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Mol Cancer Ther. 2006;5:1986-1994
© 2006 American Association for Cancer Research

Research Articles: Therapeutics

Expression profiling of ATP-binding cassette transporters in childhood T-cell acute lymphoblastic leukemia

Thomas Efferth1, Jean-Pierre Gillet2, Axel Sauerbrey3, Felix Zintl4, Vincent Bertholet5, Françoise de Longueville5, Jose Remacle6 and Daniel Steinbach4

1 German Cancer Research Center, Heidelberg, Germany; 2 Laboratory of Cell Biology, National Cancer Institute, NIH, Bethesda, Maryland; 3 Helios Children's Hospital, Erfurt, Germany; 4 Children's Hospital, University of Jena, Jena, Germany; 5 Department of Biology, University of Namur; and 6 Eppendorf Array Technologies, Namur, Belgium

Requests for reprints: Thomas Efferth, German Cancer Research Center M070, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. Phone: 49-6221-423426; Fax: 49-6221-423433. E-mail: t.efferth{at}dkfz.de


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
A major issue in the treatment of T-cell acute lymphoblastic leukemia (T-ALL) is resistance to chemotherapeutic drugs. Multidrug resistance can be caused by ATP-binding cassette (ABC) transporters. The majority of these proteins have not yet been examined in T-ALL. Using a newly developed microarray for the simultaneous quantification of 38 ABC transporter genes, we observed a consistent overexpression of ABCA2/ABCA3 in clinical samples of ALL. Therefore, we analyzed the association of these two genes with drug resistance. Treatment of CCRF-CEM and Jurkat cells with methotrexate, vinblastine, or doxorubicin led to an induction of ABCA3 expression, whereas a significant increase of ABCA2 expression was only observed in Jurkat cells. To study the causal relationship of ABCA2/A3 overexpression with drug resistance, we applied RNA interference (RNAi) technology. RNAi specific for ABCA2 or ABCA3 led to a partial decrease of expression in these two ABC transporters. Upon cotreatment of RNAi for ABCA2 with methotrexate and vinblastine, a partial decrease of ABCA2 expression as well as a simultaneous increase of ABCA3 expression was observed. Vice versa, ABCA3 RNAi plus drugs decreased ABCA3 and increased ABCA2 expression. This indicates that down-regulation of one ABC transporter was compensated by the up-regulation of the other. Application of RNAi for both ABCA2 and ABCA3 resulted in a more efficient reduction of the expression of both transporters. As a consequence, a significant sensitization of cells to cytostatic drugs was achieved. In conclusion, ABCA2 and ABCA3 are expressed in many T-ALL and contribute to drug resistance. [Mol Cancer Ther 2006;5(8):1986–94]


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Although cancer therapy for childhood T cell acute lymphoblastic leukemia (T-ALL) has remarkably improved during the past two decades, and normal life expectancy is a reality for many patients, a considerable number of patients cannot permanently be cured. In the majority of children suffering from T-ALL, remissions can be achieved by chemotherapy. Nevertheless, ~25% of the patients develop relapses. One major reason is the development of drug resistance. In patients with a T cell immunophenotype (T-ALL), drug resistance is even more commonly encountered.

The expression of one drug resistance gene, the multidrug resistance gene, MDR1, and its gene product, P-glycoprotein, has been well documented in leukemia. Its prognostic relevance for the development of drug resistance and worse outcome of patients has been shown for many tumor types including myeloid leukemia (1–3). In ALL, the relevance of the MDR1/P-glycoprotein is, however, still under debate. Although some authors found that high MDR1/P-glycoprotein expression and function is associated with the failure of chemotherapy and adverse prognosis (4–6), others have not (7–9). MDR1/P-glycoprotein belongs to the family of ATP-binding cassette (ABC) transporter, which comprises >40 different proteins (10). Other ABC transporters such as ABCC1/MRP1, ABCC2/MRP2, and ABCG2/BCRP also confer drug resistance in tumor cells. Their role in clinical treatment failure and worse outcome for patients with ALL is still a matter of discussion. Although there are other ABC transporters which also contribute to drug resistance of cancer cells (11), their role in ALL is still unknown.

