ABCB1 genotype and PGP expression, function and ... - Nature

The Pharmacogenomics Journal (2007) 7, 154–179 & 2007 Nature Publishing Group All rights reserved 1470-269X/07 $30.00 www.nature.com/tpj

REVIEW

ABCB1 genotype and PGP expression, function and therapeutic drug response: a critical review and recommendations for future research GD Leschziner1,2, T Andrew1, M Pirmohamed3 and MR Johnson1 1 Division of Neurosciences, Imperial College, London, UK; 2Wellcome Trust Sanger Institute, Cambridge, UK and 3University of Liverpool, London, UK

Correspondence: Dr GD Leschziner, Division of Neurosciences, Imperial College London, Room 10E07, Charing Cross Hospital, London W6 8RF, UK. E-mail: [email protected]

The product of the ABCB1 gene, P-glycoprotein (PGP), is a transmembrane active efflux pump for a variety of drugs. It is a putative mechanism of multidrug resistance in a range of diseases. It is postulated that ABCB1 polymorphisms contribute to variability in PGP function, and that therefore multidrug resistance is, at least in part, genetically determined. However, studies of ABCB1 genotype or haplotype and PGP expression, activity or drug response have produced inconsistent results. This critical review of ABCB1 genotype and PGP function, including mRNA expression, PGP–substrate drug pharmacokinetics and drug response, highlights methodological limitations of existing studies, including inadequate power, potential confounding by co-morbidity and co-medication, multiple testing, poor definition of disease phenotype and outcomes, and analysis of multiple drugs that might not be PGP substrates. We have produced recommendations for future research that will aid clarification of the association between ABCB1 genotypes and factors related to PGP activity. The Pharmacogenomics Journal (2007) 7, 154–179. doi:10.1038/sj.tpj.6500413; published online 12 September 2006 Keywords: ABCB1; MDR1; drug response; PGP function

Introduction Since the description of a glycosylated membrane protein isolated from cells resistant to chemotherapeutic agents,1 the ATP-binding cassette (ABC) protein P-glycoprotein (PGP) has been a particular focus of attention as a putative mechanism of drug resistance. PGP is a 1280-residue polypeptide that forms a dimer, resulting in a transmembrane pore.2 It has been shown to transport a large variety of drugs, including cytotoxic agents, protease inhibitors, immunosuppressants, steroids, statins, calcium channel blockers, beta-blockers, antihistamines, anticonvulsants and antidepressants.3 PGP has been reported to be expressed in a wide variety of tissues, including the small and large intestine, adrenal gland, liver, kidney, placenta and capillary endothelial cells of testis and brain.4–7 In the blood–brain and blood–testis barriers, it has been shown to transport substrates out of these tissues,8 whereas in the intestine, PGP limits the absorption of substrates from the bowel.9 It has Received 9 February 2006; revised 20 June 2006; accepted 27 June 2006; published also been implicated in the excretion of substrates into urine, accelerating renal clearance.8 online 12 September 2006

ABCB1 genotype and therapeutic drug response GD Leschziner et al

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Interindividual variability in response to drug therapy might be, at least in part, explained by interindividual variability in the expression or function of PGP. PGP expression is likely to be influenced by environmental or genetic factors, or more likely both. For example, rifampicin has been shown to induce PGP,10 and ABCB1 polymorphisms may be associated with, or causative of, different expression levels or function of PGP. Variants may act to alter amino-acid residues, gene promoter or enhancer sequences, or mRNA stability or processing (Figure 1); furthermore, silent splice site mutations have been shown to alter splicing activity.11 Alternatively, polymorphisms influencing PGP expression or function may also be located outside ABCB1. It has been suggested, for example, that coding sequences in ABC genes may act as regulatory regions for adjacent ABC genes,12 and therefore silent polymorphisms in one gene may influence regulation of another; ABCB1 is one of the many ABC genes located in clusters, and is adjacent to ABCB4. The first study attempting to relate ABCB1 sequence to PGP expression reported an association between a silent coding sequence polymorphism, C3435T, and protein expression and function, as measured by digoxin pharmacokinetics,13 and recently this polymorphism has been reported to affect mRNA stability.14 However, since the initial description of this association, a plethora of studies attempting to identify associations with this or other polymorphisms in ABCB1 with mRNA or protein expression, pharmacokinetics of PGP–substrate drugs, and clinical outcomes related to substrate–drug resistance or side effects, have produced mixed and inconsistent results. Here we review those studies correlating ABCB1 genotype to expression and pharmacokinetics, before proceeding to examine

clinically relevant outcomes with a critical eye on study methodology. Effects of genotype on ABCB1 mRNA and protein expression Association between genotype and mRNA expression Numerous studies have attempted to detect an association between the 3435C4T polymorphism and mRNA expression in a variety of tissues, including kidney, placenta, duodenum, peripheral mononuclear cells and acute myeloid leukaemia blast cells (Table 1). A few studies have reported associations between the C allele and increased mRNA levels, but these have generally not reached significance,15,16 or failed to account for multiple testing,17 with one exception.18 Two studies have described the opposite association, that is, the T polymorphism associated with increased mRNA expression, but once again these have been either nonsignificant19 or performed on a very small sample.20 The majority have failed to find an association,17,21–25 although two of these negative studies did show an association with haplotype rather than genotype.17,21 Most studies of the 2677G4TA polymorphism and mRNA expression levels have also been negative,17,19,21,23–25 although one study22 did report an association between the GG2677 genotype and reduced mRNA expression. Association between genotype and expression of protein Studies of PGP protein expression and 3435C4T have also been mixed (Table 2). The original study by Hoffmeyer et al. showed a borderline-significant association (P ¼ 0.056),13 but other studies have failed to replicate this finding.23,24,26 Hitzl et al.17 demonstrated no association between placental

Figure 1 Polymorphisms in ABCB1 with a minor allele frequency 45%, as confirmed by genotyping in Ensembl, as of January 2006.

The Pharmacogenomics Journal


156

Table 1

Summary of studies correlating ABCB1 genotype or haplotype with PGP mRNA expression

Polymorphism

Subjects

N

3435C4T

Japanese, live donor liver transplantation Japanese, healthy males Caucasian mothers Japanese Caucasians

2677G4TA

76T4A

1236C4T

Direction of association

Reference

69 Intestine

CC ¼ CT ¼ TT

Goto et al. (2002)

¼

13 35 24 59

CCoCToTT CC ¼ CT ¼ TT CC ¼ CT ¼ TT CC4CT4TT

Nakamura et al. (2002) Hitzl et al. (2004) Uwai et al. (2003) Fellay et al. (2002)

T ¼ ¼ C

Caucasians Caucasians Caucasians

32 Duodenum CC ¼ CT ¼ TT 31 CD56+ cells CC4CT4TT 20 Mononuclear cells CC4TT

Siegmund et al. (2002) Hitzl et al. (2001) Drescher et al. (2002)

¼ ¼* ¼*

Caucasian transplant donors Caucasian bypass graft patients Japanese

26 Liver

CC ¼ CT ¼ TT

Owen et al. (2005)

¼

51 Heart

CC ¼ CT ¼ TT

Meissner et al. (2004)

¼

13 Intestine

CCoCToTT

Moriya et al. (2002)

¼*

69 Intestine

GG ¼ GA ¼ GT ¼ TA ¼ TT ¼ AA Goto et al. (2002)

¼

35 Placenta 24 Kidney 13 Intestine

GG ¼ GT ¼ TT GA ¼ GT ¼ TA ¼ TT GGo(T/A)(T/A)oG(T/A)

Hitzl et al. (2004) Uwai et al. (2003) Moriya et al. (2002)

¼ ¼ ¼*

Caucasians Caucasian transplant donors Caucasian bypass graft patients

32 Duodenum 26 Liver

GG ¼ GT ¼ TT ¼ GA ¼ TA GG ¼ GT ¼ TT

Siegmund et al. (2002) Owen et al. (2005)

¼ ¼

51 Heart

GGoG(T/A)o(T/A)(T/A)

Meissner et al. (2004)

Japanese, live donor liver transplantation Japanese

69 Intestine

TT ¼ TA ¼ AA

Goto et al. (2002)

¼

24 Kidney

TT ¼ TA ¼ AA

Uwai et al. (2003)

¼

69 Intestine

CC ¼ CT ¼ TT

Goto et al. (2002)

¼

CC ¼ CT ¼ CT CC ¼ CT ¼ TT

Uwai et al. (2003) Illmer et al. (2002)

¼ ¼

Japanese, live donor liver transplantation Caucasian mothers Japanese Japanese

Japanese, live donor liver transplantation Japanese AML patients

Tissue

Duodenum Placenta Kidney Mononuclear cells

24 Kidney 136 AML blast cells

Conclusion

T/A

1199G4A

Japanese, live donor liver transplantation

69 Intestine

GG ¼ GA ¼ AA

Goto et al. (2002)

¼

+139C4T


69 Intestine

CC ¼ CT ¼ TT

Goto et al. (2002)

¼

129T4C

Japanese

13 Intestine

TT ¼ TC

Moriya et al. (2002)

¼

Haplotype


69 Intestine

Goto et al. (2002)

CGC

Caucasian mothers

35 Placenta

CC1236/GG2677/ CC34354TT/TT/TT4CT/GT/ CT T2677T3435 haplotype onon-TT haplotype

Hitzl et al. (2004)

GC

Abbreviations: AML, acute myeloid leukaemia; PGP, P-glycoprotein. The final column describes the conclusion of the association, that is, the allele that is associated with increased mRNA expression. For those studies in which a direction of association is described but in which the P-value is not significant, the conclusion is one of no difference (*).

PGP expression and 3435C4T in mothers or foetuses, but described a positive association between protein expression and genotype when wild-type homozygote mothers and


foetuses were compared with variant homozygote mothers and foetuses, with a P-value of 0.01, in the context of multiple testing.


