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 Table of Contents  
ORIGINAL ARTICLE
Year : 2014  |  Volume : 39  |  Issue : 1  |  Page : 13-19

Evaluation of multidrug resistance in acute leukemia using real-time polymerase chain reaction


Oncological Clinical Pathology Department, South Egypt Cancer Institute, Assiut University, Assiut, Egypt

Date of Submission10-Sep-2013
Date of Acceptance28-Oct-2013
Date of Web Publication29-Jan-2014

Correspondence Address:
Eman Ahmed Hasan
South Egypt Cancer Institute, Assiut University, Assiut, 71515
Egypt
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/1110-1067.124839

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  Abstract 

Background Despite the advances in the cure rate for acute leukemia, ~25% of affected patients develop relapses. Expression of genes for the multidrug resistance (MDR1) and breast cancer-resistance protein (BCRP) may confer the phenotype of resistance to the treatment of acute leukemia.
Objective To analyze the expression of the MDR1 and BCRP genes in new cases of acute leukemia using real-time PCR (RT-PCR) and to determine the correlation between their expression and overall survival (OS).
Patients and methods Patients diagnosed with acute myeloblastic leukemia (AML) (n = 15) and acute lymphoblastic leukemia (ALL) (n = 35), and 20 blood donors as a control group were included in this study. The expressions of mRNA for the MDR1 and BCRP genes were assessed by RT-PCR. Myeloid surface markers such as CD34, CD33, CD13, and CD14 and lymphoid surface markers such as CD3, CD5, CD2, CD4, CD8, and CD19 were analyzed using flow cytometry.
Results The groups with the MDR gene and the BCRP gene showed a highly significant difference compared with the control group (P < 0.000). The relation between MDR and BCRP in both AML and ALL groups showed no significant difference. There was a significant difference between BCRP expression in the AML and ALL groups (P < 0.01). There was no significant difference in the OS between MDR+ cases and MDR- cases in the AML and ALL groups. In contrast, the OS in BCRP+ cases and BCRP- cases showed a significant difference between AML and ALL groups (P < 0.01). No significant difference was detected between OS in AML (MDR+, CD34+) and AML (MDR+, CD34−). In contrast, OS between AML (BCRP+, CD34+) and AML (BCRP+, CD34−) showed a significant difference (P < 0.01). The difference between OS in ALL (MDR+, CD34+) and ALL (MDR+, CD34−) was not significant. In contrast, a significant difference was detected between OS in ALL (BCRP+, CD34+) and ALL (BCRP+, CD34−) (P < 0.01). OS in the AML group that was BCRP+ (CD13+) showed a significant difference (P < 0.01). In the ALL group, the association between MDR+ and CD19+ or BCRP+ and CD19+ did not affect the survival significantly.
Conclusion We concluded that the evaluation of the expression of genes for resistance to antineoplastic drugs in acute leukemia upon diagnosis, and particularly the expression of the BCRP gene, may be of clinical relevance.

Keywords: acute lymphoblastic leukemia, acute myeloblastic leukemia, breast cancer-resistance protein, multidrug resistance, multidrug resistance gene, real-time polymerase chain reaction


How to cite this article:
Mossad E, Bakry R, Badrawy H, Hasan EA, Khalaf MR. Evaluation of multidrug resistance in acute leukemia using real-time polymerase chain reaction. Egypt J Haematol 2014;39:13-9

How to cite this URL:
Mossad E, Bakry R, Badrawy H, Hasan EA, Khalaf MR. Evaluation of multidrug resistance in acute leukemia using real-time polymerase chain reaction. Egypt J Haematol [serial online] 2014 [cited 2017 Jun 25];39:13-9. Available from: http://www.ehj.eg.net/text.asp?2014/39/1/13/124839