The aim of this study was to examine whether additional ABC transporters are expressed in T-ALL and whether they are associated with drug resistance. A low-density microarray with 38 ABC transporter genes has recently been developed (12). We applied this microarray to clinical ALL samples in order to detect possible ABC transporters associated with the children's relapse, particular focus was drawn on ABCA2 and ABCA3. Their role for drug resistance was then analyzed by experiments designed to induce gene expression after drug treatment, to decrease their expression, and to sensitize the cells. Finally, the microarray-based expression of ABCA2 and ABCA3 was correlated with the 50% inhibition concentration (IC50) values of 60 cell lines for compounds of the Standard Drug Database of the National Cancer Institute, Bethesda, MD.7


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Cell Culture
The human T-ALL cell lines, CCRF-CEM and Jurkat, were maintained in RPMI medium (Invitrogen, Carlsbad, CA) supplemented with 10% FCS (Life Technologies). Cells were passaged twice weekly. All experiments were done with cells in the logarithmic growth phase. The osteosarcoma cell line 143B was maintained in DHG medium (DMEM supplemented with 4.5 g/L glucose; Invitrogen, Paisley, United Kingdom) supplemented with 10% FCS (Life Technologies). Cells were passaged twice weekly. All of the cells were incubated under standard culture conditions (5% CO2 and 37°C). The panel of 60 human tumor cell lines from the Developmental Therapeutics Program of the NCI has been described in detail previously (13).

Patients
Blood samples were obtained from 21 patients with T-ALL after obtaining informed consent. The main patient characteristics were: 14 males, 7 females; median age, 9.7 (1.8–16 years); median WBC count at presentation, 16.2 (8–450 Gpt/L); median percentage of leukemic cells in peripheral blood, 85% (18–99%). None of the patients were found positive for BCR/ABL translocation or MLL rearrangements. All samples were collected prior to the start of chemotherapy. The initial diagnosis of ALL was determined by Pappenheim-stained bone marrow smears and cytochemistry reactions (periodic acid-Schiff reaction, acid phosphatase, {alpha}-naphthyl acetate esterase, and myeloperoxidase reaction). Immunophenotype and chromosomal rearrangements were determined by standard methods (14). The patients were treated according to the multicentric Berlin-Frankfurt-Münster protocols (ALL-BFM-90, ALL-BFM-95, or ALL-BFM-2000). The main drugs used in all studies were steroids, methotrexate, cytosine-arabinoside, anthracyclines, asparaginase, and vincristine (14, 15).

In vitro Response to Cytostatic Drugs
Jurkat and CCRF-CEM cell lines were treated for 72 hours at 37°C with doxorubicin, methotrexate, and vinblastine. The concentrations used with Jurkat cells were 0.1, 0.1, 0.1 µg/mL, respectively, and with CCRF-CEM cells were 1, 1, and 0.001 µg/mL, respectively.

After drug treatment, cells were centrifuged, placed in a 96-well plate and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) solution (Sigma, Gillingham, United Kingdom) was added to each well and incubated for 2 hours at 37°C before media removal. DMSO was then added and mixed for 2 hours at 37°C. Absorbance was measured at 570 nm using a spectrophotometer (Ultramark, Bio-Rad, Hercules, CA).

Expression Profiling of ABC Transporters with the DualChip Human ABC
A detailed description of the DualChip Human ABC, its validation, the procedure protocols, and the statistical analysis was given recently (12). The chip used the Xmer technology developed by Eppendorf (Hamburg, Germany)8 as proposed by ref. (16). Each DNA probe is present in triplicates. The chip contains probes for 38 ABC transporters, 8 housekeeping genes, and a number of positive and negative hybridization and detection controls (Fig. 1A ). The total RNA was extracted using the Trizol method (Life Technologies) and reverse transcription was done from 10 µg of total RNA using the Superscript II enzyme (Invitrogen) before being hybridized on the array without amplification.


Figure 1
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  		Figure 1. A, design of DualChip human ABC. The array included 49 genes (including 8 housekeeping genes). Each capture probe is spotted in triplicate. Three complete sub-arrays are schematically drawn. Six different internal standards are placed in different areas for normalization. B, fluorescence image of the DualChip human ABC hybridized with cDNA obtained from mRNA isolated from one T-ALL sample.

 
To maximize the dynamic range of microarrays, the same arrays were scanned using different photomultiplier settings. The use of different intensities allows the quantitation of both the high and low copy expressed genes as described in ref. (17).