157

Table 2

Summary of studies correlating ABCB1 genotype or haplotype with PGP protein expression

Polymorphism

Subjects

N

3435C4T

Caucasian mothers Caucasian foetuses Caucasian mothers and foetuses Japanese Caucasians Caucasians Caucasian transplant donors HeLa transfected cells (transient transfection) Caucasian mothers Caucasian foetuses Japanese HEK293T cells

73 Placenta 73 Placenta 89 Placenta Transduced HEK293T cells 32 Duodenum 26 Liver

2677G4TA

Caucasians Caucasian transplant donors HeLa transfected cells (transient transfection)

Tissue


Reference

Conclusion

73 Placenta 73 Placenta 39 Placenta

CC ¼ CT ¼ TT CC ¼ CT ¼ TT CC/cc4TT/tt4CT/ct

Hitzl et al. (2004) Hitzl et al. (2004) Hitzl et al. (2004)

¼ ¼ C

89 21 32 26

Placenta Intestine Duodenum Liver

CC ¼ CT ¼ TT CC4CT4TT CC ¼ CT ¼ TT CC ¼ CT ¼ TT

Tanabe et al. (2001) Hoffmeyer et al. (2000) Siegmund et al. (2002) Owen et al. (2005)

¼ ¼* ¼ ¼

Cells

CC ¼ TT

Kimchi-Sarfaty et al. (2002)

¼

GG ¼ GT ¼ TT GG ¼ GT ¼ TT GG4G(T/A)4(T/A)(T/A) GG ¼ TT

Hitzl et al. (2004) Hitzl et al. (2004) Tanabe et al. (2001) Kim et al. (2001)

¼ ¼ ¼* ¼

GG ¼ GT ¼ TT ¼ GA ¼ TA GG ¼ GT ¼ TT

Siegmund et al. (2002) Owen et al. (2005)

¼ ¼

Cells

GG ¼ TT


¼

89 Placenta

TCoTT

Tanabe et al. (2001)

T

129T4C

Japanese

61A4G

HeLa transfected cells (transient transfection)

Cells

AA ¼ GG


¼

307T4C


Cells

TT ¼ CC


¼

1199G4A


Cells

GG ¼ AA


¼

Haplotype

Caucasian mothers

16 Placenta

Hitzl et al. (2004)

GC

Caucasian mothers

67 Placenta

Hitzl et al. (2004)

GC

Caucasian foetuses

27 Placenta

Hitzl et al. (2004)

¼

Caucasian foetuses

69 Placenta

Homozygote G2677C34354Homozygote TT T2677T3435 haplotype carrieronon-TT carrier Homozygote G2677C3435 ¼ Homozygote TT T2677T3435 haplotype carrier ¼ non-TT carrier GG2677gg2677/ CC3435cc3435 ¼ TTtt/TTtt AC ¼ AT ¼ GC ¼ GT ¼ TC ¼ TT

Hitzl et al. (2004)

¼

Hitzl et al. (2004)

¼

Morita et al. (2003)

¼

Caucasian mothers and foetuses LLK-PK1 cells (permanent transfection)

9 Placenta Cells

Abbreviations: HEK, human embryonic kidney; PGP, P-glycoprotein. The final column describes the conclusion of the association, that is, the allele that is associated with increased mRNA expression. For those studies in which a direction of association is described but in which the P-value is not significant, the conclusion is one of no difference (*).

Associations with other polymorphisms in the ABCB1 gene have been equally inconclusive. The majority of studies have failed to show a significant association between 2677G4TA and mRNA expression17,19,21,23–25 or with PGP expression.17,23,24,26,27 Other ABCB1 polymorphisms have similarly also failed to demonstrate positive associations.

The polymorphisms tested include 1236C4T (exon 12) and 76T4A (intron 12),21,25 þ 139C4T,21 129T4C,19 61A4G,28 307T4G28 and 1199G4A.28 A positive association has however been described with the 129T4C polymorphism in exon 1 and PGP expression in placenta tissue Japanese patients, with a P-value of 0.002.26



158

Further analyses have been based on haplotypes rather than genotypes, as theoretically haplotype analysis should increase the power to detect cis-interactions and associations with single-nucleotide polymorphisms (SNPs) that have not been directly genotyped. However, haplotype analysis will only provide more power compared to single locus tests if the functional variant(s) arise on the same haplotypic background and the haplotypic effect is large compared to a single genotype. As part of the recurring theme, these results have also been inconclusive. One study showed the 1236C4T–2677G4TA–3435C4T haplotype to be nonsignificantly associated with mRNA expression,21 but these results were based on extremely low subgroup sizes, and heterozygotes for the haplotype showed even lower levels of expression. Another study failed to show any association with haplotype in transduced cells,29 whereas Hitzl et al.17 reported no association between foetal haplotype and mRNA or protein expression, nor any association between combined maternal and foetal haplotypes and protein expression. However, this study did report an association between maternal haplotype and mRNA and protein levels, albeit with P-values of 0.02–0.04 in the context of multiple testing. In summary, the majority of studies have failed to demonstrate a significant association. Of the positive studies, most, particularly those analysing haplotypes rather than genotypes, report an association between the nonvariant allele and increased PGP or mRNA expression. However, there are several studies that also report an association in the opposite direction. In the absence of concordance between studies, the findings need to be interpreted with care. Caution must be exercised in correlating mRNA expression levels to PGP activity, as mRNA may be liable to significant posttranscriptional regulation or processing. It has recently been reported that the 3435C4T polymorphism affects mRNA stability,14 but this may not necessarily directly translate to reduced PGP levels. In correlating PGP protein levels with genotype, in patients at least, consideration must be paid to potential environmental confounders that might influence expression levels. Co-medications and possibly foodstuffs alter PGP expression,30 and the degree of alteration may itself be genetically influenced. Certainly, polymorphisms in both the regulatory and coding regions of the CYP genes affect the degree of induction of cytochrome P450 enzymes by drugs.31 This may also be a potential mechanism for the induction of multidrug resistance through PGP upregulation. To complicate matters further, (a) induction of PGP by drugs is tissue-dependent, and this may have some bearing upon the relevance of particular inducers in the study of drug resistance in different diseases32; and (b) it is also possible that baseline or constitutive expression levels may determine the maximal degree of induction that occurs in response to contact with an environmental inducer. Furthermore, the extent of induction of proteins by drugs is frequently dose-dependent and therefore differences in intracellular concentrations of inducing drugs between individuals may contribute to differences in PGP expression


levels. Disease states themselves may also influence PGP expression or may themselves be associated with polymorphisms in ABCB1. For example, chronically ischaemic cardiomyocytes in pigs have been shown to express PGP, whereas normal cells do not,33 PGP expression and activity has been shown to be different in mouse models of colitis,34 and inflammatory bowel disease has been reported to be associated with the 2677G4TA polymorphism.35 Therefore, any study attempting to correlate genotype to expression levels should make efforts to control for the potential effects of co-morbidities, co-medications and their dosages. Effects of genotype on pharmacokinetics of PGP–substrate drugs If the ABCB1 genotype has an effect on the expression or function of PGP, one would expect these genotypes to be associated with alterations in the pharmacokinetics of drugs that are substrates for PGP. PGP affects drug pharmacokinetics through its limitation of oral absorption,9 renal clearance8 and penetration into target organs such as the brain.8 In individuals with increased PGP expression or function, reduced oral bioavailability, decreased maximal plasma concentrations, increased renal clearance and reduced area under the curve (AUC) would be expected. To this end, the effect of numerous polymorphisms in the ABCB1 gene on the pharmacokinetics of a number of drugs has been analysed. Digoxin The first drug in which an association was described was digoxin13 – a borderline P-value of 0.053 was obtained for the association between the 3435C4T polymorphism and AUC0–144 after a single dose of digoxin in subjects who had been induced with rifampicin. The study also demonstrated a much more significant (P ¼ 0.006) association between the 3435C4T genotype and Cmax in a further 14 subjects (who had not been induced with rifampicin), suggesting that the CC genotype resulted in increased PGP activity. A further study36 has replicated this finding with AUC0–4, AUC0–24 and AUC0–48, describing TT individuals having a higher AUC than CC individuals and CT individuals. No significant association with the 2677G4TA polymorphism was identified in this study. Another paper described this association with AUC and Cmax in steady-state conditions, as well as Ctrough, and urinary excretion but P-values were high in the context of multiple testing.37 By contrast, other papers describe an association in the opposite direction38 or fail to find an association at all.39,40 The latter study had the largest sample size (n ¼ 50) of any of these. Analyses of other polymorphisms with digoxin pharmacokinetics described no significant association with 2677G4T, 76T4A, þ 139C4T, 61A4G, 1199G4A or 1G4A.36,40 However, one study reported a decrease in intracellular digoxin concentration in human embryonic kidney (HEK)293T cells transfected with the T2677 variant (Po0.002).27 Haplotype analysis has revealed equally inconsistent results, with the G2677/T3435 haplotype being associated


159

with a higher AUC,37 no association with transport or efflux in LLC-PK1 transfected cells,29 and an association between homozygosity for the G2677/C3435 haplotype and decreased Cmax, decreased oral bioavailability and increased renal clearance.41 Immunosuppressive drugs The majority of studies involving tacrolimus, cyclosporine and sirolimus have failed to demonstrate any significant association.21,42,42–51 A further study has shown that the pharmacodynamic effects of cyclosporine on peripheral blood mononuclear cells were affected by the level of PGP expression, but not by the ABCB1 3435C4T genotype.52 There was also no correlation between the 3435C4T genotype and PGP expression at the protein level in this study. A few studies have shown an association between 1236C4T and dose-adjusted Cmax and AUC0–4 of cyclosporine,53 3435C4T and cyclosporine concentration/dose ratio,54 2677G4T and tacrolimus dose-adjusted concentration,55 3435C4T and tacrolimus dose-adjusted concentration,56 and 3435C4T and dose-related Cmax, AUC and oral clearance (in the opposite direction to MacPhee et al.56 and Bonhomme-Faivre et al.54).57 However, these studies generally had borderline P-values, even before multiple testing, or were in very small samples. Haplotype analysis has also been uninformative, with two studies finding no association,43,47 a smaller study describing an association (P ¼ 0.04) between dose-adjusted plasma concentration of tacrolimus and the T2677/T3435 haplotype,55 and a further study reporting an association with the three-SNP haplotype and cyclosporine pharmacokinetic parameters.58 Other drugs Attempts to correlate ABCB1 genotype to variability in the pharmacokinetics of other drugs that may be substrates for PGP have also been performed, with further inconsistent findings. Thus, some studies have not demonstrated a significant association with genotype, including loperamide,59 rhodamine 12360 and vincristine.61 One study examined the efflux of verapamil, daunorubicin, vinblastine, calcein, prazosin, bisantrene, paclitaxel and forskolin in HeLa cells transiently transfected with 61A4G, 307T4C, 1199G4A, 2677G4T and 3435C4T variants, and demonstrated no substantial difference in transport for all drugs compared with wild-type PGP.28 By contrast, positive associations have been shown for the following: 3435C4T and the three SNP haplotype TTT with AUC0–4 following a single dose of fexofenadine;27 3435C4T and 2677G4TA genotypes and fexofenadine AUC0–24 and Cmax;62 CC3435 genotype and increased efflux of rhodamine 123;15,16 the T1236/G2677/T3435 haplotype and reduced renal clearance of irinotecan following intravenous administration,63 and an association between the CC3435 genotype and irinotecan Cmax.64 However, the latter study also showed an association between the three-SNP haplotype and the Cmax of SN-38, a product of irinotecan hydrolysis,

in the opposite direction to the association described with the single SNP. One of the largest studies performed18 demonstrated that the CC3435 genotype was associated with higher plasma protease inhibitor concentrations (discordant with other studies suggesting an association between the TT genotype and lower levels of PGP expression and activity), with a highly significant P-value of 0.0001. However, this study produced conflicting results, in that the CC genotype was associated with higher levels of ABCB1 expression and a lower increase in viral load. Furthermore, an association was found between nelfinavir and efavirenz levels and ABCB1 genotype, despite the fact that efavirenz is not a substrate for PGP. The authors hypothesizsed that this might be due to complex interactions between PGP, other drug transporters and the inducing or inhibiting effects of some of the antiretroviral drugs. Another study performed with protease inhibitors showed an association between 3435C4T and cellular concentration AUC, but not plasma AUC,65 suggesting that the CC genotype is more active. Although this association was not significant once correction for multiple testing had been performed, it was still felt to be significant due to the a priori hypothesis of involvement of this SNP. In summary, as with mRNA and protein expression data, studies of substrate–drug pharmacokinetics have failed to clarify the association between ABCB1 genotype and PGP activity. The majority of studies failed to show any association, whereas those studies that were positive often presented conflicting and contradictory data, sometimes even within the same study. The directions of association are detailed in Table 3. Once again, important issues that need to be addressed in many of these studies are the influence of potential confounders such as co-morbidity and co-medication with PGP inhibitors or inducers, which may include foodstuffs.66 Furthermore, some drugs used for the study of PGP pharmacokinetics, such as cyclosporine, may actually inhibit transport function.30 Moreover, some of the drugs may actually be substrates for other efflux transporters, and hence a decrease in PGP activity may be compensated for by an increase in the activity of another transporter.