  Introduction Top


Drug resistance is a multifactorial phenomenon and several mechanisms have been identified for clinical resistance to chemotherapy in solid tumors as well as in hematologic malignancies. The two important mechanisms of drug resistance in acute leukemia (AL) are the expression of drug resistance genes and activation of an antiapoptotic mechanism [1] . Classical multidrug resistance (MDR1) is characterized by expression of the permeability-glycoprotein (P-gp), a 170 kDa membrane protein that has been believed for a long time to act as a 'classical' drug efflux pump [2] . Also, ABCG2, commonly known as breast cancer-resistance protein (BCRP), which belongs to the ABC transporter superfamily, may also play a role in treatment failure in acute lymphoblastic leukemia (ALL) as ALL patients are treated with ABCG2 substrates such as methotrexate and daunorubicin [3] . Higher RNA levels of BCRP are present in the relapsed/refractory states of acute myeloblastic leukemia (AML) relative to that at diagnosis, suggesting that ABCG2 may be involved in resistance to one of the agents used commonly in the front-line treatment of AML [4] . The aim of the present study was to analyze the expression of genes related to drug resistance (MDR1, BCRP) in a group of 50 patients with AL. Further, the expression levels of these genes were correlated with phenotypically cluster designations (CDs) to confirm the association with refractoriness to the treatment or short overall survival (OS) duration.


  Patients and methods Top


The present study was carried out at the Oncological Clinical Pathology Department, South Egypt Cancer Institute. Fifty patients with newly diagnosed AL were recruited from the Pediatric Department and the Medical Oncology Department in the South Egypt Cancer Institute, Assiut University.

Samples were taken from the following two groups:

Group I (AL group): It included 50 AL patients with a mean age of 17.54 ± 16.14 years, with a male to female ratio of 1.8 : 1. This group included 15 patients (30%) with AML and 35 patients (70%) with ALL [Table 1]. The diagnosis of AL was made on the basis of morphological, cytochemical, and immunophenotypic characteristics of the leukemic blasts together with cytogenetic studies.
Table 1: Demographic data of the study groups

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Group II (the control group): 20 age-matched and sex-matched apparent healthy individuals were included in the study as a control group.

All the patients in the study were subjected to the following:

  1. Assessment of history, including:
    1. History of previous neoplasia.
    2. History of any previously received drug regimen: For the dose, duration, and date of last treatment.
  2. Full clinical examination: Particularly for hepatomegaly, splenomegaly, lymph node enlargement, bone pain, purpuric eruptions, central nervous system manifestations, skin infiltration, and testicular involvement in men.
  3. Routine laboratory examination:
    1. Complete blood picture.
    2. Bone marrow examination and cytochemistry Periodic acid-Schiff, (PAS) Myeloperoxidase (MPO).
    3. Immunophenotyping: Antigens routinely tested included CD10, CD19, CD5, CD10, CD34, CD13, CD33, CD3 CD8, CD14, CD4, and CD2 (using FAC Scan; Becton Dickinson, Mountain View, California, USA).


Sampling

A volume of 1.5 ml of venous blood or bone marrow was withdrawn into sterile vacutainer tubes containing EDTA under aseptic conditions [5] .

Total RNA extraction

Erythrocytes were selectively lysed and leukocytes were recovered by centrifugation using Qiagen kit for total RNA purification from human whole blood (Qiagen, Düsseldorf, Germany) [5] . The leukocytes were then lysed under highly denaturing conditions that immediately inactivated RNases, enabling the isolation of intact RNA. After homogenization of the lysate by a brief centrifugation through a QIAshredder spin column, ethanol was added to adjust binding conditions and the sample was placed in the QIAamp spin column. RNA was bound to the silica membrane during a brief centrifugation step. Contaminants were washed away and total RNA was eluted in 30 μl or more of RNase-free water for direct use in the Qiagen one-step real-time PCR (RT-PCR) kit. The kit contains enzyme mix, reverse transcriptase (50 μl), RT-PCR buffer (Tris •Cl KCl, (NH4)2SO4, 12.5 mmol/l MgCl2, DTT; pH 8.7), dNTP mix, and RNase-free water. HotStar Taq DNA polymerase, present in the Qiagen one-step RT-PCR enzyme mix, requires initial activation by incubation at 95°C for 15 min before amplification can take place. Quantitative RT-PCR was performed on all samples to determine the expression of BCRP, MDR1, and ABL as a housekeeping gene as follows: Primer and Taqman probe sequences were chosen using Primer Express Software (Applied Biosystem, Foster City, California, USA). BCRP (forward primer): 5′-GGATTGAAGCCAAAGGCAGAT-3′, BCRP (reverse primer): 5′-TGCAATGG-3′, BCRP probe: 5′-6FAM-TTCGTTATGATG TTT ACCCTTATGATG GTGGC-TAMRA-3′, MDR1 (forward primer): 5′-TGCAGCATTG CTGCTGAGAACATT-3′, MDR1 (reverse primer): 5′-TGCCTCACACAATCTCTTCCTG-3′, MDR1 probe: 5′-6FAM-CCTATGGAGACAA CAG CCGGGTGGTT-TAMARA-3′, ABL (housekeeping gene) (forward primer): 5′-AACCTTTCGTTG CACTGTAT GATT-3′, ABL (reverse primer): 5′-ACCCGGAGCT TTTCACCTTT-3′, ABL (probe): 5′-6FAM-TGTGGCCA CTGGAGATAAC ACTCTAAGC ATAACT-TAMARA-3′.