Real-time PCR
Real-time PCR followed a protocol described recently (12). For validation of microarray data, real-time reverse transcription-PCR was done on seven genes, i.e., ABCA2, ABCA3, ABCA7, ABCC1, ABCC5, ABCF1, and ß-tubulin (housekeeping gene). The total RNA from four ALL samples (three patients with good response and one patient with poor response) taken randomly from the 21 ALL samples studied were used in the real-time reverse transcription-PCR (n = 3), and each reaction was done in duplicate. For measurement of mRNA expression of ABCA2 and ABCA3 in CCRF-CEM, Jurkat, and 143B cells, total RNA from three independent experiments were used in the real-time reverse transcription-PCR (n = 3), and each reaction was done in duplicate. A detailed procedure for calculating the relative expression ratio of a target gene in the test sample was reported elsewhere (18).

RNA Interference Technology
Small interfering RNA (siRNA) transfection experiments were done using double-stranded RNA synthesized by Dharmacon (Chicago, IL). A nontargeting siRNA (scramble) was used as control. Cells were transfected with Dharmafect 1 (Dharmacon) according to the manufacturer's instructions. Nucleofection standard protocols have been established.9

The effect of ABCA2 and ABCA3 expression silencing on drug-induced resistance was analyzed as followed: 143B cells were seeded in 12-well plates at 100,000 cells/well 24 hours before being transfected with Dharmafect 1 for 24 hours with 20 nmol/L of ABCA2 siRNA, 50 nmol/L of ABCA3 siRNA, or with 50 nmol/L of ABCA2 + 50 nmol/L of ABCA3 siRNAs or equivalent treatment with scramble siRNA. Twenty-four hours posttransfection, media were refreshed and cells were incubated with 0.3 µg/mL of doxorubicin, 0.03 µg/mL of methotrexate, or 0.01 µg/mL of vinblastine. ABCA2 and ABCA3 mRNA level and the effect of drug treatment on transfected cell viability were measured, respectively, by quantitative reverse transcription-PCR and MTT assay 72 hours posttransfection.

Statistical Methods
COMPARE Analysis. The sulforhodamine B assay for the determination of drug sensitivity in 60 cell lines from the NCI panel were reported previously (19). The IC50 values for drugs are included in the Standard Agents Database of the Developmental Therapeutics Program of the NCI.10 The mRNA expression values of 60 cell lines of ABCA2 and ABCA3 transporter genes (represented by each of three different clones with individual GenBank accession numbers) were selected from the NCI's database. The mRNA expression was determined by microarray analysis as reported previously (20, 21). The microarray data of ABC transporters was confirmed by real-time reverse transcription-PCR analyses (22). COMPARE analysis was done to produce rank-ordered lists of cytotoxic compounds. The COMPARE methodology has been previously described in detail (23). Briefly, every standard drug of the NCI's database is ranked for the similarity of its IC50 values to the mRNA expression for the ABCA2 or ABCA3 transporters. To derive COMPARE rankings, a scale index of correlation coefficients (R values) was created. In the standard COMPARE method, greater mRNA expression in cell lines correlate with greater IC50 values, e.g., increase in drug resistance.

MTT Assay. We used Student's t test to calculate the significance of the differences in the cell viability between the treated and untreated cells.

Computation of the Theoretical Reference
A theoretical reference has been computed to obtain a global mean of the tumors by averaging all the tumor data. This reference was used as a comparison mean for each tumor and allowed the attainment of a gene expression profile with higher or lower expressed genes in each tumor.

The construction of this global mean was done in two steps. First, each array was normalized to a chosen reference array (sample no. 213). The normalization factors were obtained by the internal standards and housekeeping genes spotted on the arrays as described by de Longueville et al. (16). This normalization step levels up the intensities of the array data to a chosen one. A global weighted mean of the arrays was then computed using these factors for all data obtained at a given photomultiplier scan setting.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Expression Profiling of Clinical Samples
First, we analyzed the expression of ABC transporters in 21 clinical ALL samples by means of the DualChip Human ABC low-density microarray. The data from one representative experiment of an ALL sample (no. 42) is shown in Fig. 1B. Reliability and the reproducibility between assays were assessed by repeating the experiments with several replicates depending on the amount of RNA available. The mean coefficient of variance (CV) for the triplicates inside an array was 11.33%, calculated on the quantitative detected genes in 43 arrays. For seven samples, we could perform triplicate arrays and the mean CV of all detected genes was 14.58%.