Effects of genotype on clinical outcomes related to drug therapy A true association between ABCB1 genotype and PGP activity would be expected to alter the clinical outcome of therapy with a drug that is a PGP substrate, either in terms of successful treatment or adverse effects. Numerous studies of clinically relevant outcomes have been undertaken, and these will be considered on a drug class basis below (Table 4). However, ABCB1 genetic association studies have produced conflicting data with difficulties in replication. Clearly, this is not a problem limited to genetic association studies involving ABCB1, and to this end, guidelines have been established describing best practice:67 these are discussed further below.


160


Table 3

Summary of studies correlating genotype or haplotype with pharmacokinetic measures of PGP–substrate drugs

Polymorphism

Subjects

3435C4T

Caucasians

Renal transplant patients Volunteers Kidney transplant patients Heart transplant patients Renal transplant patients Renal and heart transplant patients Renal transplant patients Caucasian liver transplant patients Renal transplant patients Korean volunteers

14 50 24 12 32 32 15 46

Digoxin – single dose, after induction with rifampicin Digoxin – steady state Digoxin – single dose Digoxin – steady state Digoxin – single dose Digoxin – single dose Digoxin – single dose Digoxin – single dose Tacrolimus

66

Tacrolimus


Reference

Conclusion

AUC0–144

CCoTT

Hoffmeyer et al. (2000)

C

Cmax AUC0–4, Cmax, tmax AUC0–4, Cmax, tmax Cmax, AUC0–24 AUC0–4, AUC0–24, AUC0–48 Cmax AUC0–4, AUC0–24 Conc./dose

CCoTT CC ¼ CT ¼ TT CCoCToTT for AUC and Cmax CC ¼ CT ¼ TT CC/CToTT CC ¼ CT ¼ TT AUC0–4 CC4CT/TT, AUC0–24 NS CC ¼ CT ¼ TT

Hoffmeyer et al. (2000) Gerloff et al. (2002) Johne et al. (2002) Becquemont et al. (2001) Verstuyft et al. (2003) Verstuyft et al. (2003) Sakaeda et al. (2001) Goto et al. (2002)

C ¼ C ¼ C ¼ T ¼

CC ¼ CT/TT

Zheng et al. (2002)

¼

CC ¼ CT ¼ TT

Anglicheau et al. (2004)

¼

CC ¼ CT ¼ TT

Haufroid et al. (2004)

¼

CCoCT/TT

MacPhee et al. (2002)

C

Tada et al. (2005)

¼

Tsuchiya et al. (2004)

¼

Mai et al. (2004)

¼

106

Cyclosporine

Plasma conc. at 3 and 12 months Dose-adjusted Cmax, AUC0–4

100

Tacrolimus/ cyclosporine Tacrolimus

Dose-adjusted trough levels, dose requirement Dose-adjusted conc.

39

Tacrolimus

30

Tacrolimus

73

Tacrolimus

CC ¼ CT/TT tmax, Cmax, Cmax doseadjusted, AUC0–12, t1/2, CL/F CC ¼ CT/TT Dose/body weight, Ctrough, dose-adjusted Ctrough, Cmax, dose-adjusted Cmax, AUC0–12, tmax, t1/2, MRT, CL/F, Vdss/F CC ¼ CT ¼ TT Ctrough

14 10

Cyclosporine Cyclosporine

AUC, tmax, t1/2, Cmax, CL/F Cmax, AUC, CL/F

CC ¼ CT/TT CC4TT/CT

Min and Ellingrod (2002) Yates et al. (2003)

¼ T

14

Cyclosporine

AUC, Cmax, Cmin

CC ¼ CT ¼ TT

Chowbay et al. (2003)

¼

149

Sirolimus

Conc./dose

CC ¼ CT ¼ TT


¼

151

Cyclosporine

Ka, Vl/F, Cl/F, V2/F, Q/F, Tlag CC ¼ CT ¼ TT

Hesselink et al. (2004)

¼

97

Cyclosporine

AUC0–2/dose/kg

CC ¼ CT ¼ TT

Kuzuya et al. (2003)

¼

44

Cyclosporine

Conc./dose

CCoTT

C

124

Cyclosporine

Dose-adjusted Ctrough

CC ¼ CT ¼ TT

Bonhomme-Faivre et al. (2004) von Ahsen et al. (2001)

¼

Fexofenadine – single dose Fexofenadine – single dose Fexofenadine – single dose

AUC0–24, Cmax

TT4CT4CC

Yi et al. (2004)

C

AUC0–4

CT4CC4TT

Kim et al. (2001)

T

AUC, t1/2, Cmax, clearance

CC ¼ TT

Drescher et al. (2002)

¼

180

33

Caucasians

60

Caucasians

20

GD Leschziner et al

Renal transplant patients Renal transplant patients Renal transplant patients Renal transplant patients, Japanese Renal transplant patients

8

Measure

ABCB1 genotype and therapeutic drug response

Caucasians Caucasians Caucasians Normals Volunteers Volunteers Japanese Japanese, living donor liver transplant patients Paed heart Tx

Number Drug

Table 3 Continued Polymorphism

2677G4TA

Subjects

Number Drug

Caucasian HIV patients Caucasian HIV patients HIV patients

123 107 28

Nelfinavir/efavirenz Nelfinavir/efavirenz Nelfinavir

HIV patients

504

Nelfinavir/efavirenz

Measure


Reference

16 67 31

Loperamide Talinolol Rhodamine 123

Male volunteers Caucasian

41 20

Children with ALL Asian cancer patients

52 29

Rhodamine 123 Rhodamine 123 in CD56+ Vincristine Irinotecan

Caucasian Volunteers HEK293T cells (permanent transfection) Korean volunteers

50 32

Digoxin – single dose Digoxin – single dose Digoxin

AUC0–4, Cmax, tmax Cmax, AUC Intracellular concentration

GG ¼ GT ¼ TT ¼ GA ¼ TA ¼ TT CC ¼ CT ¼ TT TToGG

Gerloff et al. (2002) Verstuyft et al. (2003) Kim et al. (2001)

¼ ¼ T

33

AUC0–24

GG ¼ G(T/A) ¼ (T/A)(T/A)

Yi et al. (2004)

¼

Korean volunteers

33

AUC0–24

TT4GG4AA

Yi et al. (2004)

Complex

Caucasian

60

AUC0–4

GG4GT4TT

Kim et al. (2001)

T

Heart transplant patients Renal transplant patients Renal transplant patients Renal transplant patients HIV patients

14

Fexofenadine – single dose Fexofenadine – single dose Fexofenadine – single dose Cyclosporine

AUC, Cmax, Cmin

GG ¼ GT ¼ TT


¼

106

Cyclosporine

Dose-adjusted Cmax

GG ¼ GM ¼ MM


¼

106

Cyclosporine

AUC0–4

GG ¼ GM ¼ MM


¼

97

Cyclosporine

AUC0–2/dose/kg

GG ¼ GM ¼ MM


¼

504

Nelfinavir/efavirenz

GG ¼ GT ¼ TT

Haas et al. (2005)

¼

100

GG ¼ GM ¼ TT


¼

Dose requirement

GG ¼ GM ¼ TT


¼

81

Tacrolimus/ cyclosporine Tacrolimus/ cyclosporine Tacrolimus

Nelfinavir AUC0–12, Efavirenz AUC0–24 Dose-adjusted trough levels

Level/dose

TT4GG

Zheng et al. (2005)

G

75

Tacrolimus

Ctrough

GG ¼ GT ¼ TT

Mai et al. (2004)

¼

46

Tacrolimus

Conc./dose

GG ¼ GA ¼ GT ¼ GT ¼ TA ¼ TT

Goto et al. (2002)

¼

Sirolimus

Conc./dose

GG ¼ GM ¼ MM


¼

149

¼ ¼ ¼ C ¼ C ¼ C


100

T T C

GD Leschziner et al

161


Caucasian volunteers Caucasians Caucasian volunteers

CC4CT4TT Fellay et al. (2002) CC4CT4TT Fellay et al. (2002) CCoCToTT for cellular AUC, but Colombo et al. (2005) no assoc with plasma AUC Haas et al. (2005) Nelfinavir AUC0–12, efavirenz CC ¼ CT ¼ TT AUC0–24 CC ¼ TT Pauli-Magnus et al. (2003) AUC0–8, Cmax, tmax CC ¼ CT ¼ TT Siegmund et al. (2002) AUC0–inf, F, Cmax Rhodamine fluorescence at 5, CCoCToTT Hitzl et al. (2001) 10, 15 min Efflux CC ¼ CT ¼ TT Oselin et al. (2003) Rhodamine fluorescence TT4CC Drescher et al. (2002) 10 min CC ¼ CT ¼ TT Plasschaert et al. (2004) CL, AUC, Vd, t1/2 CCoCT/TT Zhou et al. (2005) Cmax

Renal transplant patients Renal transplant patients Lung transplant patients Renal transplant patients Japanese, liver transplant patients Renal transplant patients

Conc. Conc. Cellular AUC, plasma AUC

Conclusion

162



76T4A

129T4C

Measure


Reference

Conclusion

Caucasian Caucasian Children with ALL Asian cancer patients Male volunteers Caucasians Japanese, liver transplant pateints

67 67 52 29 41 50 46

Talinolol Talinolol Vincristine Irinotecan Rhodamine Digoxin – single dose Tacrolimus

AUC0–inf F, Cmax CL, AUC, Vd, t1/2 Cmax, AUC, t1/2, CL Efflux AUC0–4, Cmax, tmax Conc./dose

GG/GMoMM GG ¼ GM ¼ MM GG ¼ GT ¼ TT GG ¼ GT ¼ TT GG ¼ GT ¼ TT TT ¼ TA ¼ AA TT ¼ TA ¼ AA

Siegmund et al. (2002) Siegmund et al. (2002) Plasschaert et al. (2004) Zhou et al. (2005) Oselin et al. (2003) Gerloff et al. (2002) Goto et al. (2002)

