samples
Bone marrow or peripheral blood samples from untreated children initially diagnosed with BCP-ALL, T-ALL or AML were collected from the Czech Pediatric Hematology Centers. The inclusion criteria were the percentage of blasts higher than 80% and high cellularity. Within 24 h after aspiration, without freezing, the mononuclear cells were isolated by density gradient centrifugation using Ficoll-Paque PLUS (GE Healthcare, UK). All samples were obtained with the informed consent of the children’s parents or guardians. The study no. 201528848A was approved by the Ethical Committee of the University Hospital Motol, Prague, Czech Republic. Healthy controls were isolated from buffy coats (mixture of healthy individuals) using Ficoll-Paque PLUS (GE Healthcare, UK). To enrich samples for B-lymphocytes, buffy coat was pre-treated with RosetteSep™ Human B Cell Enrichment Cocktail (StemCell Technologies, USA) prior to Ficoll-Paque PLUS.
The isolated blasts were maintained in RPMI-1640 medium with GlutaMAX™ supplemented with 10% fetal calf serum, penicillin (100 U/mL) and streptomycin (100 μg/mL). For the MTS assay, insulin-transferrin-sodium selenite supplement was added to the culture media (Sigma-Aldrich, St Louis, MO, USA).
Mitochondrial FAO measurement
The cells were incubated for 4 h in culture medium containing 100 μM palmitic acid, 1 mM carnitine and 1.7 μCi [9,10(n)-
3H] palmitic acid (GE Healthcare, UK) in the presence or absence of etomoxir (100 μM, Sigma-Aldrich, MO, USA). Medium was collected to analyze the amount of released
3H
2O that was formed during the cellular oxidation of [
3H]-palmitate [
15,
16]. The procedure was performed as described previously [
7]. The measurement was performed in 3 independent experiments.
TMRE staining
The cells were incubated for 30 min in culture medium with or without 1 μM tetramethylrhodamine ethyl ester (TMRE; ThermoFisher Scientific Inc., MA, USA) and were then washed with PBS and analyzed on a flow cytometer according to the manufacturer’s instructions. The level of TMRE staining was expressed as the mean of the TMRE signal in the live cells. The measurement was performed in 3 independent experiments.
Cell survival and proliferation
To evaluate the cytotoxicity of ASNase, vincristine (VCR), and daunorubicin (DNR), MTS (dimethylthiazol carboxymethoxyphenyl sulfophenyl tetrazolium) assays were performed using a CellTiter 96 AQueous One Solution Cell Proliferation Assay (Promega Corporation, Wisconsin, USA) according to the manufacturer’s instructions and our previous publication [
7]. Range of ASNase concentration used in the MTS assay was 0 - 4 IU/ml (5-fold dilutions), for VCR it was 0 - 50 nM (5-fold dilutions) and 0 - 3 μM (5-fold dilutions) for DNR. We seeded 1.2 × 10
4 cells of leukemia cell lines and 1 × 10
5-3 × 10
5 patient cells. To evaluate the combined cytotoxicity of the 2 drugs, the number of live cells was determined by flow cytometry using DAPI (ThermoFisher Scientific Inc., MA, USA) and AccuCount Blank Particles (Spherotech Inc., IL, USA). The MTS assay was performed in at least 3 independent experiments. Cell counts were done in biological triplicates.
Glycolytic and mitochondrial respiration parameters of the leukemia cell lines were measured on a Seahorse Analyzers XFe24 and XFp (Agilent Technologies, Inc., CA, USA) using a Glycolysis stress test and a Cell mito stress test. For the Glycolysis stress test, cells were seeded in XF Base medium, pH 7.4, and for the Cell mito stress test, cells were seeded in XF Assay medium, pH 7.4, supplemented with 10 mM glucose, 1 mM HEPES, pH 7.4, 1 mM pyruvate and 0.1% BSA. The cells were plated at a density of 300,000 cells/well in XFe24 or 40,000 cells/well in XFp tissue culture plates coated with CellTak (Corning GmbH, Wiesbaden, GER), according to the Agilent Seahorse protocol for seeding suspension cells.