In order to detect an increase or decrease in the expression of the different ABC transporters in the different tumors, we compared the expression of individual ALL with the mean values obtained in the 21 T-ALL (global mean; Table 1 ) and 4 of them were very abundant. The other ABC transporters were not significantly detected.


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  		Table 1. Expression of ABC transporters in 21 clinical samples of T-ALL

 
Among the 15 genes which were well-expressed, some were expressed rather constantly in all the tumors, whereas others were expressed very variably. This is reflected in the SD of the means. Given the fact that the CV for the triplicates of each probe in one array and for experiments performed in triplicates were <15%, we concluded that the variation of the means of all the tumors really reflects the variation of expression between the tumor samples.

The expression values of high- and low-regulated ABC transporters were subjected to cluster analysis. As shown in Fig. 2 , the cluster tree clearly separates up- or down-regulated ABC transporters (red and blue color codes, respectively; black represents nondetected genes) from other ABC transporters whose expression was within the global mean. The cluster analysis in Fig. 2 illustrates that ABCA2/A3 was frequently overexpressed in several samples, indicating that ABCA2/ABCA3 may be of relevance for ALL. Moreover, we can observe that ABCA2/A3 was significantly expressed in 14 tumors with a mean intensity value of 3,709 (Table 1). Correlations between the overexpression of ABC transporters and the clinical data were not conclusive. We checked for correlations with initial response to therapy, overall and relapse-free survival, age, sex, WBC count, and percentage of leukemic cells at presentation (data not shown).


Figure 2
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  		Figure 2. Cluster analysis done using CIMMiner software (http://discover.nci.nih.gov/nature2000/tools/upload_s.jsp) on the genes which were detected on the microarray (38 ABC transporters, 1 cationic transporter, and 2 ATP-sensitive potassium channels) in 21 T-ALL samples. The data used for the clustering were the ratios obtained for one gene in each sample compared with the mean value in the 21 samples.

 
To validate the microarray data, we selected seven ABC transporter genes and analyzed their expression in four ALL samples (three from patients showing a poor response to treatment and one from a patient showing a good response to treatment as reference) taken randomly from the 21 samples by real-time reverse transcription-PCR. As shown in Fig. 3 , the mRNA expression of seven ABC transporters obtained by real-time reverse transcription-PCR of three tumor samples (poor response patient) compared with a reference from a good response patient, correlated significantly with the corresponding values obtained by microarray analysis at a significance level of P = 1.53 x 10–7 and a correlation coefficient of R = 0.935 (Pearson's correlation test).


Figure 3
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  		Figure 3. Validation of microarray data by real-time reverse transcription-PCR. The expression of seven ABC transporter genes was assayed by reverse transcription-PCR in three randomly selected poor response tumors compared with one tumor with good response to chemotherapy. The relative expression levels measured by real-time reverse transcription-PCR were first corrected for the values to the {alpha}-tubulin gene. The results of the real-time reverse transcription-PCR were correlated with the microarray-based expression values (Spearman's rank correlation test). The values are presented as the ratios of ABC gene expression in poor response tumor versus good response tumor.

 
Induction of ABCA2 and ABCA3 Expression by Drug Treatment
Because the hybridization signals for ABCA2/A3 were present in most T-ALL samples, we checked whether these two genes might be involved in the response of T-ALL to cytostatic drugs. We treated CCRF-CEM and Jurkat T-ALL cell lines with methotrexate (1 and 0.1 µg/mL), vinblastine (0.001 and 0.1 µg/mL), or doxorubicin (1 and 0.1 µg/mL). After 72 hours of incubation, we quantified the mRNA expression of ABCA2 and ABCA3 in treated and untreated cell samples by real-time reverse transcription-PCR. The ratios of mRNA expression of treated and untreated cell aliquots are shown in Fig. 4 . Although all three drugs led to an increase of ABCA3 expression in Jurkat and CCRF-CEM cells, a significant increase of ABCA2 expression was only observed in Jurkat cells. These results indicate that both genes may be involved in the drug response of T-ALL cells.