G ¼ ¼ ¼ ¼ ¼ ¼

Japanese, liver transplant patients Renal transplant patients Renal transplant patients Renal transplant patients Renal transplant patients Heart transplant patients Renal transplant patients Renal transplant patients Asian cancer patients

46

Tacrolimus

Conc./dose

CC ¼ CT ¼ TT

Goto et al. (2002)

¼

106

Cyclosporine

Dose-adjusted Cmax

CCoCT/TT


C

106

Cyclosporine

AUC0–4

CCoCT/TT


C

100

Tacrolimus/ cyclosporine Tacrolimus/ cyclosporine Cyclosporine

Dose-adjusted trough levels

CC ¼ CT ¼ TT


¼

Dose requirement

CC ¼ CT ¼ TT


¼

AUC, Cmax, Cmin

CC ¼ CT ¼ TT


¼

Sirolimus

Conc./dose

CC ¼ CT ¼ TT


¼

97

Cyclosporine

AUC0–2/dose/kg

TT ¼ CT ¼ CC


¼

29

Irinotecan

Cmax, AUC, t1/2, CL

CC ¼ CT ¼ TT

Zhou et al. (2005)

¼

46

Tacrolimus

Conc./dose

CC ¼ CT ¼ TT

Goto et al. (2002)

¼

50

Digoxin – single dose

AUC0–4, Cmax, tmax

CC ¼ CT ¼ TT

Gerloff et al. (2002)

¼

106

Cyclosporine

Dose-adjusted Cmax

TT ¼ CT ¼ CC


¼

106

Cyclosporine

AUC0–4

TT ¼ CT ¼ CC


¼

97

Cyclosporine

AUC0–2/dose/kg

TT ¼ CT ¼ CC


¼

Japanese, liver transplant patients Caucasians Renal transplant patients Renal transplant patients Renal transplant patients

100 14 149

61A4G

Caucasians

50



AA ¼ AG ¼ GG


¼

1199G4A

Caucasians

50



AA ¼ AG ¼ GG


¼

1G4A

Caucasians

50



AA ¼ AG ¼ GG


¼

Haplotypes

Caucasians

24

Digoxin – steady state AUC0–4, Cmax, tmax

Haplotype G2677T3435 higher AUC than non-carriers, haplotype GC lower AUC than non-carriers

Johne et al. (2002)

GC

GD Leschziner et al

+139C4T

Number Drug


1236C4T

Subjects


Subjects

Measure


Reference

Conclusion ¼

Digoxin, vincristine, Transport of substrates across No difference cyclosporine, verapamil monolayers


Digoxin, vincristine, Efflux of substrates cyclosporine, verapamil

No difference


Cmax Oral bioavailability Renal clearance Dose-adjusted trough levels

GG2677CC3435oTTTT/GTCT TT2677TT34354GGCC GG2677CC34354TTTT No effect

Kurata et al. (2002) Kurata et al. (2002) Kurata et al. (2002) Haufroid et al. (2004)

GC GC GC ¼

Dose/kg

No effect


¼

81

Digoxin single dose Digoxin single dose Digoxin single dose Tacrolimus/ cyclosporine Tacrolimus/ cyclosporine Tacrolimus

Level/dose

Zheng et al. (2005)

75

Tacrolimus

Ctrough/dose

T2677T3435 haplotype4non-TT haplotype All haplotypes equal

14

Cyclosporine

AUC, Cmax, Ctrough

33

15 15 15 100 100

Mai et al. (2004)

¼

Yi et al. (2004)

AUC0–4

T1236T2677T3435oCGC

Kim et al. (2001)

TTT

Renal clearance

*2 haplotype in Block2 (T1236,G2677, T3435) oothers No effect C1236G2677C3435 ¼ TTT CC1236GG2677CC34354 CTGTCT4 TTTTTT GC haplotype longer t1/2 GT haplotype shorter t1/2

Sai et al. (2003)

Complex

Caucasians

37

Japanese

49

Japanese Asian cancer patients Asian cancer patients

49 29 29

I.v. Irinotecan Irinotecan SN-36G

AUC0–24 Cmax Cmax

Children with ALL Children with ALL

52 52

Vincristine Vincristine

CL, AUC, Vd, t1/2 CL, AUC, Vd, t1/2

GC Complex

Sai et al. (2003) Zhou et al. (2005) Zhou et al. (2005)

¼ ¼ ¼

Plasschaert et al. (2004) Plasschaert et al. (2004)

¼ ¼

163


Abbreviations: HEK, human embryonic kidney; HIV, human immunodeficiency virus; i.v., intravenous; PGP, P-glycoprotein. The final column describes the conclusion of the association, that is, the allele that confers increased PGP activity.


AUC0–24

CC1236GG2677CC3435 oTTTTTT AA2677CC3435oothers

Fexofenadine – single dose Fexofenadine – single dose I.v. Irinotecan


GC

GD Leschziner et al

LLC-PK1 cells (permanent transfection) LLC-PK1 cells (permanent transfection) Japanese Japanese Japanese Renal transplant patients Renal transplant patients Lung transplant patients Renal transplant patients Heart transplant patients Korean volunteers

Number Drug


164

Immunosuppressants Tacrolimus and cyclosporin A have been the subject of several association studies, and outcomes analysed have included acute rejection following lung or renal transplantation, tremor associated with cyclosporin A toxicity and renal dysfunction following liver transplant, as well as steroid-weaning following paediatric heart transplant. The first study undertaken was by Zheng et al.42 on 69 paediatric heart transplant patients treated with tacrolimus and steroids. Outcome data were acquired retrospectively, and consisted of time to weaning off steroids, guided by endomyocardial biopsies excluding rejection. Patients were classified as being either on or off steroids at 1 year posttransplantation. Both the 2677G4T and 3435C4T polymorphisms were typed in all patients. Of the 18 patients with the CC3435 polymorphism, six were off steroids while 12 were still on steroids. For the 47 patients with the CT/ TT3435 genotype, 29 were off steroids while 18 were still receiving prednisolone 1 year after transplantation. Thus, patients with the CC genotype were significantly (P ¼ 0.04) more likely to be still on steroids 1 year after transplantation. A similar comparison with the 2677G4T polymorphism found a slightly less significant (P ¼ 0.06) association between the GG genotype and failure to wean off steroids. A subsequent study68 analysed the outcome of renal dysfunction as an adverse outcome of long-term treatment with calcineurin inhibitors (CNIs) in a retrospective, case– control study of 120 patients following liver transplantation. These patients were all non-Hispanic whites, who were still alive and were at least 3 years post-transplantation, and had had no pre-existing renal insufficiency. Patients were genotyped for the 3435C4T and 2677G4TA polymorphisms, which were both in Hardy–Weinburg equilibrium (HWE). Patients were defined as cases if serum creatinine was over 1.6 mg/dl at 3 years post-transplantation or had required haemodialysis or renal transplantation during this period, as recorded in the medical records. The reported serum creatinine was the mean of two to three values taken at 29–40 months post-transplantation. Patients were also evaluated for liver function, concomitant medication, CNI doses and trough plasma concentrations, as well as for medical conditions that might affect renal function. Analysis of the primary end point of renal dysfunction at 3 years demonstrated a nonsignificant association between more frequent renal dysfunction and the 40 GG2677 patients when compared to the 27 TT2677 patients (odds ratio (OR) for TT compared to GG 0.31, 95% confidence interval (CI) 0.09–1.00). Examination of cumulative incidence of renal dysfunction demonstrated a significantly lower frequency of renal dysfunction by 12 months posttransplantation in the TT2677 patients when compared to all other patients (OR 0.23, 95% CI 0.06–0.83). In a subgroup analysis, when patients with a preoperative serum creatinine 41.6 mg/dl were excluded, the association with the TT2677 genotype remained (OR 0.21, 95% CI 0.06–0.80). No significant association with the 3435C4T genotype was described, except in a subgroup of patients with normal preoperative serum creatinine and receiving cyclosporin A.


Comparison of the TT3435 genotype against the other genotypes produced an OR of 0.27 (95% CI 0.08–0.93). In this subgroup, there was no significant association with the 2677G4TA genotype. A further study analysed the effects of the 3435C4T and 2677G4TA polymorphisms on acute persistent rejection in adult lung transplant patients.69 Thirteen polymorphisms, including the two ABCB1 SNPs, were genotyped in 125 adult lung transplant patients, treated predominantly with tacrolimus, prednisolone and azathioprine, and who were followed up for at least 1 year post-transplantation. Acute persistent rejection was defined as two consecutive biopsies showing rejection in spite of treatment within the year. Genotypes, all in HWE, as well as the variables of age at transplantation, gender, survival status, survival days and human leucocyte antigen (HLA) mismatches were initially tested by w2 test or t-test. Univariate analysis demonstrated that only age at transplantation (P ¼ 0.016) and the 3435C4T (P ¼ 0.04, OR ¼ 0.94, 95% CI 0.89–0.98) genotype were associated with acute persistent rejection, with the TT3435 genotype being associated with reduced frequency of rejection. Of note, the analysis only reported on 76 patients, and it is not clear as to the cause of the loss of 49 patients. Four other studies used adverse effects of the immunosuppressive drugs as outcomes. The first70 analysed a sample of 174 Polish renal transplant patients, 54 with gingival overgrowth as an adverse effect of cyclosporin and 120 without gingival overgrowth. All patients were assessed at 6 months post-transplantation, and were scored for gingival overgrowth by consultant periodontal specialists. Patients were genotyped for the 3435C4T polymorphism. Frequencies of the CC, CT and TT genotypes and the C and T alleles in the patients with and without gingival overgrowth were analysed with the w2 test, and no significant differences were detected. The second study71 analysed tremor as an adverse effect of cyclosporin therapy. Polish renal transplant patients on cyclosporine (n ¼ 118) were recruited, and assessed for tremor by a consultant neurologist. Twenty-three were found to have tremor and 95 were tremor free. Patients were genotyped for the 3435C4T polymorphism (HWE was not commented upon). There was no significant difference between the tremor and control groups, either for cyclosporine dosage or plasma concentrations, or C3435T genotype. Yamauchi et al.72 genotyped 17 patients for seven SNPs in ABCB1 before liver transplantation and immunosuppression with tacrolimus. Patients were monitored for neurotoxic episodes such as convulsions, tremor and leukoencephalopathy within the first 10 days following transplantation. A stepwise discriminant function analysis was performed on the six patients exhibiting neurotoxic episodes and 11 patients without. The G2677TA polymorphism was found to correlate with the development of neurotoxicity, but the allele frequency of G2677TA was not significantly different between the patients with a neurotoxic episode and the control group.

Table 4 Field

Summary of studies attempting to correlate ABCB1 genotype or haplotype with clinical outcome

Reference

Subjects

Immunosuppressants Zheng Paediatric et al. heart (2002) transplant patients

Hebert et al. (2003)

Zheng et al. (2004)

Liver transplant patients

Adult lung transplant patients

Study type

Retrospective

N (cases, controls)

69 (30, 35)

Retrospective 120 (48, 72)


Drug

Outcome

Variant


HWE checked?

Control for stratification?