The glycolytic and mitochondrial respiration parameters of the primary leukemia cells were measured on the Seahorse Analyzer XFp using the same tests and media as in the case of the cell lines. The primary cells were plated at a density of 500,000 cells/well in XFp tissue culture plates. The procedure was performed as described previously [
17].
Final concentrations of the injected drugs were 10 mM glucose, 1 μM (for cell lines) or 2 μM (for primary cells) Oligomycin A and 100 mM 2-deoxy glucose (2-DG) in the Glycolysis stress test and 2 μM Oligomycin A, 1–4.5 μM FCCP (depending on the cell line) and 1 μM Rotenone combined with 1 μg/ml Antimycin A in the Cell mito stress test. All cell lines were measured at least in biological triplicates and 5 technical replicates on the Seahorse Analyzer.
Genomic DNA isolation and mtDNA quantification
Genomic DNA was isolated from leukemia cell lines using the QIAamp DNA Mini Kit (Qiagen GmbH, Germany) according to the manufacturer’s instructions. To quantify the mtDNA content, 2 genes were used as mitochondrial targets (
16S rRNA and
D-loop genes) and the
GAPDH gene served as a nuclear target. Quantification was performed using real-time PCR as described elsewhere [
18].
Electrophoresis and western blotting
Protein lysates were prepared as previously described [
19]. The proteins (30 μg per well) were resolved by NuPAGE Novex 4–12% Bis-Tris Gels (ThermoFisher Scientific Inc., MA, USA) and transferred to a nitrocellulose membrane (Bio-Rad, CA, USA). The membrane was probed overnight with the primary antibodies listed in Table S
2. The bound antibodies were detected with the appropriate secondary antibodies conjugated to horseradish peroxidase (Bio-Rad, CA, USA) and visualized using an enhanced chemiluminescence reagent and documented by Uvitec (Cambridge, UK).
Statistical analysis
Hierarchical clusters were generated in R using the Pheatmap package (distance measure: “Euclidean”, clustering method: “ward.D2”). Linearization method was used to calculate adjusted (bonferroni) p-values for Oligomycin A effect to ASNase, VCR and DNR sensitivity of leukemia cells (Fig.
3). Spearman rank correlations were calculated in R using the method “spearman”. P-values in Figure S1K were calculated using an unpaired, two-tailed, Mann-Whitney test in GraphPad Prism 6. Canonical Correlation analysis was done in R.
Discussion
The aim of this study was to elucidate if metabolic predisposition of leukemia cells (both cell lines and primary cells) influences their response to therapy. We wondered whether there is a basal metabolic profile that would predict which leukemia patients are sensitive or resistant to a given treatment. Since our previous work showed that ASNase affects the bioenergetics of leukemic cells, we were particularly interested in the link between the sensitivity to ASNase and the basal metabolic status of the cells as assessed before treatment. In addition to ASNase, we also tested the sensitivity to VCR and DNR, cytostatic drugs with a different mechanism of action, which are used in the treatment of a wide variety of cancers. We first examined the metabolic activity, including glycolysis, mitochondrial respiration and FAO in 19 cell lines. We performed HCA in order to cluster cell lines with a similar metabolic profile. Based on both glycolytic function and mitochondrial respiration, the cell lines were very similarly gathered into clusters. On the other hand, FAO clustered the cell lines differently. The first cluster, consisting of exclusively lymphoid leukemia cell lines, associated the cell lines with a lower glycolytic function and a lower mitochondrial respiration. The second cluster, grouped the cell lines with a higher glycolytic function and a higher mitochondrial respiration and contained all myeloid leukemia cell lines as well as some T-ALL cell lines. Next, aiming at the confirmation of this phenomenon in the primary samples, we assessed glycolytic function and mitochondrial respiration in leukemia cells isolated from ALL and AML patients and performed HCA. The first cluster which was divided according to glycolytic function consisted mostly of samples from ALL patients and the second cluster combined both, AML patients, ALL patients and healthy controls. After subsequent examination of the samples for their susceptibility to cytostatics, we found that ALLs from the high glycolytic cluster were less sensitive to ASNase treatment than their counterparts in the low glycolytic cluster. Moreover, a similar association was observed also in the tested cell lines. T-ALL lines from the high glycolytic cluster (and high respiration) were resistant to ASNase. Interestingly however, some AML patients were sensitive to ASNase to the same extent as sensitive ALL patients. This can be explained by the FAB classification according to which these AML patients fall into the M1, M4 or M5 subgroups. These morphological subgroups were shown to be more sensitive to ASNase treatment by in vitro testing [
20]. This could be potentially clinically interesting since the incorporation of ASNase is still discussed in the treatment of AML.