Figure 4
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  		Figure 4. Expression of ABCA2 and ABCA3 in Jurkat and CCRF-CEM cells after treatment with cytostatic drugs (methotrexate, vinblastine, and doxorubicin). After 72 h of incubation, the expression measured by real-time reverse transcription-PCR is given in relation with the expression of untreated cells.

 
Down-Regulation of ABCA2/A3 Expression by RNA Interference
To confirm the possible implications of ABCA2 and ABCA3 in drug resistance, we inhibited their expression by RNA interference (RNAi) and tested for the change in cell resistance to the drugs. Because CCRF-CEM and Jurkat cells were difficult to transfect with RNAi, we decided to use another cell line to address this question: the osteosarcoma 143B cell line. The control panel in Fig. 5A shows that the transfection agent Dharmafect, scramble sequences, methotrexate (0.03 µg/mL), vinblastine (0.01 µg/mL), or doxorubicin (0.3 µg/mL) applied alone or in combination had no or only a minimal effect on the mRNA expression of ABCA2 or ABCA3. As a next step, we applied ABCA2 RNAi alone or in combination with drugs. ABCA2 RNAi down-regulated mRNA expression of ABCA2. If ABCA2 RNAi was combined with methotrexate, vinblastine, or doxorubicin, an up-regulation of ABCA3 expression was observed for the first two drugs, which was not visible after treatment with ABCA2 RNAi alone (Fig. 5B). A comparable effect was found after treatment with ABCA3 RNAi. As expected, the application of RNAi alone reduced the expression of ABCA3 but not ABCA2. Cotreatment with ABCA3 RNAi plus methotrexate and vinblastine resulted in an increased expression of ABCA2 (Fig. 5C). If RNAi for both ABCA2 and ABCA3 were applied together, an increased inhibition of expression of ABCA2 and ABCA3 was observed, either with or without additional drug treatment (P < 0.05; Fig. 5D). These results indicate that the RNAi transfection specifically inhibits the targeted gene and that a combination of ABCA2 or ABCA3 siRNA with methotrexate and vinblastine leads to a compensation effect.


Figure 5
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  		Figure 5. Sensitivity of 143B cells to methotrexate, vinblastine, and doxorubicin after transfection with siRNA for ABCA2 and/or ABCA3 and after treatment with nontargeting siRNA (scramble). Cell survival was measured by MTT-assays. The results are given as the percentage of cell survival compared with untreated samples (control).

 
Next, we used the MTT assay to measure the effects of RNAi on cell viability. The transfection agent Dharmafect and scramble sequences were only minimally cytotoxic (<20% reduction in cell viability), as were methotrexate, vinblastine, and doxorubicin in the concentrations used (0.03, 0.01, and 0.3 µg/mL, respectively; Fig. 6A–C ). RNAi for ABCA2 or ABCA3 applied alone did not induce or only minimally induced cytotoxicity. A combination of the three drugs plus RNAi for ABCA2 or ABCA3 resulted in a moderate increase in cytotoxicity compared with each agent applied alone, except for the combination of doxorubicin and ABCA3 siRNA that showed a significant increase in cytotoxicity (P = 0.041 compared with transfection reagents; P = 0.012 compared with scramble). If RNAi for both ABCA2 and ABCA3 were applied together and combined with drugs, a stronger cytotoxic effect was observed (Fig. 6A–C). These differences were statistically significant when we compared these latter results with transfection reagents, and was highly significant when compared with scramble sequences (Fig. 6A–C).


Figure 6
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  		Figure 6. Transfection of ABCA2 and ABCA3 genes measured by real-time PCR in 143B cells transfected with ABCA2 and/or ABCA3 siRNA and with scramble siRNA. The results are given as ratios to the expression in untreated samples.