Thermal cycler conditions:

  1. Reverse transcription: 30 min at 50°C. However, if satisfactory results are not obtained at 50°C, the reaction temperature may be increased up to 60°C.
  2. Initial PCR activation step: 15 min, 95°C, HotStar Taq DNA polymerase is activated by this heating step.
  3. Step cycling:
    1. Denaturation: 0.5-1 min 94°C.
    2. Annealing: 0.5-1 min at 50-68°C ~5°C below Tm of primers.
    3. Extension: 1 min 72°C for RT-PCR products of 1-2 kb; increase the extension time by 30-60 s. Number of cycles: 25-40. The cycle number is dependent on the amount of template RNA and the abundance of the target transcript.
    4. Final extension: 10 min 72°C.


Statistical analysis

To characterize patients in the study, we used descriptive statistics. The Mann-Whitney U-test was used to determine differences between patients. Curves for OS were plotted according to the Kaplan-Meier method. Results were considered significant when P was less than or equal to 0.05. All analyses were carried out using the SPSS software package (SPSS Inc., Chicago, Illinois, USA).


  Results Top


MDR gene expression was detected in 22 (44.0%) patients of group I, mean 5.45 ± 1.02 compared with that of the control group (mean 0.06 ± 0.17). BCRP gene expression was detected in 40 patients (80.0%) of the study group, mean ± SE 2.49 ± 0.89 compared with that of the control group (mean ± SE 1.08 ± 0.17).

MDR gene expression was detected in four AML patients (26.7%) compared with 18 ALL patients (51.4%), with no statistical significant difference (P > 0.05). However, a statistically significant difference was detected in BCRP gene expression between AML and ALL patients (40.0 and 88.6%, respectively) [Table 2]. The OS in MDR+ cases and BCRP+ cases of AML and ALL was not significantly different (P > 0.05) [Table 3] and [Table 4]; [Figure 1]. The OS in MDR- and MDR+ cases in both AML and ALL patients showed a nonsignificant difference (P > 0.05) [Table 5] and [Table 6]; [Figure 2] and [Figure 3]. The OS in BCRP- and BCRP+ cases in both AML and ALL patients showed a significant difference (P < 0.05) [Table 7] and [Table 8]; [Figure 4].
Table 2: Relation between multidrug resistance and breast cancer-resistance protein in both acute myeloblastic leukemia and acute lymphoblastic leukemia groups

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Table 3: Overall survival in multidrug resistance positive cases of acute myeloblastic leukemia and acute lymphoblastic leukemia

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Table 4: Overall survival in breast cancer-resistance protein positive cases of acute myeloblastic leukemia and acute lymphoblastic leukemia

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Table 5: Overall survival in the acute myeloblastic leukemia group according to multidrug resistance expression

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Table 6: Overall survival in the acute lymphoblastic leukemia group according to multidrug resistance expression

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Table 7: Overall survival in the acute myeloblastic leukemia group according to breast cancer-resistance protein expression

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Table 8: Overall survival in the acute lymphoblastic leukemia group according to breast cancer-resistance protein expression

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Figure 1: Overall survival (OS) in multidrug resistance positive cases of acute myeloblastic leukemia (AML) and acute lymphoblastic leukemia (ALL).