Control for multiple testing?

Tacrolimus/ steroids

Steroid-free after 1 year

2677G4TA

CT/TT4CC

No

No

3435C4T

GT/TT4GG

No

2677G4TA

GG4TT

Yes

2677G4TA 3435C4T

GG/G(T/A)4TT CC ¼ CT ¼ TT

2677G4TA

GG ¼ G(T/A) ¼ (T/A)(T/A)

3435C4T

CC/CT4TT

Cyclosporine/ Renal dysfunction at 3 tacrolimus years post-transplant

Tacrolimus, Acute persistent prednisolone, rejection azathiaprine

P-value

Conclusion

No

0.06

G

No

No

0.04

C

Yes

No

T

T ¼ Yes

No

¼

No

0.025

C

Retrospective 174 (54, 120) Cyclosporine

Gingival overgrowth

3435C4T

CC ¼ CT ¼ TT

No

Yes

No

¼

Kotrych et al. (2005)

Kidney transplant patients


Cyclosporine

Tremor

3435C4T

CC ¼ CT ¼ TT

No

Yes

N/A

¼

Prospective

Tacrolimus

Neurotoxicity

2677G4TA

GG ¼ G(T/A) ¼ (T/A)(T/A)

No

No

No

¼

3435C4T

CC ¼ CT ¼ TT

2677G4TA

GG4G(T/A)/(T/A)(T/A) No

3435C4T

CC4TT

CD4+count

3435C4T

TT4CT/CC

Viral load

3435C4T

CC ¼ CT ¼ TT

CD4+count

3435C4T

CC ¼ CT ¼ TT

Viral load

3435C4T

CC ¼ CT ¼ TT

3435C4T

CC ¼ CT ¼ TT

3435C4T

CC ¼ CT ¼ TT

¼

3435C4T

CC ¼ CT ¼ TT

¼

Yamauchi Liver et al. transplant (2002) patients

Asano et al. (2003)


17 (6, 11)

Retrospective 136 (30, 106) Steroids

Osteonecrosis of femoral head

¼ No

No

0.046

T/A

0.052

T

0.009

C

Antiretroviral therapy Fellay et al. (2002)

Brumme et al. (2003)

HIV patients

HIV patients

Prospective

Prospective

Retrospective

96

106

455

Nelfinavir/ Efavirenz

Protease inhibitors

Not specified Time to virological success (viral load) Time to virological failure (viral load) Time to immunological failure (CD4 count)

No

No

No

¼ No

Yes

No

¼

¼ No

No

No

¼

165


Nasi et al. (2003)

HIV patients



GD Leschziner et al

Drozdzik et al. (2004)

166


Table 4 Continued Field

Reference

Subjects

AlonsoHIV Villaverde patients et al. (2005)

N (cases, controls)

Prospective

59

Drug

Outcome

Variant


HWE checked?



Efavirenz

HDL-cholesterol

3435C4T

CC4CT4TT

No

No

No

Efavirenz

CD4+count

3435C4T

CC ¼ CT ¼ TT

Viral load

3435C4T

CC ¼ TT4CT

No

No

No

3435C4T

TT4CT/TT

Yes

Yes

No

2677G4T

GG ¼ GT ¼ TT

¼

3435C4T 2677G4T

GG ¼ GT ¼ TT

¼ ¼

HIV patients

Prospective

71

HAART

Haas et al. HIV patients (2005)

Prospective

504

Efavirenz and/ Virological response or nelfinavir (viral load) Virological response (viral load) Toxicity-related failure Toxicity-related failure

P-value

Conclusion

0.024

¼ ¼

0.02

C

Epilepsy Epilepsy

Retrospective 315 (200, 115) AEDs

Multidrug resistance

3435C4T

CC4TT

Yes

Yes

No

Tan et al. Epilepsy (2004)

Retrospective 609 (401), 208)AEDs


3435C4T

CC ¼ CT ¼ TT

Yes

Yes

No

Seizure frequency

1236C4T

CC4TT

Yes

Yes

No

2677G4T 3435C4T Three SNP haplotypes

GG4TT CC4TT CGC/CGC4rest

Zimprich et al. (2004)

Temporal lobe Retrospective epilepsy

210

AEDs

0.006

C

¼

0.014

C

0.011 0.035 0.009

G C CGC

Sills et al. Epilepsy (2005)



3435C4T

CC ¼ CT ¼ TT

Yes

No

No

¼

Hung et al. (2005)



1236C4T

CC ¼ CT ¼ TT

Yes

Yes

Yes

¼

2677G4TA

GG ¼ G(T/A) ¼ (T/A)(T/ A) CC4TT CGC/TGC/TTT4others

Epilepsy

3435C4T Three SNP haplotypes Kim et al. Epilepsy (2006)



1236C4T

CC ¼ CT ¼ TT

2677G4TA

GG ¼ G(T/A) ¼ (T/A)(T/ A) CC ¼ CT ¼ TT No difference

3435C4T Three SNP haplotypes

¼ o0.0001 o0.0001 Yes

Yes

No

C

¼ ¼ ¼ ¼

GD Leschziner et al

Siddiqui et al. (2003)


Saitoh et al. (2005)

Study type


No No Yes

No Yes Yes

Bercovich Familial hyper- Prospective et al. cholesterosingle-blind (2005) laemia

76

Fluvastatin

Change in lipids

3435C4T 2677G4TA

GG lower LDL-C by 3% less than (T/A)(T/A), G(T/A) intermediate – only in atorvastatin group CC ¼ CT ¼ TT No difference 2677G4TA %Change in LDL/chol./TG/HDL 2735 (smaller Atorvastatin subgroups) Prospective Thompson Increased et al. chol. (2005)

Abbreviations: HAART, highly active antiretroviral therapy; HDL, high-density lipoprotein; HIV, human immunodeficiency virus; LDL, low-density lipoprotein; N/A, not applicable; PGP, P-glycoprotein; SNP, singlenucleotide polymorphism; TG, triglycerides.

¼ ¼

G

C 0.014

C allele smaller reduction in LDL-C T allele smaller rise in HDL-C Only in women 3435C4T

Kajinami et al. (2004) Statins

o0.01

C 0.023

GG ¼ G(T/A) ¼ (T/A)(T/ Yes A) 2677G4TA Total chol./ LDL/HDL/TG Increased chol.

Subjects Reference Field

Table 4 Continued

Study type

Prospective, randomized, placebocontrolled, double-blind

344

N (cases, controls)

Drug

Atorvastatin

Direction of association Variant Outcome

HWE checked?

No


No


P-value

¼

Conclusion

167

The final study73 examined the relationship between osteonecrosis of the femoral head (ONF) associated with steroid therapy following renal transplantation, and the 3435C4T and 2677G4TA genotypes. The study included 30 patients with ONF and 106 without ONF, all of whom had undergone renal transplantation and post-transplant immunosuppression with tacrolimus or cyclosporine plus either ‘large-dose’ or ‘small-dose’ steroid protocols. ONF was diagnosed on the basis of radiography, magnetic resonance imaging and bone scan, and patients were excluded if they had had pre-existing joint abnormalities or had not been followed up for at least 2 years post-transplantation. There was a strong positive association between steroid dose and ONF, whereas examination of the relationship between genotype and ONF revealed a significant (P ¼ 0.039) reduction of risk with the TT3435 genotype when compared to the CC genotype, as well as a significantly lower OR for TT genotype than either CC or CT. Univariate and multivariate analysis also showed a significant difference (P ¼ 0.046 in the univariate analysis) in the incidence of ONF between the GG2677 and the MM genotype (where M is the mutant allele T or A), as well as a significant decrease of OR in patients with a variant homozygous genotype when compared to the GG or GM patients (P ¼ 0.046). Analysis of the relationship between genotype and D/C10–15 (the dose/concentration ratio for a trough concentration of 10– 15 ng/ml, a presumed marker of PGP function) showed a significant difference in D/C between the three C3435T genotypes (P ¼ 0.0007), leading the authors to conclude that risk of ONF is decreased in patients with the TT3435 genotype, presumably due to a higher activity of PGP.

Antidepressants Two association studies have been performed, examining adverse effects of antidepressants. The first74 analysed the relationship between the 3435C4T polymorphism and nortryptiline-induced postural hypotension in patients treated for major depression. Only 160 of the 195 patients recruited had genotyping data available. Patients were randomized to either nortryptiline (n ¼ 78) or fluoxetine (not a substrate of ABCB1; n ¼ 82) for a period of 6 weeks. Of the patients randomized to fluoxetine, 72 completed the 6week trial, while only 54 on the nortriptyline arm completed. Of the 78 patients, eight experienced significant postural hypotension. There was a significant association between genotype and postural hypotension (P ¼ 0.034) in the nortriptyline-treated patients, with the TT patients more likely to develop postural hypotension than those with one or more C allele (P ¼ 0.042, OR ¼ 1.37, 95% CI 1.01–1.86). No mention was made for the examination of associations with fluoxetine-induced adverse effects. The second study75 examined the association in 26 patients with antidepressant-induced mania (compared with 29 patients with no such history) with the 3435C4T polymorphism. All patients were in HWE and no association was found between genotype or allele frequency and the occurrence of mania.



168

Anti-retroviral therapy Initial examination of the relationship between ABCB1 genotype and outcome in human immunodeficiency virus (HIV)-infected patients with anti-retroviral agents was performed by Fellay et al.18 Three groups of patients were included in the analysis: 67 Caucasian patients were recruited on the basis of long-term viral suppression, treatment with nelfinavir or efavirenz, stable plasma drug concentrations, and no potentially interacting co-medications; 56 Caucasian patients prospectively recruited for treatment with nelfinavir or efavirenz (randomization process not commented upon) who were assessed for CD4 count and viral load at baseline, 1, 3 and 6 months; 80 patients of unknown ethnicity, starting unknown retroviral therapy. Ninety-six patients – it is unclear as to the make-up of this sample – were examined for a relationship between viral load, CD4 count and the 3435C4T genotype. Mean baseline CD4 counts and viral load were similar between all genotypes. At 6 months, patients with a TT genotype had a significantly higher CD4 count (P ¼ 0.009) than the CT/CC genotypes. No significant association was found between genotype and viral load. The finding that TT genotypes had a higher CD4 count, implying that TT is associated with decreased PGP activity was inconsistent with the association between the TT genotype and lower drug concentrations, reported in the same study. Furthermore, it was unclear as to which patients had been included for analysis of association with clinical outcome, their ethnicity or treatment. A subsequent study76 retrospectively studied 149 newly diagnosed Caucasian HIV patients, 106 of whom had been commenced on a protease inhibitor, and the rest on a nonnucleoside reverse transcriptase inhibitor (not a substrate for PGP), in combination with two nucleoside reverse transcriptase inhibitors. CD4 counts and viral loads were measured at baseline, 3 and 6 months. In both samples, no association was found between genotype and viral load or CD4 count. The relationship between the 3435C4T polymorphism and initial anti-retroviral response was further investigated in a retrospective study of 461 drug-naive patients with HIV.77 These patients had all been initiated on anti-retroviral therapy, and as part of the provincial guidelines, patients were generally tested for viral load at baseline, 4 weeks and subsequently at 3-monthly intervals. Outcomes were defined as time to virological success, as measured by viral load response, time to virological failure (i.e. viral load increase), and time to immunological failure (exemplified by a fall in CD4 count). Time-to-event analyses were performed with Kaplan–Meier methods, log-rank testing and Cox regression analyses. In the 455 patients whose 3435C4T genotype was determined, no significant association between genotype and time to immunological failure or time to virological success was demonstrated, although a nonsignificant trend was seen in the CC3435 genotype with time to virological failure. An analysis of the effect of the 3435C4T polymorphism on efavirenz-induced increases in high-density lipoprotein