 
COMPARE Analysis
Because RNAi experiments showed that ABCA2 and ABCA3 play a role in the resistance to methotrexate, vinblastine, and doxorubicin, we investigated the role of these ABC transporters in drug resistance in more detail. We correlated the constitutive mRNA expression of these two ABC transporters in 60 cell lines of the NCI panel with the IC50 values for compounds included in the NCI's Standard Agent Database. The microarray and drug response data can be found in the NCI database.11 The two transporter genes are represented by each of three different clones with individual GenBank accession numbers. One clone of the ABCA2 gene and one clone of the ABCA3 gene have been analyzed twice in microarray experiments. They have different pattern identifier numbers (Table 2 ). Drugs for which IC50 values correlated with microarray-based mRNA expression of these ABC transporter genes with COMPARE correlation coefficients of R < 0.2 were not considered further. IC50 values of drugs correlating with at least two microarray data sets are listed in Table 2. This approach was applied to explore which compounds may be involved in drug resistance mediated by ABCA2, ABCA3, or both. The results indicate that the IC50 values of several compounds correlated with both ABCA2 and ABCA3 genes (rapamycin, triciribine phosphate, a 5,12-naphthacenedione derivative, cytoxan, 4'-deoxydoxorubicin, {alpha}-2'-deoxy-6-thioguanosine, pibenzimol, and O6-methylguanine). Other drugs correlated either with ABCA2 alone (pentostatin, mitotane, hexamethylene bisacetamide, didemnin B, tamoxifen, and teroxirone) or ABCA3 alone (lomustine and flavone acetic acid). This indicates that both ABC transporters might have an overlapping but not an identical substrate spectrum.


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  		Table 2. Possible drug substrates of ABCA2 and ABCA3 as determined by COMPARE analysis of microarray-based mRNA expression and IC50 values for compounds extracted from the Standard Agent Database of the NCI

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Many studies have been done on the clinical relevance of ABC transporters. Thus far, most of these studies analyzed one or a small group of ABC transporters, e.g., ABCB1/MDR1, ABCC1/MRP1, and ABCG2/BCRP. For most other members of this gene family, the clinical relevance has still not been examined. In the present study, we used a novel low-density DNA microarray for the simultaneous expression analysis of 38 ABC transporters, which we have recently developed (12). The present study is the first one to describe a microarray-based detection of ABC transporter expression in ALL.

One of the positive features of low-density microarray is the good reproducibility and the specific validation that was done for the group of genes detected by the chip (12, 16, 17). This validation is particularly important in a group of genes that shows as much homology as the family of ABC transporters (12, 22). The reproducibility of the arrays is well illustrated by the very low CV of the quantitative detected genes which was <15% in the triplicate spots of the same arrays and in the triplicate arrays done on the same sample.

Besides the four ABC transporters which were highly expressed, ABCA7, ABCB2, ABCC1, and ABCF1, we also observed that ABCA2/A3 was overexpressed in most of the analyzed ALL. These results suggest that ABCA2 and ABCA3 may be of importance for ALL. ABCA2 has been shown to confer mitoxantrone resistance and to transport extramustine (24, 25). Furthermore, daunorubicin and mitoxantrone are translocated by ABCA3 (26, 27). Treatment of HL60 leukemia cells with cantharidin, an investigational natural product, induced ABCA3 expression (28). Yasui et al. (29) found that a number of drug-resistant cancer cell lines showed higher copy numbers of the ABCA3 gene and a stronger expression of the gene compared with the drug-sensitive parental cell lines. ABCA3 is located at intracellular membranes (30). It does not confer a "classical" drug efflux across cell membranes but rather seems to be involved in the intracellular sequestration and vesicular drug transport (27, 30). Based on these reports, we hypothesized that ABCA2 and ABCA3 may be relevant for drug resistance in T-ALL.

To investigate this hypothesis, induction experiments were done. The T-ALL cell lines, CCRF-CEM and Jurkat, were treated with methotrexate, vinblastine, or doxorubicin. All three drugs led to an increase of mRNA expression of ABCA3 in both cell lines, whereas a significant increase of mRNA expression of ABCA2 was only observed in the Jurkat cell line. This indicates that these two transporters may contribute to the resistance of T-ALL to these drugs. These results are in accordance with the reports of the role of ABCA2 and ABCA3 in the resistance to mitoxantrone, daunorubicin, and estramustine (24–27).

The RNAi experiments of the present investigation provide evidence for the involvement of both ABC transporters in drug resistance. Because CCRF-CEM and Jurkat cells could not be transfected with sufficient efficacy in our study, we decided to use 143B cells, which are more easily transfectable. We do not know why the two leukemia cell lines could not be transfected because nucleofection standard protocols have been established (see Materials and Methods).