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Figure 2: Overall survival (OS) in the acute myeloblastic leukemia group according to multidrug resistance (MDR) expression.

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Figure 3: Overall survival (OS) in the acute lymphoblastic leukemia group according to multidrug resistance (MDR) expression.

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Figure 4: Overall survival (OS) in the acute myeloblastic leukemia group according to breast cancer-resistance protein expression.

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Correlation between immunophenotypic data and gene expression

For CD34, the OS in MDR+ AML and ALL showed a nonsignificant difference between CD34− and CD34+ cases (P > 0.05) [Table 9] and [Table 10]. However, OS in BCRP+ AML and ALL showed a significant difference between CD34− and CD34+ cases (P < 0.05) [Table 11] and [Table 12].
Table 9: Overall survival in the acute myeloblastic leukemia group with multidrug resistance positive according to CD34 expression

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Table 10: Overall survival in the acute lymphoblastic leukemia group with multidrug resistance positive according to CD34 expression

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Table 11: Overall survival in the acute lymphoblastic leukemia group with breast cancer-resistance protein positive according to CD34 expression

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Table 12: Overall survival in the acute myeloblastic leukemia group with breast cancer-resistance protein positive according to CD34 expression

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For CD13 in the AML group, the OS in MDR+ cases showed a nonsignificant difference between CD13− and CD13+ (P > 0.05), whereas OS in BCRP+ cases showed a significant difference between CD13− and CD13+ cases (P < 0.01) [Table 13] and [Table 14]; [Figure 5].
Table 13: Overall survival in the acute myeloblastic leukemia group with multidrug resistance positive according to CD13 expression

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Table 14: Overall survival in the acute myeloblastic leukemia group with breast cancer-resistance protein positive according to CD13 expression

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Figure 5: Overall survival (OS) in the acute myeloblastic leukemia group that is multidrug resistance positive according to CD13 expression

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For CD19 in the ALL group, the OS in MDR+ and BCRP+ cases showed nonsignificant differences between CD19− and CD19+ cases (P > 0.05) [Table 15] and [Table 16]; [Figure 6] and [Figure 7].
Table 15: Overall survival in the acute lymphoblastic leukemia group with multidrug resistance positive according to CD19 expression

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Table 16: Overall survival in the acute lymphoblastic leukemia group with breast cancer-resistance protein positive according to CD19 expression

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Figure 6: Overall survival (OS) in the acute lymphoblastic leukemia group that is multidrug resistance positive according to CD19 expression.

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Figure 7: Overall survival (OS) in the acute lymphoblastic leukemia group that is breast cancer-resistance protein positive according to CD19 expression.

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  Discussion Top


Drug resistance is a major obstacle and an important mechanism to explain the failure of induction chemotherapy in patients with leukemia. The two most widely characterized drug resistance mechanisms are deregulation of genes involved in apoptosis and MDR in AL. The MDR1 gene has been the most extensively studied drug resistance gene in AL. However, its role as a prognostic factor is still controversial. Overexpression of MDR1 protein in AML has been associated with poor response to chemotherapy and reduced survival [6] . In the current study, OS in MDR+ cases and MDR− cases AML showed no significant difference. Our results are consistent with previous studies [7],[8] . They established that OS in AML was also influenced by the coexistence of other MDR genes and P-gp, but not by the sole presence of MDR1. The results of our study indicated that no significant difference in OS in the ALL group, whether MDR is expressed or not, which is in agreement with the results of other studies. Andress et al. [5] found that P-gp downregulation may be occurring during lymphoid differentiation as most thymocytes are negative for P-gp. However, mature T-cell and B-cell subsets as well as NK-cells again express P-gp. The functional role of P-gp in these cells has not yet been clearly defined. P-gp may be involved in the cellular transport of cytokines. These findings support the suggestion that in ALL other resistance mechanisms may be more important for chemotherapy failure than P-gp-mediated chemoresistance.