(HDL)-cholesterol78 used a prospective cohort of 59 HIVinfected patients of undefined ethnicity who were treated with efavirenz over a 12-month period. Clinical data and serum viral load, plasma total cholesterol, triglycerides, HDL-cholesterol, low-density lipoprotein (LDL)-cholesterol and CD4 count were collected at baseline and at 12 months. Patients were genotyped for the 3435C4T polymorphism. No relationship was identified between the CD4 count and the 3435C4T polymorphism. Overall, there was a rise in total cholesterol, HDL-cholesterol and LDL-cholesterol. However, univariate analysis of variance with HDL-cholesterol showed a significant difference between genotypes (P ¼ 0.024), as did a multivariate analysis of variance (P ¼ 0.041) with the TT genotype showing a smaller rise in HDL-cholesterol than the CT and CC genotype. However, as it is generally accepted that efavirenz is not a substrate for PGP, the authors argued that this result might be due to the efavirenz-mediated upregulation of PGP, leading to increased reverse transport of cholesterol. The reported lack of association with CD4 þ count should be viewed with caution, as it was not stated if any of the other anti-retroviral drugs that the patients were treated with were PGP substrates. Saitoh et al.79 examined the relationship between virological response and the 3435C4T polymorphism. They genotyped six polymorphisms, including 3435C4T, in 71 HIV-infected children who had received at least 8 weeks of highly active anti-retroviral therapy, for whom virological data were available at 2, 4, 8 and 20 weeks, and for whom pharmacokinetic data were available at week 2. These subjects were all part of an ongoing prospective study. The patients were of mixed ethnicity, and different ethnic groups had different minor allele frequencies for the 3435C4T SNP. Analysis of viral load by genotype demonstrated no significant difference between the CC and CT genotypes at week 2, week 4 or week 20. However, at week 8, 91% of CT patients had reached a threshold of less than 400 copies of HIV mRNA per ml, whereas only 59% of CC patients had achieved this threshold (P ¼ 0.004). When subjects with the TT genotype were included (n ¼ 7), no significant difference was found between the TT and CC patients. This lack of difference was also found in the pharmacokinetics of nelfinavir. The relationship between CC and CT genotypes with viral load correlated well with the 8 h post-dose concentration of nelfinavir, but not with the CD4 count. As the authors admitted, the lack of a significant difference between the CC and TT genotypes was out of keeping with their hypothesis that the CC genotype conferred increased PGP activity, and that the overall picture was confounded by the large differences in ethnicity between the three genotypes. In a further study, Haas et al.80 analysed 504 patients with HIV within a randomized clinical trial. Patients were randomized to three or four drug therapy, receiving either efavirenz, nelfinavir or both, in conjunction with either didanosine and stavudine or zidovudine and lamivudine. Patients were assessed at baseline and then 4 weekly intervals until 24 weeks, and 8 weekly thereafter. Treatment


169

failure was defined either as virological failure (as measured by viral load) or toxicity-related failure. Plasma levels of efavirenz and nelfinavir were measured at 4 and 32 weeks. Seven SNPs were genotyped in patients, including the ABCB1 2677G4T and 3435C4T polymorphisms. Patients were analysed separately according to ethnicity. No association of either ABCB1 polymorphism was seen with nelfinavir AUC0–12 or efavirenz AUC0–24. However, a nonsignificant trend (P ¼ 0.08) was demonstrated with 3435C4T and virological response to efavirenz (TT individuals showed a more favourable virological response), and comparison of the TT genotypes vs the combined CC/CT genotypes was significant (P ¼ 0.02). This association was also significant (P ¼ 0.02) in a Cox regression analysis that controlled for race. The T3435 allele was also found to be significantly associated with a decreased likelihood of efavirenz resistance. No association was demonstrated with response to nelfinavir treatment. The authors were somewhat surprised by the results of this study, as efavirenz is not known to be a PGP substrate, in contrast to nelfinavir. However, these findings were in part consistent with those of Fellay et al.18 Epilepsy Six studies examining an association between ABCB1 genotype or haplotype and multidrug resistance in epilepsy have been published to date. The first81 examined a retrospective cohort of consecutive patients with an established clinical diagnosis of epilepsy. Patients were defined as drug-resistant if they had had four or more seizures in the year before recruitment with trials of more than three appropriate anti-epileptic drugs at maximally tolerated doses. Drug-responsive (n ¼ 115) and drug-resistant (n ¼ 200) patients were genotyped for the 3435C4T polymorphism, as well as for seven Alu polymorphisms for the purposes of genomic control. No stratification could be detected using this method, nor was there any significant difference in epilepsy syndrome type between groups. w2 testing demonstrated an association between the CC genotype and drug-resistant epilepsy (P ¼ 0.006), with an OR of 2.66 (95% CI 1.32–5.38) for the C allele and drugresistant epilepsy (P ¼ 0.008). A subsequent study using a larger cohort and identical outcome definition however failed to replicate this finding. Tan et al.82 analysed 401 patients with drug-resistant epilepsy and 208 drug-resistant cases, all of whom were genotyped for the 3435C4T SNP. There was no significant association between genotype and outcome, indicating that the initial positive finding of Siddiqui et al.81 may have arisen by chance. Another study83 partially replicated the initial finding, this time in a cohort of 193 ethnically similar patients with temporal lobe epilepsy. Patients were stratified into three outcome groups: Group A – less than or equal to two seizures per month; Group B – three to five seizures per month; and Group C – six or more seizures per month.

Three SNPs, 3435C4T, 2677G4T and 1236C4T, were genotyped in all patients, all meeting HWE. Inferred haplotypes were analysed using a logistic regression model, and the CGC haplotype was found to be significantly overrepresented in group C compared to group A (P ¼ 0.009, OR 4.67 (95% CI 1.48–14.75)). No significant difference was found between groups B and C (P ¼ 0.083). Single genotype analysis of group A vs group C demonstrated significant associations for the 1236C4T (P ¼ 0.014, OR 3.12 (95% CI 1.25–7.74)), 2677G4T (P ¼ 0.011, OR 3.44 (95% CI 1.33– 8.86)) and 3435C4T (P ¼ 0.035, OR 2.96 (95% CI 1.08– 8.07)) SNPs. Three further studies have since confused the picture. Sills et al.84 studied a consecutive series of 400 epilepsy patients, who were genotyped for the 3435C4T SNP. Patients were defined as resistant if they had experienced seizures in the previous 12 months despite exposure to at least two appropriate regimens of anti-epileptic drugs at maximally tolerated dosages. One hundred and seventy were classed responders, whereas 230 were non-responders. No significant differences in genotype or allele frequencies were found between the 170 responders and 230 non-responders, and subgroup analyses of patients with focal epilepsy (n ¼ 270) and generalized epilepsy (n ¼ 118) were also negative for any association. A further smaller study using a similar outcome definition as used by Siddiqui et al.81 and Tan et al.,82 could not demonstrate an association of outcome with 3435C4T, 2977 G4T, 1236C4T or three SNP haplotype in 108 drugresponsive and 99 drug-resistant Korean patients with epilepsy.85 In contrast, another study86 genotyped 10 polymorphisms in 108 drug-resistant and 223 drug-responsive ethnically similar patients. The definition of drug resistance was at least 10 seizures over the preceding year with trials of two to three appropriate anti-epileptic drugs, whereas drug responsiveness was defined as complete freedom from seizures for at least 2 years in patients on any antiepileptic medication. Allele frequencies were analysed between the two groups, and a Bonferroni correction was performed to correct for multiple testing. Significant associations were described for allele frequency – drugresistant patients were more likely to carry the C allele (Po0.0001, OR 2.72 (95%CI 1.94–3.91)) – and genotypic frequency (Po0.0001, OR 7.48 (95% CI 3.49–13.27) between the groups. No significant associations were found with either the 1236C4T or 2677G4TA SNPs. Haplotype testing demonstrated significant differences between the two groups for the CGC/TGC, CGC/TTT and TGC/TTT combinations of haplotypes, which were all more likely to appear in patients with drug resistance. The last result seems counterintuitive as the TTT haplotype carries the opposing alleles for the 3435C4T and 2677G4TA SNPs that might confer increased PGP activity, as described by the single genotype and allelic associations. The field of epilepsy is one of the few areas where there has been a degree of consistency in terms of outcome studied. Yet even in this field, there have been both successes and failures of replication. These inconsistent results may be due to several factors. First, both replicating



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studies were based either solely on temporal lobe epilepsy,83 or were mainly temporal lobe epilepsy patients,86 and it is conceivable that the association is only true in this subgroup of patients. Secondly, the definition of outcome has varied between studies, the exception being the Tan et al. study,82 which still failed to replicate the original finding.81 In particular, Hung et al.86 utilized extreme definitions of drug resistance and drug responsiveness, and this might explain the positive findings of this study. In addition, in this study, there were significant differences in the proportions of patients with temporal lobe epilepsy and epilepsy syndromes between case and control groups, and the association reported may therefore have been with epilepsy aetiology rather than with outcome. Finally, these studies were all retrospective cohorts, and therefore are at risk of selection bias and inaccuracy of retrospective data. Response to statins Statins are substrates for PGP; in addition, PGP activity can be modulated by lovastatin, simvastatin and atorvastatin.87 Three major studies have examined the relationship between ABCB1 genotype and response to statins. Kajinami et al.88 performed a randomized, placebo-controlled, doubleblind study with three arms: atorvastatin, lovastatin and placebo. Within the atorvastatin arm, 344 subjects (ethnicity not commented upon) were genotyped for the 2677G4TA and the 3435C4T polymorphism, which were shown to be in HWE. Plasma lipid responses were calculated on baseline values and after 52 weeks of treatment. Initially, a one-way analysis of variance was performed to evaluate differences between genotypes, and this was followed by t-testing of the homozygous wild-type vs homozygous variant groups. For associations with a P-value o0.1, wildtype homozygotes were compared to variant allele carriers in a multiple regression analysis. No significant effect was found in the whole population, although a subgroup analysis demonstrated that males with the TT3435 genotype showed a significantly lower HDL-cholesterol level posttreatment (P ¼ 0.044). However, the multiple regression analysis demonstrated no significant predictive value of genotype for drug response in males. In females, in contrast, the 3435C4T polymorphism had a significant effect on both absolute and percentage changes in HDL- and LDLcholesterol (lowest P-value 0.014). Multiple regression analysis also demonstrated that the 3435C4T allele had significant effects on post-treatment LDL levels (P ¼ 0.044), the absolute and percentage reduction in LDL (P ¼ 0.044). Similar associations were found with LDL-cholesterol (in the opposite direction). Haplotype analysis showed similar results; these results all suggest that the C allele predicts lower drug response due to lower circulating drug concentrations, but only in women. Another study89 examined the effects of five ABCB1 SNPs, including 2677G4TA in 76 patients with heterozygous familial hypercholesterolaemia (the specific mutation was characterized in 78% of patients) treated with fluvastatin. Plasma lipids were measured at baseline, after discontinuation of all cholesterol-lowering medications for 8 weeks, and