In cell lines, IC50 of ASNase did not correlate with IC50 of VCR or DNR and also, the sensitivity to these cytostatics was not associated with any specific metabolic phenotype demonstrating that their effect was less influenced by the basal metabolic status of leukemia cells.
We have previously described specific signaling pathways that regulate bioenergetic processes after ASNase treatment [
7]. We were therefore interested if a distinct metabolic profile of the leukemic cells would be associated with a different prerequisite activity of the signaling pathways. Indeed, we found a profound difference in p-AKT and c-MYC, the regulators of glycolysis [
21,
22], between T- and B-ALL cells and decreased level of p-GSK3b in B-ALL cell lines. In the case of the AML cell lines, the proteins S6 and p-S6 were more expressed than in ALL cells.
Since we found an association between the metabolic predisposition and the consecutive response to ASNase treatment, we next asked which metabolic parameter correlates with IC50 of ASNase and therefore with the potential treatment efficacy. Statistical analysis based on CCA revealed that higher ATP-linked respiration, higher OCR/ECAR and lower basal respiration significantly correlated with increased sensitivity to ASNase. To functionally confirm this relationship we used Oligomycin A, a specific ATP synthase inhibitor. After its administration, we treated the cells with ASNase and examined the changes in cell survival and growth. We confirmed that cells with lower ATP synthase activity, i.e. lower ATP-linked respiration, are more resistant to ASNase. The sensitivity to VCR and DNR did not change, which corresponded with the results of the CCA. Reduced ATP synthase activity, in relation to a resistant phenotype, has been described in colorectal carcinoma cells and 5-fluorouracil [
23]. Our assumption is that cells with less active TCA cycle followed by reduced ATP-linked respiration are less dependent on the supply of glucose or glutamine. Thus, these cells could better tolerate glutamine depletion and glucose uptake impairment, both of which are cytotoxic effects of ASNase.
Since Oligomycin A treatment leads to increased MMP [
24], we examined the so-called basal MMP of cells using TMRE labeling. We have shown that higher MMP significantly correlates with higher resistance to ASNase. Since TMRE fluorescence measuring is a standardized cytometric method, it could be established as an additional diagnostic marker to help characterize the sensitivity of individual patients to ASNase.
Even though ASNase is a crucial component in the treatment of ALL, its precise administration is still being tested in some specific ALL prognostic-risk groups [
25]. Moreover, its incorporation into the therapy of other cancer types is still under investigation [
26‐
29]. This is the first study describing the correlation between the sensitivity to ASNase and the metabolic profile of leukemia cells. Our results revealed that myeloid and lymphoid cells cluster together based on their glycolytic activity and mitochondrial respiration. Interestingly, both ALL cell lines and patients display different glycolytic activity which is associated with their sensitivity to ASNase. Further characterization of the metabolic state of the leukemic blasts, at the time of diagnosis, may help to identify patients with lower sensitivity to ASNase, individuals that are therefore more likely to fail the conventional therapy without any other detectable high-risk factors.
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