The relation between OS in the AML and ALL groups (MDR+, CD34+) and AML and ALL groups (MDR+, CD34−) showed no significant difference (P > 0.05). This finding was initially unanticipated as AL is generally considered to be an ABCB1-overexpressing malignancy. This results are in agreement with the results of Plasschaert et al. [9] and Wu et al. [10] , who concluded that this assumption, however, is based on the observation that ABCB1 is preferentially expressed in CD34+ blasts in AL similar to the preferential expression of ABCB1 in these cells in normal bone marrow, thus reflecting patterns of expression rather than a direct comparison of ABCB1 expression and function of normal and malignant cell subpopulations.

The results of this study showed that no significant difference in OS in the AML group that was MDR+ (CD13+ and CD13−), in agreement with the results of Becker et al. [7] , who reported that in AL patients the coexpression of myeloid antigens with MDR did not affect the complete remission rate or the OS duration.

In the present study, in terms of the impact of BCRP positivity on OS in both groups AML and ALL, it was shorter than that in BCRP− cases, and these results are consistent with Cole et al. [11] and Hamada et al. [12] , who reported that higher BCRP mRNA expression was associated with shorter OS that suggested a predictive value of BCRP mRNA expression in AL cases. Schinkel et al. [13] have reported that BCRP protects hematopoietic stem cells under hypoxic conditions by preventing the accumulation of heme, which causes mitochondrial death, and that BCRP expression is upregulated in stem cells under hypoxic conditions; thus, normal hematopoietic and leukemic stem cells have several characteristics that protect them from potential insults through the expression of ATP-associated transporters as BCRP. Also, as suggested by Robert and Jarry [14] , BCRP mRNA expression could well be distributed heterogeneously among all leukemic cells and this subset could survive treatment and later proliferate, causing relapse, as the expression of human ABCG2 in bone marrow (BM) cells significantly blocked hematopoietic development, leading to the speculation that ABCG2 expression might play a role in early stem cell self-renewal by blocking differentiation, which could be an explanation for our finding that high BCRP mRNA expression in AL patients was associated with poor survival.

In terms of the OS in both groups, AML and ALL, BCRP+, CD34+ was shorter than BCRP+, CD34− and these results are in agreement with those of Pornngarm et al. [15] and Schmitt et al. [16] ; they found that the expression of ABCG2 is highly conserved in primitive stem cells from a variety of sources, suggesting its role in the regulation of stem cell biology. In the hematopoietic compartment, ABCG2 expression is restricted to the most immature hematopoietic progenitors in the bone marrow and is sharply downregulated at the committed progenitor level, suggesting an important role of this ABC transporter in the earliest stages of hematopoietic development. BCRP expression in AML cells might vary depending on the stage of the stem cells in which malignant transformation occurred. In this respect, stem cells, which are CD34 cells, express high levels of BCRP mRNA and BCRP is downregulated during differentiation of normal stem cells, becoming undetectable in mature granulocytes. Chauhan et al. [8] reported that BCRP expression can be separated from those expressing other ABC proteins and suggest that BCRP is expressed in even less differentiated hematopoietic stem cells than MDR1.

The results of this study indicated that the relation between OS in groups with BCRP+, CD19+ and BCRP+, CD19− showed no significant difference. This is consistent with the results of Zolnerciks et al. [17] and Uggla et al. [18] , who explained that ABCG2 expression induced a clear reduction in the development of B-lymphoid cells and an increase in the number and proportion of myeloid cells, leading to an inversion of the lymphoid-myeloid ratio that influences a subset of committed myeloid or lymphoid progeny to produce altered frequencies of mature cells.


  Conclusion Top


BCRP expression has been associated with a lower OS rate in both groups when associated with CD34 and CD13. The study recommends further insight into the contribution of drug resistance gene and apoptosis genes in the pathogenesis of drug resistance in AL.