then after 20 weeks of treatment with fluvastatin. Five ABCB1 SNPs and five CETP gene SNPs were genotyped, and met HWE. Under an additive model, two ABCB1 intronic SNPs were associated with a decrease in LDL-cholesterol response (b ¼ 0.467, 95% CI –0.93–0.00; b ¼ 0.770, 95% CI 0.05–1.49) A univariate haplotype analysis of the six most prevalent haplotypes demonstrated a significant association between a haplotype containing the A/T2677 allele and increase in LDL-cholesterol (P ¼ 0.025), and a haplotype containing the G2677 allele and reduction in triglycerides (P ¼ 0.016). However, no significant associations were detected in a multivariate analysis. The largest association study featuring ABCB1 performed to date90 also examined the response to atorvastatin. Fortythree SNPs across 16 candidate genes were genotyped in 2735 individuals from a prospective study, comparing the efficacy and safety of statins. Plasma lipid levels were examined at baseline and after 6 weeks of treatment. The effect of each polymorphism was evaluated using an analysis of covariance for each statin. The significance value was set at Po0.01, but no correction was made for multiple testing. In the atorvastatin Caucasian subgroup – the exact sample size is not given, but a total of 1902 patients of all ethnicities received atorvastatin, although only 70% of patients overall consented to DNA analysis – patients with a GG2677 genotype showed 3% less reduction in LDL-cholesterol, with an intermediate effect in heterozygotes. No significance was achieved in any of the other drug groups. The 3435C4T SNP did not achieve significance in any analysis, and the direction of association was opposite to that described by Kajinami et al.88 Althoughs this study showed a small but significant effect of the 2677G4T SNP on atorvastatin response, the significant value of Po0.01 was almost certainly insufficient to correct for the extensive multiple testing that was performed. Furthermore, the overall effect of any positive association in determining the response to atorvastatin is likely to be small and therefore of limited clinical utility.

ABCB1 and cancer The nature of the relationship between ABCB1 genotype and phenotype in cancer is an order of magnitude more complex. Two genomes are at play – that of the patient and that of the cancer – contributing to absorption and excretion of cytotoxic drug within the body and penetration of the drug into the malignant cell. In addition to ABCB1 polymorphisms within malignant cells potentially altering PGP activity or function as in nonmalignant tissue, in cancer cells gene amplifications or random chromosomal rearrangements have been shown to cause PGP overexpression following drug exposure;91 random chromosomal rearrangement and capture and activation of MDR1 has been proposed as a mechanism of acquired drug resistance in cancers. An alternative proposed mechanism of acquired drug resistance is the induction of epigenetic modifications influencing PGP expression by


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environmental factors such as chemotherapeutic drugs, through transcriptional activation and with a potential effect on mRNA stability.92 It has been demonstrated in acute myeloid leukaemia cells that ABCB1 gene expression is inversely correlated with methylation status of CpG sites at the promoter region,93 and that cancer cells treated with a cytotoxic agent acquire both multidrug resistance and hypomethylated ABCB1 promoter regions.94 It is suggested that methylation status influences transcription through altering chromatin structure and sensitivity to Dnase I. ABCB1 is also known to have alternative transcripts, in human and animal cell lines, and the resistance status of transfected hamster lung cells to various cytotoxic drugs is affected by the proportion of mRNA transcripts expressed95: alternative splicing is likely to be under both genetic and environmental control. In cancerous cells, studies of correlation of genotype with mRNA expression have generally described an association of the T3435 allele with increased mRNA expression,96,97 but these have either not reached significance97 or did not correct for multiple testing.96 Illmer et al.96 also reported an association between the GG2677 genotype and reduced mRNA expression, with a P-value of 0.05 when corrected for the presence of multiple testing. With respect to measures of pharmacokinetics of cytotoxic drugs, the cancer cell genotype is unlikely to influence clearance or metabolism of the drug. However, in terms of outcome, penetration of the drug into the malignant cells and therefore the influence of cancer cell ABCB1 genotype is likely to have a significant impact. One of the earliest studies addressing the relationship between ABCB1 genotypes and outcome in cancer96 was in 405 previously untreated patients with acute myeloid leukaemia who were studied within the context of a prospective trial of chemotherapy. Patients were assessed for levels of ABCB1 mRNA from bone marrow samples at the time of diagnosis and were genotyped for the 1236C4T, 2677G4T and 3435C4T SNPs. Complete remission was defined as less than 5% blast cells on bone marrow sampling. Associations between genotype and ABCB1 mRNA expression have been discussed earlier, but genotypes were tested against induction treatment outcome and overall survival. ABCB1 genotypes did not show any significant influence on complete remission rate in a regression analysis, nor did genotypes show any significant impact on overall survival in the whole cohort, utilizing a Cox regression model. A subgroup analysis of patients younger than 60 years showed that the CC3435 genotype was an independent predictor for decreased overall survival (P ¼ 0.02). This genotype also showed worse disease-free survival and a significantly higher rate of relapse. However, heterozygotes did not have intermediate outcomes; in fact, heterozygotes always showed increased survival and decreased probability of relapse when compared to homozygotes for either allele. In conclusion, this was a prospective study, in contrast to the majority of association studies performed. While it showed a consistent association between the CC3435 genotype and decreased survival and increased likelihood of relapse, it also reported

an association between this genotype and lower ABCB1 mRNA expression, somewhat inconsistent with the analysis of clinical outcome. A subsequent study98 examined survival and relapse in 113 children with acute lymphoblastic leukaemia. These patients were all Caucasians of Slavic origin and were followed up for a mean of 3 years, and were all genotyped for the 3435C4T polymorphism. The CC genotype was found to be more frequent in patients with an event (defined as relapse or leukaemia-related death) than in non-event patients (12/19 vs 30/92; P ¼ 0.025). Analysis of the outcome by Cox F-test methods demonstrated an association between the CC genotype and lower event-free survival (P ¼ 0.007) and overall survival (P ¼ 0.006). Other methods of analysis confirmed this association with consistently low P-values. This prospective study can be considered to be consistent with the findings reported by Illmer et al.96 in adult AML patients. A third, more recent study,99 examined the 129T4C, 2677G4T and 3435C4T polymorphisms in 17 patients with acute lymphoblastic leukaemia and 28 with acute myeloid leukaemia. Twenty-five of the patients were refractory to induction therapy while 20 were sensitive. w2 testing of genotype vs outcome for the three SNPs revealed no significant association. However, this study was limited in a number of ways. First, a very heterogeneous patient sample was used – ALL and AML are two discrete diagnostic entities. Second, all but two of the 17 ALL patients were drug-responsive, while only five of the 28 AML patients were drug-responsive. Essentially, the comparison being made was one of AML vs ALL. A further analysis of genotype and constipation due to vincristine therapy has been performed;61 this outcome is more likely to be related to patient genotype rather than cancer cell genotype. In a prospective study of children with acute lymphoblastic leukaemia treated with vincristine, 52 patients were genotyped for the 2677G4T and 3435C4T polymorphisms and were scored for constipation over the 6 weeks of induction, using National Cancer Institute common toxicity criteria. Genotypes were compared between the 29 patients with grade 1–2 constipation and those 14 with grade 3–4 constipation. There were no differences in the pharmacokinetic parameters for vincristine between the two groups and no significant differences were identified between genotype frequencies or haplotype frequencies (lowest P-value of 0.12). Studies of ABCB1 genotype and outcome in cancer are summarized in Table 5.

Limitations of studies of therapeutic drug response and recommendations for future research There are many limitations in the studies described above, some of which have already been mentioned. Indeed, almost all of these limitations are common to many if not most of these studies. For instance, only a minority of the studies have been prospective in


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Table 5

Studies of association of ABCB1 genotype and clinical outcome in cancer Subjects

Study type

N (cases, controls)

Illmer et al. (2002)

Acute myeloid leukaemia

Prospective

405

Kaya et al. (2005)

Plasschaert et al. (2004)

Childhood Prospective acute lymphoblastic leukaemia

Acute leukaemia

Retrospective

111

45 (25, 20)

Childhood Prospective acute lymphoblastic leukaemia

Abbreviation: HWE, Hardy–Weinburg equilibrium.

52

Outcome

Variant


HWE checked?

Control for Control for stratification? multiple testing?

Cytotoxics

Complete remission after induction Complete remission after induction Complete remission after induction Overall survival Relapse

1236C4T

CC ¼ CT ¼ TT

No

No

2677G4TA

G ¼ G(T/ A) ¼ (T/A)(T/A)

¼

3435C4T

CC ¼ CT ¼ TT

¼

3435C4T 3435C4T

CT4TT4CC CC4TT4CT

Relapse or death

3435C4T

CC4CT/TT

Event-free survival 3435C4T Overall survival 3435C4T

CCoCT/TT CCoCT/TT

Drug resistance

2677G4T

GG ¼ TT

3435C4T

CC ¼ TT

2677G4T

GG ¼ GT ¼ TT Yes

3435C4T

CC ¼ CT ¼ TT

Cytotoxics

Cytotoxics

Vincristine

Constipation

No

No

Yes

No

P-value

¼

No

No

No

Conclusion

o0.01 o0.001

C C

0.025

C

0.007 0.006

C C ¼ ¼

No

No

¼

¼

GD Leschziner et al

Jamroziak et al. (2004)

Drug


Reference


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design.18,96,61,71,74,98,78–80,88–90 The use of retrospective case– control methodology brings with it the epidemiological problems of the appropriate selection of controls and cases, and ascertainment bias. A particularly stark example of inappropriate case and control selection is the study of Kaya et al.,99 in which the drug-responsive group consisted almost entirely of patients with acute lymphoblastic leukaemia, whereas almost all of the drug-resistant group were patients with acute myeloid leukaemia. Similarly in epilepsy, the Hung et al.86 study had different proportions of temporal lobe epilepsy patients in the case and control groups, and so what was reported as an association with therapeutic drug response may well have been an association with disease aetiology. Another limitation of many of these studies18,96,42,61,69,72–74,77–79,84,88,89 was the absence of any attempt to detect or prevent stratification between cases and controls, either through control for ethnicity or genetic methods of detection of stratification (some studies may have been performed in ethnically homogenous populations, but this was not stated). Indeed in two of the studies, significant differences in allele frequency between groups of different ethnicity were commented upon,42,79 and this was not taken into account when performing the statistical analysis. Data from HapMap (www.hapmap.org) and dbSNP (www.ncbi.nih.gov/SNP) demonstrate very large differences in allele frequencies between ethnically distinct populations: C1236 allele frequency ranges from 0.87 in Yorubans to 0.31 in Han Chinese, G2677 from 1.0 in Yorubans to 0.46 in Japanese, and C3435 from 0.38 in Europeans to 0.93 in African-Americans. Indeed, certain ABCB1 coding polymorphisms only exist in one of the populations genotyped in HapMap (Table 6). Therefore, population stratification is likely to have a significant impact on the result of an association study analysing these ABCB1 SNPs, and future studies should make efforts to match cases and controls for ancestral history. However, use of ethnicity labels may be insufficient to properly control for stratification.100 Therefore, studies should ideally utilize techniques of stratification detection, such as genotyping of random genetic markers throughout the genome.101 Provided these markers