Treatment with ABCA2 or ABCA3 RNAi resulted in a partial down-regulation of ABCA2 or ABCA3 mRNA, respectively. An unexpected observation was that cotreatment of ABCA2 RNAi plus methotrexate and vinblastine led to an up-regulation of ABCA3. Vice versa, ABCA3 RNAi plus these two cytostatic drugs increased the expression of ABCA2. This observation was made in three independent experiments. A possible explanation could be that methotrexate and vinblastine are transported by both ABC transporters and that the down-regulation of one transporter was at least compensated by the up-regulation of the other one. Such a mechanism would allow coping with cytotoxic challenge more efficiently.

As a strategy to further explore the possible role of ABCA2 and ABCA3 transporters as drug transporters, COMPARE analyses were done with compounds included in the NCI's Standard Agent database and these two ABC transporters, whose mRNA expression in 60 NCI cell lines has been determined by microarrays (20, 21). The COMPARE computation provided a list of drugs that could be considered as substrates for ABCA2 and ABCA3. Although such correlation analysis does not provide evidence for a compound being a true ABC transporter substrate, this strategy can be used to generate testable hypotheses. Our aim was, however, not to provide a complete list of possible substrates for ABC transporters but to obtain information that the ABCA2 and ABCA3 transporters could be considered as candidate drug transporters. The results of the COMPARE analysis reinforce the use of the DualChip human ABC as a tool to detect ABC transporter–associated drug resistance. Interestingly, the IC50 values of several compounds have been found to correlate with mRNA expression levels of both ABCA2 and ABCA3. Other compounds correlated only with ABCA2 or only with ABCA3. This indicates that the spectrum of possible substrates is overlapping, but not identical. This result fits with the RNAi experiments in the present investigation. The full spectrum of substrates has yet to be explored to compare the multidrug resistance phenotypes of ABCA2 and ABCA3 with those of the established multidrug resistance–mediating transporters, ABCB1/MDR1, ABCC1/MRP1, ABCC2/MRP2, and ABCG2/BCRP.

Among the established multidrug resistance–conferring ABC transporter genes, ABCB1/MDR1 was not overexpressed in the T-ALL samples of the present study, and underscores the debate regarding the role of the ABCB1/MDR1 gene in the drug resistance of T-ALL. In contrast to acute myeloid leukemia, in which the role of the ABCB1/MDR1 gene for drug resistance of tumors and prognosis of patients is widely accepted (31, 32), data for ALL are conflicting (7, 9, 32–40). It is, therefore, reasonable to propose that ABC transporters other than ABCB1/MDR1 may be more decisive for treatment response and prognosis of T-ALL. In our investigation, the ABCG2 (BCRP) transporter was not significantly detected whereas ABCC1 (MRP1) was highly expressed. Again, the contribution of these three ABC transporters to the treatment outcome of T-ALL patients has been shown by some, but not all authors (8, 36, 41–46), leaving the prognostic relevance of these ABC transporters open to discussion.

In the present investigation, we found several ABC transporters to be down-regulated in some T-ALL samples whereas being rather highly expressed in the overall samples. Among them, ABCF1 and ABCB2 are interesting genes because they are well expressed in the tumors but are found to be relatively underexpressed in several other tumors. The biological relevance of this finding is unknown.

Whereas overexpression is compatible with the function of ABC transporters as drug efflux pumps, down-regulation raises the possibility that ABC transporters, i.e., ABCF1, might act as influx transporters for cytostatic drugs. Indeed, we found that ABCF1 expression correlated inversely with the IC50 values for 6-mercaptopurine in the NCI cell line panel. Hence, high ABCF1 expression was associated with drug sensitivity (data not shown). Unlike the majority of ABC proteins, which are membrane-associated transporters, ABCF1 associates with the ribosome and probably functions in mRNA translation (47). A possible role for ABCF1 in drug sensitivity deserves further investigation.


   Acknowledgments
 
We thank Dr. Robert Wyn Owen for critically reading the manuscript.


   Footnotes
 
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Note: T. Efferth and J-P. Gillet contributed equally to this work.

7 http://www.dtp.nci.nih.gov. Back

8 http://www.eppendorf.com/microarrays/. Back

9 http://www.amaxa.com/jurkat.html and http://www.amaxa.com/jurkatcells.html. Back

10 http://dtp.nci.nih.gov. Back

11 http://dtp.nci.nih.gov. Back

Received 2/15/06; revised 5/27/06; accepted 6/ 7/06.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Chauncey TR. Drug resistance mechanisms in acute leukemia. Curr Opin Oncol 2001;13:21–6.[CrossRef][Medline]
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