  Acknowledgements Top


 
  References Top

1.Pulte D, Gondos, A, Brenner H. Improvements in survival of adults diagnosed with acute myeloblastic leukemia in the early 21st century. Haematologica 2008; 93 :594-600.  Back to cited text no. 1
    
2.Goemans B, Tamminga R, Corbijn C, Hahlen K, Kaspers G. Outcome of children with relapsed acute myeloid leukemia in the Netherlands following initial treatment between 1980 and 1998. Eur J Haematol 2008; 93 :1418-1420.  Back to cited text no. 2
    
3.Tamaki A, Ierano C, Szakacs G, Robey RW, Bates SE. The controversial role of ABC transporters in clinical oncology. Biochemistry 2011; 50 :209-232.  Back to cited text no. 3
    
4.Dassa E, Viasani G. Natural history of ABC systems not only the transporters. Biochemistry 2011; 19 :1458-1460.  Back to cited text no. 4
    
5.Andress EM, Linton K. ABC transporters in the balance Nicolau and their role in multidrug resistance. Biochemistry 2011; 33 :241-245.  Back to cited text no. 5
    
6.Benderra Z, Velenga E, Soria J. MRP3, BCRP and P-glycoprotein activities are prognostic factors in adult acute myeloid leukemia. Cancer Res 2005; 11 :7764-7772.  Back to cited text no. 6
    
7.Becker JP, Depret G, Van Bambeke F, Tulkens, PM, Prevost M. Molecular models of human P-glycoprotein in two different catalytic states. BMC Struct Biol 2009; 9 :3.  Back to cited text no. 7
    
8.Chauhan P, Visani G, Wakita Y. Mutation of FLT3 gene in acute myeloid leukemia with normal cytogenetics and its association with clinical and immunophenotypic features. Med Oncol 2011; 28 :544-551.  Back to cited text no. 8
    
9.Plasschaert S, Van F, Cortes J. Expression of multidrug resistance-associated proteins predicts prognosis in childhood and adult acute lymphoblastic leukemia. Clin Cancer Res 2005; 11 :8661-8668.  Back to cited text no. 9
    
10.Wu CP, Hsieh CH, Wu YS. The emergence of drug transporter-multidrug resistance to cancer chemotherapy. Mol Pharm 2011; 8 :1996-2011.  Back to cited text no. 10
    
11.Cole S, Ino T. Comparison of the functional characteristics of the nucleotide binding domains of multidrug resistance protein 1. Biol Chem 2010; 275 :13098-13108.  Back to cited text no. 11
    
12.Hamada S, Satoh K, Hirota M, Kanno A, Umino J, Ito H, et al. The homeobox gene MSX2 determines chemosensitivity of pancreatic cancer cells via the regulation of transporter gene ABCG2. J Cell Physiol 2012; 227 :729-738.  Back to cited text no. 12
    
13.Schinkel A, Jonker J. Mammalian drug efflux transporters of the ATP binding cassette (ABC) family. Drug Deliv Rev 2012; 55 :3-29.  Back to cited text no. 13
    
14.Robert J, Jarry C Multidrug resistance reversal agents. Med Chem 2009; 46 :4805-4817.  Back to cited text no. 14
    
15.Pornngarm L, Wanida C, Suneet S, Chada P, Suresh V Modulation of function of three ABC drug transporters, P-glycoprotein (ABCB1), mitoxantrone resistance protein (ABCG2) and multidrug resistance protein-1 (ABCC1) by tetrahydrocurcumin, a major metabolite of curcumin. Mol Cell Biochem 2007; 29 :85-95.  Back to cited text no. 15
    
16.Schmitt L, Tampe R. Structure and mechanism of ABC transporters. Curr Opin Struct Biol 2012; 12 :754-760.  Back to cited text no. 16
    
17.Zolnerciks, J, Wooding C, Linton K. Evidence for a Sav1866-like architecture for the human multidrug transporter P-glycoprotein. Am J Hematol 2007; 21 :3937-3948.  Back to cited text no. 17
    
18.Uggla B, Watanaba R, Kumda R. BCRP mRNA expression clinical outcome in 40 adult AML patients. Leuk Res 2011; 29 :141-146.  Back to cited text no. 18
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9], [Table 10], [Table 11], [Table 12], [Table 13], [Table 14], [Table 15], [Table 16]



 

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