Table 6

do not correlate with disease or each other, they should reflect genetic differences between cases and controls. It has been proposed that as few as 30 SNPs might be sufficient,102,103 although some have argued that this number should be significantly higher.104 Once detected, stratification may be controlled for, through techniques known as genomic control105 or structure assessment.106 Differences in allele frequencies between populations of different ancestry will also have an impact on patterns of linkage disequilibrium (LD) and haplotypes within the ABCB1 gene. The majority of studies have analysed a single three SNP haplotype rather than formally defining haplotypes within a sample representative of the study population. An exception is the study by Colombo et al.65 ABCB1 haplotypes have also been analysed at a higher SNP density in a European sample.107 Of note, this study also demonstrated that a coding polymorphism in ABCB4, adjacent to ABCB1, is in high LD with 3435C4T, suggesting, in the context of the study by Zhao et al.,12 that ABCB4 polymorphisms may be functional variants of which 3435C4T is a marker. Another potential issue that was repeatedly ignored was that of genotyping error. Many of the studies failed to comment upon their genotyping error rate or HWE.18,96,42,72–74,98,99,76–79 The use of HWE as a measure of genotyping error has limitations, in that it may result in the exclusion of variants that are out of HWE due to selection, that is, functionally significant polymorphisms, and may be too insensitive. Rather, genotyping error rates should be directly measured through the genotyping of duplicate samples. There was also lack of consideration for multiple testing, resulting in the overinterpretation of allegedly significant P-values. In fact, only one study made a true effort to correct for multiple testing.86 By contrast, another study reported up to 300 P-values without any correction.88 While the rest of the studies did not undertake multiple testing to quite such an extent, even those studies testing for one SNP performed multiple tests through subgroup analysis. Correction for multiple testing can be performed in a number of ways. At the most basic level, the Bonferroni method corrects for multiple testing by setting

ABCB1 exonic SNPs genotyped in HapMap

SNP

rs3213619 rs2214102 rs9282564 rs2229109 rs1128503 (1236CT) rs2032582 (2677GT) rs17149694

Population Caucasian

Yoruban

Japanese

Han Chinese

0.051 0.133 0.1 0.033 0.392 0.392 0

0.067 0 0 0 0.689 0 0.045

0.068 0 0 0 0.578 0.524 0

0.078 0 0 0 0.123 0.5 0

Abbreviation: SNP, single-nucleotide polymorphism. The reference allele is the minor allele in the Caucasian population.



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statistical significance at approximately p/k, where p is the level of significance for a single test and k is the number of tests (see Lynch and Walsh108). However, the Bonferroni correction is extremely conservative when the tests are not independent (as with correlated SNP data), particularly if the number of tests is high, and tends to result in very few statistically significant results. This has led to modifications to this method, such as the sequentially rejective Bonferroni test,109 which is slightly less conservative. An alternative to statistical correction in association studies is permutation testing, where the designation of case or control is randomly applied and allelic associations are analysed.110 Numerous permutations are performed, and an estimate of what constitutes significance for each individual marker is applied for the true association testing. Thus for each analysis, an empirical P-value is determined. Genome-wide permutation tests are computationally very intensive and therefore impractical, but can be made tractable by use of a reduced number of permutations to which extreme value distributions are applied and subsequently used analytically to assess significance.111 False discovery rates have been successfully applied to multiple testing problems by ranking the P-values for large sets of test results in order to retain the maximum number of significant results while minimizing the number of false discoveries. For an explanation of the important distinction between false positive rates (‘P-values’) and false discovery rates (‘q-values’), see Storey and Tibshirani.112 More recently, Bayesian techniques have been applied to this issue, where the a posteriori significance threshold is a function of the a priori likelihood. This technique and others are more extensively reviewed elsewhere.113 Inadequate sample size is a major issue. The total sample sizes were as low as 17 patients in one study,72 whereas another performed subgroup analyses on samples as low as six.42 Sample size and power calculations should be performed in the design stage of any study. For a review of genetic epidemiology design, mode of inheritance and effect size, see Terwilliger et al.114 The theory underpinning genetic power calculations is outlined elsewhere,115 and power calculation software has been reviewed previously.116 A common theme in all of these therapeutic areas is the difference in definition of phenotype, both of disease and outcome, consideration of co-morbidity and co-medication, the performance of post hoc subgroup analyses, and the analysis of treatment with multiple drugs. Precision in phenotypic definition is vital both for the detection of a true association and for its replication. The mixture of various disease phenotypes may result in the loss of a true signal that is limited to one particular phenotype, and poor definition of the outcome variable may also produce false results. The utilization of different disease phenotypes and alternative outcome measures may contribute to failures of replication, as may attempts to replicate in different populations, as different polymorphisms may contribute to drug resistance in populations of different ancestry. As has been described previously, both co-medications (drug and dosage) and co-morbidity may influence PGP expression


and activity, and so these potential confounders need to be controlled for. The performance of post hoc subgroup analyses is problematic, as sample sizes may be small, and may result in the generation of false positive associations, either through a sample size effect or through chance by multiple testing. Finally, caution should be exercised when comparing pharmacokinetic and clinical outcome studies with different drugs. Different drugs, even when used within the same therapeutic area, are transported at different rates by PGP. In addition, there is substantial overlap in substrate specificities for the different ABC transporters, and hence a relative deficiency in the activity of PGP may be compensated by another transporter. Therefore, PGP may have a lesser or greater role in both pharmacokinetics and mediation of clinical outcome, depending on the drug under investigation. The situation becomes even more complicated when the association between genotype and outcome becomes obscured when multiple drugs are being investigated and when some of these drugs are not in fact substrates of PGP. This hypothesis has certainly been put forward in the context of multidrug resistance in epilepsy, where there is debate over whether or not carbamazepine, one of the most widely used anti-epileptics drugs, is actually a substrate for PGP.117–119 Additionally, treatment with multiple drugs may result in complex drug–drug interactions that result in different outcomes, independent of ABCB1 genotype, expression or activity. Therefore, best practice would be to analyse the effects of ABCB1 genotype on outcome with a single drug, rather than with multiple drugs. Clearly, this may not always be possible, for example in resistant epilepsy where combination therapy is the norm, in which instance cases and controls should be carefully matched. The limitations outlined above, and references for the resolution of these issues, are summarized in Table 7.

Conclusions PGP has long been considered as a putative mechanism of drug resistance. Extensive research has been conducted to identify correlates between ABCB1 genotype and PGP expression, function (drug pharmacokinetics) and related clinical outcome, on the assumption that genotypic variants in the gene might directly influence expression or function. However, in all these areas, there have some positive studies, which have largely failed to be replicated. Most studies have failed to show an association with genotype, and those few studies that do show an association have often been conflicting in direction, either between studies or within the same study. This lack of a clear conclusion probably reflects limitations of studies performed to date. Of course, an additional contributing factor may be that the effect size of a functional variant in ABCB1 may be of such a small magnitude that extremely large samples are required to detect the true association. If this is the case, then it may be that such an effect is not ‘useful’ in the clinical world.


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Table 7

Limitations of current studies

Genetic epidemiological study design issue Sample selection and recall bias Unmeasured ascertainment bias Recall data

Power Sample size Inheritance model

Mapping strategy Control of type I error rate

Confounding Genetic Environmental Gene–environment Correlations and/or interactions

Example

Further reading

Retrospective definition of phenotype and outcome

Rothman and Greenland120

Recollection of first seizure, childhood medical history, past exposures, etc.

Genotype effect size, mode of inheritance, penetrance, allelic spectrum Environmental exposures Family/general population (case–control and cohort studies) Marker density, reality of mapping strategy assumptions Bonferonni single stringent threshold FDR and use of q-values (e.g. microarray and genome scans) Permutation and application of extreme value distributions to permuted data Bayesian models incorporating prior information

Terwilliger and Weiss124 Risch and Merikangas125 Storey112 Dudbridge et al.111

Clayton and McKeigue,126 Setakis et al.127

Population stratification Co-medication, co-morbidity Population confounding between genetic and environmental/social factors Gene expressed according to some environmental cue and not others

Misclassification and measurement error Phenotype Complex (and inconsistent) phenotype and outcome definition Differential affinity of PGP for substrate drugs Exposure Medical history, drug treatment (number, dosage and duration), age and sex Genotyping errors Protocol, platform and laboratory call-rate errors

Clearly, the research environment in this field is in need of improvement; otherwise, it promises to erode confidence in an otherwise promising area of research. Ideally, future association studies should include the use of prospective cohorts, control for stratification either by limiting the study population to one ethnicity or the use of unlinked genetic markers to detect population substructure, screening for genotyping error, correction for multiple testing, a sample size that would provide adequate power, control for confounders such as co-morbidity or co-medication, limiting the study to a single drug (if possible), well-defined disease phenotypes and outcome measures, and the avoidance of post hoc subgroup analyses. Controlled clinical trials are the ideal context for the performance of pharmacogenetic studies, as this would eliminate many of the confounding factors affecting current studies. Replication studies should ideally be performed in the same population, with identical disease and outcome definitions, and

Cardon and Bell121 Reich and Lander,122 Terwilliger et al.,114 Schork123

Terwilliger and Hiekkalinna128

Clayton and McKeigue126

should utilize either the same genetic markers or a higher marker density. None of the association studies that have to date examined clinical outcome meet this gold standard of methodology. This is perhaps understandable given that such gold standard studies would inevitably be large and expensive. Nevertheless, for the future, we should attempt to attain this gold standard; this is only likely to be possible through multicentre collaborations. In attempting to uncover associations that confer small effects, only studies approximating best practice are likely to be informative. This will certainly aid in the clarification of the correct relationship between ABCB1 genotype and outcome in terms of expression, drug pharmacokinetics and response (efficacy and toxicity). In addition, we would recommend that investigators consider providing phenotypic and genotypic data for individual patients in their studies – this would permit the execution of individual patient data meta-analyses that



176

could be used to tease out any association with ABCB1 genotype.

16

Acknowledgments

17

We would like to thank the Wellcome Trust for their support, and the reviewers for their constructive reviews and helpful comments. GL is supported in part by a Neurology Entry/Exit Scholarship from the Guarantors of Brain.

18

19

Duality of Interest No conflicts of interest are declared.

20

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